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+ ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "625abf4a", + "metadata": {}, + "outputs": [], + "source": [ + "options(warn=-1)\n", + "library(dplyr)\n", + "library(tidyverse)\n", + "library(lubridate)\n", + "library(zoo)\n", + "library(xts)\n", + "library('ggplot2')" + ] + }, + { + "cell_type": "markdown", + "id": "d786e051", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "0f47587a", + "metadata": {}, + "source": [ + "Series 類似於列表或一維陣列，但帶有索引。所有操作都會與索引對齊。在 R 中對行進行索引時，我們需要使用 row.names。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "f659f553", + "metadata": {}, + "outputs": [], + "source": [ + "a<- 1:9" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "9acc193d", + "metadata": {}, + "outputs": [], + "source": [ + "b = c(\"I\",\"like\",\"to\",\"use\",\"Python\",\"and\",\"Pandas\",\"very\",\"much\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "f577ec14", + "metadata": {}, + "outputs": [], + "source": [ + "a1 = length(a)\n", + "b1 = length(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "31e069a0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " a\n", + "1 1\n", + "2 2\n", + "3 3\n", + "4 4\n", + "5 5\n", + "6 6\n", + "7 7\n", + "8 8\n", + "9 9\n" + ] + } + ], + "source": [ + "a = data.frame(a,row.names = c(1:a1))\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "29ce166e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " b\n", + "1 I\n", + "2 like\n", + "3 to\n", + "4 use\n", + "5 Python\n", + "6 and\n", + "7 Pandas\n", + "8 very\n", + "9 much\n" + ] + } + ], + "source": [ + "b = data.frame(b,row.names = c(1:b1))\n", + "print(b)" + ] + }, + { + "cell_type": "markdown", + "id": "a83abe74", + "metadata": {}, + "source": [ + "其中一個常見的序列用途是時間序列。在時間序列中，索引具有一種特殊的結構——通常是一系列日期或日期時間。最簡單的方法是使用 ts 函數來創建時間序列。不過，我們將嘗試另一種方式來實現時間序列。我們需要使用 lubridate 函式庫，並通過 seq 函數來創建日期的索引。\n", + "\n", + "假設我們有一個序列，顯示每天購買的產品數量，並且我們知道每個星期日我們還需要為自己額外拿取一件商品。以下是如何使用序列來建模的：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "eeb683c7", + "metadata": {}, + "outputs": [], + "source": [ + "# We will use ggplot2 for visualizing the data\n", + "# If you want to change the plot size repr library will be used\n", + "library(repr)\n", + "options(repr.plot.width = 12,repr.plot.height=6)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "e7788ca1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"length of index is 366\"\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 360, + "width": 720 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "start_date <- mdy(\"Jan 1, 2020\")\n", + "end_date <- mdy(\"Dec 31, 2020\")\n", + "idx = seq(start_date,end_date,by ='day')\n", + "print(paste(\"length of index is \",length(idx)))\n", + "size = length(idx)\n", + "sales = runif(366,min=25,max=50)\n", + "sold_items <- data.frame(row.names=idx[0:size],sales)\n", + "ggplot(sold_items,aes(x=idx,y=sales)) + geom_point(color = \"firebrick\", shape = \"diamond\", size = 2) +\n", + " geom_line(color = \"firebrick\", size = .3)" + ] + }, + { + "cell_type": "markdown", + "id": "3f199e43", + "metadata": {}, + "source": [ + "我們正在合併 additional_items 和 sold_items，以便找出產品的總數。 \n", + "如你所見，我們在這裡遇到了問題，無法計算總數，因為我們得到的是 NaN 值。這是因為在每週的數據中，未提及的日子被視為缺失值 (NaN)。如果我們將 NaN 與一個數字相加，結果仍然是 NaN。 \n", + "為了進行加法運算，我們需要將 NaN 替換為 0。 \n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "abe41544", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 53 × 1
additional_product
<dbl>
2020-01-0110
2020-01-0810
2020-01-1510
2020-01-2210
2020-01-2910
2020-02-0510
2020-02-1210
2020-02-1910
2020-02-2610
2020-03-0410
2020-03-1110
2020-03-1810
2020-03-2510
2020-04-0110
2020-04-0810
2020-04-1510
2020-04-2210
2020-04-2910
2020-05-0610
2020-05-1310
2020-05-2010
2020-05-2710
2020-06-0310
2020-06-1010
2020-06-1710
2020-06-2410
2020-07-0110
2020-07-0810
2020-07-1510
2020-07-2210
2020-07-2910
2020-08-0510
2020-08-1210
2020-08-1910
2020-08-2610
2020-09-0210
2020-09-0910
2020-09-1610
2020-09-2310
2020-09-3010
2020-10-0710
2020-10-1410
2020-10-2110
2020-10-2810
2020-11-0410
2020-11-1110
2020-11-1810
2020-11-2510
2020-12-0210
2020-12-0910
2020-12-1610
2020-12-2310
2020-12-3010
\n" + ], + "text/latex": [ + "A data.frame: 53 × 1\n", + "\\begin{tabular}{r|l}\n", + " & additional\\_product\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t2020-01-01 & 10\\\\\n", + "\t2020-01-08 & 10\\\\\n", + "\t2020-01-15 & 10\\\\\n", + "\t2020-01-22 & 10\\\\\n", + "\t2020-01-29 & 10\\\\\n", + "\t2020-02-05 & 10\\\\\n", + "\t2020-02-12 & 10\\\\\n", + "\t2020-02-19 & 10\\\\\n", + "\t2020-02-26 & 10\\\\\n", + "\t2020-03-04 & 10\\\\\n", + "\t2020-03-11 & 10\\\\\n", + "\t2020-03-18 & 10\\\\\n", + "\t2020-03-25 & 10\\\\\n", + "\t2020-04-01 & 10\\\\\n", + "\t2020-04-08 & 10\\\\\n", + "\t2020-04-15 & 10\\\\\n", + "\t2020-04-22 & 10\\\\\n", + "\t2020-04-29 & 10\\\\\n", + "\t2020-05-06 & 10\\\\\n", + "\t2020-05-13 & 10\\\\\n", + "\t2020-05-20 & 10\\\\\n", + "\t2020-05-27 & 10\\\\\n", + "\t2020-06-03 & 10\\\\\n", + "\t2020-06-10 & 10\\\\\n", + "\t2020-06-17 & 10\\\\\n", + "\t2020-06-24 & 10\\\\\n", + "\t2020-07-01 & 10\\\\\n", + "\t2020-07-08 & 10\\\\\n", + "\t2020-07-15 & 10\\\\\n", + "\t2020-07-22 & 10\\\\\n", + "\t2020-07-29 & 10\\\\\n", + "\t2020-08-05 & 10\\\\\n", + "\t2020-08-12 & 10\\\\\n", + "\t2020-08-19 & 10\\\\\n", + "\t2020-08-26 & 10\\\\\n", + "\t2020-09-02 & 10\\\\\n", + "\t2020-09-09 & 10\\\\\n", + "\t2020-09-16 & 10\\\\\n", + "\t2020-09-23 & 10\\\\\n", + "\t2020-09-30 & 10\\\\\n", + "\t2020-10-07 & 10\\\\\n", + "\t2020-10-14 & 10\\\\\n", + "\t2020-10-21 & 10\\\\\n", + "\t2020-10-28 & 10\\\\\n", + "\t2020-11-04 & 10\\\\\n", + "\t2020-11-11 & 10\\\\\n", + "\t2020-11-18 & 10\\\\\n", + "\t2020-11-25 & 10\\\\\n", + "\t2020-12-02 & 10\\\\\n", + "\t2020-12-09 & 10\\\\\n", + "\t2020-12-16 & 10\\\\\n", + "\t2020-12-23 & 10\\\\\n", + "\t2020-12-30 & 10\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 53 × 1\n", + "\n", + "| | additional_product <dbl> |\n", + "|---|---|\n", + "| 2020-01-01 | 10 |\n", + "| 2020-01-08 | 10 |\n", + "| 2020-01-15 | 10 |\n", + "| 2020-01-22 | 10 |\n", + "| 2020-01-29 | 10 |\n", + "| 2020-02-05 | 10 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|\n", + "| 2020-10-28 | 10 |\n", + "| 2020-11-04 | 10 |\n", + "| 2020-11-11 | 10 |\n", + "| 2020-11-18 | 10 |\n", + "| 2020-11-25 | 10 |\n", + "| 2020-12-02 | 10 |\n", + "| 2020-12-09 | 10 |\n", + "| 2020-12-16 | 10 |\n", + "| 2020-12-23 | 10 |\n", + "| 2020-12-30 | 10 |\n", + "\n" + ], + "text/plain": [ + " additional_product\n", + "2020-01-01 10 \n", + "2020-01-08 10 \n", + "2020-01-15 10 \n", + "2020-01-22 10 \n", + "2020-01-29 10 \n", + "2020-02-05 10 \n", + "2020-02-12 10 \n", + "2020-02-19 10 \n", + "2020-02-26 10 \n", + "2020-03-04 10 \n", + "2020-03-11 10 \n", + "2020-03-18 10 \n", + "2020-03-25 10 \n", + "2020-04-01 10 \n", + "2020-04-08 10 \n", + "2020-04-15 10 \n", + "2020-04-22 10 \n", + "2020-04-29 10 \n", + "2020-05-06 10 \n", + "2020-05-13 10 \n", + "2020-05-20 10 \n", + "2020-05-27 10 \n", + "2020-06-03 10 \n", + "2020-06-10 10 \n", + "2020-06-17 10 \n", + "2020-06-24 10 \n", + "2020-07-01 10 \n", + "2020-07-08 10 \n", + "2020-07-15 10 \n", + "2020-07-22 10 \n", + 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A data.frame: 366 × 1
total
<dbl>
2020-01-0153.59979
2020-01-02 NA
2020-01-03 NA
2020-01-04 NA
2020-01-05 NA
2020-01-06 NA
2020-01-07 NA
2020-01-0840.93455
2020-01-09 NA
2020-01-10 NA
2020-01-11 NA
2020-01-12 NA
2020-01-13 NA
2020-01-14 NA
2020-01-1559.24704
2020-01-16 NA
2020-01-17 NA
2020-01-18 NA
2020-01-19 NA
2020-01-20 NA
2020-01-21 NA
2020-01-2238.26416
2020-01-23 NA
2020-01-24 NA
2020-01-25 NA
2020-01-26 NA
2020-01-27 NA
2020-01-28 NA
2020-01-2944.58327
2020-01-30 NA
......
2020-12-0241.74811
2020-12-03 NA
2020-12-04 NA
2020-12-05 NA
2020-12-06 NA
2020-12-07 NA
2020-12-08 NA
2020-12-0937.85650
2020-12-10 NA
2020-12-11 NA
2020-12-12 NA
2020-12-13 NA
2020-12-14 NA
2020-12-15 NA
2020-12-1646.73560
2020-12-17 NA
2020-12-18 NA
2020-12-19 NA
2020-12-20 NA
2020-12-21 NA
2020-12-22 NA
2020-12-2340.42143
2020-12-24 NA
2020-12-25 NA
2020-12-26 NA
2020-12-27 NA
2020-12-28 NA
2020-12-29 NA
2020-12-3041.20298
2020-12-31 NA
\n" + ], + "text/latex": [ + "A data.frame: 366 × 1\n", + "\\begin{tabular}{r|l}\n", + " & total\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t2020-01-01 & 53.59979\\\\\n", + "\t2020-01-02 & NA\\\\\n", + "\t2020-01-03 & NA\\\\\n", + "\t2020-01-04 & NA\\\\\n", + "\t2020-01-05 & NA\\\\\n", + "\t2020-01-06 & NA\\\\\n", + "\t2020-01-07 & NA\\\\\n", + "\t2020-01-08 & 40.93455\\\\\n", + "\t2020-01-09 & NA\\\\\n", + "\t2020-01-10 & NA\\\\\n", + "\t2020-01-11 & NA\\\\\n", + "\t2020-01-12 & NA\\\\\n", + "\t2020-01-13 & NA\\\\\n", + "\t2020-01-14 & NA\\\\\n", + "\t2020-01-15 & 59.24704\\\\\n", + "\t2020-01-16 & NA\\\\\n", + "\t2020-01-17 & NA\\\\\n", + "\t2020-01-18 & NA\\\\\n", + "\t2020-01-19 & NA\\\\\n", + "\t2020-01-20 & NA\\\\\n", + "\t2020-01-21 & NA\\\\\n", + "\t2020-01-22 & 38.26416\\\\\n", + "\t2020-01-23 & NA\\\\\n", + "\t2020-01-24 & NA\\\\\n", + "\t2020-01-25 & NA\\\\\n", + "\t2020-01-26 & NA\\\\\n", + "\t2020-01-27 & NA\\\\\n", + "\t2020-01-28 & NA\\\\\n", + "\t2020-01-29 & 44.58327\\\\\n", + "\t2020-01-30 & NA\\\\\n", + "\t... & ...\\\\\n", + "\t2020-12-02 & 41.74811\\\\\n", + "\t2020-12-03 & NA\\\\\n", + "\t2020-12-04 & NA\\\\\n", + "\t2020-12-05 & NA\\\\\n", + "\t2020-12-06 & NA\\\\\n", + "\t2020-12-07 & NA\\\\\n", + "\t2020-12-08 & NA\\\\\n", + "\t2020-12-09 & 37.85650\\\\\n", + "\t2020-12-10 & NA\\\\\n", + "\t2020-12-11 & NA\\\\\n", + "\t2020-12-12 & NA\\\\\n", + "\t2020-12-13 & NA\\\\\n", + "\t2020-12-14 & NA\\\\\n", + "\t2020-12-15 & NA\\\\\n", + "\t2020-12-16 & 46.73560\\\\\n", + "\t2020-12-17 & NA\\\\\n", + "\t2020-12-18 & NA\\\\\n", + "\t2020-12-19 & NA\\\\\n", + "\t2020-12-20 & NA\\\\\n", + "\t2020-12-21 & NA\\\\\n", + "\t2020-12-22 & NA\\\\\n", + "\t2020-12-23 & 40.42143\\\\\n", + "\t2020-12-24 & NA\\\\\n", + "\t2020-12-25 & NA\\\\\n", + "\t2020-12-26 & NA\\\\\n", + "\t2020-12-27 & NA\\\\\n", + "\t2020-12-28 & NA\\\\\n", + "\t2020-12-29 & NA\\\\\n", + "\t2020-12-30 & 41.20298\\\\\n", + "\t2020-12-31 & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 366 × 1\n", + "\n", + "| | total <dbl> |\n", + "|---|---|\n", + "| 2020-01-01 | 53.59979 |\n", + "| 2020-01-02 | NA |\n", + "| 2020-01-03 | NA |\n", + "| 2020-01-04 | NA |\n", + "| 2020-01-05 | NA |\n", + "| 2020-01-06 | NA |\n", + "| 2020-01-07 | NA |\n", + "| 2020-01-08 | 40.93455 |\n", + "| 2020-01-09 | NA |\n", + "| 2020-01-10 | NA |\n", + "| 2020-01-11 | NA |\n", + "| 2020-01-12 | NA |\n", + "| 2020-01-13 | NA |\n", + "| 2020-01-14 | NA |\n", + "| 2020-01-15 | 59.24704 |\n", + "| 2020-01-16 | NA |\n", + "| 2020-01-17 | NA |\n", + "| 2020-01-18 | NA |\n", + "| 2020-01-19 | NA |\n", + "| 2020-01-20 | NA |\n", + "| 2020-01-21 | NA |\n", + "| 2020-01-22 | 38.26416 |\n", + "| 2020-01-23 | NA |\n", + "| 2020-01-24 | NA |\n", + "| 2020-01-25 | NA |\n", + "| 2020-01-26 | NA |\n", + "| 2020-01-27 | NA |\n", + "| 2020-01-28 | NA |\n", + "| 2020-01-29 | 44.58327 |\n", + "| 2020-01-30 | NA |\n", + "| ... | ... |\n", + "| 2020-12-02 | 41.74811 |\n", + "| 2020-12-03 | NA |\n", + "| 2020-12-04 | NA |\n", + "| 2020-12-05 | NA |\n", + "| 2020-12-06 | NA |\n", + "| 2020-12-07 | NA |\n", + "| 2020-12-08 | NA |\n", + "| 2020-12-09 | 37.85650 |\n", + "| 2020-12-10 | NA |\n", + "| 2020-12-11 | NA |\n", + "| 2020-12-12 | NA |\n", + "| 2020-12-13 | NA |\n", + "| 2020-12-14 | NA |\n", + "| 2020-12-15 | NA |\n", + "| 2020-12-16 | 46.73560 |\n", + "| 2020-12-17 | NA |\n", + "| 2020-12-18 | NA |\n", + "| 2020-12-19 | NA |\n", + "| 2020-12-20 | NA |\n", + "| 2020-12-21 | NA |\n", + "| 2020-12-22 | NA |\n", + "| 2020-12-23 | 40.42143 |\n", + "| 2020-12-24 | NA |\n", + "| 2020-12-25 | NA |\n", + "| 2020-12-26 | NA |\n", + "| 2020-12-27 | NA |\n", + "| 2020-12-28 | NA |\n", + "| 2020-12-29 | NA |\n", + "| 2020-12-30 | 41.20298 |\n", + "| 2020-12-31 | NA |\n", + "\n" + ], + "text/plain": [ + " total \n", + "2020-01-01 53.59979\n", + "2020-01-02 NA\n", + "2020-01-03 NA\n", + "2020-01-04 NA\n", + "2020-01-05 NA\n", + "2020-01-06 NA\n", + "2020-01-07 NA\n", + "2020-01-08 40.93455\n", + "2020-01-09 NA\n", + "2020-01-10 NA\n", + "2020-01-11 NA\n", + "2020-01-12 NA\n", + "2020-01-13 NA\n", + "2020-01-14 NA\n", + "2020-01-15 59.24704\n", + "2020-01-16 NA\n", + "2020-01-17 NA\n", + "2020-01-18 NA\n", + "2020-01-19 NA\n", + "2020-01-20 NA\n", + "2020-01-21 NA\n", + "2020-01-22 38.26416\n", + "2020-01-23 NA\n", + "2020-01-24 NA\n", + "2020-01-25 NA\n", + "2020-01-26 NA\n", + "2020-01-27 NA\n", + "2020-01-28 NA\n", + "2020-01-29 44.58327\n", + "2020-01-30 NA\n", + "... ... \n", + "2020-12-02 41.74811\n", + "2020-12-03 NA\n", + "2020-12-04 NA\n", + "2020-12-05 NA\n", + "2020-12-06 NA\n", + "2020-12-07 NA\n", + "2020-12-08 NA\n", + "2020-12-09 37.85650\n", + "2020-12-10 NA\n", + "2020-12-11 NA\n", + "2020-12-12 NA\n", + "2020-12-13 NA\n", + "2020-12-14 NA\n", + "2020-12-15 NA\n", + "2020-12-16 46.73560\n", + "2020-12-17 NA\n", + "2020-12-18 NA\n", + "2020-12-19 NA\n", + "2020-12-20 NA\n", + "2020-12-21 NA\n", + "2020-12-22 NA\n", + "2020-12-23 40.42143\n", + "2020-12-24 NA\n", + "2020-12-25 NA\n", + "2020-12-26 NA\n", + "2020-12-27 NA\n", + "2020-12-28 NA\n", + "2020-12-29 NA\n", + "2020-12-30 41.20298\n", + "2020-12-31 NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "index = seq(start_date,end_date,by = 'week')\n", + "sz = length(index)\n", + "additional_product <- rep(10,53)\n", + "additional_items <- data.frame(row.names = index[0:sz],additional_product)\n", + "additional_items\n", + "# we are merging two dataframe so that we can add\n", + "additional_item = merge(additional_items,sold_items, by = 0, all = TRUE)[-1] \n", + "total = data.frame(row.names=idx[0:size],additional_item$additional_product + additional_item$sales)\n", + "colnames(total) = c('total')\n", + "total" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "387cb4c2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + 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A data.frame: 366 × 1
total
<dbl>
2020-01-0153.59979
2020-01-0230.41127
2020-01-0348.54839
2020-01-0439.20897
2020-01-0539.09894
2020-01-0647.53019
2020-01-0744.94766
2020-01-0840.93455
2020-01-0937.66561
2020-01-1031.68825
2020-01-1145.30576
2020-01-1226.45509
2020-01-1345.81249
2020-01-1446.84547
2020-01-1559.24704
2020-01-1629.28688
2020-01-1732.41731
2020-01-1845.23295
2020-01-1948.54330
2020-01-2036.69353
2020-01-2143.09588
2020-01-2238.26416
2020-01-2345.56863
2020-01-2425.70944
2020-01-2537.38721
2020-01-2644.53955
2020-01-2746.88427
2020-01-2848.05540
2020-01-2944.58327
2020-01-3026.19490
......
2020-12-0241.74811
2020-12-0335.03915
2020-12-0425.84637
2020-12-0527.73147
2020-12-0639.00993
2020-12-0741.03187
2020-12-0826.33862
2020-12-0937.85650
2020-12-1041.98943
2020-12-1136.68901
2020-12-1246.96883
2020-12-1339.70374
2020-12-1446.59464
2020-12-1541.24742
2020-12-1646.73560
2020-12-1732.68275
2020-12-1846.64238
2020-12-1925.22163
2020-12-2039.79997
2020-12-2134.45013
2020-12-2248.71183
2020-12-2340.42143
2020-12-2432.41991
2020-12-2539.12296
2020-12-2629.43616
2020-12-2739.09337
2020-12-2838.09288
2020-12-2941.00681
2020-12-3041.20298
2020-12-3143.25232
\n" + ], + "text/latex": [ + "A data.frame: 366 × 1\n", + "\\begin{tabular}{r|l}\n", + " & total\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t2020-01-01 & 53.59979\\\\\n", + "\t2020-01-02 & 30.41127\\\\\n", + "\t2020-01-03 & 48.54839\\\\\n", + "\t2020-01-04 & 39.20897\\\\\n", + "\t2020-01-05 & 39.09894\\\\\n", + "\t2020-01-06 & 47.53019\\\\\n", + "\t2020-01-07 & 44.94766\\\\\n", + "\t2020-01-08 & 40.93455\\\\\n", + "\t2020-01-09 & 37.66561\\\\\n", + "\t2020-01-10 & 31.68825\\\\\n", + "\t2020-01-11 & 45.30576\\\\\n", + "\t2020-01-12 & 26.45509\\\\\n", + "\t2020-01-13 & 45.81249\\\\\n", + "\t2020-01-14 & 46.84547\\\\\n", + "\t2020-01-15 & 59.24704\\\\\n", + "\t2020-01-16 & 29.28688\\\\\n", + "\t2020-01-17 & 32.41731\\\\\n", + "\t2020-01-18 & 45.23295\\\\\n", + "\t2020-01-19 & 48.54330\\\\\n", + "\t2020-01-20 & 36.69353\\\\\n", + "\t2020-01-21 & 43.09588\\\\\n", + "\t2020-01-22 & 38.26416\\\\\n", + "\t2020-01-23 & 45.56863\\\\\n", + "\t2020-01-24 & 25.70944\\\\\n", + "\t2020-01-25 & 37.38721\\\\\n", + "\t2020-01-26 & 44.53955\\\\\n", + "\t2020-01-27 & 46.88427\\\\\n", + "\t2020-01-28 & 48.05540\\\\\n", + "\t2020-01-29 & 44.58327\\\\\n", + "\t2020-01-30 & 26.19490\\\\\n", + "\t... & ...\\\\\n", + "\t2020-12-02 & 41.74811\\\\\n", + "\t2020-12-03 & 35.03915\\\\\n", + "\t2020-12-04 & 25.84637\\\\\n", + "\t2020-12-05 & 27.73147\\\\\n", + "\t2020-12-06 & 39.00993\\\\\n", + "\t2020-12-07 & 41.03187\\\\\n", + "\t2020-12-08 & 26.33862\\\\\n", + "\t2020-12-09 & 37.85650\\\\\n", + "\t2020-12-10 & 41.98943\\\\\n", + "\t2020-12-11 & 36.68901\\\\\n", + "\t2020-12-12 & 46.96883\\\\\n", + "\t2020-12-13 & 39.70374\\\\\n", + "\t2020-12-14 & 46.59464\\\\\n", + "\t2020-12-15 & 41.24742\\\\\n", + "\t2020-12-16 & 46.73560\\\\\n", + "\t2020-12-17 & 32.68275\\\\\n", + "\t2020-12-18 & 46.64238\\\\\n", + "\t2020-12-19 & 25.22163\\\\\n", + "\t2020-12-20 & 39.79997\\\\\n", + "\t2020-12-21 & 34.45013\\\\\n", + "\t2020-12-22 & 48.71183\\\\\n", + "\t2020-12-23 & 40.42143\\\\\n", + "\t2020-12-24 & 32.41991\\\\\n", + "\t2020-12-25 & 39.12296\\\\\n", + "\t2020-12-26 & 29.43616\\\\\n", + "\t2020-12-27 & 39.09337\\\\\n", + "\t2020-12-28 & 38.09288\\\\\n", + "\t2020-12-29 & 41.00681\\\\\n", + "\t2020-12-30 & 41.20298\\\\\n", + "\t2020-12-31 & 43.25232\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 366 × 1\n", + "\n", + "| | total <dbl> |\n", + "|---|---|\n", + "| 2020-01-01 | 53.59979 |\n", + "| 2020-01-02 | 30.41127 |\n", + "| 2020-01-03 | 48.54839 |\n", + "| 2020-01-04 | 39.20897 |\n", + "| 2020-01-05 | 39.09894 |\n", + "| 2020-01-06 | 47.53019 |\n", + "| 2020-01-07 | 44.94766 |\n", + "| 2020-01-08 | 40.93455 |\n", + "| 2020-01-09 | 37.66561 |\n", + "| 2020-01-10 | 31.68825 |\n", + "| 2020-01-11 | 45.30576 |\n", + "| 2020-01-12 | 26.45509 |\n", + "| 2020-01-13 | 45.81249 |\n", + "| 2020-01-14 | 46.84547 |\n", + "| 2020-01-15 | 59.24704 |\n", + "| 2020-01-16 | 29.28688 |\n", + "| 2020-01-17 | 32.41731 |\n", + "| 2020-01-18 | 45.23295 |\n", + "| 2020-01-19 | 48.54330 |\n", + "| 2020-01-20 | 36.69353 |\n", + "| 2020-01-21 | 43.09588 |\n", + "| 2020-01-22 | 38.26416 |\n", + "| 2020-01-23 | 45.56863 |\n", + "| 2020-01-24 | 25.70944 |\n", + "| 2020-01-25 | 37.38721 |\n", + "| 2020-01-26 | 44.53955 |\n", + "| 2020-01-27 | 46.88427 |\n", + "| 2020-01-28 | 48.05540 |\n", + "| 2020-01-29 | 44.58327 |\n", + "| 2020-01-30 | 26.19490 |\n", + "| ... | ... |\n", + "| 2020-12-02 | 41.74811 |\n", + "| 2020-12-03 | 35.03915 |\n", + "| 2020-12-04 | 25.84637 |\n", + "| 2020-12-05 | 27.73147 |\n", + "| 2020-12-06 | 39.00993 |\n", + "| 2020-12-07 | 41.03187 |\n", + "| 2020-12-08 | 26.33862 |\n", + "| 2020-12-09 | 37.85650 |\n", + "| 2020-12-10 | 41.98943 |\n", + "| 2020-12-11 | 36.68901 |\n", + "| 2020-12-12 | 46.96883 |\n", + "| 2020-12-13 | 39.70374 |\n", + "| 2020-12-14 | 46.59464 |\n", + "| 2020-12-15 | 41.24742 |\n", + "| 2020-12-16 | 46.73560 |\n", + "| 2020-12-17 | 32.68275 |\n", + "| 2020-12-18 | 46.64238 |\n", + "| 2020-12-19 | 25.22163 |\n", + "| 2020-12-20 | 39.79997 |\n", + "| 2020-12-21 | 34.45013 |\n", + "| 2020-12-22 | 48.71183 |\n", + "| 2020-12-23 | 40.42143 |\n", + "| 2020-12-24 | 32.41991 |\n", + "| 2020-12-25 | 39.12296 |\n", + "| 2020-12-26 | 29.43616 |\n", + "| 2020-12-27 | 39.09337 |\n", + "| 2020-12-28 | 38.09288 |\n", + "| 2020-12-29 | 41.00681 |\n", + "| 2020-12-30 | 41.20298 |\n", + "| 2020-12-31 | 43.25232 |\n", + "\n" + ], + "text/plain": [ + " total \n", + "2020-01-01 53.59979\n", + "2020-01-02 30.41127\n", + "2020-01-03 48.54839\n", + "2020-01-04 39.20897\n", + "2020-01-05 39.09894\n", + "2020-01-06 47.53019\n", + "2020-01-07 44.94766\n", + "2020-01-08 40.93455\n", + "2020-01-09 37.66561\n", + "2020-01-10 31.68825\n", + "2020-01-11 45.30576\n", + "2020-01-12 26.45509\n", + "2020-01-13 45.81249\n", + "2020-01-14 46.84547\n", + "2020-01-15 59.24704\n", + "2020-01-16 29.28688\n", + "2020-01-17 32.41731\n", + "2020-01-18 45.23295\n", + "2020-01-19 48.54330\n", + "2020-01-20 36.69353\n", + "2020-01-21 43.09588\n", + "2020-01-22 38.26416\n", + "2020-01-23 45.56863\n", + "2020-01-24 25.70944\n", + "2020-01-25 37.38721\n", + "2020-01-26 44.53955\n", + "2020-01-27 46.88427\n", + "2020-01-28 48.05540\n", + "2020-01-29 44.58327\n", + "2020-01-30 26.19490\n", + "... ... \n", + "2020-12-02 41.74811\n", + "2020-12-03 35.03915\n", + "2020-12-04 25.84637\n", + "2020-12-05 27.73147\n", + "2020-12-06 39.00993\n", + "2020-12-07 41.03187\n", + "2020-12-08 26.33862\n", + "2020-12-09 37.85650\n", + "2020-12-10 41.98943\n", + "2020-12-11 36.68901\n", + "2020-12-12 46.96883\n", + "2020-12-13 39.70374\n", + "2020-12-14 46.59464\n", + "2020-12-15 41.24742\n", + "2020-12-16 46.73560\n", + "2020-12-17 32.68275\n", + "2020-12-18 46.64238\n", + "2020-12-19 25.22163\n", + "2020-12-20 39.79997\n", + "2020-12-21 34.45013\n", + "2020-12-22 48.71183\n", + "2020-12-23 40.42143\n", + "2020-12-24 32.41991\n", + "2020-12-25 39.12296\n", + "2020-12-26 29.43616\n", + "2020-12-27 39.09337\n", + "2020-12-28 38.09288\n", + "2020-12-29 41.00681\n", + "2020-12-30 41.20298\n", + "2020-12-31 43.25232" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "additional_item[is.na(additional_item)] = 0\n", + "total = data.frame(row.names=idx[0:size],additional_item$additional_product + additional_item$sales)\n", + "colnames(total) = c('total')\n", + "total" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "bdb60236", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 360, + "width": 720 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "ggplot(total,aes(x=idx,y=total)) + geom_point(color = \"firebrick\", shape = \"diamond\", size = 2) +\n", + " geom_line(color = \"firebrick\", linetype = \"dotted\", size = .3)" + ] + }, + { + "cell_type": "markdown", + "id": "38e65fd5", + "metadata": {}, + "source": [ + "我們想要按月分析產品總數。因此，我們計算每月產品總數的平均值，並繪製柱狀圖。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "294dde87", + "metadata": {}, + "outputs": [], + "source": [ + "index = seq(start_date,end_date,by ='month')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "7542d95e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " total\n", + "2020-01-31 41.03847\n", + "2020-02-29 40.91568\n", + "2020-03-31 39.27424\n", + "2020-04-30 37.63589\n", + "2020-05-31 38.75129\n", + "2020-06-30 38.75744\n", + "2020-07-31 38.35212\n", + "2020-08-31 40.43712\n", + "2020-09-30 38.90043\n", + "2020-10-31 37.99855\n", + "2020-11-30 41.20759\n", + "2020-12-31 38.46355" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 360, + "width": 720 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "x<- as.xts(total, dateFormat =\"Date\")\n", + "(monthly<-apply.monthly(x,mean))\n", + "ggplot(monthly, aes(x=index, y=total)) + \n", + " geom_bar(stat = \"identity\", width=5) " + ] + }, + { + "cell_type": "markdown", + "id": "945feffd", + "metadata": {}, + "source": [ + "## 資料框 (DataFrame)\n", + "資料框本質上是一組具有相同索引的序列 (Series) 的集合。我們可以將多個序列組合在一起形成一個資料框。 \n", + "例如，我們正在建立一個包含 a 和 b 序列的資料框：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "88a435ec", + "metadata": {}, + "outputs": [], + "source": [ + "a = data.frame(a,row.names = c(1:a1))" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "c4e2a6c1", + "metadata": {}, + "outputs": [], + "source": [ + "b = data.frame(b,row.names = c(1:b1))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "2bb5177c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 9 × 2
ab
<int><chr>
11I
22like
33to
44use
55Python
66and
77Pandas
88very
99much
\n" + ], + "text/latex": [ + "A data.frame: 9 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & a & b\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I \\\\\n", + "\t2 & 2 & like \\\\\n", + "\t3 & 3 & to \\\\\n", + "\t4 & 4 & use \\\\\n", + "\t5 & 5 & Python\\\\\n", + "\t6 & 6 & and \\\\\n", + "\t7 & 7 & Pandas\\\\\n", + "\t8 & 8 & very \\\\\n", + "\t9 & 9 & much \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 9 × 2\n", + "\n", + "| | a <int> | b <chr> |\n", + "|---|---|---|\n", + "| 1 | 1 | I |\n", + "| 2 | 2 | like |\n", + "| 3 | 3 | to |\n", + "| 4 | 4 | use |\n", + "| 5 | 5 | Python |\n", + "| 6 | 6 | and |\n", + "| 7 | 7 | Pandas |\n", + "| 8 | 8 | very |\n", + "| 9 | 9 | much |\n", + "\n" + ], + "text/plain": [ + " a b \n", + "1 1 I \n", + "2 2 like \n", + "3 3 to \n", + "4 4 use \n", + "5 5 Python\n", + "6 6 and \n", + "7 7 Pandas\n", + "8 8 very \n", + "9 9 much " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df<- data.frame(a,b)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "6531fe0e", + "metadata": {}, + "source": [ + "我們也可以使用 rename 函數來重新命名欄位名稱\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "8f45d3a5", + "metadata": {}, + "outputs": [], + "source": [ + "df = \n", + " rename(df,\n", + " A = a,\n", + " B = b,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "0efbf2d4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 9 × 2
AB
<int><chr>
11I
22like
33to
44use
55Python
66and
77Pandas
88very
99much
\n" + ], + "text/latex": [ + "A data.frame: 9 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & A & B\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I \\\\\n", + "\t2 & 2 & like \\\\\n", + "\t3 & 3 & to \\\\\n", + "\t4 & 4 & use \\\\\n", + "\t5 & 5 & Python\\\\\n", + "\t6 & 6 & and \\\\\n", + "\t7 & 7 & Pandas\\\\\n", + "\t8 & 8 & very \\\\\n", + "\t9 & 9 & much \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 9 × 2\n", + "\n", + "| | A <int> | B <chr> |\n", + "|---|---|---|\n", + "| 1 | 1 | I |\n", + "| 2 | 2 | like |\n", + "| 3 | 3 | to |\n", + "| 4 | 4 | use |\n", + "| 5 | 5 | Python |\n", + "| 6 | 6 | and |\n", + "| 7 | 7 | Pandas |\n", + "| 8 | 8 | very |\n", + "| 9 | 9 | much |\n", + "\n" + ], + "text/plain": [ + " A B \n", + "1 1 I \n", + "2 2 like \n", + "3 3 to \n", + "4 4 use \n", + "5 5 Python\n", + "6 6 and \n", + "7 7 Pandas\n", + "8 8 very \n", + "9 9 much " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "8ac0204f", + "metadata": {}, + "source": [ + "我們亦可使用 select 函數選擇數據框中的一列\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "88b51fdc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Column A (series):\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 9 × 1
A
<int>
11
22
33
44
55
66
77
88
99
\n" + ], + "text/latex": [ + "A data.frame: 9 × 1\n", + "\\begin{tabular}{r|l}\n", + " & A\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t1 & 1\\\\\n", + "\t2 & 2\\\\\n", + "\t3 & 3\\\\\n", + "\t4 & 4\\\\\n", + "\t5 & 5\\\\\n", + "\t6 & 6\\\\\n", + "\t7 & 7\\\\\n", + "\t8 & 8\\\\\n", + "\t9 & 9\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 9 × 1\n", + "\n", + "| | A <int> |\n", + "|---|---|\n", + "| 1 | 1 |\n", + "| 2 | 2 |\n", + "| 3 | 3 |\n", + "| 4 | 4 |\n", + "| 5 | 5 |\n", + "| 6 | 6 |\n", + "| 7 | 7 |\n", + "| 8 | 8 |\n", + "| 9 | 9 |\n", + "\n" + ], + "text/plain": [ + " A\n", + "1 1\n", + "2 2\n", + "3 3\n", + "4 4\n", + "5 5\n", + "6 6\n", + "7 7\n", + "8 8\n", + "9 9" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cat(\"Column A (series):\\n\")\n", + "select(df,'A')" + ] + }, + { + "cell_type": "markdown", + "id": "45397ec4", + "metadata": {}, + "source": [ + "我們將提取符合系列中某些邏輯標準的行\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "010bcba8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 4 × 2
AB
<int><chr>
11I
22like
33to
44use
\n" + ], + "text/latex": [ + "A data.frame: 4 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & A & B\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I \\\\\n", + "\t2 & 2 & like\\\\\n", + "\t3 & 3 & to \\\\\n", + "\t4 & 4 & use \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 4 × 2\n", + "\n", + "| | A <int> | B <chr> |\n", + "|---|---|---|\n", + "| 1 | 1 | I |\n", + "| 2 | 2 | like |\n", + "| 3 | 3 | to |\n", + "| 4 | 4 | use |\n", + "\n" + ], + "text/plain": [ + " A B \n", + "1 1 I \n", + "2 2 like\n", + "3 3 to \n", + "4 4 use " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[df$A<5,]" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "082277db", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\n", + "
A data.frame: 1 × 2
AB
<int><chr>
66and
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A data.frame: 9 × 3
ABDivA
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\n" + ], + "text/latex": [ + "A data.frame: 9 × 3\n", + "\\begin{tabular}{r|lll}\n", + " & A & B & DivA\\\\\n", + " & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I & -4\\\\\n", + "\t2 & 2 & like & -3\\\\\n", + "\t3 & 3 & to & -2\\\\\n", + "\t4 & 4 & use & -1\\\\\n", + "\t5 & 5 & Python & 0\\\\\n", + "\t6 & 6 & and & 1\\\\\n", + "\t7 & 7 & Pandas & 2\\\\\n", + "\t8 & 8 & very & 3\\\\\n", + "\t9 & 9 & much & 4\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 9 × 3\n", + "\n", + "| | A <int> | B <chr> | DivA <dbl> |\n", + "|---|---|---|---|\n", + "| 1 | 1 | I | -4 |\n", + "| 2 | 2 | like | -3 |\n", + "| 3 | 3 | to | -2 |\n", + "| 4 | 4 | use | -1 |\n", + "| 5 | 5 | Python | 0 |\n", + "| 6 | 6 | and | 1 |\n", + "| 7 | 7 | Pandas | 2 |\n", + "| 8 | 8 | very | 3 |\n", + "| 9 | 9 | much | 4 |\n", + "\n" + ], + "text/plain": [ + " A B DivA\n", + "1 1 I -4 \n", + "2 2 like -3 \n", + "3 3 to -2 \n", + "4 4 use -1 \n", + "5 5 Python 0 \n", + "6 6 and 1 \n", + "7 7 Pandas 2 \n", + "8 8 very 3 \n", + "9 9 much 4 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "2be67ef7", + "metadata": {}, + "source": [ + "我們正在創建一個系列，計算 A 欄的字串長度，然後合併到現有的數據框中\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "c67f2bd0", + "metadata": {}, + "outputs": [], + "source": [ + "df$LenB <- str_length(df$B)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "cef214b2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 9 × 4
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\n" + ], + "text/latex": [ + "A data.frame: 9 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & A & B & DivA & LenB\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I & -4 & 1\\\\\n", + "\t2 & 2 & like & -3 & 4\\\\\n", + "\t3 & 3 & to & -2 & 2\\\\\n", + "\t4 & 4 & use & -1 & 3\\\\\n", + "\t5 & 5 & Python & 0 & 6\\\\\n", + "\t6 & 6 & and & 1 & 3\\\\\n", + "\t7 & 7 & Pandas & 2 & 6\\\\\n", + "\t8 & 8 & very & 3 & 4\\\\\n", + "\t9 & 9 & much & 4 & 4\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 9 × 4\n", + "\n", + "| | A <int> | B <chr> | DivA <dbl> | LenB <int> |\n", + "|---|---|---|---|---|\n", + "| 1 | 1 | I | -4 | 1 |\n", + "| 2 | 2 | like | -3 | 4 |\n", + "| 3 | 3 | to | -2 | 2 |\n", + "| 4 | 4 | use | -1 | 3 |\n", + "| 5 | 5 | Python | 0 | 6 |\n", + "| 6 | 6 | and | 1 | 3 |\n", + "| 7 | 7 | Pandas | 2 | 6 |\n", + "| 8 | 8 | very | 3 | 4 |\n", + "| 9 | 9 | much | 4 | 4 |\n", + "\n" + ], + "text/plain": [ + " A B DivA LenB\n", + "1 1 I -4 1 \n", + "2 2 like -3 4 \n", + "3 3 to -2 2 \n", + "4 4 use -1 3 \n", + "5 5 Python 0 6 \n", + "6 6 and 1 3 \n", + "7 7 Pandas 2 6 \n", + "8 8 very 3 4 \n", + "9 9 much 4 4 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "e37d50de", + "metadata": {}, + "source": [ + "根據數字選擇行\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "59fe5316", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 5 × 4
ABDivALenB
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\n" + ], + "text/latex": [ + "A data.frame: 5 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & A & B & DivA & LenB\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I & -4 & 1\\\\\n", + "\t2 & 2 & like & -3 & 4\\\\\n", + "\t3 & 3 & to & -2 & 2\\\\\n", + "\t4 & 4 & use & -1 & 3\\\\\n", + "\t5 & 5 & Python & 0 & 6\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 5 × 4\n", + "\n", + "| | A <int> | B <chr> | DivA <dbl> | LenB <int> |\n", + "|---|---|---|---|---|\n", + "| 1 | 1 | I | -4 | 1 |\n", + "| 2 | 2 | like | -3 | 4 |\n", + "| 3 | 3 | to | -2 | 2 |\n", + "| 4 | 4 | use | -1 | 3 |\n", + "| 5 | 5 | Python | 0 | 6 |\n", + "\n" + ], + "text/plain": [ + " A B DivA LenB\n", + "1 1 I -4 1 \n", + "2 2 like -3 4 \n", + "3 3 to -2 2 \n", + "4 4 use -1 3 \n", + "5 5 Python 0 6 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[0:5,]" + ] + }, + { + "cell_type": "markdown", + "id": "6abec1b7", + "metadata": {}, + "source": [ + "***分組是指根據某些條件將多個列分組，我們將使用 summarise 函數來查看差異***\n", + "\n", + "假設我們想計算每個 LenB 數值對應的 A 列的平均值。那麼我們可以按 LenB 將 DataFrame 分組，並計算平均值，將其命名為 a。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "f944a949", + "metadata": {}, + "outputs": [], + "source": [ + "df1 = df %>% group_by(LenB) %>% summarise(a = mean(A))" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "8ffd39cd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 5 × 2
LenBa
<int><dbl>
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\n" + ], + "text/latex": [ + "A tibble: 5 × 2\n", + "\\begin{tabular}{ll}\n", + " LenB & a\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t 1 & 1.000000\\\\\n", + "\t 2 & 3.000000\\\\\n", + "\t 3 & 5.000000\\\\\n", + "\t 4 & 6.333333\\\\\n", + "\t 6 & 6.000000\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 5 × 2\n", + "\n", + "| LenB <int> | a <dbl> |\n", + "|---|---|\n", + "| 1 | 1.000000 |\n", + "| 2 | 3.000000 |\n", + "| 3 | 5.000000 |\n", + "| 4 | 6.333333 |\n", + "| 6 | 6.000000 |\n", + "\n" + ], + "text/plain": [ + " LenB a \n", + "1 1 1.000000\n", + "2 2 3.000000\n", + "3 3 5.000000\n", + "4 4 6.333333\n", + "5 6 6.000000" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df1" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "3b859950", + "metadata": {}, + "outputs": [], + "source": [ + "df2 = df %>% group_by(LenB) %>%\n", + "summarise(MEAN = mean(A),count =length(DivA))" + ] + }, + { + "cell_type": "markdown", + "id": "5d3f0287", + "metadata": {}, + "source": [ + "## 列印和繪圖\n", + "當我們呼叫 head(df) 時，它會以表格形式列印出資料框。\n", + "\n", + "任何數據科學項目的第一步都是數據清理和可視化，因此可視化數據集並提取一些有用的信息是非常重要的。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "69946dc7", + "metadata": {}, + "outputs": [], + "source": [ + "#dataset = read.csv(\"file name\")" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "4976f190", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 4
ABDivALenB
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}, + { + "cell_type": "markdown", + "id": "dcca35a8", + "metadata": {}, + "source": [ + "ggplot2 是一個非常好的庫，因為它可以簡單地從數據框中的數據創建複雜的圖表。\n", + "\n", + "它提供了一個更具程式化的介面，用於指定要繪製的變量、它們的顯示方式以及一般的視覺屬性。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "515c95b2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 360, + "width": 720 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "barplot(df$A, ylab = 'A',xlab = 'no')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**免責聲明**： \n此文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.1.1" + }, + "coopTranslator": { + "original_hash": "96055e5b4288cab076079d967d88e75f", + "translation_date": "2025-09-02T07:05:05+00:00", + "source_file": "2-Working-With-Data/07-python/R/notebook.ipynb", + "language_code": "hk" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/translations/zh-HK/2-Working-With-Data/07-python/README.md b/translations/zh-HK/2-Working-With-Data/07-python/README.md new file mode 100644 index 00000000..f611d262 --- /dev/null +++ b/translations/zh-HK/2-Working-With-Data/07-python/README.md @@ -0,0 +1,282 @@ +# 使用數據：Python 和 Pandas 庫 + +| ![ 由 [(@sketchthedocs)](https://sketchthedocs.dev) 繪製的手繪筆記 ](../../sketchnotes/07-WorkWithPython.png) | +| :-------------------------------------------------------------------------------------------------------: | +| 使用 Python - _由 [@nitya](https://twitter.com/nitya) 繪製的手繪筆記_ | + +[![介紹影片](../../../../translated_images/zh-HK/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) + +雖然數據庫提供了非常高效的方式來存儲數據並使用查詢語言進行查詢，但最靈活的數據處理方式是編寫自己的程序來操作數據。在許多情況下，使用數據庫查詢可能更有效。然而，在某些需要更複雜數據處理的情況下，使用 SQL 並不容易完成這些操作。 + +數據處理可以用任何編程語言來編寫，但某些語言在處理數據方面更高效。數據科學家通常偏好以下幾種語言之一： + +* **[Python](https://www.python.org/)**：一種通用編程語言，因其簡單性常被認為是初學者的最佳選擇之一。Python 擁有許多額外的庫，可以幫助解決許多實際問題，例如從 ZIP 壓縮檔中提取數據，或將圖片轉換為灰階。除了數據科學，Python 還常用於網頁開發。 +* **[R](https://www.r-project.org/)**：一個專為統計數據處理而開發的傳統工具箱。它擁有大量的庫（CRAN），使其成為數據處理的良好選擇。然而，R 並不是通用編程語言，通常僅用於數據科學領域。 +* **[Julia](https://julialang.org/)**：另一種專為數據科學開發的語言。它旨在提供比 Python 更好的性能，是科學實驗的強大工具。 + +在本課中，我們將專注於使用 Python 進行簡單的數據處理。我們假設您對該語言已有基本的了解。如果您想更深入地學習 Python，可以參考以下資源： + +* [用 Turtle Graphics 和分形圖形趣味學習 Python](https://github.com/shwars/pycourse) - 基於 GitHub 的 Python 編程快速入門課程 +* [邁出學習 Python 的第一步](https://docs.microsoft.com/en-us/learn/paths/python-first-steps/?WT.mc_id=academic-77958-bethanycheum) - [Microsoft Learn](http://learn.microsoft.com/?WT.mc_id=academic-77958-bethanycheum) 上的學習路徑 + +數據可以有多種形式。在本課中，我們將考慮三種數據形式——**表格數據**、**文本**和**圖像**。 + +我們將專注於一些數據處理的例子，而不是全面介紹所有相關庫。這樣可以讓您了解主要的可能性，並在需要時知道去哪裡尋找解決方案。 + +> **最有用的建議**：當您需要對數據執行某些操作但不知道如何實現時，嘗試在互聯網上搜索。[Stackoverflow](https://stackoverflow.com/) 通常包含許多針對常見任務的 Python 代碼示例。 + +## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/12) + +## 表格數據與 DataFrame + +當我們討論關係型數據庫時，您已經接觸過表格數據。當您擁有大量數據，並且這些數據存儲在許多不同的關聯表中時，使用 SQL 來處理它們是非常合理的。然而，在許多情況下，我們擁有一個數據表，並需要對該數據獲得一些**理解**或**洞察**，例如分佈情況、值之間的相關性等。在數據科學中，經常需要對原始數據進行一些轉換，然後進行可視化。這兩個步驟都可以輕鬆地使用 Python 完成。 + +Python 中有兩個非常有用的庫可以幫助您處理表格數據： +* **[Pandas](https://pandas.pydata.org/)**：允許您操作所謂的 **DataFrame**，類似於關係型表格。您可以擁有命名的列，並對行、列以及整個 DataFrame 執行不同的操作。 +* **[Numpy](https://numpy.org/)**：用於處理 **張量**（即多維 **數組**）的庫。數組中的值具有相同的基礎類型，並且比 DataFrame 更簡單，但它提供了更多的數學操作，並且開銷更小。 + +此外，還有幾個您應該了解的庫： +* **[Matplotlib](https://matplotlib.org/)**：用於數據可視化和繪製圖表的庫 +* **[SciPy](https://www.scipy.org/)**：包含一些額外科學函數的庫。我們在討論概率和統計時已經接觸過該庫 + +以下是您通常在 Python 程序開頭導入這些庫的代碼： +```python +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from scipy import ... # you need to specify exact sub-packages that you need +``` + +Pandas 圍繞幾個基本概念構建。 + +### Series + +**Series** 是一組值的序列，類似於列表或 numpy 數組。主要區別在於 Series 還有一個 **索引**，當我們對 Series 進行操作（例如相加）時，索引會被考慮在內。索引可以簡單如整數行號（從列表或數組創建 Series 時默認使用的索引），也可以具有複雜結構，例如日期區間。 + +> **注意**：在隨附的筆記本 [`notebook.ipynb`](notebook.ipynb) 中有一些 Pandas 的入門代碼。我們在這裡僅概述一些示例，您可以查看完整的筆記本。 + +舉個例子：我們想分析我們冰淇淋店的銷售情況。讓我們生成一個銷售數據的 Series（某段時間內每天售出的數量）： + +```python +start_date = "Jan 1, 2020" +end_date = "Mar 31, 2020" +idx = pd.date_range(start_date,end_date) +print(f"Length of index is {len(idx)}") +items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) +items_sold.plot() +``` +![時間序列圖](../../../../translated_images/zh-HK/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) + +假設每週我們都會為朋友舉辦派對，並額外準備 10 盒冰淇淋。我們可以創建另一個以週為索引的 Series 來展示這一點： +```python +additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W")) +``` +當我們將兩個 Series 相加時，我們得到總數： +```python +total_items = items_sold.add(additional_items,fill_value=0) +total_items.plot() +``` +![時間序列圖](../../../../translated_images/zh-HK/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) + +> **注意**：我們並未使用簡單的語法 `total_items+additional_items`。如果這樣做，結果 Series 中會有許多 `NaN`（*非數值*）值。這是因為在 `additional_items` Series 的某些索引點上存在缺失值，而將 `NaN` 與任何值相加的結果都是 `NaN`。因此，我們需要在相加時指定 `fill_value` 參數。 + +對於時間序列，我們還可以使用不同的時間間隔對其進行**重採樣**。例如，假設我們想計算每月的平均銷售量，可以使用以下代碼： +```python +monthly = total_items.resample("1M").mean() +ax = monthly.plot(kind='bar') +``` +![每月時間序列平均值](../../../../translated_images/zh-HK/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) + +### DataFrame + +DataFrame 本質上是具有相同索引的多個 Series 的集合。我們可以將多個 Series 組合成一個 DataFrame： +```python +a = pd.Series(range(1,10)) +b = pd.Series(["I","like","to","play","games","and","will","not","change"],index=range(0,9)) +df = pd.DataFrame([a,b]) +``` +這將創建如下的橫向表格： +| | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | +| --- | --- | ---- | --- | --- | ------ | --- | ------ | ---- | ---- | +| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | +| 1 | I | like | to | use | Python | and | Pandas | very | much | + +我們還可以將 Series 作為列，並使用字典指定列名： +```python +df = pd.DataFrame({ 'A' : a, 'B' : b }) +``` +這將生成如下表格： + +| | A | B | +| --- | --- | ------ | +| 0 | 1 | I | +| 1 | 2 | like | +| 2 | 3 | to | +| 3 | 4 | use | +| 4 | 5 | Python | +| 5 | 6 | and | +| 6 | 7 | Pandas | +| 7 | 8 | very | +| 8 | 9 | much | + +**注意**：我們還可以通過轉置上一個表格來獲得這種表格佈局，例如： +```python +df = pd.DataFrame([a,b]).T..rename(columns={ 0 : 'A', 1 : 'B' }) +``` +這裡 `.T` 表示轉置 DataFrame 的操作，即交換行和列，而 `rename` 操作允許我們重命名列以匹配前面的示例。 + +以下是我們可以對 DataFrame 執行的一些最重要操作： + +**選擇列**。我們可以通過 `df['A']` 選擇單個列——此操作返回一個 Series。我們還可以通過 `df[['B','A']]` 選擇列的子集到另一個 DataFrame 中——此操作返回另一個 DataFrame。 + +**篩選**符合條件的行。例如，要僅保留列 `A` 大於 5 的行，我們可以寫 `df[df['A']>5]`。 + +> **注意**：篩選的工作方式如下。表達式 `df['A']<5` 返回一個布爾 Series，指示原始 Series `df['A']` 的每個元素是否滿足條件。當布爾 Series 用作索引時，它返回 DataFrame 中的行子集。因此，不能使用任意的 Python 布爾表達式，例如，寫 `df[df['A']>5 and df['A']<7]` 是錯誤的。相反，您應該使用布爾 Series 的特殊 `&` 操作，寫作 `df[(df['A']>5) & (df['A']<7)]`（*括號在這裡很重要*）。 + +**創建新的可計算列**。我們可以通過直觀的表達式輕鬆為 DataFrame 創建新的可計算列： +```python +df['DivA'] = df['A']-df['A'].mean() +``` +此示例計算列 A 與其平均值的偏差。實際上，我們是在計算一個 Series，然後將該 Series 賦值給左側，創建另一列。因此，我們不能使用與 Series 不兼容的任何操作，例如，以下代碼是錯誤的： +```python +# Wrong code -> df['ADescr'] = "Low" if df['A'] < 5 else "Hi" +df['LenB'] = len(df['B']) # <- Wrong result +``` +後一個示例雖然語法正確，但給出了錯誤的結果，因為它將 Series `B` 的長度賦值給所有值，而不是我們預期的每個元素的長度。 + +如果我們需要計算這樣的複雜表達式，可以使用 `apply` 函數。最後一個示例可以寫成如下： +```python +df['LenB'] = df['B'].apply(lambda x : len(x)) +# or +df['LenB'] = df['B'].apply(len) +``` + +執行上述操作後，我們將得到以下 DataFrame： + +| | A | B | DivA | LenB | +| --- | --- | ------ | ---- | ---- | +| 0 | 1 | I | -4.0 | 1 | +| 1 | 2 | like | -3.0 | 4 | +| 2 | 3 | to | -2.0 | 2 | +| 3 | 4 | use | -1.0 | 3 | +| 4 | 5 | Python | 0.0 | 6 | +| 5 | 6 | and | 1.0 | 3 | +| 6 | 7 | Pandas | 2.0 | 6 | +| 7 | 8 | very | 3.0 | 4 | +| 8 | 9 | much | 4.0 | 4 | + +**基於行號選擇行**可以使用 `iloc` 結構。例如，要從 DataFrame 中選擇前 5 行： +```python +df.iloc[:5] +``` + +**分組**通常用於獲得類似於 Excel 中*樞軸表*的結果。假設我們想計算每個 `LenB` 值對應的列 `A` 的平均值。我們可以按 `LenB` 將 DataFrame 分組，然後調用 `mean`： +```python +df.groupby(by='LenB')[['A','DivA']].mean() +``` +如果我們需要計算平均值和組中的元素數量，則可以使用更複雜的 `aggregate` 函數： +```python +df.groupby(by='LenB') \ + .aggregate({ 'DivA' : len, 'A' : lambda x: x.mean() }) \ + .rename(columns={ 'DivA' : 'Count', 'A' : 'Mean'}) +``` +這將生成以下表格： + +| LenB | Count | Mean | +| ---- | ----- | -------- | +| 1 | 1 | 1.000000 | +| 2 | 1 | 3.000000 | +| 3 | 2 | 5.000000 | +| 4 | 3 | 6.333333 | +| 6 | 2 | 6.000000 | + +### 獲取數據 +我們已經看到如何輕鬆地從 Python 對象構建 Series 和 DataFrames。然而，數據通常以文本文件或 Excel 表格的形式存在。幸運的是，Pandas 為我們提供了一種簡單的方法來從磁碟中載入數據。例如，讀取 CSV 文件就像這樣簡單： +```python +df = pd.read_csv('file.csv') +``` +我們將在“挑戰”部分中看到更多載入數據的例子，包括從外部網站獲取數據。 + +### 打印和繪圖 + +數據科學家經常需要探索數據，因此能夠可視化數據非常重要。當 DataFrame 很大時，我們通常只需要打印出前幾行來確保我們的操作是正確的。這可以通過調用 `df.head()` 完成。如果你在 Jupyter Notebook 中運行，它會以漂亮的表格形式打印出 DataFrame。 + +我們還看到過使用 `plot` 函數來可視化某些列的用法。雖然 `plot` 對許多任務非常有用，並且通過 `kind=` 參數支持多種不同的圖表類型，但你也可以使用原始的 `matplotlib` 庫來繪製更複雜的圖表。我們將在單獨的課程中詳細介紹數據可視化。 + +這個概述涵蓋了 Pandas 的最重要概念，但這個庫非常豐富，幾乎沒有你不能用它完成的事情！現在讓我們應用這些知識來解決具體問題。 + +## 🚀 挑戰 1：分析 COVID 傳播 + +我們將專注於的第一個問題是 COVID-19 的流行病傳播建模。為了做到這一點，我們將使用由 [約翰霍普金斯大學](https://jhu.edu/) 的 [系統科學與工程中心](https://systems.jhu.edu/) (CSSE) 提供的不同國家感染人數數據。數據集可在 [這個 GitHub 存儲庫](https://github.com/CSSEGISandData/COVID-19) 中找到。 + +由於我們想展示如何處理數據，我們邀請你打開 [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) 並從頭到尾閱讀。你也可以執行單元格，並完成我們在最後留下的一些挑戰。 + +![COVID 傳播](../../../../translated_images/zh-HK/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) + +> 如果你不知道如何在 Jupyter Notebook 中運行代碼，可以查看 [這篇文章](https://soshnikov.com/education/how-to-execute-notebooks-from-github/)。 + +## 處理非結構化數據 + +雖然數據通常以表格形式出現，但在某些情況下我們需要處理較少結構化的數據，例如文本或圖片。在這種情況下，要應用我們上面看到的數據處理技術，我們需要以某種方式**提取**結構化數據。以下是一些例子： + +* 從文本中提取關鍵字，並查看這些關鍵字出現的頻率 +* 使用神經網絡提取圖片中物體的信息 +* 獲取視頻鏡頭中人物的情感信息 + +## 🚀 挑戰 2：分析 COVID 相關論文 + +在這個挑戰中，我們將繼續探討 COVID 疫情的主題，並專注於處理相關的科學論文。有一個 [CORD-19 數據集](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge)，其中包含超過 7000 篇（撰寫時）關於 COVID 的論文，並提供了元數據和摘要（其中約一半還提供了全文）。 + +使用 [Text Analytics for Health](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health/?WT.mc_id=academic-77958-bethanycheum) 認知服務分析此數據集的完整示例已在 [這篇博客文章](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/) 中描述。我們將討論此分析的簡化版本。 + +> **NOTE**: 我們不提供此存儲庫中的數據集副本。你可能需要先從 [Kaggle 的這個數據集](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge?select=metadata.csv) 中下載 [`metadata.csv`](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge?select=metadata.csv) 文件。可能需要在 Kaggle 註冊。你也可以從 [這裡](https://ai2-semanticscholar-cord-19.s3-us-west-2.amazonaws.com/historical_releases.html) 無需註冊下載數據集，但它將包括所有全文以及元數據文件。 + +打開 [`notebook-papers.ipynb`](notebook-papers.ipynb) 並從頭到尾閱讀。你也可以執行單元格，並完成我們在最後留下的一些挑戰。 + +![COVID 醫療處理](../../../../translated_images/zh-HK/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) + +## 處理圖片數據 + +最近，已經開發出非常強大的 AI 模型，能夠理解圖片。有許多任務可以使用預訓練的神經網絡或雲服務解決。一些例子包括： + +* **圖片分類**，可以幫助你將圖片分類到預定義的類別之一。你可以使用像 [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum) 這樣的服務輕鬆訓練自己的圖片分類器。 +* **物體檢測**，用於檢測圖片中的不同物體。像 [Computer Vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum) 這樣的服務可以檢測許多常見物體，你也可以訓練 [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum) 模型來檢測一些特定的感興趣物體。 +* **人臉檢測**，包括年齡、性別和情感檢測。這可以通過 [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum) 完成。 + +所有這些雲服務都可以通過 [Python SDKs](https://docs.microsoft.com/samples/azure-samples/cognitive-services-python-sdk-samples/cognitive-services-python-sdk-samples/?WT.mc_id=academic-77958-bethanycheum) 調用，因此可以輕鬆地集成到你的數據探索工作流程中。 + +以下是一些探索圖片數據源的例子： +* 在博客文章 [如何在無需編碼的情況下學習數據科學](https://soshnikov.com/azure/how-to-learn-data-science-without-coding/) 中，我們探索 Instagram 照片，試圖了解什麼使人們更喜歡某張照片。我們首先使用 [Computer Vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum) 從圖片中提取盡可能多的信息，然後使用 [Azure Machine Learning AutoML](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml/?WT.mc_id=academic-77958-bethanycheum) 建立可解釋的模型。 +* 在 [Facial Studies Workshop](https://github.com/CloudAdvocacy/FaceStudies) 中，我們使用 [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum) 提取活動照片中人物的情感，試圖了解什麼使人們感到快樂。 + +## 結論 + +無論你已經擁有結構化數據還是非結構化數據，使用 Python 你都可以完成所有與數據處理和理解相關的步驟。這可能是最靈活的數據處理方式，這也是大多數數據科學家使用 Python 作為主要工具的原因。如果你對數據科學之旅非常認真，深入學習 Python 可能是一個好主意！ + +## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/13) + +## 回顧與自學 + +**書籍** +* [Wes McKinney. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython](https://www.amazon.com/gp/product/1491957662) + +**線上資源** +* 官方 [10 分鐘學習 Pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html) 教程 +* [Pandas 可視化文檔](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html) + +**學習 Python** +* [用 Turtle Graphics 和分形圖以有趣的方式學習 Python](https://github.com/shwars/pycourse) +* [從 Python 開始你的第一步](https://docs.microsoft.com/learn/paths/python-first-steps/?WT.mc_id=academic-77958-bethanycheum) 在 [Microsoft Learn](http://learn.microsoft.com/?WT.mc_id=academic-77958-bethanycheum) 上的學習路徑 + +## 作業 + +[對上述挑戰進行更詳細的數據研究](assignment.md) + +## 致謝 + +這節課由 [Dmitry Soshnikov](http://soshnikov.com) 用 ♥️ 編寫。 + +--- + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/2-Working-With-Data/07-python/assignment.md b/translations/zh-HK/2-Working-With-Data/07-python/assignment.md new file mode 100644 index 00000000..6ca9caee --- /dev/null +++ b/translations/zh-HK/2-Working-With-Data/07-python/assignment.md @@ -0,0 +1,26 @@ +# 使用 Python 處理數據的作業 + +在這份作業中，我們將要求你詳細說明我們在挑戰中開始開發的代碼。作業分為兩部分： + +## COVID-19 傳播模型 + + - [ ] 在一個圖表中繪製 5-6 個不同國家的 *R* 圖表進行比較，或者使用多個並排的圖表。 + - [ ] 查看死亡人數和康復人數如何與感染病例數量相關。 + - [ ] 通過視覺上相關感染率和死亡率並尋找一些異常，找出典型疾病持續的時間。你可能需要查看不同國家的數據來得出結論。 + - [ ] 計算致死率以及它隨時間的變化。*你可能需要考慮疾病持續的天數，將一個時間序列進行移位後再進行計算。* + +## COVID-19 論文分析 + +- [ ] 建立不同藥物的共現矩陣，查看哪些藥物經常一起出現（即在同一摘要中提到）。你可以修改用於建立藥物和診斷共現矩陣的代碼。 +- [ ] 使用熱圖可視化這個矩陣。 +- [ ] 作為額外挑戰，使用 [chord diagram](https://en.wikipedia.org/wiki/Chord_diagram) 可視化藥物的共現情況。[這個庫](https://pypi.org/project/chord/) 可能可以幫助你繪製弦圖。 +- [ ] 作為另一個額外挑戰，使用正則表達式提取不同藥物的劑量（例如 **400mg** 在 *每天服用 400mg 氯喹* 中），並建立一個數據框，顯示不同藥物的不同劑量。**注意**：考慮與藥物名稱在文本中接近的數值。 + +## 評分標準 + +卓越 | 合格 | 需要改進 +--- | --- | -- | +所有任務完成，圖形化展示並解釋清楚，包括至少完成一個額外挑戰 | 完成超過 5 項任務，但未嘗試額外挑戰，或者結果不清晰 | 完成少於 5 項（但超過 3 項）任務，且可視化未能有效展示重點 + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，請注意自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為權威來源。對於重要資訊，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/2-Working-With-Data/07-python/notebook-covidspread.ipynb b/translations/zh-HK/2-Working-With-Data/07-python/notebook-covidspread.ipynb new file mode 100644 index 00000000..e2225b5f --- /dev/null +++ b/translations/zh-HK/2-Working-With-Data/07-python/notebook-covidspread.ipynb @@ -0,0 +1,2456 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 新冠疫情估算\n", + "\n", + "## 載入數據\n", + "\n", + "我們將使用由[約翰霍普金斯大學](https://jhu.edu/)的[系統科學與工程中心](https://systems.jhu.edu/)（CSSE）提供的新冠感染者數據。數據集可在[這個 GitHub 儲存庫](https://github.com/CSSEGISandData/COVID-19)中獲取。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams[\"figure.figsize\"] = (10,3) # make figures larger" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "我們可以使用 `pd.read_csv` 直接從 GitHub 加載最新的數據。如果因某些原因數據不可用，你可以隨時使用 `data` 文件夾中本地可用的副本——只需取消註釋下面定義 `base_url` 的那一行：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "base_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/\" # loading from Internet\n", + "# base_url = \"../../data/COVID/\" # loading from disk\n", + "infected_dataset_url = base_url + \"time_series_covid19_confirmed_global.csv\"\n", + "recovered_dataset_url = base_url + \"time_series_covid19_recovered_global.csv\"\n", + "deaths_dataset_url = base_url + \"time_series_covid19_deaths_global.csv\"\n", + "countries_dataset_url = base_url + \"../UID_ISO_FIPS_LookUp_Table.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", + "0 NaN Afghanistan 33.93911 67.709953 0 0 \n", + "1 NaN Albania 41.15330 20.168300 0 0 \n", + "2 NaN Algeria 28.03390 1.659600 0 0 \n", + "3 NaN Andorra 42.50630 1.521800 0 0 \n", + "4 NaN Angola -11.20270 17.873900 0 0 \n", + "\n", + " 1/24/20 1/25/20 1/26/20 1/27/20 ... 8/20/21 8/21/21 8/22/21 \\\n", + "0 0 0 0 0 ... 152448 152448 152448 \n", + "1 0 0 0 0 ... 138132 138790 139324 \n", + "2 0 0 0 0 ... 190656 191171 191583 \n", + "3 0 0 0 0 ... 14988 14988 14988 \n", + "4 0 0 0 0 ... 45583 45817 45945 \n", + "\n", + " 8/23/21 8/24/21 8/25/21 8/26/21 8/27/21 8/28/21 8/29/21 \n", + "0 152583 152660 152722 152822 152960 152960 152960 \n", + "1 139721 140521 141365 142253 143174 144079 144847 \n", + "2 192089 192626 193171 193674 194186 194671 195162 \n", + "3 15002 15003 15014 15016 15025 15025 15025 \n", + "4 46076 46340 46539 46726 46929 47079 47168 \n", + "\n", + "[5 rows x 590 columns]" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "infected = pd.read_csv(infected_dataset_url)\n", + "infected.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "我們可以看到，表格的每一行定義了每個國家和/或省份的感染人數，而列則對應日期。類似的表格可以用於加載其他數據，例如康復人數和死亡人數。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [], + "source": [ + "recovered = pd.read_csv(recovered_dataset_url)\n", + "deaths = pd.read_csv(deaths_dataset_url)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 理解數據\n", + "\n", + "從上表來看，「省份」這一列的角色並不明確。讓我們看看 `Province/State` 列中存在的不同值：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Australian Capital Territory 1\n", + "Xinjiang 1\n", + "Martinique 1\n", + "Guadeloupe 1\n", + "French Polynesia 1\n", + " ..\n", + "Fujian 1\n", + "Chongqing 1\n", + "Beijing 1\n", + "Anhui 1\n", + "Turks and Caicos Islands 1\n", + "Name: Province/State, Length: 87, dtype: int64" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "infected['Province/State'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...8/20/218/21/218/22/218/23/218/24/218/25/218/26/218/27/218/28/218/29/21
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59BeijingChina40.1824116.4142142236416880...1112111311151115111511151115111511151115
60ChongqingChina30.0572107.874069275775110...603603603603603603603603603603
61FujianChina26.0789117.98741510183559...780780780782782783783784785786
62GansuChina35.7518104.28610224714...199199199199199199199199199199
63GuangdongChina23.3417113.424426325378111151...3001300730123020302330323040304330463055
64GuangxiChina23.8298108.78812523233646...289289289289289289289289289289
65GuizhouChina26.8154106.8748133457...147147147147147147147147147147
66HainanChina19.1959109.7453458192233...190190190190190190190190190190
67HebeiChina39.5490116.130611281318...1317131713171317131713171317131713171317
68HeilongjiangChina47.8620127.761502491521...1614161416141614161416141615161516151615
69HenanChina37.8957114.90425593283128...1521152215231524152515251527152815281528
70Hong KongChina22.3000114.2000022588...12049120521205712062120691207412077120941210012107
71HubeiChina30.9756112.270744444454976110581423...68287682896828968289682896828968290682906829068290
72HunanChina27.6104111.708849244369100...1181118111811181118111811181118111811181
73Inner MongoliaChina44.0935113.94480017711...412412412412412412412412412412
74JiangsuChina32.9711119.4550159183347...1583158415841584158615861587158715891589
75JiangxiChina27.6140115.72212718183672...937937937937937937937937937937
76JilinChina43.6661126.1923013446...574574574574574574574574574574
77LiaoningChina41.2956122.6085234172127...443443443443443444445446446446
78MacauChina22.1667113.5500122256...63636363636363636363
79NingxiaChina37.2692106.1655112347...77777777777777777777
80QinghaiChina35.745295.9956000116...18181818181818181818
81ShaanxiChina35.1917108.8701035152235...668668668669669669669669669669
82ShandongChina36.3427118.14982615274675...923923923923923923923923923924
83ShanghaiChina31.2020121.449191620334053...2420243224362445245124542462246624712476
84ShanxiChina37.5777112.29221116913...255255256256256256256258258259
85SichuanChina30.6171102.71035815284469...1181118211831185118511851186118711881188
86TianjinChina39.3054117.3230448101423...458459462463464465466466470472
87TibetChina31.692788.0924000000...1111111111
88UnknownChinaNaNNaN000000...0000000000
89XinjiangChina41.112985.2401022345...980980980980980980980980980980
90YunnanChina24.9740101.4870125111626...1000100710101014102110311039104710641067
91ZhejiangChina29.1832120.093410274362104128...1420142014211428142814291429142914291430
\n", + "

34 rows × 590 columns

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" + ], + "text/plain": [ + " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", + "58 Anhui China 31.8257 117.2264 1 9 \n", + "59 Beijing China 40.1824 116.4142 14 22 \n", + "60 Chongqing China 30.0572 107.8740 6 9 \n", + "61 Fujian China 26.0789 117.9874 1 5 \n", + "62 Gansu China 35.7518 104.2861 0 2 \n", + "63 Guangdong China 23.3417 113.4244 26 32 \n", + "64 Guangxi China 23.8298 108.7881 2 5 \n", + "65 Guizhou China 26.8154 106.8748 1 3 \n", + "66 Hainan China 19.1959 109.7453 4 5 \n", + "67 Hebei China 39.5490 116.1306 1 1 \n", + "68 Heilongjiang China 47.8620 127.7615 0 2 \n", + "69 Henan China 37.8957 114.9042 5 5 \n", + "70 Hong Kong China 22.3000 114.2000 0 2 \n", + "71 Hubei China 30.9756 112.2707 444 444 \n", + "72 Hunan China 27.6104 111.7088 4 9 \n", + "73 Inner Mongolia China 44.0935 113.9448 0 0 \n", + "74 Jiangsu China 32.9711 119.4550 1 5 \n", + "75 Jiangxi China 27.6140 115.7221 2 7 \n", + "76 Jilin China 43.6661 126.1923 0 1 \n", + "77 Liaoning China 41.2956 122.6085 2 3 \n", + "78 Macau China 22.1667 113.5500 1 2 \n", + "79 Ningxia China 37.2692 106.1655 1 1 \n", + "80 Qinghai China 35.7452 95.9956 0 0 \n", + "81 Shaanxi China 35.1917 108.8701 0 3 \n", + "82 Shandong China 36.3427 118.1498 2 6 \n", + "83 Shanghai China 31.2020 121.4491 9 16 \n", + "84 Shanxi China 37.5777 112.2922 1 1 \n", + "85 Sichuan China 30.6171 102.7103 5 8 \n", + "86 Tianjin China 39.3054 117.3230 4 4 \n", + "87 Tibet China 31.6927 88.0924 0 0 \n", + "88 Unknown China NaN NaN 0 0 \n", + "89 Xinjiang China 41.1129 85.2401 0 2 \n", + "90 Yunnan China 24.9740 101.4870 1 2 \n", + "91 Zhejiang China 29.1832 120.0934 10 27 \n", + "\n", + " 1/24/20 1/25/20 1/26/20 1/27/20 ... 8/20/21 8/21/21 8/22/21 \\\n", + "58 15 39 60 70 ... 1008 1008 1008 \n", + "59 36 41 68 80 ... 1112 1113 1115 \n", + "60 27 57 75 110 ... 603 603 603 \n", + "61 10 18 35 59 ... 780 780 780 \n", + "62 2 4 7 14 ... 199 199 199 \n", + "63 53 78 111 151 ... 3001 3007 3012 \n", + "64 23 23 36 46 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63 63 63 63 \n", + "79 77 77 77 77 77 77 77 \n", + "80 18 18 18 18 18 18 18 \n", + "81 669 669 669 669 669 669 669 \n", + "82 923 923 923 923 923 923 924 \n", + "83 2445 2451 2454 2462 2466 2471 2476 \n", + "84 256 256 256 256 258 258 259 \n", + "85 1185 1185 1185 1186 1187 1188 1188 \n", + "86 463 464 465 466 466 470 472 \n", + "87 1 1 1 1 1 1 1 \n", + "88 0 0 0 0 0 0 0 \n", + "89 980 980 980 980 980 980 980 \n", + "90 1014 1021 1031 1039 1047 1064 1067 \n", + "91 1428 1428 1429 1429 1429 1429 1430 \n", + "\n", + "[34 rows x 590 columns]" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "infected[infected['Country/Region']=='China']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 數據預處理\n", + "\n", + "我們不需要將國家細分到更小的地區，因此我們首先會移除這些細分，並將所有地區的資訊合併，以獲得整個國家的資訊。這可以使用 `groupby` 完成：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + 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LatLong1/22/201/23/201/24/201/25/201/26/201/27/201/28/201/29/20...8/20/218/21/218/22/218/23/218/24/218/25/218/26/218/27/218/28/218/29/21
Country/Region
Afghanistan33.9391167.70995300000000...152448152448152448152583152660152722152822152960152960152960
Albania41.1533020.16830000000000...138132138790139324139721140521141365142253143174144079144847
Algeria28.033901.65960000000000...190656191171191583192089192626193171193674194186194671195162
Andorra42.506301.52180000000000...14988149881498815002150031501415016150251502515025
Angola-11.2027017.87390000000000...45583458174594546076463404653946726469294707947168
\n", + "

5 rows × 588 columns

\n", + "
" + ], + "text/plain": [ + " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 \\\n", + "Country/Region \n", + "Afghanistan 33.93911 67.709953 0 0 0 0 \n", + "Albania 41.15330 20.168300 0 0 0 0 \n", + "Algeria 28.03390 1.659600 0 0 0 0 \n", + "Andorra 42.50630 1.521800 0 0 0 0 \n", + "Angola -11.20270 17.873900 0 0 0 0 \n", + "\n", + " 1/26/20 1/27/20 1/28/20 1/29/20 ... 8/20/21 8/21/21 \\\n", + "Country/Region ... \n", + "Afghanistan 0 0 0 0 ... 152448 152448 \n", + "Albania 0 0 0 0 ... 138132 138790 \n", + "Algeria 0 0 0 0 ... 190656 191171 \n", + "Andorra 0 0 0 0 ... 14988 14988 \n", + "Angola 0 0 0 0 ... 45583 45817 \n", + "\n", + " 8/22/21 8/23/21 8/24/21 8/25/21 8/26/21 8/27/21 8/28/21 \\\n", + "Country/Region \n", + "Afghanistan 152448 152583 152660 152722 152822 152960 152960 \n", + "Albania 139324 139721 140521 141365 142253 143174 144079 \n", + "Algeria 191583 192089 192626 193171 193674 194186 194671 \n", + "Andorra 14988 15002 15003 15014 15016 15025 15025 \n", + "Angola 45945 46076 46340 46539 46726 46929 47079 \n", + "\n", + " 8/29/21 \n", + "Country/Region \n", + "Afghanistan 152960 \n", + "Albania 144847 \n", + "Algeria 195162 \n", + "Andorra 15025 \n", + "Angola 47168 \n", + "\n", + "[5 rows x 588 columns]" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "infected = infected.groupby('Country/Region').sum()\n", + "recovered = recovered.groupby('Country/Region').sum()\n", + "deaths = deaths.groupby('Country/Region').sum()\n", + "\n", + "infected.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "您可以看到，由於使用了 `groupby`，所有的 DataFrame 現在都以國家/地區為索引。因此，我們可以使用 `.loc` 來訪問特定國家的數據：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "infected.loc['US'][2:].plot()\n", + "recovered.loc['US'][2:].plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> **注意** 我們使用 `[2:]` 來移除序列中包含國家地理位置的前兩個元素。我們也可以完全刪除這兩列：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [], + "source": [ + "infected.drop(columns=['Lat','Long'],inplace=True)\n", + "recovered.drop(columns=['Lat','Long'],inplace=True)\n", + "deaths.drop(columns=['Lat','Long'],inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 調查數據\n", + "\n", + "現在讓我們轉向調查一個特定國家。讓我們創建一個框架，其中包含按日期索引的感染數據：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " infected recovered deaths\n", + "2020-01-22 1 0 0\n", + "2020-01-23 1 0 0\n", + "2020-01-24 2 0 0\n", + "2020-01-25 2 0 0\n", + "2020-01-26 5 0 0\n", + "... ... ... ...\n", + "2021-08-25 38223029 0 632272\n", + "2021-08-26 38384360 0 633564\n", + "2021-08-27 38707294 0 636720\n", + "2021-08-28 38760363 0 637254\n", + "2021-08-29 38796746 0 637531\n", + "\n", + "[586 rows x 3 columns]" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def mkframe(country):\n", + " df = pd.DataFrame({ 'infected' : infected.loc[country] ,\n", + " 'recovered' : recovered.loc[country],\n", + " 'deaths' : deaths.loc[country]})\n", + " df.index = pd.to_datetime(df.index)\n", + " return df\n", + "\n", + "df = mkframe('US')\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "現在讓我們計算每天新增感染者的人數。這將使我們能夠看到疫情進展的速度。最簡單的方法是使用 `diff`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['ninfected'] = df['infected'].diff()\n", + "df['ninfected'].plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "我們可以看到數據有很大的波動。我們來更仔細地看看其中一個月份：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[(df.index.year==2020) & (df.index.month==7)]['ninfected'].plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "看起來數據明顯存在每週波動。因為我們希望能夠看到趨勢，所以透過計算移動平均值（即對於每一天，我們將計算前幾天的平均值）來平滑曲線是有道理的：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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04AFAFG4.0NaNNaNNaNAfghanistan33.93911067.709953Afghanistan38928341.0
18ALALB8.0NaNNaNNaNAlbania41.15330020.168300Albania2877800.0
212DZDZA12.0NaNNaNNaNAlgeria28.0339001.659600Algeria43851043.0
320ADAND20.0NaNNaNNaNAndorra42.5063001.521800Andorra77265.0
424AOAGO24.0NaNNaNNaNAngola-11.20270017.873900Angola32866268.0
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419184056037USUSA840.056037.0SweetwaterWyomingUS41.659439-108.882788Sweetwater, Wyoming, US42343.0
419284056039USUSA840.056039.0TetonWyomingUS43.935225-110.589080Teton, Wyoming, US23464.0
419384056041USUSA840.056041.0UintaWyomingUS41.287818-110.547578Uinta, Wyoming, US20226.0
419484056043USUSA840.056043.0WashakieWyomingUS43.904516-107.680187Washakie, Wyoming, US7805.0
419584056045USUSA840.056045.0WestonWyomingUS43.839612-104.567488Weston, Wyoming, US6927.0
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4196 rows × 12 columns

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" + ], + "text/plain": [ + " UID iso2 iso3 code3 FIPS Admin2 Province_State \\\n", + "0 4 AF AFG 4.0 NaN NaN NaN \n", + "1 8 AL ALB 8.0 NaN NaN NaN \n", + "2 12 DZ DZA 12.0 NaN NaN NaN \n", + "3 20 AD AND 20.0 NaN NaN NaN \n", + "4 24 AO AGO 24.0 NaN NaN NaN \n", + "... ... ... ... ... ... ... ... \n", + "4191 84056037 US USA 840.0 56037.0 Sweetwater Wyoming \n", + "4192 84056039 US USA 840.0 56039.0 Teton Wyoming \n", + "4193 84056041 US USA 840.0 56041.0 Uinta Wyoming \n", + "4194 84056043 US USA 840.0 56043.0 Washakie Wyoming \n", + "4195 84056045 US USA 840.0 56045.0 Weston Wyoming \n", + "\n", + " Country_Region Lat Long_ Combined_Key \\\n", + "0 Afghanistan 33.939110 67.709953 Afghanistan \n", + "1 Albania 41.153300 20.168300 Albania \n", + "2 Algeria 28.033900 1.659600 Algeria \n", + "3 Andorra 42.506300 1.521800 Andorra \n", + "4 Angola -11.202700 17.873900 Angola \n", + "... ... ... ... ... \n", + "4191 US 41.659439 -108.882788 Sweetwater, Wyoming, US \n", + "4192 US 43.935225 -110.589080 Teton, Wyoming, US \n", + "4193 US 41.287818 -110.547578 Uinta, Wyoming, US \n", + "4194 US 43.904516 -107.680187 Washakie, Wyoming, US \n", + "4195 US 43.839612 -104.567488 Weston, Wyoming, US \n", + "\n", + " Population \n", + "0 38928341.0 \n", + "1 2877800.0 \n", + "2 43851043.0 \n", + "3 77265.0 \n", + "4 32866268.0 \n", + "... ... \n", + "4191 42343.0 \n", + "4192 23464.0 \n", + "4193 20226.0 \n", + "4194 7805.0 \n", + "4195 6927.0 \n", + "\n", + "[4196 rows x 12 columns]" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries = pd.read_csv(countries_dataset_url)\n", + "countries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "因為這個數據集包含了國家和省份的資訊，要獲取整個國家的總人口，我們需要稍微聰明一點：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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UIDiso2iso3code3FIPSAdmin2Province_StateCountry_RegionLatLong_Combined_KeyPopulation
790840USUSA840.0NaNNaNNaNUS40.0-100.0US329466283.0
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" + ], + "text/plain": [ + " UID iso2 iso3 code3 FIPS Admin2 Province_State Country_Region Lat \\\n", + "790 840 US USA 840.0 NaN NaN NaN US 40.0 \n", + "\n", + " Long_ Combined_Key Population \n", + "790 -100.0 US 329466283.0 " + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries[(countries['Country_Region']=='US') & countries['Province_State'].isna()]" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pop = countries[(countries['Country_Region']=='US') & countries['Province_State'].isna()]['Population'].iloc[0]\n", + "df['pinfected'] = df['infected']*100 / pop\n", + "df['pinfected'].plot(figsize=(10,3))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 計算 $R_t$\n", + "\n", + "為了了解疾病的傳染性，我們會查看**基本傳染數** $R_0$，這表示一名感染者平均會傳染給多少人。如果 $R_0$ 大於 1，疫情很可能會擴散。\n", + "\n", + "$R_0$ 是疾病本身的特性，並未考慮人們可能採取的一些減緩疫情的防護措施。在疫情發展過程中，我們可以估算任意時間 $t$ 的傳染數 $R_t$。研究顯示，這個數值可以通過取 8 天的時間窗口來大致估算，計算公式如下： \n", + "$$R_t=\\frac{I_{t-7}+I_{t-6}+I_{t-5}+I_{t-4}}{I_{t-3}+I_{t-2}+I_{t-1}+I_t}$$ \n", + "其中 $I_t$ 是第 $t$ 天新增的感染人數。\n", + "\n", + "現在讓我們為疫情數據計算 $R_t$。為此，我們將取 8 天的 `ninfected` 值作為滾動窗口，並應用上述公式來計算該比率：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['Rt'] = df['ninfected'].rolling(8).apply(lambda x: x[4:].sum()/x[:4].sum())\n", + "df['Rt'].plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "你可以看到圖表中有一些空隙。這些空隙可能是由於數據集中存在 `NaN` 或 `inf` 值所引起的。`inf` 可能是因為除以 0 而產生，而 `NaN` 則可能表示缺失數據，或者沒有可用的數據來計算結果（例如在我們框架的最開始部分，寬度為 8 的滾動窗口尚未可用）。為了讓圖表看起來更美觀，我們需要使用 `replace` 和 `fillna` 函數來填充這些值。\n", + "\n", + "接下來，我們來看看疫情初期的情況。我們還會限制 y 軸的值，只顯示低於 6 的數值，以便更清楚地觀察，並在 1 的位置繪製一條水平線。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[df.index<\"2020-05-01\"]['Rt'].replace(np.inf,np.nan).fillna(method='pad').plot(figsize=(10,3))\n", + "ax.set_ylim([0,6])\n", + "ax.axhline(1,linestyle='--',color='red')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['ninfected'].diff().plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "鑑於報告導致數據出現大量波動，透過運行移動平均來平滑曲線以獲得整體情況是有道理的。我們再次專注於疫情初期的幾個月：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax=df[df.index<\"2020-06-01\"]['ninfected'].diff().rolling(7).mean().plot()\n", + "ax.axhline(0,linestyle='-.',color='red')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 挑戰\n", + "\n", + "現在是時候讓你多玩玩代碼和數據了！以下是一些你可以嘗試的建議：\n", + "* 查看疫情在不同國家的傳播情況。\n", + "* 在一個圖上繪製多個國家的 $R_t$ 圖表進行比較，或者將多個圖表並排展示。\n", + "* 查看死亡人數和康復人數與感染病例數之間的關聯。\n", + "* 嘗試找出一種典型疾病的持續時間，通過視覺上比較感染率和死亡率，並尋找一些異常情況。你可能需要查看不同國家的數據來找出答案。\n", + "* 計算致死率以及它隨時間的變化。你可能需要考慮疾病的持續天數，將一個時間序列進行時間偏移後再進行計算。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 參考資料\n", + "\n", + "你可以在以下出版物中進一步研究 COVID 疫情的傳播：\n", + "* [滑動 SIR 模型用於 COVID 疫情期間的 Rt 估算](https://soshnikov.com/science/sliding-sir-model-for-rt-estimation/)，[Dmitry Soshnikov](http://soshnikov.com) 的博客文章\n", + "* T.Petrova, D.Soshnikov, A.Grunin. [全球 COVID-19 疫情的時間依賴性再生數估算](https://www.preprints.org/manuscript/202006.0289/v1)。*Preprints* **2020**，2020060289 (doi: 10.20944/preprints202006.0289.v1)\n", + "* [上述論文的 GitHub 代碼](https://github.com/shwars/SlidingSIR)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**免責聲明**： \n此文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤詮釋概不負責。\n" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5" + }, + "kernelspec": { + "display_name": "Python 3.8.8 64-bit (conda)", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + }, + "orig_nbformat": 4, + "coopTranslator": { + "original_hash": "68a0ada1dae45c7d1d3519aa0a6d1a0f", + "translation_date": "2025-09-02T06:48:34+00:00", + "source_file": "2-Working-With-Data/07-python/notebook-covidspread.ipynb", + "language_code": "hk" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/zh-HK/2-Working-With-Data/07-python/notebook-papers.ipynb b/translations/zh-HK/2-Working-With-Data/07-python/notebook-papers.ipynb new file mode 100644 index 00000000..21ad3288 --- /dev/null +++ b/translations/zh-HK/2-Working-With-Data/07-python/notebook-papers.ipynb @@ -0,0 +1,2341 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 分析 COVID-19 論文\n", + "\n", + "在這個挑戰中，我們將繼續探討 COVID 大流行的主題，並專注於處理有關該主題的科學論文。有一個 [CORD-19 資料集](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge)，其中包含超過 7000 篇（撰寫本文時的數據）關於 COVID 的論文，並附有元數據和摘要（其中約有一半的論文還提供了全文）。\n", + "\n", + "使用 [Text Analytics for Health](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health/?WT.mc_id=academic-77958-bethanycheum) 認知服務分析該資料集的完整範例已在[這篇部落格文章](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/)中描述。我們將討論這種分析的簡化版本。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 獲取數據\n", + "\n", + "首先，我們需要獲取我們將要處理的 CORD 論文的元數據。\n", + "\n", + "**注意**：我們並未在此存儲庫中提供數據集的副本。您需要先從 [Kaggle 上的這個數據集](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge?select=metadata.csv) 下載 [`metadata.csv`](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge?select=metadata.csv) 文件。可能需要在 Kaggle 上註冊。您也可以從[這裡](https://ai2-semanticscholar-cord-19.s3-us-west-2.amazonaws.com/historical_releases.html)下載數據集，無需註冊，但這將包含所有全文以及元數據文件。\n", + "\n", + "我們將嘗試直接從線上來源獲取數據，但如果失敗，您需要按照上述說明下載數據。此外，如果您計劃進一步進行實驗，下載數據是有意義的，這樣可以節省等待時間。\n", + "\n", + "> **注意** 數據集相當大，大約 1 Gb，以下這行代碼可能需要很長時間才能完成！（大約 5 分鐘）\n" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\winapp\\Miniconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3441: DtypeWarning:\n", + "\n", + "Columns (1,4,5,6,13,14,15,16) have mixed types.Specify dtype option on import or set low_memory=False.\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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NaN \n", + "3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5... NaN \n", + "4 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5... NaN " + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(\"https://datascience4beginners.blob.core.windows.net/cord/metadata.csv.zip\",compression='zip')\n", + "# df = pd.read_csv(\"metadata.csv\")\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "我們現在將把出版日期列轉換為 `datetime`，並繪製直方圖以查看出版日期的範圍。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['publish_time'] = pd.to_datetime(df['publish_time'])\n", + "df['publish_time'].hist()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 結構化數據提取\n", + "\n", + "讓我們看看可以從摘要中輕鬆提取哪些信息。我們可能感興趣的一件事是了解有哪些治療策略存在，以及它們隨著時間的演變情況。首先，我們可以手動整理出用於治療 COVID 的可能藥物清單，以及診斷清單。然後，我們檢視這些清單，並在論文摘要中搜尋相應的術語。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " + Processing medication: hydroxychloroquine\n", + " + Processing medication: chloroquine\n", + " + Processing medication: tocilizumab\n", + " + Processing medication: remdesivir\n", + " + Processing medication: azithromycin\n", + " + Processing medication: lopinavir\n", + " + Processing medication: ritonavir\n", + " + Processing medication: dexamethasone\n", + " + Processing medication: heparin\n", + " + Processing medication: favipiravir\n", + " + Processing medication: methylprednisolone\n", + " + Processing diagnosis: covid\n", + " + Processing diagnosis: sars\n", + " + Processing diagnosis: pneumonia\n", + " + Processing diagnosis: infection\n", + " + Processing diagnosis: diabetes\n", + " + Processing diagnosis: coronavirus\n", + " + Processing diagnosis: death\n" + ] + } + ], + "source": [ + "medications = [\n", + " 'hydroxychloroquine', 'chloroquine', 'tocilizumab', 'remdesivir', 'azithromycin', \n", + " 'lopinavir', 'ritonavir', 'dexamethasone', 'heparin', 'favipiravir', 'methylprednisolone']\n", + "diagnosis = [\n", + " 'covid','sars','pneumonia','infection','diabetes','coronavirus','death'\n", + "]\n", + "\n", + "for m in medications:\n", + " print(f\" + Processing medication: {m}\")\n", + " df[m] = df['abstract'].apply(lambda x: str(x).lower().count(' '+m))\n", + " \n", + "for m in diagnosis:\n", + " print(f\" + Processing diagnosis: {m}\")\n", + " df[m] = df['abstract'].apply(lambda x: str(x).lower().count(' '+m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "我們已經在數據框中新增了一些欄位，這些欄位記錄了某種藥物或診斷在摘要中出現的次數。\n", + "\n", + "> **注意** 我們在尋找子字串時，會在單詞的開頭加上一個空格。如果不這樣做，可能會得到錯誤的結果，因為*chloroquine*也可能會在子字串*hydroxychloroquine*中被找到。此外，我們強制將摘要欄轉換為`str`類型，以避免出現錯誤——試試移除`str`看看會發生什麼。\n", + "\n", + "為了讓數據處理更方便，我們可以提取只包含藥物計數的子框架，並計算累積出現次數。這樣可以找出最受歡迎的藥物：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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6ritonavir948
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" + ], + "text/plain": [ + " Name Count\n", + "0 hydroxychloroquine 9806\n", + "3 remdesivir 7861\n", + "2 tocilizumab 6118\n", + "1 chloroquine 4578\n", + "8 heparin 4161\n", + "5 lopinavir 3811\n", + "4 azithromycin 3585\n", + "7 dexamethasone 3340\n", + "9 favipiravir 2439\n", + "10 methylprednisolone 1600\n", + "6 ritonavir 948" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dfm = df[medications]\n", + "dfm = dfm.sum().reset_index().rename(columns={ 'index' : 'Name', 0 : 'Count'})\n", + "dfm.sort_values('Count',ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dfm.set_index('Name').plot(kind='bar')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 尋找治療策略的趨勢\n", + "\n", + "在上述例子中，我們已經對所有數值進行了`sum`運算，但我們也可以按月進行相同的操作：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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hydroxychloroquinechloroquinetocilizumabremdesivirazithromycinlopinavirritonavirdexamethasoneheparinfavipiravirmethylprednisolone
publish_timepublish_time
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11272661701977910427771247744
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2021121917801787252389284119812081096805474
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3172851902958710017150828536
4198701251618360131301446037
51415513817969552110814110644
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\n", + "
" + ], + "text/plain": [ + " hydroxychloroquine chloroquine tocilizumab \\\n", + "publish_time publish_time \n", + "2020 1 3672 1773 1779 \n", + " 2 0 19 0 \n", + " 3 45 72 5 \n", + " 4 188 238 50 \n", + " 5 459 191 158 \n", + " 6 381 149 243 \n", + " 7 381 178 202 \n", + " 8 307 115 172 \n", + " 9 319 123 185 \n", + " 10 319 96 212 \n", + " 11 272 66 170 \n", + " 12 255 102 229 \n", + "2021 1 2191 780 1787 \n", + " 2 163 66 184 \n", + " 3 172 85 190 \n", + " 4 198 70 125 \n", + " 5 141 55 138 \n", + " 6 144 29 138 \n", + " 7 112 49 96 \n", + "\n", + " remdesivir azithromycin lopinavir ritonavir \\\n", + "publish_time publish_time \n", + "2020 1 2134 1173 1430 370 \n", + " 2 3 3 18 11 \n", + " 3 27 12 52 16 \n", + " 4 124 68 113 13 \n", + " 5 209 132 135 41 \n", + " 6 186 110 132 18 \n", + " 7 165 108 138 29 \n", + " 8 165 145 91 24 \n", + " 9 190 91 98 28 \n", + " 10 227 72 127 39 \n", + " 11 197 79 104 27 \n", + " 12 271 98 76 31 \n", + "2021 1 2523 892 841 198 \n", + " 2 173 85 76 9 \n", + " 3 295 87 100 17 \n", + " 4 161 83 60 13 \n", + " 5 179 69 55 21 \n", + " 6 182 75 41 12 \n", + " 7 270 64 59 5 \n", + "\n", + " dexamethasone heparin favipiravir \\\n", + "publish_time publish_time \n", + "2020 1 561 984 666 \n", + " 2 1 3 12 \n", + " 3 3 21 11 \n", + " 4 14 77 48 \n", + " 5 12 92 48 \n", + " 6 48 84 30 \n", + " 7 58 117 56 \n", + " 8 56 95 45 \n", + " 9 90 111 46 \n", + " 10 97 117 81 \n", + " 11 77 124 77 \n", + " 12 76 87 56 \n", + "2021 1 1208 1096 805 \n", + " 2 86 61 52 \n", + " 3 150 82 85 \n", + " 4 130 144 60 \n", + " 5 108 141 106 \n", + " 6 128 116 66 \n", + " 7 169 106 44 \n", + "\n", + " methylprednisolone \n", + "publish_time publish_time \n", + "2020 1 331 \n", + " 2 19 \n", + " 3 14 \n", + " 4 14 \n", + " 5 21 \n", + " 6 29 \n", + " 7 27 \n", + " 8 35 \n", + " 9 26 \n", + " 10 37 \n", + " 11 44 \n", + " 12 59 \n", + "2021 1 474 \n", + " 2 63 \n", + " 3 36 \n", + " 4 37 \n", + " 5 44 \n", + " 6 42 \n", + " 7 50 " + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dfm = df[['publish_time']+medications].set_index('publish_time')\n", + "dfm = dfm[(dfm.index>=\"2020-01-01\") & (dfm.index<=\"2021-07-31\")]\n", + "dfmt = dfm.groupby([dfm.index.year,dfm.index.month]).sum()\n", + "dfmt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dfmt.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "一個有趣的觀察是，我們在兩個時間點出現了巨大的峰值：2020年1月和2021年1月。這是因為有些論文沒有明確標註發表日期，因此被標註為該年份的1月。\n", + "\n", + "為了讓數據更有意義，我們來只視覺化幾種藥物。我們還會「抹去」1月的數據，並用某個中間值填補，以便繪製出更美觀的圖表：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "meds = ['hydroxychloroquine','tocilizumab','favipiravir']\n", + "dfmt.loc[(2020,1)] = np.nan\n", + "dfmt.loc[(2021,1)] = np.nan\n", + "dfmt.fillna(method='pad',inplace=True)\n", + "fig, ax = plt.subplots(1,len(meds),figsize=(10,3))\n", + "for i,m in enumerate(meds):\n", + " dfmt[m].plot(ax=ax[i])\n", + " ax[i].set_title(m)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dfmt.plot.area()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "甚至更進一步，我們可以以百分比計算相對受歡迎程度：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dfmtp = dfmt.iloc[:,:].apply(lambda x: x/x.sum(), axis=1)\n", + "dfmtp.plot.area()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 計算藥物與診斷的對應關係\n", + "\n", + "其中一個最有趣的關係是不同的診斷如何使用不同的藥物進行治療。為了將這種關係可視化，我們需要計算**共現頻率圖**，這可以顯示兩個術語在同一篇論文中被提及的次數。\n", + "\n", + "這種圖本質上是一個二維矩陣，最適合用**numpy array**來表示。我們將通過遍歷所有摘要來計算這個圖，並標記出在摘要中出現的實體：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [], + "source": [ + "m = np.zeros((len(medications),len(diagnosis)))\n", + "for a in df['abstract']:\n", + " x = str(a).lower()\n", + " for i,d in enumerate(diagnosis):\n", + " if ' '+d in x:\n", + " for j,me in enumerate(medications):\n", + " if ' '+me in x:\n", + " m[j,i] += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[4788., 2264., 741., 2109., 348., 2730., 975.],\n", + " [2111., 1238., 231., 998., 79., 1394., 364.],\n", + " [2186., 821., 691., 1063., 185., 1136., 573.],\n", + " [3210., 2191., 522., 1538., 160., 2191., 622.],\n", + " [1803., 773., 406., 880., 133., 909., 410.],\n", + " [1982., 1102., 379., 885., 113., 1366., 370.],\n", + " [ 504., 356., 83., 259., 23., 354., 106.],\n", + " [1419., 640., 345., 742., 108., 760., 314.],\n", + " [1537., 678., 330., 782., 93., 826., 301.],\n", + " [ 967., 634., 201., 431., 44., 656., 136.],\n", + " [ 660., 336., 293., 385., 53., 452., 148.]])" + ] + }, + "execution_count": 157, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "其中一種可視化此矩陣的方法是繪製一個**熱圖**：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(m,interpolation='nearest',cmap='hot')\n", + "ax = plt.gca()\n", + "ax.set_yticks(range(len(medications))) \n", + "ax.set_yticklabels(medications)\n", + "ax.set_xticks(range(len(diagnosis)))\n", + "ax.set_xticklabels(diagnosis,rotation=90)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "然而，使用所謂的 **Sankey** 圖表可以進行更好的可視化！`matplotlib` 並不內建支援這種圖表類型，因此我們需要使用 [Plotly](https://plotly.com/python/)，如 [這篇教學](https://plotly.com/python/sankey-diagram/) 所述。\n", + "\n", + "要製作 Plotly 的 Sankey 圖表，我們需要建立以下列表：\n", + "* 列表 `all_nodes`，包含圖中所有的節點，包括藥物和診斷\n", + "* 來源和目標索引的列表——這些列表將顯示哪些節點位於圖表的左側，哪些位於右側\n", + "* 所有連結的列表，每個連結包含：\n", + " - 在 `all_nodes` 陣列中的來源索引\n", + " - 目標索引\n", + " - 表示連結強度的值。這正是我們共現矩陣中的值。\n", + " - （可選）連結的顏色。我們將提供選項以突出顯示某些術語以提高清晰度\n", + "\n", + "繪製 Sankey 圖表的通用程式碼被結構化為一個獨立的 `sankey` 函數，該函數接收兩個列表（來源和目標類別）以及共現矩陣。它還允許我們指定閾值，並省略所有低於該閾值的連結——這使得圖表稍微簡化一些。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "link": { + "color": [ + "pink", + "lightgray", + "lightgray", + "lightgray", + "lightgray", + "lightgray", + "pink", + "lightgray", + "lightgray", + "lightgray", + "pink", + "lightgray", + "lightgray", + "lightgray", + "lightgray", + "lightgray", + "pink", + "lightgray", + "lightgray", + "lightgray", + "lightgray", + "lightgray", + "pink", + "lightgray", + "lightgray", + "lightgray", + "pink", + "lightgray", + "lightgray", + "lightgray", + "pink", + "pink", + "lightgray", + "lightgray", + "lightgray", + "pink", + "lightgray", + "lightgray", + "lightgray", + "pink", + "lightgray", + "lightgray", + "pink" + ], + "source": [ + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 1, + 1, + 1, + 2, + 2, + 2, + 2, + 2, 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"hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objects as go\n", + "\n", + "def sankey(cat1, cat2, m, treshold=0, h1=[], h2=[]):\n", + " all_nodes = cat1 + cat2\n", + " source_indices = list(range(len(cat1)))\n", + " target_indices = list(range(len(cat1),len(cat1)+len(cat2)))\n", + "\n", + " s, t, v, c = [], [], [], []\n", + " for i in range(len(cat1)):\n", + " for j in range(len(cat2)):\n", + " if m[i,j]>treshold:\n", + " s.append(i)\n", + " t.append(len(cat1)+j)\n", + " v.append(m[i,j])\n", + " c.append('pink' if i in h1 or j in h2 else 'lightgray')\n", + "\n", + " fig = go.Figure(data=[go.Sankey(\n", + " # Define nodes\n", + " node = dict(\n", + " pad = 40,\n", + " thickness = 40,\n", + " line = dict(color = \"black\", width = 1.0),\n", + " label = all_nodes),\n", + "\n", + " # Add links\n", + " link = dict(\n", + " source = s,\n", + " target = t,\n", + " value = v,\n", + " color = c\n", + " ))])\n", + " fig.show()\n", + "\n", + "sankey(medications,diagnosis,m,500,h2=[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 結論\n", + "\n", + "你已經看到，我們可以使用相當簡單的方法，從非結構化的數據來源（例如文本）中提取信息。在這個例子中，我們使用了現有的藥物清單，但如果使用自然語言處理（NLP）技術來進行文本中的實體提取，效果會更強大。在[這篇博客文章](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/)中，我們描述了如何使用雲端服務進行實體提取。另一個選擇是使用 Python 的 NLP 庫，例如 [NLTK](https://www.nltk.org/)——使用 NLTK 從文本中提取信息的方法已在[這裡](https://www.nltk.org/book/ch07.html)描述。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 挑戰\n", + "\n", + "繼續沿以下方向研究 COVID 論文數據：\n", + "\n", + "1. 建立不同藥物的共現矩陣，查看哪些藥物經常一起出現（即在同一摘要中提到）。你可以修改用於建立藥物和診斷共現矩陣的代碼。\n", + "1. 使用熱圖來可視化這個矩陣。\n", + "1. 作為進階目標，你可以嘗試使用 [chord diagram](https://en.wikipedia.org/wiki/Chord_diagram) 可視化藥物的共現情況。[這個庫](https://pypi.org/project/chord/) 可能可以幫助你繪製 chord diagram。\n", + "1. 作為另一個進階目標，嘗試使用正則表達式提取不同藥物的劑量（例如 **400mg** 在 *每天服用 400mg 的氯喹* 中），並建立一個數據框，顯示不同藥物的不同劑量。**注意**：考慮與藥物名稱在文本中距離較近的數值。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**免責聲明**： \n此文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤詮釋概不負責。\n" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5" + }, + "kernelspec": { + "display_name": "Python 3.8.8 64-bit (conda)", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + }, + "orig_nbformat": 4, + "coopTranslator": { + "original_hash": "563424d708ece56f0ef9b78b58755442", + "translation_date": "2025-09-02T06:05:44+00:00", + "source_file": "2-Working-With-Data/07-python/notebook-papers.ipynb", + "language_code": "hk" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/zh-HK/2-Working-With-Data/07-python/notebook.ipynb b/translations/zh-HK/2-Working-With-Data/07-python/notebook.ipynb new file mode 100644 index 00000000..161ef650 --- /dev/null +++ b/translations/zh-HK/2-Working-With-Data/07-python/notebook.ipynb @@ -0,0 +1,1510 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 基本的 Pandas 範例\n", + "\n", + "這份筆記本將帶你了解一些非常基本的 Pandas 概念。我們會從匯入常見的數據科學庫開始：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 系列\n", + "\n", + "系列就像一個列表或一維陣列，但帶有索引。所有操作都會根據索引對齊。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1\n", + "1 2\n", + "2 3\n", + "3 4\n", + "4 5\n", + "5 6\n", + "6 7\n", + "7 8\n", + "8 9\n", + "dtype: int64 0 I\n", + "1 like\n", + "2 to\n", + "3 use\n", + "4 Python\n", + "5 and\n", + "6 Pandas\n", + "7 very\n", + "8 much\n", + "dtype: object\n" + ] + } + ], + "source": [ + "a = pd.Series(range(1,10))\n", + "b = pd.Series([\"I\",\"like\",\"to\",\"use\",\"Python\",\"and\",\"Pandas\",\"very\",\"much\"],index=range(0,9))\n", + "print(a,b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "其中一個常見的 Series 用途是 **時間序列**。在時間序列中，索引具有一種特殊的結構——通常是一系列日期或時間日期。我們可以使用 `pd.date_range` 來創建這樣的索引。\n", + "\n", + "假設我們有一個 Series，顯示每天購買的產品數量，而我們知道每個星期日我們還需要為自己拿一件商品。以下是如何使用 Series 來建模這種情況：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Length of index is 366\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "start_date = \"Jan 1, 2020\"\n", + "end_date = \"Dec 31, 2020\"\n", + "idx = pd.date_range(start_date,end_date)\n", + "print(f\"Length of index is {len(idx)}\")\n", + "items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx)\n", + "items_sold.plot(figsize=(10,3))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Additional items (10 item each week):\n", + "2020-01-05 10\n", + "2020-01-12 10\n", + "2020-01-19 10\n", + "2020-01-26 10\n", + "2020-02-02 10\n", + "2020-02-09 10\n", + "2020-02-16 10\n", + "2020-02-23 10\n", + "2020-03-01 10\n", + "2020-03-08 10\n", + "2020-03-15 10\n", + "2020-03-22 10\n", + "2020-03-29 10\n", + "2020-04-05 10\n", + "2020-04-12 10\n", + "2020-04-19 10\n", + "2020-04-26 10\n", + "2020-05-03 10\n", + "2020-05-10 10\n", + "2020-05-17 10\n", + "2020-05-24 10\n", + "2020-05-31 10\n", + "2020-06-07 10\n", + "2020-06-14 10\n", + "2020-06-21 10\n", + "2020-06-28 10\n", + "2020-07-05 10\n", + "2020-07-12 10\n", + "2020-07-19 10\n", + "2020-07-26 10\n", + "2020-08-02 10\n", + "2020-08-09 10\n", + "2020-08-16 10\n", + "2020-08-23 10\n", + "2020-08-30 10\n", + "2020-09-06 10\n", + "2020-09-13 10\n", + "2020-09-20 10\n", + "2020-09-27 10\n", + "2020-10-04 10\n", + "2020-10-11 10\n", + "2020-10-18 10\n", + "2020-10-25 10\n", + "2020-11-01 10\n", + "2020-11-08 10\n", + "2020-11-15 10\n", + "2020-11-22 10\n", + "2020-11-29 10\n", + "2020-12-06 10\n", + "2020-12-13 10\n", + "2020-12-20 10\n", + "2020-12-27 10\n", + "Freq: W-SUN, dtype: int64\n", + "Total items (sum of two series):\n", + "2020-01-01 NaN\n", + "2020-01-02 NaN\n", + "2020-01-03 NaN\n", + "2020-01-04 NaN\n", + "2020-01-05 54.0\n", + " ... \n", + "2020-12-27 43.0\n", + "2020-12-28 NaN\n", + "2020-12-29 NaN\n", + "2020-12-30 NaN\n", + "2020-12-31 NaN\n", + "Length: 366, dtype: float64\n" + ] + } + ], + "source": [ + "additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq=\"W\"))\n", + "print(f\"Additional items (10 item each week):\\n{additional_items}\")\n", + "total_items = items_sold+additional_items\n", + "print(f\"Total items (sum of two series):\\n{total_items}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "正如你所見，我們在這裡遇到了問題，因為在每週的序列中，未提及的日子被視為缺失值（`NaN`），而將 `NaN` 加到一個數字會得到 `NaN`。為了獲得正確的結果，我們需要在相加序列時指定 `fill_value`：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-01-01 26.0\n", + "2020-01-02 25.0\n", + "2020-01-03 37.0\n", + "2020-01-04 30.0\n", + "2020-01-05 54.0\n", + " ... \n", + "2020-12-27 43.0\n", + "2020-12-28 44.0\n", + "2020-12-29 36.0\n", + "2020-12-30 38.0\n", + "2020-12-31 34.0\n", + "Length: 366, dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "total_items = items_sold.add(additional_items,fill_value=0)\n", + "print(total_items)\n", + "total_items.plot(figsize=(10,3))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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UwsPDpWnTpmqahYWF1K9fX06ePJmdf4qIiIiIiEj11k9s/kl4eLiIiDg4OOilOzg4yP3799/4maSkJElKSlJ/j46Ozs4sERERERFRHpAjs6IpiqL3O4AsaTrTp08XOzs79cfV1TUnskREREREREYsWwMbR0dHEfnryY1ORERElqc4OmPHjpVXr16pP2FhYdmZJSIiIiIiygOyNbBxc3MTR0dHOXz4sJqWnJwsx44dEz8/vzd+xsLCQmxtbfV+iIiIiIiI3sZbv2MTGxsrt2/fVn8PDQ2VixcvSuHChaVEiRIybNgwmTZtmri7u4u7u7tMmzZNrK2tpWvXrtmacSIiIiIiIp23DmzOnTsnDRo0UH8fMWKEiIj06tVL1q5dK6NHj5aEhAQZMGCAREZGio+Pjxw6dEhsbGyyL9dERERERESZvHVgExAQIAD+druiKDJp0iSZNGnSu+SLiIiIiIjoX8uRWdGIiIiIiIhyEwMbIiIiIiLSPAY2RERERESkeQxsiIiIiIhI8xjYEBERERGR5jGwISIiIiIizWNgQ0REREREmsfAhoiIiIiINI+BDRERERERaR4DGyIiIiIi0jwGNkREREREpHkMbIiIiIiISPMY2BARERERkeYxsCEiIiIiIs1jYENERERERJrHwIaIiIiIiDSPgQ0REREREWkeAxsiIiIiItI8BjZERERERKR5DGyIiIiIiEjzGNgQEREREZHmMbAhIiIiIiLNY2BDRERERESax8CGiIiIiIg0j4ENERERERFpHgMbIiIiIiLSPAY2RERERESkeQxsiIiIiIhI8xjYEBERERGR5jGwISIiIiIizWNgQ0REREREmsfAhoiIiIiINI+BDRERERERaR4DGyIiIiIi0jwGNkREREREpHkMbIiIiIiISPMY2BARERERkeYxsCEiIiIiIs1jYENERERERJrHwIaIiIiIiDSPgQ0REREREWkeAxsiIiIiItI8BjZERERERKR5DGyIiIiIiEjzGNgQEREREZHm5Vhgs2TJEnFzcxNLS0upXr26nDhxIqf+FBERERER5XE5Eths3bpVhg0bJuPGjZMLFy6Iv7+/tGjRQh48eJATf46IiIiIiPK4HAls5syZI3379pV+/fpJxYoVZd68eeLq6ipLly7NiT9HRERERER5XL7s/geTk5Plzz//lM8++0wvvWnTpnLy5Mks+yclJUlSUpL6+6tXr0REJDo6+p3ykZ4U/06ff1fvmv93ldePX4RlYOjjF2EZGPr4RVgGef34RVgGhj5+EZZBXj9+EZbBuxy/7rMA/u+dkc0ePXoEEcHvv/+ulz516lSUK1cuy/4TJ06EiPCHP/zhD3/4wx/+8Ic//OHPG3/CwsL+zzgk25/Y6CiKovc7gCxpIiJjx46VESNGqL+np6fLy5cvpUiRIm/cPzdER0eLq6urhIWFia2trUHyYEh5/fhFWAYiLIO8fvwiLIO8fvwiLIO8fvwiLAMRloGhjx+AxMTEiLOz8/+5b7YHNkWLFhVTU1MJDw/XS4+IiBAHB4cs+1tYWIiFhYVeWsGCBbM7W/8TW1vbPHkC6+T14xdhGYiwDPL68YuwDPL68YuwDPL68YuwDERYBoY8fjs7u3+1X7ZPHmBubi7Vq1eXw4cP66UfPnxY/Pz8svvPERERERERZf8TGxGRESNGSI8ePaRGjRpSu3ZtWbFihTx48ED69++fE3+OiIiIiIjyuBwJbLp06SIvXryQL7/8Up48eSJVqlSR/fv3S8mSJXPiz2U7CwsLmThxYpYhcnlFXj9+EZaBCMsgrx+/CMsgrx+/CMsgrx+/CMtAhGWgpeNXgH8zdxoREREREdF/V44s0ElERERERJSbGNgQEREREZHmMbAhIiIiIiLNY2BDRERERESax8Amh3FuBiKivFsX5tXjzgyAWg4sDyLKSZwVLRudP39eXr16JUlJSeLv7y/58+cXkYyKXFEUA+cu9+S146WsMp8D6enpYmLCPhSdvHB9nDx5Uh4/fixJSUlSp04dKVWqlIjkjXMhOjpakpKSxN7eXk3LC8dN/4x14l/yQh1I/ywnzwEGNtlk5cqV8tlnn4mTk5MEBwdLQECAdO7cWV2UNK9cyLoK+9WrVxIVFSUFChSQ/Pnzi6WlZZ4pg8xeP+a8UgbR0dFiamqqBvd52fPnzyUhIUEcHBzE3NxcRIy7YbNq1SoZMWKEVK5cWc6fPy+enp7SqFEjmTZtmogY97GvX79eVq1aJSEhIeLr6yu+vr4yevRoETHu4/4ne/bskZ9++kliY2PF29tbPvroI7GxsRGRvFMfioi8ePFCrKysRETE2trawLnJXQ8fPpRz586Joiji7u4ulSpVMnSW/hPyYp2gO2bdtZ8TZcDAJhv88ccf0qpVK1m0aJE0btxYYmJiZOzYsXL37l1p0qSJfPXVVyJi/JW47gS9cuWKdO/eXZKTkyU2Nlbq168vQ4cOlZo1a+aZC/nGjRuyZMkSuX//vvj5+UmtWrWkQYMGImL858HGjRtl6dKlEh0dLfnz55exY8dKnTp1pGjRokZ/7K9bu3atLFq0SMLCwqRixYpSq1YtmT59upiamkpaWpqYmpoaOovZ6urVq9K0aVOZPXu2vPfeexIVFSVz586Vffv2iZeXl6xfv15EjPMa2LFjh3Tv3l2mT58uNjY2curUKTl27Jh4enrK999/LyJ5ryGzfv166d+/v3Tq1EmSk5Nl79694ufnJyNHjpTmzZuLiHGeC6/bsGGDLFy4UOLj40VRFJk0aZLUr19fihYtauis5bjLly9L8+bNpXDhwhITEyPPnj2TyZMnS+/evfWeahq7kJAQ2b59uzx9+lSqVasmLVq0kGLFiuWpOuHmzZuybNkyefnypZQoUUKCgoLE1dU1+/8Q6J1t2bIF5cuXR3R0tJoWHh6OMWPGoHr16pgxY4YBc5e7wsLC4ODggKFDh+L06dOYP38+AgMDUbRoURw5cgQAkJaWZuBc5qxr166hYMGC6NChA3r06IGKFSuiRo0a+Oqrr9R90tPTDZjDnLNjxw5YWFhgxowZWL9+PTp06IBy5cph+PDhuHfvnqGzl6t2794NS0tLLF68GHv27MHIkSPh4eEBPz8/pKSkADC+a+HQoUMoWbIkwsPD1bSoqCgsW7YMlStXxsCBAw2Yu5yTlpaGTz75BEOHDlXTXr16he3bt8PR0RFt2rRR04312s8sPT0dT58+hbe3N5YuXaqm37t3DzVr1kS9evWwd+9eA+Yw9/zwww+wtLTEggULsGrVKgwYMAD58+fH8OHDcePGDUNnL0e9ePECVatWxejRoxEdHY379+9j4cKFyJcvHwYMGICwsDBDZzFXXL16FQULFkS7du3g6emJ6tWro2TJkrh06ZKhs5Zrrl27Bjs7O3Tt2hXNmzeHn58f7OzssHv37my/DzKwyQa7d+9GqVKlcP36dQBAamoqAODZs2f45JNP4Ofnh+DgYENmMdfs27cPPj4+iImJUdOuXbuGXr16wcrKCseOHQNgvDf3lJQUBAUFoVevXmranTt3MH78eLi5uWH8+PGGy1wOSk9PR0pKCt5//329xh0ATJ8+HTVq1MCgQYPw6NEjw2TQAEaPHo1+/fqpvyclJeHw4cOoWLEiatasqdYTxnQtnD17FiVKlMDhw4f10qOjozFz5kx4e3vj4MGDBspdzmratCkCAwP10pKSkrBz5064ublh5MiRBsqZYURFRaFcuXL47rvvAADJyckAgEePHqF27dqoX78+7t69C8C4rgEd3TH16dNHrx4AgG+//RalS5fGkCFD8ODBA0NkL1c8fvwYFSpUwK+//qqXvmvXLpibm2PEiBFIS0szyu9fJykpCa1bt0bv3r3VtPPnz6N9+/awtbXF77//DsD4OrkyS0tLQ/fu3dGlSxc17dmzZxgwYAAsLCzUOiK7zoO88fwrh1WtWlViYmJkzZo1IiJiamoq6enpUrRoUZk8ebLcuHFDDhw4YOBc5o64uDi5cOGCvHz5Uk2rWLGiTJw4UTp16iQDBgyQkJAQox16kC9fPgkNDZW0tDQ1rXTp0jJw4EDp06eP7NixQ1asWGHAHOYMRVEkX758kpSUJFFRUSIikpKSIiIin332mXTp0kWOHTsme/fuFZGMITnG7uHDh3L16lX1d3Nzc2nYsKEsXrxYkpKSpE+fPkY3DKd48eJiZ2cn69atk1evXqnpNjY20r9/f0lKSpJffvnFgDnMfrpzuVWrVvLixQs5d+6cus3c3FwaN24svXv3ltOnT0tERIShsmkQqamp6jVgZmYmKSkp4uzsLDt27JBr167JggULRESM6hp4XVRUlKSmpoqISHJysoiI9OvXTz7//HP57rvvZN++fSJinHViXFychIaGSnx8vIhk3BPS09Olbdu2smHDBpk7d67s3bvXqL//tLQ0efr0qXh6eqppXl5esnLlSmnVqpW0atVKQkND1fdOjJGiKPL06VMpXbq0iGQMPy1atKgsXrxYBg4cKP369ZPTp0+r79y8KwY2/4O4uDi9m7abm5ssXLhQZs+eLfPmzRORjC8SgNjb24u/v788fvzYQLnNHboLsmrVqlKlShXZsWOHWpmJZJRRUFCQmJuby8WLFw2Uy5ylKwNvb2+JjIyUp0+fqtscHR2lV69eUrNmTdmzZ49e2RiT4sWLy++//y5JSUliZmYmSUlJIiIyatQoqVOnjkyfPl1SUlKMekyxrmJu1qyZpKSk6DXkTUxMxM/PT/r16yfXrl2T+/fvGyqb2SIyMlKePHmiTufr6OgoCxculM2bN8tXX32ld57b2NhI7dq1JTw83IA5zn66c9nf31+ePn0qS5culbCwMHW7jY2NtG7dWk6ePCk3b940VDZznZ2dnYwfP17mz58vW7duFRFR6wQnJyeZNGmS/PTTT/LixQujbNTrGuseHh6yZ88eefHihZibm6sdPn379pXBgwfL+PHjJTw83KjqRN29sGzZsvLBBx/I6NGjJSQkRMzMzASApKWlSefOnSUoKEgWL14s8fHxRtuot7KyEkdHRzl8+LB6jIqiSOHChWXWrFlSo0YNGTJkiCQmJhptgKcoihQvXlx+/PFHSUlJEUVR1M7fb775Rtq0aSNBQUESExOTLdeB8VxJuWT79u3SsWNH8fHxkWbNmqm9Ue+99558/fXXMmLECJkyZYpERUWJoiiSmJgoDx48kCJFihg459nr9RuR7oKsWLGiVK9eXRYuXCi//vqrWomLiNStW1dMTU3l559/ztW85hZdGTRs2FCOHDki69atU3vqRER9WW7//v1y48YNQ2UzR02dOlXS0tKkQ4cOIiJiYWEhiYmJIiLyxRdfSHR0tJw5c8aQWcxxuoo5ICBAUlJSZN68eXoNWgsLC2nbtq1cvnxZLl++bKhsvrPNmzdL27ZtxcfHR3x9fWXv3r2SnJws9evXl++++07mzZsnw4cPl+DgYBHJ6BC6evWqODo6Gjjn7+6XX36RuXPnysiRI+XYsWMSHx8v3t7esmTJElm/fr1MmTJFrl27pu5fsGBB8fT0lAIFChgw1zkrODhYTpw4oT6BEBFp3Lix9OrVS8aNGyfbtm0TkYzzX/dfW1tbyZ8/v1E16l83cOBAqVChgrRv315evnwpZmZmap3YvXt3sbS0lJCQEAPnMnvEx8dLfHy8PHv2TE3r0aOHFCtWTMaMGSOhoaFiamqq3iuLFSsm8fHxYm1tbbSNehGRli1bypMnT2Tt2rV6bSIXFxfp3Lmz3Lp1S6+z3Bh16dJFzMzMZMqUKZKYmKhOoCMi8uGHH0p0dLReh9A7yZYBbXnE2rVrYWdnh6+++grffvstvLy84OPjo25PTk7Gt99+CwsLC9SrVw/NmjVDvXr1UKlSJfVlYWOgGwt669YtjBs3Dm3btsX8+fNx4cIFdZ+mTZvCxcUF27dvR2xsrJrerl07TJs2LbeznGOePHmC33//HTt37kRkZKQ6RnTevHkwNTXFokWLEBcXp+5/9+5dVK1aFZcvXzZUlrPNr7/+ijlz5mDw4MH4888/AWScGz/++COcnJzQunVrvf2vXLkCd3d3XLx40RDZzVE//fQTJk+ejG7dumH37t2IiIgA8NdLo61bt8aZM2fU/R89egRvb28cP37cUFl+Jxs2bICtrS3mzp2LPXv2oHnz5qhSpYreBCoHDx6Eg4MDqlevDi8vL9SuXRuVK1fWfF24evVqFClSBB07dkTJkiXh7e2NLVu2qNf+/v37UaxYMbRs2RJTpkzB/v370bhxY9SoUcNox9GvWbMG5cuXR5kyZVC0aFE0adJE3Xbx4kV89NFHKFq0KObMmYP79+/jwYMHaNGiBdq2bWtU71f88ssvmDp1KoYNG4Yff/wRQEaduHPnTvj6+qJRo0aIjIxU97979y7Kli2LkydPGijH2Sc4OBht2rSBp6cnfH19MX/+fHXbypUr4e/vj+bNm+PatWtq+uDBgxEYGIj4+HijOQ+eP3+OS5cu4fLly+p9ID4+Hh06dED16tWxefNmxMfHq/v/8ccfKF26NEJCQgyV5WwXHh6Oo0eP4tdff8Xt27cBZLxrNGLECNSoUQPTpk3Tew/75s2bcHNzU9sR74qBzb/0xx9/oGLFili9erWaFh4ejmLFiuGnn37S2/fSpUv44osv8PHHH2PixInqjVz3srCW6W7MV65cQbFixfDee++hbdu2KFu2LIKCghAVFaXu265dO5QrVw7vv/8+Zs2ahY8//hi2trbqJAtad/nyZZQrVw7VqlWDoiioV68e/vjjD3X7tGnToCgKRo0ahV9//RXh4eEYPXo0SpQooTdrlBatWrUKzs7OaNKkCby8vGBmZqbenOPj47Fr1y64urrC29sb69atw65du9CyZUv4+voaXeNu9erVKFSoEHr27Alvb29UrFgRy5cvV4/z6tWrcHNzg7+/P4YMGYLvvvsODRs2hLe3tybrhEuXLsHT0xPLly9X09LS0uDg4IC1a9eqvwMZnR9r167FmDFjsGDBArUu1Gpw8+OPP6JYsWLYsWOHmtaqVSu0aNECwF8vv546dQpBQUFwdXVFzZo10bx5c/XleWM7/7dt2wYbGxts3boV169fx2+//YZSpUph1qxZ6j537tzBzJkzkT9/fjg7O8Pd3R01atQwqjJZtWoVHBwc0KpVK9SpUweKomDLli0AMs73bdu2wcfHB66urli7di02btyIli1bolatWpqsBzILDg5GoUKFMHz4cCxYsABjxoxByZIl9dpGmzdvRosWLWBlZYXAwEA0adIENjY2RtXRdfnyZXh4eKBy5cooVKgQunXrpnb4xsbGonnz5qhVqxYmT56M6OhoREVFYfTo0ahatSpevnxp2Mxnk8uXL6NChQrw8PCAvb096tati6NHjwIAEhIS0L9/f/j4+KBXr16IiIhAWFgYPv/8c5QtWxZPnz7NljwwsPmX1qxZg4YNG6oFn5aWhvj4eJQvXx47d+5U99NV0K/3Pmi94srs4cOH6hSOOrt27UKBAgVw5coVvX3nzZuHLl26wMPDA61btzaaSiwkJATOzs4YP348wsLC8PjxY5QrV05v5hMAWLduHTw8PFC4cGFUqlQJxYsXx/nz5w2U6+yxb98+2NvbY9u2bUhKSgIA9OjRA/7+/mpPVGpqKu7evYtWrVqhXLlyqFy5Mlq0aKE2ZIzlejh48CCcnJzw/fffq2l9+/ZFtWrVAPxVH9y9exeffvopPD094efnh/bt22u2LHbv3o3WrVur03enpKQgPT0dtWvXxrJlywBk1H9/1wOrtePViY6OxuDBgzFq1CgkJSWp39/hw4dRoUIFxMTE6M3wlJKSgqioKDx79kwvzZjcv38fAQEBer3ziYmJ6NChA4KCgrLsf/fuXRw8eBBHjx5VzwNjKJP9+/fDwcEBW7duBZDRiB05ciQaN26sdvalpaXhxo0b6Nu3L0qVKoVq1aohMDBQs/WAzvPnz9GgQQOMGDFCTXv06BF8fX0xe/ZsvX0fPXqEZcuWoX///hg/frzRdHICGU8dHBwc8Omnn+LmzZtYt24dateujUWLFqn7xMXFYdCgQfD09IS5uTlq1aoFe3t7zbcJdEJCQuDk5ITRo0fj8ePHOHToENq3b49PP/1Uvc4TExPxzTffoGbNmlAUBVWqVIGLi0u2Pa0BGNj8a0lJSdi+fbv6u64yqlOnjl7vXeZtxig9PR2bN29Ghw4dcPv2bfWGnZ6ejmrVqqk9NK+XQWxsLBITE3M9vzkhISEBgwYNwocffoiEhAT1hrR582aUL18esbGxejep+/fv48yZMzh+/DgeP35sqGxni1evXqFr164YNWqUXvrq1atRtWrVN37m0aNHePr0qdE17uLi4vDZZ59h2LBhiIuLU8/5CxcuwN3dHc+fPwfwV4MlLS0NSUlJePXqlfpvaLEskpOTsW/fPvV33fG1b99eb80SIONaMRapqamYMmWK3rEDwPHjx2Fra/t/XtvG8FTidc+fP0evXr2yTOE9Y8YMdTiart5/0/FrtTGfWWxsLPr164cBAwbopX/33XcoVarUG6+Bx48fIzo62ijqxGvXriEwMBCHDh3SSx8wYIC67IGuA8xYxcbGonv37ujTp49e+sCBA1GtWjWkpKToBfL379/H+vXrsX//fqNZ3y0hIQFBQUHo1q2b3rU+ZcoUlC5dWu86SEtLQ0JCAn788UecPn0629czypc9b+oYJ92LTaampmJubi6dOnUSkYwZP8zMzEREJDExUZ39CoCMHDlSWrdura4ybyzw/6elVRRFnJ2dxd/fX8qUKaNuT09Pl8TERHn06JGIiFo+Ovnz58/V/Oa0tLQ08fX1FUtLSzWtaNGi8vz5c0lISBBra2u1zEqUKCElSpQwYG6zj62trfj5+WX5Pj09PeXVq1cSFRUlBQoUkHz58qkrKjs7O6v7paenS758xlHtWFtbi7Ozs5QsWVKsra3V9Hz58smTJ08kKipKihQpIqampiKSMamAubm5mJubi0jGNaWVsoiLi1NnNLKwsJBWrVqJSMYx6I4vJiZGnjx5oqb3799f/P39pXv37gbLd3YyNTWVTz/9VH35XXd+29vbS+HChfXqgo0bN0rHjh3FyspKTTPGF+Tt7Ozk66+/VieE0NV5JiYm6gQzuvKKjY0VW1tbvc/rzh0ty58/vzRs2DDLy++enp4CQOLj48XCwkKdKVVRFHFwcFDPB63XiUWLFpWhQ4dK48aNRSTj3mhqaiomJibq9Na6Ok8HRjbNfXx8vBQqVEjq1asnIn+VQaNGjeSPP/4Qkb+WAcmXL5+UKFFCevToYcgsZ7vU1FRxcXGR4sWLq9e/iYmJNG3aVDZt2iTJycliaWkpAMTExEQsLS2lZcuWOZIX7V5NOWzbtm1y4MABuXnzpgQGBkq9evWkTp066vbMFbhupptWrVrJpUuXZObMmYbKdrbTnZyZK6F69epJ3bp1ReSvcjA1NRUbGxu9G9X69evFzc1N/P39cz3fOcnS0lKmTJmiznSnK6NixYpJsWLFxNbWVi2vK1euSOXKlY2qUTNw4MAsaboZTlJTU9Wb9K+//ip16tTRa/AZUzmIiAwePFj9f921YGtrK7a2tnrB/dKlS+W9996TokWLqmlaubFv2rRJtm3bJnfv3pXatWtL69atJTAwUEREr7FmZmYmNjY2IpJRF169elUWL15syKy/s1OnTsnNmzfF2tpaKlasKFWrVlW36b4/MzMzMTExURvwTZo0keTkZOnatatB8pzTHjx4IKmpqWJraytFixZVg5q0tDT1+k5NTdWbvtfPz0+qVKlilGt4iWTMivp6cGJtbS3p6ekSHx8vhQsXFhGR/fv3S6tWrfTqQS3WiZnreXt7ezWoSU9PV9sA1tbWeus2ffrpp+Ls7CzDhw/XTN33b9nb20vXrl3F19dXRP6qGwoXLizp6emSnp6uNugfPnwoxYsXN2R2c0SBAgWkU6dOUqlSJRH5qwwKFCgg6enpap2gKIrcvXtXXdMmJ2jvisoFGzdulF69eom9vb24ubnJwYMHpWvXrvLdd9+JSMYXpuuJsLGxUZ/m3L17V+7duyf58uXTW6BRq3QN9jt37sisWbPkq6++krVr14rIX5WxrmEjIurUnSIiY8eOlYEDBxrF1K4iIi9evJArV65IaGiovHz5UooUKaLOx5+55y02NladyvPzzz+XQYMGaX4ax9OnT8uuXbtk+/bt8uDBAzU987SViYmJki9fPrXhHhAQIGPGjFEbe8bi2LFjMn/+fFm2bJn8+uuvb9zHwsJCrKys1GuhSZMmsm7dOrVxoyVbtmyRvn37iq+vrzRp0kRevXolHTp0kLlz56r76KY0L1SokFhYWEjHjh3lzp07cufOHU3XhatWrZLAwECZP3++fP755+Lv7y+LFy9Wj0d3446KipJXr17JixcvpE2bNnL//n35+eefjXLBvTVr1kiDBg2kcePG4ubmJqNGjZLTp0+LiP7Tl8zXfbNmzeTly5eyaNGiXM9vTvntt99k+/btsnTpUnny5In6dCrz9P5xcXGSmpoqhQoVEhGRRo0ayaBBgzR/Tly7dk26du0qLVq0kNatW8vx48fVe1zmgMXS0lItl3HjxsmcOXPEz8/PIHnOCVFRUfL8+XP1nqgLajK3CaKiouTZs2eSmpoqiqLI5MmTpWvXrkazbs+LFy/k/v376rIFuqAmLS1Nr36MjIxUO8i/+OILadCggcTExORcGWTrwDYjEBMTg6ZNm2LGjBlq2tWrVzFy5EiYmppi/fr1evvXrVsXiqKgYsWK6hh7LY+Xfd3Vq1dRqFAhNGvWDJUqVULJkiXh4+OTZWrCtLQ01KhRA9999x2mTJkCKysrnD171kC5zl6XLl1ChQoVULZsWbi4uKBevXpvnJ7zzJkzsLW1RXR0NCZMmAAzMzPNl8HKlSthZ2eHWrVqwczMDL6+vpg4caK6XTdu+I8//kCZMmXw6tUrNG/eXO96MJZpPFetWgU7Ozv4+/ujYsWKKFCgAEaPHq03nTeQ8QKlvb09bty4gTZt2qBcuXKanP0pOTkZHTt2xGeffaamPXv2DN988w1MTU316kgAaNOmDRRFQeXKlTVfF166dAnFihXD5s2bkZycjLt372LGjBkwMTHB2LFj9d4XvHr1KkqUKAFvb2+4u7tr/tj/zqFDh5A/f36sXLkSly5dwqJFi+Dn54cGDRpg//79evsuXrwYAQEBCAwMRJkyZYyqTL799lvY2dnB19cX+fPnR+XKlTFhwgS9SQLS09Nx8eJFlCpVCi9fvkRgYCAqVaqk+Trx5s2bsLW1RY8ePTBhwgTUq1cPZcuWxdixY/Hw4UMAf90TRo4cid69e2P27NmwsLDI1pfDDe3SpUuoVasWypQpg/Lly6Nv3756M53qvt+ffvoJ5cqVAwBMnDgRZmZmRlMOly5dQuXKlVG2bFnY29urSxq8fo6fO3cOrq6uSElJwaRJk3KlbcjA5jWRkZFwdXXVm6oSyJjaedSoUShSpIj6gnxaWhoCAgLg7u6u+WlM3yQhIQENGzbERx99BCBjVqBLly6hZs2aWdYj0ZVFyZIljSqoefToEVxcXDBq1CgEBwdjy5YteP/995EvXz51MgldY/XixYvw9vbGkCFDYGFhgXPnzhky6+/s0qVLcHR0xHfffYfExEQ8fPgQn376KSpXrox+/frp7atr3Hl5eaF06dJG1ZABMm7oLi4uasdGeHg4Nm7cCCsrK/Ts2VNvmvO7d+/CyckJ5cqV0wtqtFYW8fHxqFKlil5gA2Qcx/z586EoCjZt2gQg4ybWp08f1KxZ0yjqwt9//x0VKlTIMi37hg0bYGJigq+//lpNCw4OhqIo8PPz0+x3/U90DZSJEyciMDBQb9vPP/+M9u3bw9/fH7/++quaPn36dCiKAh8fH6Mqk8uXL6N48eLYtm2bOgPk8OHD4evri169eunVA/fu3UOpUqVQoUIFo6gT09PTMXz4cHTo0EEvfcKECahevTqGDh2KJ0+eqOljxoyBoiiws7MzmvYAkPG9FitWDKNHj8aOHTuwbt06uLi4oHr16jhx4oRe59Xvv/+OgIAADB8+3CjaBDoPHz5E8eLFMXbsWBw7dgzHjh1D1apV4eXlhfXr1+tNFnHr1i3UqFEDffv2hbm5ea6UAQObN+jevTs++OADPHv2TC/99u3beO+999CjRw91hoekpCSjmrYys4SEBNSsWRNr1qzRS4+JiYGfnx8qVaqkLsiXkJCApk2bwt7e3igWn9T57bff4OXlpdfAefHiBUaMGAEzMzO9efr//PNPKIqCwoULG0WvzI8//ojSpUurs3sBGce+cOFCVKhQQW96z99//90oGzI6ly5dQpkyZXD37l299KNHj8La2hoDBw5U00JDQ2FpaYm6detqvixGjhwJHx8f3LlzRy9dN/Vx/fr11UXokpOTjaYu/O2336AoijoNa+ZpnBcvXox8+fKpazPEx8frrVem9WP/O5MnT0a1atX0FmEFMhbqbd68OXr16oUXL14AyJgZsHv37kZXJr/88gucnZ0RGhqqpiUlJWHOnDnw8fHBsGHD1IDn8uXLUBRFnRUL0H459O/fHy1atEBqaqresUybNg1eXl5YtGiRWgesXbsWJUqUyLIEhNZt3boVnp6eerNbvnr1Cp6envD09NS79+/btw+KoiB//vxG0SbQOXjwYJaOn+TkZLRp0wbVqlXDjh071Pry5MmTUBQF1tbWuTatNQObN1i0aBFKlCiB1atX660QCwALFixAkSJF1Ju5jjFMW/kmnp6e6NGjh/q77jifPn2KChUq4P3331e37d69Gzdv3sz1POak/fv3Q1EUvRsZkDG94yeffIJChQohODgYABAREYGAgABcvXrVADnNfidPnoSrq6vagNOJiorC1KlTUa1aNRw5ckRNN4YFGP/OzZs3oSgK9uzZo6bpeuZ2794Nc3Nz9ekFAMyfP98oymLnzp3w8vLCxIkTszy92LZtGwoWLJgl6NHScLvX6W7GcXFxaNWqFdq0aaMGs7q1eXQL7X366adZ6n0tf9f/ly1btqBgwYLqNZ/52Ddv3gxra2u1UytzORhTmfz+++8oWbIkfvvtNwB/HVtqaiomTZoEDw8PHD9+HEDGNNdr1qwxinpAZ+zYsXB3d1c7djMPyfz4449RqlQpxMbGAgDCwsKybcHF/5KFCxeiRIkS6u+6skhISEDFihXRoEEDddu5c+fQsGFDo1qvBwC2b98OJycntdNT105OSEhA48aNUb16dTXwCw0NRbdu3XDt2rVcy1+eD2zOnz+P3bt344cfftBrvH700UewtbXFhg0b9FaEPXr0KGrUqKH3yNUY/N2Y3+XLl6Nq1arqiuLAXxX0ihUr4Onpqfm1Wd5EVx4RERGoXbs2hg0blmVl4JCQEPj7+2Pu3LlqY84Ybl46YWFhqFSpEvr165ell/bFixdwd3fXe99Gx5jKQCcpKQk9e/ZEw4YN9Xqd0tLSkJycjPfffx99+/bNsn6Tlsri5MmTWLVqFZYuXYrTp0+r6RMmTECpUqXw1Vdf6a25cOXKFXh4eODGjRuGyG62evbsGZ49e6bXEFuzZg1q1aqFwYMH4/79+3r7d+rUSa/Dx1i9fv526dIFTk5OuHXrFgD94MbNzQ0rVqzI1fzltvj4eFSoUAHt2rVT7xGZy6BixYr4+OOPs3xOS/XAP4mNjYWrqyvat2+vpuka9vHx8ShYsKDeen/G6NatW7C2tsa8efPUNF2Ad/v2bRQsWBArV65Ut0VGRuZ2FnNceHg4ChUqpDdMWTf8LCYmBvb29pgyZYq6LSYmJlfzl6dnRVuzZo20bt1avvzyS/nggw9k5MiRcu7cORERWbFihXTs2FFGjx4tX3/9tRw7dkxu3rwpX331ldjZ2YmDg4OBc5990tPTRVEUiYyMlJs3b8qtW7ckLi5OREQaN24s5cqVk/Xr18u2bdtERNRpHp2dneXVq1dGMbuHzuszHtnb20ujRo3kl19+kW3btklsbKy6b9myZcXc3FzOnj2rzoKi5fUIXr16pbcmU/HixWX27NmyatUqmTlzpjrbm0jGNJa+vr4SFhaW5d/RchnoPHnyRO7fv6/OeGNubi4dO3aUhIQEmT9/vly5ckVEMmYHNDMzk0KFCklERESW9Zu0UharV6+W9u3by8aNG2XSpEkyduxY2bt3r4iITJ48Wbp16yabN2+WkSNHyu7du+XkyZMycuRIsbW1FXd3dwPn/t1899130qVLF/H09JRu3brJ8uXLRUSkd+/e0r59ezl58qRMmDBBQkJCRCRjzYrnz5+Li4uLIbOdo/bt2ycDBw6Upk2byvz58yUyMlJERBYsWCCVKlWSBg0ayLlz59SZ0CIiIsTS0lJvOnNj8OLFCwkPD1dngLSyspKNGzfKkSNHpH///iKiPxtcQECAOmNqZlqpBzJ7+PCh7Ny5UxYuXCh3794VkYw1e5YsWSK//fabvP/++yIi6nT+L1++FCcnJ7G3tzdYnnODo6OjDBkyRNauXStbt24VkYxZANPS0sTZ2VnKly8vjx8/VvcvWLCggXKafXSz2+k4ODjItGnTZM2aNbJgwQIRybhHJicnS4ECBcTPz09d00xE1CVRck2uhlH/ITt27EChQoWwbds2xMTE4OzZs3Bzc8OcOXP09psyZQoaNGgARVHg6emp9w6Blodc6OiO4cqVK/Dx8YG7uztKly6NLl26qL2Xly9fRvPmzREQEKBOqhAdHY3PPvsMNWrUMJoeiRs3bmDIkCFo2LAhPv/8c70hWN26dUPFihUxb948vbG1Xbt2xejRozV/LmzatAkBAQEoUaIE/P39sX//fvU8X7t2LUxMTDBkyBC1dz42Nha1atXCuHHjDJntHLF+/XrUrl0b9vb28PX1xeTJk9Vtq1evRs2aNdGxY0d1yElkZCQaNWqEQYMGGSrL72T37t0oWrQotm7dirS0NFy/fh0NGjTAmDFj9PZbvXo1OnXqBBMTE3h7e6NevXqarws3bNgAa2trLF26FN9++y0GDhyIOnXq6A0nXbJkCRo2bIgCBQqgYcOG8PDwQOXKlY2mF/51q1evRpEiRdCvXz/07t0bJiYmmDRpkrr94cOHaNu2LWxtbTFgwABMmjQJTZo0gaenp1ENyd60aRP8/Pzg5uaGcuXK4fDhw+q2HTt2oECBAujatSvu3buHxMREJCUlwcfHR+/dQ626fPky3N3dUb16dbi6usLGxgZnzpwBkFH3b926FQ4ODqhfvz6OHj2Ks2fPYvz48XB2dsaDBw8MnPvsc/v2bUyYMAGffPIJJk+erNZz58+fR5cuXeDr64t169bpfaZJkyb46quvAGh39rvMbt26hSFDhqBLly748MMP1SczoaGhGDVqFNzc3DB79my9z7Ru3Vq9DgxRBnkysAkPD0eHDh3w5ZdfAvjrpjx27Fg0bNhQHV6i8/z5c5w7dw5XrlwxyiFHN27cQNGiRTFq1Cj88ccfWLp0Kfz9/bF48WL1pLx+/ToGDRoEJycnODg4oEaNGihatGiuvQyW065cuYIiRYqgd+/e6NmzJ+rVq4f27dvrVdJBQUHw8vJCnTp1MGHCBPTo0QM2NjbqOzZatX79etja2mL27Nn4/vvvUb9+fXh7e+tNY/zDDz+gSJEiqFmzJmrVqgU/Pz+jbNxt3LgR+fPnx6pVq7Br1y6MGzcOPj4+6k0dyHi3pG3btrCwsICXlxcqV66MqlWranIq15cvX6Jnz55qEKOr3+bPn4/KlSsjPj5er7Ganp6OkJAQPHz4UPN14cWLF1GlShW9YbaXLl2Cvb09du3apbdvSEgIVq1ahXHjxmHu3LlG9d5EZr/++itcXV313hdbv3497O3tswzHmzFjBtq0aYOAgAD06dNHPf+NIbhZv349bGxssHDhQuzduxfvv/8+nJyc1E689PR0/Prrr3B2dkaVKlXg6emJ2rVro1KlSpo/J+7cuaPOePX8+XNER0ejR48e8Pb2Vt+fSU5OxoULF+Dj4wMXFxd19jdjekH+8uXLsLe3R+fOndGgQQN4eHigbdu2av1+9uxZ9OnTB8WLF8eAAQPUjhEbGxujedf4ypUrKFq0KHr06IHu3bujSpUqqF69uloGd+7cwfjx45E/f3507NgR48ePR1BQEAoUKJCr79S8Ls8GNv369VN7XHVf0vz58/9Vr5NWeyffJDo6Gp06dUJQUJBeeqdOndCyZUu9tFevXuH27duYO3cutm7dmuWlYa16/PgxqlWrhtGjR6tphw8fhr29PX7//Xe9fbds2YKPPvoIdevWRZcuXTQ/A9yFCxdQtWpVvbHxCQkJKFKkCDZv3gzgr/P96tWr+PbbbzFixAjMmTPH6Bp3wcHB8Pb2xrfffqum3b9/Hy4uLli+fLnevo8ePcKBAwcwY8YMrF69WrNlERUVhXHjxuHnn3/WS9+xYwdKly6dZfKU12m5Ljxy5Ai6dOmS5cXeZs2aYcmSJQCgN23p64yhAZ9ZUlISPv/8c/Ts2VPt1EhPT8eVK1dQsmRJdZ2SzIF7UlKS3jmgtfP/TS5dugQvLy+99yTS0tJQtmzZLDOERkVFYfHixZg0aRLmzZun2XpAJykpCcOHD0fXrl3Vd2cAYNeuXShfvrwa2GR24cIFBAcHG9VEAbr3S3UdPomJidi+fTu8vLz0nubeu3cPa9asQYUKFVCrVi0EBAToLYOhZY8ePYKXl5faLkpPT8eff/6J8uXL49ChQ+p+0dHROHr0KBo2bIhGjRqhbdu2uHTpkqGyDQDQ3sDPbODg4CATJ06U4sWLi0jG+EFTU1MpXbq0FCpUSG/M7KlTp6R27dp6n9e9T2EMEhISxMHBQfz9/UUk4x0TU1NT6dSpk6xYsULS09MlPT1d8uXLJ7a2tmJrayvDhg0zbKaz2YULF6REiRLSs2dPASCKokjjxo2lQoUKcvv2bfHz85OUlBQxMzOTLl26SJcuXSQ5OVlMTEw0OXY6s7t374qbm5s0b95cRDJWzjYzM5PSpUtLUlKSiGS8bwRAKleuLJUrV9b7fFpamubLQCc2NlYqVaoktWrVEpGM94xKlCgh9erVk2fPnomISHJyspibm4uzs7M4OztLs2bN1M9rsSzs7OxkyJAhUqxYMRHJqAtNTEykRIkSUrBgQb2VxA8cOKCeJzpargtLly4tgwYNkgoVKoiIqNd+SkqKREdHi0jGuPG/k/k+YQzMzc2lRYsWcuvWLbG2thaRjGvf1dVVREQiIyPF2dlZrQ8URdErHwCaO//fJCwsTKysrKRx48YiknFcJiYmUrhwYfVdI5GM693Ozk4GDBig93kt1gM65ubm4ubmJtbW1uq7MyIi1atXl6ioKHny5ImUKlVK8uXLp9YVXl5ehstwDjly5Ij6Lg0AsbCwkAYNGsjAgQPl3r176n2wZMmS0rt3b+nRo4cAkOTkZPXa0brTp0+LtbW1fPLJJyKSURdUqlRJ0tLS5NGjR+p+NjY2Ur9+fTl8+LCYmJhIUlKSWFhYGCrbIiKizasvG+iCGl2lJSKSmJgoUVFRanpgYKDY2NiIr6+v3g3emBQpUkS6d+8uvr6+IvJXQ8XExERiYmJE5K8beFxcnOTPn98wGc1BDg4O0qpVK7Wywv+fDCExMVEiIiJERLK8FP5PDR4tadGihZibm6uNF0VRxNTUVBwcHCQ1NVVNE/mrUZ+ZMTXuSpUqJYMHDxYPDw+9dABG3dDVBTUif13/8fHxEh0drTbQWrVqJXFxcdKsWTOjqQtLlSolpUqVEhHRmwBF10DR/X+7du2kU6dO0qNHD0NkM1fVrVtX6tatmyU9KSlJYmJi1O9+yZIl0rx5cylTpoy6j7GcF61atRILCwspWbKkiIikpKSIubm5ODk56d0HTE1N33hP1Go9oDN48OAsabrOTXNzc7VOuHbtmpQtW1YvADIWFSpUkC5duoizs7OIZHT4FSpUSAoXLqx2+GWWnp4uZmZmmg1o36RmzZrSu3dvtY5MSUkRS0tLKVas2BsnyND5L7SNtNvdlk0URVEr5NjYWElMTJTExERp06aN3LhxQzZs2GA0FfabmJqaqkGNrhdOJONJTlxcnJiYmIiiKDJ+/Hhp06aNpKWlGdUsaCIZvVH9+vUTkb/KQFEUKViwoN5NavHixXLgwAFDZTPbpaeni5WVlQQGBqppuuONjo5WZzUBIAMGDJAffvjBIPnMLcWKFRMfHx8R0W/o6uoEnZYtW8rs2bNzPX+5KSYmRpKSkiQ+Pl7atm0rISEhcvjwYbW33pjoZoXUKVSokNrr2rx5c7l27Zo6A1Rek56eLmlpaWJlZaUGwI0bN5Z58+aJm5ubgXOX/XSzP2V+WqNrqCUnJ6sdXQDkww8/lJ9++skwGc0Fuus8LS1NUlJSxMTERL0uRo8eLS1atJCEhARDZjHHVKtWTYKCgkTkr6DOxMREbG1t9e4Fq1evVoMaY+Pq6iofffSRiIjeMVpaWqodfSIiy5cvl9DQULVT7L/QXs6TgY3ugj137pzcunVLTS9SpIgUKVJE2rZtKzdu3JAbN26ImZmZ2nNtrHSVuaIo6nTHdnZ2YmVlJSIi48aNkzlz5siMGTPE1NT0P3HiZpfMjbTXGzgFChRQH6l+/vnn8umnn0qJEiVyPY855U3DiHTfv4WFhTpNZcuWLWXfvn3SsWPH3MyewejOA925UahQIbVXtlmzZnLnzh0ZOnSoIbOYbXTHePz4cTl//ryabm9vL4ULF1Yb9sHBwWpdqLXr//Lly2+sw3XHnvkmrRMbGyvvvfeehIaGyrVr1/LEfQAZ79yKiKg9srp7grm5uSQkJEibNm3k8ePHcu3aNTExMckyDazWvV4nKoqiHiMAtXEXGBgoBw4ckHbt2uV2FnNc5vaASEZnl4WFhaSkpEhaWpqMHz9elixZIjt27JBChQoZMqs5JnOgYmJiojeKQ/f/EydOlH79+snt27cNksfclPlaT0lJUTtAJ06cKJ988skbn2IZUp4KbHQVt6IosnPnTmndurXeWMHU1FQ5efKkREZG6t3MjOnx4uvS0tLExMREHYKnO2F179SMHj1aZs+eLSdOnJAaNWoYMKfZ4/XeZkVRsjRYdBdwZGSkpKWlyddffy1z586VEydOSKVKlXItr9nt9UZI5oaM7h0S3Y29YMGCYmZmJh07dpTQ0FC5c+eO5MuXTw18tO718yA9PV1N0/XK6srCzMxMEhISpEOHDnL37l25evWq5hq6rx9v5rrwhx9+kLZt2+qtSZSSkiKXL1+WlJQUTdeFX375pXh5ecmxY8fUc/f1+4C/v788f/5cDWbj4uJk8uTJEhISohfQae3Y/8nrQ+90FEWR/fv3y+rVqyU5OVkURRELCwtJTEyUhg0byvXr1+XSpUtqmWj5Hau/oyuP58+f69WZtra2kj9/funSpYvcvn1b7t+/r+k68U11oK498PTpU3WtLpGMurBgwYIyZMgQmTVrlhw7dkx9F9EYvV63p6SkSEpKiiQmJoqdnZ3MmzdPZs2aJefOnZNy5coZKJc56/Uy0J3nqampUrhwYZk/f75aBrr3FP8zsncugv+Of5qtZ8uWLTAzM8OyZcv00s+fP4/u3btrfmaTf0t3fPfu3YOLiwt27Nihbtu4cSMURUGBAgWMagpH3Yw+x48fx2+//aaWwf3799GnTx+8fPlS3bd169YoVKgQ8ufPj7Nnzxokvznhiy++wLFjx9RZnXbs2IFOnTrh0aNH6j4tWrSAoiioVKmSOo2rMV4PuhWRM5dFyZIl9aa27dKlCxRFgZeXlybLInNd+PosX3v27IGVlVWWuvDOnTsYNWqUUdSFzZo1Q/HixXH48GG9mcx27tyJ/PnzY+nSpXr7f/7552jatKlRHPv/RVcf6o7x+++/h6mpKdavX6/u8/jxYyiKAm9vb6Muk/T0dLU8tm/fjrZt2+Lx48fq9vbt20NRFHh4eGiyHsgs83Vw6dIlvbXo7t27BxsbG3zzzTcAMsrl2rVrUBQFtra2RjPrl87r0/Prvtu7d++qZaBTv359lC5dGlZWVkbVJsgsPT1dPT/u3r2L8ePH691DAgMDUbBgQVhbW/9ny8AoA5vMJ+qCBQswaNAgBAUFqdMRrlmzBgsWLPjHf0OrFdab/FOQFxoaCmdnZ/Tv31+v3P744w80aNBA82u06GQ+tiNHjkBRFHWtinv37sHZ2RmDBw/W2693795wcHDQ/JTOmb//H374ASYmJjh58iQAYOvWrcifP79ewzY9PR1du3ZFzZo1jbohM2PGDAwePFj9XdfQfb2RP2fOHLRu3VqTZZH5fJ41axY++OADtGnTBhcuXAAA7N27F6tXr/7Hf0NLx5tZ5rXIGjduDGdnZxw+fBjJycmIiIiAt7d3lmm8gYygTutr9PydzHXB5s2b0apVK/UYL1y4ADMzsyyBHgCcOHFCk+f/3/m/Oj4LFCiARYsW6aV//PHHKFOmjObLITQ0FPXr1weQcT9wdnbGuXPnAGQshVGoUKEs7QEAGD16tMGn8c0pp06dwpYtW9Tz4t69e3ByckK3bt309qtZsybMzMw03ybITPc9P3/+XC89NDQUTk5O6NGjh156q1atoCiK3rTX/zVGF9hkrrAmTJiAggULokuXLihdujRKlCiB3377zYC5y12Z56EHgD///BNbt27Fjz/+iHv37gHIqKwGDhyYpRJLSUnRe3phLB49eoQVK1Zg+vTpADJWjS9VqtQbK/IjR47g7t27hshmjti8eTOWLFmirlnz8uVLlC9fHvPnz1f30ZVBQkKC2muj1Rv4/2Xt2rUoWrQorly5gujoaPj7++ut56Pz5MkTTTZ0M9eF06dPh52dHYYMGQIvLy/Y29tj48aNml6H5p+86bgaNWoEZ2dndc2esLAwve2vX//GVjaZj+fIkSP46KOPYGpqiqCgIKSkpCApKSnL/fFN9wWty1wOx48fx8mTJ3H69GkAGfeDhg0bvrFOzPxZLZfD1atXUaZMGZQrVw6KouC7775Ttx09ehTffPONXhnpjl9LCw//W7qnEw0bNkSLFi0AZJwDlSpVQlBQUJZj3717t9Gs3wf8dVz79u2Dv78/fvrpJwAZaxbWqlUL/fr1y1IGFy5cQGhoqEHy+28ZXWCj8/TpU/Tq1Qt//PEHgIyKqGXLlnB0dFQX5jRmU6dOxZAhQ/Ds2TMAGT0z5ubm8PDwgLm5OerWravXU5u50jLGCgzIGG6me5w+Y8YMNf2nn34y+uMPCQmBq6srFEXRe7weERGRZd/MNzVjady9/p2mp6fj9u3bqFu3rroQ3+sN3f/r39CKe/fuISgoSK/e6927NxwdHbF+/fo3LrpnLPbt26c+nQQyghsHBwf8/PPPmm6cvosRI0agevXq+Pjjj1GrVi04OjrqDcE2toVHM8t8DQ8fPhxFixaFk5MTChUqhA8//BAPHjx440KTmcvEGOrEhQsXQlEUlCpVSi/dGI7tbejOhzNnzqBw4cLYsWMHXr16hU2bNr2xvtfqPeCf7Nq1C1ZWVvj666/VuvLFixc4cOCAZo/XKAOblStXwtbWFjVq1MCNGzf0trVs2RLOzs44ceKEgXKXO5YvXw5FUfD555/j+vXr8PHxwbJlyxAfH4+rV69i4MCB8PLywsKFCw2d1Vy1bNkymJubo0+fPoiOjjZ0dnLM6xVSQkICdu3aBQ8PD1SvXl1NN+ZGzJu83ogfPHgwSpQokeXpprHYsGEDFEVBhQoVsoyH7tOnD5ydnbFhwwb1XSNjcuPGDTg4OKBXr156x575yU1eO/8PHTqEokWLqg2YtLQ0zJkzB56enkYf3GSuE8+ePYuyZcvi1KlTuHTpEg4dOoTChQujdevWah2h1Ubd39EdT3JyMn777Td888038Pb2hqenJ169egUg65MoYysD4M1PZqOiovDBBx/go48+UtPygqdPn6JatWr4+uuvDZ2VbGWUgc3Tp0/RqFEj5MuXT320nvlEbd26NRRFMbqX4F6na9QMHz4cHTp00OuJunv3Lj7++GM0aNDgjb32xmzp0qVQFAUzZswwyor79WEEiYmJADJuaPv370fJkiXRqFEjdR9jbMS8yfLly9GpUyf8/PPP6vsXkZGRqFGjBpYsWQJA+zfyN92Q27ZtC0VRsH79er33TgCgb9++UBRFHYKgZW/67rZt24YqVargww8/1AtuGjduDFdXV+zbty/PNGKAjElhnJ2d9cbTR0dHY8KECbC2tka/fv3Uc8RYy2XVqlXo2rUr+vfvr5d+69YtFChQAJ999pmBcpZzdNfG4cOHMX78ePUdu8uXL8PDwwMeHh56nT6HDx/Wm1DA2Jw5cwY7d+7US1u/fj2srKzU94i0fi/4N0JCQuDi4qJ29GeeQCMzrZWF5gObv6t8IyIiUKtWLVSsWBEhISEA9L+cUaNGGW2DLvNxrlu3DoqiwNTUVH1BUOfcuXNQFAW//vprLucw5+nK4MqVKzhy5Ah++OEHve26R/EzZ87U3EX7b82YMQPt2rWDr68vlixZor5XtX//fpQrVw5NmzZV9zXWRkxms2bNQrdu3WBubo6uXbtiwYIFSEtLw/vvv48PPvjA0NnLVq8PwWrWrBkcHR2zzAwGZAxbNaa6UNf7rPP999+jYsWK+PDDD/VmePTy8kKbNm1yO3u55k312vHjx1GhQgUcOnRIL/3BgwdwcXGBu7s7+vbta1TnQ2bh4eH44IMPUKhQIbRr105N13X+zJo1C15eXnjx4oXR3Rd27NgBKysrfPXVV3qduleuXFGDm3PnzmHMmDEoVaoUHj58aMDc5oz09HS8ePECPXr0gKIo6NGjBzZs2KBuf++999C5c2ejHp6b2f3791GqVCmsW7dOTdO1BQ4ePIh9+/YZKmvvRNOBTebG2OXLl3H+/Hm9i/HZs2eoXr06Kleu/MbgBsgbvdVbt26Foij45JNP8OTJEzX92bNnqFixIg4cOGDA3GU/3Xe8c+dOFC9eHFWrVkXBggXRtGlTXL58WT1vFi5cCAsLC0yePNmQ2c02ma+HiRMnonDhwhg6dCj69u2LwoULo2vXrmrD7scff0SlSpXg7e1tqOzmqH8K1I4fP47hw4fDxcUFrVq1Qr9+/aAoCvbu3ZuLOcw5/+sQLK3WhSdPnsTNmzcBAHPnzsWgQYOyvNy6bds22Nvbo0ePHnrBTV4I6L/++mscO3YMQEbDvlq1aggMDNSb1ejOnTvo1KkTpkyZAm9vb6OZZOdN3++5c+fQu3dvmJmZYePGjXrblixZgqpVqxrd0Mxr166hVKlSb5wFEABu3rwJHx8fuLq6onTp0lk6QY1NbGwsTp8+jRYtWsDT0xPe3t44fPgwvvjiC7Ro0QK3b982dBaz3ZsmwYiNjUVAQAAaNGiQZZazYcOGoUWLFpoM8jQb2GSusL744guULl0apUuXRoECBbBmzRp1Rq/nz5+jRo0a8PDwwPXr1w2V3RyX+RHi7du3cfr0afz+++9qT9Tq1auhKAqCgoJw4sQJhIaGYuzYsShYsCAePHhgyKzniMOHD6NQoULqi+F//vknFEVBw4YN8eeff6plNXPmTBQuXBgvXrwwZHaz1f379/HZZ5/hl19+UdMOHjyI6tWro3fv3oiLi0NCQgK2b9+ODz74wOgad5mPZ8+ePVi3bp16Q888zvzFixcYOnQoOnToAEVR8PHHH2f5vBbk5SFYoaGh8PHxQevWrfHkyROsXr0aRYoUwdixY9UnlDqTJk1CwYIF8d577+lNY6/VgO7fiI6ORps2bWBiYqIGK8HBwXB2dkazZs3wzTff4Oeff0bjxo3xwQcf4OXLl7CxscGcOXMMnPN3l/n8fvDgAa5evaqmhYaGomfPnmpvdVxcHMLDw9G4cWM0b97cKJ7WZG4T6J7SZw7433SMp06dQnh4eG5lMVfojvPGjRs4ePAgzp49q3aAv3z5EleuXEH79u1Ru3Zt+Pj4qO8mG5PMQxGHDRuGJk2aYNmyZQgPD8eDBw/g5OSEpk2bYtGiRdi7dy8GDBgAOzs7XLlyxcA5/99oNrDR+fLLL+Ho6IhDhw4hPT0d3bt3h62tLWbOnKmOEX3+/DlKlCiB7t27GzazOUh34u7YsQMVKlRA2bJl4evrCw8PD/XdGt07N4qi4L333oO/v7861lbrQkNDsXv3bgAZixAOGzYMEydOBJDxPlHp0qXRu3dvlC5dGr6+vjh37px6kzOmaa13794NRVFQrFgxHDlyRG/b/v37YW5urg49zPyiqDE0cgH94/jss8/g7OwMPz8/2Nvbo1mzZjh//nyWY42Pj8eCBQtgaWmp6c6PvDoEa8WKFWjQoAG6dOmCmJgYbN26FY6OjhgzZoxeQ27u3LmoX78+evXqZTTn++t0x5W50frgwQP07NkT5ubm6pOb69evo1OnTqhQoQLKlCmD+vXrIz4+HgBQu3ZtbNmyJfczn40yH/8XX3wBDw8PODk5oVq1apg9ezbi4uJw7do19OzZE4qioESJEujduzfq1aunTiSi1XMkPj4eiYmJuH//vvqdfv/993BwcEBUVBQA/br/5MmTOHPmjEHymtN058H3338PFxcXlCpVCiVLlkT58uWzTCB14MABfP3113BwcDDK9Xp++OEH2NjYoH///vjiiy/g4uKCpk2bIikpCSEhIWjbti3Kly+PMmXKoG7dupp+B11zgU3mYQdXrlxB48aNsWfPHgAZ09YVKlRIfVl25syZ6kuSUVFRRtUzp6t04+Li1LRjx46hQIECWL58OZKSkrB3714oioI5c+boDc9SFAVffvmlWslp3aNHj1C0aFFUrFhRnZP/0KFDCA4ORmRkJGrWrIl+/foBAH755RcoioLq1atr+sLV0Z0Huv8+evQIAwcOhKIoWLt2LQD9m1jlypUxc+bM3M9oLvvmm2/0Fp7btGkTFEWBv78/zp8//8a1Gfz9/fWmAf+vy+tDsDJ/d2vWrEGdOnXQpUsXREdHY/PmzXBycsLo0aNx5swZJCcno0OHDti+fbv6OWMog7+jewKtO9awsDB0794d5ubm6rTfMTExePHiBe7fv69+buzYsXB2dv7Pr1Pxb02bNg0ODg748ccfkZqaikaNGqFkyZJqwzU4OBgffvghypYtq/eUSjfSQWuuXbuGDh06oEqVKsiXLx+8vLwwefJkPH36FEWLFsWwYcOyfGbYsGGYPn16lolFtE533ztz5gxsbGywbNkyPHz4EEePHkX37t1haWmp9x6iji4YNCZhYWHw9PRUF99NT0+HjY0NRo8erbaLk5OTER0djYcPH2p+xlhNBTa6YQdt2rTBnTt3kJiYiJUrVyIxMRHHjx+Hs7OzOn1x586dUbBgQUyYMEHvSzKG4EZ3Qz537hzKlCmj3oRmzZqFQYMGAcjopStRogQGDhyofk5XcW3cuBHXrl3L3UznIF2wUrNmTbRp0wabN29Wt+3atQs1a9ZU37Hav38/2rRpA29vb80vtLVp0yb07NkTwcHBer31T548Qc+ePWFlZaX31ObVq1coXbr0G1cW17qDBw+q33tUVBT69++vjp/fsWMHChYsiDlz5qBMmTLw9/fHuXPnsgzFqFatmvqU77+OQ7Ay/FNws337dlStWhVOTk5wd3dH5cqV1caOMQw1+jtbt26FtbW1+vRRd6z3799H27ZtYW1trRfkAhmL7rVu3RpOTk44f/58ruc5u2QOSF69eoWGDRti/fr1ADI6u2xsbNRhqbrz/9KlS/joo49QsWLFLJPMaMnly5dhZ2eHgQMHYuXKldi5cyfatm0LU1NTdOzYERs3bkSRIkUwcOBAhIWFITg4WB2OruUn1a+7d++ees6npqZi5cqVaNCggV5HxpMnT9C1a1d4e3ura/3pGGPdEBYWBi8vL8TFxeHWrVtwcXFRp7YGgNOnT2d54q9lmgpsgIxhBw0bNkTnzp31FtQLCgpCnz591Mb7oEGD4O3tjTp16hjViaq7OC9evAgbGxu9Hpg+ffqgX79+ePToEYoXL663cu727dsxe/Zso2jMvMmHH34IT09PdOzYEQ0aNFBvZitWrICLi4s6pvbzzz/HhAkTNF8OUVFRKFOmDOzt7VGlShX06tULq1atUrfHxcXhgw8+gKWlJYYOHYoZM2YgMDAQVapUMbqFCX/77TcoioIaNWqoT+yOHDmC8PBwXLhwAWXKlFFXEl+/fj0URUGlSpXUpx1AxthyCwsLXL582SDH8L/gEKwMfxfcvHz5Erdu3cKmTZuwdOlSo12j5fWntqdPn0azZs3g5uamruOm27Zt2zZ1OPLr5/rixYuzrPumJQcPHsSsWbPUp7SRkZGoXLkyIiIicOjQIRQoUEDt1ElISMDy5cvVOuDixYsICgqCg4MDdu3aZbBj+F9FRETA29s7y1TVERERWLRoEWxsbBAYGIg9e/bA0dERzs7OKFOmDMqXL6/pQPZ1iYmJ8PX1RalSpdR6Yc6cOShUqJD6aoIufd++fXB1dTWqoA7IuPc/e/YMv/zyCx4+fIioqCjcu3cP9vb2+OWXX1CmTBn069dPrRMuXbqE999/36jOA80ENq/fvPz9/dG5c2e1YmrQoIHe04n27dvj4sWLbxxyolWZT0Rra+ssL7jNmTMH7du3h7Ozszr0Kj09HcnJyRgwYACGDRtmdI9ZdT10P/74I3r37o2DBw+iQ4cOqFevHn744QdERkbCxcUFZcqUQZ06dWBnZ2cU7xWlpqZi7NixWLZsGf7880/MmjULdnZ26Ny5M6ZOnYqkpCRERERgxIgRUBQFnTp1wtatW9XyMqbg5ocffoCiKKhXrx7atGmj937AokWL0LBhQ3Wtpg0bNmDgwIF4//339Rq4kZGRejMG/pdxCFZWmctk9erVqFu3Lrp06YK7d+/qbTe2oObvntpeuHABLVq0yNJwO3HiBIKCgjBnzhyjqgNWr14NFxcXfPLJJ3ozetWsWRP169eHra2tOpEMkPH0qn79+npP98+dO4fBgwdrckas8+fPo0qVKrhy5Yp6juuu88jISEyZMgW2trY4cOAAnj17hsOHD+PkyZOaqfP+rfT0dJw4cQJVqlSBl5cX0tPTcefOHVSqVAlz5szRG35/8+ZNlC5d2qjeL7p58yZ69uyJChUqwNLSEnZ2dujatSsuX76s1xbIbOzYsahZsyYeP35soFxnP80ENkDWG3r9+vXRuXNnPH/+HIsXL4aJiQk++OADeHt7o1KlSkY57ODBgwcoWrQoOnfurJe+YsUKvP/++yhTpgwKFy6sjh2NiYnB559/DicnJ033xmX24MGDLEMGIiIiUKFCBSxatAgRERHo0KED6tSpg7179+Lp06cYNWoUxo4da1RD8H766SfY2tqq48UTEhIwYcIEKIqCatWqYerUqfjxxx8xdOhQFChQAL///jsA7Y4f/yfdu3dH/fr10aFDBwQEBKhP7MaNG4fy5cvjyZMnePXqFQIDA7Fo0SL1c1pt6HIIVlavBzf16tVD9+7d9Z7sG5M3PbXN3Hi/deuWun7Rzz//jODgYLRr1w6ffPKJuo8xBDebN2+GtbU1tm7dqgZ3mV8ad3d3h7+/v7p/TEwMWrZsiYCAgCzXv1brxjVr1sDS0lL9/fXr/M6dO7Czs8P06dNzO2u5Li0tDadOnUL58uVRs2ZNABn3gapVq2LWrFkIDw9HTEwMxowZg7Jly+otXK5lly5dgpOTE/r374+1a9fi+vXrGDNmDMqUKYMKFSrgyy+/RNeuXVG2bFkcPnwY33//PYYPHw4bGxujeN84M00FNkDWG7quZ+7JkydYvnw5OnfujI8//lgdkqbVhsvfCQ0NVd8l0U3fOW3aNFhbWyM4OBhhYWFwc3NDtWrV4O7ujhYtWmh+3HRmDx48QJEiRaAoClq2bImtW7eqT+327NkDf39/REREqC9RBgQEYPv27QbOdc4ZOHAgBgwYoP5eqVIltGvXDqNHj0bLli2hKAqmTp2K7t27o2DBgka3GKuuIbJx40Z89NFHOH36NDp06AB/f3/s2bMHERERcHJygpOTE0qXLo2qVasazUuyeX0I1ptkLpNVq1bBx8dHHaJpbEHd3z21fe+99zB9+nQkJSXh6tWrCAoKgqIoKFOmDDw8PNTz3xjK4+nTp6hXr55eZwWQEbxcuHABe/bswdSpU1GpUiXUqFED7du3R506dfTKwRiuixMnTsDS0hLff//93+7j7e2tDl03hu9e58mTJzh16pReWnJyMs6cOQM3NzfUq1cPADB+/HhUqVIFlpaW8PX1hb29vdG0i3SjeMaOHZuls2Lz5s2oVasWfHx8sHLlSvTp0wd2dnaoWrUqGjdubJQzwGkusAH+ftiBbv513SNYY+iNepNbt26hefPmaNOmDT766CMUK1YMBw8eVLc/efIEmzZtwtixY/Hdd9+pwzGMwb1791CjRg3Url0b1atXR79+/VCyZEksW7YMW7duRWBgIPbv3w8gY8abxo0bIzAw0KhejMts5cqVqFOnDl68eKG+U6Y71sePH+P7779HSkoKYmNj0bZtW7i4uGh+OOIvv/yi1zMNZByri4sLVq1ahSdPnqBDhw6oW7cu9u3bh5cvX2LBggVYsmSJWicYS92QV4dg/ZPMZdKqVSu0bdvWcJnJYf/01LZGjRqYOnUqrl+/jgsXLuDUqVPqeWAs5//Tp0+zvPS/ZMkSdOrUCYqiwN3dHZ6enjh16hQGDx6MoUOHYvbs2UZXD4SFhaFYsWJo06aN3ix3mZc08PPzw4YNGwyVxRyRuaMzICAAY8eOxZEjR9QJo/744w9UrVoVderUAZDRNlq1ahV27tyZZZIVrdKN4nnvvffUtPT0dL1ze9myZShSpAhWrFgBAAgJCUFsbKzmZz/7O5oMbICsN3R/f3/06NFDHXZgTD0Sb3Lz5k00adIEVlZWmD17tppuLBX1P7l16xY6dOiAdu3aYefOndi1axcCAgLQrl07KIqCWrVqISkpCUDGolzGOhRFp2bNmlAUBfXr1//bhUZTUlLw/PlzPHr0KJdzl710M+ApioJmzZph6dKl6iJimzdvRuvWrRETE4OrV6+iY8eOqF+/vjqhgI6xNfLz2hCsf0NXJrr3qXT1gTH6u6e2n376KZo1awZFUbB69Wp1uzGd/0+fPoWLiwv69euHI0eOoGPHjqhSpQr69++PgwcPYtu2bShbtizmzp2b5bPGVA5AxuyP5ubm6NmzZ5ZV5MePH49SpUoZTWNe5969e/Dy8kL58uVRo0YN9OrVC5aWlvDy8kL37t2xdetWbNu2DWXKlEGTJk2Msl2YeRTP62vzZD7eunXron379gCM79x/nWYDGyBvDTt4k9u3b6Np06Zo0aKF3gmdF479xo0baNGiBZo2bYqbN28iNjYWp06dQmBgoPp+hbGXg+74NmzYgCpVqqgvzRrzcYeEhKBevXpo2LAhGjRogMGDB6NIkSKYO3cu5syZg4YNG6rXQnBwMAICAvQmFTFWeb0ufJNnz56hTp06ml09+9/6p6e2T548wbZt24y6w+vnn3+GnZ0dSpcuDU9PTxw5ckSdwvfly5fw8vLC+PHjDZzLnJeamoply5YhX758KF++PD788EOMGzcO3bp1Q+HChY1m2NXrQkJC0L59e7Rt2xanT5/G/fv3sXnzZtSpUwe1atWClZUVqlSpAkVR0K5dOwDGVyfqRvE0a9bsb9uCAQEB6NatmyGyl+sUABANAyCKooiISGBgoOTLl0927dpl2EzlopCQEBkyZIgAkC+++ELq1Klj6CzlmpCQEBk0aJCIiEyYMCFPHXtmjx49kpo1a8qQIUPks88+M3R2ctytW7dk7NixkpKSIkOHDpW0tDRZvny5JCQkyIEDB6Rt27by/fffi6mpqdy7d09KlCghJiYmhs52jsvrdeGbJCYmiqWlpaG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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "monthly = total_items.resample(\"1M\").mean()\n", + "ax = monthly.plot(kind='bar',figsize=(10,3))\n", + "ax.set_xticklabels([x.strftime(\"%b-%Y\") for x in monthly.index], rotation=45)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 資料框架\n", + "\n", + "資料框架本質上是一組具有相同索引的序列。我們可以將多個序列結合在一起形成一個資料框架。假設已定義了上述的 `a` 和 `b` 序列：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " A B\n", + "0 1 I\n", + "1 2 like\n", + "2 3 to\n", + "3 4 use\n", + "4 5 Python\n", + "5 6 and\n", + "6 7 Pandas\n", + "7 8 very\n", + "8 9 much" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame({ 'A' : a, 'B' : b })\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "透過轉置（然後重新命名列以匹配之前的例子）可以達到相同的結果：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " A B\n", + "0 1 I\n", + "1 2 like\n", + "2 3 to\n", + "3 4 use\n", + "4 5 Python\n", + "5 6 and\n", + "6 7 Pandas\n", + "7 8 very\n", + "8 9 much" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame([a,b]).T.rename(columns={ 0 : 'A', 1 : 'B' })" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "從 DataFrame 中**選擇列**可以這樣做：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Column A (series):\n", + "0 1\n", + "1 2\n", + "2 3\n", + "3 4\n", + "4 5\n", + "5 6\n", + "6 7\n", + "7 8\n", + "8 9\n", + "Name: A, dtype: int64\n", + "Columns B and A (DataFrame):\n", + " B A\n", + "0 I 1\n", + "1 like 2\n", + "2 to 3\n", + "3 use 4\n", + "4 Python 5\n", + "5 and 6\n", + "6 Pandas 7\n", + "7 very 8\n", + "8 much 9\n" + ] + } + ], + "source": [ + "print(f\"Column A (series):\\n{df['A']}\")\n", + "print(f\"Columns B and A (DataFrame):\\n{df[['B','A']]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "根據篩選表達式**選擇行**：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AB
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" + ], + "text/plain": [ + " A B\n", + "0 1 I\n", + "1 2 like\n", + "2 3 to\n", + "3 4 use" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df['A']<5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "它的運作方式是，表達式 `df['A']<5` 會返回一個布林系列，該系列指示對於系列中的每個元素，表達式是 `True` 還是 `False`。當系列用作索引時，它會返回 DataFrame 中的行子集。因此，無法使用任意的 Python 布林表達式，例如，寫 `df[df['A']>5 and df['A']<7]` 是錯誤的。相反，你應該在布林系列上使用特殊的 `&` 運算符：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " A B\n", + "5 6 and" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[(df['A']>5) & (df['A']<7)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**創建新的可計算列**。我們可以通過使用直觀的表達式輕鬆為我們的 DataFrame 創建新的可計算列。以下代碼計算 A 與其平均值的偏差。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ABDivA
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" + ], + "text/plain": [ + " A B DivA\n", + "0 1 I -4.0\n", + "1 2 like -3.0\n", + "2 3 to -2.0\n", + "3 4 use -1.0\n", + "4 5 Python 0.0\n", + "5 6 and 1.0\n", + "6 7 Pandas 2.0\n", + "7 8 very 3.0\n", + "8 9 much 4.0" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['DivA'] = df['A']-df['A'].mean()\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "實際上發生的是我們正在計算一個序列，然後將這個序列賦值給左邊，創建另一列。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# WRONG: df['ADescr'] = \"Low\" if df['A'] < 5 else \"Hi\"\n", + "df['LenB'] = len(df['B']) # Wrong result" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ABDivALenB
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" + ], + "text/plain": [ + " A B DivA LenB\n", + "0 1 I -4.0 1\n", + "1 2 like -3.0 4\n", + "2 3 to -2.0 2\n", + "3 4 use -1.0 3\n", + "4 5 Python 0.0 6\n", + "5 6 and 1.0 3\n", + "6 7 Pandas 2.0 6\n", + "7 8 very 3.0 4\n", + "8 9 much 4.0 4" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['LenB'] = df['B'].apply(lambda x: len(x))\n", + "# or\n", + "df['LenB'] = df['B'].apply(len)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "根據數字選擇行可以使用 `iloc` 結構。例如，要從 DataFrame 中選擇前 5 行：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ABDivALenB
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" + ], + "text/plain": [ + " A B DivA LenB\n", + "0 1 I -4.0 1\n", + "1 2 like -3.0 4\n", + "2 3 to -2.0 2\n", + "3 4 use -1.0 3\n", + "4 5 Python 0.0 6" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.iloc[:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**分組** 通常用於獲得類似於 Excel 中 *樞紐分析表* 的結果。假設我們想計算每個給定的 `LenB` 數值對應的列 `A` 的平均值。那麼我們可以按 `LenB` 對 DataFrame 進行分組，然後調用 `mean`：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Count Mean\n", + "LenB \n", + "1 1 1.000000\n", + "2 1 3.000000\n", + "3 2 5.000000\n", + "4 3 6.333333\n", + "6 2 6.000000" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby(by='LenB') \\\n", + " .aggregate({ 'DivA' : len, 'A' : lambda x: x.mean() }) \\\n", + " .rename(columns={ 'DivA' : 'Count', 'A' : 'Mean'})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 列印和繪圖\n", + "\n", + "數據科學家經常需要探索數據，因此能夠將數據可視化非常重要。當 DataFrame 很大時，我們通常只需要確認自己是否正確操作，可以透過列印出前幾行來做到這一點。這可以通過調用 `df.head()` 完成。如果你在 Jupyter Notebook 中執行，它會以漂亮的表格形式列印出 DataFrame。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " A B DivA LenB\n", + "0 1 I -4.0 1\n", + "1 2 like -3.0 4\n", + "2 3 to -2.0 2\n", + "3 4 use -1.0 3\n", + "4 5 Python 0.0 6" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "我們已經看過使用 `plot` 函數來視覺化一些欄位。雖然 `plot` 在許多任務中非常有用，並且透過 `kind=` 參數支持多種不同的圖表類型，但你也可以直接使用原始的 `matplotlib` 庫來繪製更複雜的圖表。我們會在其他課程中詳細講解數據視覺化。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['A'].plot(kind='bar')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "這個概覽涵蓋了 Pandas 的大部分重要概念，但這個函式庫非常豐富，你可以用它做到的事情幾乎是無窮無盡的！現在，讓我們運用這些知識來解決具體的問題吧。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**免責聲明**： \n此文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議使用專業的人類翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。\n" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5" + }, + "kernelspec": { + "display_name": "Python 3.8.8 64-bit (conda)", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + }, + "orig_nbformat": 4, + "coopTranslator": { + "original_hash": "1eee5e4aa24fb5c945b43cbcda282de8", + "translation_date": "2025-09-02T06:24:47+00:00", + "source_file": "2-Working-With-Data/07-python/notebook.ipynb", + "language_code": "hk" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/zh-HK/2-Working-With-Data/08-data-preparation/README.md b/translations/zh-HK/2-Working-With-Data/08-data-preparation/README.md new file mode 100644 index 00000000..61e05b12 --- /dev/null +++ b/translations/zh-HK/2-Working-With-Data/08-data-preparation/README.md @@ -0,0 +1,336 @@ +# 處理數據：數據準備 + +|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/08-DataPreparation.png)| +|:---:| +|數據準備 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | + +## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/14) + +根據數據來源，原始數據可能包含一些不一致性，這些問題會對分析和建模造成挑戰。換句話說，這些數據可以被歸類為「髒數據」，需要進行清理。本課程重點介紹清理和轉換數據的技術，以應對缺失、不準確或不完整的數據挑戰。本課程涵蓋的主題將使用 Python 和 Pandas 庫，並在本目錄中的[筆記本](../../../../2-Working-With-Data/08-data-preparation/notebook.ipynb)中進行演示。 + +## 清理數據的重要性 + +- **易於使用和重用**：當數據被適當地組織和標準化後，更容易搜索、使用和與他人共享。 + +- **一致性**：數據科學通常需要處理多個數據集，來自不同來源的數據集需要合併在一起。確保每個數據集具有共同的標準化，能夠保證合併後的數據仍然有用。 + +- **模型準確性**：清理過的數據能夠提高依賴於它的模型的準確性。 + +## 常見的清理目標和策略 + +- **探索數據集**：數據探索（在[後續課程](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/4-Data-Science-Lifecycle/15-analyzing)中會介紹）可以幫助你發現需要清理的數據。通過可視化觀察數據集中的值，可以設置對其餘部分的期望，或者提供解決問題的思路。探索可以包括基本查詢、可視化和抽樣。 + +- **格式化**：根據來源，數據可能在呈現方式上存在不一致性。這可能導致在搜索和表示值時出現問題，數據雖然在數據集中可見，但在可視化或查詢結果中未正確表示。常見的格式化問題包括解決空白、日期和數據類型。解決格式化問題通常由使用數據的人來完成。例如，日期和數字的表示標準可能因國家而異。 + +- **重複數據**：重複出現的數據可能會產生不準確的結果，通常應該被刪除。這在合併兩個或多個數據集時很常見。然而，有些情況下，合併數據集中的重複部分可能包含額外的信息，需要保留。 + +- **缺失數據**：缺失數據可能導致不準確以及結果偏差。有時可以通過重新加載數據、使用 Python 等計算和代碼填充缺失值，或者直接刪除缺失值及其相關數據來解決。數據缺失的原因有很多，解決缺失值的行動取決於數據缺失的方式和原因。 + +## 探索 DataFrame 信息 +> **學習目標**：完成本小節後，你應該能夠熟練地查找存儲在 pandas DataFrame 中的數據的一般信息。 + +當你將數據加載到 pandas 中後，它通常會以 DataFrame 的形式存在（參考之前的[課程](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/2-Working-With-Data/07-python#dataframe)了解詳細概述）。然而，如果你的 DataFrame 中的數據集有 60,000 行和 400 列，你該如何開始了解你正在處理的內容？幸運的是，[pandas](https://pandas.pydata.org/) 提供了一些方便的工具，可以快速查看 DataFrame 的整體信息以及前幾行和後幾行。 + +為了探索這些功能，我們將導入 Python 的 scikit-learn 庫並使用一個經典數據集：**Iris 數據集**。 + +```python +import pandas as pd +from sklearn.datasets import load_iris + +iris = load_iris() +iris_df = pd.DataFrame(data=iris['data'], columns=iris['feature_names']) +``` +| |sepal length (cm)|sepal width (cm)|petal length (cm)|petal width (cm)| +|----------------------------------------|-----------------|----------------|-----------------|----------------| +|0 |5.1 |3.5 |1.4 |0.2 | +|1 |4.9 |3.0 |1.4 |0.2 | +|2 |4.7 |3.2 |1.3 |0.2 | +|3 |4.6 |3.1 |1.5 |0.2 | +|4 |5.0 |3.6 |1.4 |0.2 | + +- **DataFrame.info**：首先，`info()` 方法用於打印 `DataFrame` 中內容的摘要。我們來看看這個數據集： +```python +iris_df.info() +``` +``` +RangeIndex: 150 entries, 0 to 149 +Data columns (total 4 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 sepal length (cm) 150 non-null float64 + 1 sepal width (cm) 150 non-null float64 + 2 petal length (cm) 150 non-null float64 + 3 petal width (cm) 150 non-null float64 +dtypes: float64(4) +memory usage: 4.8 KB +``` +從中我們知道 *Iris* 數據集有 150 條記錄，分布在四列中，沒有空值。所有數據都存儲為 64 位浮點數。 + +- **DataFrame.head()**：接下來，為了檢查 `DataFrame` 的實際內容，我們使用 `head()` 方法。讓我們看看 `iris_df` 的前幾行： +```python +iris_df.head() +``` +``` + sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) +0 5.1 3.5 1.4 0.2 +1 4.9 3.0 1.4 0.2 +2 4.7 3.2 1.3 0.2 +3 4.6 3.1 1.5 0.2 +4 5.0 3.6 1.4 0.2 +``` +- **DataFrame.tail()**：相反，為了檢查 `DataFrame` 的最後幾行，我們使用 `tail()` 方法： +```python +iris_df.tail() +``` +``` + sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) +145 6.7 3.0 5.2 2.3 +146 6.3 2.5 5.0 1.9 +147 6.5 3.0 5.2 2.0 +148 6.2 3.4 5.4 2.3 +149 5.9 3.0 5.1 1.8 +``` +> **重點**：僅僅通過查看 DataFrame 的元數據或其中的前幾個和最後幾個值，你就可以立即了解你正在處理的數據的大小、形狀和內容。 + +## 處理缺失數據 +> **學習目標**：完成本小節後，你應該知道如何替換或刪除 DataFrame 中的空值。 + +大多數情況下，你想使用（或必須使用）的數據集中都會有缺失值。如何處理缺失數據涉及微妙的權衡，這可能會影響你的最終分析和實際結果。 + +Pandas 以兩種方式處理缺失值。第一種方式你在之前的部分中已經見過：`NaN`，即非數值（Not a Number）。這實際上是一個特殊值，是 IEEE 浮點規範的一部分，僅用於表示缺失的浮點值。 + +對於浮點數以外的缺失值，pandas 使用 Python 的 `None` 對象。雖然你可能會覺得遇到兩種不同的值來表示基本相同的意思有些混亂，但這種設計選擇有其合理的編程原因，實際上，這種方式為大多數情況提供了一個良好的折衷。不過，`None` 和 `NaN` 都有一些限制，你需要注意它們的使用方式。 + +在[筆記本](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/4-Data-Science-Lifecycle/15-analyzing/notebook.ipynb)中了解更多關於 `NaN` 和 `None` 的信息！ + +- **檢測空值**：在 `pandas` 中，`isnull()` 和 `notnull()` 方法是檢測空數據的主要方法。兩者都返回布爾掩碼。我們將使用 `numpy` 來處理 `NaN` 值： +```python +import numpy as np + +example1 = pd.Series([0, np.nan, '', None]) +example1.isnull() +``` +``` +0 False +1 True +2 False +3 True +dtype: bool +``` +仔細查看輸出。是否有任何內容讓你感到驚訝？雖然 `0` 是一個算術空值，但它仍然是一個完全有效的整數，pandas 將其視為這樣。`''` 則稍微微妙一些。雖然我們在第一部分中使用它來表示空字符串值，但它仍然是一個字符串對象，而不是 pandas 所認為的空值。 + +現在，讓我們反過來以更接近實際使用的方式使用這些方法。你可以直接將布爾掩碼用作 ``Series`` 或 ``DataFrame`` 的索引，這在處理孤立的缺失（或存在）值時非常有用。 + +> **重點**：`isnull()` 和 `notnull()` 方法在 `DataFrame` 中的使用結果相似：它們顯示結果及其索引，這將在你處理數據時幫助你很多。 + +- **刪除空值**：除了識別缺失值，pandas 還提供了一種方便的方法來刪除 `Series` 和 `DataFrame` 中的空值。（特別是在大型數據集上，通常更建議直接從分析中刪除缺失 [NA] 值，而不是以其他方式處理它們。）讓我們回到 `example1`： +```python +example1 = example1.dropna() +example1 +``` +``` +0 0 +2 +dtype: object +``` +注意，這應該看起來像你的 `example3[example3.notnull()]` 的輸出。不同之處在於，`dropna` 已經從 `Series` `example1` 中刪除了那些缺失值。 + +由於 `DataFrame` 是二維的，它提供了更多刪除數據的選項。 + +```python +example2 = pd.DataFrame([[1, np.nan, 7], + [2, 5, 8], + [np.nan, 6, 9]]) +example2 +``` +| | 0 | 1 | 2 | +|------|---|---|---| +|0 |1.0|NaN|7 | +|1 |2.0|5.0|8 | +|2 |NaN|6.0|9 | + +（你是否注意到 pandas 將兩列數據提升為浮點數以容納 `NaN`？） + +你不能從 `DataFrame` 中刪除單個值，因此你必須刪除整行或整列。根據你的操作，你可能需要刪除其中之一，pandas 為此提供了選擇。由於在數據科學中，列通常代表變量，行代表觀測值，你更可能刪除數據行；`dropna()` 的默認設置是刪除所有包含任何空值的行： + +```python +example2.dropna() +``` +``` + 0 1 2 +1 2.0 5.0 8 +``` +如果需要，你可以刪除列中的 NA 值。使用 `axis=1` 來完成： +```python +example2.dropna(axis='columns') +``` +``` + 2 +0 7 +1 8 +2 9 +``` +注意，這可能會刪除你可能想保留的大量數據，特別是在較小的數據集中。如果你只想刪除包含幾個或所有空值的行或列怎麼辦？你可以在 `dropna` 中使用 `how` 和 `thresh` 參數來指定這些設置。 + +默認情況下，`how='any'`（如果你想自己檢查或查看該方法的其他參數，可以在代碼單元中運行 `example4.dropna?`）。你也可以選擇指定 `how='all'`，以便僅刪除包含所有空值的行或列。讓我們擴展我們的示例 `DataFrame` 來看看這一點。 + +```python +example2[3] = np.nan +example2 +``` +| |0 |1 |2 |3 | +|------|---|---|---|---| +|0 |1.0|NaN|7 |NaN| +|1 |2.0|5.0|8 |NaN| +|2 |NaN|6.0|9 |NaN| + +`thresh` 參數提供了更細粒度的控制：你可以設置行或列需要保留的*非空值*的數量： +```python +example2.dropna(axis='rows', thresh=3) +``` +``` + 0 1 2 3 +1 2.0 5.0 8 NaN +``` +在這裡，第一行和最後一行被刪除，因為它們僅包含兩個非空值。 + +- **填充空值**：根據你的數據集，有時用有效值填充空值比刪除它們更合理。你可以使用 `isnull` 來就地完成這項工作，但如果你有很多值需要填充，這可能會很繁瑣。由於這是數據科學中的常見任務，pandas 提供了 `fillna`，它返回一個 `Series` 或 `DataFrame` 的副本，其中的缺失值被替換為你選擇的值。讓我們創建另一個示例 `Series` 來看看這在實踐中的工作方式。 +```python +example3 = pd.Series([1, np.nan, 2, None, 3], index=list('abcde')) +example3 +``` +``` +a 1.0 +b NaN +c 2.0 +d NaN +e 3.0 +dtype: float64 +``` +你可以用單一值（例如 `0`）填充所有空條目： +```python +example3.fillna(0) +``` +``` +a 1.0 +b 0.0 +c 2.0 +d 0.0 +e 3.0 +dtype: float64 +``` +你可以**向前填充**空值，即使用最後一個有效值填充空值： +```python +example3.fillna(method='ffill') +``` +``` +a 1.0 +b 1.0 +c 2.0 +d 2.0 +e 3.0 +dtype: float64 +``` +你也可以**向後填充**，即向後傳播下一個有效值來填充空值： +```python +example3.fillna(method='bfill') +``` +``` +a 1.0 +b 2.0 +c 2.0 +d 3.0 +e 3.0 +dtype: float64 +``` +正如你可能猜到的，這對 `DataFrame` 也同樣有效，但你還可以指定沿著哪個 `axis` 填充空值。再次使用之前的 `example2`： +```python +example2.fillna(method='ffill', axis=1) +``` +``` + 0 1 2 3 +0 1.0 1.0 7.0 7.0 +1 2.0 5.0 8.0 8.0 +2 NaN 6.0 9.0 9.0 +``` +注意，當前一個值不可用進行向前填充時，空值仍然保留。 +> **重點:** 處理數據集中缺失值的方法有很多。你採用的具體策略（移除、替換，甚至是如何替換）應該根據該數據的具體情況來決定。隨著你處理和接觸更多數據集，你將會更好地掌握如何應對缺失值。 +## 移除重複數據 + +> **學習目標：** 完成本小節後，你應該能夠熟練地識別並移除 DataFrame 中的重複值。 + +除了缺失數據外，你在真實世界的數據集中經常會遇到重複的數據。幸運的是，`pandas` 提供了一個簡單的方法來檢測和移除重複的條目。 + +- **識別重複值：`duplicated`**：你可以使用 pandas 的 `duplicated` 方法輕鬆地找到重複值。該方法返回一個布爾掩碼，指示 `DataFrame` 中某個條目是否是之前條目的重複。讓我們創建另一個示例 `DataFrame` 來看看它的作用。 +```python +example4 = pd.DataFrame({'letters': ['A','B'] * 2 + ['B'], + 'numbers': [1, 2, 1, 3, 3]}) +example4 +``` +| |letters|numbers| +|------|-------|-------| +|0 |A |1 | +|1 |B |2 | +|2 |A |1 | +|3 |B |3 | +|4 |B |3 | + +```python +example4.duplicated() +``` +``` +0 False +1 False +2 True +3 False +4 True +dtype: bool +``` +- **移除重複值：`drop_duplicates`**：該方法返回一個副本，其中所有 `duplicated` 值均為 `False`： +```python +example4.drop_duplicates() +``` +``` + letters numbers +0 A 1 +1 B 2 +3 B 3 +``` +`duplicated` 和 `drop_duplicates` 默認會考慮所有列，但你可以指定它們僅檢查 `DataFrame` 中的部分列： +```python +example4.drop_duplicates(['letters']) +``` +``` +letters numbers +0 A 1 +1 B 2 +``` + +> **重點：** 移除重複數據是幾乎每個數據科學項目中的重要部分。重複數據可能會影響分析結果，導致不準確的結論！ + + +## 🚀 挑戰 + +所有討論的材料都以 [Jupyter Notebook](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/2-Working-With-Data/08-data-preparation/notebook.ipynb) 的形式提供。此外，每個部分後面都有練習題，試試看吧！ + +## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/15) + + + +## 回顧與自學 + +有許多方法可以探索和準備你的數據進行分析和建模，而清理數據是一個需要親自動手的重要步驟。試試 Kaggle 上的這些挑戰，探索本課未涵蓋的技術。 + +- [數據清理挑戰：解析日期](https://www.kaggle.com/rtatman/data-cleaning-challenge-parsing-dates/) + +- [數據清理挑戰：數據縮放與標準化](https://www.kaggle.com/rtatman/data-cleaning-challenge-scale-and-normalize-data) + + +## 作業 + +[評估表單中的數據](assignment.md) + +--- + +**免責聲明**： +此文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/2-Working-With-Data/08-data-preparation/assignment.ipynb b/translations/zh-HK/2-Working-With-Data/08-data-preparation/assignment.ipynb new file mode 100644 index 00000000..dc3681b1 --- /dev/null +++ b/translations/zh-HK/2-Working-With-Data/08-data-preparation/assignment.ipynb @@ -0,0 +1,155 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# 作業：評估表單數據\n", + "\n", + "一位客戶測試了一個[小型表單](../../../../2-Working-With-Data/08-data-preparation/index.html)，用來收集他們客戶群的一些基本數據。他們將測試結果交給你，請你驗證他們收集的數據。你可以在瀏覽器中打開 `index.html` 頁面，查看該表單。\n", + "\n", + "你已獲得一份[包含表單記錄的 CSV 數據集](../../../../data/form.csv)，其中包括表單的輸入記錄以及一些基本的可視化圖表。客戶指出，有些可視化圖表看起來不正確，但他們不確定該如何解決。你可以在[作業筆記本](assignment.ipynb)中進一步探索。\n", + "\n", + "## 指引\n", + "\n", + "使用本課程中的技術，對表單提出改進建議，以確保它能收集準確且一致的信息。\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "!pip install pandas\r\n", + "!pip install matplotlib" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 4, + "source": [ + "import pandas as pd\r\n", + "import matplotlib.pyplot as plt\r\n", + "\r\n", + "#Loading the dataset\r\n", + "path = '../../data/form.csv'\r\n", + "form_df = pd.read_csv(path)\r\n", + "print(form_df)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " birth_month state pet\n", + "0 January NaN Cats\n", + "1 JAN CA Cats\n", + "2 Sept Hawaii Dog\n", + "3 january AK Dog\n", + "4 July RI Cats\n", + "5 September California Cats\n", + "6 April CA Dog\n", + "7 January California Cats\n", + "8 November FL Dog\n", + "9 December Florida Cats\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 7, + "source": [ + "form_df['state'].value_counts().plot(kind='bar');\r\n", + "plt.show()" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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", 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REdGFcYNe0hTgbOAlwH7ASZL2G7XaKcA9tp8BfAr41/q5+1FdTPw5wGzgc/XrRUREn3SzR38osMz27bYfBi4A5oxaZw7w5fr+RcALVF0lfA5wge2HbN8BLKtfLyIi+kS2N7yCdAIw2/ab6sevBw6zfWpjnZvqdVbUj38LHAZ8ELja9r/X7ecCl9i+qMP7zAPm1Q+fBdy6eT8aANOBu3rwOr00iDXBYNaVmrqTmro3iHX1qqa9bA91WrB1D168J2zPB+b38jUlLbE93MvX3FyDWBMMZl2pqTupqXuDWFc/auqm62YlsGfj8R51W8d1JG0NPAm4u8vnRkTEBOom6BcD+0jaW9I2VAdXF45aZyFwcn3/BOCHrvqEFgJz61E5ewP7AL/sTekREdGNcbtubK+VdCpwKTAFWGD7ZklnAktsLwTOBf5N0jJgDdWHAfV6FwK3AGuBt9peN0E/Syc97QrqkUGsCQazrtTUndTUvUGsa8JrGvdgbERETG45MzYionAJ+oiIwiXoIyIKV1TQS3qbpGlt1wEgacaGbgNQ31HdtPXTIG0/AEmfkPSctusYTdIUSV9tu47JoP5dfbztOpraqGlgTpjqkV2BxZKuARYAl7q9o83fAwyo0WZgCNiFagRTm84CDumirZ8GafsB/AqYX58b8iXgfNv3tlgPALbXSdpL0jb1tCStkbTzhpbbXtOvWsZ4/3WSjm6zhtHaqKm4UTf1HDuzgDcCw8CFwLm2f9tyXTOB9wIvBD5j+6yW6jgCOBJ4J9UEdCN2Al5l+8A26hoxiNtP0rPqek4Cfgb8H9s/aqueuqavAPtSnavywEi77U/2uY47ePwOTaMcP62f9XQi6fPA7sA3WP939a0tpabS9uixbUl/BP5INXZ/GnCRpMtt/69+1yNpH+ADVHP/fAJ4u+2/9LuOhm2AHai2/Y6N9vuoTnZr1QBuvynAs+vbXcD1wLslvdn23H7X0/Db+rYV62/HvrK9d1vvvRG2ozpT//mNNgOtBT19rqmoPXpJ7wDeQPUf8ovAt23/RdJWwG22n97HWp5LFfDPAT5K9bW/nyeLbZCkvWz/TtJOVPl6/wDUNDDbr67nU8ArgB9Qfav4ZWPZrbaf1c96OpG0ve0HW3z/Z9v+taSOXX62r+l3TfF4pe3RTwNebft3zUbbj0h6eZ9ruR5YTtVXfyhwaNUr8WhNb+9zPaMNSfoP6r1BSfcCf297aYs1DdL2A7gB+GfbD3RY1up023UX3LlU385mSDoQeLPtt/S5lHdTzTr7iQ7LzPp7rK2Q9Ezg88Cutp8r6QDgeNsf3lJqKmaPvv6KfbPtZ7ddC4Ckkze03PaXN7R8okm6gWpKip/Uj48GPmf7gJbqGZjtN9be6YhB2EuV9AuqrraFtg+u226y/dwWatkKOML2z/r93t2QdAXwHuCctn9XbdVUzB59fST7VkkzbP9+AOrpGOSStqPqDmjbupGQB7D9U0lr2ypmwLZfp73TEQOxlwpge3nzWyLQStdg/Y3rs8DBbbx/F7a3/ctRv6vW/tZrfa2pmKCvTQNulvRL1j+SfXx7JT26t/piqlEbs4CfUB1tb9MVks4BzqcKrxOBH4/szba01zoQ28/2cf18v020XNKRgCVNBd5BNRy0LT+Q9BrgWy0Pie3kLklPp/o7H7mY0h/aLam/NRXTdQMg6ZhO7bav6Hct8Gg9fwu8lGp65qOAp7V58GyEpA0ND7Ttvu+1DuD2e0Ondttf6Xcto0maDnyaarjuVlSzy77D9t0t1XM/8ESqbxV/phpuads7tVFPk6SnUc0QeSRwD3AH8NrRx4JKrqmooB8kklYAv6c64PJt2/dLumOSDEcLQFLzXIftgBcA19hufRhqbDxJTwS2GoQRZiP6VVNpUyAcLmmxpD9JeljSOkn3tVTORcBTqbpEXlFv0IH5VJW0q6RzJV1SP95P0ikt1zRI2w/bb2vc/oHqrOEd2qqnSdLTJH1X0mpJqyR9p95LbLOmV0v6pKqpI17ZZi1Nkp4i6TNUXaY/lvRpSU/ZkmoqKuiBz1L1g98GPAF4E3B2G4XYfiewN9WBvWOpLna+i6QTJQ1CWJxH9XX/qfXj31CdLdumgdl+Y3iAapsOgq9RnTW8G9U2/AbV8ZZWSPoc8I/AjcBNwD9KGpRtdwGwGngN1Uil1cDXW62ozzUV1XWj+iK7km4YGSYo6dqR4Ust1zaVxgFZj3G19j7Ws9j285q/H0nX2T6oxZoGavtJ+i6PfQubAuwHXGj7vW3U09T8HTXarm9rCgtJvwb2HTkQWw+5vNn2vm3U09Rp2KKkG23vv6XUVNqomwdVXdf2OkkfpTqK3cq3lvrg1OhP0ZGxVH+RdDXwAds/6G9lj3qg/qo48h/zcKDtCbsGZvvVPs5j23At8DvbrV7cXo9NInaJpNOo9gxHRk0taq0wWAbMAEYOJu5Ztw2CyyTNpfoGBNUe9KUt1gN9rqm0Pfq9gFXAVOBdwJOoTgIalD844NHhls8FvtrWSRv1MMqz6jpuoppV8wTbN7RRT13TQGy/xof06Im6DDxENcdMKx/SGtBJxOoTgJ5HNbqM+v5iqjmUWhniPGo7PhF4pF60FfCnNkYEtVVTUUE/2aiaGOucPr/n84Dltv+oavrdN1P1E94CnO6Wp5UddIPwIT2IRg2NFfDfgLnAW6C9IbJRKSroG3s762lrL2cQqZrr/YW210j671Rf/d8GHETVx9ra0MHJtP3a+JAe9f5TgJcBM2l0wbrP0xSPqulgqvNG/oZqXPi33NJ03KOpmktmJuv/rtqcvbKvNZXWRz/cuL8d1R/cBi+MsAWa0thrPxGYb/ubwDclXddeWcAk2n5thnztu8D/oxrl8sg4604YVZNznVTf7qIaOaJBOrtY0gLgAOBmHvtdtTpNcb9rKmqPvhNJS23/ddt1DApJNwEH2V5bj5SYZ/vKkWWD1h2R7ddZp1E3LdXxCNVY8FNGjqVIun2QvoVJusX2fm3X0dTvmorao9f6sw5uRbWHWNTP2APnU81zcxfVqeojs1c+g5ZH3WT7bZRLJM2yfVnLdbyaqi/+R5K+T9UV2OlAcZt+Lmk/27e0XUhDX2sqao9e68/fsha4E/i47VvbqWgw1UMpdwMucz3Xev0VfIc2p+DN9uuepFcB/071gfgXWp5bpj7zew5VF87zga8AFw/AB9HIgeKFVFcte4jHfletfSPqd01FBX3ElqI+cD0HuHHQZouUNI3q+MqJtl8wAPUso7pAynrHM1qe1KyvNRUV9JK2pRoqOJP1j2Sf2VZN0b1sv+5JuhI41nZrB2InC0k/t31E23U09bum0vo/v0PVz7yU6utQTC7Zft27nWoyrEto/K7aHF45wK6V9DWqkUrN31Wbwyv7WlNpQb+H7dltFxGbLNuve3fUt23qW4ztCVRhOqvR1urwSvpcU2ldN/OBs2zf2HYtsfGy/TaepO0H4UI2MdhKm6b4aGCpqmuP3iDpRlUXwY7JIduvS5KOkHQL8Ov68YH1VMExiqRnSvpBfQ4Jkg6Q9M9bUk2l7dHv1am9zaPr0b1sv+5J+gXVjIcLG9NMD9wJb4OgnnDtPcA5g/K76ndNRfXRjwSCpF2oTqGPSSTbb+PYXi6td27SurZqGXDb2/7lqN/V2raKqfW1pqK6biQdL+k2qoNUV1CdcHNJq0VF17L9NspySUcCljRV0v8EftV2UQPqLklP57FrL5xAda2DNvW1ptK6bq6nOivvP20fLOk44HW2W70WanQn2697kqYDnwZeSHVW5WXA2zPN9OOpupbufOBI4B6qHYnXtnzCVKeaXmf7zgl5v8KCfuRSdNcDB9t+RC1eXi02TrZf9yQdZftn47VFNaWz7XX1NA1b2b6/7ZpG9KumorpugP9SdeHtnwBflfRpqgs6x+SQ7de9TvO8D8Tc7wPojnro7uHAn9ouBkDSv0h6su0HbN8vaZqkD0/Y+xW2R7891RzdAl4H7ER1JaB8nZ0Esv3GJ+kIqq/77wQ+1Vi0E/CqfPt5vPrv6uVUs2weAvwHcIHtn7ZY07UeddF7SdfYPmSs52yOIkbdaMMX4j5dUmvX+IzxSfqp7aOB/8tj23Fk+31Y0hrgY7YzTrw6C3YHqv+7Ozba76Mabhmj1CeUXQhcWE+49mmqg/1TWixriqRtbT8EIOkJwLYT9WZF7dF3olzjc9KT9BTgKtvParuWQSFpL9u/k7QT1fS2A9PvPIjqaYFPBGYDS4Cv11dWa6ue9wKvAL5UN72R6pyIj07I+5Ue9CPU8jU+Y/NI2s1220PiBoakYaqQGNmrvxf4e9tL26tqMEm6E7iWaq9+4cg1GNomaTbVqCmAy21fOmHvtaUEfURJ6qkh3mp75AphRwOfG4TLCw4aSTvZvq/tOkarzwTfx/Z/1scRpkzUN7PSRt1EbCnWjYQ8QH1gse2zPQfVTpIulrSqvn1T0h5tFiTpH4CLgJFeht2Bb0/U+yXoIyanKySdI+lYScfUE5r9WNIhWv/au1F1cS0EnlrfvstjfeNteStwFNVBdGzfBuwyUW9WxKibiC3QyDDKM0a1H0w1cun5/S1noA3Zbgb7eZLe2VYxtYdsPzwy142krXn8yMGeSdBHTEK2j2u7hknkbkmvA86vH58E3N1iPVB9I3s/8ARJLwLeQvVNY0LkYGzEJCRpV+BfgKfafomk/YAjbJ/bcmkDpz7oeRZwBNVe81VU8wL9vsWatgJOobrClIBLgS9O1IXeE/QRk5Cqa8V+iepEwAPrr/7X2t6/5dKiS5KGAGyvnuj3StdNxOQ03faFkt4HYHutpMxH3yDp9A0stu0P9a2YmqpO+TOAU6kHw9Tb7SzbZ07U+2bUTcTk9EB9xvDIfOaHU500FY95oMMNqi6T97ZU07uoRts8z/bOtncGDgOOkvSuiXrTdN1ETEL1EMqzqKb3uAkYAk6wnWvsdiBpR+AdVCF/IfAJ26taqONa4EW27xrVPgRcNnq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"ea753231e2dbdcf8d2e7e976784cbe2a", + "translation_date": "2025-09-02T08:28:38+00:00", + "source_file": "2-Working-With-Data/08-data-preparation/assignment.ipynb", + "language_code": "hk" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/zh-HK/2-Working-With-Data/08-data-preparation/assignment.md b/translations/zh-HK/2-Working-With-Data/08-data-preparation/assignment.md new file mode 100644 index 00000000..69a4c226 --- /dev/null +++ b/translations/zh-HK/2-Working-With-Data/08-data-preparation/assignment.md @@ -0,0 +1,17 @@ +# 評估表單數據 + +一位客戶正在測試一個[小型表單](../../../../2-Working-With-Data/08-data-preparation/index.html)，以收集一些關於其客戶群的基本數據。他們已將測試結果交給你，請你驗證所收集的數據。你可以在瀏覽器中打開 `index.html` 頁面查看表單。 + +你已獲得一份[包含表單記錄的 csv 數據集](../../../../data/form.csv)，其中包括表單的輸入內容以及一些基本的可視化圖表。客戶指出部分可視化圖表看起來不正確，但他們不確定如何解決。你可以在[作業筆記本](../../../../2-Working-With-Data/08-data-preparation/assignment.ipynb)中進行探索。 + +## 指引 + +使用本課程中的技術，提出建議以改進表單，使其能夠收集準確且一致的信息。 + +## 評分標準 + +卓越 | 合格 | 需要改進 +--- | --- | --- | + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，請注意自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為具權威性的來源。對於重要信息，建議使用專業的人類翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/2-Working-With-Data/08-data-preparation/notebook.ipynb b/translations/zh-HK/2-Working-With-Data/08-data-preparation/notebook.ipynb new file mode 100644 index 00000000..162064e1 --- /dev/null +++ b/translations/zh-HK/2-Working-With-Data/08-data-preparation/notebook.ipynb @@ -0,0 +1,4241 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "rQ8UhzFpgRra" + }, + "source": [ + "# 數據準備\n", + "\n", + "[原始筆記本來源：*Data Science: Introduction to Machine Learning for Data Science Python and Machine Learning Studio by Lee Stott*](https://github.com/leestott/intro-Datascience/blob/master/Course%20Materials/4-Cleaning_and_Manipulating-Reference.ipynb)\n", + "\n", + "## 探索 `DataFrame` 資訊\n", + "\n", + "> **學習目標：** 完成本小節後，您應該能夠熟練地查找存儲在 pandas DataFrame 中的數據的一般資訊。\n", + "\n", + "當您將數據載入 pandas 後，數據通常會以 `DataFrame` 的形式存在。然而，如果您的 `DataFrame` 中的數據集有 60,000 行和 400 列，您該如何開始了解自己正在處理的內容呢？幸運的是，pandas 提供了一些方便的工具，可以快速查看 `DataFrame` 的整體資訊，以及前幾行和後幾行的內容。\n", + "\n", + "為了探索這些功能，我們將導入 Python 的 scikit-learn 庫，並使用一個每位數據科學家都看過數百次的經典數據集：英國生物學家 Ronald Fisher 在其 1936 年的論文《The use of multiple measurements in taxonomic problems》中使用的 *Iris* 數據集：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "id": "hB1RofhdgRrp", + "trusted": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sklearn.datasets import load_iris\n", + "\n", + "iris = load_iris()\n", + "iris_df = pd.DataFrame(data=iris['data'], columns=iris['feature_names'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AGA0A_Y8hMdz" + }, + "source": [ + "### `DataFrame.shape`\n", + "我們已經將 Iris 數據集載入到變數 `iris_df` 中。在深入分析數據之前，了解我們擁有的數據點數量以及整個數據集的大小是很有價值的。查看我們正在處理的數據量是很有幫助的。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LOe5jQohhulf", + "outputId": "fb0577ac-3b4a-4623-cb41-20e1b264b3e9" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(150, 4)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris_df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "smE7AGzOhxk2" + }, + "source": [ + "我們正在處理 150 行和 4 列的數據。每一行代表一個數據點，每一列代表與數據框相關的一個特徵。基本上，這裡有 150 個數據點，每個數據點包含 4 個特徵。\n", + "\n", + "`shape` 在這裡是數據框的一個屬性，而不是一個函數，所以它的後面沒有一對括號。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d3AZKs0PinGP" + }, + "source": [ + "### `DataFrame.columns`\n", + "現在讓我們深入了解這個數據框的四個欄位。每個欄位具體代表什麼呢？`columns` 屬性將提供數據框中欄位的名稱。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YPGh_ziji-CY", + "outputId": "74e7a43a-77cc-4c80-da56-7f50767c37a0" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n", + " 'petal width (cm)'],\n", + " dtype='object')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris_df.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TsobcU_VjCC_" + }, + "source": [ + "正如我們所見，有四（4）列。`columns`屬性告訴我們列的名稱，基本上沒有其他內容。當我們想要識別數據集包含的特徵時，這個屬性就顯得重要了。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2UTlvkjmgRrs" + }, + "source": [ + "### `DataFrame.info`\n", + "透過 `shape` 屬性提供的數據量以及透過 `columns` 屬性提供的特徵或列名稱，可以讓我們對數據集有初步了解。現在，我們希望更深入地探索數據集。`DataFrame.info()` 函數在這方面非常有用。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dHHRyG0_gRrt", + "outputId": "d8fb0c40-4f18-4e19-da48-c8db77d1d3a5", + "trusted": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 150 entries, 0 to 149\n", + "Data columns (total 4 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 sepal length (cm) 150 non-null float64\n", + " 1 sepal width (cm) 150 non-null float64\n", + " 2 petal length (cm) 150 non-null float64\n", + " 3 petal width (cm) 150 non-null float64\n", + "dtypes: float64(4)\n", + "memory usage: 4.8 KB\n" + ] + } + ], + "source": [ + "iris_df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1XgVMpvigRru" + }, + "source": [ + "從這裡，我們可以作出以下幾點觀察：\n", + "1. 每列的數據類型：在這個數據集中，所有數據都以64位浮點數形式存儲。\n", + "2. 非空值的數量：處理空值是數據準備中的重要步驟，稍後會在筆記本中處理。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IYlyxbpWFEF4" + }, + "source": [ + "### DataFrame.describe()\n", + "假設我們的數據集中有大量的數值數據。像平均值、中位數、四分位數等單變量統計計算可以分別對每一列進行。`DataFrame.describe()` 函數可以為我們提供數據集中數值列的統計摘要。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "id": "tWV-CMstFIRA", + "outputId": "4fc49941-bc13-4b0c-a412-cb39e7d3f289" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", + "145 6.7 3.0 5.2 2.3\n", + "146 6.3 2.5 5.0 1.9\n", + "147 6.5 3.0 5.2 2.0\n", + "148 6.2 3.4 5.4 2.3\n", + "149 5.9 3.0 5.1 1.8" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris_df.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "31kBWfyLgRr3" + }, + "source": [ + "在實際操作中，能夠輕鬆查看 `DataFrame` 的前幾行或後幾行非常有用，特別是在檢查有序數據集中的異常值時。\n", + "\n", + "上面展示的所有函數和屬性，通過代碼示例的幫助，讓我們能夠快速了解數據的概況。\n", + "\n", + "> **重點提示：** 即使僅僅查看 `DataFrame` 的元數據或其中的前幾個和最後幾個值，也能讓你立即對所處理數據的大小、形狀和內容有一個初步的了解。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TvurZyLSDxq_" + }, + "source": [ + "### 缺失數據\n", + "讓我們深入了解缺失數據。缺失數據是指某些欄位中沒有存儲任何值。\n", + "\n", + "舉個例子：假設某人對自己的體重非常在意，因此在調查中不填寫體重欄位。那麼，該人的體重值就會是缺失的。\n", + "\n", + "在現實世界的數據集中，缺失值是非常常見的。\n", + "\n", + "**Pandas 如何處理缺失數據**\n", + "\n", + "Pandas 有兩種方式來處理缺失值。第一種方式你可能在之前的章節中已經見過：`NaN`，即「非數字」（Not a Number）。這實際上是一個特殊值，是 IEEE 浮點規範的一部分，僅用於表示缺失的浮點值。\n", + "\n", + "對於非浮點類型的缺失值，Pandas 使用 Python 的 `None` 對象。雖然遇到兩種不同的值來表示同樣的事情可能會讓人感到困惑，但這種設計選擇有其合理的編程原因。實際上，這樣的設計使得 Pandas 能夠在絕大多數情況下提供良好的平衡。不過，無論是 `None` 還是 `NaN`，它們都帶有一些限制，這些限制需要注意，特別是在使用它們時的方式上。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lOHqUlZFgRr5" + }, + "source": [ + "### `None`：非浮點型的缺失數據\n", + "由於 `None` 是 Python 的一部分，因此它不能用於數據類型不是 `'object'` 的 NumPy 和 pandas 陣列。請記住，NumPy 陣列（以及 pandas 中的數據結構）只能包含一種數據類型。這正是它們在大規模數據和計算工作中展現強大能力的原因，但同時也限制了它們的靈活性。這類陣列必須向“最低共同分母”進行類型提升，即能夠涵蓋陣列中所有內容的數據類型。當陣列中包含 `None` 時，意味著您正在處理 Python 對象。\n", + "\n", + "以下示例陣列可以幫助您理解這一點（注意它的 `dtype`）：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QIoNdY4ngRr7", + "outputId": "92779f18-62f4-4a03-eca2-e9a101604336", + "trusted": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, None, 6, 8], dtype=object)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "example1 = np.array([2, None, 6, 8])\n", + "example1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pdlgPNbhgRr7" + }, + "source": [ + "數據類型向上轉型的現實帶來了兩個副作用。首先，操作將在解釋型的 Python 代碼層面執行，而不是編譯型的 NumPy 代碼層面。基本上，這意味著任何涉及包含 `None` 的 `Series` 或 `DataFrame` 的操作都會變得較慢。雖然你可能不會注意到這種性能下降，但對於大型數據集來說，這可能會成為一個問題。\n", + "\n", + "第二個副作用源於第一個副作用。由於 `None` 本質上將 `Series` 或 `DataFrame` 拉回到原生 Python 的世界，因此在包含 ``None`` 值的數組上使用像 `sum()` 或 `min()` 這樣的 NumPy/pandas 聚合操作通常會產生錯誤：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 292 + }, + "id": "gWbx-KB9gRr8", + "outputId": "ecba710a-22ec-41d5-a39c-11f67e645b50", + "trusted": false + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "ignored", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mexample1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[0;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 45\u001b[0m def _sum(a, axis=None, dtype=None, out=None, keepdims=False,\n\u001b[1;32m 46\u001b[0m initial=_NoValue, where=True):\n\u001b[0;32m---> 47\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhere\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m def _prod(a, axis=None, dtype=None, out=None, keepdims=False,\n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'NoneType'" + ] + } + ], + "source": [ + "example1.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LcEwO8UogRr9" + }, + "source": [ + "**主要重點**：整數與 `None` 值之間的加法（以及其他運算）是未定義的，這可能會限制您對包含這些值的數據集所能執行的操作。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pWvVHvETgRr9" + }, + "source": [ + "### `NaN`：缺失的浮點值\n", + "\n", + "與 `None` 不同，NumPy（因此也包括 pandas）支援使用 `NaN` 來進行快速的向量化操作和 ufuncs。壞消息是，任何涉及 `NaN` 的算術運算結果都會是 `NaN`。例如：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rcFYfMG9gRr9", + "outputId": "699e81b7-5c11-4b46-df1d-06071768690f", + "trusted": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "nan" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.nan + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BW3zQD2-gRr-", + "outputId": "4525b6c4-495d-4f7b-a979-efce1dae9bd0", + "trusted": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "nan" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.nan * 0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fU5IPRcCgRr-" + }, + "source": [ + "好消息：在包含 `NaN` 的數組上運行的聚合不會出現錯誤。壞消息：結果並不一律有用：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LCInVgSSgRr_", + "outputId": "fa06495a-0930-4867-87c5-6023031ea8b5", + "trusted": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(nan, nan, nan)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example2 = np.array([2, np.nan, 6, 8]) \n", + "example2.sum(), example2.min(), example2.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nhlnNJT7gRr_" + }, + "source": [ + "### 運動：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true, + "id": "yan3QRaOgRr_", + "trusted": false + }, + "outputs": [], + "source": [ + "# What happens if you add np.nan and None together?\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_iDvIRC8gRsA" + }, + "source": [ + "請記住：`NaN` 只適用於缺失的浮點值；整數、字符串或布爾值並沒有 `NaN` 的等效。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kj6EKdsAgRsA" + }, + "source": [ + "### `NaN` 和 `None`：pandas 中的空值\n", + "\n", + "儘管 `NaN` 和 `None` 的行為可能略有不同，但 pandas 已設計成可以互換處理它們。為了更清楚地了解，請考慮一個整數的 `Series`：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Nji-KGdNgRsA", + "outputId": "36aa14d2-8efa-4bfd-c0ed-682991288822", + "trusted": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "1 2\n", + "2 3\n", + "dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "int_series = pd.Series([1, 2, 3], dtype=int)\n", + "int_series" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WklCzqb8gRsB" + }, + "source": [ + "### 運動：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true, + "id": "Cy-gqX5-gRsB", + "trusted": false + }, + "outputs": [], + "source": [ + "# Now set an element of int_series equal to None.\n", + "# How does that element show up in the Series?\n", + "# What is the dtype of the Series?\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WjMQwltNgRsB" + }, + "source": [ + "在將數據類型向上轉型以建立 `Series` 和 `DataFrame` 的數據一致性過程中，pandas 會自動在缺失值之間切換 `None` 和 `NaN`。由於這種設計特性，將 `None` 和 `NaN` 視為 pandas 中兩種不同形式的「空值」是很有幫助的。事實上，pandas 中一些處理缺失值的核心方法名稱也反映了這一點：\n", + "\n", + "- `isnull()`：生成一個布爾掩碼，用於指示缺失值\n", + "- `notnull()`：與 `isnull()` 相反\n", + "- `dropna()`：返回過濾後的數據版本\n", + "- `fillna()`：返回填充或估算缺失值後的數據副本\n", + "\n", + "這些方法非常重要，掌握並熟悉它們是必不可少的，因此我們將逐一深入探討這些方法。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Yh5ifd9FgRsB" + }, + "source": [ + "### 檢測空值\n", + "\n", + "既然我們已經了解了缺失值的重要性，在處理它們之前，我們需要在數據集中檢測它們。 \n", + "`isnull()` 和 `notnull()` 是檢測空值的主要方法。這兩個方法都會返回布爾掩碼，覆蓋你的數據。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true, + "id": "e-vFp5lvgRsC", + "trusted": false + }, + "outputs": [], + "source": [ + "example3 = pd.Series([0, np.nan, '', None])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1XdaJJ7PgRsC", + "outputId": "92fc363a-1874-471f-846d-f4f9ce1f51d0", + "trusted": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 True\n", + "2 False\n", + "3 True\n", + "dtype: bool" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example3.isnull()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PaSZ0SQygRsC" + }, + "source": [ + "仔細看看輸出結果，有沒有令你感到驚訝的地方？雖然 `0` 是一個算術上的空值，但它仍然是一個完全有效的整數，pandas 也將其視為整數。至於 `''` 則稍微微妙一些。雖然我們在第 1 節中使用它來表示空字串值，但它本質上仍然是一個字串物件，而不是 pandas 所認為的空值。\n", + "\n", + "現在，讓我們換個角度，使用這些方法更接近實際應用的方式。你可以直接使用布林遮罩作為 ``Series`` 或 ``DataFrame`` 的索引，這在處理孤立的缺失值（或存在值）時非常有用。\n", + "\n", + "如果我們想要計算缺失值的總數，只需對 `isnull()` 方法生成的遮罩進行求和即可。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JCcQVoPkHDUv", + "outputId": "001daa72-54f8-4bd5-842a-4df627a79d4d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example3.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PlBqEo3mgRsC" + }, + "source": [ + "### 運動：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true, + "id": "ggDVf5uygRsD", + "trusted": false + }, + "outputs": [], + "source": [ + "# Try running example3[example3.notnull()].\n", + "# Before you do so, what do you expect to see?\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D_jWN7mHgRsD" + }, + "source": [ + "**主要重點**：當在資料框中使用 `isnull()` 和 `notnull()` 方法時，兩者會產生相似的結果：它們會顯示結果以及這些結果的索引，這將在您處理數據時提供極大的幫助。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BvnoojWsgRr4" + }, + "source": [ + "### 處理缺失數據\n", + "\n", + "> **學習目標：** 完成本小節後，您應該了解如何以及何時替換或移除 DataFrame 中的空值。\n", + "\n", + "機器學習模型無法直接處理缺失數據。因此，在將數據傳入模型之前，我們需要先處理這些缺失值。\n", + "\n", + "如何處理缺失數據涉及微妙的取捨，可能會影響您的最終分析結果以及實際應用的效果。\n", + "\n", + "主要有兩種處理缺失數據的方法：\n", + "\n", + "1. 刪除包含缺失值的行\n", + "2. 用其他值替換缺失值\n", + "\n", + "我們將詳細討論這兩種方法及其優缺點。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3VaYC1TvgRsD" + }, + "source": [ + "### 刪除空值\n", + "\n", + "我們傳遞給模型的數據量會直接影響其性能。刪除空值意味著減少數據點的數量，從而減少數據集的大小。因此，當數據集相當大時，建議刪除包含空值的行。\n", + "\n", + "另一種情況可能是某一行或列有大量缺失值。在這種情況下，可以刪除它們，因為該行/列的大部分數據都缺失，對分析的價值不大。\n", + "\n", + "除了識別缺失值之外，pandas 還提供了一種方便的方法來從 `Series` 和 `DataFrame` 中移除空值。為了更好地理解這一點，我們可以回到 `example3`。`DataFrame.dropna()` 函數可以幫助刪除包含空值的行。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7uIvS097gRsD", + "outputId": "c13fc117-4ca1-4145-a0aa-42ac89e6e218", + "trusted": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "2 \n", + "dtype: object" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example3 = example3.dropna()\n", + "example3" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hil2cr64gRsD" + }, + "source": [ + "請注意，這應該看起來像您從 `example3[example3.notnull()]` 的輸出。這裡的不同之處在於，`dropna` 不僅僅是基於遮罩值進行索引，而是已經從 `Series` `example3` 中移除了那些缺失值。\n", + "\n", + "由於 DataFrame 是二維的，因此它提供了更多刪除數據的選項。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "id": "an-l74sPgRsE", + "outputId": "340876a0-63ad-40f6-bd54-6240cdae50ab", + "trusted": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2\n", + "0 1.0 NaN 7\n", + "1 2.0 5.0 8\n", + "2 NaN 6.0 9" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example4 = pd.DataFrame([[1, np.nan, 7], \n", + " [2, 5, 8], \n", + " [np.nan, 6, 9]])\n", + "example4" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "66wwdHZrgRsE" + }, + "source": [ + "（你是否注意到 pandas 將其中兩列提升為浮點型，以容納 `NaN` 值？）\n", + "\n", + "你無法從 `DataFrame` 中刪除單個值，因此必須刪除整行或整列。根據你的需求，你可能需要選擇其中一種方式，因此 pandas 提供了兩種選項。在數據科學中，列通常代表變量，行則代表觀察值，因此你更可能刪除數據的行；`dropna()` 的預設設定是刪除所有包含任何空值的行：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "id": "jAVU24RXgRsE", + "outputId": "0b5e5aee-7187-4d3f-b583-a44136ae5f80", + "trusted": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 0 1 2 3\n", + "0 1.0 NaN 7 NaN\n", + "1 2.0 5.0 8 NaN\n", + "2 NaN 6.0 9 NaN" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example4[3] = np.nan\n", + "example4" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pNZer7q9JPNC" + }, + "source": [ + "> 關鍵要點：\n", + "1. 只有在數據集足夠大的情況下，刪除空值才是明智的選擇。\n", + "2. 如果整行或整列的大部分數據都缺失，可以刪除這些行或列。\n", + "3. `DataFrame.dropna(axis=)` 方法可用於刪除空值。`axis` 參數表示是刪除行還是列。\n", + "4. `how` 參數也可以使用。默認情況下設置為 `any`，因此它只刪除包含任何空值的行或列。可以設置為 `all`，以指定僅刪除所有值均為空的行或列。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oXXSfQFHgRsF" + }, + "source": [ + "### 運動:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true, + "id": "ExUwQRxpgRsF", + "trusted": false + }, + "outputs": [], + "source": [ + "# How might you go about dropping just column 3?\n", + "# Hint: remember that you will need to supply both the axis parameter and the how parameter.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "38kwAihWgRsG" + }, + "source": [ + "`thresh` 參數提供更細緻的控制：您設定一行或一列需要擁有的*非空*值的數量，以便保留該行或列：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "id": "M9dCNMaagRsG", + "outputId": "8093713a-54d2-4e54-c73f-4eea315cb6f2", + "trusted": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " 0 1 2 3\n", + "1 2.0 5.0 8 NaN" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example4.dropna(axis='rows', thresh=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fmSFnzZegRsG" + }, + "source": [ + "這裡，第一行和最後一行已被刪除，因為它們僅包含兩個非空值。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mCcxLGyUgRsG" + }, + "source": [ + "### 填充空值\n", + "\n", + "有時候，用可能有效的值來填充缺失值是合理的。有幾種方法可以填充空值。第一種方法是使用領域知識（即數據集所基於主題的知識）來大致估算缺失值。\n", + "\n", + "你可以使用 `isnull` 直接進行操作，但這可能會很繁瑣，特別是當你需要填充大量值時。由於這是數據科學中非常常見的任務，pandas 提供了 `fillna` 方法，它會返回一個 `Series` 或 `DataFrame` 的副本，並將缺失值替換為你選擇的值。讓我們創建另一個示例 `Series`，來看看這在實際操作中的效果。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CE8S7louLezV" + }, + "source": [ + "### 類別型數據（非數字型）\n", + "首先讓我們來看看非數字型數據。在數據集中，我們有一些包含類別型數據的欄位，例如性別、True 或 False 等。\n", + "\n", + "在大多數情況下，我們會用該欄位的 `眾數` 來替換缺失值。假設我們有 100 個數據點，其中 90 個選擇 True，8 個選擇 False，還有 2 個未填寫。那麼，我們可以將這 2 個未填寫的值替換為 True，基於整個欄位的情況。\n", + "\n", + "此外，我們也可以在這裡使用領域知識。讓我們來看一個用眾數填充的例子。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "id": "MY5faq4yLdpQ", + "outputId": "19ab472e-1eed-4de8-f8a7-db2a3af3cb1a" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ID class\n", + "0 10 0\n", + "1 20 2\n", + "2 30 1\n", + "3 40 1\n", + "4 50 1\n", + "5 60 0" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class_labels = {'business class':0,'economy class':1,'first class':2}\n", + "label['class'] = label['class'].replace(class_labels)\n", + "label" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ftnF-TyapOPt" + }, + "source": [ + "正如我們所見，輸出結果與我們預期的一致。那麼，我們什麼時候使用標籤編碼呢？標籤編碼通常在以下情況之一或兩者中使用：\n", + "1. 當類別數量很大時\n", + "2. 當類別具有順序性時。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eQPAPVwsqWT7" + }, + "source": [ + "**獨熱編碼**\n", + "\n", + "另一種編碼方式是獨熱編碼。在這種編碼方式中，每個欄位的類別都會被新增為一個獨立的欄位，而每個數據點會根據是否包含該類別而被賦予 0 或 1 的值。因此，如果有 n 個不同的類別，則會在資料框中新增 n 個欄位。\n", + "\n", + "例如，讓我們以同樣的飛機艙等例子來說。類別包括：['商務艙', '經濟艙', '頭等艙']。如果我們進行獨熱編碼，以下三個欄位將被新增到數據集中：['class_business class', 'class_economy class', 'class_first class']。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + }, + "id": "ZM0eVh0ArKUL", + "outputId": "83238a76-b3a5-418d-c0b6-605b02b6891b" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ID class_business class class_economy class class_first class\n", + "0 10 1 0 0\n", + "1 20 0 0 1\n", + "2 30 0 1 0\n", + "3 40 0 1 0\n", + "4 50 0 1 0\n", + "5 60 1 0 0" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "one_hot_data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_zXRLOjXujdA" + }, + "source": [ + "每個獨熱編碼的列包含 0 或 1，指定該數據點是否存在該類別。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bDnC4NQOu0qr" + }, + "source": [ + "我們何時使用獨熱編碼？獨熱編碼通常在以下其中一種或兩種情況下使用：\n", + "\n", + "1. 當分類數量和數據集的規模較小時。\n", + "2. 當分類沒有特定的順序時。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XnUmci_4uvyu" + }, + "source": [ + "> 主要重點：\n", + "1. 編碼是將非數值數據轉換為數值數據的過程。\n", + "2. 編碼分為兩種類型：標籤編碼和獨熱編碼，可根據數據集的需求進行選擇。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K8UXOJYRgRsJ" + }, + "source": [ + "## 移除重複數據\n", + "\n", + "> **學習目標：** 完成本小節後，您應該能夠熟練識別並移除 DataFrame 中的重複值。\n", + "\n", + "除了缺失數據外，您在現實世界的數據集中經常會遇到重複的數據。幸運的是，pandas 提供了一個簡便的方法來檢測和移除重複的條目。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qrEG-Wa0gRsJ" + }, + "source": [ + "### 識別重複值：`duplicated`\n", + "\n", + "你可以使用 pandas 的 `duplicated` 方法輕鬆找出重複值。該方法會返回一個布林遮罩，指示 `DataFrame` 中某個條目是否是之前條目的重複值。我們來創建另一個範例 `DataFrame`，看看這個方法的實際應用。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "id": "ZLu6FEnZgRsJ", + "outputId": "376512d1-d842-4db1-aea3-71052aeeecaf", + "trusted": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " letters numbers\n", + "0 A 1\n", + "1 B 2\n", + "2 A 1\n", + "3 B 3\n", + "4 B 3" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example6 = pd.DataFrame({'letters': ['A','B'] * 2 + ['B'],\n", + " 'numbers': [1, 2, 1, 3, 3]})\n", + "example6" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cIduB5oBgRsK", + "outputId": "3da27b3d-4d69-4e1d-bb52-0af21bae87f2", + "trusted": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 True\n", + "3 False\n", + "4 True\n", + "dtype: bool" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example6.duplicated()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0eDRJD4SgRsK" + }, + "source": [ + "### 刪除重複項：`drop_duplicates`\n", + "`drop_duplicates` 會返回一份數據副本，其中所有 `duplicated` 值均為 `False`：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "id": "w_YPpqIqgRsK", + "outputId": "ac66bd2f-8671-4744-87f5-8b8d96553dea", + "trusted": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " letters numbers\n", + "0 A 1\n", + "1 B 2\n", + "3 B 3" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example6.drop_duplicates()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "69AqoCZAgRsK" + }, + "source": [ + "`duplicated` 和 `drop_duplicates` 預設會考慮所有列，但你可以指定它們僅檢查 `DataFrame` 中的一部分列：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 111 + }, + "id": "BILjDs67gRsK", + "outputId": "ef6dcc08-db8b-4352-c44e-5aa9e2bec0d3", + "trusted": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " letters numbers\n", + "0 A 1\n", + "1 B 2" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example6.drop_duplicates(['letters'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GvX4og1EgRsL" + }, + "source": [ + "> **重點:** 移除重複數據是幾乎每個數據科學項目中不可或缺的一部分。重複數據可能會改變分析結果，並導致不準確的結論！\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 真實世界數據質量檢查\n", + "\n", + "> **學習目標：** 在本節結束時，你應該能夠熟練地檢測和修正常見的真實世界數據質量問題，包括不一致的分類值、異常的數值（離群值）以及帶有變化的重複實體。\n", + "\n", + "雖然缺失值和完全重複的數據是常見問題，但真實世界的數據集往往還包含更微妙的問題：\n", + "\n", + "1. **不一致的分類值**：同一分類以不同方式拼寫（例如：\"USA\"、\"U.S.A\"、\"United States\"）\n", + "2. **異常的數值**：極端的離群值，可能表明數據輸入錯誤（例如，年齡 = 999）\n", + "3. **近似重複的行**：表示同一實體但有輕微變化的記錄\n", + "\n", + "接下來，我們將探討檢測和處理這些問題的技術。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 建立一個範例「髒」數據集\n", + "\n", + "首先，讓我們建立一個範例數據集，其中包含我們在真實世界數據中常見的問題類型：\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Create a sample dataset with quality issues\n", + "dirty_data = pd.DataFrame({\n", + " 'customer_id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n", + " 'name': ['John Smith', 'Jane Doe', 'John Smith', 'Bob Johnson', \n", + " 'Alice Williams', 'Charlie Brown', 'John Smith', 'Eva Martinez',\n", + " 'Bob Johnson', 'Diana Prince', 'Frank Castle', 'Alice Williams'],\n", + " 'age': [25, 32, 25, 45, 28, 199, 25, 31, 45, 27, -5, 28],\n", + " 'country': ['USA', 'UK', 'U.S.A', 'Canada', 'USA', 'United Kingdom',\n", + " 'United States', 'Mexico', 'canada', 'USA', 'UK', 'usa'],\n", + " 'purchase_amount': [100.50, 250.00, 105.00, 320.00, 180.00, 90.00,\n", + " 102.00, 275.00, 325.00, 195.00, 410.00, 185.00]\n", + "})\n", + "\n", + "print(\"Sample 'Dirty' Dataset:\")\n", + "print(dirty_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. 檢測不一致的分類值\n", + "\n", + "注意到 `country` 欄位中，同一個國家有多種表示方式。讓我們找出這些不一致之處：\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check unique values in the country column\n", + "print(\"Unique country values:\")\n", + "print(dirty_data['country'].unique())\n", + "print(f\"\\nTotal unique values: {dirty_data['country'].nunique()}\")\n", + "\n", + "# Count occurrences of each variation\n", + "print(\"\\nValue counts:\")\n", + "print(dirty_data['country'].value_counts())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 標準化分類值\n", + "\n", + "我們可以建立一個映射來標準化這些值。一個簡單的方法是將值轉換為小寫，並建立一個映射字典：\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a standardization mapping\n", + "country_mapping = {\n", + " 'usa': 'USA',\n", + " 'u.s.a': 'USA',\n", + " 'united states': 'USA',\n", + " 'uk': 'UK',\n", + " 'united kingdom': 'UK',\n", + " 'canada': 'Canada',\n", + " 'mexico': 'Mexico'\n", + "}\n", + "\n", + "# Standardize the country column\n", + "dirty_data['country_clean'] = dirty_data['country'].str.lower().map(country_mapping)\n", + "\n", + "print(\"Before standardization:\")\n", + "print(dirty_data['country'].value_counts())\n", + "print(\"\\nAfter standardization:\")\n", + "print(dirty_data[['country_clean']].value_counts())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**替代方法：使用模糊匹配**\n", + "\n", + "對於更複雜的情況，我們可以使用 `rapidfuzz` 庫進行模糊字符串匹配，自動檢測相似的字符串：\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " from rapidfuzz import process, fuzz\n", + "except ImportError:\n", + " print(\"rapidfuzz is not installed. Please install it with 'pip install rapidfuzz' to use fuzzy matching.\")\n", + " process = None\n", + " fuzz = None\n", + "\n", + "# Get unique countries\n", + "unique_countries = dirty_data['country'].unique()\n", + "\n", + "# For each country, find similar matches\n", + "if process is not None and fuzz is not None:\n", + " print(\"Finding similar country names (similarity > 70%):\")\n", + " for country in unique_countries:\n", + " matches = process.extract(country, unique_countries, scorer=fuzz.ratio, limit=3)\n", + " # Filter matches with similarity > 70 and not identical\n", + " similar = [m for m in matches if m[1] > 70 and m[0] != country]\n", + " if similar:\n", + " print(f\"\\n'{country}' is similar to:\")\n", + " for match, score, _ in similar:\n", + " print(f\" - '{match}' (similarity: {score}%)\")\n", + "else:\n", + " print(\"Skipping fuzzy matching because rapidfuzz is not available.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. 檢測異常數值（離群值）\n", + "\n", + "查看 `age` 欄位時，我們發現一些可疑的數值，例如 199 和 -5。讓我們使用統計方法來檢測這些離群值。\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display basic statistics\n", + "print(\"Age column statistics:\")\n", + "print(dirty_data['age'].describe())\n", + "\n", + "# Identify impossible values using domain knowledge\n", + "print(\"\\nRows with impossible age values (< 0 or > 120):\")\n", + "impossible_ages = dirty_data[(dirty_data['age'] < 0) | (dirty_data['age'] > 120)]\n", + "print(impossible_ages[['customer_id', 'name', 'age']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 使用 IQR（四分位距）方法\n", + "\n", + "IQR 方法是一種穩健的統計技術，用於檢測異常值，對極端值的敏感度較低：\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate IQR for age (excluding impossible values)\n", + "valid_ages = dirty_data[(dirty_data['age'] >= 0) & (dirty_data['age'] <= 120)]['age']\n", + "\n", + "Q1 = valid_ages.quantile(0.25)\n", + "Q3 = valid_ages.quantile(0.75)\n", + "IQR = Q3 - Q1\n", + "\n", + "# Define outlier bounds\n", + "lower_bound = Q1 - 1.5 * IQR\n", + "upper_bound = Q3 + 1.5 * IQR\n", + "\n", + "print(f\"IQR-based outlier bounds for age: [{lower_bound:.2f}, {upper_bound:.2f}]\")\n", + "\n", + "# Identify outliers\n", + "age_outliers = dirty_data[(dirty_data['age'] < lower_bound) | (dirty_data['age'] > upper_bound)]\n", + "print(f\"\\nRows with age outliers:\")\n", + "print(age_outliers[['customer_id', 'name', 'age']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 使用 Z 分數方法\n", + "\n", + "Z 分數方法根據與平均值的標準差來識別異常值：\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " from scipy import stats\n", + "except ImportError:\n", + " print(\"scipy is required for Z-score calculation. Please install it with 'pip install scipy' and rerun this cell.\")\n", + "else:\n", + " # Calculate Z-scores for age, handling NaN values\n", + " age_nonan = dirty_data['age'].dropna()\n", + " zscores = np.abs(stats.zscore(age_nonan))\n", + " dirty_data['age_zscore'] = np.nan\n", + " dirty_data.loc[age_nonan.index, 'age_zscore'] = zscores\n", + "\n", + " # Typically, Z-score > 3 indicates an outlier\n", + " print(\"Rows with age Z-score > 3:\")\n", + " zscore_outliers = dirty_data[dirty_data['age_zscore'] > 3]\n", + " print(zscore_outliers[['customer_id', 'name', 'age', 'age_zscore']])\n", + "\n", + " # Clean up the temporary column\n", + " dirty_data = dirty_data.drop('age_zscore', axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 處理異常值\n", + "\n", + "一旦檢測到異常值，可以用以下幾種方式處理：\n", + "1. **移除**：刪除包含異常值的行（如果它們是錯誤）\n", + "2. **限制**：用邊界值替換\n", + "3. **替換為 NaN**：視為缺失數據並使用填補技術\n", + "4. **保留**：如果它們是合法的極端值\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a cleaned version by replacing impossible ages with NaN\n", + "dirty_data['age_clean'] = dirty_data['age'].apply(\n", + " lambda x: np.nan if (x < 0 or x > 120) else x\n", + ")\n", + "\n", + "print(\"Age column before and after cleaning:\")\n", + "print(dirty_data[['customer_id', 'name', 'age', 'age_clean']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. 檢測近似重複的行\n", + "\n", + "注意，我們的數據集中有多個 \"John Smith\" 的條目，且數值略有不同。我們來基於名字相似度識別潛在的重複項。\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# First, let's look at exact name matches (ignoring extra whitespace)\n", + "dirty_data['name_normalized'] = dirty_data['name'].str.strip().str.lower()\n", + "\n", + "print(\"Checking for duplicate names:\")\n", + "duplicate_names = dirty_data[dirty_data.duplicated(['name_normalized'], keep=False)]\n", + "print(duplicate_names.sort_values('name_normalized')[['customer_id', 'name', 'age', 'country']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 使用模糊匹配尋找近似重複項\n", + "\n", + "為了進行更高級的重複檢測，我們可以使用模糊匹配來尋找相似的名稱：\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " from rapidfuzz import process, fuzz\n", + "\n", + " # Function to find potential duplicates\n", + " def find_near_duplicates(df, column, threshold=90):\n", + " \"\"\"\n", + " Find near-duplicate entries in a column using fuzzy matching.\n", + " \n", + " Parameters:\n", + " - df: DataFrame\n", + " - column: Column name to check for duplicates\n", + " - threshold: Similarity threshold (0-100)\n", + " \n", + " Returns: List of potential duplicate groups\n", + " \"\"\"\n", + " values = df[column].unique()\n", + " duplicate_groups = []\n", + " checked = set()\n", + " \n", + " for value in values:\n", + " if value in checked:\n", + " continue\n", + " \n", + " # Find similar values\n", + " matches = process.extract(value, values, scorer=fuzz.ratio, limit=len(values))\n", + " similar = [m[0] for m in matches if m[1] >= threshold]\n", + " \n", + " if len(similar) > 1:\n", + " duplicate_groups.append(similar)\n", + " checked.update(similar)\n", + " \n", + " return duplicate_groups\n", + "\n", + " # Find near-duplicate names\n", + " duplicate_groups = find_near_duplicates(dirty_data, 'name', threshold=90)\n", + "\n", + " print(\"Potential duplicate groups:\")\n", + " for i, group in enumerate(duplicate_groups, 1):\n", + " print(f\"\\nGroup {i}:\")\n", + " for name in group:\n", + " matching_rows = dirty_data[dirty_data['name'] == name]\n", + " print(f\" '{name}': {len(matching_rows)} occurrence(s)\")\n", + " for _, row in matching_rows.iterrows():\n", + " print(f\" - Customer {row['customer_id']}: age={row['age']}, country={row['country']}\")\n", + "except ImportError:\n", + " print(\"rapidfuzz is not installed. Skipping fuzzy matching for near-duplicates.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 處理重複項目\n", + "\n", + "一旦識別出來後，你需要決定如何處理重複項目：\n", + "1. **保留第一次出現**：使用 `drop_duplicates(keep='first')`\n", + "2. **保留最後一次出現**：使用 `drop_duplicates(keep='last')`\n", + "3. **聚合信息**：合併重複行的資訊\n", + "4. **人工審查**：標記以供人工審查\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Example: Remove duplicates based on normalized name, keeping first occurrence\n", + "cleaned_data = dirty_data.drop_duplicates(subset=['name_normalized'], keep='first')\n", + "\n", + "print(f\"Original dataset: {len(dirty_data)} rows\")\n", + "print(f\"After removing name duplicates: {len(cleaned_data)} rows\")\n", + "print(f\"Removed: {len(dirty_data) - len(cleaned_data)} duplicate rows\")\n", + "\n", + "print(\"\\nCleaned dataset:\")\n", + "print(cleaned_data[['customer_id', 'name', 'age', 'country_clean']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 摘要：完整的數據清理流程\n", + "\n", + "讓我們將所有內容整合成一個全面的清理流程：\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def clean_dataset(df):\n", + " \"\"\"\n", + " Comprehensive data cleaning function.\n", + " \"\"\"\n", + " # Create a copy to avoid modifying the original\n", + " cleaned = df.copy()\n", + " \n", + " # 1. Standardize categorical values (country)\n", + " country_mapping = {\n", + " 'usa': 'USA', 'u.s.a': 'USA', 'united states': 'USA',\n", + " 'uk': 'UK', 'united kingdom': 'UK',\n", + " 'canada': 'Canada', 'mexico': 'Mexico'\n", + " }\n", + " cleaned['country'] = cleaned['country'].str.lower().map(country_mapping)\n", + " \n", + " # 2. Clean abnormal age values\n", + " cleaned['age'] = cleaned['age'].apply(\n", + " lambda x: np.nan if (x < 0 or x > 120) else x\n", + " )\n", + " \n", + " # 3. Remove near-duplicate names (normalize whitespace)\n", + " cleaned['name'] = cleaned['name'].str.strip()\n", + " cleaned = cleaned.drop_duplicates(subset=['name'], keep='first')\n", + " \n", + " return cleaned\n", + "\n", + "# Apply the cleaning pipeline\n", + "final_cleaned_data = clean_dataset(dirty_data)\n", + "\n", + "print(\"Before cleaning:\")\n", + "print(f\" Rows: {len(dirty_data)}\")\n", + "print(f\" Unique countries: {dirty_data['country'].nunique()}\")\n", + "print(f\" Invalid ages: {((dirty_data['age'] < 0) | (dirty_data['age'] > 120)).sum()}\")\n", + "\n", + "print(\"\\nAfter cleaning:\")\n", + "print(f\" Rows: {len(final_cleaned_data)}\")\n", + "print(f\" Unique countries: {final_cleaned_data['country'].nunique()}\")\n", + "print(f\" Invalid ages: {((final_cleaned_data['age'] < 0) | (final_cleaned_data['age'] > 120)).sum()}\")\n", + "\n", + "print(\"\\nCleaned dataset:\")\n", + "print(final_cleaned_data[['customer_id', 'name', 'age', 'country', 'purchase_amount']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 🎯 挑戰練習\n", + "\n", + "現在輪到你了！以下是一行包含多個質量問題的新數據。你能否：\n", + "\n", + "1. 找出這行數據中的所有問題\n", + "2. 編寫程式碼來清理每個問題\n", + "3. 將清理後的數據添加到數據集\n", + "\n", + "以下是有問題的數據：\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# New problematic row\n", + "new_row = pd.DataFrame({\n", + " 'customer_id': [13],\n", + " 'name': [' Diana Prince '], # Extra whitespace\n", + " 'age': [250], # Impossible age\n", + " 'country': ['U.S.A.'], # Inconsistent format\n", + " 'purchase_amount': [150.00]\n", + "})\n", + "\n", + "print(\"New row to clean:\")\n", + "print(new_row)\n", + "\n", + "# TODO: Your code here to clean this row\n", + "# Hints:\n", + "# 1. Strip whitespace from the name\n", + "# 2. Check if the name is a duplicate (Diana Prince already exists)\n", + "# 3. Handle the impossible age value\n", + "# 4. Standardize the country name\n", + "\n", + "# Example solution (uncomment and modify as needed):\n", + "# new_row_cleaned = new_row.copy()\n", + "# new_row_cleaned['name'] = new_row_cleaned['name'].str.strip()\n", + "# new_row_cleaned['age'] = np.nan # Invalid age\n", + "# new_row_cleaned['country'] = 'USA' # Standardized\n", + "# print(\"\\nCleaned row:\")\n", + "# print(new_row_cleaned)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 關鍵要點\n", + "\n", + "1. **分類不一致**在真實世界的數據中很常見。務必檢查唯一值，並使用映射或模糊匹配來標準化它們。\n", + "\n", + "2. **異常值**可能會對分析產生重大影響。結合領域知識和統計方法（如 IQR、Z-score）來檢測它們。\n", + "\n", + "3. **近似重複項**比完全重複項更難檢測。考慮使用模糊匹配並對數據進行標準化（如轉小寫、去除空格）來識別它們。\n", + "\n", + "4. **數據清理是迭代的過程**。可能需要應用多種技術並審查結果，才能最終完成清理後的數據集。\n", + "\n", + "5. **記錄你的決策**。追蹤你所採用的清理步驟及其原因，這對於可重現性和透明度非常重要。\n", + "\n", + "> **最佳實踐：**務必保留一份原始的“髒”數據副本。切勿覆蓋你的源數據文件——創建清理版本並使用清晰的命名規則，例如 `data_cleaned.csv`。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**免責聲明**： \n本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "colab": { + "name": "notebook.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.4" + }, + "coopTranslator": { + "original_hash": "6301339d1c9a301b00639c635dc9b731", + "translation_date": "2025-10-03T19:22:23+00:00", + "source_file": "2-Working-With-Data/08-data-preparation/notebook.ipynb", + "language_code": "hk" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/translations/zh-HK/2-Working-With-Data/README.md b/translations/zh-HK/2-Working-With-Data/README.md new file mode 100644 index 00000000..9840b428 --- /dev/null +++ b/translations/zh-HK/2-Working-With-Data/README.md @@ -0,0 +1,20 @@ +# 處理數據 + +![數據之愛](../../../translated_images/zh-HK/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) +> 照片由 Alexander Sinn 提供，來自 Unsplash + +在這些課程中，你將學習一些管理、操作和應用數據的方法。你會了解關聯式和非關聯式數據庫，以及數據如何存儲在其中。你將學習使用 Python 管理數據的基礎知識，並探索多種使用 Python 管理和挖掘數據的方法。 + +### 主題 + +1. [關聯式數據庫](05-relational-databases/README.md) +2. [非關聯式數據庫](06-non-relational/README.md) +3. [使用 Python](07-python/README.md) +4. [準備數據](08-data-preparation/README.md) + +### 鳴謝 + +這些課程由 [Christopher Harrison](https://twitter.com/geektrainer)、[Dmitry Soshnikov](https://twitter.com/shwars) 和 [Jasmine Greenaway](https://twitter.com/paladique) 用 ❤️ 編寫。 + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/09-visualization-quantities/README.md b/translations/zh-HK/3-Data-Visualization/09-visualization-quantities/README.md new file mode 100644 index 00000000..d683a3f5 --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/09-visualization-quantities/README.md @@ -0,0 +1,213 @@ +# 視覺化數量 + +|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/09-Visualizing-Quantities.png)| +|:---:| +| 視覺化數量 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | + +在這節課中，你將探索如何使用眾多可用的 Python 庫之一，學習如何圍繞數量的概念創建有趣的視覺化。使用一個關於明尼蘇達州鳥類的清理過的數據集，你可以了解許多關於當地野生動物的有趣事實。 + +## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/16) + +## 使用 Matplotlib 觀察翼展 + +一個非常出色的庫可以用來創建各種簡單和複雜的圖表和圖形，那就是 [Matplotlib](https://matplotlib.org/stable/index.html)。一般來說，使用這些庫繪製數據的過程包括：識別你想要針對的數據框部分，對數據進行必要的轉換，分配其 x 和 y 軸值，決定要顯示的圖表類型，然後顯示圖表。Matplotlib 提供了多種視覺化選擇，但在這節課中，我們將重點放在最適合視覺化數量的圖表上：折線圖、散點圖和柱狀圖。 + +> ✅ 根據數據結構和你想要講述的故事選擇最合適的圖表。 +> - 分析時間趨勢：折線圖 +> - 比較數值：柱狀圖、條形圖、餅圖、散點圖 +> - 展示部分與整體的關係：餅圖 +> - 展示數據分佈：散點圖、柱狀圖 +> - 展示趨勢：折線圖、條形圖 +> - 展示數值之間的關係：折線圖、散點圖、氣泡圖 + +如果你有一個數據集並需要了解某個項目的數量，第一步通常是檢查其數值。 + +✅ Matplotlib 有非常好的「速查表」，可以在 [這裡](https://matplotlib.org/cheatsheets/cheatsheets.pdf) 找到。 + +## 建立鳥類翼展數值的折線圖 + +打開本課文件夾根目錄中的 `notebook.ipynb` 文件並添加一個單元格。 + +> 注意：數據存儲在本倉庫根目錄的 `/data` 文件夾中。 + +```python +import pandas as pd +import matplotlib.pyplot as plt +birds = pd.read_csv('../../data/birds.csv') +birds.head() +``` +這些數據是文本和數字的混合： + +| | 名稱 | 學名 | 類別 | 目 | 科 | 屬 | 保育狀況 | 最小長度 | 最大長度 | 最小體重 | 最大體重 | 最小翼展 | 最大翼展 | +| ---: | :--------------------------- | :--------------------- | :-------------------- | :----------- | :------- | :---------- | :----------------- | --------: | --------: | ----------: | ----------: | ----------: | ----------: | +| 0 | 黑腹樹鴨 | Dendrocygna autumnalis | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 樹鴨屬 | LC | 47 | 56 | 652 | 1020 | 76 | 94 | +| 1 | 棕樹鴨 | Dendrocygna bicolor | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 樹鴨屬 | LC | 45 | 53 | 712 | 1050 | 85 | 93 | +| 2 | 雪鵝 | Anser caerulescens | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 雁屬 | LC | 64 | 79 | 2050 | 4050 | 135 | 165 | +| 3 | 羅斯鵝 | Anser rossii | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 雁屬 | LC | 57.3 | 64 | 1066 | 1567 | 113 | 116 | +| 4 | 大白額鵝 | Anser albifrons | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 雁屬 | LC | 64 | 81 | 1930 | 3310 | 130 | 165 | + +讓我們從使用基本折線圖繪製一些數字數據開始。假設你想查看這些有趣鳥類的最大翼展。 + +```python +wingspan = birds['MaxWingspan'] +wingspan.plot() +``` +![最大翼展](../../../../3-Data-Visualization/09-visualization-quantities/images/max-wingspan-02.png) + +你立即注意到什麼？似乎至少有一個異常值——這是一個相當大的翼展！2300 厘米的翼展相當於 23 米——明尼蘇達州有翼龍在飛嗎？讓我們調查一下。 + +雖然你可以在 Excel 中快速排序以找到這些異常值（可能是輸入錯誤），但繼續從圖表中進行視覺化分析。 + +在 x 軸上添加標籤以顯示涉及的鳥類類型： + +``` +plt.title('Max Wingspan in Centimeters') +plt.ylabel('Wingspan (CM)') +plt.xlabel('Birds') +plt.xticks(rotation=45) +x = birds['Name'] +y = birds['MaxWingspan'] + +plt.plot(x, y) + +plt.show() +``` +![帶標籤的翼展](../../../../3-Data-Visualization/09-visualization-quantities/images/max-wingspan-labels-02.png) + +即使將標籤旋轉設置為 45 度，仍然太多以至於無法閱讀。讓我們嘗試另一種策略：僅標記那些異常值並在圖表內設置標籤。你可以使用散點圖來為標籤留出更多空間： + +```python +plt.title('Max Wingspan in Centimeters') +plt.ylabel('Wingspan (CM)') +plt.tick_params(axis='both',which='both',labelbottom=False,bottom=False) + +for i in range(len(birds)): + x = birds['Name'][i] + y = birds['MaxWingspan'][i] + plt.plot(x, y, 'bo') + if birds['MaxWingspan'][i] > 500: + plt.text(x, y * (1 - 0.05), birds['Name'][i], fontsize=12) + +plt.show() +``` +這裡發生了什麼？你使用 `tick_params` 隱藏了底部標籤，然後對你的鳥類數據集進行了循環。通過使用 `bo` 繪製帶有小圓形藍點的圖表，你檢查了任何最大翼展超過 500 的鳥類，並在點旁邊顯示其標籤。你在 y 軸上稍微偏移了標籤 (`y * (1 - 0.05)`) 並使用鳥類名稱作為標籤。 + +你發現了什麼？ + +![異常值](../../../../3-Data-Visualization/09-visualization-quantities/images/labeled-wingspan-02.png) + +## 篩選數據 + +禿鷹和草原隼，雖然可能是非常大的鳥類，但似乎被錯誤標記了，其最大翼展多加了一個 `0`。不太可能遇到翼展 25 米的禿鷹，但如果真的遇到，請告訴我們！讓我們創建一個新的數據框，去掉這兩個異常值： + +```python +plt.title('Max Wingspan in Centimeters') +plt.ylabel('Wingspan (CM)') +plt.xlabel('Birds') +plt.tick_params(axis='both',which='both',labelbottom=False,bottom=False) +for i in range(len(birds)): + x = birds['Name'][i] + y = birds['MaxWingspan'][i] + if birds['Name'][i] not in ['Bald eagle', 'Prairie falcon']: + plt.plot(x, y, 'bo') +plt.show() +``` + +通過篩選掉異常值，你的數據現在更加一致且易於理解。 + +![翼展散點圖](../../../../3-Data-Visualization/09-visualization-quantities/images/scatterplot-wingspan-02.png) + +現在我們至少在翼展方面有了一個更乾淨的數據集，讓我們進一步探索這些鳥類。 + +雖然折線圖和散點圖可以顯示數據值及其分佈的信息，但我們想要思考這個數據集中固有的數值。你可以創建視覺化來回答以下關於數量的問題： + +> 有多少類別的鳥類？它們的數量是多少？ +> 有多少鳥類是滅絕、瀕危、稀有或常見的？ +> 根據林奈分類法，有多少屬和目？ + +## 探索柱狀圖 + +當你需要展示數據分組時，柱狀圖非常實用。讓我們探索這個數據集中存在的鳥類類別，看看哪一類最常見。 + +在 notebook 文件中，創建一個基本柱狀圖。 + +✅ 注意，你可以篩選掉我們在上一節中識別的兩個異常鳥類，編輯它們翼展中的錯誤，或者保留它們，因為這些練習不依賴於翼展值。 + +如果你想創建柱狀圖，可以選擇你想要關注的數據。柱狀圖可以從原始數據中創建： + +```python +birds.plot(x='Category', + kind='bar', + stacked=True, + title='Birds of Minnesota') + +``` +![完整數據柱狀圖](../../../../3-Data-Visualization/09-visualization-quantities/images/full-data-bar-02.png) + +然而，這個柱狀圖因為數據未分組而難以閱讀。你需要選擇你想要繪製的數據，所以讓我們看看基於鳥類類別的長度。 + +篩選數據以僅包含鳥類的類別。 + +✅ 注意，你使用 Pandas 管理數據，然後讓 Matplotlib 繪製圖表。 + +由於類別很多，你可以垂直顯示此圖表並調整其高度以容納所有數據： + +```python +category_count = birds.value_counts(birds['Category'].values, sort=True) +plt.rcParams['figure.figsize'] = [6, 12] +category_count.plot.barh() +``` +![類別和長度](../../../../3-Data-Visualization/09-visualization-quantities/images/category-counts-02.png) + +這個柱狀圖很好地展示了每個類別中鳥類的數量。一眼就能看出，這個地區最多的鳥類屬於鴨/鵝/水禽類別。明尼蘇達州是「萬湖之地」，所以這並不令人驚訝！ + +✅ 嘗試對這個數據集進行其他計數。有什麼讓你感到驚訝嗎？ + +## 比較數據 + +你可以通過創建新軸嘗試不同的分組數據比較。試試基於鳥類類別的最大長度比較： + +```python +maxlength = birds['MaxLength'] +plt.barh(y=birds['Category'], width=maxlength) +plt.rcParams['figure.figsize'] = [6, 12] +plt.show() +``` +![比較數據](../../../../3-Data-Visualization/09-visualization-quantities/images/category-length-02.png) + +這裡沒有什麼令人驚訝的：蜂鳥的最大長度最小，而鵜鶘或鵝的最大長度最大。當數據符合邏輯時，這是件好事！ + +你可以通過疊加數據創建更有趣的柱狀圖視覺化。讓我們在給定的鳥類類別上疊加最小和最大長度： + +```python +minLength = birds['MinLength'] +maxLength = birds['MaxLength'] +category = birds['Category'] + +plt.barh(category, maxLength) +plt.barh(category, minLength) + +plt.show() +``` +在這個圖表中，你可以看到每個鳥類類別的最小長度和最大長度範圍。你可以安全地說，根據這些數據，鳥越大，其長度範圍越大。真是有趣！ + +![疊加數值](../../../../3-Data-Visualization/09-visualization-quantities/images/superimposed-02.png) + +## 🚀 挑戰 + +這個鳥類數據集提供了關於特定生態系統中不同類型鳥類的大量信息。在網絡上搜索，看看你是否能找到其他與鳥類相關的數據集。練習圍繞這些鳥類構建圖表和圖形，發現你之前未曾意識到的事實。 + +## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/17) + +## 回顧與自學 + +這第一節課提供了一些關於如何使用 Matplotlib 視覺化數量的信息。研究其他方法來處理數據集進行視覺化。[Plotly](https://github.com/plotly/plotly.py) 是我們不會在這些課程中涵蓋的一個工具，看看它能提供什麼。 + +## 作業 + +[折線圖、散點圖和柱狀圖](assignment.md) + +--- + +**免責聲明**： +此文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/09-visualization-quantities/assignment.md b/translations/zh-HK/3-Data-Visualization/09-visualization-quantities/assignment.md new file mode 100644 index 00000000..247f755f --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/09-visualization-quantities/assignment.md @@ -0,0 +1,14 @@ +# 折線圖、散點圖與柱狀圖 + +## 指引 + +在這節課中，你學習了如何使用折線圖、散點圖和柱狀圖來展示這個數據集中的有趣事實。在這次作業中，深入挖掘數據集，發現關於某種特定鳥類的事實。例如，創建一個筆記本，視覺化展示你能找到的所有關於雪雁的有趣數據。使用上述三種圖表，在你的筆記本中講述一個故事。 + +## 評分標準 + +優秀 | 合格 | 需要改進 +--- | --- | -- | +筆記本包含良好的註解、完整的故事敘述以及吸引人的圖表 | 筆記本缺少其中一個元素 | 筆記本缺少其中兩個元素 + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，請注意自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為具權威性的來源。對於重要資訊，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/09-visualization-quantities/notebook.ipynb b/translations/zh-HK/3-Data-Visualization/09-visualization-quantities/notebook.ipynb new file mode 100644 index 00000000..8109b556 --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/09-visualization-quantities/notebook.ipynb @@ -0,0 +1,130 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# 一起學習關於鳥類吧\n", + "\n", + "## 鳥類是什麼？\n", + "\n", + "鳥類是一種擁有羽毛的溫血動物。牠們是現存唯一擁有羽毛的動物群體。大多數鳥類都能飛行，但也有一些鳥類是不能飛的，例如企鵝和鴕鳥。\n", + "\n", + "[!NOTE] 鳥類的羽毛不僅用於飛行，還能提供保暖和保護。\n", + "\n", + "---\n", + "\n", + "## 鳥類的主要特徵\n", + "\n", + "1. **羽毛** \n", + " 羽毛是鳥類最顯著的特徵，能幫助牠們飛行、調節體溫以及進行求偶展示。\n", + "\n", + "2. **喙** \n", + " 鳥類沒有牙齒，牠們用喙來進食。喙的形狀和大小因鳥類的飲食習慣而異。\n", + "\n", + "3. **骨骼** \n", + " 鳥類的骨骼輕盈但堅固，這有助於減輕牠們的體重，方便飛行。\n", + "\n", + "4. **蛋** \n", + " 鳥類是卵生動物，牠們會產下有硬殼的蛋。\n", + "\n", + "[!TIP] 如果你想觀察鳥類，可以在清晨或黃昏時分去公園，這是牠們最活躍的時間。\n", + "\n", + "---\n", + "\n", + "## 鳥類的分類\n", + "\n", + "鳥類可以根據牠們的特徵和行為分為不同的類別。以下是一些常見的分類：\n", + "\n", + "- **猛禽** \n", + " 例如老鷹和貓頭鷹，牠們以鋒利的爪子和喙捕食其他動物。\n", + "\n", + "- **水鳥** \n", + " 例如鴨子和鵜鶘，牠們通常生活在水域附近，並擅長游泳。\n", + "\n", + "- **鳴禽** \n", + " 例如麻雀和畫眉，牠們以悅耳的鳴叫聲聞名。\n", + "\n", + "[!WARNING] 不要靠近猛禽的巢穴，牠們可能會變得具有攻擊性。\n", + "\n", + "---\n", + "\n", + "## 鳥類的生態重要性\n", + "\n", + "鳥類在生態系統中扮演著重要角色：\n", + "\n", + "- **傳播種子** \n", + " 許多鳥類通過進食水果並排泄種子來幫助植物繁殖。\n", + "\n", + "- **控制害蟲** \n", + " 一些鳥類以昆蟲為食，幫助控制害蟲數量。\n", + "\n", + "- **指標物種** \n", + " 鳥類的數量和健康狀況可以反映環境的健康程度。\n", + "\n", + "[!IMPORTANT] 保護鳥類及其棲息地對維持生態平衡至關重要。\n", + "\n", + "---\n", + "\n", + "## 如何幫助鳥類\n", + "\n", + "1. **提供食物和水源** \n", + " 在你的花園或陽台放置鳥食器和水盆，吸引鳥類。\n", + "\n", + "2. **種植本地植物** \n", + " 本地植物能提供鳥類所需的食物和棲息地。\n", + "\n", + "3. **避免使用化學品** \n", + " 農藥和化肥可能對鳥類有害，應盡量避免使用。\n", + "\n", + "4. **支持保育組織** \n", + " 捐款或參與志願活動，支持保護鳥類的行動。\n", + "\n", + "[!CAUTION] 不要餵食鳥類人類的食物，例如麵包，這可能對牠們的健康有害。\n", + "\n", + "---\n", + "\n", + "## 結語\n", + "\n", + "鳥類是地球上最迷人的生物之一。通過了解牠們的特徵、行為和生態角色，我們可以更好地欣賞並保護這些美麗的生物。希望這篇文章能激發你對鳥類的興趣，並鼓勵你為牠們的保護作出貢獻！\n" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**免責聲明**： \n此文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。\n" + ] + } + ], + "metadata": { + "orig_nbformat": 4, + "language_info": { + "name": "python", + "version": "3.7.0", + "mimetype": "text/x-python", + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "pygments_lexer": "ipython3", + "nbconvert_exporter": "python", + "file_extension": ".py" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3.7.0 64-bit" + }, + "interpreter": { + "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" + }, + "coopTranslator": { + 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ScientificName \\\n", + "0 Black-bellied whistling-duck Dendrocygna autumnalis \n", + "1 Fulvous whistling-duck Dendrocygna bicolor \n", + "2 Snow goose Anser caerulescens \n", + "3 Ross's goose Anser rossii \n", + "4 Greater white-fronted goose Anser albifrons \n", + "\n", + " Category Order Family Genus \\\n", + "0 Ducks/Geese/Waterfowl Anseriformes Anatidae Dendrocygna \n", + "1 Ducks/Geese/Waterfowl Anseriformes Anatidae Dendrocygna \n", + "2 Ducks/Geese/Waterfowl Anseriformes Anatidae Anser \n", + "3 Ducks/Geese/Waterfowl Anseriformes Anatidae Anser \n", + "4 Ducks/Geese/Waterfowl Anseriformes Anatidae Anser \n", + "\n", + " ConservationStatus MinLength MaxLength MinBodyMass MaxBodyMass \\\n", + "0 LC 47.0 56.0 652.0 1020.0 \n", + "1 LC 45.0 53.0 712.0 1050.0 \n", + "2 LC 64.0 79.0 2050.0 4050.0 \n", + "3 LC 57.3 64.0 1066.0 1567.0 \n", + "4 LC 64.0 81.0 1930.0 3310.0 \n", + "\n", + " MinWingspan MaxWingspan \n", + "0 76.0 94.0 \n", + "1 85.0 93.0 \n", + "2 135.0 165.0 \n", + "3 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NameScientificNameCategoryOrderFamilyGenusConservationStatusMinLengthMaxLengthMinBodyMassMaxBodyMassMinWingspanMaxWingspan
0Black-bellied whistling-duckDendrocygna autumnalisDucks/Geese/WaterfowlAnseriformesAnatidaeDendrocygnaLC47.056.0652.01020.076.094.0
1Fulvous whistling-duckDendrocygna bicolorDucks/Geese/WaterfowlAnseriformesAnatidaeDendrocygnaLC45.053.0712.01050.085.093.0
2Snow gooseAnser caerulescensDucks/Geese/WaterfowlAnseriformesAnatidaeAnserLC64.079.02050.04050.0135.0165.0
3Ross's gooseAnser rossiiDucks/Geese/WaterfowlAnseriformesAnatidaeAnserLC57.364.01066.01567.0113.0116.0
4Greater white-fronted gooseAnser albifronsDucks/Geese/WaterfowlAnseriformesAnatidaeAnserLC64.081.01930.03310.0130.0165.0
\n", + "
" + ] + }, + "metadata": {}, + "execution_count": 2 + } + ], + "metadata": { + "tags": [] + } + }, + { + "cell_type": "markdown", + "source": [ + "讓我們通過展示一個非常基本的線圖來可視化這些鳥類的翼展\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 19, + "source": [ + "\n", + "plt.title('Max Wingspan in Centimeters')\n", + "plt.ylabel('Wingspan (CM)')\n", + "plt.xlabel('Birds')\n", + "wingspan = birds.MaxWingspan \n", + "wingspan.plot()\n" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 19 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "顯示鳥類名稱與其翼展的對應關係\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 20, + "source": [ + "\n", + "plt.title('Max Wingspan in Centimeters')\n", + "plt.ylabel('Wingspan (CM)')\n", + "plt.xlabel('Birds')\n", + "plt.xticks(rotation=45)\n", + "x = birds['Name'] \n", + "y = birds['MaxWingspan']\n", + "\n", + "plt.plot(x, y)\n", + "\n", + "plt.show()" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " x[:, None]\n", + "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " x = x[:, np.newaxis]\n", + "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " y = y[:, np.newaxis]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": 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+ }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 21, + "source": [ + "plt.title('Max Wingspan in Centimeters')\n", + "plt.ylabel('Wingspan (CM)')\n", + "plt.tick_params(axis='both',which='both',labelbottom=False,bottom=False)\n", + "\n", + "for i in range(len(birds)):\n", + " x = birds['Name'][i]\n", + " y = birds['MaxWingspan'][i]\n", + " plt.plot(x, y, 'bo')\n", + " if birds['MaxWingspan'][i] > 500:\n", + " plt.text(x, y * (1 - 0.05), birds['Name'][i], fontsize=12)\n", + " \n", + "plt.show()\n", + " \n", + "\n", + " \n" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "移除那些異常值並重新繪製翼展\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 22, + "source": [ + "plt.title('Max Wingspan in Centimeters')\n", + "plt.ylabel('Wingspan (CM)')\n", + "plt.xlabel('Birds')\n", + "plt.tick_params(axis='both',which='both',labelbottom=False,bottom=False)\n", + "for i in range(len(birds)):\n", + " x = birds['Name'][i]\n", + " y = birds['MaxWingspan'][i]\n", + " if birds['Name'][i] not in ['Bald eagle', 'Prairie falcon']:\n", + " plt.plot(x, y, 'bo')\n", + "plt.show()\n", + " " + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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" 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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+ }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "顯示各類別中的鳥類數量，並水平顯示標籤以便我們閱讀類別名稱\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 42, + "source": [ + "category_count = birds.value_counts(birds['Category'].values, sort=True)\n", + "plt.rcParams['figure.figsize'] = [6, 12]\n", + "category_count.plot.barh()" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 42 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "顯示每個類別的最大長度\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 43, + "source": [ + "maxlength = birds['MaxLength']\n", + "plt.barh(y=birds['Category'], width=maxlength)\n", + "plt.rcParams['figure.figsize'] = [6, 12]\n", + "plt.show()\n" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": 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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "將每個類別的鳥類最小和最大長度重疊\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 77, + "source": [ + "minLength = birds['MinLength']\n", + "maxLength = birds['MaxLength']\n", + "category = birds['Category']\n", + "\n", + "plt.barh(category, maxLength)\n", + "plt.barh(category, minLength)\n", + "\n", + "plt.show()" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": 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+ }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**免責聲明**： \n本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為權威來源。對於重要資訊，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。\n" + ] + } + ] +} \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/10-visualization-distributions/README.md b/translations/zh-HK/3-Data-Visualization/10-visualization-distributions/README.md new file mode 100644 index 00000000..991f90fe --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/10-visualization-distributions/README.md @@ -0,0 +1,208 @@ +# 視覺化數據分佈 + +|![由 [(@sketchthedocs)](https://sketchthedocs.dev) 繪製的手繪筆記](../../sketchnotes/10-Visualizing-Distributions.png)| +|:---:| +| 視覺化數據分佈 - _手繪筆記由 [@nitya](https://twitter.com/nitya) 繪製_ | + +在上一課中，你學習了關於明尼蘇達州鳥類數據集的一些有趣事實。你通過視覺化異常值發現了一些錯誤的數據，並比較了不同鳥類分類的最大長度差異。 + +## [課前小測驗](https://ff-quizzes.netlify.app/en/ds/quiz/18) +## 探索鳥類數據集 + +另一種深入了解數據的方法是查看其分佈，即數據如何沿著某個軸排列。例如，你可能想了解這個數據集中鳥類的最大翼展或最大體重的整體分佈。 + +讓我們來發掘一些關於這個數據集中數據分佈的事實。在本課文件夾根目錄中的 _notebook.ipynb_ 文件中，導入 Pandas、Matplotlib 和你的數據： + +```python +import pandas as pd +import matplotlib.pyplot as plt +birds = pd.read_csv('../../data/birds.csv') +birds.head() +``` + +| | 名稱 | 學名 | 分類 | 目 | 科 | 屬 | 保育狀況 | 最小長度 | 最大長度 | 最小體重 | 最大體重 | 最小翼展 | 最大翼展 | +| ---: | :--------------------------- | :--------------------- | :-------------------- | :----------- | :------- | :---------- | :----------------- | --------: | --------: | ----------: | ----------: | ----------: | ----------: | +| 0 | 黑腹樹鴨 | Dendrocygna autumnalis | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 樹鴨屬 | LC | 47 | 56 | 652 | 1020 | 76 | 94 | +| 1 | 棕樹鴨 | Dendrocygna bicolor | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 樹鴨屬 | LC | 45 | 53 | 712 | 1050 | 85 | 93 | +| 2 | 雪雁 | Anser caerulescens | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 雁屬 | LC | 64 | 79 | 2050 | 4050 | 135 | 165 | +| 3 | 羅斯雁 | Anser rossii | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 雁屬 | LC | 57.3 | 64 | 1066 | 1567 | 113 | 116 | +| 4 | 大白額雁 | Anser albifrons | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 雁屬 | LC | 64 | 81 | 1930 | 3310 | 130 | 165 | + +通常，你可以通過使用散點圖快速查看數據的分佈，就像我們在上一課中所做的那樣： + +```python +birds.plot(kind='scatter',x='MaxLength',y='Order',figsize=(12,8)) + +plt.title('Max Length per Order') +plt.ylabel('Order') +plt.xlabel('Max Length') + +plt.show() +``` +![每個目中的最大長度](../../../../3-Data-Visualization/10-visualization-distributions/images/scatter-wb.png) + +這提供了每個鳥類目中身體長度的一般分佈概覽，但這並不是顯示真實分佈的最佳方式。這通常需要使用直方圖來完成。 + +## 使用直方圖 + +Matplotlib 提供了非常好的方法來使用直方圖視覺化數據分佈。這種類型的圖表類似於條形圖，通過條形的升降可以看到分佈情況。要構建直方圖，你需要數值數據。構建直方圖時，可以將圖表類型定義為 'hist'。以下圖表顯示了整個數據集範圍內最大體重的分佈。通過將數據數組分成較小的區間（bins），它可以顯示數據值的分佈： + +```python +birds['MaxBodyMass'].plot(kind = 'hist', bins = 10, figsize = (12,12)) +plt.show() +``` +![整個數據集的分佈](../../../../3-Data-Visualization/10-visualization-distributions/images/dist1-wb.png) + +如你所見，這個數據集中大多數的 400 多種鳥類的最大體重都在 2000 以下。通過將 `bins` 參數設置為更高的數值（例如 30），可以獲得更多的數據洞察： + +```python +birds['MaxBodyMass'].plot(kind = 'hist', bins = 30, figsize = (12,12)) +plt.show() +``` +![使用更大 bins 參數的分佈](../../../../3-Data-Visualization/10-visualization-distributions/images/dist2-wb.png) + +這個圖表以更細緻的方式顯示了分佈。通過僅選擇給定範圍內的數據，可以創建一個不那麼偏向左側的圖表： + +篩選數據以僅獲取體重低於 60 的鳥類，並顯示 40 個 `bins`： + +```python +filteredBirds = birds[(birds['MaxBodyMass'] > 1) & (birds['MaxBodyMass'] < 60)] +filteredBirds['MaxBodyMass'].plot(kind = 'hist',bins = 40,figsize = (12,12)) +plt.show() +``` +![篩選後的直方圖](../../../../3-Data-Visualization/10-visualization-distributions/images/dist3-wb.png) + +✅ 試試其他篩選條件和數據點。若要查看數據的完整分佈，移除 `['MaxBodyMass']` 篩選條件以顯示帶標籤的分佈。 + +直方圖還提供了一些不錯的顏色和標籤增強功能可以嘗試： + +創建一個 2D 直方圖來比較兩個分佈之間的關係。我們來比較 `MaxBodyMass` 和 `MaxLength`。Matplotlib 提供了一種內建方式，通過更亮的顏色顯示匯聚點： + +```python +x = filteredBirds['MaxBodyMass'] +y = filteredBirds['MaxLength'] + +fig, ax = plt.subplots(tight_layout=True) +hist = ax.hist2d(x, y) +``` +可以看到，這兩個元素之間沿著預期軸線存在預期的相關性，並且有一個特別強的匯聚點： + +![2D 圖表](../../../../3-Data-Visualization/10-visualization-distributions/images/2D-wb.png) + +直方圖對於數值數據默認效果很好。如果你需要查看基於文本數據的分佈該怎麼辦？ +## 使用文本數據探索數據集的分佈 + +這個數據集還包括關於鳥類分類、屬、種、科以及保育狀況的有用信息。讓我們深入了解這些保育信息。鳥類的保育狀況分佈如何？ + +> ✅ 在數據集中，有幾個縮寫用於描述保育狀況。這些縮寫來自 [IUCN 紅色名錄分類](https://www.iucnredlist.org/)，該組織記錄了物種的狀況。 +> +> - CR: 極危 +> - EN: 瀕危 +> - EX: 滅絕 +> - LC: 無危 +> - NT: 近危 +> - VU: 易危 + +這些是基於文本的值，因此你需要進行轉換以創建直方圖。使用篩選後的數據框架，顯示其保育狀況以及最小翼展。你看到了什麼？ + +```python +x1 = filteredBirds.loc[filteredBirds.ConservationStatus=='EX', 'MinWingspan'] +x2 = filteredBirds.loc[filteredBirds.ConservationStatus=='CR', 'MinWingspan'] +x3 = filteredBirds.loc[filteredBirds.ConservationStatus=='EN', 'MinWingspan'] +x4 = filteredBirds.loc[filteredBirds.ConservationStatus=='NT', 'MinWingspan'] +x5 = filteredBirds.loc[filteredBirds.ConservationStatus=='VU', 'MinWingspan'] +x6 = filteredBirds.loc[filteredBirds.ConservationStatus=='LC', 'MinWingspan'] + +kwargs = dict(alpha=0.5, bins=20) + +plt.hist(x1, **kwargs, color='red', label='Extinct') +plt.hist(x2, **kwargs, color='orange', label='Critically Endangered') +plt.hist(x3, **kwargs, color='yellow', label='Endangered') +plt.hist(x4, **kwargs, color='green', label='Near Threatened') +plt.hist(x5, **kwargs, color='blue', label='Vulnerable') +plt.hist(x6, **kwargs, color='gray', label='Least Concern') + +plt.gca().set(title='Conservation Status', ylabel='Min Wingspan') +plt.legend(); +``` + +![翼展與保育狀況的對比](../../../../3-Data-Visualization/10-visualization-distributions/images/histogram-conservation-wb.png) + +最小翼展與保育狀況之間似乎沒有明顯的相關性。使用這種方法測試數據集中的其他元素。你也可以嘗試不同的篩選條件。你發現了任何相關性嗎？ + +## 密度圖 + +你可能已經注意到，我們目前看到的直方圖是“階梯式”的，並沒有平滑地呈現弧線。若要顯示更平滑的密度圖，你可以嘗試使用密度圖。 + +為了使用密度圖，請熟悉一個新的繪圖庫 [Seaborn](https://seaborn.pydata.org/generated/seaborn.kdeplot.html)。 + +加載 Seaborn，嘗試一個基本的密度圖： + +```python +import seaborn as sns +import matplotlib.pyplot as plt +sns.kdeplot(filteredBirds['MinWingspan']) +plt.show() +``` +![密度圖](../../../../3-Data-Visualization/10-visualization-distributions/images/density1.png) + +你可以看到，這個圖表與之前的最小翼展數據圖表相呼應，只是更平滑了一些。根據 Seaborn 的文檔，“相較於直方圖，KDE 可以生成一個更簡潔且更易於解讀的圖表，特別是在繪製多個分佈時。但如果底層分佈有界或不平滑，它可能會引入失真。與直方圖一樣，圖表的質量也取決於良好平滑參數的選擇。” [來源](https://seaborn.pydata.org/generated/seaborn.kdeplot.html) 換句話說，異常值仍然會影響圖表的表現。 + +如果你想重新訪問第二個圖表中那條鋸齒狀的最大體重線，你可以通過這種方法將其很好地平滑化： + +```python +sns.kdeplot(filteredBirds['MaxBodyMass']) +plt.show() +``` +![平滑的體重線](../../../../3-Data-Visualization/10-visualization-distributions/images/density2.png) + +如果你想要一條平滑但不過於平滑的線，編輯 `bw_adjust` 參數： + +```python +sns.kdeplot(filteredBirds['MaxBodyMass'], bw_adjust=.2) +plt.show() +``` +![較少平滑的體重線](../../../../3-Data-Visualization/10-visualization-distributions/images/density3.png) + +✅ 閱讀此類圖表可用的參數並進行實驗！ + +這種類型的圖表提供了非常直觀的視覺化效果。例如，只需幾行代碼，你就可以顯示每個鳥類目中的最大體重密度： + +```python +sns.kdeplot( + data=filteredBirds, x="MaxBodyMass", hue="Order", + fill=True, common_norm=False, palette="crest", + alpha=.5, linewidth=0, +) +``` + +![每個目中的體重密度](../../../../3-Data-Visualization/10-visualization-distributions/images/density4.png) + +你還可以在一個圖表中映射多個變量的密度。比較鳥類的最大長度和最小長度與其保育狀況： + +```python +sns.kdeplot(data=filteredBirds, x="MinLength", y="MaxLength", hue="ConservationStatus") +``` + +![多個密度圖，重疊顯示](../../../../3-Data-Visualization/10-visualization-distributions/images/multi.png) + +或許值得研究這些“易危”鳥類的長度聚集是否具有意義。 + +## 🚀 挑戰 + +直方圖是一種比基本散點圖、條形圖或折線圖更為複雜的圖表類型。在互聯網上搜索一些直方圖的優秀示例。它們是如何使用的？它們展示了什麼？它們通常在哪些領域或研究範疇中使用？ + +## [課後小測驗](https://ff-quizzes.netlify.app/en/ds/quiz/19) + +## 回顧與自學 + +在本課中，你使用了 Matplotlib 並開始使用 Seaborn 來繪製更為複雜的圖表。研究 Seaborn 中的 `kdeplot`，這是一種“在一維或多維空間中顯示連續概率密度曲線”的方法。閱讀 [文檔](https://seaborn.pydata.org/generated/seaborn.kdeplot.html) 以了解其工作原理。 + +## 作業 + +[應用你的技能](assignment.md) + +--- + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為具權威性的來源。對於重要資訊，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/10-visualization-distributions/assignment.md b/translations/zh-HK/3-Data-Visualization/10-visualization-distributions/assignment.md new file mode 100644 index 00000000..3d52c962 --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/10-visualization-distributions/assignment.md @@ -0,0 +1,14 @@ +# 運用你的技能 + +## 指引 + +到目前為止，你已經使用了明尼蘇達州的鳥類數據集，探索了有關鳥類數量和種群密度的信息。現在試試運用這些技巧，選擇一個不同的數據集，例如從 [Kaggle](https://www.kaggle.com/) 獲取的數據集。建立一個筆記本，講述這個數據集的故事，並確保在討論時使用直方圖。 + +## 評分標準 + +卓越 | 合格 | 需要改進 +--- | --- | --- | +提供了一個包含有關此數據集的註解（包括其來源）的筆記本，並使用至少 5 個直方圖來探索數據的事實。 | 提供了一個包含不完整註解或有錯誤的筆記本。 | 提供了一個沒有註解且包含錯誤的筆記本。 + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原文文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/10-visualization-distributions/notebook.ipynb b/translations/zh-HK/3-Data-Visualization/10-visualization-distributions/notebook.ipynb new file mode 100644 index 00000000..fffa3407 --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/10-visualization-distributions/notebook.ipynb @@ -0,0 +1,32 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# 鳥類分佈\n" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**免責聲明**： \n本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。\n" + ] + } + ], + "metadata": { + "orig_nbformat": 4, + "language_info": { + "name": "python" + }, + "coopTranslator": { + "original_hash": "e5272cbcbffd1ddcc09e44d3d8e7e8cd", + "translation_date": "2025-09-02T09:06:30+00:00", + "source_file": "3-Data-Visualization/10-visualization-distributions/notebook.ipynb", + "language_code": "hk" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/10-visualization-distributions/solution/notebook.ipynb b/translations/zh-HK/3-Data-Visualization/10-visualization-distributions/solution/notebook.ipynb new file mode 100644 index 00000000..57b6e2f4 --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/10-visualization-distributions/solution/notebook.ipynb @@ -0,0 +1,571 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + }, + "orig_nbformat": 4, + "kernelspec": { + "name": "python3", + 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autumnalis \n", + "1 Fulvous whistling-duck Dendrocygna bicolor \n", + "2 Snow goose Anser caerulescens \n", + "3 Ross's goose Anser rossii \n", + "4 Greater white-fronted goose Anser albifrons \n", + "\n", + " Category Order Family Genus \\\n", + "0 Ducks/Geese/Waterfowl Anseriformes Anatidae Dendrocygna \n", + "1 Ducks/Geese/Waterfowl Anseriformes Anatidae Dendrocygna \n", + "2 Ducks/Geese/Waterfowl Anseriformes Anatidae Anser \n", + "3 Ducks/Geese/Waterfowl Anseriformes Anatidae Anser \n", + "4 Ducks/Geese/Waterfowl Anseriformes Anatidae Anser \n", + "\n", + " ConservationStatus MinLength MaxLength MinBodyMass MaxBodyMass \\\n", + "0 LC 47.0 56.0 652.0 1020.0 \n", + "1 LC 45.0 53.0 712.0 1050.0 \n", + "2 LC 64.0 79.0 2050.0 4050.0 \n", + "3 LC 57.3 64.0 1066.0 1567.0 \n", + "4 LC 64.0 81.0 1930.0 3310.0 \n", + "\n", + " MinWingspan MaxWingspan \n", + "0 76.0 94.0 \n", + "1 85.0 93.0 \n", + "2 135.0 165.0 \n", + "3 113.0 116.0 \n", + "4 130.0 165.0 " + ], + "text/html": [ + "
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NameScientificNameCategoryOrderFamilyGenusConservationStatusMinLengthMaxLengthMinBodyMassMaxBodyMassMinWingspanMaxWingspan
0Black-bellied whistling-duckDendrocygna autumnalisDucks/Geese/WaterfowlAnseriformesAnatidaeDendrocygnaLC47.056.0652.01020.076.094.0
1Fulvous whistling-duckDendrocygna bicolorDucks/Geese/WaterfowlAnseriformesAnatidaeDendrocygnaLC45.053.0712.01050.085.093.0
2Snow gooseAnser caerulescensDucks/Geese/WaterfowlAnseriformesAnatidaeAnserLC64.079.02050.04050.0135.0165.0
3Ross's gooseAnser rossiiDucks/Geese/WaterfowlAnseriformesAnatidaeAnserLC57.364.01066.01567.0113.0116.0
4Greater white-fronted gooseAnser albifronsDucks/Geese/WaterfowlAnseriformesAnatidaeAnserLC64.081.01930.03310.0130.0165.0
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" + ] + }, + "metadata": {}, + "execution_count": 3 + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "顯示 MaxBodyMass 數據的直方圖\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 4, + "source": [ + "birds['MaxBodyMass'].plot(kind = 'hist',bins = 10,figsize = (12,12))\n", + "plt.show()" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "嘗試使用箱子\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 5, + "source": [ + "birds['MaxBodyMass'].plot(kind = 'hist',bins = 30,figsize = (12,12))\n", + "plt.show()" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "篩選數據並建立新的直方圖\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 6, + "source": [ + "filteredBirds = birds[(birds['MaxBodyMass'] > 1) & (birds['MaxBodyMass'] < 60)] \n", + "filteredBirds['MaxBodyMass'].plot(kind = 'hist',bins = 40,figsize = (12,12))\n", + "plt.show() \n" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "建立一個二維直方圖，顯示最大體重與最大長度之間的關係\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 7, + "source": [ + "from matplotlib import colors\n", + "from matplotlib.ticker import PercentFormatter\n", + "\n", + "x = filteredBirds['MaxBodyMass']\n", + "y = filteredBirds['MaxLength']\n", + "\n", + "fig, ax = plt.subplots(tight_layout=True)\n", + "hist = ax.hist2d(x, y)" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "使用過濾後的數據集，創建一個標記且堆疊的直方圖，將保育狀況與最大體重重疊展示\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 8, + "source": [ + "x1 = filteredBirds.loc[filteredBirds.ConservationStatus=='EX', 'MinWingspan']\n", + "x2 = filteredBirds.loc[filteredBirds.ConservationStatus=='CR', 'MinWingspan']\n", + "x3 = filteredBirds.loc[filteredBirds.ConservationStatus=='EN', 'MinWingspan']\n", + "x4 = filteredBirds.loc[filteredBirds.ConservationStatus=='NT', 'MinWingspan']\n", + "x5 = filteredBirds.loc[filteredBirds.ConservationStatus=='VU', 'MinWingspan']\n", + "x6 = filteredBirds.loc[filteredBirds.ConservationStatus=='LC', 'MinWingspan']\n", + "\n", + "kwargs = dict(alpha=0.5, bins=20)\n", + "\n", + "plt.hist(x1, **kwargs, color='red', label='Extinct')\n", + "plt.hist(x2, **kwargs, color='orange', label='Critically Endangered')\n", + "plt.hist(x3, **kwargs, color='yellow', label='Endangered')\n", + "plt.hist(x4, **kwargs, color='green', label='Near Threatened')\n", + "plt.hist(x5, **kwargs, color='blue', label='Vulnerable')\n", + "plt.hist(x6, **kwargs, color='gray', label='Least Concern')\n", + "\n", + "plt.gca().set(title='Conservation Status', ylabel='Max Body Mass')\n", + "plt.legend();" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "使用 Seaborn，建立一個關於 MinWingspan 的平滑圖表\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 9, + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "sns.kdeplot(filteredBirds['MinWingspan'])\n", + "plt.show()" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " x[:, None]\n", + "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " x = x[:, np.newaxis]\n", + "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " y = y[:, np.newaxis]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "嘗試使用 MaxBodyMass 繪製 kdeplot\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 11, + "source": [ + "sns.kdeplot(filteredBirds['MaxBodyMass'])\n", + "plt.show()" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " x[:, None]\n", + "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " x = x[:, np.newaxis]\n", + "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " y = y[:, np.newaxis]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "嘗試調整圖形平滑參數\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 12, + "source": [ + "sns.kdeplot(filteredBirds['MaxBodyMass'], bw_adjust=.2)\n", + "plt.show()" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " x[:, None]\n", + "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " x = x[:, np.newaxis]\n", + "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " y = y[:, np.newaxis]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "建立一個2D核密度圖，比較MinLength和MaxLength，並以色調顯示ConservationStatus\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 13, + "source": [ + "sns.kdeplot(data=filteredBirds, x=\"MinLength\", y=\"MaxLength\", hue=\"ConservationStatus\")\n" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/distributions.py:1078: UserWarning: Dataset has 0 variance; skipping density estimate.\n", + " warnings.warn(msg, UserWarning)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 13 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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HCXcMPyHp33333SxevBioLSllldVQpDfj466hbYvzM+mDTPySJEknGD16NN98+y2Hy2o4Um3Bz1PL2lU/MnbM6HrPF0fn8B8oMWCyOYnwP3UPoPPB+RuZJEnSOXDtjbewYsVKqo1mIgM8cVWXUFRYSP/+/U86t7Y1g5X8ShM6jZq2od4EeZ9f9fz6nJ+fQyRJkgB+ngbFaU17zbDOMPS1er9VZrSid7rTuVsPDu7cSLfbb+WTOQsZMWLECcncdbRdc7XFjtXuoqW/B0Febud9wj9GjvglSbrkCSEoqDRTWGXGR6fh3jvHsmTxdwAsXLiQ0aNH1yV1g8XOgSNGivUWtGoVEQG1HTYvlKQPcsQvSdL5rIGReVNyCUFuuYlqi50QH3fCfHUE33orjz76KDt37sRkMtGjRw+Sd+2muKScrLIa3DUqooO9sNVUExbaotljbGpyxC9J0iXLJQQ5R5N+S38Pwv08UBQFb29vBg0axD333MOIkaPIrzSBbzjFxYUYirNpG+pD5ZFCdu/eTWJi4rl+GadNjvglSbokCSHIrzBjsNiJCPAgyOvEDVGGjxzJ8Ntv58V3P6XSZCc8yIcFX3/Now8/gMViQavV8tlnn+Hn53eOXsGZk4lfkqRLUpnRRpXZRpiv7oSkb7Y7KTdYaXfZlaTmVRHgWdti2U2jpuUV/dmyZcs5jLppyMQvSdIlRwhBqcGKt7uGEB937E4X1WY7lSY7JpsDlaIQ6OlGsI8b7ppz20mzOcjEL0nSJUcAAoHR6iCz2IDNWbvxik6rJtxPR4Cn23m9AOvfkolfkqRLjkpRiA/1oVhvQQgIclPh7a5Fp1VdUNMyz5RM/JIkXZK0ahVRgU3TvfNCc/F+lpEkSZLqJRO/JEnSJUaWeiRJkv5GURSmTp3KW2+9BcCbb76J0WhEq9Xy/fffA5CWlkbnzp0BuOeee5g8efI5i/d0ycQvSZL0N+7u7ixZsoTp06cTHBxcd/zpp5/m6aefBsDb25uUlJRzFeK/Iks9kiSdNiEEJpuDYr2Fw6VGDpYYya80YT86LfJCp9FomDBhAm+//fa5DqVZyBG/JEmN5nC6qDTZqaixYXU4UVDqpkBW1Njwdtfg7+nWZM83c9tM9lXsa7LrAbQPbM+TvZ78x/MmTpxIly5deOKJJ5r0+c8HMvFLkvSPbA4XpUYrlTU2XELg6aYhMsATX50GjVpFkd6MyebATXPxFBF8fX258847mT17Nh4eHuc6nCYlE78kSQ0q0pupNNnILDaAAv4eWoK93fFw+6uNQbXZTqnBSqCXG55uTZtSGjMyb05Tpkyhe/fujB8//pzG0dQunl/PkiQ1Gb3JzqurMhjwxnpMNieBXm60C/UhKtDzhKRvsNjJrTDhoVXT0u/iGhUDBAYGMmLECObOnXuuQ2lSMvFLklTH4XTx5eZsBry5jk82HOaGLuGE+roTEeBxUhlHb7aTXW7CTaMiJtgLleribHXw6KOPUlZWdq7DaFKy1CNJEgDbsip47qc97Cs20Cc2iGdv6EjHlr5kZGSccJ4QgvIaG4VVZjzdNEQHeV50Dc2MRmPdv0NDQzGZTKc850IjE78kXeIqamy8vDKDH3bmE+HvwUd39OCahNB6m5W5hKCoykx5jQ1fnZZWgZ4X7Uj/YiYTvyRdooQQ/JRSyIsr0qk223loYByTBrc9oYZ/PJvDRW6FCZPNUbc37aXQyfJiJBO/JF2CjlRbeGpJGr/tK6FbK39eu60L7cJ8Gjy/2mwnv9KES0DrQE/8mnCuvnT2ycQvSZeYZbsLefbHPVgdTp65vgPjL49B3UC5xmJ3UmWyYy+vQadV0yrQE5324tuR6lIjE78kXSIMFjvP/bSXpbsKSIzyZ9aIrsSGeDd4/p4CPY8sSuGJ3j609q4t7ch6/sVBJn5JugSk5lcx6dtd5FeamXJVWx4e1KbBmThWh5P31h7kg/WHCPZ2I9jbjZb+F98c/UvZxTUHS5KkEwgh+GpLDsM+3Izd4WLhhN5MuSq+waSfnFPB9bM3MmftQW5ObMmvUwZckqUdtVpNYmJi3Z/XXnsNgIEDB9KzZ8+683bs2MHAgQPPUZRnTo74JekiZbE7efbHPXyfnM+A+BDeGZlIgFf9N2WrTDZm/i+Tb7fl0tJPx7zxSQxq1wKAwrMZ9HnCw8OjwZbLJSUl/PzzzwwdOvQsR9V0ZOKXpItQudHKhK+SSc6pZNLgNjxyVXy99XmnS/D9jjxe/yUTvdnOvf1imDokHi93mRoa8vjjj/Pyyy/LxC9J0vkjq6yG8fO2UaS38P6Y7lzfJbze8zYfKuflVensKagmKTqAF2/uRIdw37Mc7akVv/IK1oymbcvs3qE9YU89dcpzzGYziYmJdV9Pnz6dkSNHAtCnTx+WLl3KunXr8PFpeArs+Uwmfkm6iKQXVnPn59twCcE39/emR+uAk87JLDbwxi+ZrMk4Qks/HbNHd+PGLuFyMdZxTlXqAXjmmWf473//y8yZM89iVE2n2RK/oig64A/A/ejzLBZCzFAUJQZYCAQBycA4IYStueKQpHNFCEFBlRkvN02DtfWmlF5YzehPt+Dppuare3vTpsWJUzUPlRqZ/dsBlu8uxMtdw+PXtOPefjHn9c3bfxqZnyuDBw/mmWeeYcuWLec6lDPSnCN+KzBYCGFUFEULbFQU5WdgKvC2EGKhoigfAfcCHzZjHJJ01rhcgj8PlbMitZB1mSUcqbaiVilc3iaYOaO64eepbZbnLdKbGT9/G55uar77Tx+iAj3rvpeSV8Wnfxxm1Z4idBo19/eP5YEBcWfll9HF7JlnnuGBBx4gNjb2XIdy2pot8QshBHCsfZ326B8BDAbGHD3+BfA8MvFLF7gaq4NF2/OY/2c2uRUmvNzUDGzfgstiAlmWUsgf+0vZmVdZN1OmKTldgknf7KLG6mTxg7VJ32J3siqtiAVbc0nOqcTHXcMDA+K4t18Mwd7uTR7DxebvNf5rr722bkrnMddddx0hISFnO7Qm0aw1fkVR1NSWc9oA7wOHgCohhOPoKflARAOPnQBMAGjVqlVzhilJZ6zKZOPzTdnM25SFweIgKTqAR6+O55qEMHRaNRa7k2+25hLq607fuKBmiWFXbiU7cioZ2imM1Hw9H6w7xNp9JRitDqKDPHnuho6MSIrCW87UaTSn01nv8fXr15/wdXJy8lmIpuk160+CEMIJJCqK4g8sBdqfxmM/AT4B6Nmzp2ieCCXpzBitDuZuyOKzDYcxWB1cmxDGhAGxdG/1181Up0swZWEK+4oNzBufhLumeWrp2qOLsX7eU8zPe4oJ8nLjhi7h3NS1JX3iguRNW+kkZ2UIIISoUhRlHdAH8FcURXN01B8JFJyNGCSpKTicLr7dlss7aw5QXmPj2oQw/u+qtidNg3S6BI8v3s3/9hbz7A0dm6XEc0zXKH9+mXIF1RY7AZ5a4kK8ZbKXTqk5Z/WEAPajSd8DGALMBNYBw6id2XMX8FNzxSBJTWnDgVJeXJ7OgRIjvWMDmTu0A4lR/iedZ3U4mbpoNyvTipg6JJ57+8U0e2ynaqncFIQQ8pfJeaz2lmrjNeeIPxz44midXwV8J4RYoShKOrBQUZT/AruAi2sXY+miU1hl5sXl6fxvbzHRQZ58PK4HV3esf4eqKpONCV8msy27gmeu78B9/S+8GR9/p9PpKC8vJyhIlo3OR0IIysvL0el0jX5Mc87qSQW61XP8MNCruZ5XkpqKw+li/p/ZzFq9H5cQPH5NO+7rH9Ngrf5giZH7vthOYZWFd0clcnNivfMWLjiRkZHk5+dTWlp6rkORGqDT6YiMjGz0+fI2vyTVI6OomicWp5JWoGdw+xa8cFPCCXPj/+63jCNMWZiCu1bFN/dfRs/owNN6vhqrg935VaQXVpNbYcJidxLo5c7IpChigr3+7cv5V7RaLTExzV+uks6eRid+RVH6AtHHP0YI8WUzxCRJ54zd6eLD9YeY/dsB/D21zBndjRtO0c7A5RLMWXuQd37bT0JLXz4e15OIRvSuF0JwqNTIytRi1u8vITVfj9NVW6f10WnwdFNTbrSxcHsuW5+6stlmBEmXpkYlfkVRvgLigBTg2ARXAcjEL100DpUamboohd35em7s2pIXbkog8BSrW/VmO1MXpfDbvhJu6xbBK7d1/sf2BwVVZr7fkcey3YUcLq1BUaBrpD8PDoijR+sAukb51z3nd9vzeOKHVPIqTLRpcWE2A5POT40d8fcEOorTvXUsSRcAIQSLtufxwvJ03LUqPhjbnes619/R8pj0wmoeXJBMQaWZ52/syF19oxv8VGB3ulidfoSvt+Tw56FyFAV6xwQxvm80VyeEEepb/025w2U1aFQKkQENl5gk6Uw0NvHvAcKAomaMRZLOOoPFzvQlaaxILeLyNkHMGpHYYCI+5ofkfJ5amoa/p5ZF/+lNj9b11/NLqi18sTmbRdvzKTNaifD3YOqQeG7tFnHK+wXH7MytpFOE33ndRE26MJ0y8SuKspzako4PkK4oyjZqm68BIIS4qXnDk6Tmk1FUzUMLdpJbYeLxa9rx4IC4U24mbnO4eGlFOl9tyaF3bCBzRncnxOfkvjd7C/V8+sdhVqQW4RKCwe1DGd0rip7RgWSX1ZBRVE2p0UqHMF883BpO6mVGK3Gn2Axdks7UP4343zwrUUjSWbZ0Vz7Tl6Thq9PyzX2XcVnsqfvoGK0OHvgqmY0Hy5hwRSxPXNPuhH1rnS7BL3uLmbcpi+3ZlXi5qRnXpzX92wSTkq/n3d8OsLewuu4GLkCorzvLJ/WjhU/9nzASo/z5ZU8xewv1JLT0a5oXLkmA0piyvaIoM4UQT/7TsebSs2dPsWPHjrPxVNJFzuF08erP+5i7MYteMYG8P6b+UfvxDBY7d36+jdR8Pa/d1pnhPaPqvme2OVm0PZfPNmaRX2kmOsiTW7tFoCgKK1IL2X/EiEqB7q0C6BsXRKcIP0J9dWSX1/D496mM7d2KGTcm1Pu8+ZUmhn+0GbPdyc//159wv3+eLSRJx1MUJVkI0fPvxxtb4x8C/D3JD63nmCSdt6otdiYu2MmGA2Xc3Teap6/vUNfgrCEul2DiN7tIy9fz/pjuXNspDIC0fD3fbs9l+e5CDBYHPVsHMLpXFBlFBt5fdwib00V0kCdJ0QHotGo0KoW4Ft5c2SEUtUqha5Q/7645QInB2uBzRwZ4suC+y7h+9kZm/bqfN4Z3bdL3Q7p0/VON/0HgISBWUZTU477lA2xqzsAkqSnlVZgYP3872WU1zLy9MyOTGtfqe3t2BX/sL2XqkHjC/XTM+e0AK9OK2FdsQKdVMTQhjNbBXqzdV8Ibv+xHp1Hh56Gl1Gglu9xEmcFKVJAXBmsNU5f9yc/Z7swcOgZfN19KDVb6/8NmKLEh3rRp4U1xtaUp3gZJAv55xP8N8DPwKjDtuOMGIURFs0UlSU1oV24l93+5A5vDxVf3Xkaf0+iLHxvijZtGxazV+5m1ej8APVsH8NR17XE4Bd9sy2FpSiGebipUClgcLhSrA7VKwekS2LTZuIXtwebYhqfDzCY9vLD5AM/1eg2D1fGPs3ssdicHSgyM7iX3pJCazikTvxBCD+gVRZn49+8piqIVQtibLTJJagIrUgt59LvdhPrqWDgh6aR9aP9JiI87397fm82HyogO9qJtiDfLU4t4f91B9GYHHtraUpHF7sIlQKVA+3BvWobncNi+jHxzOodq3Liy1TUkp7fE6PclpaZSjt3j/adbbL/vL8Vid3Fl+9AzefmSVK/G1vh3AlFAJaAA/kCxoihHgPuFEBfmNjTSRUsIwezfDvL2mv30aB3AJ+N6EHSGWw72aB1AS38dH/9+mEe/S8HqELhpaqd9mu0uoHYGzi3dIggJyeKLfe/xR/lewr3CGRY9kXm/tOCG3n35edcbuGNmbMexBHhqiQn2YnX6Ee6/ouEOntuzaj9YV5psrE4/Qk55DTnlJo5UW3C4BC39ddzRuzXtw3wbvIYk/V1jE/9qYLEQ4hcARVGuBm4H5gEfAJc1T3iSdPpMNgePf5/KyrQibusWwau3dz7jXje7ciuZ/2c2K1MLcbpAfXSev80h0GlVjOgRxZ19o+YcXHQAACAASURBVKlRDvF28vMk708mwjuCF/q+wI2xN/LOmkOoxCG2H9mIW8hqere4kmtaX4OiKIzp1YqXV2VwsMTY4CeRUb2iWJtZwqRvd9Ud89VpCPfzQKVS2HiwjJ9SCtn61JV4usmei1LjNPYnpbcQ4v5jXwghflUU5U0hxH8URZE7N0vnjUOlRh76eicHSgxMH9qeCVfEnnYPebvTxaq0IuZtyiYlrwqtqrYkIwCHSxDk5cZ/BsQyqlcr9LZi3tk5g19zfiVQF8hTlz3FsLbD0Kq1uFyCpTsL6Na2mu/z3sJlackbA/9bF0/HlrWj9GK9pcHE36aFDysm9WPL4XICvdyJCvAg0Mut7hofrD/I6//LxGBxyMQvNVpjf1KKFEV5ktpdswBGAkeObrLiapbIJOk0CCH4YWcBz/20B51WzfzxvbgiPqTRj7c7XWw5XM6qtGJ+3VtMeY0Nb3c1igJHqzlEBXgw6cq23JIYgdVVwyeps1mQsQCNSsODXR/k7oS78dT+dbN2e3YFxZbDONw+x83lj7pyAv4efyX4dftKUKsUOoSfugGbp5uGwfXU+C12J4t35BMf6k3IGZaxpEtTYxP/GGAG8OPRrzcdPaYGRjRDXJLUaFUmG0//uIeVqUVcFhPIO6MST1jsVG60sj27gsNlNZRUW3G4ajO5ELUdNguqzBw4YsRodaDTqAjw1AJgtNY2oo0P9eaRq+K5JiEMgYulB5cwZ9ccKi2V3NzmZiZ1m0QLz5P31P1q5xY8W8/F192HKMuj5Lj9tUL3cKmRL7fkcHPXlmd87+HllRkcLqvhy3t6nbLVhCT9XaMSvxCiDJjUwLcPNl04knR61mWWMO2HVMqNNh67Op4HB7ZBrVKoqLHxQ3I+K1IL2Z2vrzvfx12DVqPiWJr09dAS4uNGj9b+FFdZyCwxUlxdu6gqoaUvU4fEM7h9bVJfdWAzH6S9Ra5xP91bdOfDqz6kY1DHeuNKLdnL79Uv4q5257MhnzH2o0MktKwd7TucLqZ+txtPNzVPDm1/Rq977sYsvtqSw/39Y07rk40kQeP78ccDj3HyRiyDmycsSTo1vdnOyyvT+W5HPu1CffjsziQ6R/qxr7iaT//IYkVqIVaHiy6Rfjx2dTx92wTTLtQHL/e/fuQPlRr5ITmf77bnUVZj49iguVsrfyZd2ZaB8SEcKjXy7PLNLM//DJfXdlx2P6wlo+kXM5KOQXH1xpZSksKEXx/E5XLjye6z2ZWlpqDKzLSjSf69dQdJyati9uhuJ3QCLdZb2J5dQXpRNVUmG2G+HjwwMPakG9Pfbc/jpRXpXJsQxrShHZr4nZUuBY0t9XwPfAR8xl8bsUjSObEm/QjP/LiHUqOVBwfGMeWqtuwvNnL/lztYnX4ETzc1w3tGcmefaOJDa+vnQgjyK838sb+UrVkVbDhQyqHSGoC6hD+4fQseHBhHYlQAv2UcYcxnm9lRsQr3kF9QednpEzicayPvYOqiDCpM9S9h2Vy4mf9b93847N74VT5Er8i23PLBJrpG+XNd53D2HzEw+7cD3NotgqGdwvjzUBm/Z5by+/5S9hUbANCqFfw93Sg1WMkqM/LOqL+2rl6wNYenl+6hf9tg3hmVWDfLSJJOR2MTv0MI8WGzRiJJ/6DcaOXFFen8lFJI+zAfPh7XAy93NY8sSmFVWjF+HlqmXNWW4T0iOVBiZEVqEQeO7CerrIa8ChM1ttoxi1at1M2A0aoVbk6M4D9XxBId7MWPuwp47PtUcowZ+EQsQxeWT/eQXsy4/Gli/WJ5cXk6ANd1OnmjltU5q3nyjyfx07Qk++AdPH99TyZ8tQOXS/DuyNok/Xtm7eKt5JxKery0mmqLA61aoWfrQJ68tj392gTTLswHi8NJv9fWolbVLhATQvDB+kO88UsmV7ZvwQd3dJfbMUpnrLGJf7miKA8BSzmxH79s2yA1OyEEy3YX8sLydAwWO1OuasuInlG8t+4gi7bn4aFV88CAOCL8dazOKOGDo03SVApEB3nRKsiDcD8derOd/Udv4vroNNzbL4ZRvaII8HRjyc587v1iB7lVZYRHr8U7ZBNBHsE8kfQG10TXzrv/fX8p8/7M4o7eregceWKb5IX7FvLK1leI9U1gT/LtXBHXiuW7C8gqq+GL8b2IPrph+sB2IazLLAHg8jZBDGzXgn5tgk8oQQkhePT7PdTYnNzZpzUOZ+0+AF9szuHmxJa8MawrbppTN5eTpFNpbFvmrHoOCyFEw0sOm5Bsy3zpKqwy88yPe1i7r4TEKH9eurkTGw+W8f66g1jsTm7sEo6bRsWK1CJqbE5aB3nSv00wIT469GYbewqqScmrwuZ04avTcGWHUIb1iKRPbBBOIViyM5/31h0kr8JEbPR+zD5LMTsNjOkwhomJE/HS1ibs/UcM3P7hn0T4e7Dkob51nxiEEMzZNYdP0z6lW/Dl7Np+PS18fAnw1LArT8+c0d1O2sax1GBlXWYJGw+UkZxTSYCXlrl3JRHqq0MIwUsrMvh8UxaPX9OOOy5rzeSFu/h9fyn39Yvhqes6yBk8UqP9q7bMQoiYpg9JkhrmcgkWbM1h5v8ycboEz97QkdZBnkz6difZ5SZ6xwSi06r5aXchakWhb1wQAV5uZB4xsGBbLkLUlnESWvpxV9/WDG4fSs/oALRqFQ6ni8U783lv7UFyK0x0iLLSo+1P7K/eSWf/zjzX5znaB/412yavwsRdn2/DQ6vms7t61iV9u9PO85ufZ9mhZQwIv5Hf/+yHv4eOQC8tyTmVzBqRWJf0zTYnP+8pYumuAjYdLMMloIWPO10i/fnjQClTFqaw4L7LeGllOvM2ZXN332iu7hjKze9vpKDKzKu3dZaN2qQm09gRvycwFWglhJigKEpboJ0QYkVzBwhyxH+pOVxqZNoPaWzLrqBfm2CmXNWWT/44zK/pR4gK9CDC34OtWRV4aNV0CPelsMpMkd6CRqWQFB1In7ggLosJpGuU/wn71TqcLn5KKWTO2gNkl5tIiPCic8cU1hR9jValZXL3yYyIH4FAsC5vHasOr2J78Q4sld2xlVzHwgl96lbbGmwGpq6fypaiLVwfdRc/rUugha+O2GAv1mWW8sawLgzrEcn27EqW7MxnZWoRBquDyAAdN3ZtyfWdW5LQ0pft2ZWM/nQLtyZG4BSCpbsKuOfyGPrGBfLId7txU6v4eFwPekbXv6+vJJ1KQyP+xib+RUAycKcQotPRXwR/CiESmz7Uk8nEf2lwOF18uiGLt9fU9rV/6roOGK0OZq3ej9PlomuUP7tyq1AUiAny5lCpEacQ9GsTzK3dIhjSMRQfnbbe6/6UUsh76w6SVVZDx3Bfbuvj4ufi2eyv3M9Vra5iWq9pBOoC+enQT8xNm0u+MZ8A90AqrRVgjeSrod+SGOUPQIGxgIlrJpJTncNtrabw5eoQ4kK86dcmmM82ZnFT15ZEB3uxMrWQQ6U1uGtUtAr0xGB1UKyv7as/8/bOdI7wZ/SnW/D30NLCx53tOZVMHRJfezN47QE6hvvy8bgeRAb888bsklSff7sDV5wQYqSiKKMBhBAm5XQboEjSKewt1PPE4lT2FlZzbUIYd/Ruxeu/ZJKar6dDmA9HDFa2ZVUSFehBfoWZ3AoTd/RuzT2Xx9AqqP7EaHe6WLqzgPfXHySn3ETHcF9mj+5Ipm0xczK+JlgXzDuD3mFQ1CBWHl7Je7veo7CmkISgBMbFT+StP1aD91oe6HZ3XdJPK01j0tpJ2Fw2hkW+wGe/qOneyp/P70ri4W93ArBsdyEKEBngQbC3G2VGG9nlNfSODSIuxItNB8vZf8TAqz/vQ6tWcLhc7C7Q88JNCazdV8Lv+0u5tVsEr9za+ZSbsUvSmWps4rcpiuJBbZ8qFEWJ47jZPZJ0pix2J7N/O8DHfxwmwNON2aMSOVBi5O552/HWaWjbwpuMYgPB3u5oVAplBhsTBsTynyviCGxg9yqL3cl3O/L4+PfDFFSZ6RThyyfjeuDlf4iXtjxIgbGA4fHDeaTHI2RWZDJ65WjSy9PpENiBZ/s8S5CqM2O+ewuH/1qujLiZiUkjAfg1+1ee3vg0QR5BXO37PJ/8WsOgdiF8MLYHHm5qhveIRKdRUWW2sztPT16lmZ6tA3jimvZc0ymMUoOFUZ9sJdDLja825+Dn6Ua12Y7ioeWp69rzwfqDVJrsvHxrJ8b0anXazeUkqbEam/hnAP8DohRFWQBcDtzdXEFJl4ath8uZviSNw2U1DO8RyS3dWvLC8nT2HzHSsaUv+4ursdideLmpKa+xMiopikeuiqfFcatdj6c32/l6Sw7zNmVRZrTRvZU//72lE4mttbyZ/CbLdiwj2jeaedfMI9w7nBl/zmB1zmpCPUN5pd8rXB97Pan51Yxc9BYiaCmXhQ7grcEvIITg49SPeT/lfRJDErnc9zFeW5HPjV1bMmtEV+xOFwu35bJgay5pBXq83NSMSDpxAdmeAj13fb4Ns92JyeYkxMedUoOV7q386RzhxwvL04kJ8mLe3b3q7iNIUnNpVI0fQFGUIKA3tRuxbAHchBCFzRhbHVnjv7joTXZeWZXBoh15RAV68OJNCSTnVPHh74fw99Di5a4ht8JEkJcb5TW1CfzFmzvRKcKv3uuVG618vimLL//MwWB1MCA+hAcHxtErOoDVuat5ZesrVFurGd9pPHcl3MXXGV8zb888FBTu6XwPdyfcjYfGg82HyrnvpzdQBa2kd2h/3h/yDk7hZMamGfyc/TM3xN7AyOip3P7hdvrGBXFf/1hWpxezLKWQaouDdqE+jO3ditu6R+J93Lz8dZklTFywE6dLYHW48PfUUmWyM7xHJPuKq0krqGZkzyhm3NRRtlaWmtS/rfEjhCgHVh53wVxAzi+TGs3lEizZVcArqzLQm+3854pYhnYOY/qSPWQUVdMpwo99RXrMdidatYLN6eKVWzszKimq3rnrhVVm5m7M4putuVgcToZ2CuOhgW3oFOFHqamUR9Y/wtq8tXQM6sgnQz6huKaYkStGUmAsYGj0UKb2nEqYVxgAv2eW8OCql1EHrWVgxBBmDZ5JpaWSSWsnkVGewZTuU7in0z2sSivG6RJsOFDGhgNleGjVXNUxlHG9W5MUHXBCecblEnyw/iBv/bofjbp2D153jQoh4I7erVicnI9Oq+ajO7pzbT0rgSWpufyb4YUsQEqNlppfxYxle9mVW0X3Vv68cFMn/jxUxoiPtuDlriY+1Js9BXoCvdyoqLExuH0LXrm1M2F+J5d18ipMfLD+EIuT83AJuLlrSx4a1IY2LbwRQvDTwZ+YuX0mNqeNR3o8wtWtr+aN7W+wNm8tcX5xfH7N5ySFJdVdb01GIZNXP4M6cDs3RN/Kf/vPIKMig8lrJ1Njr+HdQe8yqNUgAHrFBDKkYyjB3m4MbNeCK9qG1HsDtqLGxtTvUlifWYoCaFQKdqegfZgPPh5avt6SS/+2wbw5vOsJjdok6Wz4N4m/cTUi6ZKWW27izV8zWba7kGBvd14f1oU+MUE8+v1utmVX0DXKj8OltfvIemjVWOxOXrutMyOTok66uVmstzBn7QG+25GHgsKInlE8ODCubrpjcU0xL2x+gY0FG+kaksgLfZ5nU+Emblt2G0IIpnSfwp0d70Sr/mvK5y8ZOUxd9yhqv0zu6nAvjyb9HysOr+D5P58n2COYr677iviA+LrzQ3zc+fTOkz45n2Dr4XImfbuLUkPt/Ad3jQqL3cVNXcPZdLAcQ5GBZ67vwD2Xx8hVuNI5ccrEryjKHOpP8Mc2XJekemUUVfP5xiyW7CpAo1KYOCiO/1wRy+r0EobO3oAQgj6xQWw+XE6QlxsGi4OuUf68OzKxrq/NMXqTnQ9+P8j8Tdm4hGBkUhQTB7Wp22zFaLEzc9MXLM//BKfLiaXkRlLyo5lQ/Sgl1kP0j+jP072fJsI74oTr/pq5n0c3TELlWczjPZ5mTMdhvLnjTb5M/5KeoT2ZNXAWAbqARr9mu9PFnLUHmbP2AAq1XT+dAoK83Wgf5suy3UW0D/Nhwf2Xyc3RpXPqn0b8p7qjKu+2SifIKa/ht4wSlu0uJCWvCp1WxbjerXloYBxatYonf0jj5z3FdI7ww2Rz1CX98hob/7kilseuaYdW/VfzMavDyVebc5iz9iDVFju3JkbwyJB4ogI9EUKwI7uCuVt28kflh6g896OytKGv373UxP/Jbv17lJt9eWvQWwxpPeSkTw+rMpN5cuP/odJaeLXf2/SN7MYDax5ga9FWRrcfzeNJj6NVnbwYrCFZZTVM/nYXaQW1m764qRWsTsGgdiHsKzawLrOEBwbE8ciQtrKrpnTOnTLxCyG+AFAURSeEsBz/PUVRgpszMOn8ZrDYSSvQs7egmj2FepJzKsmvNAPQIdyXZ67vwPAeUfh5alm3r4QnfkilymTjxi4t+W3fERRAp1XhEoJ5dycxqP1fWxcKIfhlbzGvrNpHboWJ/m2DmT60Ax1b+mKxO1m0PZf5f2Zz0Pwb7qEr0XrC6DZTuCyqDa9ue4XC6kI0pr509RrL1dFXnBT7kn2/MGPzUyh48v7gubTw0TF65WhKTaW8dPlL3NLmlka/D0IIFmzN5cUV6dgdrhN29kqI8GNdZimxwV4sfrAv3Vs1/tODJDWnxtb4tymKMkEIsQVAUZTbgVeB+FM/TLpYWB1Othyu4PfMUrZmlZNeVM2xmcBhvjq6tfLnvn4xDGzXoq5UY7DYmfZDKgu35xEf6k2P1gEsTy0k1MedIwYr3Vr5896Y7kT4/7U/7r7ial5Yls7mw+XEh3rzxT29GBAfgt5s5/11B5m3KYtySwlB0T+h88+gR4skHk96lK8yvmLSuneI9YvlP21m8eZyGyPHtj3hNQgh+Hj3XN5PmY2wRfLR1e9RIw4y7udn8NH6MP/a+XQO6dzo96RYb+HR71PYdLAcAK1Kwe4SXB4XxOHSGv442lHzsWvandAzSJLOtcYm/rHA54qirAdaAkHAKbddVBQlCvgSCKX2PsEnQoh3FUUJBBZRu41jNjBCCFF5JsFLzcvlEmw5XM6SXQX8srcYg8WBu0ZF91YB/N+VbUmMql18VN9m4esyS3hm6R6K9GZG9YoiNV/P//YUE+aro7jawl19WvP09R3r+sobLHbeWXOA+X9m4+2u4aWbExjdq1Vtr55fM5m3KRuD1U5Cu0wU7fcoiounuj+Fr7svD655EIPdwANdH2Bc+3u4fvZm4kO9uTohrC4eq9PKjE0zWJm1EruhK7MGv8SOymV8lvYZXUO6MmvgrHo3TK+PEIIfUwp4eukeTLa/NqTz89DSLsyHTYfKiQvxYvEdcpQvnZ9OZwHXLcBXgAG4Qghxyk3WFUUJB8KFEDsVRfGhtsnbLdSu+K0QQrymKMo0IEAI8eSpriUXcJ1depOd73bk8fXWHHLKTfi4axiSEMoNXcLpGxd8ytFricHCyysz+CmlkLYtvLk5MYKP/ziE0yVQKQouIXj1ts7cnFh7o1UIwcq0Il5cnk6p0cqopFY8cU071GqFuRuy+HxjFgarg0Ed3XEELiKlfAs9Q3syuftkPk/7nPX56+kc3JkX+r6AgsJHG3exeJOOBff14fI2tdXIElMJU9ZNIa0sDWvJEO7qPIZc9af8Wfgnw+KHMb3XdNzU9bd/qO/1Pbk4lXWZpQBoVOBwQZ/YQPaXGKky1a5PmHxlWznKl865f7WAS1GUuUAc0IXa8s4KRVHmCCHeb+gxQogioOjovw2KomQAEcDNwMCjp30BrAdOmfils6OwysxnG7JYuD0Xk81Jz9YBTB0SzzUJYf+YxGwOF1/8mc3stQew2l08NCAWvcXBm79m0sLHnRKDlXahPrw/tjttWngDtfPxn/lxD7/vLyWhpS+f3tmT9uE+fLU5h/fX1fatuaZjKD06HWZ+5svYq+w8mfQkOo2OiWsmYnfZeaznY3Rv0Z3Xtr3GtuJtAHRIuIrL29wIwO7S3Tyy7hGMNiOqkrtp5xvJhppnKDGV8Fyf5xgeP7xR782xXcCeWppGjfWvUX6QlzuRgZ5sPlxBx3Bfvhjfq8EVxpJ0vmhsqScNuE/UfjzIUhTlMmBWY59EUZRooBuwFQg9+ksBoJjaUlB9j5kATABo1UouEG5OtQuiDvL9jnwAbuzakvv7xzaqZ4zTJVi+u5BZq/eTW2FiQHwIYy9rxVu/7ifziKEu6Y9KimLGjQl4uKmxO118vrG2/bJaUZhxY0fG9W7Nqj3FPLRgJwVVZvq3DWbCwBC+z3mHOWnr6NaiG5O6TeLT1E/ZXLSZnqE9mdRtEj8c+IE3d7yJn9tfyfaO7v0AWHpgKS9teYlQz1AGBD/FV7lbKPR8A3+XH/OvnU+XkC6Nen9KDVam/ZDKb/tqt0xUH52m2Sc2kLQCPWkFep64th339489YVaSJJ2vGl3qOeMnUBRv4HfgZSHEEkVRqoQQ/sd9v1IIccpCqCz1NI8Sg4X31h7km625qBSFUb2imHBFbKP6vxssdn5MKeSzDYfrWh4/OiSeA6VGZv26HzeNCqdLoFEp/PfWTnWlndT8Kqb9kEZ6UTVDOoby4s0JFOstvLginV25VXQI9+Xp6zpg1O7g5S0vY7KbGBF3P95uHnyZ+QECwaRukzDajczbMw+ny8no9qNZc3APBbZkhoTfycyrpvD6ttdZmLmQ3uG9ebX/q9z1w0xynf8j1rszd7d5Dofdi4oaG2qVQtcof5Ia2OhkRWohT/6QesIoP8Jfh5+HlvQiA5fFBPLa7V2I+dvaA0k6H/zbUk9bamfxdATq1pf/0567iqJogR+ABUKIJUcPH1EUJVwIUXT0PkBJI1+D1ERMNgef/pHFx38cwuZwMSIpismD29bbHuF4JdUWNh8u55e9xazbV4rZ7qRLpB8fju1ObLAXTy5JIyWvqu4Gbo/WAbwzMpGoQE9MNgdvr97P3I1ZBHu789EdPeje2p+ZP2fyw858QnxqV/VemeDN85teYn3BatydrakuvJ35hv+h8TqEp7M9j/Udwed751JgLGBI6yE80PUBXtjwJgW2ZNq5jeCp/uO575f72Fmyk7sT7ubOjncybcM0cp1bcVVdzu6M63hk+6ETXpe3u4atT115wobn5UYrT/6QypqM2h/PYwtsk6ID2ZlbSbXFwau3dWZkz/r7CEnS+ayxpZ551LZmfhsYBIwHTvmZ9uhGLXOBDCHE8WWhZcBdwGtH//7pNGOWztCxOvVrP++jSG9haKcwnry2/UkrZYUQ5Fea2VuoJ6PIwL7iavYWVtfN0w/xcee27hEM7xlF+1BvPvj9MJMX7sJNrcJXp6HMaOXRIfE8ODAOjVrFxgNlTF+aSl6FmdG9WvH4NfEs3VXIY9/vxuZw8cCAOB4e3IbF6b8w5LtXsQkjttIhBPgE4Gy1ALWiIkYzirTyVF7a+iJt/Nsw9+q5tPJtxQOrJ3Ko6iDexhE8efuNjFwxkmprNTP7zyTGL4Y7Vt1BmbmMly5/iatb3cC+YgMalYLf0S6g18/egMnqxOZw4XV0ctLPaUU8vjgVo9VR9560DvREAFuzKhjaKYwXbkposD20JJ3vGpv4PYQQvymKogghcoDnFUVJBp47xWMuB8YBaYqipBw99hS1Cf87RVHuBXKAEWcYu3QaMosNPPvTHrZlVdApwpfZo7udUN4oM1pZnX6EdftKSM6ppLzGBoCiQHSQF12j/LmzT2suiwmiU4QfKgXW7ivh6m93kVthIsJfR0GVhfZhPrw5vCudIvyoMtl4eWUG3yfnExPsxaIJvdGoVYz5dCv7ig0MiA/h+ZsSKKyu4PpvHqZCtQmsLekf9CDVwWtI/f/27js8qmJ94Ph3tifZ9B6SEAgBQq/SEQSUakFQqoAKKqKiV73iDxUbYsNeURAbWCgiKCgIonQIEAKEkpDe+2Z7Ob8/NuZig+tVSTDzeZ487J6cnH3PhH0zO2fOO+Xf0juyN90iu/HOkWVo/FxMbT2bu3vdTHp5OhM3TKTCYsZRMIPJw8K5ZfONRPhG8MHIDzhVeYqpX08lSB/E8hHL6RDWAaB+eqXV4ea2jw5QYrLzxuTuBPvpqLI4eGD1ETamFXnPHVCrBZ1jg0jJriQiQM/bU7v/bJqoJF2M/tvEbxdCqIBTQog5QD5gPNcPKIryI79fwXPIfx+i9GdYHW5e2nKKd37IxGjQsPAabwE0tUrg8ShsSS/hg93Z/HiqFI8CzYJ8GNw2gi5xQXRoFkibSP9fVZ88UWTiiQ3H+OFUGWFGHb46NaUmB3cP9fbydRoVXx8p5KEvjlJpcXDboESm903gxc2nWLE3h+hAA29O6UbzUF/u3/AZx5zvoNKY6OY/jsHdWvFG6stgg1s738qugl28fvh1jJ52aEvHct/069iUtYn5O+ajF0GYMm+mX/cM3jr2Lr2je/Nk/ydZlraMD49/SI/IHjx36XOE+oT+LP5Sk52Z7+/ncF4VT13TkeEdotiaXsxdKw9RY/tPLz8x3I8am4sD2ZVM7hXPv0e0JeA31vSVpIvNf7vYek/gON7CbI8DgcAzP93J+3eTF3f/NztOlzFv9RFyKiyM7x7LvJHJhPjpcHsU1h70rkWbWWomOtDAtd1iGd05mjaR/r+75F9uhYWXt5xiVYq3jnygj5bCahu9W4bw5DUdSQw3Ulxj4+Ev0th0tJgOzQJ46pqOZJVbePTLo1RanMzom8DkXvG8su0oG/LfRRe8iyBNM/6vz/2syfiEnQU76RnZk+TQZFakr8BX68t1LWazZI2GBb0F+bpNLCndQ0f8GJWjZnWUjZM6BzPaTmJqx5t54IcH2Fu0lynJU7inxz2/qrdzosjEje/to9xs56UJXembGMpDa9NYe8i7ppAAdBoVbaL8Sc2rJjHcj0XXdvrdi7+S1Jj93sXdv31Wz19BJv4/xmTzrnC1qtFuOgAAIABJREFUYm8uLcL8eGpsR3q39PZ6t58s5ckNxzlRbCI5OoDZgxIZ0SEKzTmmIZ493VMI73j36VIzEf56/m9UMld2jgFg5b5cFn51HIfLw93DWjOyQxSPrDvK1hOldIoN5NEr27PjdBmv79qCiFiJSlfO+FaTaBeexOL9i3EpLiYmjWNH3nZOmLIZ7hPPv12+uE8fxkAp/w4PZYevD2NrLfQ2aXkqXItVDY+XltGy9RjudOVQaillQd8FjEkc86vz+OZoEXM/OYRRr2Hp9J6YbE5u/yiFCouzfp/WEUaKTXbMdhezByVy+2WtZFE16aL1PyV+IcS6cx1UUZQr/4LYzksm/v/ezowy7vsslcJqKzMHtOTuYa0xaNWU19p5ZN1R1qcWeodYrmjLiA5R55yRkldp4bWtdQkfb/G1E8UmFODm/i24fXAr/PQazpSZmbc6ld2ZFfW9/52ny1j0dToeBe4ZlkR8qB+Pb0ilRP0l+rDvidQF80BEX74o3cc2ezFd3Cra2O187qMh2O3hofIKLrPaqfZtzqe2EFbGm6kQduZ1uIX1p6ykWN8nwieSN1teTe7WR5kXFY3REMxLg1/6Vb0dj0fhpS2neGnLKTrHBvLyxK68vT2Tj/bk1O/jo1XRPNSP9CITneOCeObaTrSJ8v+bfkuSdGH8r9M5+wC5wAq8N1/JeWuNlMPl4dlN6bzz4xlahP68GuTOjDLmrjxElcXJ3UNbc+uglufsxZbV2nn1u9N8tCcbFOiREMzJ4lpS86sZ0zmG+69oQ1yIL063h9e2nublLafQaVQ8NbYjlyQE88CqVPZlVTIgKYw5g1vx8dYD7Ny6Dp+Y3eh1dq4x1dLTWsqj1lIsKhVX1woO+an5xFfLlYZY7vdrQ2CwFVt1MZ+Xn+LN2BKCPIJ3EifwcVUeB23riNJ24vPQJD7e8RRvRIbTKaQNL1z28q/q7ZTX2rnn08N8f7KUa7vFMumSeK5/azdFNf8pNts6wkhelZWscjPzRyUzo18L1HKKpvQPdr4evxoYBkzEW65hA7BCUZSjFyY8L9njP7czZWbuWJFCWn4NU3rH8+DI5PpFuz/cnc0j646SEOrLKxO7nfNuXJvTzbs/nuGNbRlYHC76J4WRU24hq9xCz4RgHhyZTNe6PyaHcqt4YFUq6UUmhreP4uExyXx5uJDF355ErxE829dDcN4W9Nmb2RdUwWvBgQR5FG62h7AeFWm+FfhbA4h1B3LCL5cANzxWWcPg2ioA7GodD4dE85VRoZsw8mDuKR6LiSVV5SawvCvrfM7wmFLAFj9frmwxiof7PYpe/fNicQeyK5jz8UHKax3MH51MQaWVt7Zn1q8s5KtTEx1oIKPUTL9WoSwa24m4kPPfvCZJF4v/qcevKIob2AhsFELo8f4B2CaEeFRRlFf/nlClP+KrI4Xc/3kqGrX41VTDJdszefKr4wxpG8FLE7ti1P/+r3vT0SIe+/IY+VVWLmkRgtPtYfvJMlqE+fHmlO5c0T4SIQS1dhfPbTrB8l1ZRPobeGtqd1qG+XHbhymI/AO8GnGY/s5d+Ows4IxawwPNojmmD2KAXcuAKjNLQsupVKuYUG3ikMHBcZ8aRput3FjqIkqrA58QCoXCbeFRZKjMDPIbyIySndwWFUGtRsVtpliuqt3AzIAoTmv9uL/nfUxJnvKrRc7f/iGTZzedoFmQDy9e35lnNp0gq9xSv09ShB/Z5RZKTHaeubYT43vE/u5FbUn6pznvdM66hD8Kb9JPAF4G1vy9YUnn4/YoPLMpnbe+z6RrfBCvTOz6s1ILB7IrWPj1cUZ1jOalCV1+9+JtWa2d+WvS2Hi0iFbhfgxMCuOH02UE+Wh59Mr2TOoVX19/ZtPRIhasO0pRjY2pvZtz99Ak1n+/i5Mr3+cV9Y/E64txVavJ9YSyJTCSt0O0aBSFR4or2GsIZmGkjnhFywiVkZUBAj9F4cXiUoZYrNgULXa/tvwgyphnVOGklltq4ogpXcvNYcFEqnx5s6AIq6eAsc3isajUzEx6hKntxvzqfO759DDbT5YyokMUrcKNzFlxEE9dN99PpybUqONUiZmhyZE8eU0Hudi51OScb83d94EOwFfAo4qipF2QqKRzqrW7uHPFQb5LL2Fyr3geGdO+vq79T97bmU2Ir46nx3X63aT/XXox93+eSrXVyeA2Eew9U05OhZVZA1oye3ArAn28UyELqqwsWHeUb44V0zbKn9cntCcq/1tyX7yNqc5UPGqwGiIx2Xywqh08Hamww9dAL4udqTXVPBcSTJZOxdgaE7laLe/7OBnk8PCIKoLQfrP41/5AymotdAn5lKUeLa3sLhaVlLA60MIj4SH0sTl4triAH1sP4GFnLsH6cMwnJ7PTGcadff9zPttPlnLPp4epsTm5Z1gS6w8X8nXdzVjgnZefXW6m1u7m5YldGdMpWvbypSbpfD3+KYAZuAu486w3iQAURVHkitEXWHGNjenL9nGy2MTjV3dgau/mv7lfUbWVuBDf3xzesbvcPPVVOu/tzKJ5qC8BBi1bT5RwWdsIHh7drr6Eg8vtYfmubBZ/cwK3ovD44CAmiG9wfTwVH2cVPhixaoPxcVZisJbwjZ8vT4ZFYReCaaVQaojhzigD4Ro/bvNL5CPScCpuHikr51qTGUEex1Vt6Vt1hC+iilnqMXCNqZZbK6p5KLoZe3UwtdrE3KjBvNW+OW9nrKJ7ZHcuDfwXj6bkMHxwVP35PLvxBO/8eIakCCNju8bw8pbTuOq6+QatihBfHRmlZkZ3iubRK9v/5uIxktRUnG+MX9aYbUSyysxMXbqHiloHS6f35NLW4b+7b7f4YJbuOIPN6f5ZLf3cCgu3f5xCal41HWICOFZYQ7i//mfj+OCtovngmiOk5dcwLaGS+wO+xXf3l3gUD+WeUCJVGoKoxeHQYFIJnggP4yujDy1t0MfUkh2xbk7bs7lcE45vbTFvuA7Tzm7n6ZJyElzeu2PdWiMFZWtYHBeKS+hYWFJJmE3PzJgICjUKj/t14IoRj/DAsSV8m7GKsUljSVLfwMNr0+nXKpSpfRI4WWzizhUHSS8ycU3XZpwoquHtH87Un298iA95lVacHkWWW5CkOv9tyQapgeWUW5i4ZDd2l4ePZ/amc1zQOff3TrdUKDc76te0/fFUGXNWpOBweYgJNJBWUMPES+KYNzK5vhRBrd3F89+cYPnOLIb5nmZZ/EbCi3biLPEh2xNGgigmRlWJUjc3ZrtvME+HaSlVq5lWaSNS48+roadRW+CeqmrW+Ts4bdAxvaqGOyur0OqMICxYUXg2QMdnAUbaOdwsLK+lSDi5OzYIvdaHpQOfJza8HTd/dxdHyo5wT/d/UZhzCfO3p3Np63Bem9SV5TuzWLQxHX+9homXxPHZ/rz6Xr5eLQjw1ZFTYWV891jmj2pHoK8styBJIBP/RaG81s7UpXuwOt2smNmb5Ojzj7DtyignwKAhKsCAoigs25HF4+uPEWbUYXZ7cHkUls3oyeA2/5n3vuV4MQ+tTSPClMa3oV+QWHsAd3UwZzQtaeHKJEblxoUWPG7W0Yf0sExW+msIcuoYrptAeut8lpd+R5zVj4mWfF4JDsJHUVhcXEs/swWtAJxWjmvVPBAeRqZOyw3VJu6qqORZ/9Z8EmqnVXArXhvyGianickbJlNpr2Rhv+dY9UMQW0+cYUrveGYNbMmsDw6wM6OcvomhVJjtrNibW38eMUEGCqps6NQq3r/xEgae45ORJDVFMvE3ch6Pwt2fHqaw2vZfJ/29Zyr4Kq2QWwYmoigKD607yoe7cwgz6iitdTA0OZJnxnUixM+7zmx5rZ1HvzzG/sOpLPT/jEG6H1DcoeSGDyKsZCfRwkSVIY4gWy5b3Z1ZGXMdKfovsKm1jDa5iOryGB+fXoStpJpbK6sp15h4JjSE7k6FZwoLCXN78CBwActCw3ndqCXY7ebVMgu92k1kVE4xRYZ99I4awEuXPcf+4v3c9/19GLVGFvR4jWfXmsmtKOPxq9rjq1Mz6uUfcXsURneKZmNaIS6P97z1GoGvTkNBlY0b+jTn/uFtzzmFVZKaKvmuaOTWHspn+8lSHr+6A92bn3OhMsBbefLOFQeJC/blhj7Nmb5sHz+eLsOoV1NtdbJgTDum9U2oH8vfkFrIgrWHGef8gud9V6FRVJh7zOFM2i46lG4jxacXfr6+JJVvY6GYwa6kcLLc7+DrEbxSVMJRvQ9vnZ5HM5eTeyvMvBsWwhG1hxlVNdxZY0XjdmNTNJzwC2NRkEKaXkc3k5broqfTY9JUrlo7lyJDCoMix/HisPmsSF/Bs/ufpU1wGy4Pm8c9Hxbhb9DyxpTufLY/l2+OFdOxWSB2l5v1qYX15x0VqKeo2k6zIB1vTe3BJS1kUTVJ+j0y8TdyK/fm0jrSyORLzr/usNnu4qbl+6iyOnh5QlcmLdlNToUFtRAE++l4bVI3OsV6rw1UWRzMX5vGwdRU3je+QbI6HZJGsr35HIK+uYtkJZPNifezt1TPgxWPszRwLKv8c3AoWxhgc3JnRRmLQoM54GNgRK2VyW5f7gsVVAkXi0uqGGauxW4Ip9xt5pMQLcsCtegVHT75l9Ov/0y6d/dj3IYZ5NvP0Mkwgxcuv5NFexex8sRKBjYbDCWTeGJHAX1ahjKmczT3f34Ys8PNkLYRbDtRgrtuXr5eI9Bp1JTU2LltUCJ3DUk678LwktTUycTfyJWYbMQE+Zx3eT+Hy8NtH6WQll/NvZe34d7PDmNzefAoMKRtOIuv61J/cXPn6TLu+fQwceYjbDYuxqD2YBv5Fg+dasOAr+fRX32aL9suYtfxLB5VvcAHgS15OfAwWuHg0dIKQt1ubo6OwCEEj1fZSDJXcWuUHrUieK+onHaGMBxY+VpjYXFcBJUaDxH0JfPUEO4f1p1LO7iYuH4yZZZqgky38MK1E5mzZQ47CnYwPHYCO/b1orimgtsGJZJVVsuDa9JoE+mP3e2uX/AcIDJAT3GNncRwX54Z14kOzQJ/r3kkSTqLTPyN3LXdYnn+25M8uOYIC37jRi3w1ti5/aMUtp8sZdIl8bzw7UlUKoHD5WHu0CTuvCwJlUrgcnt4YfNJXt+WwZVBWbzgswhVQAzHBr/D7K8quLn2Ca5U72K1/2RUx77gQe0u7ghtyS6jk042O4+XlrPG38gj4aG0djh5rqQUl1rLtKhI/BV4p9pFkMPGhz423mrWgiqdg1aBrQkqHUNqRigPj25H2xaF3PD13dgdepy5s1k4tQ8zt0wjpyaXPgG3smpzArHBau4aksSyHWeotbvo1yqUnafL62vsGLQqBFBpcXL/8DbMHNCy/u5iSZLOTyb+Rm724FbUOly89X0mGpXgsas6/Oz7dpeb2R+l8F16CVe0j+TjvTno1CqEgDendGN4h2jA+8nhzhUH2Z1Zwdz2Nu7KewoCY3mvzeu8tuIkrxjepLf6IOsZQK+aTeT51XJ5WCJmjYuhrrbcVbyNeZHhpBm0XF9j4r7KavQeD3eER2JSq7i2qobnAkP4ITQBJ2707mju63ILH20NIKPEzAvXd0Ix7mX2lsfQuKMxZU7lXyNDmLf7ZjwKhJrm8M2xKK7sHIPZ7mTxtydpHWlEr1Gz43R5/fmG112gvqRFCIvGdqRl+DkXgpMk6TfIxN/IqVWCeSOSqTQ7+GB3Ng+OTK4fw7Y53dzywQG+P1lKrxYhbDpajEYlCPLVsnR6z/qhjwPZFdz2YQo1NifLhqkYvP9e3LoA7tUv4MzWXWzye5UAdyXvuoYzQbOZF0Ij+CQggiBFz/1loTRz7eKG2HAcQvBsSRnDrU7wiwBTER0tZrbpA3gvKACDCMRc3pruIUO5b+hQZn90kGqrlXemdSfV/AlLdi7Bx9WOsszrmDKkiiWn5hGgiaLw1GTsqkim941hzcF8rE43PZoHsz+7sr4d/PRqHE4PNpeHhdd0ZELPuPMOf0mS9Ntk4r9IlNU6CPbVoa8b6qm1u5j1/n52ZpTTISaAPWcqEEBSpJGl03sSHei9aeuTfTnMX5tGTJAPqweVErv1buyGUKbYHyS2ajefG96kzBPC1bZHuDPkE2YEhnFcr+Hamlrurcjlg4BA5oYFkeh0srjGQwunCjxOEBoYPJ9Z3W6gp8nC/LVHOZoDtw9uRfuYQCa/sxejXsOHN3dj5Znn+Drra3SWvtQUjGTEgAOsyV2Dn7s92enj6d8yFrdH4b2dWbSJ9KfEZPtZ0g/21VJpcTK8fRSPXtVeFlWTpD9JJv6LwPcnS/kuvYT7rmiDEAKTzcmMZftIyamkVYQfaQU1AAxoHc7rk7th1GvweBQWbUzn7e2ZDGwVwttx32D4djFlwZ0ZU3IbPXxLWKx+lf1KMnd57iE48Rj3qU0YFR2LSi0MqK3kwYhQtvr5Msrm5uGiYnyFGloPh243QKuhoFKzMa2Qf686hcej5bVJHUnNr2b2RyneVazGJ7LwwH2klKTgLhsJjj507rGG7cUH8FQNoLZiNGM7x7AxrRAFQfuYAI7WnQtAkI+WaqsTnUbFm1O6M7yDLLcgSX8FmfgbueIaG//+PJWWYX7c1L8F1RYnNyzbS1peFXHBPmSUmAG4qksMz43vjFatwu5yc8+nh9mQWsj0S6J52Pkiql1fkBJ2JRPyrqVHYiTTPNsQhQoabTWtQxdwQKtigMXKgrIKMglmUmwseRp4oLyCSb4tEJffDh2vA6P3LliTzcljX6bx2YE8OsUG8uCIZJ7ZlE5KThWTesVzwwAf5n5/IwW1RVjzJtIuLInaoNc5XlmKtXAcXYOHYQ/3sPpgPm2j/MmuMNcnfRXeoZ0qq5PJveL594i29SUlJEn682Tib8RsTjez3t9Pjc3J0uk9MdtdTFu2lxOFJiIDDORWWlGAaX2a88iY9qhU3k8Ds94/wK7McuYNb8Os0icQx7/g46BZPJh3KVN7J1Baa+fOtL6Mj85ipX8hLqEwu9aP4SUOTvtquTfcgEYIlkRcRs9RsyG8zc/i2naihAdXH6GoxsbtgxNpGebHzPe9K6S9NKEL4eHZTNs0C6tDYM65meGdDeyseRpnrR6Kb+Oy2G5sPVGCj1ZNy3DvOrc/CfHVUmFxEhnow7KxHemRIG/EkqS/mkz8jZTT7eGulQc5nFfNW1O7ExVoYNKS3WSW1hJq1FFcY8ejwOxBifVDQOW1dqYv28fxwhpeuL4z14TkwrY1LNdP4onSwcy7IpHdh47grkglMXEnS3XFtLM7WVRaSnOHm9eCWvJOiJ5W/vG8cvnbxBhjfhZTSY2NJzYcZ93hAlpFeK8lfLo/l9e2ZtC9eTAvXNeZbUVreWjzM7jtEfhXz6RfjxNsL/sUl6U5SdyOWe/DN8eKSY7251SxicxSb6VOnVrU/eFycc+w1tx6aeJvTl2VJOnPk4m/EVIUhQdXH2HT0WIevbI9PZoHM2nJbjJKawnz01FicuBWFOYOTWLu0NaAd+WpyUv2kFVu5u0bunNZ20iyj5yhOTDN/jETfb5E/b2ZLgYt8xNCKVWrmVwj6O/pwbPmBIrbujipbGBAswE8d+lz+Gr/s5qXw+Vh+c4sXtpyCofLw11DWhEZYGDuJ4ew2N3cd0UbbuwXx7+/f4ytBetwmpK5LGwWGf7v8WPZYTD1pqvPdHaeqiLM6CQqQM/xwl/38nvFB7NwbEcS5RRNSfpbycTfyCiKwuPrj/PZgTzuHJLEmM4xTH5nD5l1Sb+01pv07x7amruGJgHeOfqTluwhr9LCsuk96dsqjAPZFdy42kpP9ZPMTSxk38lTbA2t4GBAMTp7AJcFzKHEN5kpKXn06r6Hk5YNXJl4JQv6LkCr8o6nezwKX6cV8eymdLLKLQxqE86EnnG8vT2TlJwqejT3JmqtzsyITydT7j6B1jSE61uP4PO8J3Cpqoh2TqGmuju78qtoG+X/s2EdP50Km8uDW0GueytJF5BM/I2Ioigs/vYkS3ecYUa/BG7qn8CkJXvIKDER4a+nsMaG2wN3XtaqPumX19qZ8s4eCqqsvDfjEnq3DOXHU2XMfH8/kQF6Bg8cw7XfbMInbg9uTTGOij7c0uUucsvdfHIgl8t7Z7Crei3jW49nfu/5qIQKRVHYcryEF7ecJC2/htaRRp4f35kdGWXc+mEKYUY9z4zrxNguzXhl5xaWnnoURWWhm+/tOHR2VuY/gEoxEm+7l2NZwTQLUmHUqX+W9AMNGqptLq7qEsNDo9sRJlfEkqQLRib+RuTN7zN55bvTXN8jjnuGtWbGsn2kF9YQH+JLdoUFjwKzBrbk7mHe4Z0am5Op7+4lp8LC0uk96d0ylO9PljJz+X5ahvsxoVcMj/34Arq4rQglEGvOTTw14lqqrU4+2X+cWwfF8Fnpw/SK6sX83vNxuhU2pObx9vZM0otMxIf4smBMO/KqrMxbcwSA2wYlMntQIscKahj67iLK9J+iVQUzPHwBG7K/QDHuxUgyptzryHD50SLMwJkyS/05BvlqqbI4CfTV8cqkbrJWviQ1AKEoyvn3amA9evRQ9u/f39Bh/K0+2J3NQ2vTuLJzDE+P7ciNy/ez50w5ieFGMkpr8Sgw8ZJ4Fl7TASEEVoebacv2kpJdyTvTejCoTYQ36b+/n8RwI5d38bAkfSFqQwGB7t4UZFzBC+P7MDApnN5PbWFAUhgPXxPJ6DWjUVDwU4VhsfpjLryClv4dmNgrnoIqKx/vycHqdDO2ayx3D0uixurimW9S2V29BG1QCrH6rigVw8hRv49aX4yv9QqKswfSMtyf3HILzroVsbQq0KhVOFwebrk0kTsuS8JHJ6toStLfSQhxQFGUHr/cLnv8jcCqA3k8tDaNocmRPDW2I3NWHGRXZjkdmwVyJL8agNGdonnyam/Sd3sU7lhxkH1ZFbw0oSuD2kSw43RZXdL3ISlpL+9mfoBO70sid5ByqhnPj+/MVV2asSG1ELvLw+mSWt7YrCew6g6KXYdxak1oA1No224H7enPoq/Tcbk9jO4Uw51DWmGyuXj0y2NsPp2GMe4jtEHFJGqu4WimAUP0S+iEFlPuDLRKe8KMgsxSc/35hfhpqTA76RQXyMJrOtImyr+hmlqSJGTib3BfHynk/lWp9E0M5dWJXXlwzRG2pJfQNS6Ig7lVCLx35C6+rgsqlfCuqPVFGpuPe2f8XNk5hgPZFdy0fB9xEbWI6KVsLU0nQOnGiKjZvPt9GfNHJTO2WywAQ5IjmNwrnh9OlbExrYiOsZ2ZljQEp7qAN07O4XS+DxnlBYzrHsuN/RJILzIxb/UR9mVV4h96hKBWq1ALHbb8GZwwpGNothNszanNm0RCUAwZZyV8o06F1eXB5VZ4amxHru8h6+tIUmMgE38D2n6ylDtXHqRLXBBvT+3Ooo3prD6YT8+EYPZlVaJWCTrEBPDG5G71c9rf/D6Tj/fkcNugRKb1TeB4YQ0z3ttLYOQuKgM24DCpCbffyJPDpjD53b2M7dqMm/q3qH9Ng1bNk9d0xOn2UGqyszolj2V7UqkKfAGV2oeb2t3KkDaJbD5ewsQleyg12YkNUdO75xaO1n6Lx9aC6oIrCGn+FXZVDo7y/sR4xlGoOOqTvgowGjTU2Fxc3SWG/xvVjnB/efFWkhoLmfgbyP6sCma+v59WEf4snd6TZTuyeG9nFr1ahLDnTAV6jYroQAPLZlyCX926sRtSC3l6YzpjOsdw/xVtKKiyMmX5V4joFVj0mags7QisncBnt47k7k8OEWbU81jd8NBPKs0Ovj1WzLrDBezIKEMRFsKS3kWvtnBV5GP8cMLO69/tQq0SDG4TwYB2DpZnPMlRUy728kFE+yRAwnvYPAJd6Y0YnR05U22rP36YUUdZrYMwfz1vTulO31ZhF7ppJUk6D5n4G0BafjU3Ld9PsyAfPrq5F+sOF/D8tyfp2TyYPWcq8NGq8dWpeW/GJfULoh/OreKeTw/Ro3kwz43vRJXVwXUfP48jcjU+Oh2Blmnk5bdn2Zz+hPjq2JVRTqsIIwdzKrE5PaTmVbEro5yUnEo8CsSF+DC5Txg/mhZS4SzGnDWNZcc8dGwG80clM7RdOC/vW85zR9/F4/Yh1DILTdgxSnkPxdqcaMfNnCrTAnbAOyff5QGTzcW9l7dm5sCW6DXy4q0kNUZyVs8Flllay9g3duKn07ByVm8O51Vxx4qDdI0LIjWvCoNWg9Pt4ZNb+tAlzrs+bkGVlStf/RGDVs3a2/thV8oZv2ouJnGcdkE96Bt4Gy9sLGXxdZ3rx/JX7s3hyQ3HMdm9JRHUKm/1y+7xwWjVgv25OaSL51Hpyggxz2Rc8jDGdI7BV6dmyc6DfJr1PIrPCXycHWhvHMN+0zso2jKCnVdQnDUQh9s79KQCAuvKJg9pG8GCK9sTF+L7m+cuSdKF9XuzemTiv4ByKyxc/9YubC4Pa2b3Jb/KyrSle2kT6U9WuRkQmO0uXp3UjVGdvCtnWR1urn97F5mlZtbM7kNazRYe37UIh9vF8OhZLBx6M70WbqFDs0Dev/GSnw3r1NicHMyupNrqJKvcwtYTJRzMqUJoaghssRS0Fczv8SzDEwey5Xgxaw7msaNwC/qotahVbroZp3I4vxRHwAY0GBFlk6gsb15//J9Ww4oL8WHBmPYMSY680E0qSdI5yOmcDazEZGPKu3uotbtYMas3tXYXM5fvJzbYhyqrE48CFoeL+65oU5/0FUXhvs8PcyS/msUTEnk57f/YlrcNt6UFg0Pu4NkrhnK0oIZKi5MRHaIRwjvrJ6/SyqHcKvacKef7k6XkVlgBaBcdwG1DQtlS9RqV9mpGRy7gy91GHvz4W5xKDYFxX2JodphIXWssRSPZbd6AJugUBkdHyrOuRnH7AeCnU2N3eTDZvQXVZg1sWb8qmCRJjZ/s8V8AVRYH49/cRX6VlQ9u6kVUoIFrX98JKITVLLFAAAAVmUlEQVQZ9ZwoNuF0K1zTtRmLr+tc32t/b8cZFnx5jHEDK9hbswSz04y2ZhQa80C+mTsIX52GWruLvk9twWR3oVOrUAmB1ekGwKjX0KtFCANahxEdaOBQwRk+zf8/HEot5pwZeKzNSQjzpVXCaY7al2N1m9HUDKeqOgDfmC9AOLAWjcJZ1QsQqAX46NTU2t2M6hTN/41MJibIp+EaVpKkc7rgPX4hxFJgNFCiKEqHum0hwCdAApAFXKcoSuXvHeOfwGx3cfPy/WSXW3jvxp60ijAy9vUdmO1OerYI5bv0EvQaFZ3jAll0bcf6pH8gu5LHv06hRfK3bCrdQbvQdlwWchdPratkyQ0d8dV5f3VGvYZVt/Xly8MFWBzehN881Bd/Hy0l1TZ+zCjnqa/ScYgyfJu/hVrtpL/x/xg2pgfNI228mvoMe0p2gT0ec8EUImNT8PFfj8vWDFv+9XgcEcB/lj9sEWbk4THt6Cnr5EvSRevvHOp5D3gVeP+sbQ8AWxRFWSSEeKDu+b//xhgalMPlYeb7+zmYW8UrE7vSo3kIM97z1ta5tlssK/flEuijRa9R8fbU7vWzYCwOF3esWYVfyw+oFNXc0vEWbul8C/d8kkaEv4WhyRE/e51WEUau7tqMfVkV7MwoZ/XBfCrMDgBahvtxdQ8je2wv4PAoLB2+nBjfeJ748Q0ePfIRbo/AUTqajuHJ5LRYhkkpx1E2GEfpEEBDqJ+OcrMDjVrF8+M7c03XZvImLEm6yP1tiV9RlO1CiIRfbL4KGFT3eDmwjX9o4nd7FO7+9BA7M8pZfF1nRnSI4t7PUtlxupyb+rXg3R1niPDXU2F2sGxGn/oFxJ0eJzeseYyaoC+I8Ilm8eCX6BLRBfDOka+yOLnlgwN0ig2k3Owgs9RMWn415XWJPsyoZ2BSGANbh9M3MQyjj4upX0/F4q5mZqtFLPxmOwdrPwJtBSpzF4ZHTyfTZzMnbM+hOIKxFtyKx9qcYF8tFoebaquDWy5tyZzBrfCXyx9K0j/Chb64G6koSmHd4yLgd6eBCCFmAbMA4uPjL0Bofx1FUXhkXRobUgt5cGRbxnaL5c3vM1iVkscNfZrz6f5cIgL0lNTYefzqDnSLDwYgpyaHu767l9O24zTXDeLTsYvw0/rVH/eWgYlYHW5WH8znm2PF+PmYiAitZUCbjvRKiKZnQjCJ4UZq7S5OldTyXXoJH2c8R7bzNM7KPrxgega1Tx5+ujjGNb+P/AqFr0ufQqUvxlnVC3vxSAINfqh8BZUWJ0OTI3lwZFtayoVRJOkf5W+9uFvX419/1hh/laIoQWd9v1JRlODzHediu7j74uaTvLj5FLdc2pJ5I5LZfKyYm9/fz4gOUWSWmcmrsGBzehjZKZqXJ3h7819mfsmTu5/E6RJYC8fy45y7CP2NGvVuj5vvcr7jw+MfkVJyAIBLIgbTz/9uDuZWcSi3sn4WD3jwbzsfhAeAUH0k4xNv5OjJFmwpWoE29HsUlxFb4Th83e3wN2goqLKRHB3A/FHJ9JN33UrSRa2xTOcsFkJEK4pSKISIBkou8Ov/7T4/kMeLm08xrnssDwxvy+kSE3M/OUSHZgEYDRpOFJkI99cT7KfiyWs6YHFZeGL3E6zPXE/3yO6kHRrFZfGJv0r6HsXDN1nf8Nqh18iqySJAE0GQ0o0qkcKP6Xa2FB8jKsBA1/ggJvSMp3WkP22j/Emr0lJqLaeoXM8Ph8N5Pj0dQ/RD6MKKcVZ1Q1RcTaxfENkVFox6DYuv81bxVMtxfEn6x7rQiX8dMA1YVPfvFxf49f9W358s5d+rUunXKpSnxnbE4nAz64MDGLQqpvVJ4L7PU+kWH0RKThUrZvamwJLBv7b9i7zaPGZ3ns2sTrPosXcLO06XkVFaS2SAgYpaB19n/MDK029Q5sxAsUdhK51ErTWegJZv4asJY/6ldzMwsTnRgf+ZWun2KKTkVLL9UAxfHAKL04Yu7DN8E7ajuPzxFN5IlLoLWXYLtRoXj4xpx8RL4uV8fElqAv7O6Zwr8F7IDRNC5AGP4E34nwohbgKygev+rte/0E6XmLjj4xSSIoy8NbUHGpXg3jVHOFNm5p0bejBv9RESw/04kl/NVZ2jyXFuZs6GZwgyBPHu5e/SI8r7aeyJqzsy95ODDHn+e4S2DH3kV2j9j+FxBhJkm8qgZlfQsbueD7Pvp9RqZ+kVS2kf1hZFUcitsLA/u4IfTpaxJb2EaqsTALVvBn5xa1DpylCbexHpGseJKjc6XycPjmzLlN7N66eHSpL0z/d3zuqZ+DvfGvJ3vWZDKa+1M23pPnQaNUtu6IFRr2HNwTy+OFTAv4a15mBOFSUmO71ahJBTWYkn/COe2PMN/Zr1Y2H/hYQY/jMnflSnaDrEGXjsh5fZX7kWjUrL6PibmdP9RsKN/pidZm7ceDMF5nyui13AZzvhiaLdpBVUY7J56/IIAYoCQmXBEPkVmqD9qN1hRFrnciInCpdeMHdoEjf1byFn6khSEyS7eX+S0+3hjhUHKa21s+rWvsSF+FJea+exL4/RLT6IKb2b0//p7xjZMYofs48RkvQB2wuKuLPrndzU8SZUQlV/LEVR2JKzhaf3PU2RuYgxLcdwd/e70RLI7sxytp8+wcayx3FosrDmTeHNoyrUqmy0KoHN5b2AqxZg0KqwGw5giFyPUFsJcgwjN3MADq0Pcwa34Mb+LeqrfkqS1PTIxP8nPbH+GDszynlufGc6xgYC8Nw3JzHZXCy6thMbjhRidrhJiMtgu/0F9Gpf3h7yNr2ie/3sODk1OSzcu5Ad+TtoHdyaW5IfoqAompuXnSA1txpFOPGNW4bK9wy2/Em4a9sB3ro5oUY9VoeLoho7irYEn/j1qLQn0bkSqDxzJTpNAvdd3oLJveIJ8pUJX5KaOpn4/4SNaUUs35XNjf1aMK67txxyQZWVz/bnMqV3c1pH+vP8pnTC4rbwwZlv8TjiuL3jU/SK7lx/DLvbztK0pSxJfQcVGtpop5BxuDP37jQBJlQCFOHCJ/ZD1L5niHbeSHBYLyp9HGSVm6mxuaixuegYqychaRfHLOuxe/TYCq8hRH0p/xqZxNhuzeRFW0mS6snE/z/Kq7Rw32eH6RwbyL9HtKnfvjolD7eicFP/FpidZg45X8BuPMJVidewdUd/Xt9cxumCNFpFGDlSsY9t5W9hVYpxVnfCXjKaSk8gbo+35k6AQUNUkJ5Sw7u4fE5gKxzLyaokDNoq2kUHMLVPAp2aBXDUtI1VZ97CYanCWdWd9oZJzBzdkWHtouS0TEmSfkUm/v+Boig8tDYNt6Lw6qRuP1tpauuJUjrFBqHRVTP169uxaDLQVI7lrs7zGN/cxpMbjrPy4BFE6JdoA46gOMJwl8zEZkoEIDxAh1qlorDaSo3NiVOsQeNziO7GGxg7YjLtov1pHuLLyZJa3t33HQ/vfwePLgfscQwJ/xezhw+hdaR/QzWNJEkXAZn4/wdfpxWx9UQp80cl/2q1qTNlZnonW5j01XzsLjv3dnyGhas99Fu0leggNS7jdnTNv0IB7CXDUNdcRmywPybhoqjGRlGNnaQII7demojT7wdWZu5garup3NJ+LvuyKli+M5tNJw9R4/slWv9jaLVBXB33L+7rNwmjXo7fS5J0fjLx/0Eej8Jzm07QJtKfGf1a/Or7ar8T7DS/R6RfKEtGLKFVcCt6RlXz6u517DMtx0YxwXQjxn0duW4D+U4bp0pq6RIXxIx+CVzePor4EF82Zxzi/p2vE6HuyqYfevL6qm9Q6YvwCd+KKjIVo8qHqcmzmdVlOj4aWRNfkqT/nkz8f9COjDIyy8y8NKHLr8bPt+Vuwx76Dm5bBL1DF1BVHcLGkkMsPf4yx037MBAFRTPJqUwkT0CvFn7cPKAlw9pFYnW4+f5kKQ+tTeNAdiVEvYXKoKckZzSt4k7QrtlOcm0H8dX4Min5Jqa3n06gPrCBWkGSpIuZTPx/0KajRfho1VzRPupn27fnbefubXfTNrgtuvJb+GBPFitPv4s2aB94dNjLRhHEEIa0iqRPYigDWoWRV2VlfWoh17+1m/wqb2G11pFGru8Zx4bqQmweM4YWz3LabSNCRDCnyxwmtJ0gE74kSX+KTPx/UGpeNd2bB/9seuSpylPc+/29tA5uzZP9nuDzU59zXPM5LsVF77DRTGx9M52jmxHspyOjtJY1Kfk8s/EE+VVWNCrBwNbh3D64FQNbhxEb7L1mcFneM2zO3oyv1pf+zfrTO7o3GpX8dUmS9OfJTPIH1VidJIT6/WzbSykvYXVZMagNjF8/HkVRGJM4hlmdZhHnH0dxjY3PDuSyOiWf9CLv3PwBSeHMHZrEsHaRv3lT1cDYgQyMHXihTkuSpCZEJv4/KCLAwKmSWhRFqV8f96eLq1k1WUxoM4Ep7abgcQSz6VARXx3ZQUpOFQBd44N4eHQ7RnaMJirQ0GDnIElS0/a3LsTyV2lMC7F8sDubh9amMfGSeKb0jicx3EiFpYYzFZUUV2o5kF3FzoxyzpSZAUiODmBUxyhGdIwmUa5kJUnSBdRYFmK56E3pFc/JIhMf781hxd6cX33fX6+hZ4sQpvRuzpC2ESSE+f3GUSRJkhqOTPx/kBCCx6/uwO2DW7Ezo4yCKis+Og0R/nqSIo0kRfjLMgmSJDVqMvH/j6ICDYztFtvQYUiSJP1hqvPvIkmSJP2TyMQvSZLUxMjEL0mS1MTIxC9JktTEyMQvSZLUxMjEL0mS1MTIxC9JktTEyMQvSZLUxMjEL0mS1MTIxC9JktTEyMQvSZLUxMjEL0mS1MTIxC9JktTEyMQvSZLUxMjEL0mS1MTIxC9JktTEyMQvSZLUxMjEL0mS1MTIxC9JktTEyMQvSZLUxMjEL0mS1MQ0SOIXQgwXQpwQQpwWQjzQEDFIkiQ1VRc88Qsh1MBrwAigHTBRCNHuQschSZLUVDVEj/8S4LSiKJmKojiAlcBVDRCHJElSk9QQib8ZkHvW87y6bT8jhJglhNgvhNhfWlp6wYKTJEn6p9M0dAC/R1GUt4G3AYQQpUKI7F/sEgaUXfDAzq+xxgWNNzYZ1x8j4/pjmnJczX9rY0Mk/nwg7qznsXXbfpeiKOG/3CaE2K8oSo+/OLY/rbHGBY03NhnXHyPj+mNkXL/WEEM9+4AkIUQLIYQOmACsa4A4JEmSmqQL3uNXFMUlhJgDbALUwFJFUY5e6DgkSZKaqgYZ41cU5Svgqz95mLf/ilj+Bo01Lmi8scm4/hgZ1x8j4/oFoShKQ722JEmS1ABkyQZJkqQmRiZ+SZKkJuaiTPyNtdaPECJLCHFECHFICLG/AeNYKoQoEUKknbUtRAjxrRDiVN2/wY0krgVCiPy6NjskhBjZAHHFCSG2CiGOCSGOCiHuqtveoG12jrgatM2EEAYhxF4hxOG6uB6t295CCLGn7n35Sd2svcYQ13tCiDNntVeXCxnXWfGphRAHhRDr6543XHspinJRfeGdCZQBtAR0wGGgXUPHVRdbFhDWCOIYCHQD0s7a9gzwQN3jB4CnG0lcC4B7G7i9ooFudY/9gZN460g1aJudI64GbTNAAMa6x1pgD9Ab+BSYULf9TeC2RhLXe8C4hvw/VhfTPcDHwPq65w3WXhdjj1/W+jkPRVG2AxW/2HwVsLzu8XLg6gsaFL8bV4NTFKVQUZSUuscm4DjeMiIN2mbniKtBKV61dU+1dV8KcBnwed32hmiv34urwQkhYoFRwDt1zwUN2F4XY+L/r2r9NBAF+EYIcUAIMauhg/mFSEVRCuseFwGRDRnML8wRQqTWDQVd8CGoswkhEoCueHuLjabNfhEXNHCb1Q1bHAJKgG/xfgqvUhTFVbdLg7wvfxmXoig/tdeTde31ghBCf6HjAl4E7gc8dc9DacD2uhgTf2PWX1GUbnhLTt8uhBjY0AH9FsX72bJR9ISAN4BEoAtQCDzfUIEIIYzAKmCuoig1Z3+vIdvsN+Jq8DZTFMWtKEoXvCVXLgHaXugYfssv4xJCdADm4Y2vJxAC/PtCxiSEGA2UKIpy4EK+7rlcjIn/D9f6uVAURcmv+7cEWIP3DdFYFAshogHq/i1p4HgAUBSluO7N6gGW0EBtJoTQ4k2uHymKsrpuc4O32W/F1VjarC6WKmAr0AcIEkL8dFNog74vz4preN2QmaIoih1YxoVvr37AlUKILLxD05cBL9GA7XUxJv5GWetHCOEnhPD/6TFwOZB27p+6oNYB0+oeTwO+aMBY6v2UWOtcQwO0Wd1467vAcUVRFp/1rQZts9+Lq6HbTAgRLoQIqnvsAwzDe/1hKzCubreGaK/fiiv9rD/eAu84+gVtL0VR5imKEqsoSgLefPWdoiiTacj2augr3f/LFzAS7wyHDOD/Gjqeupha4p1hdBg42pBxASvwDgE48Y4d3oR3THELcArYDIQ0krg+AI4AqXgTbXQDxNUf7zBOKnCo7mtkQ7fZOeJq0DYDOgEH614/DXi4bntLYC9wGvgM0DeSuL6ra6804EPqZv40xBcwiP/M6mmw9pIlGyRJkpqYi3GoR5IkSfoTZOKXJElqYmTilyRJamJk4pckSWpiZOKXJElqYmTil/6xhBCKEOLDs55rhBClZ1VHvFKcp7qrECLh7Gqif1Occ4UQvmc9rz3X/pL0Z8nEL/2TmYEOdTfzgPeGnvq7IxVFWacoyqIGiezn5gK+591Lkv4iMvFL/3Rf4a2KCDAR701kAAghpgshXq17/J4Q4mUhxE4hRKYQYtxvHIuzfjZRCLGxriDfD0KItuc6jhBCJYR4XQiRLry1/b8SQowTQtwJxABbhRBbzzr+k3V15XcLIRpTQT3pH0AmfumfbiUwQQhhwHtn555z7BuN927Z0cD5Pgm8DdyhKEp34F7g9fMcZyyQgLee/lS8tW1QFOVloAAYrCjK4Lp9/YDdiqJ0BrYDM897lpL0B2jOv4skXbwURUmtK2k8EW/v/1zWKt7CZ8fO1cuuq5bZF/jMW/4FgLNL/f7WcfoDn9VtLzq7d/8bHMD6uscH8A5RSdJfRiZ+qSlYBzyHt05K6Dn2s5/1WPzuXt5PylWKt/zvnznO73Eq/6ml4ka+T6W/mBzqkZqCpcCjiqIc+SsOpnhr4p8RQowHb9VHIUTn8/zYDuDaurH+SLx/hH5iwru0oiRdEDLxS/94iqLk1Y2l/6/aCCHyzvoaD0wGbhJC/FSN9XzLf67CW5H0GN4KkSlAdd333gY2nmf4R5L+MrI6pyRdIEIIo6IotUKIULzlePspilLU0HFJTY8cO5SkC2d93UIhOuBxmfSlhiJ7/JIkSU2MHOOXJElqYmTilyRJamJk4pckSWpiZOKXJElqYmTilyRJamL+H/BKkpK5fOjOAAAAAElFTkSuQmCC" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**免責聲明**： \n此文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原文文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。\n" + ] + } + ] +} \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/11-visualization-proportions/README.md b/translations/zh-HK/3-Data-Visualization/11-visualization-proportions/README.md new file mode 100644 index 00000000..a77b421c --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/11-visualization-proportions/README.md @@ -0,0 +1,196 @@ +# 視覺化比例 + +|![由 [(@sketchthedocs)](https://sketchthedocs.dev) 繪製的速記筆記](../../sketchnotes/11-Visualizing-Proportions.png)| +|:---:| +|視覺化比例 - _速記筆記由 [@nitya](https://twitter.com/nitya) 繪製_ | + +在這節課中，你將使用一個以自然為主題的數據集來視覺化比例，例如在一個關於蘑菇的數據集中有多少不同種類的真菌。讓我們使用一個來自 Audubon 的數據集來探索這些迷人的真菌，該數據集列出了 Agaricus 和 Lepiota 家族中 23 種有鰓蘑菇的詳細信息。你將嘗試一些有趣的視覺化方式，例如： + +- 圓餅圖 🥧 +- 甜甜圈圖 🍩 +- 華夫餅圖 🧇 + +> 💡 微軟研究院的一個非常有趣的項目 [Charticulator](https://charticulator.com) 提供了一個免費的拖放界面來進行數據視覺化。在他們的一個教程中也使用了這個蘑菇數據集！因此，你可以同時探索數據並學習這個工具庫：[Charticulator 教程](https://charticulator.com/tutorials/tutorial4.html)。 + +## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/20) + +## 認識你的蘑菇 🍄 + +蘑菇非常有趣。讓我們導入一個數據集來研究它們： + +```python +import pandas as pd +import matplotlib.pyplot as plt +mushrooms = pd.read_csv('../../data/mushrooms.csv') +mushrooms.head() +``` +一個表格被打印出來，包含一些很棒的分析數據： + +| 類別 | 菌蓋形狀 | 菌蓋表面 | 菌蓋顏色 | 是否有瘀傷 | 氣味 | 鰓附著方式 | 鰓間距 | 鰓大小 | 鰓顏色 | 菌柄形狀 | 菌柄根部 | 菌柄環上表面 | 菌柄環下表面 | 菌柄環上顏色 | 菌柄環下顏色 | 菌幕類型 | 菌幕顏色 | 環數 | 環類型 | 孢子印顏色 | 群體數量 | 棲息地 | +| --------- | --------- | ----------- | --------- | ------- | ------- | --------------- | ------------ | --------- | ---------- | ----------- | ---------- | ------------------------ | ------------------------ | ---------------------- | ---------------------- | --------- | ---------- | ----------- | --------- | ----------------- | ---------- | ------- | +| 有毒 | 凸形 | 光滑 | 棕色 | 有瘀傷 | 刺鼻 | 自由 | 緊密 | 狹窄 | 黑色 | 擴大 | 相等 | 光滑 | 光滑 | 白色 | 白色 | 部分 | 白色 | 一個 | 垂飾 | 黑色 | 分散 | 城市 | +| 可食用 | 凸形 | 光滑 | 黃色 | 有瘀傷 | 杏仁 | 自由 | 緊密 | 寬廣 | 黑色 | 擴大 | 棍狀 | 光滑 | 光滑 | 白色 | 白色 | 部分 | 白色 | 一個 | 垂飾 | 棕色 | 多數 | 草地 | +| 可食用 | 鐘形 | 光滑 | 白色 | 有瘀傷 | 茴香 | 自由 | 緊密 | 寬廣 | 棕色 | 擴大 | 棍狀 | 光滑 | 光滑 | 白色 | 白色 | 部分 | 白色 | 一個 | 垂飾 | 棕色 | 多數 | 草原 | +| 有毒 | 凸形 | 鱗片狀 | 白色 | 有瘀傷 | 刺鼻 | 自由 | 緊密 | 狹窄 | 棕色 | 擴大 | 相等 | 光滑 | 光滑 | 白色 | 白色 | 部分 | 白色 | 一個 | 垂飾 | 黑色 | 分散 | 城市 | + +你會立刻注意到所有的數據都是文本格式。你需要將這些數據轉換為可以用於圖表的格式。事實上，大部分數據是以對象形式表示的： + +```python +print(mushrooms.select_dtypes(["object"]).columns) +``` + +輸出結果為： + +```output +Index(['class', 'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor', + 'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color', + 'stalk-shape', 'stalk-root', 'stalk-surface-above-ring', + 'stalk-surface-below-ring', 'stalk-color-above-ring', + 'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number', + 'ring-type', 'spore-print-color', 'population', 'habitat'], + dtype='object') +``` +將這些數據中的「類別」列轉換為分類： + +```python +cols = mushrooms.select_dtypes(["object"]).columns +mushrooms[cols] = mushrooms[cols].astype('category') +``` + +```python +edibleclass=mushrooms.groupby(['class']).count() +edibleclass +``` + +現在，如果你打印出蘑菇數據，你可以看到它已經根據有毒/可食用類別分組： + +| | 菌蓋形狀 | 菌蓋表面 | 菌蓋顏色 | 是否有瘀傷 | 氣味 | 鰓附著方式 | 鰓間距 | 鰓大小 | 鰓顏色 | 菌柄形狀 | ... | 菌柄環下表面 | 菌柄環上顏色 | 菌柄環下顏色 | 菌幕類型 | 菌幕顏色 | 環數 | 環類型 | 孢子印顏色 | 群體數量 | 棲息地 | +| --------- | --------- | ----------- | --------- | ------- | ---- | --------------- | ------------ | --------- | ---------- | ----------- | --- | ------------------------ | ---------------------- | ---------------------- | --------- | ---------- | ----------- | --------- | ----------------- | ---------- | ------- | +| 類別 | | | | | | | | | | | | | | | | | | | | | | +| 可食用 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | ... | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | +| 有毒 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | ... | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | + +如果你按照這個表格中呈現的順序來創建你的類別標籤，你可以製作一個圓餅圖： + +## 圓餅圖！ + +```python +labels=['Edible','Poisonous'] +plt.pie(edibleclass['population'],labels=labels,autopct='%.1f %%') +plt.title('Edible?') +plt.show() +``` +完成，一個圓餅圖展示了根據這兩類蘑菇的數據比例。正確排列標籤的順序非常重要，尤其是在這裡，因此請務必核對標籤數組的順序！ + +![圓餅圖](../../../../3-Data-Visualization/11-visualization-proportions/images/pie1-wb.png) + +## 甜甜圈圖！ + +一個更具視覺吸引力的圓餅圖是甜甜圈圖，它是一個中間有洞的圓餅圖。讓我們用這種方法來查看我們的數據。 + +看看蘑菇生長的各種棲息地： + +```python +habitat=mushrooms.groupby(['habitat']).count() +habitat +``` +在這裡，你將數據按棲息地分組。共有 7 種棲息地，因此使用它們作為甜甜圈圖的標籤： + +```python +labels=['Grasses','Leaves','Meadows','Paths','Urban','Waste','Wood'] + +plt.pie(habitat['class'], labels=labels, + autopct='%1.1f%%', pctdistance=0.85) + +center_circle = plt.Circle((0, 0), 0.40, fc='white') +fig = plt.gcf() + +fig.gca().add_artist(center_circle) + +plt.title('Mushroom Habitats') + +plt.show() +``` + +![甜甜圈圖](../../../../3-Data-Visualization/11-visualization-proportions/images/donut-wb.png) + +這段代碼繪製了一個圖表和一個中心圓，然後將該中心圓添加到圖表中。通過更改 `0.40` 的值來編輯中心圓的寬度。 + +甜甜圈圖可以通過多種方式進行調整以更改標籤。特別是標籤可以被突出顯示以提高可讀性。了解更多信息請參考 [文檔](https://matplotlib.org/stable/gallery/pie_and_polar_charts/pie_and_donut_labels.html?highlight=donut)。 + +現在你已經知道如何分組數據並將其顯示為圓餅圖或甜甜圈圖，你可以探索其他類型的圖表。嘗試華夫餅圖，這是一種不同的方式來探索數量。 + +## 華夫餅圖！ + +華夫餅圖是一種以 2D 方格陣列視覺化數量的方式。嘗試視覺化這個數據集中蘑菇菌蓋顏色的不同數量。為此，你需要安裝一個名為 [PyWaffle](https://pypi.org/project/pywaffle/) 的輔助庫並使用 Matplotlib： + +```python +pip install pywaffle +``` + +選擇數據的一部分進行分組： + +```python +capcolor=mushrooms.groupby(['cap-color']).count() +capcolor +``` + +通過創建標籤並分組數據來製作華夫餅圖： + +```python +import pandas as pd +import matplotlib.pyplot as plt +from pywaffle import Waffle + +data ={'color': ['brown', 'buff', 'cinnamon', 'green', 'pink', 'purple', 'red', 'white', 'yellow'], + 'amount': capcolor['class'] + } + +df = pd.DataFrame(data) + +fig = plt.figure( + FigureClass = Waffle, + rows = 100, + values = df.amount, + labels = list(df.color), + figsize = (30,30), + colors=["brown", "tan", "maroon", "green", "pink", "purple", "red", "whitesmoke", "yellow"], +) +``` + +使用華夫餅圖，你可以清楚地看到這個蘑菇數據集中菌蓋顏色的比例。有趣的是，有許多綠色菌蓋的蘑菇！ + +![華夫餅圖](../../../../3-Data-Visualization/11-visualization-proportions/images/waffle.png) + +✅ PyWaffle 支持在圖表中使用任何 [Font Awesome](https://fontawesome.com/) 提供的圖標。嘗試進行一些實驗，用圖標代替方格來創建更有趣的華夫餅圖。 + +在這節課中，你學到了三種視覺化比例的方法。首先，你需要將數據分組到分類中，然後決定哪種方式最適合展示數據——圓餅圖、甜甜圈圖或華夫餅圖。這些方法都很有趣，能夠讓用戶快速了解數據集。 + +## 🚀 挑戰 + +嘗試在 [Charticulator](https://charticulator.com) 中重現這些有趣的圖表。 + +## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/21) + +## 回顧與自學 + +有時候，何時使用圓餅圖、甜甜圈圖或華夫餅圖並不明顯。以下是一些相關文章： + +https://www.beautiful.ai/blog/battle-of-the-charts-pie-chart-vs-donut-chart + +https://medium.com/@hypsypops/pie-chart-vs-donut-chart-showdown-in-the-ring-5d24fd86a9ce + +https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c6.htm + +https://medium.datadriveninvestor.com/data-visualization-done-the-right-way-with-tableau-waffle-chart-fdf2a19be402 + +進行一些研究，找到更多關於這個選擇的相關信息。 + +## 作業 + +[在 Excel 中嘗試](assignment.md) + +--- + +**免責聲明**： +此文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議使用專業的人工作翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/11-visualization-proportions/assignment.md b/translations/zh-HK/3-Data-Visualization/11-visualization-proportions/assignment.md new file mode 100644 index 00000000..eed4f3a3 --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/11-visualization-proportions/assignment.md @@ -0,0 +1,14 @@ +# 在 Excel 中嘗試 + +## 指示 + +你知道嗎？你可以在 Excel 中建立甜甜圈圖、圓餅圖和鬆餅圖！使用你選擇的數據集，直接在 Excel 試算表中建立這三種圖表。 + +## 評分標準 + +| 優秀表現 | 合格表現 | 需要改進 | +| --------------------------------------------------- | --------------------------------------------- | ------------------------------------------------ | +| 提供的 Excel 試算表包含所有三種圖表 | 提供的 Excel 試算表包含兩種圖表 | 提供的 Excel 試算表僅包含一種圖表 | + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，請注意自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/11-visualization-proportions/notebook.ipynb b/translations/zh-HK/3-Data-Visualization/11-visualization-proportions/notebook.ipynb new file mode 100644 index 00000000..01bff0bc --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/11-visualization-proportions/notebook.ipynb @@ -0,0 +1,30 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**免責聲明**： \n此文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤詮釋概不負責。 \n" + ] + } + ], + "metadata": { + "orig_nbformat": 4, + "language_info": { + "name": "python" + }, + "coopTranslator": { + "original_hash": "397e9bbc0743761dbf72e5f16b7043e6", + "translation_date": "2025-09-02T08:38:13+00:00", + "source_file": "3-Data-Visualization/11-visualization-proportions/notebook.ipynb", + "language_code": "hk" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/11-visualization-proportions/solution/notebook.ipynb b/translations/zh-HK/3-Data-Visualization/11-visualization-proportions/solution/notebook.ipynb new file mode 100644 index 00000000..442304c2 --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/11-visualization-proportions/solution/notebook.ipynb @@ -0,0 +1,1311 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "導入蘑菇數據集\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 4, + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "mushrooms = pd.read_csv('../../../data/mushrooms.csv')\n", + "mushrooms.head()" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " class cap-shape cap-surface cap-color bruises odor \\\n", + "0 Poisonous Convex Smooth Brown Bruises Pungent \n", + "1 Edible Convex Smooth Yellow Bruises Almond \n", + "2 Edible Bell Smooth White Bruises Anise \n", + "3 Poisonous Convex Scaly White Bruises Pungent \n", + "4 Edible Convex Smooth Green No Bruises None \n", + "\n", + " gill-attachment gill-spacing gill-size gill-color ... \\\n", + "0 Free Close Narrow Black ... \n", + "1 Free Close Broad Black ... \n", + "2 Free Close Broad Brown ... \n", + "3 Free Close Narrow Brown ... \n", + "4 Free Crowded Broad Black ... \n", + "\n", + " stalk-surface-below-ring stalk-color-above-ring stalk-color-below-ring \\\n", + "0 Smooth White White \n", + "1 Smooth White White \n", + "2 Smooth White White \n", + "3 Smooth White White \n", + "4 Smooth White White \n", + "\n", + " veil-type veil-color ring-number ring-type spore-print-color population \\\n", + "0 Partial White One Pendant Black Scattered \n", + "1 Partial White One Pendant Brown Numerous \n", + "2 Partial White One Pendant Brown Numerous \n", + "3 Partial White One Pendant Black Scattered \n", + "4 Partial White One Evanescent Brown Abundant \n", + "\n", + " habitat \n", + "0 Urban \n", + "1 Grasses \n", + "2 Meadows \n", + "3 Urban \n", + "4 Grasses \n", + "\n", + "[5 rows x 23 columns]" + ], + "text/html": [ + "
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classcap-shapecap-surfacecap-colorbruisesodorgill-attachmentgill-spacinggill-sizegill-color...stalk-surface-below-ringstalk-color-above-ringstalk-color-below-ringveil-typeveil-colorring-numberring-typespore-print-colorpopulationhabitat
0PoisonousConvexSmoothBrownBruisesPungentFreeCloseNarrowBlack...SmoothWhiteWhitePartialWhiteOnePendantBlackScatteredUrban
1EdibleConvexSmoothYellowBruisesAlmondFreeCloseBroadBlack...SmoothWhiteWhitePartialWhiteOnePendantBrownNumerousGrasses
2EdibleBellSmoothWhiteBruisesAniseFreeCloseBroadBrown...SmoothWhiteWhitePartialWhiteOnePendantBrownNumerousMeadows
3PoisonousConvexScalyWhiteBruisesPungentFreeCloseNarrowBrown...SmoothWhiteWhitePartialWhiteOnePendantBlackScatteredUrban
4EdibleConvexSmoothGreenNo BruisesNoneFreeCrowdedBroadBlack...SmoothWhiteWhitePartialWhiteOneEvanescentBrownAbundantGrasses
\n", + "

5 rows × 23 columns

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" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# 圓餅圖\n", + "\n", + "建立一個圓餅圖，顯示有毒蘑菇與可食用蘑菇的比例\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 5, + "source": [ + "print(mushrooms.select_dtypes([\"object\"]).columns)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Index(['class', 'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',\n", + " 'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',\n", + " 'stalk-shape', 'stalk-root', 'stalk-surface-above-ring',\n", + " 'stalk-surface-below-ring', 'stalk-color-above-ring',\n", + " 'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number',\n", + " 'ring-type', 'spore-print-color', 'population', 'habitat'],\n", + " dtype='object')\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 6, + "source": [ + "cols = mushrooms.select_dtypes([\"object\"]).columns\n", + "mushrooms[cols] = mushrooms[cols].astype('category')" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 7, + "source": [ + "edibleclass=mushrooms.groupby(['class']).count()\n", + "edibleclass" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " cap-shape cap-surface cap-color bruises odor gill-attachment \\\n", + "class \n", + "Edible 4208 4208 4208 4208 4208 4208 \n", + "Poisonous 3916 3916 3916 3916 3916 3916 \n", + "\n", + " gill-spacing gill-size gill-color stalk-shape ... \\\n", + "class ... \n", + "Edible 4208 4208 4208 4208 ... \n", + "Poisonous 3916 3916 3916 3916 ... \n", + "\n", + " stalk-surface-below-ring stalk-color-above-ring \\\n", + "class \n", + "Edible 4208 4208 \n", + "Poisonous 3916 3916 \n", + "\n", + " stalk-color-below-ring veil-type veil-color ring-number \\\n", + "class \n", + "Edible 4208 4208 4208 4208 \n", + "Poisonous 3916 3916 3916 3916 \n", + "\n", + " ring-type spore-print-color population habitat \n", + "class \n", + "Edible 4208 4208 4208 4208 \n", + "Poisonous 3916 3916 3916 3916 \n", + "\n", + "[2 rows x 22 columns]" + ], + "text/html": [ + "
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cap-shapecap-surfacecap-colorbruisesodorgill-attachmentgill-spacinggill-sizegill-colorstalk-shape...stalk-surface-below-ringstalk-color-above-ringstalk-color-below-ringveil-typeveil-colorring-numberring-typespore-print-colorpopulationhabitat
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Edible4208420842084208420842084208420842084208...4208420842084208420842084208420842084208
Poisonous3916391639163916391639163916391639163916...3916391639163916391639163916391639163916
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" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 8, + "source": [ + "labels=['Edible','Poisonous']\n", + "plt.pie(edibleclass['population'],labels=labels,autopct='%.1f %%')\n", + "plt.title('Edible?')\n", + "plt.show()" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {} + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 9, + "source": [ + "capcolor=mushrooms.groupby(['cap-color']).count()\n", + "capcolor" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " class cap-shape cap-surface bruises odor gill-attachment \\\n", + "cap-color \n", + "Brown 2284 2284 2284 2284 2284 2284 \n", + "Buff 168 168 168 168 168 168 \n", + "Cinnamon 44 44 44 44 44 44 \n", + "Green 1856 1856 1856 1856 1856 1856 \n", + "Pink 144 144 144 144 144 144 \n", + "Purple 16 16 16 16 16 16 \n", + "Red 1500 1500 1500 1500 1500 1500 \n", + "White 1040 1040 1040 1040 1040 1040 \n", + "Yellow 1072 1072 1072 1072 1072 1072 \n", + "\n", + " gill-spacing gill-size gill-color stalk-shape ... \\\n", + "cap-color ... \n", + "Brown 2284 2284 2284 2284 ... \n", + "Buff 168 168 168 168 ... \n", + "Cinnamon 44 44 44 44 ... \n", + "Green 1856 1856 1856 1856 ... \n", + "Pink 144 144 144 144 ... \n", + "Purple 16 16 16 16 ... \n", + "Red 1500 1500 1500 1500 ... \n", + "White 1040 1040 1040 1040 ... \n", + "Yellow 1072 1072 1072 1072 ... \n", + "\n", + " stalk-surface-below-ring stalk-color-above-ring \\\n", + "cap-color \n", + "Brown 2284 2284 \n", + "Buff 168 168 \n", + "Cinnamon 44 44 \n", + "Green 1856 1856 \n", + "Pink 144 144 \n", + "Purple 16 16 \n", + "Red 1500 1500 \n", + "White 1040 1040 \n", + "Yellow 1072 1072 \n", + "\n", + " stalk-color-below-ring veil-type veil-color ring-number \\\n", + "cap-color \n", + "Brown 2284 2284 2284 2284 \n", + "Buff 168 168 168 168 \n", + "Cinnamon 44 44 44 44 \n", + "Green 1856 1856 1856 1856 \n", + "Pink 144 144 144 144 \n", + "Purple 16 16 16 16 \n", + "Red 1500 1500 1500 1500 \n", + "White 1040 1040 1040 1040 \n", + "Yellow 1072 1072 1072 1072 \n", + "\n", + " ring-type spore-print-color population habitat \n", + "cap-color \n", + "Brown 2284 2284 2284 2284 \n", + "Buff 168 168 168 168 \n", + "Cinnamon 44 44 44 44 \n", + "Green 1856 1856 1856 1856 \n", + "Pink 144 144 144 144 \n", + "Purple 16 16 16 16 \n", + "Red 1500 1500 1500 1500 \n", + "White 1040 1040 1040 1040 \n", + "Yellow 1072 1072 1072 1072 \n", + "\n", + "[9 rows x 22 columns]" + ], + "text/html": [ + "
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classcap-shapecap-surfacebruisesodorgill-attachmentgill-spacinggill-sizegill-colorstalk-shape...stalk-surface-below-ringstalk-color-above-ringstalk-color-below-ringveil-typeveil-colorring-numberring-typespore-print-colorpopulationhabitat
cap-color
Brown2284228422842284228422842284228422842284...2284228422842284228422842284228422842284
Buff168168168168168168168168168168...168168168168168168168168168168
Cinnamon44444444444444444444...44444444444444444444
Green1856185618561856185618561856185618561856...1856185618561856185618561856185618561856
Pink144144144144144144144144144144...144144144144144144144144144144
Purple16161616161616161616...16161616161616161616
Red1500150015001500150015001500150015001500...1500150015001500150015001500150015001500
White1040104010401040104010401040104010401040...1040104010401040104010401040104010401040
Yellow1072107210721072107210721072107210721072...1072107210721072107210721072107210721072
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9 rows × 22 columns

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" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# 圓環圖\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 10, + "source": [ + "habitat=mushrooms.groupby(['habitat']).count()\n", + "habitat" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " class cap-shape cap-surface cap-color bruises odor \\\n", + "habitat \n", + "Grasses 2148 2148 2148 2148 2148 2148 \n", + "Leaves 832 832 832 832 832 832 \n", + "Meadows 292 292 292 292 292 292 \n", + "Paths 1144 1144 1144 1144 1144 1144 \n", + "Urban 368 368 368 368 368 368 \n", + "Waste 192 192 192 192 192 192 \n", + "Wood 3148 3148 3148 3148 3148 3148 \n", + "\n", + " gill-attachment gill-spacing gill-size gill-color ... \\\n", + "habitat ... \n", + "Grasses 2148 2148 2148 2148 ... \n", + "Leaves 832 832 832 832 ... \n", + "Meadows 292 292 292 292 ... \n", + "Paths 1144 1144 1144 1144 ... \n", + "Urban 368 368 368 368 ... \n", + "Waste 192 192 192 192 ... \n", + "Wood 3148 3148 3148 3148 ... \n", + "\n", + " stalk-surface-above-ring stalk-surface-below-ring \\\n", + "habitat \n", + "Grasses 2148 2148 \n", + "Leaves 832 832 \n", + "Meadows 292 292 \n", + "Paths 1144 1144 \n", + "Urban 368 368 \n", + "Waste 192 192 \n", + "Wood 3148 3148 \n", + "\n", + " stalk-color-above-ring stalk-color-below-ring veil-type \\\n", + "habitat \n", + "Grasses 2148 2148 2148 \n", + "Leaves 832 832 832 \n", + "Meadows 292 292 292 \n", + "Paths 1144 1144 1144 \n", + "Urban 368 368 368 \n", + "Waste 192 192 192 \n", + "Wood 3148 3148 3148 \n", + "\n", + " veil-color ring-number ring-type spore-print-color population \n", + "habitat \n", + "Grasses 2148 2148 2148 2148 2148 \n", + "Leaves 832 832 832 832 832 \n", + "Meadows 292 292 292 292 292 \n", + "Paths 1144 1144 1144 1144 1144 \n", + "Urban 368 368 368 368 368 \n", + "Waste 192 192 192 192 192 \n", + "Wood 3148 3148 3148 3148 3148 \n", + "\n", + "[7 rows x 22 columns]" + ], + "text/html": [ + "
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classcap-shapecap-surfacecap-colorbruisesodorgill-attachmentgill-spacinggill-sizegill-color...stalk-surface-above-ringstalk-surface-below-ringstalk-color-above-ringstalk-color-below-ringveil-typeveil-colorring-numberring-typespore-print-colorpopulation
habitat
Grasses2148214821482148214821482148214821482148...2148214821482148214821482148214821482148
Leaves832832832832832832832832832832...832832832832832832832832832832
Meadows292292292292292292292292292292...292292292292292292292292292292
Paths1144114411441144114411441144114411441144...1144114411441144114411441144114411441144
Urban368368368368368368368368368368...368368368368368368368368368368
Waste192192192192192192192192192192...192192192192192192192192192192
Wood3148314831483148314831483148314831483148...3148314831483148314831483148314831483148
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7 rows × 22 columns

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" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 11, + "source": [ + " \n", + "labels=['Grasses','Leaves','Meadows','Paths','Urban','Waste','Wood']\n", + "\n", + "plt.pie(habitat['class'], labels=labels,\n", + " autopct='%1.1f%%', pctdistance=0.85)\n", + " \n", + "center_circle = plt.Circle((0, 0), 0.40, fc='white')\n", + "fig = plt.gcf()\n", + "\n", + "fig.gca().add_artist(center_circle)\n", + " \n", + "# Adding Title of chart\n", + "plt.title('Mushroom Habitats')\n", + " \n", + "plt.show()" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {} + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "華夫圖表\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 12, + "source": [ + "pip install pywaffle" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: pywaffle in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.6.3)\n", + "Requirement already satisfied: matplotlib in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pywaffle) (3.1.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib->pywaffle) (2.8.0)\n", + "Requirement already satisfied: numpy>=1.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib->pywaffle) (1.19.2)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib->pywaffle) (2.4.0)\n", + "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib->pywaffle) (0.10.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib->pywaffle) (1.1.0)\n", + "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.1->matplotlib->pywaffle) (1.12.0)\n", + "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib->pywaffle) (45.1.0)\n", + "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.2.3 is available.\n", + "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 13, + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from pywaffle import Waffle\n", + " \n", + "# creation of a dataframe\n", + "\n", + "\n", + "data ={'color': ['brown', 'buff', 'cinnamon', 'green', 'pink', 'purple', 'red', 'white', 'yellow'],\n", + " 'amount': capcolor['class']\n", + " }\n", + " \n", + "df = pd.DataFrame(data)\n", + " \n", + "# To plot the waffle Chart\n", + "fig = plt.figure(\n", + " FigureClass = Waffle,\n", + " rows = 100,\n", + " values = df.amount,\n", + " labels = list(df.color),\n", + " figsize = (30,30),\n", + " colors=[\"brown\", \"tan\", \"maroon\", \"green\", \"pink\", \"purple\", \"red\", \"whitesmoke\", \"yellow\"],\n", + ")\n", + "\n", + "\n" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "image/png": 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"3-Data-Visualization/11-visualization-proportions/solution/notebook.ipynb", + "language_code": "hk" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/12-visualization-relationships/README.md b/translations/zh-HK/3-Data-Visualization/12-visualization-relationships/README.md new file mode 100644 index 00000000..c9a05ec4 --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/12-visualization-relationships/README.md @@ -0,0 +1,179 @@ +# 視覺化關係：關於蜂蜜 🍯 + +|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/12-Visualizing-Relationships.png)| +|:---:| +|視覺化關係 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | + +延續我們研究的自然主題，讓我們探索一些有趣的視覺化方式，展示不同種類蜂蜜之間的關係。這些視覺化基於一個來自[美國農業部](https://www.nass.usda.gov/About_NASS/index.php)的數據集。 + +這個包含約600項目數據的數據集展示了美國多個州的蜂蜜生產情況。例如，您可以查看每個州在1998年至2012年間的蜂群數量、每群產量、總生產量、庫存、每磅價格以及蜂蜜的生產價值，每年每州一行數據。 + +我們可以視覺化某州每年的生產量與該州蜂蜜價格之間的關係。或者，您也可以視覺化各州每群蜂蜜產量之間的關係。這段時間涵蓋了2006年首次出現的毀滅性“蜂群崩潰症”（CCD，Colony Collapse Disorder）（http://npic.orst.edu/envir/ccd.html），因此這是一個值得研究的數據集。🐝 + +## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/22) + +在本課中，您可以使用之前使用過的 Seaborn 庫，這是一個很好的工具來視覺化變量之間的關係。特別有趣的是使用 Seaborn 的 `relplot` 函數，它可以快速生成散點圖和折線圖，視覺化[統計關係](https://seaborn.pydata.org/tutorial/relational.html?highlight=relationships)，幫助數據科學家更好地理解變量之間的關聯。 + +## 散點圖 + +使用散點圖展示蜂蜜價格每年每州的變化。Seaborn 的 `relplot` 可以方便地將州數據分組，並顯示分類和數值數據的數據點。 + +首先，導入數據和 Seaborn： + +```python +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +honey = pd.read_csv('../../data/honey.csv') +honey.head() +``` +您會注意到蜂蜜數據中有幾個有趣的列，包括年份和每磅價格。讓我們探索按美國州分組的數據： + +| 州 | 蜂群數量 | 每群產量 | 總生產量 | 庫存 | 每磅價格 | 生產價值 | 年份 | +| ----- | ------ | ----------- | --------- | -------- | ---------- | --------- | ---- | +| AL | 16000 | 71 | 1136000 | 159000 | 0.72 | 818000 | 1998 | +| AZ | 55000 | 60 | 3300000 | 1485000 | 0.64 | 2112000 | 1998 | +| AR | 53000 | 65 | 3445000 | 1688000 | 0.59 | 2033000 | 1998 | +| CA | 450000 | 83 | 37350000 | 12326000 | 0.62 | 23157000 | 1998 | +| CO | 27000 | 72 | 1944000 | 1594000 | 0.7 | 1361000 | 1998 | + +創建一個基本散點圖，展示蜂蜜每磅價格與其來源州之間的關係。將 `y` 軸設置得足夠高以顯示所有州： + +```python +sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); +``` +![scatterplot 1](../../../../translated_images/zh-HK/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) + +接下來，使用蜂蜜色調展示價格隨年份的變化。您可以通過添加 'hue' 參數來展示每年的變化： + +> ✅ 了解更多 [Seaborn 可用的色彩調色板](https://seaborn.pydata.org/tutorial/color_palettes.html) - 試試美麗的彩虹色調！ + +```python +sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); +``` +![scatterplot 2](../../../../translated_images/zh-HK/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) + +通過這種色彩方案的改變，您可以清楚地看到蜂蜜每磅價格隨年份的明顯增長趨勢。事實上，如果您查看數據中的樣本集（例如選擇一個州，亞利桑那州），您可以看到價格每年增長的模式，只有少數例外： + +| 州 | 蜂群數量 | 每群產量 | 總生產量 | 庫存 | 每磅價格 | 生產價值 | 年份 | +| ----- | ------ | ----------- | --------- | ------- | ---------- | --------- | ---- | +| AZ | 55000 | 60 | 3300000 | 1485000 | 0.64 | 2112000 | 1998 | +| AZ | 52000 | 62 | 3224000 | 1548000 | 0.62 | 1999000 | 1999 | +| AZ | 40000 | 59 | 2360000 | 1322000 | 0.73 | 1723000 | 2000 | +| AZ | 43000 | 59 | 2537000 | 1142000 | 0.72 | 1827000 | 2001 | +| AZ | 38000 | 63 | 2394000 | 1197000 | 1.08 | 2586000 | 2002 | +| AZ | 35000 | 72 | 2520000 | 983000 | 1.34 | 3377000 | 2003 | +| AZ | 32000 | 55 | 1760000 | 774000 | 1.11 | 1954000 | 2004 | +| AZ | 36000 | 50 | 1800000 | 720000 | 1.04 | 1872000 | 2005 | +| AZ | 30000 | 65 | 1950000 | 839000 | 0.91 | 1775000 | 2006 | +| AZ | 30000 | 64 | 1920000 | 902000 | 1.26 | 2419000 | 2007 | +| AZ | 25000 | 64 | 1600000 | 336000 | 1.26 | 2016000 | 2008 | +| AZ | 20000 | 52 | 1040000 | 562000 | 1.45 | 1508000 | 2009 | +| AZ | 24000 | 77 | 1848000 | 665000 | 1.52 | 2809000 | 2010 | +| AZ | 23000 | 53 | 1219000 | 427000 | 1.55 | 1889000 | 2011 | +| AZ | 22000 | 46 | 1012000 | 253000 | 1.79 | 1811000 | 2012 | + +另一種視覺化這種進展的方法是使用大小而不是顏色。對於色盲用戶，這可能是一個更好的選擇。編輯您的視覺化，通過點的圓周大小展示價格的增長： + +```python +sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspect=.5); +``` +您可以看到點的大小逐漸增大。 + +![scatterplot 3](../../../../translated_images/zh-HK/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) + +這是否是一個簡單的供需問題？由於氣候變化和蜂群崩潰等因素，是否每年可供購買的蜂蜜減少，因此價格上漲？ + +為了探索數據集中某些變量之間的相關性，讓我們研究一些折線圖。 + +## 折線圖 + +問題：蜂蜜每磅價格是否每年都有明顯上漲？您可以通過創建一個單一折線圖來最容易地發現這一點： + +```python +sns.relplot(x="year", y="priceperlb", kind="line", data=honey); +``` +答案：是的，但在2003年左右有一些例外： + +![line chart 1](../../../../translated_images/zh-HK/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) + +✅ 由於 Seaborn 將數據聚合到一條線上，它通過繪製均值和均值周圍的95%置信區間來顯示每個 x 值的多個測量值。[來源](https://seaborn.pydata.org/tutorial/relational.html)。這種耗時的行為可以通過添加 `ci=None` 禁用。 + +問題：那麼，在2003年，我們是否也能看到蜂蜜供應的激增？如果您查看每年的總生產量呢？ + +```python +sns.relplot(x="year", y="totalprod", kind="line", data=honey); +``` + +![line chart 2](../../../../translated_images/zh-HK/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) + +答案：並不完全。如果您查看總生產量，實際上在那一年似乎有所增加，儘管總體而言蜂蜜的生產量在這些年中呈下降趨勢。 + +問題：在這種情況下，2003年蜂蜜價格的激增可能是什麼原因？ + +為了探索這一點，您可以使用 Facet Grid。 + +## Facet Grids + +Facet Grids 將數據集的一個方面（在我們的例子中，您可以選擇“年份”，以避免生成過多的 Facets）。Seaborn 可以為您選擇的 x 和 y 坐標生成每個 Facet 的圖表，方便比較。2003年是否在這種比較中脫穎而出？ + +繼續使用 Seaborn 的 `relplot` 創建 Facet Grid，正如 [Seaborn 文檔](https://seaborn.pydata.org/generated/seaborn.FacetGrid.html?highlight=facetgrid#seaborn.FacetGrid) 所推薦的。 + +```python +sns.relplot( + data=honey, + x="yieldpercol", y="numcol", + col="year", + col_wrap=3, + kind="line" + ) +``` +在這個視覺化中，您可以比較每年的每群產量和蜂群數量，並將列的 wrap 設置為3： + +![facet grid](../../../../translated_images/zh-HK/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) + +對於這個數據集，關於蜂群數量和每群產量，按年份和州比較並沒有特別突出的地方。是否有其他方式來尋找這兩個變量之間的相關性？ + +## 雙折線圖 + +嘗試使用多折線圖，通過將兩個折線圖疊加在一起，使用 Seaborn 的 'despine' 移除其上方和右側的邊框，並使用 `ax.twinx` [源自 Matplotlib](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html)。Twins 允許圖表共享 x 軸並顯示兩個 y 軸。因此，展示每群產量和蜂群數量的疊加圖： + +```python +fig, ax = plt.subplots(figsize=(12,6)) +lineplot = sns.lineplot(x=honey['year'], y=honey['numcol'], data=honey, + label = 'Number of bee colonies', legend=False) +sns.despine() +plt.ylabel('# colonies') +plt.title('Honey Production Year over Year'); + +ax2 = ax.twinx() +lineplot2 = sns.lineplot(x=honey['year'], y=honey['yieldpercol'], ax=ax2, color="r", + label ='Yield per colony', legend=False) +sns.despine(right=False) +plt.ylabel('colony yield') +ax.figure.legend(); +``` +![superimposed plots](../../../../translated_images/zh-HK/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) + +雖然在2003年沒有明顯的異常，但這讓我們以一個稍微樂觀的結論結束本課：儘管蜂群數量總體上在下降，但蜂群數量正在穩定，即使每群產量在減少。 + +加油，蜜蜂們，加油！ + +🐝❤️ +## 🚀 挑戰 + +在本課中，您學到了更多關於散點圖和折線圖的其他用途，包括 Facet Grids。挑戰自己使用不同的數據集創建 Facet Grid，也許是您之前使用過的數據集。注意它們的生成時間以及如何小心選擇需要繪製的 Facets 數量。 + +## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/23) + +## 回顧與自學 + +折線圖可以簡單也可以非常複雜。閱讀 [Seaborn 文檔](https://seaborn.pydata.org/generated/seaborn.lineplot.html) 中的各種構建方法。嘗試使用文檔中列出的其他方法來增強您在本課中構建的折線圖。 +## 作業 + +[深入蜂巢](assignment.md) + +--- + +**免責聲明**： +此文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業的人類翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/12-visualization-relationships/assignment.md b/translations/zh-HK/3-Data-Visualization/12-visualization-relationships/assignment.md new file mode 100644 index 00000000..d0ed3501 --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/12-visualization-relationships/assignment.md @@ -0,0 +1,14 @@ +# 探索蜂巢 + +## 指引 + +在這節課中，你開始研究一個有關蜜蜂及其蜂蜜生產的數據集，涵蓋了一段時間內蜂群數量整體下降的情況。深入分析這個數據集，建立一個筆記本，講述蜂群健康狀況的故事，按州和年份進行分析。你是否在這個數據集中發現了任何有趣的事情？ + +## 評分標準 + +| 卓越表現 | 合格表現 | 需要改進 | +| ------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------- | ---------------------------------------- | +| 提供了一個筆記本，包含至少三個不同的圖表，註解展示數據集的各個方面，按州和年份進行分析 | 筆記本缺少其中一個元素 | 筆記本缺少其中兩個元素 | + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為具權威性的來源。對於重要信息，建議使用專業的人類翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋不承擔責任。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/12-visualization-relationships/notebook.ipynb b/translations/zh-HK/3-Data-Visualization/12-visualization-relationships/notebook.ipynb new file mode 100644 index 00000000..a61baa82 --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/12-visualization-relationships/notebook.ipynb @@ -0,0 +1,32 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# 視覺化蜂蜜生產 🍯 🐝\n" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**免責聲明**： \n本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，請注意自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。\n" + ] + } + ], + "metadata": { + "orig_nbformat": 4, + "language_info": { + "name": "python" + }, + "coopTranslator": { + "original_hash": "0f988634b7192626d91cc33b4b6388c5", + "translation_date": "2025-09-02T08:58:08+00:00", + "source_file": "3-Data-Visualization/12-visualization-relationships/notebook.ipynb", + "language_code": "hk" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/12-visualization-relationships/solution/notebook.ipynb b/translations/zh-HK/3-Data-Visualization/12-visualization-relationships/solution/notebook.ipynb new file mode 100644 index 00000000..c867226d --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/12-visualization-relationships/solution/notebook.ipynb @@ -0,0 +1,389 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# 視覺化蜂蜜生產 🍯 🐝\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 177, + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "honey = pd.read_csv('../../../data/honey.csv')\n", + "honey.head()" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " state numcol yieldpercol totalprod stocks priceperlb \\\n", + "0 AL 16000.0 71 1136000.0 159000.0 0.72 \n", + "1 AZ 55000.0 60 3300000.0 1485000.0 0.64 \n", + "2 AR 53000.0 65 3445000.0 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3vF7mzrPvv8fR3dGSds508aSLWyS3sxo9nW0pMfV0tsHtWnjVK9t6Kp13u6p12wdRe6qHdLFuufriooyViCqDKR6PG/6I2caNG7Flyxa0t7cnj/n9frS3t8PhcODuu+/GypUrk+d2796NH/3oR9i7dy+czuyXk4y+4uHxOOE7PgUpJONUMIL6uurkB3fiWF2NFWF5Bo4a6+y5ueGYsLCvEeHOmafBaUMsFk8/pylz3AWppqtmn/XwS9Nwu2pmi475D5YmZFtPpfMKx3PKV9Se6mFerMsaF8PvDxQ6KqFK4TdEPTHf8lYK+Wa64iGk8DCakMKjyDdZT8y3vFVavkDl5cx8y1sp5FvwWy1EREREAAsPIiIiEoiFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMIIKTwCgQBuv/12XHHFFbjqqquwefNm/PWvf02eHxsbQ2trK/bs2SMinPyZAOmMjHFfENL0DFCV5bUpS//55/WISc2YavqcbfPmuwG8c2oagUhUn3i1UBOnGXhvKoy//f00fIEwYDZ4vnzaG6mYYiEiSsNi9ASxWAzXX389Vq5ciUceeQQWiwUvvPACrr/+ejz22GNYvHgxBgcHsW7dOvT392PdunVGh5QfEzA6fhp9A8MIy1Gc57aja20LdgyOICxHYbOa0dPZhoGnx/COPwSb1YzeLi9WNC0C4gv7LzivQ0yqxszUJ0Ob7o4WNHocaF7izD1evXMzA8NvnMD//r/v78HX/qEN3g81AFED5sunvZGKKRYiIgWGX/E4ePAg3nvvPfT29sJima1zVq1ahe3btyMWi2FmZga7d+/Gli1bcPjwYYyPjxsdUl6kkJz8YAeANd7GZNEBAGE5ih2DI1jjbUy+7hsYhhSS0/aff16PmNSMqaZPujb9e8dwdFLKK14t1MT53qlwsuhItPnf/3cE750KGzJfPu2NVEyxEBEpMfyKx+HDh9HW1oaqqtQa59JLLwUAPP3001iyZAmWLVuGtWvXor+/H9/5znc0zeF2O3SLV4nH4wQAHDviS36wAwBMSH2Ns69Nqa9DchTNTQ0L+887n4tcxszUB5jNV6lNLB7PK14t1OT2t79LaduclML4yAfPUTWP4v6mmU9rfKJoiSWRbyWptJyZb3kr5XwNLzyqqqoQjytf5x0cHMQVV1wBANiwYQO2bt2Km2++GdXV1arn8PsDiMWMu5bs8Tjh800BAOw2C2xWc8oHfLrXcy9t26xm2K1m+HxTiv0T53ORy5iZ+gDIGGuVyZRXvFqoyc29yJa2zWKXTVWMavZXKV8j9jNXamOZm2+lqLScmW95K4V8MxVGht9qufDCC3H48OEFxcfdd9+Nxx9/HAcOHMBvfvMbtLe349Zbb4UkSXjqqaeMDitnrloLeru8s8UFgKFXJtDT2ZZ8nXjGY2h4Ivm6t8sLl92atv/883rEpGZMNX3StenuaEHzElde8WqhJk7PIhu+9g+pe/C1f2jDufU2Q+bLp72RiikWIiIlpnimyxE6iMfjuPrqq3HJJZfgpptugtlsxtDQEG655RZce+21+Mtf/oL7778/2f7ee+/FX/7yFzz44IOq5xB5xQPA7F8OhGScCkZQX1cNV50VUjDDa7s19eG++f3nn89FLmMq9EnJ92yb41IYNTYLXLUWOGosYh9WVJObefZZjxPSNBpcNbNFh8oHS7Pub7a1NGI/c6UillL4bUlvlZYz8y1vpZBvpisehhceAHDixAls374dhw4dgsViweLFi7Ft2zZ897vfxZYtW9De3p5s6/f70d7ejsHBQTQ3N6saX3jhUeaYb3mrtHyBysuZ+Za3Usg3U+Fh+DMeANDQ0IB/+Zd/WXD80UcfXXDM7Xbj1VdfFREWERERCcb/z6VEREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMIY/r9OGwgEcNddd+HFF1+E2WyGy+XCtm3b8JGPfATBYBA//elP8eyzz6K2thYOhwPf/OY38elPf9rosIxlAqSQjFOBCOqdNrhqLUA8/Tmz2YTTQRnT4Rmcs6gmtW0h49TSJpdx84lN6dz843YLpGD2/CZ9AfhOTRdmD0Q6uz7Hjvhgt1n0228iIg0MLTxisRiuv/56rFy5Eo888ggsFgteeOEFXH/99XjsscfQ29uLFStW4LHHHkN1dTUOHz6MG264AXfddRdWrlxpZGjGMQGj46fRNzCMsByFzWpGb5cXK5oWAUg9d57bjv912Yfw612HFrY1+sM+S5xZ2yjFl0sfjbGlPbd0EUbfTl3brrUt2DE4ohyHCTg6OYUJXwD9e8fE74FIavZFj70jIsrC0FstBw8exHvvvYfe3l5YLLM1zqpVq7B9+3Y8//zzmJycxC233ILq6moAwIc//GHceOON+Nd//VcjwzKUFJKTH9wAEJaj6BsYhhSSF5xb421MFh3z2xYyTi1tchk3n9iUzvmlyIK1TRQdmfI7Oikli45c4y0FRu03EZFWhl7xOHz4MNra2lBVlVrfXHrppbj//vtx4YUXwmQypZz75Cc/ibvuukvTPG63I+9Ys/F4nKraHTviS35wJ4TlKEJyFIgj9ZwJim2bmxryjjnnODGbb6Y2SvHl0kdTbPPX8Oy5k4Gw5rU9dsSHWDxesD0QSc2+6LF3xUztz3C5YL7lrZTzNbTwqKqqQjye/hqtyWRCNBpdcFyWtf925fcHEIsZdy3Y43HC55tS1dZus8BmNad8gNusZtitZsBkSnsuXVu18+UqY5wAfL6pjG2U4sulj6bYFNZwscOmeW3tNguqFMYTsQciqdkXPfauWGn5GS4HzLe8lUK+mQojQ2+1XHjhhTh8+PCC4uPuu+/G9PQ0Dh06tKDQGB4eRltbm5FhGcpVa0Fvlxe2s1/gifvkLrt1wbmhVyZw/VUXpm1byDi1tMll3HxiUzrndlUvWNuezras+TUvcaG7o6UgeyCSUftNRKSVKa50SUIH8XgcV199NS655BLcdNNNMJvNGBoawi233IJHHnkE27Ztw/nnn4/vfe97sFqtOHToEHp6ejQ/XFpMVzwAvP+XAcEI6uuqZz+45/9FxtlzZkvV+3/V4rKltjWaQpwp+WbKReO4esSW8dz843XW2b9qyRSHCZDjJvhOnSnMHoh0dn1CchR2q1lxPfLeuyJUCr8h6on5lrdSyDfTFQ9DCw8AOHHiBLZv345Dhw7BYrFg8eLF2LZtGz784Q9jenoaP/vZz7B//36YzWYsWrQIvb29mv+ctugKjxLHfMtbpeULVF7OzLe8lUK+BS08RGDhoS/mW94qLV+g8nJmvuWtFPIt2DMeRERERHOx8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkjEXkZBMTE7j88svR3Nyccry+vh5XXXUVOjs7RYajjQkITM9AOjOD6fAMzllUA1etBcj0P4pbBfilCPzSNNyLauB2VgMxY2OUQjJOBSKod9qyx5em73EpjHelMKotVTgzLcNhr85pnJxiUKJ1HfWIQYe11GUNjFhPIqICElp4AMC5556LXbt2pRzbtm2b6DA0icXiODo5hQlfAP17xxCWo7BZzejt8mJF06L0XwRVwKtHT2DH4EiyfU9nGy5qbjCm+DABo+On0TcwrC6+LH27O1pgqzbj0aE38aXLV+Q8juoYlGhdRz1i0Hktc14DI9aTiKjAeKtFhXeOB3F0UkoWHQAQlqPoGxiGFJLT9vFLkeSXZaL9jsER+KWIITFKITn5BaUmvmx9+/eOQQrKWONtzGsctX2VaF1HPWLQey1zXQMj1pOIqNCEX/F47733cNVVVyVfb9y4Me8x3W5H3mNkMnLEh1g8nvwCSAjLUYTkKJqbGhb0eePYZNr2JwNhLF/m1j3GY0d8muJT0zcWjwOm/MdR01eJ1nXMNQaPx5n3GPn2NXKs+ebmWykqLWfmW95KOd+yuNXi9wcQixl37bnBVYsqkwk2qznli8BmNcNuNcPnm1rYx2lL236xw5a2fb7sNoum+NT0rTKZEIvF8x5HTV8lWtcxlxg8HmfKOSPWMpc1MGI9gYX5VoJKy5n5lrdSyDdTYcRbLSqcd04dmpe4Zp97sJoBIHm/3WW3pu3jdlajp7MtpX1PZxvcrmpDYnTVWtDb5VUdX7a+3R0tcNVZMTQ8kdc4avsq0bqOesSg91rmugZGrCcRUaGZ4vG4sMfUJiYmcO2112Lfvn0px7dt24ZPfepTOf9Vi9FXPDweJ3zHp1L/qsVlm/0CUPtXLa6a2S9LEX/VEoygvq46e3xp+h6XwrDXWmGzVOFMWIajxprTODnFoETrOmqMIe1vDzqspS5rYMB6lsJvS3qrtJyZb3krhXwzXfEQfqtFyQ9/+EPceeedyde//vWv8YlPfKKAEc0TBxw2Cxw2S8qxjGKA21ENt6M6+dpQccBVa4Wr1pp8nUvfxJt6sT2/cTT3VaJ1HfWIQae1zHl+I8YiIioCQq94GEXIFY8iry71xHzLW6XlC1Rezsy3vJVCvnzGg4iIiIoCCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwggpPGZmZvDLX/4S69evx4YNG7Bu3Trs2LEDc/+HcX/yk59g1apViEQiIkIyngmQzsh4890A3jk1jUAkCphSz02eCMEfiGD8eBDS9Mz753Wcf9xnwNi5zGkCpGkZR49NYeJECCfPyMrvPqNiVxg3FovnN9+8cQPhGbHrniYGYfMSEWlkETHJ7bffjuPHj2Pnzp1wuVwIBAL4+te/DqfTiX/6p3/CzMwMnnjiCXzsYx/Dk08+iSuvvFJEWMYxAaPjp9E3MIywHIXNakZ3RwsaPQ40f8CJ0bdP4/dPjqJj5VLs3DuWbNPb5cWKpkVAPPsUWufXbexc5kT69Ti3oRYrmuqBmIDYlcZdugjPj7yDnz38cm7zKez1Y39+C1NB2fh1z5Sb0fMSEeXA8Csex44dw+7du/HjH/8YLpcLAOBwOHDbbbfhnHPOAQD813/9F84//3x88YtfxM6dO40OyXBSSE5+CQBAWI6if+8Yjk5K8EsR9A0MY423MVl0JNr0DQxDCsmGzK/X2LnMqbQe/9+7QfiliOpxjIjPL0WSRUcu8ynl1v7xJiHrrhSDiHmJiHJh+BWP1157Dc3NzVi0aFHK8ebmZjQ3NwMABgcHcfnll+PSSy/FLbfcgiNHjuCCCy5QPYfb7dA15nQ8HqfqtseO+JJfAglhOYpYPI6TgfDsORPStgnJUTQ3NeQVq9L8WsbWkm+2ORFPn2tiPZYvc+sau5b4kvuR43xK4yZudei1p7nEkGlerftbDiotZ+Zb3ko5XyG3Wkym9284P/nkk/jlL3+JWCyG6upq/PrXv8azzz6LO++8EzU1NbjsssvQ39+PW2+9VfX4fn8AsZhx15Q9Hid8vinV7e02C2xWc8qXgc1qRpXJhMUOG2xWc/LY/DZ2q1nTXFrmVzu21nyzzQmTKeN6zJ0r39i1xpfYj1znUxo3cYtDrz3NJQaleXPZ31JXaTkz3/JWCvlmKowMv9XykY98BEePHkUgEAAAXH755di1axd++ctf4uTJk9i9ezfi8Tj+8R//Ee3t7Xj++eexa9cuTE9PGx2aYVy1FvR2eVMKjO6OFjQvccHtqkZvlxdDr0xgU0dLSpveLi9cdqsh8+s1di5zKq3H+f9PHdyuaiGxK43rdlVjy9UX5zyfUm77XhoXsu5KMYiYl4goF6b43D8tMchtt90Gv9+P7du3w+VyIRqN4uGHH8bvfvc71NbW4sYbb8SGDRsAALFYDJ///Odx0003obOzU9X4xXbFA8DsXxmEZByXwqixWVQjT/oAACAASURBVOCqtcBRY5n9TfjsucC0DJvVguC0jPq66tkvCr3SODvHqWBE89g5V9OZ5jz7Vxe+02HYrFWoq7FiscOa+mCpDrHnEp/b7cBbEydzn2/euFVVJpyYCuu/pxpiyDRvKfy2pLdKy5n5lrdSyDfTFQ8hhUcsFsNvf/tbPProo4jH44hEIvB6veju7sbXv/51PPPMM7Ba3//t7MEHH8Sjjz6KgYEBVeMXZeFRwphveau0fIHKy5n5lrdSyLfghYfRWHjoi/mWt0rLF6i8nJlveSuFfAv6jAcRERFRAgsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhClI4TExMYH29vYFx1tbWxXPERERUenjFY9iZgKkMzLGfUFI0zNA1ezryRMh+AMRjB8/e9yk0N6UcXRjYsw2p8gYE3MdD8IfiGDyRCj3OasAfyCCsUkJ/mBE3U9OplwLsVckDve3tFTafhU4X4vY6Ug1EzA6fhp9A8MIy1Gc57aja20LBp4eQ8fKpdi5dwxhOQqb1YzeLi9WLF2E0bffb5883rQIiIuJMeucWtvrHNumjhbsPfg2vnT5Cm1zVgGvHj2BHYMjybF6OttwUXMDEFM/fzJXCFwHEk/k+5zyV2n7VQT5FuyKx3vvvYerrroq5f/ofVJITr4xAGCNtxE7BkewxtuYLDoAICxH0TcwDL8USWmfOC6FZGExZptTa3u9Y9u5dwxrvI2a5/RLkWTRkRhrx+AI/FJE0/yJeUWuA4nH/S0tlbZfxZBvwa54nHvuudi1a1fKsdbW1pzGcrsdeoSUkcfjNHyOuY4d8SXfGAAA0+wbJPnfOcJyFCcC4bTHQ3IUzU0NmudXk++CGLPMqbV9PpTmSqzf/Dkz5fvGscm0Y50MhLF8mVvT/CE5CsTT76ER66BE9Pu5GIjKWeT7PJNK2+Nc8y2W/dKqlPMti1stfn8AsZhx14g8Hid8vinDxk/HbrPAZjWnvEFsVnPyv/OPNzhsaY/brWbNsavNVylGpTm1ts+H4vrFF86ZLd8GZ/q1XeywKfbLlCtMJmHrkE4h3s+FJjJnke9zJZW2x/nkWwz7pVUp5JupMOLDpUXKVWtBb5c3WWwMvTKBns42DL0ygU0dLSlFSG+XF25XdUr7xHGX3Sosxmxzam2vd2ybOlowNDyheU63sxo9nW0pY/V0tsHtqtY0f2JeketA4nF/S0ul7Vcx5GuKx+PCH5+ZmJjAtddei3379qUcb21txZ/+9Ke05zIpxyseAGafPA7JOBWMoL6uGq46K6SgjMC0DJvVguC0PHvcbp19KGh++8RxjTTlq3VOnWLUGltdjRVheQaOGuuCOVXlWzX7rIdfmobbVTNbdCg9WJpm/gW5ilyHeSrtt2GgADkXcH+BytvjvPMt8H5pVQr5ZrriUZDCQ29lW3gUCPMtb5WWL1B5OTPf8lYK+fJWCxERERUFFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhLEUYtLW1la8/vrrmJiYwOWXX47m5mYAwPT0NFpbW3HbbbfhnHPOKURoREREZKCCX/E499xzsWvXLuzatQtPPvkkli5dit7e3kKHlSIWi0M6I2PcF4Q0PQOY5jUwQdX5yRMh+AMRjB9XaJdNtnm0Uhgva76i41Qat0rDPBli0pTvnHECkag+eRq1XkRERaggVzyUmEwmfPOb38Qll1yCv/3tb1i+fHmhQwJMwPMj7+BnD7+MsByFzWpGb5cXK5oWAfHZ86Pjp9E3MJzx/O+fHEXHyqXYuXcsfTsVcWScJ4e80o63dFHmfEXHmWHcns42DDw9hnf8oczzZIoJWfZXYRxnnRVfWL0M/bnup9HrRURUpAp+xWO+6upqLF26FG+++WahQwEASCE5+aUEAGE5ir6BYUghOXk+8aWR6fwab2Oy6EjXTk0cmebJJa904/mlSMZ8RceZadwdgyNY423MOk+mmLLtr9I47R9vShYd+eRp1HoRERWrorrikWAymVBTU6O6vdvtMCyWY0d8yS+FhLAcRUiOormpQf15EzK2yzcOvfI6GQgXVZzZxp17W0JpnkwxIa5+X1LGyXM/1cSWz3pl4/E4DRu7WFVazsy3vJVyvkVXeEQiEbz11lu44IILVPfx+wOIxYy5Lm23WWCzmlO+HGxWM+xWM3y+KdXnE8eV2uUbh155LXbYiirObOPOvR2hNE+mmGAyqY53/jh65GnUemXi8TgNG7tYVVrOzLe8lUK+mQqjorrVEovFcO+99+Kiiy5CU1NTocMBALhqLdhy9cUpxUNvlxcuuzV5vrfLm/X80CsT2NTRothOTRyZ5sklr3TjuV3VGfMVHWemcXs62zA0PJF1nkwxZdtfpXH2/fc4uvPYTzWxERGVI1M8Hhf+CJvSn9PGYjGsWLEC3/ve91BfX696PCOveACzt3LemjiJU8EI6uuqZ78U5k5nmr1Xn+18YFqGzWpBcFpO3y6bbPNopTBe1nxFx6k0bp0VUlDlPBli0pTvnHEanDbEYvH88zRqvRSUwm9Lequ0nJlveSuFfDNd8ShI4aE3owuPUthkPTHf8lZp+QKVlzPzLW+lkG/J3GohIiKi8sbCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwBS08WltbAQAHDx7E5s2bCxkKERERCWApdABUhkyAFJIROCPDVm1BcFpGvcOGKhNwQgqj3mmDq9YCxAsT16lARFsMufbLlwkITM9AOjODcGQGLns1wpEZOOzVhVm/szEVZC20mhenOxbPeN5lt0AKGpRXqaxZOSmVNS+VOHXGwoP0ZQJGx0/j90+OomPlUuzcO4awHIXNakZ3Rwse+/NbmArK6O3yYkXTInE/ZGfj6hsYTsaTjCHXfkbGbgKOTk5hwhdA/5w13NTRgr0H38aXLl8hdv3OxlSQtdAqTZxbrr4YLR9wzsaZ5nxPZxsGnh7DO/6QvnmVypqVk1JZ81KJ0wB8xoN0JYVk9A0MY423MVl0AEBYjqJ/7xjaP96EsBxF38AwpJAsPK658aiJIdd+esR7dFJKFh2JuXfuHcMab6Pw9UvEVIi10CpdnD97+OVknOnO7xgcwRpvY/K1XnmVypqVk1JZ81KJ0whlccXD7XYYPofH4zR8jmKSa77Hjvhmf5BMSP5AJSSOJ/4dkqNobmrIN1Rtcc2LJ3T2mFK+mfoZGfuxIz7E4nHFNcw3hlz2t1BroVW2OJXOJ96b89sbGYuRKvUzq1zep9mU8v6WReHh9wcQm38PV0cejxM+35Rh4xebfPK12yywWc0AAJvVnPKDZbOak5cQbVYz7FazsHVNxDU/HvvZWJXiyNTPyNjtNguqTCbFNcwnhlz3t1BroVW2OJXOz728rVdehVqzSv7MKpf3aSalsL+ZCiPeaiFduWot6O3yYuiVCWzqaEkpQro7WrDvpfHkvUyX3So8rrnxqIkh1356xNu8xIXueWu4qaMFQ8MTwtcvEVMh1kKrdHFuufriZJzpzvd0tmFoeCL5Wq+8SmXNykmprHmpxGkEUzweL9hjLK2trXj99ddx8OBBXHfddaipqUme27hxI+644w5V4/CKh77yzjfxVy3TMmzWs3/VUleNqioTTkyFUV9XPfvDVai/aglGUmLImq9CPxHxLvirFnkGjhprXjHktb+FWgut5sW5rHEx/P6A4nlXnXX2r1qMyKsAa1bxn1kl+j5VG2cp7G+mKx4FLTz0wsJDX8y3vFVavkDl5cx8y1sp5MtbLURERFQUWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEqYghUdraysAYGJiAq2trXjuuedSzre3t2NiYqIQoREREZGBCn7Fw2q14gc/+AECgUChQyldJkA6I2PcF4Q0PQOYUo9PngjBH4hg/Pi887mMmUd8b74bwDunphGIRPMbL884kjlVzb4eOeLLnKOatZjTJhCJ6rd2Rsl1f7X00/M9pJdijImowlgKHcC5556L1atX4yc/+QnuvPPOQodTekzA6Php9A0MIyxHYbOa0dvlxYqlizD69mn8/slRdKxcip17x1LPNy0C4umHjMXi6cfM0EdLfN0dLWj0ONC8xKl9vFzNi+M8tx1da1uwY3Akc45K6zu33Zw2zjorvrB6Gfo1rLdoOe+vmrXIpa0oxRgTUQUq+BUPANi2bRueffbZBbdcKDspJCc/SAEgLEfRNzAMvxRB38Aw1ngbk0XH3PNSSFYc853jwbRjZuqjJb7+vWM4OinlNF6u5sexxtuYLDoScaXLUWl957ab26b9403JoiPTuIWU6/6qWYtc2opSjDERVaKCX/EAAIfDgTvvvBM/+MEPsHv3bs393W6HAVGl8nichs+Ri2NHfMkP0oSwHMWJQHj2uAlpz4fkKJqbGtKOOaIwZqY+WuOLxeM5jZerBXGoXBel+Oe2S2mTw3qLluv+qlmLXNqKEopEiy4mIxXrZ5ZRmG/pKIrCAwA+85nPJG+5aOX3BxCLGXet1ONxwuebMmz8fNhtFtis5pQPVJvVjAaHDTarOfl6/nm71ayYU4OrVnMfrfFVmUw5jZcrpTiy5ajUb267+W30Wjuj5Lq/atYil7YieDzOoovJSMX8mWUE5lt8MhVGRXGrJSFxy+W9994rdCglw1VrQW+XN6XI6O3ywu2qRm+XF0OvTGBTR8uC8y67VXHM886pSztmpj5a4uvuaEHzEldO4+VqfhxDr0ygp7Mta45K6zu33dw2+/57HN0a11u0XPdXzVrk0laUYoyJqBKZ4vG48MeqWltb8frrr2NiYgLXXnst9u3blzz37LPP4itf+Qr+9Kc/obGxUdV4lXzFA8Dsk/ohGaeCEdTXVc9+kMbfPx6YlmGzWhCcllPPK/B4nPAdn0o/Zh7xHZfCqLFZ4Kq1wFFjEf9A3/x1qrNCCsoIyVHYrWblHJXWV6FNg9OGWCyuz9oZIK/9VbMWubQ1WPJnuIhiMlLRf2bpjPkWn0xXPApSeOit4gsPnTHf8lZp+QKVlzPzLW+lkG/J3GohIiKi8sbCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwBSs8WltbU16PjY2htbUVe/bsKVBEBjIB0hkZ474gApFo8t/S9AwC4Znkv2Fa2D7leCHpFZPWcTK1VzqXOH48CH8ggskTIV1injwRgj8QwfjxItqXhPlrUSXoPVSM71UiKmqWQgeQMDg4iHXr1qG/vx/r1q0rdDj6MQGj46fRNzAMZ50VX1i9DP17xxCWo7BZzejuaMFjf34LU0EZvV1erFi6CKNvz7ZPtOnt8mJF0yIgXvgc8opJ6ziZ2kPhXJr129TRgr0H38aXLl+Rc8y/f3IUHSuXYuecvSv4vsyLMZHzeW47uta2YMfgiLGx6vW+IKKKUhS3WmZmZrB7925s2bIFhw8fxvj4eKFD0o0UkpMfzO0fb0oWHQAQlqPo3zuG9o83ISxH0TcwDL8USbZPtOkbGIYUkosih3xi0jpOpvZK59Kt3869Y1jjbcwr5jXexmTRkc8aGGH+WqzxNiaLDsC4WPV6XxBRZSmKKx779+/HkiVLsGzZMqxduxb9/f34zne+o7q/2+0wMLpZHo8zp37HjviSH8ww4f1/nxWWo8nL02E5ihOBcNo2ITmK5qaGnGLIxdx8U3LIIyat42Rqj3j6tTypsH6JtVeaS2l/kzEo7J3ofUlnwTqpiDXX93PGedPMU0z0yLmUMN/yVsr5FkXhMTg4iCuuuAIAsGHDBmzduhU333wzqqurVfX3+wOIxYy7tuvxOOHzTeXU126zwGY1Jz+g5/478TpxWdpmNaPBYUvbxm415xyDVvPznZ9DrjFpHSdTe5hMac8tVlg/xJXnyrS/iRgS4xRyX5QorZNSrPm8n9XMWwxrMp9eOZcK5lveSiHfTIVRwW+1+P1+HDhwAL/5zW/Q3t6OW2+9FZIk4amnnip0aLpw1VrQ2+WFzWrGvv8eR3dHS8oXWXdHC/a9NJ68P+52VSfbJ9r0dnnhsluLIod8YtI6Tqb2SufSrd+mjhYMDU/kFfPQKxPYNG/vCr0vCfPXYuiVCfR0thkeq17vCyKqLKZ4PF6Qx8BaW1vx+uuv47e//S2ee+453H///clz9957L/7yl7/gwQcfVDVWMV/xADD75H9IxqlgBA1OG2KxOE4FI6ivq0ZVlQknpsKor6ue/cCOp7ZPOS5I2nz1iknrOJnaK52bc7yuxoqwPANHjVVxrqz7e3a8wLQMm9WC4LRckH3JaP5a1FkhBdOvm66/LRX4vapWKfyGqCfmW95KId9MVzwKfqtlcHAQW7ZsSTl2zTXX4P7778fRo0fR3NxcoMh0FAdctVa4at//TXDuvx02S7Jd2vbF8EGuV0xax8nUXuncgvWu1jVmtyPP8YwwP+eYoPdQMb5XiaioFazweP311wEAjz766IJzbrcbr776quiQiIiIyGAFf8aDiIiIKgcLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCCCk8Dh48iM2bNydfBwIBbNq0CT/+8Y/xt7/9Dddeey2uvPJKfOELX8D3v/99hEIhEWGVHxMgnZEx7gtCmp4BTArHlNpqGVfDucmTIbw3FcYb70jwByPGvutyyGvkiC/3tRHBiHjyGTPfeIptfYlIKIvoCYPBIL761a/ik5/8JLZu3Yr169fjRz/6ET72sY8hFovh9ttvx89//nPccsstokMrbSZgdPw0+gaGEZajsFnN2HrNxYjMxFKO9XZ5sWLpIoy+fXrh8aZFQDz7uMm2yH7u90+OomPlUuzcO5Zs09PZhouaG4CY8WugOS8tayOClpxUisXiuY+ZbzwG5ENEpUXorZZQKIQbbrgBq1atwtatWwEAx48fx/T09GwwVVX4xje+gfXr14sMqyxIITn5YQ4AYTmKo5PSgmN9A8PwS5G0x6WQrGrcRFs159Z4G5NFR6LNjsER+KWIkDXQmpeWtRFBS05qvXM8mPOY+cZjRD5EVFqEXfE4c+YMvva1r+GNN97AL37xi+TxW265BTfeeCPOPfdcrFy5Ep/73Ofw2c9+VtPYbrdD52gX8nichs+Rj2NHfMkP84RYPL7gWFiO4kQgnPZ4SI6iuakBwPv5phs30RZxZD9nSt/mZCCM5cvcuSecRqZYE3lla6tmbUTSkpNaI3mMmW88RuSjVrH/DOuN+Za3Us5XWOExMjKCb33rW/jgBz+IW2+9Fffddx8AoLOzE5///Ofx/PPP489//jO2bduGjRs34vvf/77qsf3+AGIx467TejxO+HxTho2vB7vNApvVnPKhXmUyLThms5rR4LClPW63muHzTaXkm27cRFsojD/3XOLY/DaLHTbd1zRTrPPnUmqbbW1E05KTWg2u2pzHzDceI/JRoxR+hvXEfMtbKeSbqTASdqvF6/XipptuwrZt2/DGG2/g4Ycfxv/8z//gF7/4BRwOBzo6OvDDH/4Q/f39+MMf/iAqrLLhqrWgt8ub8mXfvMS14FhvlxduV3Xa4y67VdW4ibZqzg29MoFNHS0pbXo62+B2VQtZA615aVkbEbTkpNZ559TlPGa+8RiRDxGVFlM8Hjf8ka6DBw/ivvvuw4MPPggAePXVV/HlL38Z999/P2688Ubcc889+PSnPw0AePrpp/Fv//ZvePjhh1WPzyseZ5lm76GfCkZQX1ed/DBfcCyu0PbsEi7IN0NbNecC0zIsFjNOByJocNpmiw69HyxVE6tC25Achd1qVrU2BaFzPB6PE77jU7mPmW88BVjfkvkZ1gnzLW+lkG+mKx7C/6oFAC666CJcd911+OEPf4h7770XfX19uPXWW2G1WrFs2TLcfffdhQir9MUBV60Vrlpr8jWQ/phSWy3jaj13rtM2e9yooiNbPAptm5saZn+Ic1kbEYyIJ58x842n2NaXiIQScsXDaLzioS/mW94qLV+g8nJmvuWtFPItimc8iIiIiFh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhJGSOFx8OBBbN68Ofk6EAhg06ZNWLt2Ldrb2xEKhZLnjh8/jtWrV+Ovf/2riNCo0EyAdEbGuC8IaXoGMOXRxwQEwjOYPDWNN98NqB9Pz9hzyUdUbPn0zTZeofIuhvUW/b4TIbGux4PwByKYPBEqj7yoKFhETxgMBvHVr34Vn/zkJ7F161b09vbi5z//OW655RYAwB133IFrrrkGH/nIR0SHRqKZgNHx0+gbGEZYjsJmNaO3y4sVTYuAuMY+Sxfh6N+nMOELoH/vmPrxdIx96zUXIzIT05aPEXJZ1wx9s+aVz3yFylPHGI5OCnzfiZBmXTd1tGDvwbfxpctXlG5eVDSE3moJhUK44YYbsGrVKmzduhUAcNttt+HRRx/F4cOHsW/fPvz9739HT0+PyLCoQKSQnPxwA4CwHEXfwDCkkKy5j1+K4OiklPzwVzuenrEfnZQ052OEXNY1U99seeUzXz4KNe/8GES+70RIt647945hjbexpPOi4iHsiseZM2fwta99DW+88QZ+8YtfJI+fc8452LZtG26//XZMTU3h3nvvhcWiLSy326F3uAt4PE7D5ygmIvI9dsSX/HBLCMtRhOQompsaNPU5GQgjFo9rHi9Ba77p4shnfj2pWVelfHPJK5d91IPWeY14Tx874iuafZ8v13yV1hWm4shLCT+jS4ewwmNkZATf+ta38MEPfhC33nor7rvvvuS5K6+8En/4wx+wfv16NDc3ax7b7w8gFjPu2p/H44TPN2XY+MVGVL52mwU2qznlQ85mNcNuNSvOr9RnscOGKpNJ83hAbvmmiyPX+fWWbV0z5ZtLXrnsox60zGvUe9pusxTNvs+VT75K64p44fNSws/o4pOpMBJ2q8Xr9eKmm27Ctm3b8MYbb+Dhhx9OOf+BD3wAH/jAB0SFQ0XAVWtBb5d39kMNSN4bd9mtmvu4XdVoXuJCd0eLpvH0jL15iUtzPkbIZV0z9c2WVz7z5aNQ886PQeT7ToR067qpowVDwxMlnRcVD1M8Hjf8MaGDBw/ivvvuw4MPPggAePXVV/HlL38ZAwMDuOCCCwAA27Ztw6c+9Sl0dnZqHp9XPPQlNF/T7D3lU8EI6uuqZz/UVDwAmbaPCQhMz0A6M4Pp8AzOcdlUjZdzvuniQA75GCHDumbNN5e8ctlHPaic19D3dI7vOyPlne+cda2rsSIsz8BRYy14Xkr4GV18Ml3xEP5XLQBw0UUX4brrrsOWLVvwH//xH7DZbIUIg4pBHHDVWuGqtSZf59wnDjhsFjhslpS2hlGIQ3M+RshlXbP0zThePvPlo1DzzotB6PtOhPnriurkcaJ8CbniYTRe8dAX8y1vlZYvUHk5M9/yVgr5FsUzHkREREQsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhJGWOFx8OBBbN68Oe25sbExtLa2Ys+ePaLCISIiogIoiiseg4ODWLduHfr7+wsdyiwTIJ2RMe4LQpqewcxMLOU1TOnbT54IwR+IYPy4QjuV86Xtp6aNHn30GlPvdvNVAf5ABGOTEvzBSG7v5LNzjxzxiV8f0WMRERUJS6EDmJmZwe7du/HQQw+hu7sb4+PjaGpqKlxAJmB0/DT6BoYRlqM4z21H19pW7Bh8DWE5CpvVjN4uL1Y0LQLi77f//ZOj6Fi5FDv3jqVvp3K+tP3UtMll3DzXRnFMvdvNVwW8evQEdgyOJPv1dLbhouYGIKZzLlroOaYR8RERFYGCX/HYv38/lixZgmXLlmHt2rUFv+ohheTkhz0ArPE2JosOAAjLUfQNDEMKySnt13gbk0VHunZq50vXT02bXMbVSu2Yerebzy9FkkVHot+OwRH4pYjuuWih55hGxEdEVAwKfsVjcHAQV1xxBQBgw4YN2Lp1K26++WZUV1erHsPtdugWz7EjvuSHPQDAhNTXmH0dkqNobmp4v32WdqrnS9NPTZtcxs3E43HmPKbe7eZ749hk2n4nA2EsX+bOmls+c4sa04j45kq3v+Wu0nJmvuWtlPMtaOHh9/tx4MABHDp0CA888ADi8TgkScJTTz2VLEbUjRNALKbP9We7zQKb1ZzyoZ/utd1qhs83lWyfrZ3W+eb2U9Mml3GVeDzOtG3Ujql3u/kanLa0/RY7bFlzy3duUWMaEV+C0v6Ws0rLmfmWt1LIN1NhVNBbLbt378aqVatw4MAB7Nu3D8888wx6enqwc+fOgsXkqrWgt8ubLCaGXplAT+dHU4qL3i4vXHZrSvuhVyawqaNFsZ3a+dL1U9Mml3G1Ujum3u3mczur0dPZltKvp7MNbpf6q2SFXJ9CxUdEVAxM8XhcyKNqBw8exHXXXYeamprkMY/Hg23btqG9vT15zO/3KtiJSAAAIABJREFUo729HYODg2hublY1tp5XPADM/jVBSMapYAT1ddVYuqQeb0+eSr522a0LHpKUQjIC0zJsVguC03L6dirnS9tPTZtcxk0jYzWtdky9281XNfush1+ahttVM1t0qH2wdN7cITkKu9Wsfm4VY2rOx+ix5iiF35b0Vmk5M9/yVgr5ZrriIazwMJLuhcc8pbDJemK+5a3S8gUqL2fmW95KId+ivdVCRERElYWFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhLEYOPjExgc997nPYtGkT7rjjjuTx0dFRfPGLX8T27dtx3333oaamBlarNXn+wx/+MLZv325kaERERFQAhhYeAFBfX4+hoSFEo1GYzWYAwOOPP46GhoZkm1/96ldobGw0OpTiYQKkkIxTgQjqnTa4ai1APMt5zDtmt0AKZhijWGXLPV0bLbmqGT+PeKtMwAkpvHBsvefNR5b1c8fimdsX43upFGIkIlUMLzzq6uqwfPlyvPjii1i1ahUA4LnnnsPq1auNnro4mYDR8dPoGxhGWI7CZjWjt8uLFU2LZj9I05zfes3FiMzEUo71dLZh4OkxvOMPLRyjWGXLPU2b89x2dK1twY7BEeU+WsbPM97ujhY89ue3MBWU3x8bOs+bDxXrt+Xqi9HyAafi+63o3kulECMRqSbkGY/169djz549AIDXXnsNra2tKbdWbrjhBlx11VXJ//vjH/8oIqyCkEJy8gMUAMJyFH0Dw5BCsuL5o5PSgmM7BkewxtuYdoxilS33dG3WeBuTX5pKfbSMn2+8/XvH0P7xppSx9Z43H2rW72cPv5zx/VZs76VSiJGI1DP8igcAXHbZZbjnnnsQi8XwxBNPYP369Xj88ceT5/O91eJ2O/QIMyOPx6nLOMeO+JIfoAlhOYqQHEVzU0Pa87F4PG0fmNKPoQe98p0rW+5p25iQtY+W8ZWky1dpvMS6J8ZGXH2MRtO6fvmsmSh6xWjEe7qYMd/yVsr5Cik8HA4Hli9fjpdeegkvvPACvv3tb6cUHvny+wOIzb9vrSOPxwmfb0qXsew2C2xWc8oHqc1qht1qhs83lfZ8lcmUts/cy8xzx8iXnvnOlS33TG0y9dEyfjpK+SqNl1j3xNhQ2B+99kMLreuX65qJpEeMRr2nixXzLW+lkG+mwkjYn9OuX78ed911Fy688EJYLELqnaLkqrWgt8s7+wUGJO9Xu+xWxfPNS1wLjvV0tmFoeCLtGMUqW+7p2gy9MoGezraMfbSMn2+83R0t2PfSeMrYes+bDzXrt+XqizO+34rtvVQKMRKReqZ4PG7YpYKJiQlce+212LdvH4LBIFavXo0dO3bg05/+NLZt24ZPfepTaf+ctra2Fv39/arnKaUrHgDef0I/GEF9XfXsB2i6v2qZex7zjtVZZ/9SQWmMPBhaTWfLPV0bLbmqGX+ejPnOG6+qyoQTU+GFY+cwr2GyrN+yxsXw+wPK7QsZu5I8YyyF3xD1xHzLWynkm+mKh6GFhyglV3gUOeZb3iotX6Dycma+5a0U8i2KWy1ERERELDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCWNo4TExMYHW1lbcdtttKcdHR0fR2tqKwcFBtLe3Y8OGDbjqqquwYcMGdHd347XXXjMyLCIiIioQi9ET1NfXY2hoCNFoFGazGQDw+OOPo6GhIdnmV7/6FRobGwEA+/fvx/XXX48nnngipQ0VIRMghWScCkRQ77TBVTv7dlpwLF7gOPVS7PnOi6/KBJyQwoWPi4hoDsMLj7q6OixfvhwvvvgiVq1aBQB47rnnsHr16rTtP/vZz+KjH/0o/vM//xPXXnut0eFRrkzA6Php9A0MIyxHYbOasfWaixGZiaUc6+3yYkXTotL/0kuTb2+XF9WWKvz0/7xc+HzTxNfd0YLH/vwWpoJy+ewDEZU8Ic94rF+/Hnv27AEAvPbaa2htbYXValVs/6EPfQhvvvmmiNAoR1JITn7JAUBYjuLopLTgWN/AMKSQXMhQdZEu376BYRydlIoi33Tx9e8dQ/vHm8pqH4io9Bl+xQMALrvsMtxzzz2IxWJ44oknsH79ejz++OOK7U0mE2pqalSP73Y79AgzI4/HafgcxSRbvseO+JJfcgmxeHzBsbAcRUiOormpuG+b5ZJvWI4iFo8vOFaIfJXigyl9XJX2fgYqL2fmW95KOV8hhYfD4cDy5cvx0ksv4YUXXsC3v/3tjIXH66+/jnXr1qke3+8PIBYz7hqyx+OEzzdl2PjFRk2+dpsFNqs55cuuymRacMxmNcNuNRf1+uWar81qRpXJlNKuUPkqxZe4tTI3rkp7PwP8GS53zLf4ZCqMhP057fr163HXXXfhwgsvhMWiXO/s27cPo6OjWL9+vajQKAeuWgt6u7yzX26Y/WJrXuJacKy3ywuXXfm2WqlIl29vlxfNS1xFkW+6+Lo7WrDvpfGy2gciKn1CrngAs7dbvv/97+Nb3/rWgnM33HBD8pmPxYsX4/7774fDYfztE8pDHFjRtAg/uWk1TgUjqK+rTn6xLThWDg80Fnu+aeKrqjJh2RJXee0DEZU8UzweL/mPI95q0RfzLW+Vli9QeTkz3/JWCvkWxa0WIiIiIhYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIQRXnhMTEygtbUVt912W8rx0dFRtLa2YnBwEJs3b8bBgwdFh0ZEREQGK8gVj/r6egwNDSEajSaPPf7442hoaChEOPoxAdIZGeO+IKTpGcA051wV4A9EMDYpwR+M5L7ymeYQ0V/DHG++G8A7p6YRiESBKjHzBsIzmDw1jTffDczOk8u8+axRKexPIeYiIjrLUohJ6+rqsHz5crz44otYtWoVAOC5557D6tWrCxGOPkzA6Php9A0MIyxHYbOa0dvlxYqmRYAJePXoCewYHEme6+lsw0XNDUBMpzniAvrnGON1X1iB2horfvnH1wyd9+jkFCZ8AfTvHUNYjuI8tx1da1tS1j3rvPmsUSnsTyHmIiKao2DPeKxfvx579uwBALz22mtobW2F1WotVDh5k0Jy8kMcAMJyFH0Dw5BCMvxSJPnllzi3Y3AEfimi2xwi+uc6x+mgnCw6jJz36KSULDoAYI23ccG6Z5s3nzUqhf0pxFxERHMV5IoHAFx22WW45557EIvF8MQTT2D9+vV4/PHHcxrL7XboHN1CHo8z4/ljR3zJD/GEsBxFSI5iKiSnPXcyEMbyZW7VMWSao7kp+20qLf3/f/buPbqN8s4f/3ssjeWLLBs7yhZqzIJDHLNJ8VJIgkm2EOpt3Sa4DT0myy5uz6aUkAVzutAepzSwqVloSgOpuWVp2W+htKWmNSSFXEhx2U3chtIUl1BMQhJ+eIVJozgYRZItjSX9/nCkWLYuM9LMo4vfr3N6Go2ey+fzPIr00cwoJMtXyxzBUCituNXOO20eCZrzTWeNRe5Purz+gLC5skWqr+lcxXzzWy7nm7HCw2q1Yt68edi/fz/27duH22+/PeXCY3jYjWDQuPPDdnsZnM5TCduUWMywyKaoN3OLbEKJbIJcVhDzubOslqTjqp1DzThq+6vJV8scBZKUVtxq5403j5Z801ljUfuTLru9TNhc2SKd13QuYr75LRfyTVQYZfTntM3Nzdi0aRPmz58PszljNZAubMVmtLc2wCKbACByzdxWIqOqrBBrVi6Iem7NygWoshXqNoeI/qnOUV4q4+ZrP2H4vLXn2LCqaW5knj2vO6ate7J501mjXNifTMxFRDSZFAqFhN5K5nA40NbWht7eXng8HjQ2NmLLli24/PLL0dHRgYULF+K5557DLbfcgkWLFqkaMxvOeACY+JWAV8GIx4+K0sKJN/FwWAXAsMuPYdcYqmxFE0WHlhtL1cyhU/+0q+nTc5xw+VBkMcNWbIa12AyXJ424Vc7rHhuHa3QcY75xzLJZYCuVk847Ld901ljA/qQrkq+AubJFLnxD1BPzzW+5kG+iMx7CCw8jZE3hkSeYb36bafkCMy9n5pvfciHfrL3UQkRERDMLCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwhhaeDgcDtTV1eGuu+6KOj4wMIC6ujr09PRg2bJlePDBB6Oe7+joQE9Pj5Gh6UMCXKMKBp0euMbGASnOsQJg2O3HoSEXhj1+ceWe2vi0jFGgsb8o8fKasvYeJZA8drVrpHUtjZRNsRARJWA2eoKKigrs2bMHgUAAJpMJALB9+3ZUVlZG2jz55JNoamrC/PnzjQ5HPxIwMPgRurr74VMCsMgmtLc2oNBcgO//7E+RY3dcfwk8vnE89qs3IsfWrFyAi2srgaDY+O64/hL4x4PTYq6vKQdC6sZYs3IBun9zCB8Me5P3FyXOXtT/bTn+fPgktvQciBy/sWU+dr36/2HwmPtM7GrGmpqj2nYiZFMsRERJGP7du7S0FPX19Xjttdcix/r6+tDY2Bh5fNNNN2HdunXw+/1Gh6Mbl1eJvNEDgE8JoKu7H0eGXFHHjgy5IkVH+NiWngMYdhmba6z4jgy5Ysbs8iqqx9jScwBLG6pV9Rcl3l4cH/FFio7w8R9ufRNf+NScuLHHGyvVdiJkUyxERMkYfsYDAJqbm7Fr1y4sXrwYb7zxBurq6hAKnfkqtmLFChw4cACPPPIIvv71r2sev6rKqme4MdntZVGPjx12Rt7ow3xKAMFQ9FfMYCgUs92Hbh/mnV9lTLBx4osXi1cJoLamMuq43V4WN8fJp/Hj9RcpXpwnXb6Yx8f8Zz6gvaefD+9vvLGm5qi2nQipxDL19TwTzLScmW9+y+V8hRQeV111FTZv3oxgMIgdO3agubkZ27dvj2qzYcMGtLS0oKmpSfP4w8NuBIPGnVO228vgdJ6KOlZiMcMim6Le8C2yCQVS9MX1AkmK2e4sq2XamHqKFV+8WEpkU1Qs4Xzj5Tj59H2s/qLFi7PKZol5vKjQFPlziTzx53D88caamqPadiJojSXW6znfzbScmW9+y4V8ExVGQm5ztFqtmDdvHvbv3499+/ZFXWYJs9vt6OjowLp166Ao2X+K2FZsRntrw8QHMRC5rl57ji3qWO05Ntx87Seijq1ZuQBVtkLh8dWeY4sZs61EVj3GmpULsKffoaq/KPH2wl5hwZqVC6KO39gyH8//z+G4sccbK9V2ImRTLEREyUihUMiwUwUOhwNtbW3o7e3FCy+8gKeffhoXXnghOjs70dHRgYULF+Lhhx/GU089herqifsG1q5di//93//Fd77zHaxcuVLVPJk44wFg4pcEXgUjHj8qSgsjb/TTjknAsMuPYdcYqmxFE0WHkTeWao1vytJF5Tt1jFIZLk/i/hkRK9cQJn7VMmntiywmDH80FtVm2v7GG0vtnJmgIZZc+Lakt5mWM/PNb7mQb6IzHkIutQATl1vuvPNO3HbbbQnbbdiwAcuXLxcUVZpCgK1Yhq1YjjwGYhwLAVXWQlRZT5/lEFF0aIlPyxhBjf1FiZMrglPWHkDprNLoNmrHSrWdCNkUCxFRAoae8RAlY2c88hTzzW8zLV9g5uXMfPNbLuSb8Xs8iIiIiAAWHkRERCQQCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEMbTwcDgcqKurw1133RV1fGBgAHV1dejp6cHbb7+NtrY2XHPNNfj85z+PO++8E16v18iwUicBrlEFg04PXGPjgBTnWLL+JzwYdvsxdNKbvE+iuVW0HzrpxbDbn9qcRkuUkwS4feMYGhnD/w17Ijlojj2V/VG7xnrIxJxERBlkNnqCiooK7NmzB4FAACaTCQCwfft2VFZWAgC+/vWv495778Xf//3fIxgMYsOGDfjBD36AdevWGR2aNhIwMPgRurr74VMCsMgmtLc2oNBcgO//7E9Rx+pryoFQ8v7XNc3F7lffw798tj52nyRzx+1zuv3TOwfQtOg8/GL3Ie1zGi1RTgCODJ2Cw+nGS6++Ny2HhLmrnUPF/qieJ1WZmJOIKMMMv9RSWlqK+vp6vPbaa5FjfX19aGxsBACcOHECY2NjE8EUFOCWW25Bc3Oz0WFp5vIqkQ8IAPApAXR19+PIkGvaMZdXUdX/F7sPYWlDddw+yeaO1yfcfmlDdeQDW+ucRkuUk8ur4MiQC8+cjnVqDmpj17JuWtdYD5mYk4go0ww/4wEAzc3N2LVrFxYvXow33ngDdXV1CIUmvtKtW7cON998M2bPno1Fixbh6quvxpVXXqlp/KoqqwFRR/P6A5EPiDCfEkAwFJp2zKsEUFtTGXX82GFnzP6Q4vdJ1jden0j702OnMqfdXhbzuF4S5YQQEAyFEuaQKHY1c0ztG29/1cyTKq37qiej9zcbzbScmW9+y+V8hRQeV111FTZv3oxgMIgdO3agubkZ27dvBwCsXLkS//iP/4jf//73+N3vfoeOjg6sWLECd955p+rxh4fdCAaNOzdtt5ehxGKGRTZFfVBYZBMKpOiL8hbZhBLZBKfzVNTxeP0Rit8nWd94fcLtw+20zmm3l8WNRS+JcoIkoUCSEuaQaL3UzDG5b6L9VTNPqjIxJyBmf7PNTMuZ+ea3XMg3UWEk5FctVqsV8+bNw/79+7Fv377IZZbx8XE88sgjsFqtaGpqwt13341nnnkGzz77rIiwNLEVm9He2hD1Ydje2oDac2zTjtlKZFX9r2uaiz39jrh9ks0dr0+4/Z7XHbiuaW5KcxotUU62YjNqz7FhVdPcmDmojV3LumldYz1kYk4iokyTQqGQYacKHA4H2tra0NvbixdeeAFPP/00LrzwQnR2dqKjowPz5s3DY489hs2bN+Pyyy8HAPzmN7/BE088gZ///Oeq5xFxxsPpPDXxCwSvghGPHxWlhZEPiGnHEtwkGm5bWiTDp4zDWiQn7hOjb9J5JrV3jymwyGZ4xhTVcwqrphPlJAHusXG4RscRCARQYpHhGVPU5a52jtMS7q/RN3lmYM5c+Lakt5mWM/PNb7mQb6IzHkIutQATl1vuvPNO3HbbbZFjNpsNjz/+OO6//358+9vfhizLOP/88/HAAw+ICkubEGArlmErliOPgdjHVPVHYfI+SeZW277KWqh9TqMlyikEWC1mWC1nXqKRHLTErmXdtK6xHjIxJxFRBhl6xkMUYWc8Zgjmm99mWr7AzMuZ+ea3XMg34/d4EBEREQEsPIiIiEggFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJY1jh4XA4UFdXh7vuuivq+MDAAOrq6tDT0wMA+PGPf4zm5mYsX74cLS0t+OlPf2pUSMaTANeogkGnB66xcUBS0aYgTh81Y4mKWe8xjJzz9PGjf3Xjg5ExuP0B49ZOa2xG9dM49vvH3amPLep1ma+4fkQwGzl4RUUF9uzZg0AgAJPJBADYvn07KisrAQAPPfQQXnvtNfzkJz/BrFmzcPLkSaxduxYjIyP4t3/7NyND058EDAx+hK7ufviUACyyCe2tDaivKQdCsducXVWC1k/PxZaeA9F9zivHwHtJxjIw5qpKq755p9Ney5wx1m1V01xU262oPadM37XTGluy/PRYF6PHNjLGmYDrRwTA4EstpaWlqK+vx2uvvRY51tfXh8bGRoyOjuKJJ57Avffei1mzZgEAKisrcc899+BHP/oRRkdHjQxNdy6vEnlDAQCfEkBXdz9cXiVum6UN1ZGiY3KfYZc/6VhGxvzBCU/aY8SLVWt7LWPEWrdndh/CkSGX7munNbZk8+uxLkaPbWSMMwHXj2iCoWc8AKC5uRm7du3C4sWL8cYbb6Curg6hUAijo6MoLi5GdXV1VPs5c+agsLAQR48exd/93d+pmqOqSsM39BTZ7WUJnz922Bl5QwnzKQF4lQBqaypjt5EQs8+Hbl/SsfQQL+aTp0axoNae1hjxYtXaXsuc8dYtGAolHT/Z/qqVan56rIvRYxsZowh67XGqRK9fpvMVjfnmDsMLj6uuugqbN29GMBjEjh070NzcjO3bt0OSJAQCgZh9xsfHNc0xPOxGMGjcuUq7vQxO56mEbUosZlhkU9Qbi0U2oUQ2RfrGazP18VlWS9Kx9BAvnsqyYtXzqMk7nfZa5oy3bgWSlHB8NfurVqr56bEuRo9tZIxG03OPUyVy/bIhX5GYb/ZJVBgZ/qsWq9WKefPmYf/+/di3bx8aGxsBAEVFRVAUBUePHo1q/8477yAYDOKCCy4wOjRd2YrNaG9tgEWeuJclfP3WViLHbbPndQfWrFwwrU+VrTDpWEbGfPas0rTHiBer1vZaxoi1bqua5qL2HJvua6c1tmTz67EuRo9tZIwzAdePaIIUCoUMOVXgcDjQ1taG3t5evPDCC3j66adx4YUXorOzEx0dHVi4cCFOnDiB3/72t3j44YdRVVWF4eFh/Nu//RuWLl2q6ebSbDjjAWDijnWvghGPHxWlhRNvKFPDmtqmVIbLE6OPmrH0EGMe+yyN1bTWWPXILd4Yp4+fcPlQZDHDVmyGtciccHzdvz2kmp+Rez5p7I9VWVFYEEptbFGvS51lzTdEQeuXNfkKwnyzT6IzHoZfagEmLrfceeeduO2226KOf+1rX0NZWRm+8pWvIBQKQZIkrFq1Cv/8z/8sIiz9hQBbsQxbsRx5nLRNME4fNWOJilnvMYycc+rxVMdPR6r5Gbnnk8a2262pv2mJel3mK64fkXFnPETKmjMeeYL55reZli8w83JmvvktF/LN6D0eRERERGEsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJY3jh4XA4UFdXh7vuuivq+MDAAOrq6vCrX/0KLS0taGlpwfz58/G5z30OLS0t2LBhg9Gh6UcCXKMKBk94MOz2Y+ikF66xcaDg9HGnJ/ZjyYAY1Iwdq+2kY+8fd8fvr2Ueo8Vbd2l6m6GTXgy7/Rg8kULcqeacqbXKpj0iIprCLGKSiooK7NmzB4FAACaTCQCwfft2VFZWQpIkbN26FQCwbNkyPP7446iurhYRlj4kYGDwI3R198OnBGCRTbiuaS52v/oeWj89F92/OYQPhr04u6oErZ+eiy09ByLt2lsbUF9TDoT0jyHu2DHa3nH9JfCPB5P31zKP0RKs+798tn4iJky0eXrnAJoWnYdf7D6kPe5Uc87UWmXTHhERxSDkUktpaSnq6+vx2muvRY719fWhsbFRxPSGcnmVyJs8APiUAH6x+xCWNlRjS88BLG2YKKLCjye36+ruh8urGBJDvLFjtT0y5FLVX8s8Rku07uGYwm2WNlRHig6tcaeac6bWKpv2iIgoFiFnPACgubkZu3btwuLFi/HGG2+grq4OoZA+X8Gqqqy6jJOI3V4W8/ixw87Im3yYTwkA0pn/B3Dm8ZR2XiWA2prKtGKLF0OssWO1DYZCqvprmcdoydbdqwSA0JS9mNJ2ctxa9zdZzplaK7Xzxss3n820nJlvfsvlfFUXHjt37sTAwADWrFmDl19+GcuXL9c00VVXXYXNmzcjGAxix44daG5uxvbt2zUHHMvwsBvBoHHnke32Mjidp2I+V2IxwyKbot7sLbIJCJ35/8nHp7YrkU1xx1YrXgyxxo7VtkCSVPXXMo/Rkq17iWwCTucVfi5e3Knsb7KcM7VWauZNlG++mmk5M9/8lgv5JiqMVF1qefzxx/Hzn/8cO3fuxNjYGB5++GE88sgjmoKwWq2YN28e9u/fj3379uXFZRYAsBWb0d7aEPUBd13TXOzpd2DNygXY0+8AAOx5feLx5HbtrQ2wlciGxBBv7Fhta8+xqeqvZR6jJVr3cEzhNnted+C6prkpxZ1qzplaq2zaIyKiWKSQiusdLS0tePbZZ9Ha2ornn38eLpcL1113HXbs2JF0AofDgba2NvT29uKFF17A008/jQsvvBCdnZ3o6OjAwoULsXLlSgATN5c+9dRTmm8uzeQZDwATvyLwKhjx+FFaJMOnjMNaJMNWKsPlmTheUVo4/XGJrN8Nf5NiSDp2rLY4c+xjVVYUFoRi99cyj9HirfvkmE63cY8psMhmeMaUaXFr2V9NOWdqrZLMmwvflvQ203JmvvktF/JNdMZD1aUWs9mMwsLCyGObzQazWfvtIVdddRXuvPNO3HbbbZr7ZrUQYCuWYSsOf6s8vVbBKcenPtbzQ2hqDInGjtM2fMxut8Z/UWuZx2jx1j0Uv02VNUYbrfOo7ZuptcqmPSIimkJV9XD22WfjlVdegSRJ8Pv9eOKJJ/Dxj39c1QTV1dXo7e0FMPHrlj//+c+R57773e9GtQ23IyIiovykqvBYv349vvnNb+LgwYNoaGjAxRdfjE2bNhkdGxEREeUZ1ddLnnzySYyOjiIQCMBqteLw4cNGxkVERER5KOGvWkZGRjAyMoIbb7wRH330EXw+HwKBAE6cOIG1a9eKipGIiIjyRMIzHrfffjv6+voAAIsWLTrTyWwxdpCjAAAgAElEQVTGpz/9aWMjIyIioryTsPB44oknAADr1q3DfffdJyQgIiIiyl+q7vG47777MDIygtHRUYRCIQQCAQwODuKKK64wOj4iIiLKI6oKj66uLvzXf/0XAMBkMkFRFMyZMwe//vWvDQ2OiIiI8ouqfzL9+eefx29/+1t85jOfwUsvvYTvfve7mDNnjtGxERERUZ5RVXhUVlZi9uzZuOCCC/D222+jpaUF7733ntGxERERUZ5RVXiYzWYMDg7iggsuwB//+EeMj4/D5XIZHRsRERHlGVWFx0033YT169fjyiuvxO7du3HllVdi8eLFRsdGREREeUbVzaUXXXQRnnzySQAT93u89957KChQVbMQERERRWj6l0tHRkbg8/kwa9Ys3HLLLaoncTgcqKuri/xjZGHLli2Dw+HA/Pnz0dLSEvW/n/70p6llRERERFlL879cKkkSTCaT5n+5VJZlrF+/Htu2bYPVao16bvbs2di6daum8bKKBLi8CkbcflSUWVAgASddPlSUWWArNif/z5JP6a+qT4wx3GPjcI2OY8w3jlnlRdHjqJ1DAt4/7saJD72wFJrhGVNQYU0xJlGS5RZrbUrMcHkUuEcVfDg6jlMen7g89dhvkeMSEelI9b9cunbtWpx77rnYtWsX3nnnHXz5y1/WNNHs2bPR2NiIjRs3orOzM/WIs40EDAx+hK7ufviUACyyCaua5uLF372LUx4F7a0NqK8pj/8BEKN/0j4xxjgydAoOpxvP7D40fRyonON0LE/vHEDTovPwi1hjZdsHWbL1i7M2N1/7Cez6/bu47O/OFpunHvstclwiIp2p/lXLD3/4Qxw5cgT33HMP3n//fdx5552aJ+vo6MDevXunXXI5fvz4tEstBw8e1Dx+Jri8SuTNHgB8SgDP7D6EZZ+sgU8JoKu7Hy6voql/sj6xxjgy5Ip8sE4dR+0c4XZLG6ojH8apxiRKstzirc1jv3oDy5fWCs9Tj/0WOS4Rkd5U3Vz6l7/8Bb/85S/x+OOP4wtf+AJuv/12XHvttZons1qt6OzsjFxyCUv3UktVlTV5ozTZ7WUxjx877Iy82Yf5lAAgnfmzVwmgtqZSU/9EfWKNEQyF4o6DEFTNEYlFUtc+GyRbv0RrM+YLCM9Tj/3WY9x4r+d8NtNyZr75LZfzVVV4hEIhFBQUoK+vD2vWrAEAjI6OpjThkiVLIpdc9DI87EYwaNz5ZLu9DE7nqZjPlVjMsMimqDd9i2yKnN62yCaUyCbN/RP1iTVGgSTFHQcJnps8RziW8PPpxCRKsvVLtDbFFpPwPPXY73THTfR6zlczLWfmm99yId9EhZGqSy01NTW48cYb4XA4sHDhQtx+++2YN29eygGFL7kcP3485TGyha3YjPbWhqgP7FVNc9G7fzBynd1WImvqn6xPrDFqz7FhVdPcmOOonSPcbs/rDlwXZ6xskyy3eGtz87WfwK/3HBGepx77LXJcIiK9SaFQKOmpAq/Xi927d+OTn/wkqqur8fOf/xxf+MIXUFxcrGoSh8OBtrY29Pb2Ro7t3bsXq1evxssvv4zPfvazqK2tjepz2WWX4dvf/raq8TN5xgPAmV8TePyoKC1EQYGEk6d8qCgtnHjjV/urltP9VfWJMUbULzdsluhx1M4hAf6ghBMjHljk079qSTUmUZLlFmttSuWJX7WMKSguKpz4VYuoPPXY7zTGzYVvS3qbaTkz3/yWC/kmOuOhqvDIdhkvPPIM881vMy1fYOblzHzzWy7km/alFiIiIiI9sPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJIzhhYfD4UBdXR36+vqiji9btgyf+tSn8MILL0Qd93q9WLRoEU6ePGl0aERERCSYkDMesixj/fr1cLvdUcdvvfXWaYXHSy+9hEWLFqGyslJEaJSPJMA1qmDQ6YFrbBwomPJY0tA3Udt0+ujZPx1a585krNkgm/OfFNv7x93qY8vmnIwyE3POImYRk8yePRuNjY3YuHEjOjs7I8c/8YlP4Hvf+x5GRkZQUVEBANi2bRu+/OUviwiL8pEEDAx+hK7ufviUACyyCWtWLkD3bw7hg2EvLLIJ7a0NqK8pB0LJ+8Ztm04fPfunQ+vcmYw1G2Rz/qnGls05GWUm5pxlhN3j0dHRgb1790ZdcikpKcHVV1+NnTt3AgD++te/4t1338XSpUtFhUV5xuVVIm8oAOBTAtjScwBLG6ojj7u6++HyKqr6xmubTh89+6dD69yZjDUbZHP+qcaWzTkZZSbmnG2EnPEAAKvVis7OTqxfvx7btm2LHL/22muxefNmrFq1Cr/+9a9xzTXXoKBAWz1UVWXVO9xp7PYyw+fIJrma77HDzsgbSphPCUSdSvUpAXiVAGprzlzOs9vL4vad2lbNfIn66Nk/Vankm6lY9ZLuazqb8081tmzOSSu1+5svOefqezQgsPAAgCVLlkQuuYRdeumlcDqd+OCDD7Bt2zY8/PDDmscdHnYjGDTuHJndXgan85Rh42ebXM63xGKGRTZFvbFYZFPUKVSLbEKJbIrkGM43Xt/JbdXOl6iPnv1TkWq+mYhVL3q8prM5/1Rjy+actNCyv/mQcy68RycqjIT/nDZ8yeX48eORY1/84hfx2GOPoby8HDU1NaJDojxiKzajvbVhotgAIvd47Ol3RB63tzbAViKr6huvbTp99OyfDq1zZzLWbJDN+acaWzbnZJSZmHO2kUKhkKG30zgcDrS1taG3tzdybO/evVi9ejVefvllVFdX49ixY7j66qvxn//5n/jCF76geQ6e8dBXzucrTVzHHfH4UVFaCFupDJdn0uMSOeoMSFS+U/tOaatqPjV99OyvUVr5Co5VL7q9prM5/0mxfazKisKCkOobnLM2J5U072+O55wL79GJzngYXniIwMJDX8w3v820fIGZlzPzzW+5kG9WXWohIiKimYuFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhhBUeDocDdXV16Ovrizq+bNkyOByOyOOenh50dHSICouIiIgEEnrGQ5ZlrF+/Hm63W+S0xpEA16iCQacHrrFxQIpzTK+x9WirYs4Dh52pj5MoFr3iTGVuncd3+wPGzqUxnnhrPXTSi2G3H4MnMhhnqozeT6Jk+Bo0hFnkZLNnz0ZjYyM2btyIzs5OkVPrTwIGBj9CV3c/fEoAFtmEO66/BP7xYNSx9tYG1NeUA6H0xo47jpa2es2ZyhjQKU4j41c5flmpjM83no9ndh8yZi6N8cRb66d3DqBp0Xn4xZQ4qyqtgoJMg9H7SZQMX4OGEX6PR0dHB/bu3TvtkkuucXmVyAsSAHxKAEeGXNOOdXX3w+VV0h473jha2uo1Zypj6BWnkfGrHX/ZJ2siRYcRc2mNZ2oM4eeWNlRHio7JbT444REWZ6qM3k+iZPgaNI7QMx4AYLVa0dnZifXr12Pbtm26jFlVZfw3OLu9LOrxscPOyAsyLBgKTTvmUwLwKgHU1lSqnivW2PHG0dJWrzlTGQMh6BJnKnOrGX/q/iYcXzI2FzVUrXWcOE+eGsWCWruQOFOl1+t6smR7nG+Yb3qMeA3qKZf3V3jhAQBLliyJXHIBgGPHjkGWZfzN3/wNQqEQTCaTpvGGh90IBo0792W3l8HpPBV1rMRihkU2Rb0wCyRp2jGLbEKJbJrWP5FYY8cbR0tbveZMZQzotDZGxB9rf5ONb2QuaqhZ63hxVpYVC4szVXq9rsPU7HE+Yb7p0/s1qKdc2N9EhVHGfk4bvuRy/Phx/OAHP8BvfvMbAMDBgwdx7rnnZios1WzFZrS3NkS9wdeeY5t2rL21AbYSOe2x442jpa1ec6Yyhl5xGhm/2vF7/ziIVU1zDZtLazxTYwg/t+d1B66LEefZs0qFxZkqo/eTKBm+Bo0jhUIhIbfJOBwOtLW1obe3N3Js7969WL16NXbu3IlvfvObcLvd+NjHPobNmzejvLxc9diZOOMBYOKOZ6+CEY8fFaWFkRfktGOphBZr7HjjaGmrYk6vEkCJbEptnESx6BVnKnMnoPrbw6TxK8ssCAZDxuWihoq1do8psMhmeMaUSBv7rOz/tgRA19dLLnxD1BPz1YnR71kpyoX9TXTGQ1jhYaSMFR55ivnmt5mWLzDzcma++S0X8s3KSy1EREQ087DwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCSMoYWHw+FAXV0d+vr6oo4vW7Ys8txdd90V9dzAwADq6urQ09NjZGhERESUAYaf8ZBlGevXr4fb7Z72XEVFBfbs2YNAIBA5tn37dlRWVhodlngS4BpVMOj0wDU2DkhJnpMAt28cQyNjOPpX9/Q+mYxXS5uwAmDY7cfB9134q8sHz3jAuHySxTXl+WAwpGq8o39144ORMbj9KmLXsjZExL8zM4jZ6Almz56NxsZGbNy4EZ2dnVHPlZaWYt68eXjttdewePFiAEBfXx8aGxuNDkssCRgY/Ahd3f3wKQFYZBPaWxtQX1MOIPZzpUVmvPuBC8/sPjS9T5LPyXQFg6H48YbnTpTT1PgKgD8fOYktPQcibW9smY9Z5UWomV2qbz7J4orx/Nf/6RLM/XhZ7DhitF/VNBfVditqz1HfR9TeEeUk/p2ZUYTc49HR0YG9e/dOu+QCAM3Nzdi1axcA4I033kBdXR1kWRYRljAurxL5CwUAPiWAru5+uLxK3Oe8vkCk6Jjax2gfnPDEjVdNTlMNu/yRoiPc9odb34TXF9A9n2RxxXr+wZ//KW4csdo/s/sQjgy5NPURtXdEuYh/Z2YWw894AIDVakVnZyfWr1+Pbdu2RT131VVXYfPmzQgGg9ixYweam5uxfft2TeNXVVn1DDcmu70s5b7HDjsjf6HCfEoAXiUAhBDzuVH/eNw+tTXGXoo6kCDe8NyJcpoa3zvHhuLmqHc+yeLSEnei8YKhkOY+IvZOrXRez7lqpuWcS/nq8Xcml/LVQy7nK6TwAIAlS5ZELrlMZrVaMW/ePOzfvx/79u3D7bffrrnwGB52J79Onwa7vQxO56mU+5dYzLDIpqi/WBbZhBLZBEhSzOeKC+P3SScWNSptxUnnTpTT1Pgqyyxxc9Q7n2RxaYk70XgFkqS5j4i9UyPd13Mummk551q+6f6dybV805UL+SYqjIT+nDZ8yeX48eNRx5ubm7Fp0ybMnz8fZrOwWkgYW7EZ7a0NsMgmAIhcv7SVyHGfK7FM3EsQq4/Rzp5VGjdeNTlNVVVWiDUrF0S1vbFlPkosJt3zSRZXrOe//k+XxI0jVvtVTXNRe45NUx9Re0eUi/h3ZmaRQqGQYacKHA4H2tra0NvbGzm2d+9erF69Gi+//HLkOY/Hg8bGRmzZsgWXX345Ojo6sHDhQqxcuVLVPNl+xgPAxB3bXgUjHj8qSgsn/kKFEjwHwD02DtfoOMZ845hls0T3MZDdXgbniVPx41WT01QFE/d6nHCNobzUgrISM0oLTcbkkyyuKc+fX30Whoen/+pqavsTLh+KLGbYis2wFpkTx65lbQTLhW9LeptpOedkvmn8ncnJfNOQC/kmOuNhaOEhSk4UHjmE+ea3mZYvMPNyZr75LRfyzZpLLURERDSzsfAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJIzhhYfD4UBdXR36+vqiji9btgw33XQT2tvbo47v3bsXV199NdzuBP+Z8kyTANeogkGnB66xcUBKsY+WcVKZM1fEyy3Rmp3wwHnKh3ePu/HhqALXWAbXJlv2ZlIcnvEAht1+HBpyYdjjz8xXjGxZFyLKKmYRk8iyjPXr12Pbtm2wWq2R47fffjvWrFmD3t5eLFu2DF6vF//xH/+Be++9N6pdVpGAgcGP0NXdD58SgEU2ob21AfU15UBIW59CcwG+/7M/JR8nlTlzRbzczivHwHvJ1+zLn6+H47gHP9v1dmbWJlv2ZlIcNR+z4jOL/hY/3PpmJKY1Kxfg4tpKICg+nrx7zRJRWoR8D5o9ezYaGxuxcePGqOMlJSW455578J3vfAderxddXV1YtmwZFi1aJCKslLi8SuTNFAB8SgBd3f1weRXNfY4MuVSNk8qcuSJebsMuv6o1c3mUSNExuZ2otcmWvZkcxxc+NSdSdIRj2tJzAMMuf0biCceQL69ZIkqPkDMeANDR0YEVK1agr68PV1xxReR4Y2MjlixZgnXr1uHo0aN49tlnNY9dVWX82RG7vQwAcOywM/JmGuZTAvAqAdTWVMbsG69PMBSadizWOKnMma5wvkaLl9tJt0/VmgVDIV3WJtV8M7E3yeIY8wVixvSh24d551cBMH5/s2VdJhP1ms4WzDe/5XK+wgoPq9WKzs7OyCWXyTo6OnDllVfikUceQVFRkeaxh4fdCAaNO39rt5fB6TwFACixmGGRTVFvqhbZhBLZFGkzVbw+BVL0Re9446QyZzom52u0eLlVWi2q1qxAktJem3TyFb03auIoLjLFjOksqwVO5ykh+5st6xIm8jWdDZhvfsuFfBMVRkJvOVuyZEnMSy5WqxU2mw0f//jHRYaTEluxGe2tDbDIJgCIXLu2lcia+9SeY1M1Tipz5op4uVXZClWtma1UxvWfmZextcmWvZkcx3OvHMaNLfOjYlqzcgGqbIUZiSccQ768ZokoPVIoFDL0Vi+Hw4G2tjb09vYCANxuN1asWAGn04mdO3eiuroawMSvXJ566qnIYy1EnvEAMHG3vlfBiMePitLCiTfTZNPH6gMN46QyZ4qEV9PxckuyZkUWMzyjCiqsFpgKgBF3amuTdr4C90ZtHFXlRRjzBTDsGkOVrWii6Dh9Y6mw/c2WdUFufEPUE/PNb7mQb6IzHsIutYSFL7msXr1a9NT6CQG2Yhm2YjnyONU+qsdJZc5cES83NWtWZokMYyvK0Npky95MiaNUNqHKevosh6hfsySIJ69es0SUMsMLj+rq6sjZjrAlS5bg4MGDUcemtiEiIqL8w3+5lIiIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGMMLD4fDgbq6OvT19UUdX7ZsGRwOB9xuNzZs2IDly5ejpaUFN9xwA/7yl78YHRblEwlwjSoYdHrg9gcif3aNjcPtG4/8GdL09q6xcQSDCf577VPaxhsjclyEWHNLgNs3jqGRMRz9q1tsTJlci3jU7rGW2PXOMxvXjdTj/qXMLGISWZaxfv16bNu2DVarNXI8FArhxhtvxKJFi/D888/DbDZj3759uPHGG/Hiiy/irLPOEhEe5TIJGBj8CF3d/SgrlfH5xvPxzO5D8CkBWGQTVjXNxYu/exenPAraWxtQf145Bt6baB9u8/V/ugRzP14GTP1smjR2uG28MdpbG1BfUz59DAPznTx3aZEZ737gispdSEzx1kjEWmiIKeYea4ld7zyzcd1IPe5fWoRcapk9ezYaGxuxcePGqOP79u3D8ePH0d7eDrN5ogZavHgx7rvvPgSDQRGhUY5zeZXIX/5ln6yJfPACgE8J4Jndh7DskzXwKQF0dfdj2OWPtA+3efDnf4LLqyQcO9w23hhd3f0xxzAy38lze32BabmLiClePCLWQktMsfZYS+x655mN60bqcf/SI+SMBwB0dHRgxYoV6OvrwxVXXAEAcLlcWLBgAQoKouufT33qU5rGrqqyJm+UJru9zPA5skmu5HvssDPylx8Szvz5NJ8SiJwC9SkBnHT7YrbxKgHU1lTGH3tS2w81jKG3eDGN+sc1xaTX/saLR8RapBuTltj1zlPEuuXK32G9iMw3G173uby/wgoPq9WKzs7OyCUXACgoKEAolP55qeFhd+Lr9Gmy28vgdJ4ybPxsk0v5lljMsMimyJvA5D+HH4dPfVpkEyqtlphtSmTTtJynjh1ue5aGMfQWL6biwtjHY8Wk5/7Gi0fEWqQbk5bY9c7T6HXLpb/DehCdb6Zf97mwv4kKI6G/almyZEnUJZf58+fjrbfemlZ8PPDAA9i3b5/I0ChH2YrNaG9tgEU2ofePg1jVNHei2AAi93j07h+MXIOtshVG2ofbfP2fLoGtRE44drhtvDHaWxtijmFkvpPnLrGYpuUuIqZ48YhYCy0xxdpjLbHrnWc2rhupx/1LjxTS45RDAg6HA21tbejt7QUAuN1urFixAk6nEzt27MA3vvENXHHFFVi7di1MJhP27NmDdevW4fnnn8esWbNUzcEzHvrKuXyliWuuIx4/KsssCAZDGPH4UVFaiIICCSdP+VBRWjjxphCKbl9RWojzq8/C8LA76diJxogcF5xvZG4A7rFxuEbHMeYbxyybJW5Muu9vJtdCZUxx91hL7HrnaeC65dzf4TRlJN8Mvu5zYX8TnfEQdqklLHzJZfXq1ZAkCY8++ijuu+8+LF++HGazGWeddRYef/xx1UUHEUKArViGrfjMt43Jf7ZazJF2sdoXFCT4HdzUseOMIfSDNs7cVov5TK4iY8rkWsSjdo+1xK53ntm4bqQe9y9lhhce1dXVkbMdYUuWLMHBgwcjj++//36jwyAiIqIswH+5lIiIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGMMLD4fDgbq6OvT19UUdX7ZsGRwOB+bPn4+Wlha0tLTgM5/5DNrb23HixAmjw9KHBLhGFQye8GDY7cfQSS9cY+OANOk5p+fMsan9Yj2nde5kY8RrpyUGPeLVWwEw7Pbj0JALH44qcI0liS9WDqePHTjs1C+vTK1VNu4REVEMZhGTyLKM9evXY9u2bbBarVHPzZ49G1u3bgUAhEIhPPDAA2hvb8fPfvYzEaGlTgIGBj9CV3c/fEoAFtmE65rmYver7+FfPluPQnMBvv+zP0Wea29tQH1NOYDp/SLPhVKfO+YY8dqdV46B91TGoHYukQqAPx85iS09B1BWKuPzjefjmd2H4scXI4c7rr8E/vGgvnllaq2ycY+IiOIQcqll9uzZaGxsxMaNGxO2kyQJt956K9555x28/fbbIkJLmcurRN7oAcCnBPCL3YewtKEaXd39ODLkinquq7sfLq8Ss1/4uXTmjjVGvHbDLr/qGPSIV2/DLj+29ByATwlg2SdrIkVHvPhi5XBkyKV7Xplaq2zcIyKieISc8QCAjo4OrFixAn19fbjiiivitissLMR5552Ho0ePYt68earGrqqyJm+UJru9LOrxscPOyBt9mE8JANLE/wdDoWnPeZUAEELMfl4lgNqaSlWxxJt76hjx2p10+5L2D+erdi6R3jk2dCYmKfl6xsohGArpnlem1iqVeae+nmeCmZYz881vuZyvsMLDarWis7MzcsklEUmSUFRUpHrs4WE3gkHjzinb7WVwOk9FHSuxmGGRTVFv+BbZBIQm/r9Air7IbpFNKJFNgCTF7Fcim6bNEU+8uaeOEa9dpdWSsP/kfNXOJVJlWXT8yeKLlUOBDvswVabWSuu8sV7P+W6m5cx881su5JuoMBL6q5YlS5YkveTi9/vx7rvvYs6cOQIj085WbEZ7a8NEsQFE7vHY0+9Ae2sDas+xRT3X3toAW4kcs1/4uXTmjjVGvHZVtkLVMegRr96qygqxZuUCWGQTev84iFVNcxPGFyuH2nNsuueVqbXKxj0iIopHCoVCht5+5nA40NbWht7eXgCA2+3GihUr4HQ6sXPnzqjngsEgHnzwQbz11lt44oknVM+RiTMeACZ+SeBVMOLxo7RIhk8Zh7VIjrzhh5+rKC2cOBaa3m/ac2qpHSNeuwT9p+WrR7x6K5i412PYNQZ7RTFMBcCIO0F8sXLAxDGvEkCJbNInr0ytlYZ5c+Hbkt5mWs7MN7/lQr6JzngIu9QSFr7ksnr1agDA8ePH0dLSAmCi8Kivr8emTZtEh5WaEGArlmErDn+zLIwcB6Y8F0rQL5UPJrVjxGunJQY94tVbEKiyFqLKWhg5ZCtKEF+cHGzFMmprKif+EuuRV6bWKhv3iIgoBsMLj+rq6sgZjbAlS5bg4MGDAIA333zT6BCIiIgoS/BfLiUiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEsbwwsPj8WDDhg1oamrCNddcg+uvvx6///3vAQAdHR3o6emJav/QQw/hoYceMjqs7CIBrlEFg04PXGPjgKSiTYGKPinG8v5xt/7jimACjp/y4e33P4LT7QNMULe2YVraJpKJOXNJopzTWY9cWctciZPIIGYjBw+FQlizZg3q6+vx4osvorCwEG+99Ra+9rWvYdOmTUZOnTskYGDwI3R198OnBGCRTWhvbUB9TTkQit9mzcoF6P7NIXww7I3dx6hYspUJ6H/nJP7ruQOR2G/64gLMrihC5/97LXk+euWuZZxcXu9UJcoZaaxHrqxlrsRJZCBDz3j84Q9/wNDQENatW4fCwkIAwEUXXYSbb74Zjz76qJFT5+QqVeIAACAASURBVAyXV4m8CQGATwmgq7sfLq+SsM2WngNY2lAdt49RsWSr4yO+SNEBTMT+X88dQCAEVfnolbuWcXJ5vVOVKOd01iNX1jJX4iQykqFnPA4cOID58+dDkqLPJV522WXYtGkTzj77bHR1deHJJ5+MPHfixAmsWrVK0zxVVVZd4k3Ebi8zZNxjh52RN6EwnxKAVwmgtqYyYZvJp2in9jEqlmz19vuumLG7vP5px2Ll4/UHdMldyxpmcr2Nej0nkyhnTCoSpz6XbD3UrGWmcp5M5J5nQ74iMd/cYWjhIUkSAoHAtOOKcqa6b29vx8qVKyOPU7m/Y3jYjWDQuPOUdnsZnM5ThoxdYjHDIpui3owssgklsikyZ7w2k0/NTu1jVCzZqqrcEjN2W0lhVLtY+djtZbrlrmWcTK23ka/nZBLlDElKeT2SrWUmc55M1J5nS76iMN/sk6gwMvRSy8UXX4w333wzqtAAgP7+fixYsMDIqXOGrdiM9taGiUICiFzztZXICdusWbkAe/odcfsYFUu2spdbcNMXF0TFftMXF8AkQVU+euWuZZxcXu9UJco5nfXIlbXMlTiJjCSFQiFDb2n66le/inPPPRff+ta3IMsy3nzzTaxZswabNm3Cc889h4ULF8Y843HrrbeqniOXz3gAmLjL3atgxONHRWnhxJtQjBsRo9qUynB5kvRJMRZ/UMKxYbe+44pgmrjX46RrDJW2IsyusADB5Gsb2V81+6CGlnH0mlODjH9bSpRzOuuRoG/Gc55MwJ5nVb4CMN/sk+iMh6GXWgDg4YcfxoMPPojly5fDZDKhvLwc999/PxYtWoTnnnvO6OlzQwiwFcuwFcuRx0nbBFX0STGWj8+2olAK6TuuCAFgdpkFs8sskceAhnVSsw9qaBlHrzlzSaKc01mPXFnLXImTyCCGn/EQIefPeGQZ5pvfZlq+wMzLmfnmt1zIN2P3eBARERFNxsKDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYYQUHh6PBxs2bEBTUxOuueYaXH/99fj9738PALjhhhvw6quvRtq+8MILuPLKK3H06FERoREREZFAZqMnCIVCWLNmDerr6/Hiiy+isLAQb731Fr72ta9h06ZNUW137NiBBx54AD/+8Y/xt3/7t0aHRtlAAlxeBe5RBZZCMzxjCiqsFtiKzbH/c+Gn24+4/agoS9BOa9s0YjdsfFFzEBEJZHjh8Yc//AFDQ0N46qmnIEkSAOCiiy7CzTffjEcffTTS7qWXXsIDDzyAJ598Eueee67RYVE2kICBwY/w9M4BNC06D7/YfQg+JQCLbEJ7awPqa8qjP2RPt+/q7k/cTmvbNGI3bHxRcxARCWb4pZYDBw5g/vz5kaIj7LLLLsOBAwcAAC+//DL+/d//HStWrGDRMYO4vAq6uvuxtKE6UnQAgE8JoKu7Hy6vErN9snZa26YTu1Hji5qDiEg0w894SJKEQCAw7biinHnz7O3txY9+9CO0t7fjqquuwoIFCzTNUVVlTTvOZOz2MsPnyCYi8j122DnxoSoh8uEa5lMC8CoB1NZUTm+fpJ3WtoD2fLWOnwoj55hpr2dg5uXMfPNbLudreOFx8cUX4yc/+QkURYEsy5Hj/f39WLBgAYLBIO6++24sXrwYd9xxB+644w709PSgtLRU9RzDw24Eg8ade7bby+B0njJs/GwjKt8SixkW2QQAsMimqA9Zi2xCiWyKiiPcPlk7rW1TyVfL+Kkyao6Z9noGZl7OzDe/5UK+iQojwy+1XHrppZgzZw7uvffeyFmON998E4899hjWrl0LACgsLAQAtLa24vzzz8d3vvMdo8OiLGArNqO9tQF7Xnfguqa5UUVIe2sDbCVyzPbJ2mltm07sRo0vag4iItGkUChk+G1qY2NjePDBB/HKK6/AZDKhvLwc7e3tuPzyy3HDDTfglltuwaJFiwAAw8PDWL58Ob71rW9hxYoVqsbnGQ99Cc03/KuWMQUW+fSvWkoLJz5cE/2qxeNP3E5D25Tz1RJLqgyYY6a9noGZlzPzzW+5kG+iMx5CCg+jsfDQF/PNbzMtX2Dm5cx881su5JvRSy1EREREYSw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQljNnoCj8eD73//+9i7dy+Ki4thtVpx66234vLLL0dHRwf27duH8vJyAMDo6CgqKipw3333oba21ujQiIiISDBDz3iEQiGsWbMGsizjxRdfxLZt2/Dtb38b3/jGN/Dqq68CANrb27F161Zs3boVL730Ei6++GI89NBDRoaVGRLgGlUw6PTANTYOSCm0UztGqu1VjuP2jac+ptb81B7TK2ct/eO11Wvds91MyZOIdGXoGY8//OEPGBoawlNPPQVJmnhXuuiii3DzzTfj0Ucfxdlnnx3V3u/3w+l0Rs6A5A0JGBj8CF3d/fApAVhkE9pbG1BfUw6EVLaDyjG0zplC7Kua5uLF372LUx5F25ga87vj+kvgHw9Oa19oLsD3f/an9NconTWL1/a8cgy8p8O6Zzu9Xl9ENOMYesbjwIEDmD9/fqToCLvssstw4MABAEBXVxeuueYa/MM//AM+//nP4+yzz8Y3vvENI8MSzuVVIm/QAOBTAujq7ofLq6hup3YMrXOmEvszuw9h2SdrNI+pNb8jQ66Y7Y8MuXRZIy3xqW077PLrsu7ZTq/XFxHNPIae8ZAkCYFAYNpxRTnz5tTe3o6VK1fi6NGj+Nd//VcsWrQIVqtV0zxVVdrap8JuL0u577HDzsgbdJhPCcCrBFBbU6mqHUJQNYbWOeMJ5xtvnPBpdS1jas0vGArFbB8MhaYdS2WNJrPbyzStWby2J92+tNZdlHRez0D6r69MSDfnXMN881su52to4XHxxRfjJz/5CRRFgSzLkeP9/f1YsGBBVNsLLrgAd9xxB771rW9h165dKCtTv6jDw24Eg8ad37Xby+B0nkq5f4nFDItsinqjtsgmlMimqHETtYMkqRpD65yxTM433jjh0+lqx0wlv4I4ORdMOYOW6hpNzVfLmsVrW2m1pLzuoqT7egbSe31lgh455xLmm99yId9EhZGhl1ouvfRSzJkzB/fee2/kLMebb76Jxx57DGvXrp3Wfvny5Tj33HPx6KOPGhmWcLZiM9pbGyY+sIHI9XBbiay6ndoxtM6ZSuyrmuaid/+g5jG15ld7ji1m+9pzbLqskZb41LatshXqsu7ZTq/XFxHNPFIoFDL0VrCxsTE8+OCDeOWVV2AymVBeXo729vbIz2kXLlyIlStXRtrv378fX/nKV7B9+3ace+65qubI9jMeACZ+AeBVMOLxo6K0cOINOs5NoXHbqR1D65xTTMt3yjgFBRJOnvJpGjPl/KDyWKprNDVfLf3jtU1x3UXR7dtSluc5WS58Q9QT881vuZBvojMehhceIuRE4ZFDmG9+m2n5AjMvZ+ab33Ih34xdaiEiIiKajIUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImHMIiZxOBz47Gc/i9raWkiSBEVRMHv2bNx333342Mc+BgC49tprYbfbsWXLFhEhERERUQYIO+Mxe/ZsbN26Fc8//zxefPFFzJ8/H52dnQCAgwcPQpZlvP322/jggw9EhZTdCoBhtx+HhlwY9vgndkoCXKMKBp0euMbGAUnlWKn2U8vo8bXONaWN2zduTGyn5xk66cWw24/BE1PmELkuqdAaX7bno0WyvSMiwwg54xHLpZdeit7eXgBAT08PrrjiCoyMjKC7uxu33XZbpsLKDgXAn4+cxJaeA/ApAVhkE9asXICzrBbc99QfI8faWxtQX1MOhBKMJQEDgx+hq7tfWz+1jB5f61wx2qxqmosXf/cuTnkU/WI7Pc/TOwfQtOg8/GL3oeiYzivHwHuC1iUVWvdN5D4bLdne5WJORDkkI/d4KIqCHTt24JJLLoGiKNi2bRuam5vR3NyMX/7ylxgfH89EWFlj2OWPFB0A4FMC2NJzAF5fIOpYV3c/XF4l4VgurxL5sNDSTy2jx9c6V6w2z+w+hGWfrNE1tvA8SxuqIx9ck2MadvmFrUsqtO6byH02WrK9y8WciHKJsDMex48fR0tLCwDA7/fjE5/4BG6//Xb8z//8D+x2O+bMmYNQKISCggL89re/RVNTk+qxq6qsRoUdYbeXGT5H2DvHhiJvhmE+JYBR//i0Y14lgNqayrhjHTvsjDlWsn5q8011/FSomStem/Ap9Hixad3fyDwSYs73odsnbF1S4fUHNMUncp+NEt7jZHuXSzklIvI9Kxsw39whrPAI3+Mx1a9+9St88MEHWLZsGQDA7XbjmWee0VR4DA+7EQwad27Ubi+D03nKsPGnqiyzwCKbot4ULbIJxYXR22WRTSiRTQljK7GYY46VqJ+WfFMZP1Vq5orXJnzqPFZsqexveJ7wmFPnO8saew+NWBet7PYyzfsmcp+NMHmPk+1druSUiOj3rExjvtknUWGU0Z/TnjhxAn19fXjhhRfQ29uL3t5ePP/889i3bx/+7//+L5OhZVRVWSHWrFwQ9ea4ZuUClFhMUcfaWxtgK5ETjmUrNqO9tUFzP7WMHl/rXLHarGqai979g7rGFp5nz+sOXNc0d1pMVbZCYeuSCq37JnKfjZZs73IxJ6JcIoVCIcNvo3I4HGhra4vcTBr23//933j99dfx0EMPRR2/9dZbcd555+GOO+5QNX6+nfEAMPGrFpcfw64xVNmKUGUrBEIT16dHPH5UlBZOvEGqSVvS1k9zvhrHT4uauaa0KSiQcPKUL277lPf39DzuMQUW2QzPmBI9h8h10SCSr9b4sjQfNabtcbK9y3G58I1YT8w3+yQ64yGk8DBaXhYeGcR889tMyxeYeTkz3/yWC/lm7aUWIiIimllYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiISxixikp07d+Lxxx/H+Pg4QqEQWlpa8NWvfhU33HADjh07hpKSEgQCARQWFuK2227Dpz71KRFhERERkWCGFx5//etfsXHjRvT09OCss86Cx+PBDTfcgPPPPx8AcM8992DRokUAgAMHDuCrX/0qfvrTn2LOnDlGh6YPCXB5FYy4/agos6BAAk66fKgos8BWYobLc+Y5W7EZCCXuH7ONinl16ycBQ043nCNjGPONY1Z5kfqxRVCTdwEw7PLj5KkxVFgtUMYDsBYXxl3/94+7ceyER9s66hWrETI1LxGRCoYXHh9++CEURcHY2BgAoLS0FN/97ndhsVimtV2wYAGam5vx7LPPYt26dUaHlj4JGBj8CF3d/fApAVhkE1Y1zcWLv3sXpzwK1qxcgO7fHMIHw15YZBPaWxtQX1N+5kMgRv9pbVTOq0s/CTgydAoOpxvP7D6kbWwR1ORdAPz5yEls6TkQaXNd01zsfvU9/Mtn6/VZf71iNUKm5iUiUsnwezzmzZuHq6++Gp/+9KfxpS99Cffffz+CwSDOO++8mO0vvPBCHD161OiwdOHyKpE3eADwKQE8s/sQln2yBj4lgC09B7C0oTryXFd3P1xeJWH/qW3UzqtHP5dXwZEhV6To0DK2CGryHnb5I0VHuM0vdh/C0oZq3dZfr1iNkKl5iYjUEnKPx4YNG7B27Vrs3bsXe/fuRWtrK77//e/HbCtJEoqKijSNX1Vl1SPMhOz2smnHjh12Rt7gw3xKAJCm/zn82KsEUFtTmbD/5DaxGNXv2GEngqFQSmOLoCbvd44Nxd0TvdZfr1iNoHbeWK/nfDfTcma++S2X8zW88HjllVfg9Xrxuc99Dtdeey2uvfZadHd345e//GXM9gcPHkRtba2mOYaH3QgGjTuPbLeXwek8Ne14icUMi2yKeqO3yKbIKe3Jfw4/LpFNkbHi9Z/cJhaj+pVYzCiQpJTGFkFN3pVllrh7otf66xWrEdTMG+/1nM9mWs7MN7/lQr6JCiPDL7UUFRVh06ZNcDgcAIBQKITDhw+jvr5+Wts33ngDu3btwpe+9CWjw9KFrdiM9taGiQ82IHKPR+/+QVhkE9asXIA9/Y7Ic+2tDbCVyAn7T22jdl49+tmKzag9x4ZVTXM1jy2CmryrygqxZuWCqDbXNc3Fnn6HbuuvV6xGyNS8RERqSaFQyPBbzp577jk88cQTUJSJ68xLly7FN7/5TaxevTryc1pJklBcXIz29nZcfvnlmsbP1BkPAGd+QeDxo6K0EAUFEk6e8qGitBC2UnniVy2nn7OVyPF/1ZKojYp5desnAUpIgnNkdOJXLTaL+rFFUJN35FctPpRbCzE+HoC1SI67/v6ghGPDbm3rqFesRkgyby58W9LbTMuZ+ea3XMg30RkPIYWH0TJaeOQh5pvfZlq+wMzLmfnmt1zIN6OXWoiIiIjCWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEsZs9AQbNmzAn/70JyiKgsHBQdTW1gIA2tra8L3vfQ8vvvgiZs2aBQBwOBxoa2tDb2+v0WFRLpj0n3cvLZLh84/DWlIIW7E55n/e3j02DtfoOMZ845hVXgRbiRkuj4IRtx8VZZbY/QAEgyG4RpO30xRzrLESPZfKeCnG5Q9JKCxA4nH0mjdd6cZxuv+xw06UWMyZy0MPBcCwy49h1xiqyotQVVYIBDMdFJF2hhced999N4AzRcXWrVsBAD09PfB4PLj77rvxyCOPGB0G5RoJGBj8CF3d/fApAVhkE65rmovdr76Hf/ls/f/f3r3HR1Xe+QP/zD2ZZCYx4+BKIRSjUdZForAlIVArGAHBC6FGCgWLbrd4C9pqVXRrq4UC8lo1YqF1V9taV8xqlkt0+RnBdg1YbCCp0SK3UgISMATC5EJmJjPn90fMMTOZ+5zzzIXP+x+cc57L9/s8c/nmzIyDMfk5Pi/oh4534lhbFzbU7Zfb3z33KrxRtw+t7T0wGXSorCjy7fdl3w+bW/Hs63vkfgHbxRizPBZCnAs2T6jxonzxjWocpeaNV7xxJEseStACfzl0GutrmuVclpSPxbiCPBYflHIS+lbL9OnTceTIEWzZsiWRYVAScvS45RcMAHC6PXijbj+mFI1AVXUTHD1un7aHjjvkomOg/bq3PsaUohHybf9+A30Hio5Q7WKNeWCsUOdiGU+puNScN17xxpEseSih3eGSiw6gP5f1Nc1od7gSHBlR9FS/4hGKwWDAL37xC/zgBz9ASUlJzOPYbNkKRhWY3W5RfY5kkuh8Txxsk59kBzjdHkDT/2+P24OC/Dy5rVeSgrYffHtwv1Dz+LeLJ+YetweQEPU8SsUW7ThKrkk84o0jWfJQwoETxwPmcqbLiStG2wL2SfRjWDTmmzoSWngAwNixY3HbbbfhySefxGOPPRbTGO3tXfB61bt2ardb0NbWqdr4ySYZ8jWb9DAZdD5PtiaDDpD6/zUbdHKMZpMeWo0maPvBtwf3CzWPf7t4YjYbdECQ+ELNo1Rs0Y6j5JrEI944kiUPJeRZTAFzuSDbFDCXZHgMi8R8k0+owigpvtVy77334siRI6itrU10KJQkrJl6VFYU9RcPgPwZjw+ajqGyoghWs8GnbcFwK+aVFfq0v3vuVfig6Zh827/fQN8Hv3ONT79A7WKNeWCsUOdiGU+puNScN17xxpEseSjBZjFiSflYn1yWlI+FzWpMcGRE0dNIkiTkY1b+31ipqanBRx99hJUrVwIAmpubsXDhQuTl5UX9rRZe8VBW0uTr/60Wdx+yMwz9LxzhvtViNcGaZej/Vku3C7lZxsD90P9W3eFjZ8K2izbmIWOFOhfLeDHG9Q+2bBi1UmTfalFiTeIRbxxf9u9xe2A26BKXhxIGf6vFmtFfdAT5YGnSPIYFYb7JJ9QVj4S/1TJg7NixuOOOO/hBU/qKBFgzDbBmDvyFapSPB2qbbdIj2zToLu316x/kBUer1UTULqaYpQjPxTJejHHZ7dnhn7SUmjde8cbxZf+C/Lz+nFO16AAAL2DLNsKWbZRvE6UiYVc81MQrHspivuntfMsXOP9yZr7pLRXyTfrPeBAREdH5gYUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImH04ZvE72c/+xn27NkDt9uNlpYWFBQUAADKysrw0Ucf4be//S00Gg0kScLdd9+N0tJSLFy4UERo8dEAjh43OrpcyLWYYM3U9//s9pfHu865YTLq4XT3wWTQo7vXjdzsQe3UjMWsh6M7QGx+7U85nMg06WExG5Bt0sUfV7A1UUqYNY86Hw3w+RddOHGqW514I6H2mvnNc+JgG8wmfWJyJaLznpDC48knnwQAHDt2DIsWLcKmTZsAAB6PBx988AF+85vfYPHixXj55Zeh1WpTpujY23IWVdVNcLo9MBl0qKwowphROdh75Cx+v3UvyiaOQt2uIyibOApv1O33bZefo9yTvl8sF9vMqLi+EOtrmgPPGSD2eWWFGGHPRsHw4D9lHPOaKJVrmDUPmk+wudWON56clI4hGXIlIkKC32rR6XR45pln8Ktf/QqbN2/G66+/jhUrViQypIg5etzykzgAON0eVFU3od3hQlV1E6YUjcAbdfvlf/3bOXrcqsUypWiEXHQEmjNQ7Bvq9uPQcUdccQVbE6VyDbfm0eajdryREBVDMuRKRAQIuuIRSn5+PpYuXYqHH34Yv/nNb5Cbmxv1GDZbtgqR+bLbfa8EnDjYJj+JD3C6PTjT5ew/roHvv37tetweFOTnKRLbkFjCzBksdq8koefL4/75xhQHlM012PinB9bc7/hAPsHmVjveSIiKIRlyTbRY7tOpjPmmt1TON+GFBwA0NDTgwgsvxP/93/+hpKQk6v7t7V3wetW7Xmy3W9DW1ulzzGzSw2TQ+TyZmww6XJBtgsmgk28P/OvfzmzQDRkzVsFiCTZnsPZajQbmL2OOJbZg4yqVa7Dx875c82D5BJtb7XgjISqGZMg1kQI9htMZ801vqZBvqMIo4d9qeeutt3D48GG8+eab2LRpEz788MNEhxQRa6YelRVFPsVFZUURbFYjKiuK8EHjMdxeVij/69/OajaoFssHjcewpHxs0DkDxT6vrBAFw61xxRVsTZTKNdyaR5uP2vFGQlQMyZArEREAaCRJEvbRsoEPl27fvh0A8Le//Q3f/e538fvf/x6XXHIJ6urq8POf/xybN29GTk5OxOMm4ooHgK++jdDtQm6Wsf9JfPC3WnrdMBn8vtUyuJ2S/GPJMvR/q8U/Nr/2pxxOZHz5DYfsjP5vOcRVTQdbE6WEWfNA+YQbz+XV4ER7l3p7E47aa+Y3T4/bA7NBl5hcEyQV/kJUEvNNb6mQb6grHgl7q8XlcuGHP/whli5diksuuQRA/9dr33//fTz55JN47rnnEhVa5CTAmmmANdMg3w54HEYAgC3b6NtOzVi8QWIL1l6puIKtiVIiXvMI55aArw3LhlEjRd5HaWqvmd88Bfl5/U9a50nRQUTJRWjhMWLECPlqh9FoxMaNG4e0SZVvtRAREVH0Ev4ZDyIiIjp/sPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJIyQX6c9duwYFi1aJP8y7YDLL78c119/PY4dO4aenh6cOnUK+fn5AICHHnoIU6ZMERGecjSAo8eNji4Xci0mWDP1/T89rgXaHS60O3phy8mASa/FaYfTt02C4szLyYDX4/WNOYZxci0mWM16OLoD5B9rfN0uZGUY4HT1IdtsjG+tgu1NuDaIoJ8asajRl4goCQgpPEJ58cUXAQC7du3C2rVr8eqrryY4ohhpgL0tZ1FV3QSn2wOTQYfKiiKM+XoO/nLwNNbXNMvH55UV4u2dh9HZ7e5vk58j7sVjUJyWLANmTRqNDXX7fWK25WVHNY7T7cHFNjMqri/0yTOm3AKs4+1lhajbdQTfnTEmtrUKtjeDxwrSxqjXYs1/7Ykvp2hjUaMvEVGS4FstCnH0uOUXBABwuj2oqm7CFx1O+cV44PiGuv2YOj5fbuPocSckzqnj8+WiY3DMrae6oxoHAKYUjRiSZyy5BVrHN+r2Y0rRiJjXKtjeDB4rWJtDxx1x5xRtLGr0JSJKFgm/4qEEmy2Cv9DjZLdbQp4/cbBNfkEY4HR7cNrhDHgcmq/+u8ftQUF+nqLxRhSnBoFj7jyHsQX2yMcJMVa0uQVbx4HxY1mrYGMOHqvH5QnYxitJIftFK5JY1OjrL9z9OR2dbzkz3/SWyvkKKTy02qEXViRJgkajUWT89vYueL3qXWu22y1oa+sM2cZs0sNk0Pm8MJgMOtispoDHBy6Nmww6mA26sOMrxT/OQLHlWTJjztf/drS5BRsXUuxrFWzMgbHsdkvQNlq/+2i8+xUuFrX6DhbJ/TndnG85M9/0lgr5hiqMhLzVYrVa0dnpu0jt7e3IyckRMb0Q1kw9KiuK+l8kAfn9d3uuCUvKx/ocn1dWiO27W+Q2dc5QvwAAIABJREFUVrMhIXFub2jBvLLCITFffGFWVOMAwAeNx4bkGUtugdbx9rJCfNB0LOa1CrY3g8cK1qZguDXunKKNRY2+RETJQiNJkpCPpX3729/G97//fUyfPh1A/4dKDxw4gOeeew5AfB8uTYYrHgB8vo2Rm2Xsf0EI9q2WTqdvG5EGxZlnMcHrlXxitl8YY75Zhv5vtfjnH0d8WRkGON19yM4wxLdWwfYGg/Y3UBsE7xezELGo2vdLqfDXktLOt5yZb3pLhXxDXfEQ9hmPZ555Bj/96U/x4osvwu124/LLL8dPfvITUdOLIQHWTAOsmQb5NgDAC9iyjbBlG+Wm2Sa9bxuR/OMEhsYcyzjeIPnHHZ8xvvECjRlorCBtFMkp2ljU6EtElASEFR6jR4/Gb3/726DnJ06ciIkTJ4oKh4iIiBKAX6clIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJIyQH4mbP38+5s+fj9mzZ8vHenp6cO211+Kiiy6CTqdDS0sLLrzwQpjNZowYMQIvvviiiNCIiIhIICGFR3l5OWpra30Kj3fffRclJSWoqqoCACxcuBD33Xdf+vxCrQZw9LjR0eVCrsUEa6a+/yfMgx1XY65Iz8cr0vG1QLvDhXZHL2w5GbBZjIBXwThioQE+/6ILJ051K782aq97jHN7vRIc51SIK9Z8I+mXyLVMZ1xXSgAhhcfMmTOxevVqdHR0IDc3FwCwefNm3HHHHSKmF08D7G05i6rqJjjdHpgMOlRWFGHMqBzsPRLgeH5O7A/2YHMNjBnuvFq5+o+vBf5y6DTW1zTL7ZaUj8W4grzEFR9qro3a6x7r3AA+bG7Fs6/vUTauWPONpF8i1zKdcV0pQYR8xiMrKwvTpk3D1q1bAQAnT57E4cOHMWXKFBHTC+foccsPZgBwuj2oqm5Cu8MV8Lijx634XANjhjsfr0jHb3e45KJjoN36mma0O1yKxBELNddG7XWPdW5Hj1suOpSMK9Z8I+mXyLVMZ1xXShQhVzwAYO7cuXjuuecwb948bNmyBTfffDO0WmXqHpstW5FxQrHbLRG3PXGwTX4wD3C6PTjd5Qx4vMftQUF+XkxxBZtrYMxw54OJNN9Ixz9w4njAdme6nLhitC2iuZQW69okeux45oYEVeKKNd9I+im1ltE8htNBuHwTeR9VA/c3dQgrPCZMmIC2tja0trZi8+bNWLt2rWJjt7d3wetV79qg3W5BW1tnxO3NJj1MBp3Pg9pk0CEv2xTwuNmgi2r8SOYaGDPc+UCiyTfS8fMsgXO/INsUc+7ximVtkmHseOaGRqNKXLHmG0k/JdYy2sdwqosk30TeR5XG/U0+oQojoV+nnTNnDtatW4ecnBzk5+eLnFooa6YelRVFMBl0ACC/d2qzGgMet5oNis81MGa48/GKdHybxYgl5WN92i0pHwub1ahIHLFQc23UXvdY57Zm6vHgd65RPK5Y842kXyLXMp1xXSlRNJIkCfsY0YkTJzBt2jQsX74ct956q8+5eL7VkmxXPAB89Wnxbhdys4z9D2YpxPF4hBszyjmjzjfS8Qd/q8Wa0V90JMG3WlxeDU60dym3H4PGVnyvFZjbZsvG4WNnlI8r1nwj6RfnWqbCX4hKijjfRN5HFcT9TT6hrngILTzUkpSFRwpjvuntfMsXOP9yZr7pLRXyTZq3WoiIiOj8xsKDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTCqFx7z589HbW2tz7Genh5MnDgRp0+fBgDMnTsXS5YsUTsUIiIiSjDVC4/y8vIhhce7776LiRMnIi8vD/v27YPBYMBnn32G1tZWtcMhkTSA45wbLW3dcPT2AZo420YzXhTxNR9sU2Y8v3EVizNR80Q7vhZo73Jh/3EH2rtd6j67fBnb3052obWjF10uj3rrTPGL974q6jEVi2SLLdniCUCv9gQzZ87E6tWr0dHRgdzcXADA5s2bcccddwAAampqUFpaio6ODlRXV2Pp0qVqh0QiaIC9LWdRVd0Ep9sDk0GHyooijMnPAaQY2kYzntLxJcO4oueJdnwt8JdDp7G+plluv6R8LMYV5AFeBeIJE9u8skKMsGejYLhF2XWm+MV7XxX1mIpFssWWbPEEofoVj6ysLEybNg1bt24FAJw8eRKHDx/GlClT4Ha7sXnzZsycORMzZ87Em2++ib6+PrVDIgEcPW75zg8ATrcHVdVNcPS4Y2obzXhKx5cM44qeJ9rx2x0uuegYaL++phntDpci8YSLbUPdfhw67lB8nSl+8d5XRT2mYpFssSVbPMGofsUD6P8Mx3PPPYd58+Zhy5YtuPnmm6HVarF9+3bY7XZceumlkCQJWq0W77//PsrKyqIa32bLVinyr9jtFtXnSCbx5nviYJt85x/gdHvQ4/agID8v6rbRjKd0fMkwrtLzhNvfaMc/cOJ4wPZnupy4YrQtbDzRCBabV5JC5s/HcGLEe1+NtH8i8hX1eA8kUL6JjCcaQgqPCRMmoK2tDa2trdi8eTPWrl0LAHjrrbfQ2tqKqVOnAgC6urqwYcOGqAuP9vYueL3qXUey2y1oa+tUbfxko0S+ZpMeJoPO50FgMuhgNuiGjB1J22jGUzq+ZBhXyXki2d9ox8+zmAK2vyDbpPhjJ1hsWo0maHx8DCdOvI+JSPonKl9Rj3d/wfJNVDyBhCoEhX2dds6cOVi3bh1ycnKQn5+PU6dOYceOHaitrcX27duxfft2bNy4EX/6059w9OhRUWGRSqyZelRWFMFk0AGA/F6j1WyIqW004ykdXzKMK3qeaMe3WYxYUj7Wp/2S8rGwWY2KxBMutnllhSgYblV8nSl+8d5XRT2mYpFssSVbPMFoJEkS8pGTEydOYNq0aVi+fDluvfVWvPzyy2hsbMQLL7zg0+7+++/HqFGj8NBDD0U8Nq94KEuxfDX97zl2dLuQm2Xsv/MH26ZI2kYzXhTx9bg9MBt08Y+nVpwKzxPx/kY7vrb/sx7tjl7YrBn9RYfSHyz1i+2Uw4kMkx7WTD2yM/RB4+NjOMHifUyE6Z/QfEU93gcJmW8C4gkk1BUPYYWHmlh4KIv5prfzLV/g/MuZ+aa3VMg3Kd5qISIiImLhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYVQqPxYsXo66uTr69atUqXH311XC5XPKxyZMn4+jRowCAuXPnYsmSJWqEQkRERElElcKjpKQEe/bskW/v3LkT48aNw+7duwEAR44cgdlsxsiRI7Fv3z4YDAZ89tlnaG1tVSMcdWkAxzk3Wtq64ejtQ5ezT/5vaH3PQRPZGEHbCYgfWuDzL7rExaIBupx9ON7Ri7+d7EJ7twvHT/coP3ewNdaEyffLfn872YXWjl50uTxi9ycaibwfqSld8yI6T+nVGLS4uBgrVqwAAJw8eRJGoxEzZsxAfX09SkpK0NDQgEmTJgEAampqUFpaio6ODlRXV2Pp0qVqhKQODbC35SyqqpvgdHtgMugwr6wQb+88jM5uN5aUj0X1e/vR2t4Dk0GHyooijMnPAaTQYwRsJyD+i21mVFxfiPU1zWJi0QCHjnfiWFsXNtTtl+ecP/1ybP3w7/jujDHKzB1sjUflYO+REGsfZH9H2LNRMNyi/v5EI5H3IzWla15E5zFVrnhceeWVaGlpgdPpRH19PUpLS1FaWor6+noAQENDAyZPngy3243Nmzdj5syZmDlzJt5880309fWpEZIqHD1u+QkRAJxuDzbU7cfU8flwuj1YX9OMKUUj5HNV1U1w9LjDjhGonYj4pxSNkIsOEbE4etw4dNwhFx0Dc/7X/9uHKUUjFJs72Bq3O1wh1z7Y/h467hCyP9FI5P1ITemaF9H5TJUrHjqdDuPGjUNzczPq6+uxYMECjBw5Er29vTh79iwaGxuxbNky/PGPf4Tdbsell14KSZKg1Wrx/vvvo6ysLKr5bLZsNdLwYbdbhhw7cbBNfkIc4HR/dSl+8H8P3O5xe1CQnxd2DP92ahgytwZCYzlxsA1eSQq6hkrNHWyNT3c5Q+YbrJ9XkoTsTzSivR8Fuj8nIyUfH6mSs1KYb3pL5XxVKTyArz7n8fHHH+OZZ56Rj23btg25ubmwWCx466230NraiqlTpwIAurq6sGHDhqgLj/b2Lni96l13tdstaGvrHHLcbNLDZND5PDGaDDr5EvDg/x64bTbofMYKNoZ/OzUEm1tULGaTHlqNJugaKjV3sDzzsk0h8w3WT6vRCNmfaERzPwp2f05GSj0+UilnJTDf9JYK+YYqjFT7Om1xcTE2bdqEwsJC6PX99U1paSleeeUVlJaW4tSpU9ixYwdqa2uxfft2bN++HRs3bsSf/vQn+dsuyc6aqUdlRVH/CyUgfwZg++4WmAw6LCkfiw+ajsnnKiuKYDUbwo4RqJ2I+D9oPIYl5WOFxWLN1KNguBXzygp95pw//XJ80HRMsbmDrbHNagy59sH2t2C4Vcj+RCOR9yM1pWteROczjSRJql0qKC0txT333IMFCxYAADo7O1FcXIxXX30VTU1NaGxsxAsvvODT5/7778eoUaPw0EMPRTxPoq54AOj/xH2PGx3dLuRmGaHVanC604ncLCOsWQY4ur86ZzUbAn8gzm+MoO3U4D93lgEujwYn2rvExKIBunr74DjXh15nH3KyjXC6+pCdYVB27mBrrAFc3hD5ftnvlMOJDJMe1kw9sjP0yfnBxgjvR6nw15IPBR4fKZdznJhvekuFfENd8VC18BAloYVHGmK+6e18yxc4/3JmvuktFfJNyFstRERERP5YeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiISRpXCY/Hixairq5Nvr1q1CldffTVcLpd8bPLkybjrrruG9H3sscfwu9/9To2wiIiIKMFUKTxKSkqwZ88e+fbOnTsxbtw47N69GwBw5MgRGI1GNDU1ob29XW537tw5vP/++7jpppvUCIuUpAEc59xoaeuGo7cP0IQ5HknfEG26nH2h2/v1+9vJLrR29KLL5QndPlVFsobx9FdqH5Wi1lz+42oF5hQpketMJIBejUGLi4uxYsUKAMDJkydhNBoxY8YM1NfXo6SkBA0NDfjmN7+J3t5evPPOO1i4cCEA4L333kNxcTEuuOACNcIipWiAvS1nUVXdBKfbA5NBh8qKIowZlYO9RwIcz88BpDB9w7SZV1aIt3ceRme3e2j7EHHNKyvECHs2CoZbhrZPVZGsYQherxS6f4x7FE0MIvONZtwl5WNR/d5+tLb3qJtTHDEmPCaiOKlyxePKK69ES0sLnE4n6uvrUVpaitLSUtTX1wMAGhoaMHnyZJSXl6O2tlbut3HjRsydO1eNkEhBjh63/EQIAE63B1XVTWh3uAIed/S4w/YN12ZD3X5MHZ8fsH24foeOOwK2T1WRrGEorae6Q/aPdY+iiSEaas0VaNz1Nc2YUjRC0XmUjjHRMRHFS5UrHjqdDuPGjUNzczPq6+uxYMECjBw5Er29vTh79iwaGxuxbNkyZGdn48yZMzh69CgyMjLw97//HaWlpVHPZ7Nlq5CFL7vdovocySRUvicOtslPhAOcbg/OdDkDHu9xe1CQnxeybyRtBi4x+7cPF5dXkgK2jzTfZBPJGobSHKZ/PHsUaQzRUGou/z0Odz+LdR4lxZN7Kt2nlcB8U4cqhQfw1ec8Pv74YzzzzDPysW3btiE3NxcWS/+i3XrrraitrUVGRgZuvvlmaLXRX4Rpb++C16vedUe73YK2tk7Vxk824fI1m/QwGXQ+T4gmgw4XZJsCHjcbdPJ4wfpG0mbg0rJ/+3BxaTWagO0jzTfZRLKGoeRZM0P2j2ePIo0hGkrMFWiPw93PYplHabHmnmr36Xgx3+QTqjBS7eu0xcXF2LRpEwoLC6HX99c3paWleOWVV3yuasyZMwd1dXXYunUrysvL1QqHFGTN1KOyoqj/SRqQ33e2WY0Bj1vNhrB9w7WZV1aI7btbArYP169guDVg+1QVyRqGcvGFWSH7x7pH0cQQDbXmCjTukvKx+KDpmKLzKB1jomMiipdGkiTVLhWUlpbinnvuwYIFCwAAnZ2dKC4uxquvvoprrrlGbnfXXXfB6/XilVdeiWkeXvFQVkT5avrff+7odiE3y9j/RCiFOB5J3xBttFoNTnc6g7f363fK4USGSQ9rph7ZGfqQH8RLyf2NZA2DsNstaDvVGbq/UvuolDjnCrrH/uNmGeDoFpRTpGLIPSXv03Fgvskn1BUPVQsPUVh4KIv5prfzLV/g/MuZ+aa3VMg3IW+1EBEREflj4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGGGFx+LFi1FXVyffXrVqFa6++mq4XC752OTJk/HII4+gpqZGVFjiaYH2Lhf2H3egvdvVvwMawHHOjZa2bjh6+wBNmDGiba82v3i6nH1DYxvUpsvliT3+eHL376vtv918sC30WMm4P0rOkWz3JyJKa3pRE5WUlGDPnj0oKysDAOzcuRPjxo3D7t27UVJSgiNHjsBsNkOjSeNnPS3wl0Onsb6mGU63ByaDDkvKx+KCbBN+8bsG+VhlRRHG5OcAUoAxNMDelrOoqm6KrL3aAsQzr6wQb+88jM5ud39so3Kw90h/G0uWAbMmjcaGuv3Rxx9P7n59L7aZUXF9oc9eBBwr2jlF7I+ScyTb/YmI0p6wKx7FxcVobGwEAJw8eRJGoxEzZsxAfX09AKChoQGTJk0SFU5CtDtc8gsdADjdHqyvaUaP0+NzrKq6CY4ed8AxHD1u+UUikvZqCxTPhrr9mDo+X46t3eGS20wdny8XHdHGH0/u/n2nFI0YsheBxop2ThH7o+QcyXZ/IqL0J+yKx5VXXomWlhY4nU7U19ejtLQUpaWluO+++/Dwww+joaEB06ZNw3vvvRf12DZbtgoR+7LbLXGPceDEcfkJfoDT7cE5V9+QYz1uDwry84aMceJgW8AxgrWPVaT5Botn4HK90+3BmS7nV200iDn+eHIf0jfCOKKdU8T+KDHHwP6Kuj8lAyUew6mE+aa3VM5XWOGh0+kwbtw4NDc3o76+HgsWLMDIkSPR29uLs2fPorGxEcuWLYup8Ghv74LXq951Ybvdgra2zrjHybOYYDLofJ7oTQYdMo2+22Ay6GA26ALOaTbpA44RrH0sosk3WDwDl+lNBh0uyPbNO9b448k9WN9wY0U7p4j9iXeOwfsrIt5koNRjOFUw3/SWCvmGKoyEfqtl4HMeH3/8MYqKiuRj27ZtQ25uLiyW1K3gImGzGLGkfGz/CzMgf8bDbNL5HKusKILVbAg4hjVTj8qKoojbqy1QPPPKCrF9d4scm81qlNtsb2jBvLLCmOKPJ3f/vh80HhuyF4HGinZOEfuj5BzJdn8iovSnkSRJ2EfI9u/fjwcffBD5+flYt24dAKCurg5VVVW44YYbcP/99+PRRx/FN77xDZSXl0c8bqpc8QDQ/60Whwvtjl7YrBmwWY2A1P9ee0e3C7lZxv4n/VDpaKJsH6Wo8/WLR6vV4HSn0ze2QW3yLCZ4vVJs8ceTu3/fLAMc3W70uD0wG3TBx4p2TpX3J945huyviHgTLBX+QlQS801vqZBvqCsewt5qAYDCwkJ0dHRg/vz58rHi4mI88MADKC0tFRlK4ngBW7YRtmyjfBsArJkGWDO//Csz3JO+FGV7tfnHAyDbpJfPBWsTU/zx5O7f19t/uyA/r/9BHGysaOcUsT9KzpFs9yciSmtCCw8A2LFjh89ti8WCTz/9VL69cuVK0SERERGRIPw/lxIREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCaNK4bF48WLU1dXJt1etWoWrr74aLpdLPnbVVVfhn//5n3Hy5En5WG9vL8rKyvD++++rEZZ6NIDjnBstbd1w9Pahy9kn/zc0EZwXEFPQeQa163J54DjnRvPBtshii3QOpfjPpw0wf4B8FIkv3lyVWivRa05EpDC9GoOWlJRgz549KCsrAwDs3LkT48aNw+7du1FSUoIjR47gH/7hHzBt2jQ8/fTTWLt2LQDg+eefx4QJE3DdddepEZY6NMDelrOoqm6C0+2ByaDDvLJCvL3zMDq73Xho/jVw9XmDnq+sKMKY/BxAUjemgPMMamfJMmDWpNHYULc/dJ9o51App4ttZlRcX4j1Nc0+8xv1Wqz5rz3R5xPF3FGPpdRaiV5zIiIVqHLFo7i4GI2NjQCAkydPwmg0YsaMGaivrwcANDQ0YNKkSVi6dCkOHDiAbdu2Ye/evairq8Pjjz+uRkiqcfS45RcCAHC6PdhQtx9Tx+fD6fbg0HFHyPNV1U1w9LhVjynQPIPbTR2fL79Ih+oT7Rxq5TSlaIRcdAye/9BxR0z5RDN3tGMptVai15yISA2qXPG48sor0dLSAqfTifr6epSWlqK0tBT33XcfHn74YTQ0NGDatGnIyMjA8uXL8dhjj8Fms+Hpp59GdnZ21PPZbNH3iZbdbgl4/MTBNvmFYIDT7ZEvgXslKeR5p9uDHrcHBfl5isUaLCb/eXzaaRBRn2jnUMqQ+YLE65WkkOeDxRdsfwPOHWYspfsrPQ4QOt90db7lzHzTWyrnq0rhodPpMG7cODQ3N6O+vh4LFizAyJEj0dvbi7Nnz6KxsRHLli0DAEyYMAETJkyAJEkoKSmJab729i54vepda7bbLWhr6wx4zmzSw2TQ+bwgmAw6+dK3VqMJed5k0MFs0AUdPxbBYvKfx79dJH2inUPtnPxvazWakOcDxRdqf0PNHWmuSq2VUuOEyzcdnW85M9/0lgr5hiqMVPtWy8DnPD7++GMUFRXJx7Zt24bc3FxYLF8FNXz4cHzta19TKxRVWTP1qKwo6i8mAPkzHNt3t8Bk0KFguDXk+cqKIljNBtVjCjTP4HbbG1owr6wwbJ9o51Arpw8aj2FJ+dgh8xcMt8aUTzRzRzuWUmsles2JiNSgkSRJlUsF+/fvx4MPPoj8/HysW7cOAFBXV4eqqirccMMNuP/+++W2L7zwAgD4HItGIq94AOj/pkGPGx3dLuRmGaHVanC604ncLKP8ohDyvEofxhw8Z9B5BrXLs5jg9UrocXtgNujCxxbpHErxny/LAEe33/wYmk+4+CL66yHeXJVaKwXGSYW/lpR2vuXMfNNbKuQb6oqHKm+1AEBhYSE6Ojowf/58+VhxcTEeeOABlJaWqjVtYkiANdMAa+ZXf3lmm/TyOSD8edVjCjZPgNgL8vP679ThYot0DqX4z+cNPL9/PorEF2+uSq2V6DUnIlKYaoUHAOzYscPntsViwaeffjqkXaxXOoiIiCi18P9cSkRERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkjKq/Tjtg69at+PWvf42+vj5IkoRbbrkF//Iv/wIA2LhxI1599VX09fXB6/Xitttuw6JFi0SElVo0gKPHjY4uF3ItJlgz9enzk+jpnJtauGZElKJULzxOnjyJVatWoaamBhdccAG6u7uxcOFCjB49GqdOncKGDRvwq1/9CsOGDYPD4cCdd96JzMxM3HbbbWqHljo0wN6Ws6iqboLT7YHJoENlRRHG5Oek/otNOuemFq4ZEaUw1d9qOXPmDNxuN3p7ewEAWVlZWLlyJS699FKsW7cOjz76KIYNGwYAsFqtWLVqFQoLC9UOK6U4etzyiwwAON0eVFU3wdHjTnBk8Uvn3NTCNSOiVKb6FY8rrrgC06ZNw/XXX48xY8Zg4sSJuOmmm2CxWNDa2opx48b5tC8oKIh6DpstW6lwg7LbLarPEcyJg23yi8wAp9uDHrcHBfl5qswpKt9E5BZIIvc3WkqsWSrlq5TzLWfmm95SOV8hn/H42c9+hnvuuQf19fWor69HRUUF1qxZAwCQpPivDbe3d8HrVe8as91uQVtbp2rjh2M26WEy6HxebEwGHcwGnSpxicxXdG6BJHp/oxXvmqVavko433JmvuktFfINVRip/lbLH/7wB7zzzju46KKLMHfuXDz77LN44okn8Oabb2LkyJH45JNPfNp/9NFHclFC/ayZelRWFMFk0AGA/J6+1WxIcGTxS+fc1MI1I6JUpvoVj4yMDDz99NO46qqrMGLECEiShIMHD2LMmDG47rrrsHLlSqxfvx52ux2nT5/GypUr8Z3vfEftsFKLBIzJz8Gqeyaho9uF3Cxj/4tMOnyQMJ1zUwvXjIhSmOqFR3FxMe677z4sWbIEbnf/h9+mTJmCe++9F0ajEW63G3feeSc0Gg0kScLtt9/Ob7QEIgHWTAOsmQb5dtpI59zUwjUjohSlkZT4kEWCpftnPERjvuntfMsXOP9yZr7pLRXyTehnPIiIiIgGsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiuUYaVAAAKRUlEQVSEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhNEnOgAlaLWatJgjmTDf9Ha+5Qucfzkz3/SWyvlqJEmSEh0EERERnR/4VgsREREJw8KDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPC40tbtmzBjTfeiLKyMrz22mtDzq9duxbXXXcdbrnlFtxyyy0B26Sarq4uzJ49G8eOHRtybu/evZg7dy6mT5+Oxx9/HH19fQmIUHmhck63PV67di1mzZqFWbNmYfXq1UPOp9seh8s33fYXAJ5//nnceOONmDVrFl555ZUh59Ntj8Plm457DACrVq3Co48+OuT48ePHsWDBAsyYMQN33303uru7ExBdDCSSTpw4IV133XXSmTNnpO7ubummm26SDhw44NPmBz/4gbRnz54ERai8pqYmafbs2dKVV14pHT16dMj5WbNmSY2NjZIkSdJjjz0mvfbaa6JDVFy4nNNpj3fs2CHdfvvtktPplFwul7Ro0SLp3Xff9WmTTnscSb7ptL+SJEm7du2S5s2bJ7ndbuncuXPSddddJx06dMinTTrtcST5ptseS5Ik7dy5U5o4caL0yCOPDDn3r//6r1Jtba0kSZK0du1aafXq1aLDiwmveADYuXMniouLkZubC7PZjOnTp2Pr1q0+bT755BO89NJLuOmmm/DUU0/B6XQmKFplVFdX48knn8SwYcOGnPv888/R29uLoqIiAEB5efmQ9UhFoXIG0muP7XY7Hn30URiNRhgMBhQUFOD48ePy+XTb43D5Aum1vwDwjW98A7/73e+g1+vR3t4Oj8cDs9ksn0+3PQ6XL5B+e9zR0YFnn30WS5YsGXLO7Xbjz3/+M6ZPnw4gtfaXhQeAL774Ana7Xb49bNgwnDx5Ur7d3d2NMWPG4JFHHsH//M//wOFw4Je//GUiQlXM8uXLMWHChIDn/NfDbrf7rEeqCpVzuu3xZZddJr/g/P3vf8c777yDa6+9Vj6fbnscLt90298BBoMBVVVVmDVrFkpKSnDRRRfJ59Jtj4HQ+abjHv/kJz/Bgw8+CKvVOuTcmTNnkJ2dDb2+/0fmU2l/WXgAkAL8QK9G89VPDmdlZeGll17CqFGjoNfrceedd+KPf/yjyBCFCrce6Shd9/jAgQO488478cgjj+DrX/+6fDxd9zhYvum6vwBQWVmJDz/8EK2traiurpaPp+seB8s33fb4v//7v3HxxRejpKQk4PlU3l8WHgAuuuginDp1Sr79xRdf+FyOP378ON588035tiRJcpWZjvzXo62tLejbE+kiHfd49+7d+N73vocf/ehHmDNnjs+5dNzjUPmm4/4eOnQIe/fuBQBkZmbihhtuwL59++Tz6bbH4fJNtz1+5513sGPHDtxyyy2oqqrC9u3bsWLFCvl8Xl4eurq64PF4AKTW/rLwADBp0iR8+OGHOH36NM6dO4d3330X3/zmN+XzGRkZeOaZZ3D06FFIkoTXXnsNZWVlCYxYXV/72tdgMpmwe/duAMDGjRt91iMdpdset7a24t5778WaNWswa9asIefTbY/D5Ztu+wsAx44dwxNPPAGXywWXy4Vt27Zh/Pjx8vl02+Nw+abbHr/yyiuora3Fpk2bUFlZialTp2LZsmXyeYPBgAkTJuCdd94BkFr7y8ID/X8ZPPjgg1i0aBFuvfVWzJ49G1dddRW+//3vo7m5GXl5eXjqqadw9913Y8aMGZAkCYsXL0502IobyBcA1qxZg1/84heYOXMmzp07h0WLFiU4OnWk6x7/53/+J5xOJ1auXCl/tfD1119P2z0Ol2+67S8AXHvttbj22mtx6623Yu7cubj66qsxa9astN3jcPmm4x4H8vjjj2Pbtm0AgCeffBLV1dW48cYb0dDQgAceeCDB0UVGIwV6o4iIiIhIBbziQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iUszzzz+PjRs3JjSGqVOnorm5Gbt27cLs2bMTGgsRDZW6/1s3Iko6S5cuTXQIRJTkWHgQUVi7du3C6tWrcdFFF+Ho0aPIyMjAypUr8dJLL6GjowNHjx7Ft771LbS3t+Oyyy7DXXfdhb/85S/4+c9/jnPnzsFgMODHP/4xSkpKcOjQISxfvhwdHR3weDxYuHAhvv3tbwedo6CgAC6XC2vWrMGf//xneDwe/OM//iOeeOIJZGdnY+rUqbjqqquwb98+/PCHP/SJu6enB5WVlThy5AisViueeuopjB49OkGrSEQA32ohogj99a9/xZ133oktW7agvLwcDz/8MACgt7cXb7/9tnwb6P/J7nvvvRf33nsvamtr8fTTT2PFihVwuVyorKzEj370I9TU1OD3v/89Xn75ZTQ1NYWc49e//jV0Oh1qamqwefNmDBs2DGvWrJHnu+yyy/C///u/Q/4X2a2trfje976HTZs2Yfbs2fjxj3+s9jIRURi84kFEEbniiiswYcIEAMDcuXPx1FNPYdiwYT6/lzFg//790Gq1+Na3vgUA+Kd/+ids2bIFBw8eREtLi89vTvT29uKvf/0rCgoKAs5x5swZ/OEPf0BnZyd27twJoL+wsdls8hgDffxdfvnluOaaawAAc+bMwU9/+lN0dnbCYrHEvyBEFBMWHkQUEZ1O53NbkiRotVqYzeaAbf1/onv//v2QJAlWqxWbNm2Sj586dQoWiwVNTU0B59DpdPB6vVi2bBmuvfZaAEB3dzecTqfcLlAMAKDV+l7U1Wg0Kf2LpUTpgG+1EFFEPvvsM3z22WcAgDfeeAPXXHMNrFZrwLaXXHIJNBoNduzYAQD49NNPcccdd2D06NEwmUxy4dHa2orZs2fjk08+CTnH5MmT8dprr8HlcsHr9eLf/u3f8O///u9hY963b5/8U+pvvPEGxo8fj8zMzPgWgojiwtKfiCJy4YUX4rnnnsPnn3+OvLw8rF69GmvXrg3Y1mg04oUXXsCKFSuwevVqGAwGvPDCCzAajfjlL3+J5cuX4z/+4z/Q19eHpUuXYvz48di1a1fAOQDgnnvuwapVqzBnzhx4PB6MGTMGjz76aNiYL7nkEqxduxZHjx6FzWbDypUrFV0TIooef52WiMLatWsXnn76adTW1qb0HESUeHyrhYiIiIThFQ8iIiIShlc8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETC/H/JqTe7nLRfAgAAAABJRU5ErkJggg==" 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rn+58rOqkd7L5ib90m6dl1+vdxgZLNt7Cmgd+02187+vrh2xNIYQQhTestyNXrVrFkiVLADjvvPO48cYbueGGG3Ach9WrV7N27Vr+8Ic/9HveysrIYJfaTVVVtG8H+l5HOMkTANVxD9K2baqqut6XpbNxSB5yuAoCHo4T7Pu6PfDild1LKRlNsLSCcGnv844aVYKuPB4OzR6V08HbH3pMC5R5SDBS2CUVVJX1ve5MrgyzrBqvrT5v3IpWUnFQ/zpW1v1ks4RAOEIw0PN6aSqwy0aTa8sPnqFRVYzaP3frhOO6jm/YTcWEqTS+sSnv+MioqqN6LnqTCmnGTD2eN156Nm+8YszYIVsT+vGafhMptp6LrV8ovp6Lrd83m2ELYU1NTTz++OOsX7+ee++9F601sViMhx56iJkzZ7J8+XL+53/+h2i0/y+opqb4kL6JuaoqSkNDe5+ODYdMQoESDBL7RxSYNaDS+H6QeNwlk+naLQs6IcLOVIzstq5JIifi49AeU+Tcvq3bk0B4KvbsJfgb/6+zFnPxN2nOlqB76edAv9GaM3Aiv0bF9+yfsAw98wOoxJ87vs5uR8+4FLW56z19/vSO92i5fbxeAEZgHDUf/Q57fnINB95MFjnlfBh7Qt51D5hVlFS+A6PpwM6Wwpt4Ba3tIXSs5/WUKmfGdXew4buXds5d9a4Po0fP6Jy79Pj5lE+bS+vW9ex6fDULrv0uf/6Pm/DdHACT33YWZVPm9vk10H+KC5d+kx9dcwFutmP3c+pbT6Nm+luGbM3+vKbfLIqt52LrF4qv55HQr4TE3ik9LB/7grvvvpunnnqKFStWdI7dcccd/P3vfyeVSvGxj32Mf/mXfxnQ3MdSCAMIhRQBJ4fCRxkOWis0Bum0Jp3u/pG/oOMRstsxSIEZwvVDpDIO2dzRf0Iy4MdwWl+HVAu6fAqpkql4mL2ec3C/JboRO7YFfA+3dBpJewwldhzTawRl4qlRGKk6VKYeHagiZU8k6/fvE5gAFjmsfS/j1r2OEanEGH8i6WBNt+OCZpqAvxflxfDtahK6Bs/v/a66woN9r5Gp24oVHY01YQ6+k7+rFvZbqd/4Il42TemUE8hkM7Rs34wTKaViyhyMksp+99QfSkFs71bqt71GIByh+rgTcKJDt+ZI+OY92Iqt52LrF4qv55HQr4Sw3g1bCLvgggtYunQpCxcu7BxramrijDPOwDRNpk+fnnd8dXU1d911V5/mPtZC2EhXbP1C8fVcbP1C8fVcbP1C8fU8EvqVENa7Ybsd+cADD3Qbq6ysZNOmTT0cLYQQQgjx5iZ/MV8IIYQQogAkhAkhhBBCFICEMCGEEEKIApAQJoQQQghRABLChBBCCCEKQEKYEEIIIUQBSAgTQgghhCgACWFCCCGEEAUgIUwIIYQQogAkhAkhhBBCFICEMCGEEEKIApAQJoQQQghRABLChBBCCCEKwBrOxTZv3swFF1zA7bffzuLFiwG47LLLqKurIxwOAxCPx5k4cSLf//73GT169HCW1yvDgHAYDOXjuibJlMayIBRwUcoDFQRl4PuQTHr4fte5AcfHMVOAQSobwvWGpkalFGGaMN02tFWKjregDYtMeDyutjuPM3Fx4rXgpjFLR2FkW/GdcpJmNb4emtr6oizQjuk1os0S2t3qw14npcBONaDb6lCRStzoePxDCjf9NLp5J9r3MSon45mhjnP9LLnGHWjXxamejLbCA6pV4ZNu2EEulaCkZhIqUDqgeYaLl47Rtm8XTjBMpHpSocsRQgjBMIewVatWsXjxYlauXNkZwgC+9a1vsWDBAgB83+f666/n7rvv5vOf//xwlnd42qesNI3FbkATMG0CzkSUvxfT2w5ag4pCcCYYLo4Tpa0NPE8TDWdwMs+gMg2Awg7OJmkeTzo7uJfeUFCaeQnrle/ijzmf7Et/w9/5HKAInnQp7tuuI22WY7sx1DN3EX/0R6B9rIknUvbOD+JsvBv7jK/TVnnmoNbVV5VOLWrrnZBtBGVTNvFDxIMLyLhO3nFKgb17LXU/ugIv1oARilJzzZ3o489BowAwEvXsXvkN6h/9Vcfc889n0lX/jmcEeG3lv7PlvhWgNWNOXchJn/0hqnxCv2rV2SSb/nQPT9z5dXzXZfRxs1jyrXsJ1EwbnIsxyOL12/jlzVdTu+llDMvi3E/cxNlXfoZh/ucvhBDiEMN2O9J1Xe6//36WLl3Khg0b2LlzZ4/HJZNJWlpaKCsrG67SjkynsKgFDuy25DBVG6a3rWtMt0O2FrSBacYoKTExDYXtvo5yGw5MhJHeQNBqHfQSwzRirfsW2irBrdu5P4B1rOm/+Cvs+hcAsPa9TOqR20F3bNW5u14muek5dHQS5uM3E8ntHvTajiQaSMGOuzsCGIDOoXb+khK1p9uxdqqBuh9fiRfruKZ+qp29P/43nLbtncckX32sM4ABNK15kLbn/kTrpqfZsvqujtAM1D33KLse+Q1K9a/etu2v8I87vozvugA0vrGJJ37yFZSf6d9Ew0Bpl4f+67vUbnoZAN91+dOPv8329c8XuDIhhBDDFsIee+wxxo0bx9SpU1m0aBErV67sfOyWW27hwgsv5Mwzz+RDH/oQp59+OldeeeVwlXZkOnvIgIHSqe7HeU2gOy6pYfjYtoeRq+12mPKaUP39yX8ERrYRvDREp+NtW9vtcb3rOUxT4ddt6vZYZvPT+KNmg++iEnsHta6+sIihUj1cpwOh7CA6VofXVp8/6OVwmzvOtyyDluf/1u289teeofHlp7qN1z5+P4bXv/AU27Oj29i2Zx7BTQx+uD5amXgrG57ofj32bXu9ANUIIYQ42LDdj1i1ahVLliwB4LzzzuPGG2/khhtuALpuR77wwgtcf/31nHXWWTiO09t0eSorI0NScyc/fuhAx3vADmWUgerYZbFtG8sKQqwaMvnnm84oRpcObs06VgXKgmQt5rgTcBs25z1ujT+RUaMipMdMJ3nIufakeRjtOwCFXTYWgKqq6KDW1xudTkOgCjINeePKqaCqPL+OrD8GI1SKn4oddKBBuHo8wf01x088k4Ynf593XmTqPEJlE9jMz/LGx5z6biqqRgF977ltXPfbl+PmvY2K6moCkeG7bn2RKTGY+pb5bHzq4bzxyvGTh/U5PlYUW8/F1i8UX8/F1u+bzbCEsKamJh5//HHWr1/Pvffei9aaWCzGQw89lHfcySefzGWXXcZNN93Efffdh2X1rbympni3N2YPpqrRITyqMTmwA2PgUYYyxmD4dfvHHHCmgMrheRFisSyuqykNz8HO1aH8jujj2xNIZ8tJtrcPao2mMYrSE5Zivnob1tSz8HY8h27dBYCacgbZmlNobWgnUDUP560Xkn3p/o5OSmuInLwY9dIP8BZ8gXbGUgE0NAxufb0LUjn5StSW22H/LT1d815SahzJQ+owrGpqPv5T6n58JdrNgjKouvz7JMITad9/bMmJi4jOWkD7pmcBCE+eQ/mCi3ENh5pTF7LvuUcBiEyYxpTz/o3GpgRVVdE+9xydeAJved9VrPvjzwEIlY3iXZ/9LrGUgtRwXre+WXL9V6nduI725o6QO//CS5ky75Rhfo4Lrz/P8ZtBsfULxdfzSOhXQmLvlNZ6yD8Pd/fdd/PUU0+xYsWKzrE77riDNWvWAPDpT3+684352WyWc889l6uuuoqPfvSjfZp/yENYVZSWljjhkI9SHr5vEU+AY2uCTgalfJQZwtcmWiuSSR/X7aonHHSxVRyUQcaNkM4OzV1gU3mU6H2obDOYZfht9WjTIVc6lYzq2nmz/QRWyxuQS2KXV6PcVrRTTiIwAU/bBfmHbRgGZfY+VHYfWFGSjCOds3s8VqGx27bjt+zGKK3GrZiKr/KPNTOtuPu2oH0Pe8wMvGDHbpdKx0jtfR3fzREePwPClUD/v5npbJL43q1kE22UjZuGXTGWof+XNHCZ1jqadr+BEyph1ITp1Iwfc8x/8x5sI+EH1mAqtn6h+HoeCf1KCOvdsOyErVq1iqVLl+aNXXrppaxYsYJIJP+2nOM43HDDDXznO9/hoosuIho9Np5A19XE2hUHX7JMVpHJHnxb0u92HkAybQHlQ1ofgKdNYowDZ1zHwKiJPR6XM0rIVc4DIO+dbQUMEb7v05KpAqog1/uxGkW2bCqUTT3sMV6gHDXpVBRw8F+60MFSglNPOep6lRMmOnle17zHcAADCJSPYVz5mEKXIYQQ4iDDEsIeeOCBbmOVlZWsW7eux+MvvPBCLrzwwqEuSwghhBCiYOQv5gshhBBCFICEMCGEEEKIApAQJoQQQghRABLChBBCCCEKQEKYEEIIIUQBSAgTQgghhCgACWFCCCGEEAUgIUwIIYQQogAkhAkhhBBCFICEMCGEEEKIApAQJoQQQghRABLChBBCCCEKQEKYEEIIIUQBWMOxSDwe59Zbb2Xt2rWYpklpaSnLli3jhBNOAGDz5s1ccMEF3H777SxevHg4ShowpaAkDKbpAgaptInrakpCLoZKo3HIeQEcxwA0nqdIJDy0PngORUkwjUkCrRzS2TBZ9+jysEOaYGYnKtuOVzKBhFmTt2ZPDHxC6d2o9j1QMpp0aBKusvOOCXhtWM2vo7wUZkUVXmg0CT36iHMPtnAgg+03gzLI6ArSOSfvcceP4+/ZQLZlL4GaKeiaE3D1wF7eSoHfuofk3q1Y4VKCY2eg7XCv53jt9bTVbsF0gkTHT8cIlg5o7cGQizfRuGsLhmFQOXEGVri8YLUIIYQ4vCEPYb7vc80117BgwQJWr16NZVk888wzXHPNNTz44INUVFSwatUqFi9ezMqVK4/5EFYW1djGdsADwAxXgrYxcy+D9gFwnBkoOwrKAxSWVU5rq9s1RziGlXgEdMeYFZxNXM0mmxtYEAuQoGTTCowNvwbAdiJYi35Ma3DWYc9RShHZ90/c1Z9Be1lAEXrPl0gd/4HOY4LpvWT+9zMkt63t6LVqKuUXfYqycafSqqYOqNaBiAbacZp/h8o1A2AFJmKWX0Ai0xGMbK+d5odX8MYvvglao2yHEz53F/ZbL8bz/H6vl61dzz++cBHZWAsA086/kplXfgWCPYeZ9L4trFr6ftr37e44/sxzefeNP8CMVg+k3aOSaNjBz//fJex7YzMAU06cz0e+fRehUeOHvRYhhBC9G/Lbkc8++yz19fVcf/31WFZH5jvttNNYvnw5vu/jui73338/S5cuZcOGDezcuXOoSxqwQEBhGXs5EMAATKUxc68CXT/sVe71g77UWFacQMAEIBjwMVNPdwYwACO9kZDdPuC6gomtnQEMgGwc85lvE1TJw54TytXjPvgF8LKddXqPfJtgckdHDwr01sdx9wcwAK9hG6nXXsbctpKAmR5wvf1hGgo7/XJnAANQmV04uR0otb/yvRt4495vcGB7TueybPrxUuzm1/q9nuEmeenHN3UGMICtD95DYvsrPR6v8Hjulz/sDGAAW5/8C/s2rOn32kfLMBQv/GllZwAD2P7yGjY/80jntRJCCHHsGPKdsA0bNjBv3jwMIz/vnXXWWQA8/PDDjBs3jqlTp7Jo0SJWrlzJF77whX6tUVkZGbR6D6eqKgp+FrxU/gMaINf9BJ0FOoKXUjlKSx3AQrsxaI51O9w2Mh1rDIBuaeo2ppo3E3GyREtrejzH2/MG6XTboTNhZzrCzujRUVp3PN/tvOyOl2HiyUQDLqUlA6u3P7Sfg9rt3cZNdzeja+YDULduT7fH3fZm3FgDVdNP7vNaVVVR4nUtNG3q3neupa7H5yfZ2sTudU93G2/etom3LLmkz2sPBi+XY/Oz/+g2vu2lZ1h0xae6jQ/09TaSFVvPxdYvFF/Pxdbvm82QhzDDMNC9vIFo1apVLFmyBIDzzjuPG2+8kRtuuAHHcQ57zqGamuL4/tC9SamqKkpDQzuOrYiWRDA4aNdKaVBB0AfvDClQAaBjt8v3HWKxDLlcioANEasK5TbkrZHzQ7Q1DGw3rCw4BvuQMT3mbbTnQmQPM2fIqMCI1qDb93UNGha5UA0W0NDQTnDGO2Dtb/POC858O9gWsUyAbHLgu3d9ZRiK8tAsjMzevHHPmULL/t6CNVNQhrucSBMAACAASURBVIn2u3YoA6PHY4+aQEMfr+mB59hQYcbNP5vapx7MezxQPbnHuRQ2089awnO//lHeeM2sk/q89mBRSvGWsy9i27r8XbhZpy/qVsuBfotJsfVcbP1C8fU8EvqVkNi7Ib8dOXfuXDZs2NAtiN1222386U9/4vHHH+fnP/85Cxcu5JZbbiEWi/HQQw8NdVkDks1pcn4NWgf3jyg8HcS15u0PXQAm2pkLxv5bY9oilyshl+u4P5nJKdzgfLQZ7TzeC88nmSkZcF3J8HF4C24CsyO46tIpuPM/T9YPHPactFWBeeHtqJKqjgEngnXhD0mGJnYeoyefTuC0j3DgXpYz6ywCU47DnXopWe/Q2Dc0fF+TCczGD03fP6LwIyeRNiZ01Tn2BGbf8BPMcMc1dUaNYfbSn5KJTur/ekaAuR/7GuXHdXxoxLBs3vrJbxGaNLfH4zWKeRdfzcSTzuyozjCYf9kNVM48pd9rHy2tNXPfdSHzFp7fOXbaxR9l6snvGPZahBBCHJnSvW1TDQKtNZdccglnnHEG1113HaZp8sQTT3DzzTdz+eWXs2bNGlasWNF5/B133MGaNWv4xS9+0ec1hmsn7IBgUOHYHlobJFMK39eEghrLzOL7FumsTShkoJQmm4V02us2ZyjgYRspfGxSmSDeUdZvGppQrg4jlyAXGkNKH/kWrVIQdJsxk/X4wXLSgbH4vs7r1yKHE9+J6adQkXJy9ihSXu+fFBwKjuURMGKgDNJelNwhnya1bROj/lVybfU4VZPIlU7B637ZD+vQ59jIxEg37MIKlWBWTkIf6feVbIJk/U4MJ0CoahJaDcsHj3uk3TSxuh0owyBaMxlldt9VHgm/QQ+2Yuu52PqF4ut5JPQrO2G9G/IQBtDc3Mzy5ctZv349lmVRUVHBsmXLuOmmm1i6dCkLFy7sPLapqYmFCxeyatUqpk2b1qf5hzuEvdkVW79QfD0XW79QfD0XW79QfD2PhH4lhPVuWH5dHzVqFP/xH//RbfyBBx7oNlZZWcm6deuGoywhhBBCiIKRv5gvhBBCCFEAEsKEEEIIIQpAQpgQQgghRAFICBNCCCGEKAAJYUIIIYQQBSAhTAghhBCiACSECSGEEEIUgIQwIYQQQogCkBAmhBBCCFEAEsKEEEIIIQpAQpgQQgghRAFICBNCCCGEKAAJYUIIIYQQBWAN9QLxeJxbb72VtWvXYpompaWlLFu2jBNOOIFEIsH3v/99nnzySUKhEJFIhM985jO8/e1vH+qyhlQo6BOwUyhcPB0mnrTx/Y7HHEcRCnoYKocyHNAeymtD+x45XUo8FUTr4anTUjlKUttR8Vp0eDSp8DSyhPOOCbv1mE2bwXfxK48nGRh3xPpsrx2rcRM63ohROYV0+XR87H7VFrSzBFUj+Gl8s4JErhzPVx3zG1nC7m5Uug7tVJJyJpH1g5iGR4mqw8g2oK0yUukwqV2b8RJtBMcdj66agUblraMU2LGdNNfWEd+3F18rIlPmYlZNQw/XEzGM/Hgjzds3UJ9JUDphBqEx0/OfT+3RWruZxp1bCZePomrqHOxwecHqFUKIN7MhDWG+73PNNdewYMECVq9ejWVZPPPMM1xzzTU8+OCDXH/99cyePZsHH3wQx3HYsGEDH//4x7n11ltZsGDBUJY2ZMJBn7C5EeW1AGChMEtOpqU9hGVBJNyOSR0A2i+D9pfAa0MBAWVhlCyiLREZ8joNpYnufQjzn9/oHDPnXEp89ieAKACRTC3+H67Ca9vdcUCwjMiHfkF7eNph57W9OPqhbxFfs7JzLPLRO8nMOB/f71uoCdoZwom/YSQ37R9RmNUfpDU3CUNpIq3/wNxwe1fdUz9IYtwlhLMvY+74LwAygQVs/eVqWl5+omMG0+KEr9+HMfX0vLWcpk20rvk/Xvi/P9C24zUArGCYd//gT9gTTuxTvSOF217PX79+NbUv/hMAw7L51x/dT9mM+UBHIN3+/N9Z8dlLOgPoW86+iIu/cKsEMSGEGAJDejvy2Wefpb6+nuuvvx7L6sh7p512GsuXL+fpp59mz5493HzzzTiOA8CcOXO49tpr+clPfjKUZQ2pgJ1A6ZaDRjSmt4lgEMIh3RnAQIGbAK/toENdzOyr2EO+Pwlhdx/mmn/PGzM2/JpgaldHdUqhtj0GBwIYQLoN1v0G08jfTTqY1bKFzEEBDCDxh5twUnWHOaO7oGo6KIABaMymPxGyM4R0A+amn+bXve13hN0dmLW/6BxrbzI7AxiA9lze+NnnMHOxzjHTgNjf/5v2jN8ZwADcdJINv/x3TJ3rc80jQdPr6zoDGIDv5vj7D2+GbAKAbLyJ337js3k7gOv+dh+N2zcOe61CCFEMhvTH/YYNG5g3bx6GkZ/1zjrrLFasWMHcuXNRKv8H+tve9jZuvfXWfq1TWTn0O0dVVdG+HZhrBS9/SJEiWmIB7kGPmeAlu51ueK2UVTgoI3A05R6RbtgJbrrbuOO3AzB6dIR08+vdz6vfQEW5gzKdHudN72nrNqZTMYIqQ7SP11C3Z7sPenHCIcDPdvx3CNNrBy/V+XUukep2TGrPFkKmS2h/HV4mSaxhB6mg2e3Ytm0bCQc0wbI+Pu8jwK725m5jLTu3EDBdSqui7IvvJda4r9sxmXhb31//x7g3Sx99VWz9QvH1XGz9vtkMaQgzDOOw76tRSuF5XrfxXK7/uw9NTfE+3+oaiKqqKA0N7X06tiwS4tB44htjaGvzCAQUYXt/GMMFexQckhV8ZxqtLTn8HoLGYAqpUYTLjkO1vdE1aIVIB8cRAhoa2oketxDW/T7vPDXvAzS1ZNA60/O8ZZPBcsDtqt+adDJJq5JsH69haaAMBwV0Pac6NJ32pI3pVxAOj0Ml93SdYIbIBcZjhSahUjsBCFdXdpu3+qwPkTTKiO+vQylFycnn48W6h+Gp536EhBugvY81jwTlk2Z2G5vz3g+TNTte3yo4itlnLGLjUw93Pq4Mg4rxU/v8+j+W9eff8ZtBsfULxdfzSOhXQmLvhvR25Ny5c9mwYUO3IHbbbbeRTqdZv359t9D10ksvMW/evKEsa0jFUwFc60TYH8V8o4aMnozrapJJTU5PRhPsONgwITIflAModGAmGSYPaaA8IKUjuGd9F119EgC6dDLuoh+RMMd2HpOtPgXz3TeDHQbTwTztk+QmvavXN+anI5OJXv1LzFGTALCnn0noX28ja5T0ubZErgKv+l/BKu2oLTSDbNkisjmDlF+Ce+KX0eWzOx4rmYR7yneI+TXkJn0cHZ0FQEmkjhOW3YNTXg1KMfrM9zPugzfh07XrpbXGnncekUiIU65ehh0pQ5kmMy76GBMXXTosz8Nwik6awwXfvptwxWiUYTDn3H/l5Es+g97/bUBZAd73+e9xwjsXA1BeM56P3/FbysbNKGTZQgjxpqX0EH4ETGvNJZdcwhlnnMF1112HaZo88cQT3HzzzaxevZply5YxceJEvvjFL2LbNuvXr+eTn/xkv9+YfyzthEHHDkso6GMYPpmsyaGbe8GAwrI1nmdgmgaWyuB5PqmMjdt9c3BIBVQa223FsyKkdMdt3YP7NRQEcw2AJmOPxtN9y+0Brw0jG8cNVpJTwX7XpRSE7CyGypH1QmTd/HUDRhpbx3GNMGmv6xOdASuHreJ4OkTaC2IkGyGXhmg1nur5FqqhfKK6lXhrDE/ZGGVj8sLam4lSCq+9Adv08QMVaKP7p1a1lyHd2oAVLMGJVAzbp3WH2kjYNRhMxdYvFF/PI6Ff2Qnr3ZCGMIDm5maWL1/O+vXrsSyLiooKli1bxpw5c0in0/zgBz/gsccewzRNysrKuP766/v9JyqOtRA20hVbv1B8PRdbv1B8PRdbv1B8PY+EfiWE9W7IQ9hwkBA2uIqtXyi+noutXyi+noutXyi+nkdCvxLCeid/MV8IIYQQogAkhAkhhBBCFICEMCGEEEKIApAQJoQQQghRABLChBBCCCEKQEKYEEIIIUQBSAgTQgghhCgACWFCCCGEEAUgIUwIIYQQogAkhAkhhBBCFICEMCGEEEKIApAQJoQQQghRABLC+kmpjv/6wxjmq9zf+oZ6nmIl108IIURvrOFcrLa2lnPPPZdp06bljZeXl3PRRRfx/ve/fzjL6ZeSMAScHAYp8FP4hEllS0ilD5+wwiFNyGoDrxllRMipUbS1D90lD+oYwdhGjLat+GXTSJfOIa2O/P9gr5Qi2PY67FwDXpZMzXGUl0RRfgs4ETIlx5P0y/tUQzQQx3J3g87g2+OJ50bh+UeXRkwDAo0vkX59Lcp2CE6fT6p8Nr5/uH5ANW4hvvlZ8FwiMxegq2ehdd/XVEqR27uR5g1rUIbJqDnzsWpm9GkOP9lC0+sv0rRtE5VTZzFqxkmY4Yq+L34w7dG8YwM7XllLsKSUSfNOpaRqysDmEkIIcUwZ1hAGUF1dzX333Zc3tmzZsuEuo390jpDTiPKT4O4EwATCVg2+M51MtnvIsEwIqZ2o2NrOMduqpjRyJrH44G+NOSpDeP1PMDb/EejY4gzPvgRvznXktNPruaHWTcR/cjE6m9xfaIiy930ZO2xh1v+UUNlM9KxlpPySXueJBuIEGn8JXhwAE0VpzaW0ZMYeVW+BurXs/M7FaDfb0Vu4jElfXE2ifG6Px6v6Tbxy89l4qY46DDvAvOUPwdgT+7xmtvYVHv3sOXiZNABWqISF//k3rLGzej/RTbP2nuW89If/7hx66weuYcHHvwZmsM/rH1C3aS0//viF6P2Js3R0DZ+660FKqqf0ey4hhBDHFrkd2Rc6hcLsDGAHGP4+gk6mx1MiJTlU8sX8QbceW8WGpMRgurYzgHXWt/E3hDJ7ej3PNBW553/XFcAAcimy214gt+nv6KozUM0v4WR2Hn6S/Sy3tjOAddAYrY8RsL3+tJLHsTTNf7mzM4AB+Mk24i/+GaOH+7yGoWj656rOAAbg5zLU/fkuTLNvO3KmafDGAz/vDGAAbirBrr//DsPofY5E3ba8AAbw0u/vIlm3vU9r5/Ey/OnH3+oMYACxxn1sW/d0/+cSQghxzBn2nbD6+nouuuiizq8vuOCCo56zsjJy1HP0yms97EOOraiq6n7LT7vNoN1u4wq3x+OPlt6T67k+M3fE9Vraugc1L94MpQGwwgDYxpHn0S3ZbmPKSxCN2JRave+iHY6XTdDcsq/buNu6j+rKnufc27S321i2sZaKshDKNA+71oH+tNYk63d1ezzVsPuIr7XUzu7XAED52X4/74nWHLHG+m7j6VjLoLyGhuJ1eKwrtp6LrV8ovp6Lrd83mzfF7cimpji+3483/PRTVWUQVCsYUfDbux5QAZIZm0RLe7dzQiGHsDMJlT1oB0lZeKqUlobuxx+tsD2WUHQCqr22c0yXTSVp1pA6wnqhUz9E5uUH88aCM8/A9JqhaS1YJaStccSPME9ZYBw2Cuh6LvzS+bS0arQeeM8Vi64k9fqavLHoqUtoOEw9o9/xQfY98su8sZr3XkNjc7LH46HjG9nB80278Gr2rn0k75jJZ19y2DUPCIyeSMWk6bTs3NI5NmrydAKVE4547qGUcnjXR6/l98s/f9CYYspb3t7vuQ51aL/FoNh6LrZ+ofh6Hgn9Skjsnfm1r33ta8O1WCwW449//CNXXHFF3vjDDz/M+PHjmT179oDmTaWy/XrTdX+VRErI5QwMK4pCgc6izUpyajbtiZ5zrOtCMFKNMgyUnwCrCh05k9Z4cEhqzRHEnnw6KhdDpVvwJ78H9203kTCqjnxytJrQpBPw9m7ECJcTWfQp7LJRWKUWGAp37o3E1bgjTuPqEHZ0MirXiFIGfvk7SZqzcL3D7z71RaByDKFxU8jUbsaqqGHMv/0HHPcOPOwej7fKqimfeRLJHeuxSso47urvEpq3CG0e/r1xJSUBksmuXazAqDFUTptD27ZXCZaP5pQbfkDZ3HeA0fOaByg7xLTTF5JLxEg2NzDjXUtY+LnbsMuPfP16Ujl+MqPHTaDujdeomjiVS7/xE8bMPBXU0b2T4NB+i0Gx9Vxs/ULx9TwS+i0pCRS6hGOa0noo40u+2tpaLr/8ch599NG88WXLljF//vwBfzpyyHfC9v+24dgGjqPR2ieTVbjd7zZ2Y9sGoaCH6xokU0N/qS3Dw/aT5IwSXL/vP6iVAttLYJEjVBImlnOw/DieCpL1+rdhGrA9DOWTcZ1Be14MA4JeC1qZpCg94vFKgZmLo7WP75QeMfj29BulUkCmHaUU2on079OV2sVLtWOGomh1dBvOSilyyVZMy0bZ4aOa64CR8Bv0YCu2noutXyi+nkdCv7IT1rtj5o35X/3qVznppJM6/3vuuecKXVI32ZxPPKFJJPsWwAByOZ9YuxqWAAbg+iYpov0KYABaQ9YoIWmUY0QqybmQ8iP9DmAAmZxJKmsPajD2fUiqij4FMOjox7UiePaRA1hvc2gnim/3L4ABaGVhhCuOOoB11KGxQmWDFsCEEEIcG4Z1J2yoDNdOWLEotn6h+Houtn6h+Houtn6h+HoeCf3KTljvjpmdMCGEEEKIYiIhTAghhBCiACSECSGEEEIUgIQwIYQQQogCkBAmhBBCCFEAEsKEEEIIIQpAQpgQQgghRAFICBNCCCGEKAAJYUIIIYQQBSAhTAghhBCiACSECSGEEEIUgIQwIYQQQogCkBA2hJQCyzIwTQOlVLfHTdPAsgwMo/tjg8E0h27ugaxpGArbNnAc64jzGIbCNIfm5Xm4uY92zQO9KzV0tfdGKfa/1oZ9aSGEEANw5J+Gg8B1Xe666y7uv/9+lFJ4nsf73vc+PvGJT3SGk+9973v88Y9/5PHHH8dxnOEoa0hFwh4BqwWlW8CoQKsIWTdIexyCQYNQ0Mf0m9GZHWDauNZk2pMl+Pro17ZwiSQ2YGz7M9gRvCmLaQ9OH5S5D8c0fKJ2I2ZyPVrZeOE5tGcq8XVHOAjFtuK9cj9+8w7sGacTKhuNXzWbdnNs3jyGgqi5B7NtLbgJ/IrTiDMZ1zePukalgL3raXzy97jJGFXv/BDm5JPRhkNq6wtk//l7vFg9kdM+iD/hFDwj0Kd5tZum9fUX2PjX/yVcMYrJbz2NhjV/Y8yp76F09tshWHbUtR9JJlbP1rWPsemfjzD91DOZcdoiQhVjj3yiEEKIglFa6yH80dzhy1/+Mo2NjXzve9+jtLSUeDzOpz71Kc455xw+8pGP4LouixYtYvbs2bz3ve/lwgsv7Nf8TU1x/CFMGFVVURoa2vt8fDCoiZivonRr16BZjTYqSLvVOAEfw22CtscOOssgV3IObcnIUddb3v481t+uO2hth9x7/4c257g+nd/ffgHKA3VY+3550IhBruYK2jKVhBM7iP/4AnSqrfPR6JKbML1Wsqf9PzJ+sHO81NqL8/q3Qec6x9xpn6NVH9+venqi6l7l5WUL0bls59jcr99PcFQNe759NjqT7Bwfe8NvcKcv6tO89S8+zB9v/FDn13YozKJP3sz6n93CW6/9NuPPv5ah/Ffm51KsXv5ZXvjrqs6x6W97B5d9917MYLTHcwbyHI90xdZzsfULxdfzSOi3qqrn70Giw5DfM6mrq+P+++/nu9/9LqWlpQBEIhG+8pWvMHr0aAD+8Y9/MHHiRC6++GJ++9vfDnVJQy5gpfMDGIBXj1IWjpPDMDKQ3HjIWT6mt+eobx/ahof5yn8fsnYWc89TPd4SHQyWBWbs2UNGfcz0JgxD4e95OS+AASSe+iW4OYKJ7Z1jSims9pfzAhiAue9BbMs/qhoNQ9H6/F/yAhhA7R9uxW/YmhfAAJpXfw/bTx1xXuWmefq/l+eN5VJJ2poasUtKWX/Pd9Dt+46q9iOJ1e3IC2AAW9Y+QXPtliFdVwghxNEZ8tuRL7/8MtOmTaOsLP+WzLRp05g2bRoAq1at4txzz+Wss87i5ptvZsuWLUyfPr3Pa1RWHv3u0ZH0K817WUj3/JBpKDSanrZGDEMfdS86lwY/123cwGX06L7P3Z9+tfYg4fWwpk9lZYRkT9nP90CZmEb+WnpX9+uidI6y0iDK7NvtwcNp6eG6aDcLPdXn5YhGA5jh3q9Dut3Hc7vP63seyjDw3SyhgEVkCH8bTNT1HK4tS/X6PBbjb6jF1nOx9QvF13Ox9ftmMyzvCTt4B+Yvf/kLd955J77v4zgOd911F08++STf/OY3CQaDvPvd72blypXccsstfZ7/WLsdGQ7ahI0IinjXoFGJxiWTsXACoMKzIFZ/0FmKnBpH2yBsLVfM/TfMR5ceNLVJbuwZfZ57QLcjo/OxUm8cNKJwg7NobWgnVHMCygmjs127TeHTPgReC+nQJJIHrVVWMheb+4CunS+v+nxamrNA/i5Wf5Wd8l7U/34f7XcFxvHvW4pROQFMG7yuMFV+wedoSRroRO/XQSmD0678PA986crOMcOyqagZy+72VuZecTMZp5LUEN4yCFVOYPbp72HjPx/pHJsw+y1Eq6cc9nkcCbcxBlux9Vxs/ULx9TwS+pWQ2Lshf0/Y7t27Of/883nyySeJRLp2Ympra7n88su5/PLLue222zpvTabTaXK5HE888QTBYPBw0+Y51kIYQLTExTHqMXQzmKPwjQpybohYO4RCJsFAruON+cnXQDl4zkxiqVL8o7vrBoBNhpK2lzA3/gYdiOLPuoRYaBZ+H+8+D6Rf2/QpMfditq8FZeFF30Z7rhrP7/i0YKhlA7mn78Fr2kHoxMVYo8agx8wlZk3Mm8dQmlJjF0bjwygvgTd6EQljGjn/6D+sYeDj177I3gd+ipdsZeySa7GnvR1tBQk3v0rTX3+K17qP0kUfR009Hdcq6dO8fjZBw/qneOl3/0W4YjSz372EPY/9gfFvfy+VJ78HwpVHXfuRpFp2s/7RB3jl0QeYdcYi3nrOvxAePemwx4+Eb96Drdh6LrZ+ofh6Hgn9Sgjr3bC8Mf8rX/kKTU1NLF++nNLSUjzP4ze/+Q333HMPoVCIa6+9lvPOOw8A3/c555xzuO6663j/+9/fp/mPxRAGHe9DsiwAhevqvBoP/PkKQyk8z8f1Br9+2wKtVb/nPpp/2JalQCtcr3uaNAyFbXWEoYxr9PqcWaZCKci5g39dTEOhlMY96A5qVVWU5uY4SmvcAQZh02D/RzAVaA9fqyF9Q/6hlFIYykfT+7WFkfHNe7AVW8/F1i8UX88joV8JYb0blj9m9LWvfY2TTz6Zyy+/nIsuuoglS5bw6quvcuutt9LS0sLZZ5/dVZBhcMUVV7By5crhKG1I+b4mm9Vks363H4paQy7nk8l6QxLAAHIuQzb34biu7jGAQcf1yGQ1qaw6YkhwPT0kAQzA8/MDWOe4N/AA1jFvxxye5+P5wxvAALTWeP6Rr60QQohjw7DshA21Y3UnbKQqtn6h+Houtn6h+Houtn6h+HoeCf3KTljv5C/mCyGEEEIUgIQwIYQQQogCkBAmhBBCCFEAEsKEEEIIIQpAQpgQQgghRAFICBNCCCGEKAAJYUIIIYQQBSAhTAghhBCiACSECSGEEEIUgIQwIYQQQogCkBAmhBBCCFEAEsKEEEIIIQqgICGstraWhQsXdhufOXPmYR8rVqapCARMDEMBYNsGtm10jDtgHPIMmoYi4GjM/ccPB8OAgO31eU3DUDgqg2kM/f93vGVqAka243pZOSzzKOayDEK0Yxt+v86zVRZLuT0+ZuJh+RnU8D1dYhiZOoehc4UuQ/SRUqC8LAY9/3t9M1JotJvq/BkjhpdV6ALE/2fvzuPsqMrE/39Obbfu1vuSPWSBJuwRWUVAZFgUBPU7M+oI6oCA48rIMDCgIs6gOOPK4syIOP4YxRVFHBWViOxBQCAQspA9na2709tdazu/P27SSed2d7o73X076ef9euX16ntunVPPU1W37tOnqjqDq662sKwChuETBi6KEKV3gKqCoB0VdRHGGvGiaWSyJulEETtcg+G3Etmz8M359OZi4xpjKpbBKbyMkVlN5M7Hj59AbzE96PIJfwfmqoeIXvsVzozFqMWX0RufN+ZxKaWoKqzCfPV7GD0b0Ie/HZV2iKIsYcN59ITN6BHUgMncOjJP/5idf/4/4gvfQM1ffZhs9dFD9rHCLPbGJyg8+i1ULEXirZ+g0PwGIqzSiW/zX9jws6/gtbcy/aKPkDz+fCK35gAzF5OB8vP0vPY4r/3gaxiWzaL3fZpUy2lo06l0aGIQUaGHLc8/wvP330W8poFTPvCP1Bz+RvQhfMGoZ8sqHr//v1j/8rOceOFfc8IFf41bM73SYU0pUoRNUsmkheN0A6VZF9PwIdwI1IG3EnS+1B5mcFU3KtmCk38cFXYBYITdONY23NhZFIrjcxJxnYBY10OoYmtpnV4bTn4t8dr3kvfKv2xs5WM8+VXC1x4CQLetgtcfIfHen5Czm8Y0trS/AfvhKyEoAKCWroLj3o/hbsfo/gvJBZ8lEwyv4HHJ0vGjz9H7/G8AKG5+jcxLS5h106/IunMG7WdveorM/3dV32v/9SdIf/RBcvXHw/bXWHbTBeigNEuy+pvXsPAjXyf55g+iR1Idikmpd9UzPH7Te/pe73jpCc752m9wF55SwajEYJSCzc/+jt98/uq+tvVLH+F99zxCau5xFYxs/BS6tvKtqy+hd2cbAL+641a2rVnBpTd8HeSXhQlTsRJ/x44dXHLJJf3+iT0sK2J3AQYWRD27ftZ9BdhuSu/EVr19BVhfe9CBY2THLUaH7r4CrG+dfhs2XQMvn99KtKsA65Ntx+haO+axmV2v9xVgfV57ANInQJjF8rcMeyyja11fAbZb0LmVYOuKQfvYKqD4p2/1b9SaYPnDGIYiu/rPfQXYbpt++hXM4sDbThw8LFPx+i/+q6x9/e/vxzQP3VmVg5qX48/3fb1fk44iNj3/30dKIQAAIABJREFU2CF7q0Db+lV9Bdhuz//mJ2TaWwfpIcZDxWbCmpqaePDBB/u1tbS0jGqs+vrUWIQ0pMbGwS+xjY/iXj9r+urlQc4IhjnwrnRiDo2pkcc+nHx1oXfgdToOjdXl/cN2l4JhQ9S/+HBisTHfvrpngMuwVhx23Z9j2Q6Ntf3XOVgMhawDhglR2K/dsJxB++jAozNeXdZuxquork9RdBMDvJckVZXETk/MsTbxx3TlTUTOWmucVPksa6yqhrq65Livf2+yj4fHyymcZHk/N5mioWFyb8PR7uO2uFvWZlgWiaQ7JY+bSjkkLkd2dGSIovG7hNPYmKatbeCCY7wkEiaJhIVSARCCkYawc9fPtRB19i0bGbPJFRPEnTkY3sY97c48MlkHzx9Z7MPN17GSpFLHYWRe3rPOeAvZYopipry/qRpInHoV4VN39bWpaceQSx1GYYy3byp1OLFEMyq3fU/j8e+HnufR7kzyTCO31zqHytlJz6f+gqvp+PXdfW3ugjegpi0acjslzv4o3oo/svvmM+UkUIefQ1tbL7EFb8RK1xH07uxb/rD330JXwYJBituxVIljutImMucFl17Fhj/+DB2WCnfDdph19v+b0G0u+3hkTr/yBn76yXf1vY6lqph+/Jsm9TY8kHxrZh3OnKMXs/HVv/S1nXfFpzFTzWOasxR0QzskirBDUS4XolQKxwkxjIAodID5KN2LsuaDLkDUQ0QNBT9NvmCgYouJJWajwg4is4FiVI9XHL+5dC8wyCfOJOYuwChuRsdmUjRmUvQGPqxCbeAd+36caceiNzyNamohnHkqBaN2zGPLGE2ov/pPrG3PYGRaYcaJaKuItt6NH19ELhj+7KkXWSTP/QfcBW8gt/xJ3LlHE1t0Jlm7ech+haYTSH/0lwSv/QEVS2K2vIV8dQtoTVQ7j2Nu+x09L/4Br3MbtSdegDnnBEb23KWYrNy5i3nrHY+wbenDGJZF88nn4cw8ekQPg4iJVX/U6bz3v3/H2id/i1tdx9yTzyU+/fBDdp/ZyTouu/17rH3uMbasfpXDTzqTWUeffEg/iDAZSRE2iWWzIdksGIZNFEWAQqlqlIIoimMYdf1mAHNFmxzTMYwZRN7EnDnynkueBRj2QqJg/+ssGlUUm9+MMf3McZ29BOg1Z6BmvQulFFGkMQyF1ho9iqfP804ztFyCe/Q7CYJoWA+wR1jk6o/DOPN4tC5dptr7jB7VLSD91gV98UkBdujQKJzZx3HY3D37/lD9Mj9kGDZVC07kDYe/ccrss3jtDI7+q/dw7Plq3M/HYmAVKcJmzZrFkiVLytpXrlwJMOB7U9neH469TwyDfWgq8WEa6TonKsa+4meM1hkEIy+Vhlrv3vGJQ498sR18puI+m4o5TxYy7yiEEEIIUQFShAkhhBBCVIAUYUIIIYQQFSBFmBBCCCFEBUgRJoQQQghRAVKECSGEEEJUgBRhQgghhBAVIEWYEEIIIUQFSBEmhBBCCFEBUoQJIYQQQlSAFGFCCCGEEBUgRZgQQgghRAVIESaEEEIIUQFWJVba0tLCypUr2bx5MxdccAELFiwAoFAo0NLSwmc/+1kaGhoqEdqg4i4oFVEoGkRR+fuWpYjFIAigWOz/P9IbBriuidagIx9TRRR8izAcWQxKKeK6G9AUjBrG4n++t1WAHXQTmEk83D35mBEx3UugXIphbMTjOjqPEWTxnRpCPTaHmVLgmgWUCimESaIIDEPhWkUA8n4MrQffJkqBXegAw8J3qtl3UcvrhtAnTDSUvdd/HAW5jtILJ0kx242TrAHTGX1ukU+Q68R002DFRz2OEEKIg0dFirC9NTU18eCDDwKgtearX/0qn/jEJ/jBD35Q4chKLAsItpIyV4L2cZNzyfvN5At7JhGr0mCbOzDoQdtx4u50unsUWkM8bhKP+xhGDrwiOvMCKuzFjc2n6BxJJj+8L26HHMmtSzD/8i3QEbETriY341yKpEadW6q4CZ76BtHrS7CnH0fs7BvJpFvQvRupWvc9jO1PoFPziLdcQ7e5cMgCZzelFImOl8k9+FmCra8RO+5tJN56Lbn47FHHCWAZIenwNcxN90OYw206D7/mdCx/E2bH44AiVnMWWbUQL7DL+tvFDopPf5/239yBGa+i/j1fQB/xViIjhoqKbP/Tb3n9nhsIMl3MvPTj1J19OWGisTwQL0PbMw+x/AdfYe7fXMujD/6Y9cueZ9GbzuVtH/sMqeYFI87Na1/P0/fcxuuP/R/NixZz9ie+SPqwY4csBIUQQhz8JtXlSKUUH//4x1m9ejUrVqyodDgApOJ5KL4IOg8EmOEa4nZ7aTYEiMUUjtmKQWmGSpHDVutIJkuzNPF4gGlmS1Nk3UtQYScQoIqrcIJXsMzhxZHsehHzqS9Avh0KOzGf+SKJnc+POi9XZ9G/vo5oxW8gKKI3/ZnwJx8i5W+BZV/H2PIIhEVU9wqs528gxfbhjZvdQO9//Q3BxhfAz1N8/mcUfn4Dti6MOlaAJJsx190BXjuEOYytv8DpeRaz+0kIsxBmMDv+j6TaUtZXKYX3wi/o+OkXiLJd+O0b2XbnBzC3vARAtOklXvm391LYvoEg282G7/8r3c88gGGosrF6VzzDs//+UZrffAn3f/ULrH7uSfxigZeX/Ir/+acPEOS7R5aYl+G3X7iGFb//GUGxQOuLT/PTT7wDr2PzqLaTEEKIg8ekKsIAHMdh7ty5rF27ttKhoJTCoKus3Yw2E4uVpikcO0KR32eJCMvwcRwD09z1XpAB9rlM6a3Ddbz9xmFZBsaaX5W1G6t/jjXcKm4fZm4Letsr/RsL3Rhda6Djhf7tYQGz0Dq8gdvXor1cvyZ/9RNY2a2jihNKlxHN7Kry9vY/gTunX5uRXYZl9T+srSBDz5J7yvoXVj6OYSh6Xn2y7L2tv/k2RrGn/ziWwcZHfgyAbyXIdLb3e3/bmhX0bN80vKR2ybVtZusrf+7XVsz00N26ZkTjCCGEOPhU/HLkQJRSuK67/wV3qa8f/SW5/fJd2PfeLeWSTiVIp02IChAq9i2wbNvBjrlADgjBKL9EhuHixuPEU4n9hqFrDoMN+4RRM5/a2v33HUioqiiYDoT9i0AzXg1mHML+haXlpmhsSO933GJnTVmbcpK46SpSw+g/GL2jtrzRqYOof5zKqae2NtmvLfIsuhvn4m19fZ/uM6irT1FonFE2tNs8l6q6Gky3//atmnM4ALZdXvwapkm6pprGxuHn2ZGtxnRihF6xX3uypnZE44zGeI8/GU21nKdavjD1cp5q+R5qJl0R5nke69atY+HChcPu09GRGZOb1AeSTlbhKhf6LqcpfGM+Xe2l2R7TVFSnp2Oy5zJYpKvJZqBQzFJTk8C2PbASYDVAsGf2JIyfRFdniNa9+42jas55OK/9BLxdszN2En/+RXS37b/vQEzVROKsTxMu+WJfm3H0JWSTC0gfdQ0s+9qefJpOI6dmUBjGupyq+TjHnI/3ysN9bYl3fI4u6tGjjBUgZS8g5jSivLZSgzLR0y9B7fzNnoUMF89dRM8A66l5xz+TW/44OigVnVb9LMyFb6KtrRf3iNOINc2huGNjaWjLZvbf/gs7e0Po7T/WjDMuYeXPvkXvsic56YJ38uff/rzvvQs/ciN29QzaRpCnSk7jrI/dypKv/nNf26Lz/xq3ef6Ixhmpxsb0uI4/GU21nKdavjD1cj4Y8pUicWhKD+du6zG299ORl19+OUuWLAEgiiK+9rWvsXz5cr7zne8Me7zxLMIAGusNAq8LCAl1kkzO6feEpG1DMh6iDA+tLYpFm/yums0wIJUyMc0IpX1U0EUUFolUFZliethPSCoFqaAVq2slaAhqjiBjzzqgm7dtncftXgldGyHVhFd7JEWzhoZaE799BUZuM9qpoxifRz6qGva4MX8nxvZX0L1tGA3z8eoXERjDn9kcTMrqwvY2QuQRxmaR1c0k7F6scAcAgdlMxksPuE2UArtjJUHrqyjbxZx1HF5qVt/7SX87na89R1TMEZ97LDS1oCm/J0wpCNvX0b3mZcxkDT1Fn56d7dTNPIzG+cdixpJlffZHezm6179Cd+takg3TqJ1/HGaybsTjjMTBcPIea1Mt56mWL0y9nA+GfKUIG1rFZ8J27NjBJZdcApSKsEWLFvGVr3ylwlHtw0jS2TvA36XYxfehyzeB8j8tEEXQ07O70rKA0f3pDa2h15wJ9TP3ahzVUH18FcevOQFqTujXrqwE3ebhkC5demPw1AdUtOtg1pkHFtwAMkENGDWlOxl3bdLeYhWwq0AMBu+rNXh1LVDXApRfYU7MWEjWbi4tO0QMWoNRP4/a+nmwZ80HRDkJao44mZojTh6D0YQQQhwsKlKErVy5EoBZs2bxyiuv7GdpIYQQQohDz6R7OlIIIYQQYiqQIkwIIYQQogKkCBNCCCGEqAApwoQQQgghKkCKMCGEEEKICpAiTAghhBCiAqQIE0IIIYSoACnChBBCCCEqQIowIYQQQogKkCJMCCGEEKICpAgTQgghhKgAKcKEEEIIISpAijAhhBBCiAqoaBHW0tICwNKlS7nssssqGYoYQ0opkglFOqVJJiGdDEglQ9Ipk3TaxHHMisVmGpCKZUnHerEsPaw+SkHCDUjH87ixaJwj7M+1ilRb7dREG0jrNlJmN2m2EjOLExrH3gwDEt42koVN2MqrWBzDEQu6iPeuJRZ0lr1nhnmMnWsxM1swDAWAHfTgdK3BKbSj1NjGooo9hDteh+zYjy0GpoM8vdvXUujc2rePJyOFxu/cTLFtPSqa3J8pMbasSgcgDi2GAdVpD0u1gqpF+62oaCdggn04WAliMZNiMUlvbzChsbm2R8J/CWPHE6Aj7NRxFBNnkPXig/YxFFQldmLlnoYoj2PWEEueTnc2Me7xVts7sDpfRK34PWx8DMty4Zj3QbgNJyoSO/KT9OjmcY9jb7bO4a74BeGfvoIOCsRazsd58/Vk7WkTGsf+KAWJ9pfI3v9xwo4NmHWzKV52N6r2OLQGs3sD6779abr+8ghmIs38K26n5sg3suO/Poy36VXM6maar7yLYN6b0WPwu2qw9TWe/eKH6Vq3HLeumVNu+E9Si85EM3kLg4Ndrm0DD9z+T6x4egluMs07r/8SR519CcpyKx1aP1Ghl5W//h5P3fNFAq/IUef/Nad++DPYNTMqHZqYAHI5UoypVFJjqQ2ADcGOXQUYQAjeCogilPJwnAKWNbFfQC5bMbr+BDoENEbmJWLeiiF/Q07GC1iZRyHKA6DCLqz8E8Rj4fjGahWxtj6Aan0ZNj5WagwK8OK9kF6E6lyG9fo9OMYEF7Kdywkf+bdSLEC08mHUsvsxJ9mZJFbYTu93P0DYsQGAcOcmuv77/biF7Zj4tP7oNrr+8kjpvVwvq+/4B7LLH8fb9GqprXs7W77+Xuzu9Qcciyp08fStl9O1bjkAhZ3befymvyXsOPCxxSC0z+++fTsrnl4CQCHby/2f+yjt65dXOLByO1c9z2N3fY6gWACtWf7bH7Pyt/fLbOkUMclOneJgZyi/9IOKQ9hevsCuYsYwCjjOxB1+hqEwCuvK23PLsM3BCypDZ4H+lyBV2INlFMY6xH5selGGCxueKH+zpxWcaoy2Z3DoGdc49qYUsG1ZWbte/hB2MHFxDIfq3oLO9r8EqfPd0LUZleuk7alflPUpdnf0bwh9wrb1BxyLt3MLvZvX9GuLfI/c1gMfWwzM6+3kxd8/WNbetvH1CkQzOMNQbH7xqbL25Q//GF3MVCAiMdEOicuR9fWpcV9HY2N63NcxmYw63ygDIaB9MJIQ7fPlrJxdP1gkky7J5MQVYrprGvTu0+jOJF1dynWgnLWfh9w+jcom5iZwU+N3TOiiB1EOaudDZmv/N+P10JmFxEzcVDXx2OjiGM0+9uvnsm/JqqYdTaqunrQ9eS7z+H4jGcOCaK+ZQsMkVtdErKqe5JyjyKx5sV8fyy2/xOzWTyNxgJ/9nmIjdiKNn+t/8KWamsf9vDLVzltQyjkXC5l++FFsevUv/d6rbZw26bZJ44Ijy9qmL1pMbVM9lu0M0GOf/pMsHzEyh0QR1tGRIYqGd5P1aDQ2pmlr2/fb+9B1IPk6jiKdaMCgHezDoPgq7P7aNurBiAEBvp+iqys7ViEPSyo2h5jdjPK3lxrMBH7ijXS3ZwfN2bZipN2jMQqv9rWFiVPp7jaIovE7JpSKUdPwFkxnIWx/Gbxd62pYBIYHShEc9Um6ekzKK8v9G+0+TtQfi5p1Enrzn0sNsTTqtI/T3uUD/ojHGy+GPZ3kpV8g+8CNfW3pSz9Pjz2dKG8y/6r/4JXPXETklWY06095O8kFi+lRCnTpXFJ70bUUquaRPcDPvhFv4qTr7uCpL3yob+yj3v9PqLp543pemWrnLdg7Z5N3X387d131DvxiaR8f85a3UT/v6Em3TZqPOY1pi97AttdeAMCtquWN7/8UnV1FYOgHcA6GfSxF4tCU1nr8qpf9aGlpYeXKlSxdupQ777yT++67b1TjSBE2tg40X8dRxN0ApSIMpdBhHgwLjDhaG4ShIpOJqMShl3CKOHSgdUBoNJAplmY/hsrZsSLiThal80QqRbaQIJyAhyQNQ5MyOrCK7ajMDrSZRCdq0V4noTuTrJrGaA/7A9nHbtSN3fk6BHmi2vnkYjOo3FlkcKb2cLrXQNcWqJ5B4rBj6Ogu/UKglELtXEtx62rMRBX29CPRTgqrcw1B+wbMqiZ04xEE5tg8gKF0iL99Ndmt63HrphGbcQTY4/twx1Q7b0H/nJVS9G5bS/vG14mlqmg87EiseHWFIxxYlO2ga8MKAq9A7dwWnLpZw/pMHQz7WIqwoU2ambDnnnuOxYsX972++OKLufXWWysYkRgtz9N4ngns/lMUe38IJ/ZPPOwr58XIMbKnjrzAwAvS9M9j/EWRoidqALMBqve6ZLH7ql+FCp+CUU2h/sQ9DZOwAAMIlUO+ZhHULAIg5STYPWuotUbXzsOunVdadlcfr64F6lrG/CjVysSadiTV08ovPYnxobUm1TyPVPO8SoeyX0aynrqj3tT3ejL+UiPGR0WLsJUrVwJwyimn8Nprr1UyFCGEEEKICSVPRwohhBBCVIAUYUIIIYQQFSBFmBBCCCFEBUgRJoQQQghRAVKECSGEEEJUgBRhQgghhBAVIEWYEEIIIUQFSBEmhBBCCFEBUoQJIYQQQlSAFGFCCCGEEBUgRZgQQgghRAVIESaEEEIIUQFShAkhhBBCVEBFirCWlhYANm/eTEtLC08++WS/98855xw2b95cidAOSo5jUlVV+ue65l7tiqq0piqtqU57VMW7ScWLGGp448ZdTVWqQFXKw7ZHH19M5an2VlPnv0aNvYOE441+sAPkGB5VeiNV4VpcM4/rdxBvfwl/y3JMggH7mEqTLG4m1fUyiWAHapDt55g+VeZW0uZWErkN2Fuex8lsHnT5SjOz24g2Poexcy0G0fA7hkVyW1bSs/ZFdL5ryEUNP4ff+ireppdRXu8BRjw2lFLkO7ewY9XzZNs2ALrSIQkhpiir0gHYts1nPvMZfvnLX5JKpSodzkEnkTCJx/MYRhEAx7GxrBRhGJKItWPQiw5d6H0GdACY2MnT6c43Ew3x3VOVCnCiZagoC4Adm0XWmEuhOLKKIhm14f7531CtTwNgzDwF84R3Y1UvpqdQNaqcRythdOOuuxdj6xJQJmruR+j64eeIureSMUwSF1yPOvEDBEa8r49JQGL9bwgf/iw69MCtJvXOu8nUnoDea/slrAxu249RmVVkeo+l9Tv/gi7mULEk0z5yD+Hh5/ZbvtI6lz3OK5//f/g9HSjLZsGH/4PU6e8hMpwh+4W5Tp7/3u385affBqBh/iIuuu0+Yo3zypbVvdt58T//mc2P/RKApuPP4MTr7kbVzBr7hIZJKUXrsse599OXU8j0YMdc3nfr3Rz+potAyYUBIcTEqvhZp6mpidNPP53bb7+90qEclGKxqK8AA1DKx3GKuE4Rg060TkLv0l0FGECImX2SpFsYfFAdYbMRRbavyYg2E3cyI4pNKYWz9bG+AgyA1qWotvVYmaexrRHMvoyBWO+rpQIMiOpPpefXdxJ1by29GYXkfv1F7I6V/frEcxsIf30jhLtm7wrdRA9dixvs7D92cSVG51J88w203nMjupgDQBezbPvWFdg9m8Y3uREwCztZ/uUP4vd0AKADn9e/9Ul02+r99u1Y+XxfAQbQvvY1nvv+11GE/ZZTCjpe/GNfAQaw46UnaH30pxjDnYodB/nOrfzP9R+ikOkBwC8WuO+mq8hsX1+xmIQQU1fFizCAG264gSeeeKLssqQYmmkaGIY/QHuIofKlFzoEve8yEQb5wQfWPobuKGtWZEd0ac00DYxNj5W/0foCRpjBHiD28WIYCtX1St/rKDaTYNPLZcvp7n0ug2e2se/lKp3ZgVHYU4SZpkJlVwHgZwtor/+21cUcUc+2A8xg7ESZDgrbN5S1e+2tQ/ZTCtrXLC9rX//MI4SF/pcaDcNg+wuPli275enflBVsEymzczu57s5+bVEQ0N22pUIRCSGmsopfjgRIpVJ84Qtf6LssOVL19eN/GbOxMT3u6xgdBWUFlV2abYgAZZX+6b3vdzKwY2kaU4PkpEMw6iHs/8VkWmkaGka2HfScN8OWZ/o3Tj8BzCSJdBXJ/Vz+Gks6dyxseggAw9uKNfMogtb+RYXTMIf0Xvs6DGZSQLF3IaaSjbi1zSSq9yynwxZofxQr5aJsF+3vmWlUTpxE0yxik+QYKprTiTXOotjWv+BMTptD9X5ibG45pqztsJPPpn5aM5bdf1/OOPFsNvzhx/3aZp52AXX11aOM/MA1zphFPF1Nvre7r80wTRpnzp7En/HROxRz2p+plvNUy/dQMymKMIAzzjhj1JclOzoyREPd4HSAGhvTtLVNjpuK95VMmriug2GULpdpbVEs2gSBIhGrxlAZdPpk6FkKhIBBmDyN7h6LKBo4p8bGNEU9B4dO1K4CLzJmkMvHyRdGth1S084kNu0R1LYXSg3NJ0DzfPzUCXR3FIHikP3HUiJxFG7zmzC2P4nR/jRVb/8EXT+4mSjTDkoRP+/T5KoW0rPXvjatmSTOv5Xw95+HKAAnhXnRV9jpp9F7LZewDsetfgNO7kVm/P2/suXem9B+EWW7NF/93/Ta0/qNW0lKpTjq+v9h2S3vJsh2owyT+Vd8Cb923n6P85r5J3DsxZex7KH7AKidvYAT338tnV3l+7LmuLOYccp5bFn6OwDqj3wj0896d8U+S42NaYg3cPmXvsN3r7scL5/DtG3ee8udOLWzJu1nfLQm83lrvEy1nA+GfKVIHJrSeuJvF25paWHlypVs3ryZyy+/nCVLSvfpZDIZLr74Ytra2vjtb3/LrFnDu4F3KhdhALGYgesqtNb4viKfL13ucRyFGwtBawwVEAV5NC7Zoks0xO1Yu/NNxDW2WUBjUijaeP7o7uVxyeIWNpViSDbgmTXkiu6oxjpQMaNALNgKUYAfm4nOZVDdm4hV1dHrziKk/DFQkxC3sBmV20mUnk7emc5AH5uY6RGjDTT4PUX8rnaM6mb89Bw0k+sRycbGNDvXrsRv34RVVQ91hxGpYf5OFuTJbl1LUMyTnj4PI1k/6KLKy1DctoYoiohPn4+OVXAWbNdxrRRk2zbR3baFdF0Tqaa5h+RN+ZP9vDUeplrOB0O+UoQNbdLMhMGey5JXXHFFpUM5qBSLEcUBJpQ8T+N5u79cTCA2onFzeQXE97vc/hRIUnCPLL0Idv2rkGLkUjTmle6GDIFYApqaSDWmCQc5mYWYZN254M4tNQzye0sxdCgys/QiBaTmj3n8YylMz8BIzxjJH6coseIkZx89rEW1k8KZc3zp55GuZ5xoDYmG2SQaZlc6FCHEFFeRImzlytITaLNmzeqbBdvtjDPO6HtfCCGEEOJQdejNwQshhBBCHASkCBNCCCGEqAApwoQQQgghKkCKMCGEEEKICpAiTAghhBCiAqQIE0IIIYSoACnChBBCCCEqQIowIYQQQogKkCJMCCGEEKICpAgTQgghhKgAKcKEEEIIISpAijAhhBBCiAqQIkwIIYQQogKsSq24paWFlStX9r1etWoVF198Md/85jc5//zzKxXWuEglFZbpAQZ+aGIqD0MVMQyXCAeUgedBLhcCkHR9LNULSuFHaXKFiu2mPpYKSERbMIrtRLFGsuYMwsgc8TiGikh5rahsK7h15Nw5+LiDLq8UuNmN0LEO4tUEtQvxzVTf+/GwA7NzDUopgpoF5M06ABJ2lli4BaV9cGrxSZPx0mitRxyzGRUw2lejs52oVAOFndtQsST29BbCWM2IxxsPSgf429eQ37EJt2E6TvNCIh2R2byafGcbVdPnEp82Hz3Gv3cpBdm2TbRvep1YMk39nCOw3KoxXYcQQhyqKv/tvssDDzzA+eefzw9/+MNDqgirrgLHWAuEoGNYVgzlryq9GYLpHAlWAstSGEYKFWVx8n9ERVkALLMaI34mmXysYjmYKiLd9QfM1+7a1aIwj/lHuqvOIorUsMdRSlHd9SzmHz8NUQBA+rgryCy8HG+QQizR9hd6/vu94OcBiJ30HpzzbsKzqkgVNxP9/Cqizg2l8esXkrz0blQiQWzDnajC5tIgTj2xWZdguvPpKjSOLPeogPfHu9n5iy9R974vs/K7lxNkuwGoPfF8DrvmDsLEyMYca0ppOp76Bc986WrYVWSe+pl72bRqBc98998BMCyLS770fZrf8FejKkQH07F2GXdf/Q4K2V4Ajn/rO7jk+n/HSdWP2TqEEOJQNSkuRwZBwC9/+UuuvfZali9fzsaNGysd0piwLIVltAGlGS5UCuWv7r+QtxIihVIBth1hh+vbAObqAAAgAElEQVT7CjAAFXZj6y0oNfxiZ6wl9TbMFf+5V4vGXH4HyahtROMkog7MJz/XV4ABGC9/h3hhw4DLx6IM2Z9e31eAART//EPM9tcwDIVa+X/QuVffjtcx1vwBu7ByTwEG4HVAZj1mz1PE7HBEMRsdr7Pz518kccw5bPnTA30FGEDn8w9TWP+XEY03HsKOjTz7lY/3FWAAPVs39RVgAFEQ8Nt//Qf87m1jtl4dFHjwqzf1FWAALz3yS7atfnnM1iGEEIeySTET9uijjzJjxgzmzZvHueeeyw9/+EOuv/76Yfevr0/tf6ED1NiYHnknHUCQ36shAvadhdCl5QDTjCDYUTaMGXXQ0HDcyNd/APbOV7dlQe9TvEQeMSOLWz/87aLbW6HQVdZuB90Dbt+gvYPc9lVl7Waug/r6FPnNz5avpPV5zMMayttzm1DWfNJJqLIHjnmgGHo37gTAappH5sWfDBDjJmaM5tgYQ9u39hB5xX5tXrFQtly+qwPlZ/vyHNUxvZfObT1sXvFSWXumY9sBjz1eJmtc42Wq5QtTL+eplu+hZlIUYQ888AAXXXQRAG9729u47rrr+NSnPoXjOMPq39GRIYrG7hLLvhob07S19e5/wX2YpqImXYVBe6lBGZQ2ebDXUhYYNuAThibYczH3KcRCcyado1j/aO2bb0rV4ppxCPcqKJ1q8tSQHUFccVVNono+qnvtnkZl4sWm0TPAODYp7CPfgr/ij/3ao5o5tLf3kjry7bDhmf6dDj+fMDaTsrvVqhYRWTa9GYUflK9rsH3sVM0E06K4eil1i9/C9j/9tN/7sVlHjerYGEtGqgm3tpFC556ZyVg8gTJNdLineK4/7AiMZD1tbb2jPqb7rzjBsedcxHO/+lG/5vrZCyq+TQYyJjkfRKZavjD1cj4Y8pUicWgVvxzZ0dHBY489xr333ss555zDzTffTE9PD7/73e8qHdoBC0NNMaghYtdBqLvRsWNB7bq/S8UgdiwYAVGUIJ9XFJlBFFuwawRFFDuSQlTZe46yNBIu/jzESje94zYSHP85crp2ROPkdYrgzH9DVx1WaohVE77l38k4swZc3ieGe9EtWLNPAEDFUiT/9msUa49AawjmnIVx/N8CCpSBsfjv8GeeRsE+HN10XqkdBXUno91GgtRp+MHIDnm/Zj7TP/G/BDtbqV94DDXHnAGAYceY9/e3Yc2Z2BnKgaiqZt506/0km2cD4NY1M+2YU7jkS/+LW1XaR3WHHc7bbv0Oyq0ewxVbnHfl9Sx8Y2mb2G6cd9/wZRrnHTt26xBCiEOY0mN5l+4I7H468rvf/S5PPvkk99xzT997d9xxB88++yz33XffsMaarDNhuzmOIuZEgCJfUDh2iGkEoBxQJqDI5ULCsJSDbYFrly4n5T2XYGS3MR2wgfJVCuKqBzPsITSryes0oz1y4iqDVWwnstPkjIb93ijuRFnMzFZwkhTi0/ut11IBsfxWUIqiO51Al+bALDMibnRiqYDQiFMMExT9wSd+h9rHSins3HZ0vhMj1YDXsxPDdtE1sxnHw25ElAJyO/G72rCq6lGpBkDhd2/Fy3QTr5vWrwAby9+gtZ+jt60VKxYn2TBr1MfFeDsYZg3G0lTLF6ZezgdDvjITNrSKX4584IEHuPbaa/u1ve997+Oee+5hzZo1LFiwYJCeBw/P03je7hvrNUFgALsvtZZXWH4AfjD4n22oBK0hp6tAVZVubTsAeZ0CJ7Vn4P3wjCRULdy1fP/3Am0RuLPL3gtCg95wbJ7Q01rjxZsg3lRqaKgv7bVJVGxoDcTrsOJ1e16jsaqmYVVNG9d1KztB1YzD91qvEEKI4ahYEbb7b4Q99NBDZe/V19fz0kvlN/wKIYQQQhwqKn5PmBBCCCHEVCRFmBBCCCFEBUgRJoQQQghRAVKECSGEEEJUgBRhQgghhBAVIEWYEEIIIUQFSBEmhBBCCFEBUoQJIYQQQlSAFGFCCCGEEBUgRZgQQgghRAVIESaEEEIIUQFShAkhhBBCVIAUYUIIIYQQFWBNxEqWLl3KnXfeyX333QdAJpPhiiuuYPHixVx66aXcdtttdHV1EYYhJ5xwAjfddBOJRGIiQjukOI5BIgFKabQ2yOU0phEQs/MoPCKdIJOPEYbgOgGu1YvSeSKVIldM4YcD1+QJ3YnTswrl9RBWzSPrziPU5q51KhJugFI+WtvkCxZFTwNgKE0itw4ju4OwcwvKsDCmLyKTOpwoGp9tYChNytyBWdyCNhx8ezbZoGrAZU0jIml3YYYd6EyShFNPzothEGG1ryDYuhKVqMKYcSx+vGl8Ah4ms2czhQ3LiEKf+Jyj0fUL0Hr04wWFHMUNL5JtfZ1YbRPJw46FeO2w+0e5TjrXLiPX2UbNrPmk5xwFZmzY/fM7W9m2ehlB4DFtwVFUTT8cfSAJCSHEQWhCirC9ZbNZrrzySk466SSuu+46LrzwQm677TYWL15MFEV8/vOf5xvf+AY33njjRId2ULMsg3TaxzByfW3pZBK8VRhhe19bdfJYcoU0Cf0iRnY9ACaQSpxKd2EW0T7fgwndRfypf0Ftf2HXsgrz3G/QVX0KpqlIJXow2V5aWIEZbyCKavEDTWrnC+itr9L1f19B57pKiyRqqLnmB/Skjx6X7VBlbMBa/WXQYSledwbM+RTZoLrfckpBlbUJc/tPgVLScfcwdNVFBKufo/U/3g26VCnGFryR+mu+V7FCzOhcy6ufuwivYysAppvkmNsehubRbUPDUKz73Y946rar+tpmn/1Ojv3Y1yA2cMG6t6jQw+Pf/GdW/P5nfW0XfOZu5p3zHqJ9D6ABZNvW85/XXErnts0A2G6cj3/n19QdduwoshFCiIPXhF6OzOVyXHXVVZx66qlcd911ALS3t1MoFErBGAYf+9jHuPDCCycyrENCIqH6FWAABlkM3d6vzQxWkHB6Mbz1/dvzzxF3vbJxnd5VfQVYicZ85ou4upe4G+0pwHavU7UTj0fEdI7ouXspbnqtrwAD0Lkuii/8HMsa+0MvZgWYW37SV4ABqMIWHG9t2bJx28Ps+C27C7DSsuuxi220f//6vgIMoLjmOaLWl8c83uFQStH9wsN9BRhAWMiy7Vd3Y45yE4ZdW/jz1z/dr23Toz+nsHnFsPr3bFrZrwADWPLVf8bbuWW/fZWC1//8WF8BBuAX8iz53jdQhEP0FEKIQ8+EzYTl83muvvpqVq9ezV133dXXfuONN/KRj3yEpqYmTjnlFN761rdy9tlnj2js+vrUGEdbrrExPe7rODDF8iYdDLCcj6H8AZeNxxSJVCnP3fnqzmz5stkdJGMRKmYw0PdmzFHYbkQxDAi7y7+Ygx1rqatNDpXMqGi/C7z2snYz6qWxuf/+095OCDNly6qgiN++uby92FuxY6Bt27qyttymlVSnbMyYO+LxOns24Od6y9p1MTOsHHcuK99uXrYXE29Y/Tu3bixr27F+NamEiZsc3208+T/HY2uq5QtTL+eplu+hZsKKsGXLlvHJT36S+fPnc/PNN3PnnXcC8K53vYvzzjuPp59+mqeeeoobbriBiy++mJtuumnYY3d0ZIZ1GWS0GhvTtLWVf2lNJsmkRSJhAHvdbGXGAcXesz3aaMCPUtiY7F1BaauRTM6k6PX2y7cqORdHGf1mhqIFb6fHT+PkIBmzgb2LOotcQVHwksQbF+E2uXgrHu0Xa/zES8ZlexqGSU39WRjbHuzX7jtz6d5nfZZlUZU8GiP7yt4joJMNVJ35frr/8O09zUphNC6s2DFQe9KFbPn1t/u1Tbvg7+ns9dE9AxTU+2Ekmmg64Qx2vPhEX5sZc4k1zx9Wjsnp87BiLkGx0Nc2e/GbUMnGYfU/4pS38Id7v96v7fR3f4hMXtE7QHE4Vg6Gz/FYmmr5wtTL+WDIV4rEoZm33HLLLeO9ktbWVtatW8eXv/xlTj31VO6++24syyKdTvP973+fM844gwULFnD22Wdz7rnn8tnPfpZrrrlm2OPn894B3aS8P8lkjFyu/FLdZOL7EbadwDBClIrQ2sb3U2ijHhX1oAiIjGY8FtKbi+EkZ6DCnShdJLJnE8TeSK5gA/3zDaxq7JmLUW3LUEGOaOHFeMd8mAIp/ABspwZDFVH4aBIE0SwyWYi0wmqcj5XbhjntiNJN7naM1Nv+CVrOwzfG/sELrcGIT8M0FSq3EexqwrlXkDEWEun+1+6iSGEmpmMoH+W1gV1H2HApvUEj7uxFqNCjuPEV7PpZNF/934SzT0JX6GFiq6qJ9Kz59K5YilIGc9/zL1Sf9m4iKz6q8bRhM/fkN5Nr30LvptXUzD+a0z/zPzgzh3ePmZ2sZcGpZ7F9xV/Id7VzxNkXcfanbsdMNw6rf6KmgZkLj2D9S88CcN6HP80JF/wtpjO6fIbrYPgcj6Wpli9MvZwPhnyTyeE/sDMVKT0BjyTt+3TkSy+9xIc+9CHuuecePvKRj/D1r3+d0047DYA//OEPfOc73+H+++8f9vgyE7ZHLGZg2waep/G80kyX64JlRBQ8g2CvK5SuE2GZIUXfxt+rfaB84yqDGRUpGDUEu56M3M1xFI4NfgDFYv/9YKqIuO7B8HvRhkU+NpMgGKdHI3ev09C4ZoZIWxTC+JAFumVqXKuAm0jS3rknLpMQK9+OtmL4Ts24FvnDoZTCzLejo5Ao2XTA8TQ2punY0UGU2YkRSxI5o/ht1csQFDLYqXq0YY+oq1IKL9OBjgJiVQeez3AcTJ/jsTDV8oWpl/PBkK/MhA1twp+OBDj++OP54Ac/yOc+9znuuOMOvvnNb3LzzTdj2zbz5s3jq1/9aiXCOiQUixHFYv8ip/TcQ/ksTsEzBmwfSF6nQKX2vrLZp1TwDdwv1AYZasCuKTWMcwEGEEaKbDS8D34QKjJhnHhVEthzMgsxCePNpReT4C8naK0J3PpdL8ZmzEg5kJ7GqPeIk8JyUqMKR2uNnazb9fNoAxBCiIPbhMyEjTeZCRtbUy1fmHo5T7V8YerlPNXyhamX88GQr8yEDU3+Yr4QQgghRAVIESaEEEIIUQFShAkhhBBCVIAUYUIIIYQQFSBFmBBCCCFEBUgRJoQQQghRAVKECSGEEEJUgBRhQgghhBAVIEWYEEIIIUQFSBEmhBBCCFEBUoQJIYQQQlSAFGFCCCGEEBUgRZiYFAxDjVkfw1AopVAjH3JUBopjNPmMl9HGMtA2HM5YaqI2/D4qvc2VKuVe6TjG2u58KrVfhTiUWROxkqVLl3LnnXdy3333AZDJZLjiiivo6OggiiJ+9atfkUgkAGhvb+cd73gH3/72tzn66KMnIjxRQekUWFYehU+kE2RzFr4/dB83pok7GQzdTaRSFIMqcnmDWMwgEY8woy60vx2tXHya6M254xJ7wingRK0Y/g4iZzoFZmDpHHZhFarYRpRqIWfMxQvtcVn//iRVJ07mVVRmI1HNUeTjR1CMEvvtZ6qIVH415ralaCtBOO1k8mYj9vZlBGuewqidiZp3Gvn4rP7rK27GaF0KmTaYcyr5mkUExMYrvT52vo1w3VK8TcuIzTsRY+4b8WN1477e3ZQC1f46hR0b6Nm8lkz7NhqPfzOpBW9Ex1ITFsdYK3RtY8NLT7Nt7QrmLjoOChm07zNj8RnEGg6rdHhCHBImpAjbWzab5corr+Skk07iuuuu4xOf+ATf+MY3uPHGGwG49dZbed/73icF2BSQSipi5gYUHgCmgnRyNp3dLlrrAftYFiTtzRjhBqA0lWsYDej4kcTjAUbQAd1/QgEKiCkX4ufSmx/bQsy1A9ze32HkV/XFkax/O2z8Eaq4tdS2HZJzPkTgnEYUDZzPeIkbGdzlt6O6Xt0T38IPEDS9mzAaekYjnX0V6+GrQEcAmFWzCWa/l94f/mPfMmbDYbhX/ohCbBoASW8L+seXE2a2lxZ48g7i77ybzPSzGGRXjgk7zNDzo38m89xDfW3V536YxCW3EChn/Fa8F9Wxhh1L/pdVT/yBrrXLS40/+BqL/+GLzHz71UTRhIQxpvzsTv73xr9n/cvP9rWd83dXs/P5R3nq3tv5m7t+g103a4gRhBDDMaGXI3O5HFdddRWnnnoq1113HQCf/exneeihh1i+fDlLliyhtbWVa665ZiLDEhViW0WU8vq1mWwlPkS9FI/5GNGGfm2GbiceK2KoImRe6t9BF7DYOVYh94mpzr4CbDdV3NFXgO1mtv4Y1+wd8/Xvj1Pc1FeA7Was/QFx3T5kP9sIMV++p68AA4imnU7mV//Wb7mwfT1q+/K+12bbq7C7ANvd79HbiUWZ0aYwPG1r+hVgAN2P3IPRtWGQDmNLKeh95TF0vGZPAbbLsnu/QNi5ZULiGGtt61f2K8AAHvvJ/7DwnEvI7NhC2+oXKxSZEIeWCZsJy+fzXH311axevZq77rqrr72hoYEbbriBz3/+8/T29nLHHXdgWSMLq75+/Kf8GxvT476OyWRC8g13QtksQUgy4ZBMDTKLEXZDobzZVBqNBl1+LdM0wmHlM5Kcda5tgMawvC0qkHBNkjUTe/zo7QPF4uM6inhVKZaB8tV+Dopd/dssF10oL6bMyO8bw99ULB+r2EsybpBOj1/ume1BeaPW2EZI9QD5jcdx3VvIEEXl2zv0CsRsBoxjoow23y2RV9YWeEXUrnOz9guT9pw4WeMaL1Mt30PNhBVhy5Yt45Of/CTz58/n5ptv5s477+x77x3veAc/+clPuPDCC1mwYMGIx+7oyIzr5Z7GxjRtbRM/m1EpE5VvVdoiZipgz76LaKCryyMMy7/UAWKOQdquRenOvjaNS9GP4cQCVOJIyDy/Vw+Fr+vo3k8+I805EUsTt6pRQfeeRqcWjBhEe2KPGt5Kd84l7J3Y4ydlzsB1qsHbE1/UfAYZvwavrXfQfJWCmqMvw3zspr42Y8sTJM68gtySu/csaMcJGw7vGyNVfyQYNkR7imDz5Cvp9JJE43gsOTWHYTfNw9+xrq/NXXgSXmoW2X3WO17HderoN1N84ffYqWr8zJ7tfdj57yWIN1Xs3HEg+dbOXEiypo5s155Z5GPOPI+tLz6FYdnUzjtmUp4T5Vw9+UiRODTzlltuuWW8V9La2sq6dev48pe/zKmnnsrdd9+NZVkce+yxfcs8++yzLFq0iEWLFo14/HzeG9f7TpLJGLlc+W+Gh6qJytfzDexYDYYKUChC3UjBq8IbYtVhCKZTj2GYpZv5jUYCdQQ9GRPTdDCdBMqshjCDtmoJE6fRm0+zv8NjpDn7oYVddTiKCKWLRKnj8RLHomtORAW9KDRR84UUqs/GC8f/5vR9ecSxp5+MivKosEg052K8WX9NXpdmjYfKVyeasRoWYvRuQlfNJTzx40Rzz8KqnUHUtRn7sJNI/M1XKdQc2dcncGpxDz8Dnd2OMizMMz6Bv/Dt+Gp8HorYLbQSVJ/wVpQOifI9VL3576h552fw3MayZcfruFbpBuJujGnHnkIYhmgd0fLujzD/0o+CWz3m6xuuA8nXjldx3NnnUcz2EhSLnP6uy5h3xJFktm3kgs98i/TcYyjddTm5yLl68kkmJ/78dzBRerA7oMfQvk9HvvTSS3zoQx/ixz/+MQsXLgTghhtu4OSTT+Zd73rXiMeXmbCxNdH52rbCMMDzGPSG/H2ZpsK2IoJQEex1RcowwHFMDBXg+wp/gKtVAxltzpYJlhngBzbhrmPQNqPS+qPYhN+QXxafEWEpDy9y2TuU/eWrFDiqiMbAi0pPdxqGwgqzREaMQJsD9rNVgKF9PJUY9r4cC6aKMPwcoZ0k0gMXB+N9XJvaw9A+Yaghlq74vh+LfJUOCb08VjxNVMygTAdtVOZp3+GQc/XkIzNhQ6vI3wk7/vjj+eAHP8i1115LsTjwZScxdfi+pljUI/rSDkNNodi/AAOIIigUQnL54RdgByIIoeBZfQUYgB8aFAOn4l/CAEFkUAj7F2DDoTUUo1hfAQYQRRpPJQYtwAB8bVEkPqEFGECoDXwrNWgBNiExKAffSBLZqUmx78eCViZGbFc+dnJSF2BCHIwmZCZsvMlM2NiaavnC1Mt5quULUy/nqZYvTL2cD4Z8ZSZsaPIX84UQQgghKkCKMCGEEEKICpAiTAghhBCiAqQIE0IIIYSoACnChBBCCCEqQIowIYQQQogKkCJMCCGEEKICpAgTQgghhKgAKcKEEEIIISpAijAhhBBCiAqQIkwIIYQQogKkCBNCCCGEqIAJK8KWLl3KZZddNuB7q1atoqWlhYcffniiwtkvpRSWZaKUAsA0DSxr8M2lFFiWieOYmObIN6th7F7f4MtYloFpDrHAAPYX92iVYhl63N3bxDCGjnl37qMRi1nY9oHlN9p1D+ZA8tnXcLehEEKIg49V6QAAHnjgAc4//3x++MMfcv7551c6HFJJhWNlMFQvUVQFgUdNohWli4RqJtlCCs/fs3wiYeI6Hka4A51fA1aKwJ1PTy6F1v8/e3ceH1V5L37885xt9mSysYYdDCgquFwVtRVcMC5Xq7cu9fant4utta2tdtFS7dX2Yq8tlVarvdZuetu6tLS1LUivVi0FUcQFUGRHCEhIQpaZzH7O+f0xMGHIQgKZDJjv+/XiZeaZ5zzP93vOJPPN85yJ7kHnK7HrMDY9i2pYjTO+lkTV6cRVOPe8peIE4m+j7ViE6xuBPfx82rTR9DS0plwCkXW4q56E2B7UidcQr5xORnkP59RgOAmsXa+TXP4Yyl+G/7R/J15+LK6bXyQErDhWZjNa+7u4nrGkPDVEk8G8PkpBib4bvWUZKlGHU342cauGRObgMZrx3cTefpHtLz2Fb+gYhs26Dqf6JBynD8nEm2le9RJbF/+a8mOmUT3rKvQhk/owQGeJ+o28u/hJ6t99k+Muvo4R0z+M5i87pLFSkUY2vvp3Vi56inEnnsb0C/6NwNBxhxWfEEKII0fRi7BMJsMzzzzDr3/9a6655hq2bdvG6NGjixaP16PwmjtRtAOg615IvolG9t3doIGA73hS6VIATFPD50uhku9DZDkKIA0mmwgFZtMW8/U4X8BpwPy/m1Cx3dn5di7HN/UGUpM/i+0olFIEIq+gr5kHgAK0HYsJnTqfNkZ0P250A/avrwFnb7W44Tl8H3mIyLAPH/rJAcwdLxP5+Q25x8nXniZ08zPEyo7NtVmGg6f9RbT2t7Mxx7fgMdfglF1DLOXJ9QvqezA3fRfsvee6bTX+kdeS8s3EcbqvMHVdo3XZ79j48zkANAP1Lz3Nid/5K87Q43uVh6Zg298eY9WjdwPw/orn2fiXXzLrgedRZYf2+kvt2c7TN19MrLkRgK2v/J2zP3snU6/6Ej2k0w2bfzz+Q1789cMArHv5RV595rfc/OgiPKVDDyk+IYQQR5ai3xP24osvMmLECMaNG8d5553HE088UdR4LMvOFWCggxMD8pdXdHsLHiv7tderUKSgfc0BI2XQ3aaDzmdGNucKsH20d36NL1OfHV9rR9/0eP5Bdhw9uqHbMZVS8N6yjgJsL2f5w1gqddCYuo1VpUg8/8ABsaTJbHgxt20L4NEjuQIsF1O6AdNtzmvTU9tzBdg+2q4/4tWjPcaht21n24If5ocRi9C+5a3epoLTVs/bj9+X15ZsbaJt6zu9HuNAeza/kyvA9nn5l98n1Vrf57HaG3fwjyceyR9/5zZ2b1l7yPEJIYQ4shR9JWzBggVccsklAFx00UV85Stf4Utf+hKWZfV6jIqK4ME79ZYTBXvfg66XLxSKkhIvKBNI43azBabrGlVVoR6nc2Nd3zvk9Vr4SkO4SZvs+lffxk7pWkcaB8StrECPMQFdju2kYjR3cdOaruuUVnZcAzfR3qkPgGkZVIU7xnUbu8pd4fdZBMLd5xZPm122K6UOer73idp78grHXIym3usxDtTYxb13Sil8PotwH8d0oiZd3SBomsYhx3eg/hrnaDLYch5s+cLgy3mw5ftBU9QirKmpiX/84x+sWbOGxx57DNd1aWtr429/+1uuMOvdONEet6/6wuNRhHxBFFHAAc1PdsGwo9LKaONobkwACQxDo7TUQgWOh8jLHQMpg5RTRltDpMf5Ar6xeAPDUO27cm3O1I/TmgljN0RQSic84ePoa77fcZDuI+Wb0OPYoerTQbfA7lj5Umd8jsZWB+g5pqqqEA3djO2f9UXSP/9/HQ2GhZrwobz+luEjGDgerX11rs21hhFPlxDbr1/QGIHXCEKmY+XLGX45LVELp637GDXPMEZ/9FY2/vT2jjACpQTGndht3AdSRjnHffzrvPXTb+XavGVV+EZO7vUYByodPRl/eRWxPQ25tjM+8XVsT1mfx9QCQ/jwxz7LC489mGurqB5LWfWkQ45vfz1d4w+qwZbzYMsXBl/OR0O+UiT2TLm9uXO8H7zyyis8+OCDPP54x9baL37xC5YuXcqjjz6aa3vggQd49dVX8/odTH8WYQCBAHjMdjSiOG4IXTNx0jtRpLDVCKJxP+lMxypF9sb8NJrdhJvYAlqQjD6Wtri/x5vn9wnZOzC3PYdqXIMzdjbxylNJqNLc85aWwB9/B33n33B9w7GHzaJNjTrIjfkQiKyDtxfgxpvRjv83YhUnksHT/UF79fSNrbtJPLveIPXqb1GBMObJV3V5Y77fiuPJbEVLrMf1jCFlTSKa7LwCV2Lsxmh9BZXciROeQcycRNLuxY35iQbi7/yD+iUL8A0dzZAPfbTvN+YnWmhZ80/ee+4JyiedyIgPfQS9amIfBuhiyPqNrH9+AQ0bVjNl9lUMO/FsNF/44Ad2IRVtYvNrL/Hm3xYw5vhTOeHcywgMGXtY8e1zNPzw7m+DLefBli8MvpyPhnylCOvZgBZhN9xwA15vxxtsVVUVt99+O7Nmzcq1NTU1MWvWLBYsWMCECRN6NXZ/F2GQ3QnSdQ3bdtP5M34AACAASURBVKisDLFnTxSlFJlM1+/y+/orpbBtp8/xaJpC07ofH7J/FsJx3D6Nreuqx7i70ptvbMPQcF2w7e7H7TiHbo+fEu1N7t3xeAxs2zmkY/cxDI2yskC//TA7nHy6cijX/WCOhh/e/W2w5TzY8oXBl/PRkK8UYT0bsO3I0047jbVrD35TcUVFBW+91fsbrAvFdcl7E7Vtl+7uEeuqf1/15k32UMY/WNyHqjex9PacHE6BkUxmDum4/fVXsbRPfxdM/R2fEEKII0PRPx0phBBCCDEYSREmhBBCCFEEUoQJIYQQQhSBFGFCCCGEEEUgRZgQQgghRBFIESaEEEIIUQRShAkhhBBCFIEUYUIIIYQQRSBFmBBCCCFEEUgRJoQQQghRBFKECSGEEEIUgRRhQgghhBBFIEWYEEIIIUQRFLQIq6uro6amhrvuuiuvfe3atdTU1LBgwQJmzZrFRRddxGWXXZb7d8cddxQyrCOCpim8Xh3T7HwJlAKPlf3X0abwWg6Wuf8Y4PUqTFMNQMT9R9cUXiuNYbjd9vHoaTx6ErU3NaXAcmOYbuKg4yul8BpJLMPul3gNN4mZiaCUyo7ttmOpVNdxa0k8Kp6Lu5gMN4mR7ojbTUZQTrLb/pqTQiXb0LQjIPjuuBkyiTYU3b92hBDiaGEUeoJwOMySJUuwbRtd1wFYuHAh5eXluT6PPPII1dXVhQ7liBEI6Hg8aXS9HdfVyWQCtLbauK6LZboEfW3o9hZcFH7PeNIZL5a7Ay21Dlf5sQMnYFOKZbai0YKDj4xTRVtE4R7h701BTwwr8SZa29s41gjskjNpS4ZzcetahlDiHfRNj6EyMexxV5H0n4Cz6Z8knn8AZXrxX/AVktVnYCur0/heox1f7A30hr/hmmHsYR+hjbE4Tt8LC6VczLoV7PnDXDLN7xM+/9MER43HXfJ99OAQPGfdQnvZiTiuhkGGUNtKtDd+grLj2Mf/B+2VZ5FSgcM9ZX2m4WBvfYVNv/4OqZZ6qq+fS93WLbzx9KMEh47kzBvnUFH+4f3yhOR7b7Lml3OJ1m1k/MXXM3LWNajQ0AGPvSdtO9fz/M9/wJa3XuXE8/6VM678JP7KUcUOSwghDlnBtyMDgQBTpkxhxYoVubalS5cyY8aMQk99RDIMhdebRNfbARulUphmC6FQtkANeKPomdXgRlFuBMNZh4f30OKvgR1BZeoxos/h0ZvRaADSaLRhaVsJDPz7fZ9YpoMVeR6tdRlkWtFiazF3/5qAFcv1CaY3Y7w+B9W6Dtq3o699EG3LP2j/zRew69eTqVtF5Of/D0/Dqk7ja5rC3/4aet3jkKxHRddhbLyPkNp5SPGau99hx3//K/F3l5Ku30zD/95B9O1lkEnibl+B/cT1BCIbs3HH1qI/90VU0zvQsgV9yV0E9rx6aCfqMLnvv83qOy+h9Z1luJk0G19dwt/v/wbNdZvZvnIJT918CbvefTPXP1O/gRduvYhdK54j+v5WVj16N1v++DBKOUWJvyvJll08/NnLWbnod+zZuY0XHnuQ3997K046dvCDhRDiCDUg94TV1tayePFiAFatWkVNTQ2m2bGvduONN+ZtR/7+978fiLCKwuvV0bT4Aa0uum5jmhq6W5f/jFaBir/bqT+ZFmD/1R0bQ0sXIOL+49GiaLF1+Y1OHN1pArJFlN6YX7i4pVOJLf3fTmOlV/0VXc9f3fJocbSGZw/o6aDFNx9SvKn33gAnf0uzeclTMPZDe4OzoX41mqbQtr3Q6Xjtnd9g6f2zJdoX0fWvgpMtoEKn1PLmwqfynncymbwiLPLeWuxk/jbv+j/8BDeyu/DB9lLj9o1EmvLjWbf8BSK767o5QgghjnwF344EmDlzJvPnz8dxHBYtWkRtbS0LFy7MPX+425EVFcH+CLNHVVWhfhopQ7Z4yt83NAyDcKkHkgdssbkZ0CywD7gXSulA/hu8aRr9Fmf/5dvBTaWycbsHxG15qApn53Obw/kH2XG0YDkH0ksqCZfnX3c3o4EeAPbk9zX9vcrnwD4tgZJOfTR/KaTbO8b2BamoCOIGKjr1Vf4KSkoDKN3s9FwhpYOlua+deARPqJRYc2NeH8sXyOUb83deQjUDJQRCQfyV/f86OBR7gp1j1AyDQKh313afQryuj2SDLV8YfDkPtnw/aAakCAsGg0yePJmVK1eyfPlybrvttrwi7HA1NUVxnMLdDFVVFaKhIdIvY2maIhwOoettuTbXNYjHHdrbo4RD1ZjsYl+RppxmHP90VOSljv6aD4wKFO93tOEnFtOIxQ8/zv7Md3+6ZlFaejZay4u5Ntczilg6THzvfCWlJ2EZQchEAVBt6/F96A6Sbz8PTibb5gmiTb6gyxjDwz+KsfkHHQ1mKUlzHJGD5NNVztaoaRhlw8k0d5znqgv+A/f1H2fjCA4hXTmVloYIJcPOwjJ/1lGgKZ3Msf+Plj0J4OAfJuhPngmnYIWHkGrZTcvyZzjjo99g4bxv5p4PDR3J8ONOzuXrG3UsoVGTiGzfkOsz/XP3EiNEewFeB4eiZNh4psw4l7XLns+1nf+JWzFCw3v9Wi3U6/pINdjyhcGX89GQrxSJPRuQIgyyW5Lz5s1j6tSpGMaATXvEcRyXaFTD7w+jaWlcVyed1mhvz64OtbV7CAVORSe73Zhxw8QSJoHABejOblzlJUMVybgHv28smorhuhaptI9Y/Mi+K992IGpMwz9kOCq5A9csJ62NJJ7qWP2LqBGETpmH3roGZSeww8cTM8YQ+sKfsTcvB8ODNu504iUTDlxMzB6vTSI4cQ56+zpcI0TGdwyRTOeVtN5IBUcz9Gt/Jr1+GXa0Ce8xZ2CF/CifAl8ZzohTaLdGZue1xhC68JfoDa+jMgnsoScT8U7sMsZCc8JjOfY7i2l/dynptiZKTjiLa//nWerefJlA+RCGnziDijGTcj+8Vclwzpr7e/a8/TKx3XVUHn8G/nHTcI+gT3kYvlL+7c4HqFuzgl2b32X0cSczfPJJe1eEhRDi6DRg1dDMmTOZM2cOt9xyS6fnbrzxxrx7xHw+H0888cRAhTbgUimHVAqU0ve+0XVszzkOtEYslBoCkPvUYGumBKVK8j79mEoZKFW6d4wj5w2zJ6mMQYqRKK0aN9M5ZteFNkZA6QiU2pu/A7HwsaiTj8316S5d29FoZTTKPzrbL3OY8YbGwMlj0BWkXEgB6rgpnT6F6rrQZo6GEaM74i7iJXHKxuI7Yyz+vbGUAMdN+pduCysVrqbizI9SqRSu6x6RryZPyRAmzLiYiWdeckQViEIIcaiU+wH4aXY0bUceDQZbvjD4ch5s+cLgy3mw5QuDL+ejIV/ZjuyZ/MV8IYQQQogikCJMCCGEEKIIpAgTQgghhCgCKcKEEEIIIYpAijAhhBBCiCKQIkwIIYQQogikCBNCCCGEKAIpwoQQQgghikCKMCGEEEKIIpAiTAghhBCiCKQIE0IIIYQoAinChBBCCCGKQIowIYQQQogiKGgRVldXR01NDXfddVde+9q1a6mpqWHBggXMmjWLiy66iMsuu4yLLrqIa665hlWrVhUyLNGPfD6dYNDANLMvJaUUfp9LwO+g60UOrgAMHYK+NH6vjVLZNtNUBIMKn1cVNzjA0F2CZhs+M45S4LXSBD0xLNMpdmhCCCEOYBR6gnA4zJIlS7BtG33vu/LChQspLy/P9XnkkUeorq4G4MUXX+TTn/40ixYtyusjjiyaBqWlOroeQSkbj8dDOu3FYA96Zj2QxusbRcKppj32wVhwDXhTeJx1aLH1oHnx+E7B0Ydg6PVoRHB1C69nJK0RHacINU/QbMFqeAZtzzKwyvCPug4iG1HxjXh8E0mXnktbIjTwgQkhhOhSwd8dA4EAU6ZMYcWKFbm2pUuXMmPGjC77n3POOZxwwgn85S9/KXRo4jAEgwaG0YJSNgCalsTSo+iZNUAKcNGcbXj0erQPQA2mawqPuwktuQ5wwYmjty/BVE1oRABQKoWhthIMDHx8hu5gNf4Fbc/SbHypPahND6KsIdnY4hsxm/+Mx8wMfHBCCCG6NCBvj7W1tSxevBiAVatWUVNTg2ma3fafNGkSmzdvHojQxCHSNPuAFoVyWjv1050deCx3YIIqII+ZRktu6vxEphXYfxvSRdNSAxVWjldrz66A5XEhE889UsmdWFr7wAYmhBCiWwXfjgSYOXMm8+fPx3EcFi1aRG1tLQsXLuy2v1IKr9fb6/ErKoL9EWaPqqoG1zbOwfNNHPDYBeXp3E35CQb9BEMD8lI7LD3l7DopyIQgc0DemocDz4VpWAP+enHTLlgVkNyd/4Ta75cdZeHx+vGWZGMbbK9pGHw5D7Z8YfDlPNjy/aAZkHfGYDDI5MmTWblyJcuXL+e2227rsQhbt24ds2fP7vX4TU1RHKdwqy1VVSEaGiIFG/9I05t8vV6dQMDKW/WxVRiNAIp9qy0aaW08LY3xrgc5gvQm53BgOkb0OSB7w5erl4FegWJHro/jltAehURyoF8vinD1xzE2/QDIfi+4wYkokrkedvlsWiMWjhMZdK9pkO/jwWCw5Xw05CtFYs8GbHmitraWefPmMXXqVAyj+2n//ve/s3btWubPnz9QoYlDkEjYKOXH4/GhlIvjaESjDl7PiVh6O+Bgu36i0e63nY82bbESgoEL0dw2UAYpp5Rk1CQYGI+mUrgYJJImiWRxtl/b1CSCk/4TPf0+rubD8YxAc6Io71gcvYz2TFlBf1kRQgjRNwNWhM2cOZM5c+Zwyy23dHruxhtvzN0jVlZWxqOPPkowWPgtRnF44nGbeG6RK3uPWHtMo50P5m8+jgttMT/gz2tvbdOAfdvnxStyHEfRxjDQhmUbkgAl2a/lfnwhhDjiKNd1j/pfjWU7sn8Ntnxh8OU82PKFwZfzYMsXBl/OR0O+sh3Zsw/AHw8QQgghhDj6SBEmhBBCCFEEUoQJIYQQQhSBFGFCCCGEEEUgRZgQQgghRBFIESaEEEIIUQRShAkhhBBCFIEUYUIIIYQQRSBFmBBCCCFEEUgRJoQQQghRBFKECSGEEEIUgRRhQgghhBBFIEWYEEIIIUQRDHgRVldXR01NDXfddVde+9q1a6mpqWHBggV8/OMf55VXXhno0A6bx4JQIEUokEHXVf5zHkVZWFFWBn7/oZ92XVMEfUlCvhgey+nz8ZZKEUpvI5Spw9AyhxxHTzRN4U/XU9q+llJ7GwGtBV0DX2w7vrYNWE57QeYF8FkpSr0tlJk7CZl7MHSw4rsxG9diJZtQ6uBjAOiZKGr3u+it29B6eQyAUuC21JF+fy1aMnJIOShcUk3biO14FzcVPaQxestJx2jdsZ723VtR9P31JIQQ4tAZxZg0HA6zZMkSbNtG13UAFi5cSHl5eTHC6RehoI3HXYtyWwAN0z+eaGIoqbQiFNLwGE0otwlc0D0lWNYwWlr69qZnGQ5BYxta7A3AxjIq8Phn0Bbz9Or4gNOIZ9WDaJsXgdIwJ3+U+ORPEFfhvifcDZ0MgbqXyDw7h3QyggqPxrrwGzib3yb67DzIpDDGnor/oz8g5h/db/MClHr3YCa3QP0LEN2AZpaTynyI9x/5MnakCaNsBEM/93PSI07GdXvIoXkLG390I5H1r6FZXsZd/x1CZ30Mx/D1OL+ykzQsW8DrP/oqmUSM8ISpnD7n52hVE3udg5uOs/H/fsOLD9yJnUoyfOqpXHjnT7Aqx/Z6jN6KNW3n9//1Zda98iK6aTL701/lX674FIavpN/nEkII0VlRtiMDgQBTpkxhxYoVubalS5cyY8aMYoRz2AwDLLbtLcAAHHR7I35vHADLjGcLsL2U24ahtWEYfTv9PiuCFn8NsLPjZJow0qswe1FKK6Wwdr6YLcAAXAdt7ZN4m1b2KYaDxhjbRuaZL8HeVSC3ZRvpXduI/uVeyKQAyGxdQeqFH6Gr/luJ81kpjLZ/QMtqiG4AIGVMZ8cDn8GOZM99pnknux74OGZ8d7fj6G6K7b+5h8j61wBwUgk2/fQr2DvWHDSG5I53efW+m8kkYgC0bFrDGw9+Fc2O9zqP1i2reX7e17BTSQDeX7OClx+di3LSvR6jNxzbZtlTP2XdKy8CYKfTLHxoLvUb3urXeYQQQnSvaPeE1dbWsnjxYgBWrVpFTU0NpmkWK5zDYpkumtP5jV2jHdPUUG7nLSXltOLx9GGfC9C6GEdL1WEZB3+DNnQHfeviznHULUHX++9loNrqwN1vhU8zsFvrO/VLrXkWM9Xab/MaKobSg9D6dq4t0xbDTeUXQHZbA27Lzm7HUfEWmlYs7NSerN9y0Bjad23t1Fb/+ku5IrA3Wnd0nmfjP/5Kpr2512P0RrSliTf/70+d2re/80avt2yFEEIcnqJsRwLMnDmT+fPn4zgOixYtora2loULO7/59UZFRbCfo+usqirU/ZOuDYlScBrzmnXDTzgcALsd3APeRJUfv8/bp/vD3GSgc6NRji8QxB+yDn788FNh96q8Nm3YiZSXdx63x3x7YCeG7V2n28vJoAXKOvUzRp1IoLySkLd/rp2btiESA/8oaMsWYnogAErLKwqV5cNXMYxwF/lVVYVItzuEJkyj7d1X857zVY2g8iDnJF01vFNbyZgaSqqG4A337ny2DBvRqW3Y5GmUDanCEzy0a9KVdNJi7Imn8ubfduS1D59QQ2Vl/81zpDnU1/XRarDlC4Mv58GW7wdN0YqwYDDI5MmTWblyJcuXL+e222475CKsqSmK4/Rwk89hqqoK0dDQ803WpaHxWLQC2VUpR6siFvcRT0QoCwfQ8aHIrsq4mDiUs6exbzeoe60gfmssWmprtkGZZLwn09KUBJIHPT409mKsTYtR0bpsHBWTSQ6ZQfSA3HqTb3dMzyi8Z96MvfTH2QbNxBw2Hs8JtSRXZbdCla8E30XfpCniQuTQ5jmQUgbh4HR0ayTE3oNMFIu1DP3YndT/+u69nTSGfPJBIuZQ2rrNWWPcJ+9jzbcuxY5l+1Sdcw1q+HEHPSfmiMlMvOxTbPzTowDoHh+n3vojImmLSC/PZ2jMVKbM/ihrFz8NgBUI8eFbvktbXEG8f84VZPM9/1NfZeOKfxJtzv7yMOXM8xhWM/2Qr/2R7nBe10ejwZYvDL6cj4Z8pUjsWdGKMMhuSc6bN4+pU6diGEUN5bC1RS0CvlPQtTigk0x7SSSz+zrNLQ4lJaMxjRS4LrZj9fmmfIBEysA1T8IbOAbcNDYh2mMHXwHbJ6KPxH/+o5jRraA00sGxxCjtcxw9SSsfHP9JvONnocV2oQUryPjLMS77b8yzPgvJKFSOJ+YbAf1YN7uuS2u6moAnjDnpNki14Cg/1uhKRh5zLnbLLvTyajLhcbgcZL9t5Imc8P2lJHdtQveXoA+bhGP24geJp4Sa67/FmPM/RjrajH/4BPSKMbg9fQrgALq/jLO/9H2mXXkjqViE0pETsCqqe/wgwaEqHVnDlx5/gcbtGzG9PipGHSM35QshxAAqauUzc+ZM5syZwy233FLMMPqF60I0pgNdb6+1tdmAvvfRof8pgGRaI5k+9DfKmCqDUOftwf6UVh7SwckQnNzRqAFV0zoeF6CocFxFJBkCQsB+RV7FFKiYkr9N2gPXBbt0FEbpqOy4fYjBNQN4xkxj3+dV+1KA7aOsICUTTsqLp1C8ZcOpLuu8jSqEEKLwlHso7xJHmCNhO/KDZLDlC4Mv58GWLwy+nAdbvjD4cj4a8pXtyJ7JX8wXQgghhCgCKcKEEEIIIYpAijAhhBBCiCKQIkwIIYQQogikCBNCCCGEKAIpwoQQQgghikCKMCGEEEKIIpAiTAghhBCiCKQIE0IIIYQoAinChBBCCCGKQIowIYQQQogikCJMCCGEEKIIpAgTQgghhCiCghZhdXV11NTUcNddd+W1r127lpqaGhYsWMCsWbO4//77856//fbbWbBgQSFD6xeW6VISTFIajBPwOQB4vYrSEoeSkI3PpwAoDTmUh1qpKIkS8LsDFp+HGCWJ9ZS0r8FPazY+p41gy2qCbW/jcaMHHcOyo/iaVuNreAOPnR3Dr0cpyayjxNmMR0sUNIfe0twMVstGjO3LsSLbUNlTj6EyWM1rUZufx9O6Hq+ZodRqpMSqx2+luh8wWk9sw3LSO99BZbrJ0bVJ7N7MnrXLSDa+h2Lgrm0XwdDe8B47336ZaP1mcO0ixiKEEKI3jEJPEA6HWbJkCbZto+s6AAsXLqS8vDzX51e/+hXnn38+U6dOLXQ4/cbrcQiYW9GcnQBYmhdP+EQ0GlG0Z9t0H36rEtX2AjjRvceNQQ+eRFtUL2h8fncP3tfnob33HABmyRiss+eS/us3cOrfBsAYMwP9/P8iZg7pOsdkPak/fI34uhez/UdMpeS6+Zib7ke1b8+2DTkTfcJnibnhgubTE91JY698kh2PfwUcG+XxM/yL/wvjZtD+yhOse+DLuHYGzfJy3Ff/h+DYNkjtwjSr0MuvIJIM5Y2XeX8t/7j9ChJ76gE45sqbmHjN18Bb2tHJzfDeSwv4271fwMlkMDxeLv2vXzH0pPNx3YEtxpSCLSue45dfvYFMKolumlz7nw8y+cOXgyrs60wIIcShK/h2ZCAQYMqUKaxYsSLXtnTpUmbMmJF7/JnPfIY77riDVKqHlYkjjNeK5QowANwEWmbzAashLir+Tq4AA1DJ97BoKnh81p63cgUYgHJSuO/8GXdvAQbgvrcMffs/c6tG+1MK2LyE9N4CDCCzcw2p13+HctK5Nm33Uqz2tzsPMID0ls00PHYrONnVHzcZo/4nN6Lq38kVYABOKsE793+eWHoyACrdgBl/E13rOAEqE+fNh+/IFWAA63//MLH3VufNmdi9lcVzP4+TyY6dSSZY+J+fJtVcV9BcuxJr2sHjd3yKTCoJgJ1O85tv3Uyk/r0Bj0UIIUTvFXwlDKC2tpbFixdz+umns2rVKmpqavJWCy699FJWr17Nj3/8Y7785S/3efyKimB/htulqqr81RJSjXDAjo9yWoBKILa3xYL0rs6D2c1UVU0oQJQd3I3r8h+XjsXe/lrnjnUrqDzt+k7NlZUhWra80qk9uWE5gbNOQsU78jIiG6kac+HhB32IItvq4YDVJzvSSKppR64Ay7XHIqTbWmHv5dQSWyirPhfIXuPorj00rn650xypPe8zZr/XwKb1Dbh2/gsgGW3DibVQNWlyf6TVaxu37SEZa89rczIZEm2NjD/+xG6P6/SaHgQGW86DLV8YfDkPtnw/aAakCJs5cybz58/HcRwWLVpEbW0tCxcuzOtz9913c9lll3H++ef3efympiiOU7gtoKqqEA0Nkby2cMiHeUA/Vy9HkdyvJQnWSEisz+9nlNN4wHj9raxiKvtvRKnmjejjanF2vJnfcfSMTrnty9c74SxY8VTec57JH0IdsPKVKZlMS4Hz6YkVGg5KA9fJtRllw7GqRqFMCzfdscJqBMNY4dJcAe34jqGlOUVFpYeGhgiaCjDslFnsXL44bw5PZXXeefKEh6EZJk6mY1XQV1qOHqjodD4LzROqxBcqJR5pzbUZlgd/eEi3sXT1mv6gG2w5D7Z8YfDlfDTkK0Vizwbk05HBYJDJkyezcuVKli9fnrcVuU9VVRW33347d9xxB+l0uotRjizxlB9HG5N77BLA1sbh5NW1Jq53Mugd97+53mNIORWFjy98PM4xV3bMGxiGqrkINfbMXJs25SIy1Wd0O4Y7bgae6R/JPTYnnok1/SO43spcm1NdSzIwpX+D76NMeBxDP/M/KNMLgB4sZ+hNP8OpmsKxt/0PujcAgBEo5bivPYJPy24tup5RpLxT2b9+dzQPx3/6HkLV2ZVKpWkc/x9z8I05Pm9O75AxXPLtn2P6/NnHoTCX/tcvMcPDC5xtZ77yEdzwvV/hC2XvWfP4A1z/3z8nMGTMQY4UQghRTAOyEgbZLcl58+YxdepUDKPraf/1X/+VZ599lsWLF3PmmWd22edIkUwqbHsUfu8wwCad8RJvBcsaitdTBbgkUzrJpEsoMBNTawdlEEt5SbQfbPTDl1Cl2Md/Cd8xV4GTJuUbQcINYNXOx4puB6WTDFSTxtt9jlYV5qXfJfjhz4FjY5eOpk0L4D32bqzMLlAmCX0oKefANcGB5SgDNfUyRtxzEm60CRUeQdo/FNcB68TLOWn+VNKt9ZhlI1FlY0mpFnBtUm4piWTn16I+ZBIf+sFiEru3YfiDGJVjcVV+PxeNEaddxL8/tox4cwOByuGY4REDflM+gOu6VJ9wNrf+dgnRpnoCZVUEKqsP3KEVQghxhBmwImzmzJnMmTOHW265pcd+d999N5dccskARXV4Mhloi5qw38ZkKuWSSilAwd6b9CPtCij8fWsHSrsWaWts9sHeN+QUflLBmt6PobykS4/Ja0s4PhLauOwDp4uDisBFkQqNhtDofQ0A2A7YoYkQmpjdKE5BgoN/ktP1leMZU77/UJ37uGCVj8IqH7X3cfGqHtd18ZWNwFc2IhebEEKII5tyi/nO0U+KcU/YB9lgyxcGX86DLV8YfDkPtnxh8OV8NOQr94T1TP5ivhBCCCFEEUgRJoQQQghRBFKECSGEEEIUgRRhQgghhBBFIEWYEEIIIUQRSBEmhBBCCFEEUoQJIYQQQhSBFGFCCCGEEEUgRZgQQgghRBFIESaEEEIIUQRShAkhhBBCFIEUYUIIIYQQRSBFmBBCCCFEERiFHLyuro5zzz2Xq6++mnvuuSfXvnbtWi6//HLuvfdejj32WObOnUtLSwu2bTNt2jTmzJmD3+8vZGiHxDAg4HNRWgrXNYnFNZTS8PkUSjk4jkZ7u4Ntu10e7yWKp30zKtWGFq7GKakAdBIpH4mk6nZepRRBdqHHt4PmI+kdQ9wJ9tAfAt4kBhFIZcjU12FHmjHKhkEmCpqHWvCALgAAIABJREFUVHgiSa3kcE/JYQlYcUy3CXDIaJVEkx3X3GsksFK7yDTuIt26B0pHEW/chTJMrOpjsf1DejWHysRJ7FhHoul9/ENHYQ6bBLqny75OMkLbtneJtzQSHjke3/CJoPT+SLVbdrKdpm3vEmnaTcXIsYRHTgKtoN+WQgghjhAF/2kfDodZsmQJtm2j69k3tIULF1JeXg7Al7/8ZebOncv06dNxHIe7776bH/7wh9xxxx2FDq1PNA1KgnF0dmQbFBiBIaAF0fT2XD/DKKGlReE4+YWYlwj+N76HtmXx3gENtAvuQ1Vq6GYlMLHbQqzU2YTx2tfATgKgl5+ImvxVYk5pl/1L/DHM9udwMiVE/vYnEisW7J1Tp+Qj/4m74ieYo05FzbyHhN71GIUW8kSxmp5EZZoBMPQAWtV1tCVK8BoJfHuepXX1Fup/O5fya/6L9fffQrq1EQBf9THUfONpnNLRPc6hnCTb//I/rPpZxy8Ap972AEPP+RgO+ec63trMqz+9m7f+8IvssZrG5f/9G4aefAGu23VRfbicVIwXf/E9Xnj8weycSnH9fb/gmLMu7fT6EUII8cFT8O3IQCDAlClTWLFiRa5t6dKlzJgxA4DGxkYSiUQ2GE3j85//PLW1tYUOq88CftDZmdemsRtNs/PadD2C39/5tHojGzsKMAAnA0t/gJsJoTn1eK14l/N69BT6+kdzBRiA2vMWntjGLvsbOuipd8FNkdmT7CjAAByb6P89iDblUpwNz2E2v3uwtAtCKYWZ2pQrwACw2zFib6FrCo+9k0wkRf1T92GNnEzTu6/nCjCAeN16Im8+h+p+8RCAdP0WVv3823ltKx/4Cpk92zr13b1hTa4AA3Adh8X3foFM2+5DS7IXmus25AowANd1efKeW4jt2dnDUUIIIT4oBmTfo7a2lsWLF3P66aezatUqampqcqsLd9xxBzfddBNDhgzhtNNO49xzz+Wcc87p0/gVFd1vzfUXr0eDrrYZXZv8RRUXn8/A58vfTnWbWzsfG6mDjAsGmHqGqqpQ5+HjjRDd2qldT++halQX/Z0UNO8BwIm2dXreaatHebOrX3qyucs5gW7b+4v7/q5ObVpqB2VVPtjTTiyeBDuDUTWGPXUbOvWNvbeasZU9x7hzcyscsIrlpJKoZFun/Na+2dB5jj0NaHa8YOdi56rmTm3xSCtuOlbw8w+Fv8ZHosGW82DLFwZfzoMt3w+aASnCZs6cyfz583Ech0WLFlFbW8vChQsBuOKKK7jgggt4+eWXWbZsGbfffjuXXnopc+bM6fX4TU3Rgm7fVFWFaI+5BDwmkN7vGQ3Q89pcV6e9PUM8nswbo8RfjYUC9otz1FlgOQCkMl5aWyKd5tY1i9Jh56Bt/3Nee9o3mtaGzv01TRG2xqHF30CvGApKA9fJPW+OORm3cT0AmZLRtHQxRlVViIYu2vtTme8Y9OjqvDbbfzwte2KEtErMoI5eUkVy00oqTruWyIbX8/qGT5p90BiN8pGY/hDpWEc/f9UIVMnwTseWjZ6A0nVcu2Nlc/jUU1H+ioKdi5IhozFMi0w61THnxClYoSEFP/8DcY2PNIMt58GWLwy+nI+GfKVI7NmAfDoyGAwyefJkVq5cyfLly3NbkZlMhh//+McEg0HOP/98vvWtb/HEE0/w9NNPD0RYfRKLu6Sd0bhu9qZu1zXJMJaM42XfaXRdg3S6hHjc7nR8u3cs9jn/Dd5wtmH4KXDqDSgtQcY4gWjc6nJe21GkRl2BM/TMbIPhxz7uS8TMcV32dxyXJKNxPBMxSmKEb/gRWrAye+iYkwmcdgVO3WsYl8wjEZx0GGfk8MQZiRP+0N4b3zWc0KkkjQm4LrS7Q9GGTWfU576HHizF48QZfu61KE1HMz2Mue5OPJNOP+gcWtkoPnTv7wgMHwtAybgpnPnt30KgslPfIROO4yPfe4JAefaG/xHH/wuzv/FjsAL9mXae4LBxfPqBpwgPHQHA6GOn8fG5j6J75YeWEEIMBgP2Maza2lrmzZvH1KlTMYzstLFYjMcee4yTTjqJM844A4CNGzcyZcqUgQqrT1raFF7vaEzDxbYV8YQL2Ph8Jeg6pNOQSGS6PNZ2DVoqzsFXezy6HcfxVaJZOo6jEYvoPd78HXUqsSZ+Bc/4T+AokzjlPa78tSdMkvo0vP4k1Gj4vzgDUlGUvxwn1QqTLiRqVRX15u9E2iKt/Qu+IcdlH2cCZPYuCNmORos6Ft+4UYy8/STs9hgl/gqGXnk7StNxS0bguAe5IYzsPVae8adyzo+eJxNtxghV4nq6/kSoZhgMmTaLa3+xhEw8gic8FMxCf0JXUX3CWdzy2Ask29vwh6vQPIXfWhdCCHFkGLAibObMmcyZM4dbbrkl11ZSUsIjjzzC9773Pb75zW9imibjxo3jBz/4wUCF1WeJhEv2YwQdBUws1nnlqyuu6xJT5dmznma/XcyDF0Mp2yDFkL1dD94/Y0M0vvdPMZgV2X8Anr2rLEfAp+9sB6LJrleaXNcllg6CHoR9dVPp3lXEPobuesvQvWUHPcx1QQ9Wogc7r5QViuuCGazADFYM2JxCCCGODMot1OfvB9BA3BN2pO+796fBli8MvpwHW74w+HIebPnC4Mv5aMhX7gnrmfzFfCGEEEKIIpAiTAghhBCiCKQIE0IIIYQoAinChBBCCCGKQIowIYQQQogikCJMCCGEEKIIpAgTQgghhCgCKcKEEEIIIYpAijAhhBBCiCKQIkwIIYQQogikCBNCCCGEKAIpwoQQQgghikCKMCGEEEKIIjAKNXBdXR3nnnsuV199Nffcc0+ufe3atVx++eXce++9XHHFFfzyl7/kySefRNd1dF3nqquu4rrrritUWAWj64qgL4Gm2gGdtB0kGtM79fN6wOvNoKkUjmvh2g6a0wxuBluFicYDOC4Egzqm6QIOjmMQiWRwnALEjY0vsgHVuA48IeyqqUDf/q/3upvG0/wuzq53Uf4y3OEnkPAM6ba/prkEE5vQm9eD6ScdPpao1n3/LudUNkF7G1r7FjCCpAITaXfK0TSXkPY+RuI9lG7ieIaQ0UqJJMO4rtunOQ6VkW6FnavJ7N6KMWQsDD+BjFVy0OOUgkzDZlo3vAmuQ+mk6RhDJtAfYSsFqmkTsc1vohREa05BhcYc0tjp5joa1r9JOhalatIJBKqn4KIOP8hBQCmI7NrMjnffwnUdRk6eRsnw/rnGQoijT8GKMIBwOMySJUuwbRtdzxYkCxcupLy8HIAHHniAFStW8Pjjj1NZWcmePXv43Oc+R0tLCzfffHMhQ+t3JYF2jMzrucc6PvBPyyvETFMR8DWj0QiARhi3/RWUE997jEZJ8DwyqgyvN4JSdu7Y0tIwzc0dj/tLoGklmac/Ce7eCq98PM51vwLKe3W8UmC99xKRX34y12ZUn4j34z8jYVV1eUxJdDXG4s+Cm81HC46A8x4mqg3rddwlyTXor98JZN+9vMExcMI9WDSjr/8+kM1HsyqxRtRS4j+W1kTvcjocupMk/uwPaFn8UK4tfOHn8V18Oxnl6fHY9PvreOFLs0m3twFg+ALMnP8s5ojjDjsutftdVn/jAuxYJBunL8jUuX+DIVP6NE6qaTsLvnQZrTvfy46r61z14J8JH3PaYcc4GLTUreOBT9SSiGavsccf4As/X0R41LFFjkwIUQwF3Y4MBAJMmTKFFStW5NqWLl3KjBkziMfj/OxnP2Pu3LlUVlYCUF5ezne+8x0effRR4vF4IUPrVx4LdHtDXpsijqlH8tp8XidXgIEGmbZcAZbloCdXY5lOXgEGoOtRPJ7+vVweYmReuLejAAPYsxln56pej2FlWoj9YU5eW6buLVT9O13PqSXRVz6QK8AAVHQnZtPqXs/p1eNo637CvgIsO8Z7eGIb0XY+zb4CDIBUI9hJ9Ogb6Hrhd9/15i15BRhAy7MPojVv7fE4TVNsf+HpXAEGkIm3s3XR44cdt6YpGl96IleAAdjxKI1//zW63rcVrF1rlucKMADXtvnnw3ej7MRhxTgYaJrijWd/lyvAAJKxdl754+FfYyHE0amgK2EAtbW1LF68mNNPP51Vq1ZRU1OD67rE43F8Ph/V1dV5/SdOnIhlWWzevJnjjuvdCkBFRbAQoeepquphi85JQCLZqdnQ7fzjnAjkag8NnM6FpnLaUVrnfUelHEpKvEDnLc5D5URixKO7O7W7ibae891PpqmZWKSxU7ueiXU5hhtPQ6zznHq6tddzuvEEJPd0atecOKSbOx9gJ9BUirIyH0p1/2bX2/l70t7Q+XUAYDgJSg8y/qr3t3Rqi+7YRHl54LDjqqvvPHZ810bKy/v2vbMh0vm8R3bvxO9R+MsO//wVWn9c48PRtGNrp7bGbZsoK/OjVP9v6RY732IYbDkPtnw/aApehM2cOZP58+fjOA6LFi2itraWhQsXopTCtrveXstkMn2ao6kpiuMU7qaKqqoQDQ2Rbp/XNAgHRqHbm/LaU3aQ1paO4/x+hd80USoNZMCs6DSWbdXgZAws64B2209LS7xf89Q1P/7pH8Netv/KjUIbOqXHfPPGUCV4T/sYiZcf62jUDOyKiV2OoWkW4clXoa38UV57umwqrb2dU/NQWn0J2tan9h+ZtG8seuV5aO//Li8fPOXY5hCaG9u7HfNg17i3rJJqzKoxpBs6VovMIWPIlIw66PhjzruGbS/8Ia9t/CX/0S9xVc28jsZlz+S1DT3vhj6PPfTYUzu1Tf/ojcQdL+39EGch9dc1PhynXHQ1byzOv8anX/kfNDZG+32uIyHfgTbYcj4a8i1kkfjNb36T8vJybr31VgCeeeYZFi9ezJVXXsnDDz9MOp3G6/Xy9a9/nenTp9PY2Mhdd91FU1MTDQ0NjBw5kvnz51NRUcGsWbM44YQTWLduHbfeeivnn39+weLeX8HXwIPBIJMnT2blypUsX76cGTNmAOD1ekmn02zevDmv/4YNG3Ach/Hjxxc6tH7jOJDIDMXWxwMmLgEyxjSisfx7gGIxl7Q7BocQoOFqOm7ow6CHQHlwfNNJuCOJRh0ymVJcVwcUth0gkTD6vdC0HcgcdzX6jM+BtxSqJmFc9TO04cf3fgxXx/jQzXg//FmUrxRj5PGEPvMkiZKJXfZ3HJfE6Itwpn8OvGHc0rHY5/2IqL/r/l3HrUiOuARn/NVghnCD47FPnktUH00idAbO8I+AEQTvcBh3A7ZRSUyN7vX4hyPlqWDoLb8lcNJFaL4QgZMuYugXnyBlHfx+tNDkMzjt6z/BP6Qaf9UITv3KA5Qed3a/xOWZdAaTvvgwnsqReCpGUPPFh/Acc2afxykZezxXzHuK8rHH4AtXcPZn72TSeVcV9JegD5Lq48/gum8/RNmwasJDR3D1XT9k7LSzih2WEEel6667jgULFuQWbp588knOPvts7r//fh555BH++Mc/8u1vf5svfOELxGIx/vrXvzJt2jSefPJJnn/+ebxeL3/6059y402aNIlFixYNWAEGA7ASBtktyXnz5jF16lQMIzulz+fjpptuYs6cOTz44INUVFTQ1NTEnXfeyac+9Sl8Pt9AhNZvYnGNhDYCr2c4jqtItHXdr7UNTHMYpgnpNGQyLh7rfDTlkkjtK7RcmptdvN4QmgaJhI3j9P9N+QBxoxJ9+s1YUz+Go3uI4adKN4He3+MT9w5Dm3k7/jM/g6N7iWn+HvvHVBmJ8dfjHXc5jjJIuIH9b+/qlXanjMSwf8cz/DJsTJK2FxyIOUES/gvxTTgbTVdkHINE2oNrD1yRkAxPouQTPyWcbMXxlJLEOvhBAFaQyrOvZuYpFwAu+Mr77ROdjhnCf/q1HDvtQhQuZdWjD+03aM2katq5/NvD/4KbSaMHyuWTfX2gewIce+7VTDo9e43NQP9dYyEGmylTplBdXc2LL77IuHHj2L17N7Zts3v3bm644YZcP6UU27Zt4/rrr+e1117jF7/4BVu3bmXDhg2ceOKJuX6nnHLKgOcwIEXYzJkzmTNnDrfcckte+4033kgoFOKGG27AdV2UUlxzzTVH5Z+ogOyKWCx+8Ps60mmXdLrjcSK5b0Ey/4dxIlGYwutAtgNxveywxnBcRcLs/acPHRdilPa5+Nqf7UCsiz+n4bjQng5A7hwP/JtcBgs8XX86tCeu64I3vO9Bv8bkui62J9wvYykrhLL6PcRBwXVdDH8497UQ4tBdd911/P73v2fs2LFcddVVOI7DGWecwfz583N93n//fYYMGcL3vvc9Vq1axZVXXslpp51GJpPJ+x70+3teQCiEghVh1dXV/P3vfweyn5J86623cs9997vfzX197bXXcu211xYqDCGEEEJ8QM2ePZt58+axfv16nn76aZqamvjRj37Epk2bmDBhAi+99BJf+cpXeOmll/jnP//JF77wBc477zzq6+tZtmwZl112WVHjH5CVMCGEEEKI/mZZFrNnz6axsZHy8nLKy8u55557uPXWW7OrzobBww8/jN/v5+abb+a+++7joYceQtd1TjrpJLZt21bU+KUIE0IIIcRRKRaLsWLFCr71rW/l2mpra6mtre3U94ILLuCCCy7ocpx9O3cDTf5CoBBCCCGOOkuWLOGcc87htNNOy7vB/mgiK2FCCCGEOOqcffbZvPrqq8UO47DISpgQQgghRBFIESaEEEIIUQRShAkhhBBCFIEUYUIIIYQQRSBFmBBCCCEGnQcffJCLL76Yiy++mPvuuw+AZcuWcemll3LBBRdw//335/quXbuWK6+8ktmzZzNnzpzc/69y586dXHfddVx44YXcdNNNtLe39ykGKcKEEEIIMagsW7aMf/7zn/zhD3/gj3/8I2+//TZ/+ctf+MY3vsFDDz3EwoULWbNmDS+99BIAX/3qV7nzzjtZvHgxruvy1FNPAXD33XfzsY99jGeffZapU6fy0EMP9SkOKcKEEEIIcWRK74DYC9C+KPvf9I5+Gbaqqorbb78dy7IwTZMJEyawdetWxowZw6hRozAMg0svvZRnn32WHTt2kEgkmDZtGgBXXHEFzz77LOl0mhUrVjB79uy89r6QIkwIIYQQR570DkitATeRfewmso/7oRCbNGlSrqjaunUrCxcuRClFVVVVrs+QIUOor69n9+7dee1VVVXU19fT3NxMMBjEMIy89r6QIqwf6bpC0xRKqU7t+5qUyj4uhL6MrVQ21v1pWue27vSlb6FpmkLXtR7j2Xdeuro+vXE4161Y56qrayyEEEeN9HrAOaDR2dvePzZs2MAnPvEJvv71rzN69OhOzyulcF23T+19UfC/mF9XV8e5557L1VdfzT333JNrX7t2LZdffjlz587lscceA2DTpk2MHj0a0zQ56aST8v5fUEcyr8fB74miuU2gBXG1EOmMl1Raw+cDTUvhujpuOg3pnWhOG7ZnJLF0Gal0/9TBocxOjF3L0Nrewx55NrHSqaTwd+qnFARiW1BbXsSNNqAmncf/Z+/O46Oq7sf/v85dZs9OEjBhEQQUrWChRSkCglUQIyBdbPsR/blUWsTWUiVFqLuiX1xqqfajpZ9a29pqK4tpACtY2kArGBcEAVFIJBCykmUy+73398fAYJwEMmSFnOfj4UPynnvPPe+ZyeQ955x7byD1fPS6T4jsXA9YhEZORyQNxyL+zaQqBp7wp6iHt4DmJtLnYhrFgBbfjJ1NYKFXfoj/vQKEsEgaNQktZwg+K5NwRAXArTVgC+zBaqyisdZF1dsbUD3ppH3lKqys4bSl21bdQao++Ce1e96n7+hJpJx3CbjST76jGaFh/wd8sqkAm8vD4PFX4e4/otOfKyGg/uBedhWtp6H6MF+aNJ2soReh6I5OPa4kSVKHOjYC1tZ4goqLi7njjjtYtGgR06dPZ+vWrVRXV8cer6ysJCsri+zs7GbxqqoqsrKySE9Px+v1YhgGqqrG4onoktsWpaam8u9//zvWUYDCwkLS09MRQrB69WoAJk+ezPPPP09ubm5XdKtDaJrArR9EiZRGAwYIJQm7notuS0dRvABYEQW8/wIzeuaEFvwYt+srRIxBmGb7/ii7zSpsG36AaDocbXv3K7gvWUQkZ2Zc225/KebL34VgQzRQ/CKOa1+g7vnrwYye7eF/61ck/XAVvowL446VFNyNVpwf+9m2/xWSxjxJg+jfrhxOhX74fQ4+Mg1MA4Cavy9n0MLf4Rk4lDoxGLsaxF7+Iop3N9W+S9n+yPdi+5a99iQXLN0AfYad+CD+WrY+fCM1u4sB+PT1FZz7rTsYcv0SLNQT7lq39x3+Mu9qjlV6b7/0FN95/h+4cs5rR9Yn13B4H7+8aSr+xnoANv3x19z6i5cZOObrnXpcSZKkDiUcLRdcov1fKMvLy5k3bx5PPfUUl1xyCQAjR45k//79lJaWkpubS0FBAbNnzyYnJwe73U5xcTGjR49m1apVTJgwAV3XGTNmDIWFheTl5cXiieiS6Ui32815553Htm3bYrHNmzczbty4rjh8p3LawyjmZ82DZiNYAsXyH48Z3lgBdozqfx+nLdTuPugNe2MFWKzt4l/iMGrjthWH3z9egAEicxiBba/GCrBo/w1CW/+EqjZ/e+iqgbr/T80bNAJoR949pSm+9lBVhcZ//l+sAIv2JULdf9ejNr2PQ49gNytRGndgOM+j5LXfNu+230vjjk2crNv+g3tjBdgxe/72K4yaz1rZI0rBYOvvn+TzQ22RgJ/9m9d3+nP12YdbYwXYMX9f/iBmKLFTpyVJkrqVPoz4MkU5Gm+fFStWEAwGWbp0KTNmzGDGjBm89tprLF26lPnz53PVVVcxePBgpk6dCsCyZct49NFHmTZtGn6/nzlz5gBw77338sorr3DVVVfxzjvv8OMf/zihfnTZDbynTZvG+vXrufjii9m+fTvDhw/vsGmZjAxPh7RzIpmZSS0/YDRAoKU8rKP/Hfvxi/Pa0ZjTqeNKaqXtNrLqW2jbCOFyqri/0HZ4X4jPlS0I1YYV9hMn2Eh6urv5cSJ+iMRvq1gh+vTp/Nfgi7z+xriYGWgCC9xuHY7WHBYaRsAX30A4SJ8+x5+fll7jg58YcTHLMNA1QXpr7wkgHAgQ8nnj4kbI3+nPlRkJx8VCAT9ul44n7cT5nul6W869LV/ofTmf0fnqOdH/hz+OjogJR7QAOxZvh8WLF7N48eIWH1uzZk1c7Nxzz+Wvf/1rXDwnJ4eXXnrplPvR5iJs3bp17Nq1i7lz57JhwwauvvrqhA502WWX8fTTT2OaJmvXrmXatGkUFhYm3OGW1NR42z2ldyKZmUlUVcX/wQew6YIkezaK+bkzIoQDhIIlnIhjlYCWBEIH6/gfSNNxHvX1AsNsue22SnKfjV13Q/j4SId5wfXUh5IxvtBvT9ZIUHQwo/0wD+/EMf1JgtvXNs9r7PUt5px29rdQP3j4cxGFcNpo6lt5fjpT8uSb8BYXNIuljpuO4Tybujpwqn1wOs5CC+5mwDXXs+uZ49OoQlFJOv/SWI6tvca2vkNwZeXiqyyLxfpPmomV1K/V98QxY753Bwe3H58CRQgGjbvypPu1V/8Ro1F1HSN8/L12+c0/IWDY8Z8k3zNZb8u5t+ULvS/n0yHfdheJek6HFF09lXrffffdd7KNnn/+eQoLC3n//feZOXMmCxcupL6+nq9+9asnPUBDQwMrV67k1ltvZevWrWRnZ/Pyyy+zYMECNm7cSE5ODuedF10j8+KLLzJr1iySk5MTSsLvD7VpgfWpcrvt+HwtTxsaJihaGqpmRxDGUjJBH0TE9BAK6yiKhqKYWDhAH4RlGQghMOwXEGAQoUj7Z4TDajL6oEsRZgghVIyR3ycw4CpCxM+bR+zp2IdNQATqwe5BnXgXRs7F2IdfitVQgZren+Rv/T8C2aOxRPyaJ9PeBy39XESwGpLOxjj/x3i1IS0u4u9sIqUvySMuIVJfgS1rEH3n3IdjyAgC6mAihkrY1NHSvoSiKLiSwPOlGQRrK3EPGcXQectRckfF+t3qa2zz0H/cFaiqihkOMfwb8xhy7e3gSD1p/9x9zqL/yK/QWHmIzCEjuGLRL0kZchGIzl0F4Ejpw5cunYKvoQ5nUjLX/PgBho67EkWzH+/bCd7TZ6relnNvyxd6X86nQ75ut/3kG/VibRoJ+/vf/86rr77Kt771LdLS0njllVf49re/zbx58xI62LRp03jiiSe44IILYtfVOBM0+RT8Sj90vR9CCCJ+iERMwEAIBV33YBgmhqGiqaNRFItwkA4rHC0L6m2D0Ubdg4JB2NJabdu0BI1J56N9/UkUDELm0dfhrK+hXX8xAI6MVBpb+XYVMh2EnF9FP380oBA2RLNZ165kCDvG2ZNInXcpqgKmolAfEZ8fbKQxkoGaPAstNYKerXHOqNlYQsGwlLgTn1ujZg5h2E2PMMwMgWpv86irsLnIHn0l14yeAgjMkyzk7ziCPkMu4roHf4tlRrCE3kXHlSRJkhLRpq/kmqZhs9liPycnJ59SEXXZZZexa9currrqqoT37elM0yIYtAgEzKMFWJRlWYRCBoYR/cMdMSxC4Y4rwD4vYgpCZusFWEvbNo+pRMy2FQphQ40WYD1AxFIJGiqhsGgxd8O0CEZUTNMigoZhJT4SZVpgCtspTXubaF1YgH3uuJaQBZgkSVIP1qZKql+/fvzzn/9ECEEoFGLFihXk5LRtjjY3N5eNGzcC0bMkP/jgg9hjS5cubbbtse0kSZIkSZLOdG0qwpYsWcLdd9/Nnj17GDVqFCNHjuSJJ57o7L5JkiRJkiSdsdo8p/jiiy/i9/sxDAOPx8Mnn3zSmf2SJEmSJEk6o51wcUxdXR11dXXceuut1NfXEwwGMQyD6upqfvjDH3ZVHyVJkiRJkjrU8uXLmT59OtOnT+fxxx8HYMuWLeTl5XHFFVfw1FOgRsjWAAAgAElEQVRPxbbdtWsXs2fP5sorr+See+4hEole4Ly4uJjZs2czY8YMbrjhBg4eTOzm4icswhYsWMDFF1/Mxx9/zNixY7n44osZO3YskyZNYsSIEYnmK0mSJEmS1GZWwwdY+5/E2ntv9P8NH5x8pzbYsmULRUVFrFy5klWrVrFz504KCgpYtGgRzz77LIWFhezYsYNNmzYBcNddd7FkyRLWr1+PZVm88sorsfjDDz/M6tWrycvL46GHHkqoHycswlasWMHu3buZNWsWu3fvjv23Y8cOnn766VNMXZIkSZIk6cSshg+g8nWIHL0NW6QeKl/vkEIsMzOT/Px8bDYbuq4zZMgQSkpKGDhwIP3790fTNPLy8li3bh0HDx4kEAgwatQoAK699lrWrVtHKBTiRz/6Eeeeey4Aw4cPp7y8PKF+tOlc/UcffZS6ujrKy8s5dOgQBw4cYPPmzQmmLEmSJEmS1EY1G5rdZQaI/lyzod1NDx06NFZUlZSUUFhYiBCCzMzM2DZZWVlUVFRQWVnZLJ6ZmUlFRQU2m40ZM2YAYJomy5cv5/LLL0+oH21amP/MM8/wv//7vwCoqko4HOacc87h9ddfT+hgkiRJkiRJbXJsBKyt8VOwd+9ebrvtNhYuXIimaezfv7/Z40KIFu9zLcTx62SGQiHy8/OJRCLcdtttCR2/TSNhq1at4q233uLKK6/kjTfeYOnSpZxzzjkJHUiSJEmSJKnNtJTE4gkqLi7mxhtvZMGCBcyaNYvs7Gyqq6tjj1dWVpKVlRUXr6qqIisrC4CmpiZuueUWIpEIzz33HLqe2AWy21SEpaenk5WVxeDBg9m9ezczZsygtLQ0oQNJkiRJkiS1WcYU+OJdP4QejbdTeXk58+bNY9myZUyfPh2AkSNHsn//fkpLSzEMg4KCAiZMmEBOTg52u53i4mIgOjA1YcIEILowf+DAgfziF79odmehtmrTdKSmaXz22WcMHjyYd955h/Hjx9PQ0JDwwSRJkiRJktpCJI+M3pq4ZkN0ClJLgYwpiOSR7W57xYoVBIPBZnfuue6661i6dCnz588nGAwyceJEpk6dCsCyZctYvHgxTU1NjBgxgjlz5vDRRx+xYcMGzjnnHGbOnAlE15G98MILbc/Rammy8wveeustfve73/Hcc88xa9YsmpqamDRpUsKnYnaWmhrvKd3Tr60yM5OoauWG1mei3pYv9L6ce1u+0Pty7m35Qu/L+XTINzMzqbu70KO1aSRsxIgRvPjii0B0GK60tBRFSfwmyJIkSZIkSVJUQlfMr6urIxgM0qdPH26//fY2H6SsrIzhw4fHXdZi8uTJlJWVccEFFzBjxoxm//3xj388tYy6iRACXReoqkDXFXQ9sSJVVaL7f+6Ei4SpqsBmU7GpBooS35AQoCsmqnridizLQtdMFEWgKSbqaVBvayroWuujoUKALgx09Xj+iiLQ9ej/jUgYVem80dSWqIqFIjr2mEKAgoH8jiRJktTznXAkbMGCBbHCaezYsUC02FBVNeFrYei6zpIlS1izZg0ej6fZY1lZWaxevTqh9noSt8vArtagWhVYSiqo6WA1Ylh9aGzSiURO/Ic22eVHi3yCYtRg2M8maJ2FL9j2MyyEgORkDa3pM8THa+GzzVgDJhIcMBWvkg2Aw6hDP/BvrO1/hoxzEBf9D173UL44GZ0kKmDXn0jyewmFMvD95y8oyX1xXXor/owLsKx2VImdQAhBslqGWv0mIlSFmTEFv+1cAoYzto0eqMLYvw1v1UEOb16DntyH3Jk/JCVHx2ws5/B+k22v/gbdmcSwb96Oc/BoLHGSSrUdrEiAmt1vU/yn5ai6jdHfuZ20YWNASeysmi8KN9Wy979vsuWv/0ffIecy/tu3kjZgRNxrLEmSJPUMJyzCVqxYAcDPfvYzfvjDH9K/f3/Wr1/P3r17ueGGGxI6UFZWFuPGjeOxxx7jwQcfPPUe9zA2HRzKfhTjMADCrAejAmyD0SjB4x5EXX3rT7PHGcLm2wBWAAA1Uo3DPpyQeiERo21/PT0eFd2ogI33QPVH0X5U78Re9h8ilz5JCBf6ntcwNj0R3eHg+7B7Le7/+Rtee/9YOy6lAdv790G4jqA+iYaVP4k9FvywkKTb/44vdXgiT0+nS1IPo+99NHZBP9W7F2f/OQRt47EsCxUD/8bnCGvJ7H3x/th+NW8XMPqRl6irbuCfS34Qi5dt+TuX//If2AZ+udP6XLtnK3/70bWxnz8tWsd1v15HytCvnHKbQkBxwR8peCaaY8n2bbz3xkrufGkjnuzB7e6zJEmS1PHaNGmhaRovvPACn376KQ899BAHDx7knnvuSfhg+fn5FBUVxU1LVlZWxk1H7tmzJ+H2u4PdFkYxDzcPWgGODT9oohZNa/1p1miIFWDHKMGPcdgCrezRQhuaCXXlsQLsGFH5HjZ/GY5IDcZ/ft18p1ATorr5c6wHDyJ8ZZipY2gq+nPz7Y0wRunb7Zou7QxqoDTuisrq4ddxaL7ov72HCFeXcWjT35ptYxkRvJV1fPTqS80btCwOFv29xencjqCpgvf+8uu4+K71f0Ftx7xvsL6Sf6x4onmsycvhT3aecpuSJElS52rTwvydO3fy17/+leeff56ZM2eyYMECZs+enfDBPB4PDz74YGxa8pj2TkdmZHhOvlE7tXqGh9EILdZL0T/iQiikpblbbdcK1rUQVXA4bDg9bT2rxI/VyiIgm92O7nDgV+OnujRdb5aXVX10G8tAqPHXO1Ftdvr06VlnulhVLbyFFQ2324FHTyJk2KmzDBQ9Ph9FCFS7PS6uO52d9p6yLAvd4YyL6w4X6emtv09Ops5sRNNtBL/Yrs3W6nu3N5611Nty7m35Qu/Lubfle6ZpUxFmWRaKorB582bmzp0LgN/vP6UDjh8/PjYt2VG68xIVmiZIcQxAMT87HhQeEBZYEDbTqTvBKcRuhxuHkoQwj29jOs+nvl7BMNt26rHHo+JIOQv6j4cDRcfbGXg5XiWbSNiJe8ICjPVLjnfRnUkodRhHPtc3p9IPV8oIlCPbcE/6HvV//tz2jiSs3K/0uNOhk7SB2FU3GE2xmNFvNnX1AstqRNEycQwaxVmDvsKeve/GtlEcLtzZKVzwrRsof+efsZFLxWYn++JpnZrnqG/9gD1vrcEyzegxNY3hX/9Gu46paMlcPf9e/vLgj2KxpIwsMgePaLHd0+HU9o7W23LubflC78v5dMi3JxeJy5cvZ+3atQBMnDiRu+++my1btvDoo48SDAaZNm0ad955JwC7du1i8eLFeL1exowZw/3334+maaxatYply5aRkZEBwKRJk2L7tEWbirABAwZw6623UlZWxle/+lUWLFgQu2v4qcjPzycvL4+qqqpTbqOniEQsfJFcHHoKilUNSgqobrCCGNZgGr0q0HqB2BTQEc5J6FY5wjyCpeXgj6RjJFBUer0GIqkPtq/9DA5thfL3sPqOIZAxhpDlAMsiMGgqjtl9sfa+gUgfhHX2ZLy2fs3a8ZtulBF34/S+j95UReqt/0dgx0ZEcjbaiKn4kwefKJVu4TUyEEN+htb4HiJUg5E6hiYxECta32BaAtsl12M7tJ3zf/oCtduL0NOy6TMuD1dfJ07/Qa54ZjWlmwpRHR5yxl+DLef8Tl3MnjLkIr7zv2/w8caVKLrO0EkzSBr4pXY9taZpMeKyGXw/6yw+fOt1Mgecw4gJU3Fl9D/5zpIkSb3Mli1bKCoqYuXKlQghuOWWWygoKGDZsmW89NJL9OvXj9tuu41NmzYxceJE7rrrLh566CFGjRrFokWLeOWVV/jud7/Lhx9+SH5+PldfffUp9aNNF2v1+Xz84x//YPTo0eTm5vLyyy8zc+ZMnM74aZWWlJWVMWfOHDZu3BiLFRUVcfPNN7NhwwamTp3KkCFDmu3zla98hcWLF7ep/Z5ysVZVVTBNM3Zjz0T6JAQoioJhmKfcTyFEbC1Ta+1E+2i1eEPSYzIzk6itbcIwTFRVwbKsTn1+O8Kx3E/0/KmqghAWpnn8tVHV6HOemZnU6e+jLzr2WnX0Mdv6Gvf0b9Adrbfl3Nvyhd6X8+mQb3tHwqy9BbD1KfAeBk9f+OqdiKGnVvB83t69e2lqamLUqFEAPPDAA6Snp7Nt27Zm10V9++23uf3227nhhht48803AXjnnXd45pln+P3vf8+3vvUtkpOTqaysZNiwYSxZsoSUlLbf27JNI2Eul4sZM2bEfv7Od77T5gMA5ObmNivAIDoteWzx/Y4dOxJqr6c6VgC0oa6NY1mtF05tb8PCOMkZlW09xrHt2tunrnKquX8+1tWFZmcd73R5zSRJkk7E2lsA//o5RI4uvPaWw79+jgXtLsSGDh0a+3dJSQmFhYVcf/31ZGZmxuJZWVlUVFRQWVnZLJ6ZmUlFRUXs39///ve58MILefLJJ3nggQd44onmJ0mdiLykoyRJkiRJPc/Wp44XYMdEAtF4B9m7dy833XQTCxcuZMCAAXGPCyFaHFg5NuP1q1/9ipEjR8amNP/1r38ldHxZhEmSJEmS1PN4DycWT1BxcTE33ngjCxYsYNasWWRnZ1NdXR17vLKykqysrLh4VVUVWVlZNDY28rvf/S4WtywLTWvTBGOMLMIkSZIkSep5PH0TiyegvLycefPmsWzZMqZPnw7AyJEj2b9/P6WlpRiGQUFBARMmTCAnJwe73U5xcTEQXSs2YcIEXC4Xv/nNb/jggw8A+MMf/sDXv/71hPqRWMkmSZIkSZLUFb56Z/M1YQCaIxpvpxUrVhAMBlm6dGksdt1117F06VLmz59PMBhk4sSJTJ06FYBly5axePFimpqaGDFiBHPmzEFVVZ5++mnuu+8+AoEAgwYN4vHHH0+oH206O7Kn6ylnR54pelu+0Pty7m35Qu/LubflC70v59Mh3556dmRPIUfCJEmSJEnqkcTQq+EMKrq+SK4JkyRJkiRJ6gayCJMkSZIkSeoGsgiTJEmSJEnqBrIIkyRJkiRJ6gayCJMkSZIkSeoGsgiTJEmSJEnqBvISFZIkSZIk9TrLly9n7dq1AEycOJG7776bLVu28OijjxIMBpk2bRp33hm9MOyuXbtYvHgxXq+XMWPGcP/991NfX89NN90Ua6+xsZEjR47w3nvvtbkPnT4SVlZWxvDhw9m8eXOz+OTJk5k4cSIFBQXN4j6fj7Fjx1JbW9vZXZPOYEKAHmnEFq5HCIGqClxOE5vt5PtqmsDlFOi6aPPxFEXgcqk4HOop9VdTTFxKIzYlckr7t4cQ4NCDOLVA7Ka0J2O3mbjsBorS9ufoTGLXDVy2IGoPm0sQAuxGI3ajIeF9NSWCy6pD74b3YHey4cdp1qH0sNdSivJte5XKJRdy+PYMKpdciG/bqx3S7pYtWygqKmLlypWsWrWKnTt3UlBQwKJFi3j22WcpLCxkx44dbNq0CYC77rqLJUuWsH79eizL4pVXXiEjI4PVq1ezevVqVq5cSU5ODg888EBC/eiSt52u6yxZsgSv19ssPn/+/Lgi7I033mDs2LGkp6d3RdekM5Bq+BA71lD1yBQqHroM8d7LJLEft9hGsr6T1ORgq8VGchKkeKpw2/eR7D5MSvLJj+d0qqSmmrjdDXg8XtLSFFS17cVJklJJculzuLbNJenjh0nhM9pYC7WbroRJNT7AU/Iw7pL7SQ1txq4GWt1eVSzS3LV4IhtwhdaRavsEp733/NFWBKQ6qvA0vIKrZgUpkSLcdl93dwsAzWjCvmc1weeuIvjsVfi2/AHdbGrTvslGGcnvPoSr8FskbVtCSqS0k3vb/RQskuveQ191E+Kla3C/9ytckaru7pb0Ob5tr9Lwpx9jHikDLMwjZTT86ccdUohlZmaSn5+PzWZD13WGDBlCSUkJAwcOpH///miaRl5eHuvWrePgwYMEAgFGjRoFwLXXXsu6deuatfe3v/0Np9NJXl5eQv3okiIsKyuLcePG8dhjjzWLX3jhhbz77rvU1dXFYmvWrGH27Nld0S3pDCU+e4eK524mXLmfSM0BKn4zH99H7wAGwjqCHnkXjzsct5/DIbCpZSjUAQYKjdiUEtzu1isiIQROZwRV9QImQkTQtHo8nrb9atnVILZdT6McXAfhBkT1O+jF+bhF14wEu60S1JJnIVgBoVrUAy/iCu9udXuPw4vq3Ygw6sH0o/jfwyk+6zUjYh5bPVrFHxDBg2D4UBr+i71pC5ra/Xd/sx3aRtPLd2DUfIZRe4CGP92BVvb2Sfdz0oj+z7tQ9q2FYD1K6Ub0DfNxWUe6oNfdx+X7lMhfbsQq3w6+Gowtz6J88AdU0f2vpRTlXfMghP3Ng2F/NN5OQ4cOjRVVJSUlFBYWIoQgMzMztk1WVhYVFRVUVlY2i2dmZlJRURH72TAMnnvuORYsWJBwP7psADY/P5+ioqJm05Iul4spU6bEKsqKigr279/PpZde2lXdks4wqqrQ9PZf4+JH3vwTljg2rGWgivjRC5tuIPjiKFAETY0v2I6x2xVU1R8XV9Vwm6b2bJEqxJEPmwfDjaiBgyfdt71UVUGtL46LK9Vvomvxf4iEANWsjt8+uAe73jtGwxSjBiyjecy7HYcW/x7oSpqmEHznlbh46O0/omkn/pjXA4cQ9fuaB5sq0HxlHdnFHkfU7AWz+fvWfPcP2CNyKUxPYR5p+XOwtfip2Lt3LzfddBMLFy5kwIABcY8LIWjpFtuf/3z/97//zdlnn83w4cMTPn6XLcz3eDw8+OCDLFmyhDVr1sTis2fP5umnn+a6667j9ddf55prrkFJcHI+I8PT0d2N096bkJ5uTud8I336x8X0rFyEMODo75Ku2eJytNtsYMTtik23kZnpbuVoJtDEF3dUVY0+fVrb5zjL6wZFB7N5oac73GRmdO5rkJ7uxvLFT/sLWx9SUj0IEf97aPkd8Q0pLtweN55kvTO62aHafTNhbwv5qw4cTifO5O79nWnI6E/oCzE1YyApaSd+H1o1SYAg9stxlM2VdFp+DrS1z5EqT9yvu3Bn4EhKwu0+ffI+HV+jtlLSco5ORcbHO0JxcTF33HEHixYtYvr06WzdupXq6uNfNCsrK8nKyiI7O7tZvKqqiqysrNjPb775JlddddUp9aFLz44cP3583LTkmDFjqKqqory8nDVr1rB8+fKE262p8WKanTeEfDrcqb4jne75Or58Dcr6X2P6o4uThe4gbfJ1YEZzskQqTX4b/sDxHDMzkzhSb5DsyUKlMhY3SaWx0SIUav35SEtzo2l1n4soBAKCxsaTP4eqkkrKOXNQPl5x/JhZX8NHPwKd+Boce42T3aOwqWvBODoyKHQiGV+nrrrltUQeZzp2xYUwj40kCgzHSI7UBCBuFLFn6Yj3tcuWjtPWDxEqj8WMtCuoq1ewrO79nXGNvBax+SWsUPS1EzYX2uhvnzRnTWSRfMEclB0vxmLm0Fl4lb6ETrPPgUReY1fqcOgzFKr3xmLKlMXU+m1YvtMj79Phs7o9RaLnmiU0/OnHzackdSeea5a0u1/l5eXMmzePp556iksuuQSAkSNHsn//fkpLS8nNzaWgoIDZs2eTk5OD3W6nuLiY0aNHs2rVKiZMmBBr6/333+fWW289pX50+SUq8vPzycvLo6rq+ALIWbNm8dxzz5GSktLicKAkJSKUMZx+i/9BuORdMCLYB38Zvf8ADBqxsBOKePAH4kd5IhGLJn8KTocLRQSxLBuBoE4odOICv77eICkpDVWNYFmCSESlsbFt03OGKfD2mYoraRiiqQTL2Zeg8xwCpvOUck9Uo5GFZ8g9aMH9YBkYjrPxGtmtbu/128E5BZ1aIIwp0mn0d/5IdE/hCzkQqddisw6D4cXUsmky+rQ4XdHV/KnDcd9egHXwPbDAMXgM9Y5BcJK+RSyNpqFzcPYbi6gvwUrqTyDlXEJWC6N+ZxCfnoVr5vOoldvBX4uVNQJf8rkne7qkLuT6yjeB6Now88hBlLQcPNcsicXbY8WKFQSDQZYuXRqLXXfddSxdupT58+cTDAaZOHEiU6dOBWDZsmUsXryYpqYmRowYwZw5c2L7HThwgL59+55SP4TVyZ8eZWVlzJkzh40bN8ZiRUVF3HzzzWzYsIHc3FwOHz7MlClTePjhh5k5c2bCx5AjYR2rt+ULvS/n3pYv9L6ce1u+0PtyPh3yPZOnSztCp4+E5ebmNivAIDotuWfPntjPffv2ZefOnZ3dFUmSJEmSpB5DXp5OkiRJkiSpG8giTJIkSZIkqRvIIkySJEmSJKkbyCJMkiRJkiSpG8giTJIkSZIkqRvIIkySJEmSpF5n+fLlTJ8+nenTp/P4448DsGXLFvLy8rjiiit46qmn4vZZuHAhr732WuznQ4cO8b3vfY+pU6fygx/8gKamli903RpZhEmSJEmS1Kts2bKFoqIiVq5cyapVq9i5cycFBQUsWrSIZ599lsLCQnbs2MGmTZuA6L2t586dG7vX9TH3338/3/3ud1m3bh0XXHABzz77bEL9kEWYJEmSJEk90r51L/PXmUN48RIbf505hH3rXu6QdjMzM8nPz8dms6HrOkOGDKGkpISBAwfSv39/NE0jLy8vVnS9/vrrTJkyhWnTpsXaCIfDbNu2jSuvvBKAa6+9Nq5IO5kuv22RJEmSJEnSyexb9zJbls7FCETvVdt0+DO2LJ0LwOCp32lX20OHDo39u6SkhMLCQq6//noyMzNj8aysLCoqKgC45ZZbgOhNv485cuQIHo8HTYuWUpmZmbHt20qOhEmSJEmS1OO8++vFsQLsGCPg491fL+6wY+zdu5ebbrqJhQsXtnjvaiFEq/u2dNfHE23fElmESZIkSZLU4zRVHEgonqji4mJuvPFGFixYwKxZs8jOzqa6ujr2eGVlJVlZWa3un56ejtfrxTAMAKqqqk64fUtkESZJkiRJUo/jzu6fUDwR5eXlzJs3j2XLljF9+nQARo4cyf79+yktLcUwDAoKCpgwYUKrbei6zpgxYygsLARg1apVJ9y+JXJNmCRJkiRJPc6X5z7UbE0YgOpw8eW5D7W77RUrVhAMBlm6dGksdt1117F06VLmz59PMBhk4sSJTJ069YTt3HvvveTn5/Pcc8/Rr18/nnzyyYT6IayWJjU7QVlZGVOmTOG3v/0tX/va12LxyZMn8/vf/57c3FwAXnvtNbZu3drsiTmZmhovptl5aWRmJlFV1RgXVxSB0wlCWASDgnDYQlUFTnsEAH9QwzBOrV/RtlWEgGDQJBw2T7AtOO0hBBAI2YgYp3RIhBA4dT8up6C+SSccSXygVFEEjlAVwgwRtGcRsZrX+U4aUQ0vYS2VoOU8tY6egF0Nopv1mMKF3/LQ1nd3a6/xF+mqiV2px8JGxOvDCvsx3X2JCFs7e35qhACr4TCWEUFN7YeJ2uwxW7iOSKARry+EnpSB5kwF2p5vd1OtEFbDYYTuwHRnt7gGo61Ol5w7Sm/LFzovZ4FBqLYcoarY0vphtv5x3KVOh9c4MzOpXfvvW/cy7/56MU0VB3Bn9+fLcx9q96L8nqRLR8J0XWfJkiWsWbMGj8fTlYfucLoGSR4/KuWAgV1LJWJmoFrlqMZ+AOyuAfjCZ+EPJFbM6LpCUpKFqtYBFna7nWDQidcbX13ZdQO3WoLi2w6Y2BznEtCG4QvqCR1TUw2SxD7U6jfADJDkOpdQ0iS8QXfb+00AxyeFGP98HCvUhOOCmZhj5+PTsxACUpq2o26+H9FYhr3PBTguWUK9Piihfp5IijiItvMZRN1H4MzCfv5PqNdHYFqJLZRsjUevx1a5EpoO0liTQ8Wfl2I01uAZczWp33iAoKf9Q+QJCXk5vOkVPvjNfRhBP4OvuoFh3/kpIqkvQlg4D7/D4dJ9/OWF5yn96AP69D+b79y3nL7nje3afp4iteEAB176OdX/WY3mSWPI95fhHHkVpmrv7q5JvUikoZL3//wM7/3tBVTdxtduvYehV34XxZHc3V3rFQZP/c4ZVXR9UZeuCcvKymLcuHE89thjXXnYTuF2GaiUAdHCSKEeXRxBNT4FTMBEMUpw6HUkeLIEbrdAVRuA6Ld+RQlit4dQ1fiGnHotiv+9o/2wUAK7sItDCZ+h4VZrUKtXg+mPtuPbha3pv2jqSXeNcRz5CGP9Egg2gmVifvga6s5XURTwRA6hvTkf0VgGgKjegbbpbpx0zLc4h+pH+/DxaAEG4K9EfXcJbhI7Xbg1qgK22g0oR/5DMDKMQy/chdFYA4D3nQLqC5ahEumQY7VV06fvUvzMT4n4vFiGwaev/5aDb70SHY1sLKX+ndW8+PRTlH70AQDVB/bz63nfwFtZ2qX9PBUKBuUrn6R6yyqwLCKNtex54ibM8o+6u2tSLyKEYP+/V1P8l+cwIxHCfh//fOYeqndv6+6uSWeILl+Yn5+fT1FREZs3b+7qQ3coRQl8IeJAmFVx26lmOZqW2NOsKPF/zBUlgN3evB1VVVAiB+OPGd6HriU2baMYNfGxpp3YtC/m2TIhgPLtcXFzx2vYjAaUpkMQad6WaChFC1Ym1M/W6JEahHffFw4eRvGXd0j7dsWHcmQLAMHq+OfK+9+/ogVqO+RYbaEogqrtRXHxknV/hKAXakto1DOoKP2k2ePhgJ/aQz2/CBP+Wir/9Upc3H9wTzf0Ruq1jCA7Cv4UFy75zz9a/FIsSYnq8oX5Ho+HBx98MDYt2REyMjp/ajNuXtuMHBsEOyoMigeM5oWYUFNJTW37lF6Uv4WYhtvtwO1uXohZvnQIfnHTDJKTPAmNhlneFubt9Qxcbg/u5Lat3Qqn5/LFCVORNRxPajpY6fE7aE7sSWk4Utq3ZgDAakoBzQ2R5vft0l1pZGa0rf0TrV2wDA0cOeDdjep2xT1uO2sYrrR0kj3tz6WtagcMjYulDb2Q1Mx0DH8mduSj7BMAACAASURBVMXE5nQR8je/zk5qnz5A+9dqdKZwYwRXzjC8n77fLO5Mz25Xv3tyzp2ht+ULHZuzGYnQ99yRVH7c/Atm1jkjSE/vGUtqeuNrfCbplrMjx48f32xa8vDhw+i6TnZ2dOGtqiYwB0b3LMx3uzScNheCY3/gDEwlB0E5gmMjPjaCZjYNCS6cdLtVnE4NIY6NiAnCYTd1dfE3BvU4s7ErSQjz6DGEnbB6DvXV3oSO6bL1wWkfiAgeHSURGpG0r1NXG4E2Thm6Mi5E9PsSVvmH0YDuRBl3B9X1EWzKWXguuAFlx4ux7Y2LF1IfTsfsgIWlipJMyog7ULc/GouZA67BS19CbWi/LQtcU/t9A+3Tx3GmRnCfP56mndGRKKHZ6PO9xzni18DfdYtkU84bR8rg86nftxMA3ZXE8G//mNq6ILrnbNJcCt/84Z388YmHY/tMvmE+7syzAXr4gl6Nwbc8zoc/vxorHAIgdeQklJwLTrnfp8Mi5o7U2/KFzsl51Dd/wMdvvU6gsQ6AtAHncNaXJ/WI5/Z0eI1lkXhiXXp25Jw5c9i4cSMAXq+XvLw8qqqquOiii5g6dSrf+973eOSRR0hPT2fu3Lltbru7zo50OQU2WxgwMU0bjV6ByxlBV6PrqiKGC68vsYIy1rZLxWazAAvDUPF6jVbPDHM7wuhKA1gmEZLx+k9t4bLTFsRu1aCpYUJWGt5QMok+rU6jFq32Y4gEsNKH0OQYEOu3DT9O3z6EvxrTk4PPPohIB34PUBUDt1GG4i/HsqXh1wcQauMZmG35MBNC4FGrUEPlmGE7gYoKDJ8Xte9QwmnntPlMzA7lrcRb+hFmOIBnwHmoGQNj/dCNRsSR/VRVH6GmqgZPdn8yBp6HavecFh/eQoCo/oTAoY9RnUnYckZgODNOub3TIeeO1Nvyhc7JWQgIVh/gSOkuFFUn7ewRaMnZHXqMU3U6vMayCDuxbivCAIqKirj55ptZt24dd999N16vl759+/L000+TkpLS5ra7qwg7U/W2fKH35dzb8oXel3Nvyxd6X86nQ76yCDuxLpuOzM3NbVaAQXRacs+e6ELbV199tau6IkmSJElSL7d8+XLWrl0LwMSJE7n77rvZsmULjz76KMFgkGnTpnHnnXc222fhwoWMHTuWa6+9Foje+uiRRx4hEomQmprKI488Qk5OTpv7IG9bJEmSJElSj/T2mpf52aSh3Dbcwc8mDeXtNS93SLtbtmyhqKiIlStXsmrVKnbu3ElBQQGLFi3i2WefpbCwkB07drBp0yYAKioqmDt3LuvWrWvWzl133cXDDz/M6tWrycvL46GHEruavyzCJEmSJEnqcd5e8zJ/WPxDag99BpZF7aHP+MPiH3ZIIZaZmUl+fj42mw1d1xkyZAglJSUMHDiQ/v37o2kaeXl5saLr9ddfZ8qUKUybNi3WRigU4kc/+hHnnnsuAMOHD6e8PLHLIskiTJIkSZKkHmfVkz8nFGh+iZ1QwMeqJ3/e7raHDh3KqFGjACgpKaGwsBAhBJmZmbFtsrKyqKiIXvD7lltu4Zvf/GazNmw2GzNmzADANE2WL1/O5ZdfnlA/ZBEmSZIkSVKPU1t+IKH4qdi7dy833XQTCxcuZMCAAXGPt+V6m6FQiJ/+9KdEIhFuu+22hI4vizBJkiRJknqc9H4t34+3tXiiiouLufHGG1mwYAGzZs0iOzub6urq2OOVlZVkZWWdsI2mpiZuueUWIpEIzz33HLqe2H2bZREmSZIkSVKPM/MnD2BzNL9Dic3hYuZPHmh32+Xl5cybN49ly5Yxffp0AEaOHMn+/fspLS3FMAwKCgqYMGHCCdu56667GDhwIL/4xS+w2WwJ96NbrpgvSZIkSZJ0ImOv+Q4QXRtWW36A9H79mfmTB2Lx9lixYgXBYJClS5fGYtdddx1Lly5l/vz5BINBJk6cyNSpU1tt46OPPmLDhg2cc845zJw5E4iuI3vhhRfa3I8uu1hrZ5IXa+1YvS1f6H0597Z8offl3Nvyhd6X8+mQr7xY64nJ6UhJkiRJkqRuIIswSZIkSZKkbiCLMEmSJEmSpG4gizBJkiRJkqRuIIswSZIkSZKkbtCpRVhZWRnDhw9n8+bNzeKTJ0+OPfbznze//cCuXbsYPnw4r732Wmd2rcvZNItkl58kZxN2m9nsMUWBJHeEZE8AlzN6lqemCZKTVdLSBKkpFm6XoA0X7u1QDtFEcmAvSeHP0JRI3OOqAkn2RpJt1bhswRO2ZVcjpAb2kNb4LmniEG69sVPz0awgzvq9OOv2oFv+Zo8pmNgaStArtmML1Z60LSHAOvIZof3b4PBOtMayNvXd9NXSWLKdYNV+BObJd5CkXk4I8FUfoPrTDwg1VHb5Z54kdbVOv06YrussWbKENWvW4PF4mj2WmprKv//9bwzDQFVVAAoLC0lPT+/sbnUppz2C09qB0vQJADa9L7rzq3j9NjQNkp01qMYesEzsigN7yoUoqoYifGAcAmGh2zR0fQB19V0zeJlkHMK2eTGieicIBf386/ENm0OA6GuoqQZJ1m7UyjfAMrBpKeh9vkF9IC2uLZd1BL34RUL//S1YBqLPMOxX34vuSaPeHEhHXyTFHqrCWP8I3nejhbxtxOU48h4m4OiLagQw3vkzh16+BysSQs8cSPb8P0LmmBbbElaEI1sL2Pr/bicS8OFIz2L0zYtwpqZj/9JULNHyr5D/0G5WL/wu9YdKUXUbk+54mHOu+C5ojo5NVpLOFJbB3i1r+dPPf0jI7yMpPZObnvwDWcNGd/hnhCT1FJ3+Fz0rK4tx48bx2GOPxT3mdrs577zz2LZtWyy2efNmxo0b19nd6lJ2pRol+EnsZxE+jM0sQVEEbkcQ1dgFx0ZKrABqZCeKsMA4CBz79ImgiwO4nJ3/1dAywth2/zFagAFYJsqOF3HUfxTbxq3VodauBcs42r16tNq/49DjR8xstTsJ/+eF2LZW9ceEt/wfat17OFVvh/df7Csi+O7xkdTQR29ifRS9OatatYeql+7CioQACFeVUv37OzGa6ltsK1y5j/88fAuRozeRDdRW8t7v/h91OzZDzb6WOxD28ebjd1J/qBQAIxxiwxN30XhgdwdmKUlnlobD+/h9/s2E/NHftcbaKn53940EG2u6uWeS1Hm6ZFglPz+foqKiuGlJgGnTprF+/XoAtm/fzvDhwxO+91JPpqoC1aiIiyuRA+iqhRDx03jCagIrvpiBMKpqdEIvvyDUiDjwr7iwcmRPbHpAGPFFiwgdRlMCcXHrSGlczCj5L6hZqGZD+/v7OZqmENnzVlw8vKMQVbWI1HwW91jgk21EGqpabM9fVYZlNp9K9FUdRHGnEa5q+SayYW8thz7cGhdvKI9/HiRJiqqvOIhpNP98q68sp6km/vNTks4UXXLbIo/Hw4MPPhiblvy8yy67jKeffhrTNFm7di3Tpk2jsLAwofYzMjwn36id2nPVX8ufBcG9zYP6WSR5PAgzAl+sq4QTUFtoScPhcOJwJn5/qkRYkRD0+zJ8uq5ZXMk4hz59os+D5UuN31HPwOFKxpnsbhaOVOXEbaqcdSEYtdjdF+FwdOwVlZuGXkLw/dXNYrbhk0hKS6IpMzdue1vOuaieVDKT4vsh+sZvb0/tgxX04s7OJbmF94VPz6LPkPOo/nRXs3jqWf171NWje1Jfukpvy/l0ytd3Vi5CCD5/Exd3ajoZ/fqSkUAep1POHaG35Xum6bJ7R44fP77FaUmPx8O5555LcXEx//3vf1mwYEHCRVhPv22R25GBXeuHEikHwFKTCSmDaaz2ousaSY4hqManR7dWMbTzEQgUJRvMY98CBRFyqasJYlknXgTfXpmZSYTOvxnt8PuIpsMAmIO+jt9zLv6jz4NNS8aTMh6lvii6k2Inkj6duiMm0Py58mScj3pBHsaO16MBVwa2CXMx3W7qvC6sxo697YZz8CS0s79KZH90NErtNwLxpZlUVTWipQ8j7eqfcKTgyWi3nclk3vRLtKQ+Lb/GaQP58vzHeXf5QrAsVLuDi264C4fTQThlUCvvCxtX/OyX/O3H1xL0Rkf6xs65E1e/YT3mFiOnw+1OOlpvy/l0y9eZMYDZCx/nb48vxDJNNJud/3n4eSx7RpvzON1ybq/TIV9ZJJ5Yl97AOz8/n7y8PKqqmk/9TJs2jSeeeIILLrgATTvz7ineFLBh2C7Gbm8CyyRsJeHzRUe6wmFosPrhcmQgCGNYDpoaNHRd4HKloipJWKZBOKLS5KPLFqjWawNwXfE79KbPQHMQcPQnyPG72YciGo3aV3FmD0eYfgwlhaaQh+Nr2I7zKpm4JtyDbdS3IdSIkppL2J5Ok5FCZ9y61O/oh/17K3Ac2QemiZk+GL8WHbmLaG7sV/yEnDEzMJvqUPoMIuw5q/XGVDv9Lr+BK0ZOIFR9ALsnBT0pHdL6Y4rWp82TB1/E935XROPhUuxJqbj7DpaL8iXpBIRqY+T0/+HsL38N75EqUrNzcWcO6JTPCEnqKbq04jk2LXnzzTc3i1922WXcc889/OhHP+rK7nSpQEglEEpu8bFIBBq8NuDYNKNFKGQRCh3boqWpyc7nE2ngiT/b8ZiwoRA20oBj27T+YemzkvAlXXQ80NKStw4UVJOhz6gWHzMUO0afEdDnaOAkn/GWoqNlD0PLHgbQpotNWBboaTmkp8VPxUqS1DKh6KTkDCMlZ1h3d0WSuoSwzoCvGT19OvJ009vyhd6Xc2/LF3pfzr0tX+h9OZ8O+crpyBOTV8yXJEmSJEnqBrIIkyRJkiRJ6gayCJMkSZIkSeoGsgiTJEmSJEnqBrIIkyRJkiRJ6gayCJMkSZIkSeoGsgiTJEmSJEnqBrIIkyRJkiRJ6gayCJMkSZIkSeoGsgiTJEmSJEnqBrIIkyRJkiRJ6gayCJMkSZIkSeoGsgiTJEmSJEnqBlpnH6CsrIwpU6bw29/+lq997Wux+OTJkxk6dCh2u51nnnkmFi8qKuLee+9l9erVeDyezu5ewoQAt9NAVfyAhj/kIBQ6+X5Op8CmRwCLcETH57Nwu1U0zQIEwSAEAkarx3Q5Qmh4QWgEwh6C4TOnflYVE7fegDC9WEoyTeEkDFOgEsEZKIOGckjKxu8aiGIZOIMHUIJHUDxpYHixhINwgw8jEICMwQQc2VhW1+Zg81diVn6C0O2QNZSwlty1HSD6PlEaygge3ofmSkHJ6E9D2Sc01VSQmjsYZ855mF38vSviq6Pms48xTZM+A4aiezK69PiSJEk9WacXYQC6rrNkyRLWrFnTrLBasGABc+fOZePGjUyePBmfz8d9993HI4880iMLMICUpCB65D0wIwBotrNoEmcTCIpW93G7wakfROADQNdtONIGoqiNCGEcjemoqoempkjc/skuH3rTBrCCAKi2gSj2i/AHu+Tl61SqYpEsdqNWrAUsQEHtM4NGazCuz/5B5O8LwTJAKLinPoSqRVC3Pn50ZzvGhIdpfGs5wQ/fAEC40/Hc+md8KcO7LAd73SccfuJaIkfKAXCNvIK0/3mKkDOry/oAwMEP2H7vNRi+BjKn3kw1aWz53ZMAqDY7Mx5aQfZXpmGaXdMdf+1B/rjoFko+3AZA1qBzuPmpP+POGtQ1HZAkSerhuuRrcVZWFuPGjeOxxx5rFne5XDz00EM88MAD+Hw+nnnmGSZPnszYsWO7olsJs9tAMz4GjhdKinkIh83X6j5CgE3zxwqwaCyEQi2i2XZhbLb4AsymW2jB92IFGIASKsWu1rUrl57CrTei1qwjWoABmKg1BbjDpUTW3hMtwAAsk8j6nyMC9cd3NoJEyj+NFWAAVlMtgbWPoNOG4ckOoGJQv/YXsQIMwPfBG0RKtnXJ8WP9iDSx739/guFrAMAx4rJYAQZghIKsW3on4eqSLumPEPDxfzfECjCAypJPeOfvL6MorX9hkSRJ6k26bCglPz+fvLw8Nm/e3Gxacty4cYwfP56f/exn7Nu3j1dffTXhtjMyOn/ULDMzCUw/+BvjHtPVcPTx1kSOHK8xjhKWD3ABx6cgNc0gMzO12XaW0QS1NXFNasJ34mO2U2e2/XlWUyXwhaEZK4wSqAUj2DxuRrCMzxWqqg2jsTauzchn75Kqh1BTE5v6OpWcI4011Hz837i4cWg3WROuS7i9U+WvqKHx0/diP/sajsRt46utItRYy4DhXwI6/zUu3b41LrZ36yZm3nkfmm7r1GO3pqve1z1Fb8sXel/OvS3fM02XFWEej4cHH3wwNi35efn5+UyaNIlf/epXOByOhNuuqfFimp23CCgzM4mqqkZ0XZBsz0Axq5o9Hjbs1NXFF2fHpKW40ETzQspSkhGi+chXJKJx5EjzdjQVkvVclNC+5ttaSdRVtX7M9jiWb1fw2F04hA5W+HhQcWK6+oI9GYINx+O6C/H5sVsjhJbWN65N24grqI84MBLI4VRzVoSOe/TV1K1b3iyuDbqoy55DABU36V++gtri9QB40vogFAXrc3OPqTln40jvR1VVY5e8xsMvnsLW1//cLDbq6zOpqw9hWcFW9uo8Xfm+7gl6W77Q+3I+HfKVReKJdekq3fHjx7c4LenxeEhOTiYnJ6cru5OwcNgizBAscWzkTcFQh+ILnLhwDATtmBwflTFJwjBTsKzjNbBpOgkE4l+OiAFh/Xws7dj+CqZzFL5w1y/87gy+kIdI5mxQnNGA6iaSOZtGLQdt1nJwHc3bmYY2azlmn/NAc0VjnrPQsgfgvvwHoOoAaAMuwjb5RxhW13y/MC2Be9JNOM+7NBpQVNKuvhPRf3SXHP8YQ3Ew8MaHcQ86H4DGTX/gqnueweaKvleT+/Zn+s9/hUjq12V9GjT6UsZ94/9DiOj048jLr+GCyTOwuvqsCUmSpB5KWJ38iVhWVsacOXPYuHEjAF6vl7y8PKqqqli3bh25ublA9GzJ3//+97GfE9FVI2HHOBxgU0NYKPgCOoZx8mNrmsDpiI5KBEMKoZCFzaZit0cf9/stIpHWV0w7bCa66gdUfEEHRicuru7qb1dCCFw2H6rlJ4ILf9iBZUXXFTnD1SiBWkxHOn49E4HF/8/OnQfGVZaLH/++Z5s9SdtM2tKN0pYutGxSSlkEKXhBqSgqbheuF5CL/qQuoAKuiIqIBRXkqle5AupVRECwgiKbbLXK2lJaSlvapmuaNMvsZ3l/f6SddDppmrSZTEOezz/tvPOe932ec06SZ95zzkT9bRiFdoxYLcprRZtx3HSBoJDDT4ylYMT6HMOB5mz7aWjdgLJC+LXj8AdukbmEWWjDb2nECMcx6w8l07iSbHsziVHjUTVdH3IG7BgHBdq3rkcHATUjx6Osvq9095fBsGrQn4ZavjD0ch4M+cpKWM8G/C/FrsuSF1988UBP3W9yOcix656W3hV/nqfpSO26Iblzm0LB79XXWwDkCgY5+l5cDAZaa9L5CBDZox0yVj3E64sNGkgZDRBu6LydTiU7bykr33xAuWYMRkyrXgA7+U4tjKolAAJPY486HLv8iu3AMRxqRk+uYgBCCHHwqngRNnbs2OIq2C4nn3wyK1euLGnbs48QQgghxFvZW+cbP4UQQgghBhEpwoQQQgghqkCKMCGEEEKIKpAiTAghhBCiCqQIE0IIIYSoAinChBBCCCGqQIowIYQQQogqkCJMCCGEEKIKpAgTQgghhKgCKcKEEEIIIapAijAhhBBCiCqQIkwIIYQQogqkCBNCCCGEqAKr0hM0NjYyb948br/9dk466aRi++mnn86dd95JXV0dCxcu5J///CemaVJTU8NVV13FEUccUenQxFtENGriOBqlNL5ngNuC4TVjmBbYdWgV4OsYqYxNEEDY8Qlb7aDzBCpBOhfb69gRrxmrZSW4GfTwyWSihxJoiEYUjlNAEeAHDqm0IggGJl9T+cTy6zA61oFTSy4+Gd+MEFXbsNw2yG0nsOrIhSeSDRIDEpOTasTftLwzvkNmUIiPHZB5e2IFWezmlei2jajaQwjiRwOqrJ+Z2UZ+w6sEuRThsdPQ9VPQuvsxdftmUm8uI/BcEhNmYNYfute+vRK4pBpX0ta4msiwJHWHzsCI1B3AgGKgFdqb2LJ6GYVshoaJU6k9ZPKBnRNiSKl4EQZg2zZf/epXeeCBB4jH48V2rTWf+MQnmDNnDvfffz+WZbF48WI+8YlPsGjRIoYNGzYQ4YlBLBYziURSKOUBYBYysPX/QHe+xhkJyROx9KuY8WPI5kJEg39ipBt3jqAwY6cC5cVK1GtC/ely/M1LOxtMh/iH78QbeSTR0CYUaQAsE8zERHa0mRXOFpSCmtYlmI9+Duj8TR8dfxrq6I+iCttg6Y9B+5hAdPgxqGlXkglqKhpTqPUNNn/vPfjtTQCYNUlGf/EB8nWTKzpvT0w8zOfvIPXn64tt+l1fwpx9Kf5uv/bM1BZWff/fSa16HgBlO8y67s8w7m1lYwYt63jq6veR3vQmAHashnfc/BDW6On7FaNhKBoXP8yDX/54sW3GWR/i5Mu/iwpX9piJ/pFr3cKdX7yA9a++CIDlhPj0/zxI/eRjqxyZGCwG5HJkQ0MDJ554IjfccENJ++LFi9m2bRsLFizAsjp/MZ5wwglcf/31BAO1rCAGtc4VsM6CS/sGND/ZVYABFLaCmwXA9FYSsdsx3MbdRtCY2SVoP1M2trltKXpXAQbgF/CfvJGQShcLsGJfNhEOV/7HKaLbMJ/7NrsKMABj/ROo1DZ44w+g/WK7ankRJ7u2ovEYhiL9j3uKBRiA395EZsm9GEb5qtNAcTrWk3mo9PdN+qEbcTrWlbRl175QLMAAtFvgzTu/hhnkSvopBU3PP1oswADcdDur7vtvDLV/yx6F1s08csPnStqWP/w72jas3K/xxMDb+NrzxQIMwCvk+dMt30R7uR62EqLLgKyEAVx11VXMnz+fZ555pnhZsr29nVmzZmEYpX+8Tj311D6NPWJEfN+dDlAyOTCXdQ4WgyffVNd/dQBuS3kXPwOWgSKDqQrl7wcZ0B7JZOnqg7umGX+PrrplLYaXBae0XakCiZhNIrHHG/1Mt7VApqn8DTcH2S1lzZZO7/VY9tcxXrf+lbK2wvpXGDUAP5d7k29Ld54Pu9MBtpcivlveG9q2lm2bbVxJ1PIIjyjdP6saV5X1bVu9jJq4hR2O9jnGprb15Np3lLX7mbZ+OzaD5+e4/wxkzi+3bCtr27r2dUKWT+0AxTEUj/FbyYAVYfF4nOuuu654WRLAMAx0P1w8b25OEQSVuwifTCZoauqo2PgHm8GUb22tibOr7jENSMyC1udKOznDgS0ExiF4OoGNYveVpMAeh2FEy3JOjJhaNp8x8zy88DAcSv94Bgyjra2A5+X7Iau9c4w48XFvx9jw965GZUAkASPnwpand+utcJ3RtHVzLPvzGMdP/BDplx8paYvNPb+q51A4OhqjZiRBe1eRZcTrKcRG075bXKHxs8q2HTnvAtI6Tsce8Y+a/U5ev/enJW0T33UBbakA3bEfuUbrGXv0iTS+9GxXjJZNbNTEftl3g+nnuL8MdM6jp5SfP3PO/Riuig1IHIPhGEuR2LMBfTry5JNPLrksOXPmTJYvX15WiN10000sXrx4IEMTg1Q6HeD7NYCBUgG65mh04hjAACMKybNApQmMkeT1BFLZKH7stM736CzAXOdolFH+eSRbOw3rnIUQGQbKxDjyAwRHfoxU2sBjPLs+wwTUkfdG4HmVvxu3EDi4b/scwfjTOhtio/DP+BFu3QwYPbezEENBaDj+0V8jZU6oeEzm5Lcz4gNfQTkRlBNhxAe+hjnl7RWftyc5u574RXdgjT0KAGvMLOr+6zfkQw0l/cyxs5h6xe3YNSNQhsnIMy8kedYn6O4zXWLKbN624PvY0QSG7TD9I59j5Ann7P8HSTvKGVf9iIlzzwCgbuxEPvCDe4mMPGz/xhMDLnnYTC74zs+IDRuBYZrMPe8/OOG8i+TGfNFrSvfHUlQPGhsbufDCC3nssccASKVSzJ8/n6amJh566CG+8IUvcNJJJ/GpT30K0zR56qmnuPrqq7n//vupr6/v1RyyEta/Blu+SikiEQPDUORyAWifsJVFY6LNEIYKyOUNPG9Xf4g4Pobhk3cdXG/vOSulCPstGEGBvJPEC4yuMcIKw4RcjgEpwHbnKBfH34FvRMipBFpDyCoQMrKooICrI2SD+F7/GPT3MTYU2JktgMKNjRywJ0X3xQnSmIU2fKeW2pGj9nqMjUwTeAV0YiQBe3/AwjBU5+qa9lE1I9G6Hz7H+jnc9u2YkThGuP+ejBxsP8f9oRo5K6XItzcReAUidSPRqvIP6OwyGI6xrIT1bMAuR+6y67LkxRdfjFKK2267jeuvv55zzjkHy7IYNmwYP/vZz3pdgAmhtSaT2f3uLUXK3/0eHWOP/pDJm9DDH9vdx84awzqHCHZvh0y2eh93C9qmYOxc1dkZRt5zyO95s9oACTTkI6N2vqhKCN0qGDEI7/0rSKDzGPuR3v2+CQIN8Yad2x1weJ3MMPaw6n+lh9g/WmucROf5Iwtgoq8qXoSNHTu2uAq2y8knn8zKlV1PAN14442VDkMIIYQQ4qAi35gvhBBCCFEFUoQJIYQQQlSBFGFCCCGEEFUgRZgQQgghRBVIESaEEEIIUQVShAkhhBBCVIEUYUIIIYQQVSBFmBBCCCFEFUgRJoQQQghRBVKECSGEEEJUgRRhQgghhBBVIEWYEEIIIUQVSBHWD5Qq/be793rarj/m3t9+fYmhP+Ltb8ZuZ3Bv4uvrMdpf1dpXB+MxEkII0T2r0hM0NjYyb948br/9dk466aRi++mnn86dd97JWWedxaRJkwDI5XJMnTqVr33ta9TX11c6tAOWiPlYRjuGcsGoQROgtUW+EEJjEA5rlPIJApNcDnK5AADbyXR4BQAAIABJREFU0kSddsxgO1rFKOjhpHNOn+Z2yBJNrcRoXk4QH4M7fCZpNaKsX9Rtwtz2MrQ1wugjydVNx1URwoUm1MYXCZrfxDhkFt6oI3GNWLdzxYxmnI7XUPlm/JrpZJyJuIHd9x3WjxyymFteIrfmBaxDZpHZtpF8RxuJqXMwDplJYJTGZxiQsJsx3I1g2HjWWDoyNrk3X6H5tX/SPno88SlvQ9UcckBxua2b2Lb8n3Rs28ioGcdRd9iRYIUPaMxezZtpZcvrL7Hp9WWMnDiVQ6YfixMvPx+EEEIcPCpehAHYts1Xv/pVHnjgAeLxeMl7DQ0N/PGPfwRAa81NN93EggUL+M1vfjMQoe23RMwjpF9E+fmuRucIUJsxQ3G0mcQw0gCYJphmFM+zCAKI2xsx088VNwubw9Dht5PJ9a6wMQyIr1+EseTGzteAOfIYghO/R1bVdI3rtaAWfQZ/88tdbe/8JuahZ1C453O4q54qtkff/RWM2ZcQ6NKllJjaQfiVr6NS64pzxY+8mtbYSWitexVvfzMMYOVjrP/hx6mbfyVrfvD/yDc1Ft8/4qt/wJx2esk2NfYWrK2/BjpjNu16NiyN8uy3Lyv2qZs0k7nX/R6VGLlfcXntW3nwqo/QtGpZse3sr/+UCad+sLL7yi/w97tu5vE7f1xsOu6cD/GeK76H4XRfWAshhKi+Abkc2dDQwIknnsgNN9zQYz+lFJdffjmrVq1ixYoVAxHafrOMFIp8aaO7DlQCRQcGXslbhpEhEjEIOy5m9vmS95S/A8do7/XcUW8bxgu3lI6x9UWc9JqSNrt1FXq3AgzAf+J7WE3LSwowgMxfbiSU2VQ2l51dWyzAdjFX/ISI6n28/S3iNrPtN98AZeBilRRgAGvv+DKWlyq+dmyN2fYUuwowgEy+gedvuaZku9bVy0itW8b+alnzakkBBvDED67GTzXt95i90bZ1HU/cdVtJ27/+9Dt2bFqzly2EEEIcDAZkJQzgqquuYv78+TzzzDMllyX35DgOEyZMYM2aNUybNq1XY48YEd93pwOUTCZKG9wW8Pfs5QLmzv8HZWOEwxba8iDrlr1nmUH5HHuhd2wDL1fWbqtCyRhuU6E8xEIavGz5oF6ekBUQ37n9rnF0Y6GbvimiEYNYtHfx9rfCtq34qRYwTAK3fF+67c1EQxAavjMHPwet6ZI+fmBSSJcXksrN9/o47GlbN8ckn27HsWDYfo7ZGx0b3e5X2vxCj7nsb56D2VDLeajlC0Mv56GW71vNgBVh8Xic6667rnhZsidKKcLh3t9H09ycIggqd7knmUzQ1NRR0lYbj1F2F5d1COgUnYWYRWdR1klri1TKw3MVtaHJqPyq3TY0yftxOvaYY28cNYz4hHkY6x7drTFOLjyO1G5jxGomgh0FN1NsM454D7p+KipSi862dW0+/QzSdgNuU0dJvjWhcTiGDUFXLsH499CWjeKnexdvf7OskQx758U0//FmQolalGGig65yc8z8T5PSCdp35qAU1CWOx2z5c7FPNLyVyfM/zqr7f15sM0NhImMOLzvWvVUz7nCsUBgv31WMHfXe/8QPDdvvMXsjOmIsY6cdSeOKV4ptww8ZT6Jhwl7n7e6cfqsbajkPtXxh6OU8GPKVIrFnA1aEAZx88sn7vCxZKBRYu3YtkydPHsDI+i6VDVMTOxbTX40ij7bGogybQPt4QRK0jWUFGIaL74dw3TC5XOclypw9g1A4jFFYjTZr8UNHkspE2f1yWU8K2qZwzGdwEmMx1jxEMGIa/lGXkbZGlwyRCY8j8eG78J/5ITStRB3xXoKZHyLrjCT+X78n/8hNeI0v4Rx1LtYJ/0GWUHmeagw1x30P841fojKbCMaeTb7hDPygeo/heT7ET/0PDCdMx8sPM+Mzt7Lh4V9SaN7M6Hf/F3Unno+/W1GuNWStSUSGn4XZvhhtRNB1p3H4+WcSqk2y5s93UjPhcGb+51ewRk7Z7/u3IqOmcP6P/8QzP/0WLetXMXP+Bcx41wXo4upoZZjhGi64/nae+NWtLH/qL0w+7hTOuOjzOImD/+EWIYQYypSu8N3VjY2NXHjhhTz22GMApFIp5s+fT1NTEw8//HDJe0EQcPPNN7N8+XJ+8Ytf9HqOaqyE7RJywDA0rmdi2xrPA9ftjMWyDGxbUSgE+H5pfIaCkOPjBwYFd/8KGtMAJ+jAM8I9Pq1oKxfTz+JaNfi7XSW1cDG9DK5dU3JDfnf5OqaLiUs+iFLBXd0nlmUQyjfhW2Fc30R5eYJw3V7PBaUUIbuA1gZ519zZBmRbqU0Op7Wj/BLy/lB+Hr+QxYzWMbDPLvh4mQ6sSBxUz5+vBsMn6P421HIeavnC0Mt5MOQrK2E9G9CVMOi6LHnxxRcDsG3bNs4991ygswibPn06CxcuHOiw9lu+AKCAAK/0Xnw8r7xtl0BDNn9gKyR+AFkS3d1+VsLVNq5hl/XzsPGs2l4twBV8G6ju11LsyfMCPHNEZ/wG4ITpqULUWpMr2Hu0AeE67HAMOvrnl5k2QxiR0AAXYAAmVrRuoCcVQgixnypehI0dO7a40rXLySefzMqVKwFYtmz/n0YTQgghhBis5BvzhRBCCCGqQIowIYQQQogqkCJMCCGEEKIKpAgTQgghhKgCKcKEEEIIIapAijAhhBBCiCqQIkwIIYQQogqkCBNCCCGEqAIpwoQQQgghqkCKMCGEEEKIKpAiTAghhBCiCqQIE0IIIYSoAinCDhJKKSyr58NhmgaGoUraLMtAKbWXLfbfvmI5mEUiNqbZtU+U2rWf9r1tb45Db1RjzsHGMBSm2X3eB7JPuvs5ORgZxtA87kKILlalJ0in03z/+9/n6aefJhKJEI/Hufzyy5k7dy5XXXUVxx9/POedd16x/y233ALA5ZdfXunQDhqJBNhmBwZZfF1DNh8ml9PF9x1bEwunMfQWUBFcGnC9MOFwgGnmCAIb17Xo6PAPOJZ4JI9Or6dWbSGIjCEfNJDJ2wc87kCo8zfA+sX4q5/CGT0DY/I8CjVjcNzVGJl1BJFJ5M1DyRQi3W6fUFuxm59Dta8iSJ5INnEkuSDR5ziczGYKyx+lY9njRGa+A2fGGRSio7rtG8k2Erz2V7x1zxM+4t/Qh55E3hnR5zkHE6WgsGEp6x+7m0KqjUPP/AiRw44FMwRAoXkda55axJblLzD51HMYffQpmLF97xPt5tj8+vP860+/JT6snmPO+gDDJ8xEa73PbQeSUqCaXqflufvIblxN8u0fxJkyl8COVzs0IcQAq2gRprXmsssuY/r06SxatAjHcVi+fDmXXnopCxcurOTUg0YspggZb6JwAbBUG7FwA65bi+9rlIJ4pA3TW1bcJsQGnMjbUGYOANPMYxgmQZAgnd7/QiwS8gnlnwW/GQWYhfWEnUm41jG43gGlWXERM4//3O34L90NgL/qUdSriwi9/yaMjr8AYGZWEI4dgRd5JwXPLNleZ7fivPQ1VHZTZ98tfyd62EdwR30EP+j9qortp9jxq8+TeeVvAKT+9QCxo95J7UU/wzVjJX3DbgvZOy/C37ISgMIrfyJ8ysWY867B1xX/fFQ1hY2v8uhn3klQyAOw9qFfceoN9xGbcSp+xzbuv+KD7NiwGoCVj97H8Rd+lmP/48vofSzcb1j6LD+7/Pzi66d++z985s6/UTd2WuWS2Q+q5U2WfeUsvI4dADQ99XumfPpWoif++0FXMAohKquia+FLlixh06ZNXH311TiOA8CMGTP45Cc/yW233VbJqQcN2yqglFvSZtBEJNL5yzgc0pj+6j228lBBe0mLUj62fWC/wB0zhfKbS2MprCZsZw9o3IEQTq/Ff/mekjbdvAbdvL6kzUi/StjsKB+gfW2xACv2Xft7Irq5vG9PmtcWC7Bd0i//FdW8tqyr2r6qWIDtknv6f3FSG/s25yCilGLrkkeKBdgur/1mIYYu0LpuRbEA2+Vfv7mVfHNjzwP7ef7ysxtKmtx8jjeWPNmrS8IDKbfulWIBtsu6X1+HkevjuSaEGPQq+nF76dKlzJw5s+yepdmzZ7Nw4UJGjx7Nj370I+64447ie9u3b+fDH/5wn+YZMaLyy/jJZN8vS/VK0AZli1eaSMgmEglDkIdcd8VVeZttGySTsW769o4udFOcAKGQSTheofz7ib9BQ7erCOVtjmOSrCnNR28Jut02HLaI9CH3TEv3n2ts26B2j3Mo39RddaAJhUzilTrfdlOxc3ofNlC+rwPfpbYmQotZvk+07twn9T3Em0tD4HW3XBtQX9+1XbVy3t3mbk4R7XvEIg6hEf0b38GQ70AbajkPtXzfaipahCml8P3yy2Ou27Xys2DBgm7vCeuL5uYUQVC5ZfxkMkFTU/cFyoGKx0zCloVSXX9AAuppa/PwvA6UUgyLT8T0X9ttKwNt1KDoWk3Q2iCbhXR6/+OMhqNEzFqU39YVizOeVNqh4FYm//4Si0/EnDkff9kDxTZVOwY1fDykXiq2BZHJpHNR8h2l+dTXTESH6lH57V19x82nPV+Dl+197nbNeMJT55Jb+VyxLTz1JAqJ8aT3OIfCtYdhjpiA37yu2Baa/SFS9ii8Cp1vu1TynN6Xkcedifrld9F+1zk//SOfY0eHT3zMFBIjx9CxtWs18Oj3XYyKjdpnvGdecgW3X3Fh8bVp20x62ynF7aqZ8+6cMTMwI3H8bKrYNv5DV5MiQXs/xnew5DuQhlrOgyFfKRJ7Zn7jG9/4RqUGD4KAX/3qV3zsYx/DNLvuwXnkkUdIpVIkEgnGjBnD9OnTi+8tWbIEgDlz5vR6nmy20P0iSD+JxUJkMoWKjF1wwXLqMIzOpxw9GsjmExR2my4ggmnXoZSHNobjGtPIuxEMw8YwIAhCuG6MVOrAbtxyPQMrMhbLjgIBfmg6BWMKuYK5z22rzQ0somMmYw4bg/YKmNPeiXPqZykkxmDYcRQ+QWIO+ejxZAuhsu1jdSMo1B6NsiMow8Cf+BHyDe8kH4T7FEdghEgccQrOiEPAy1N7xieoedfnccP1ZX09M0rkiNMxo7UoNOF3fBpz9gUUzMr/0qrkOb0vVk0D4+aegXbzRJNjOObTN1Az7SQwbYxQnMNP+TdC8RoMQ3HCf36Bae/6d1Ro36vdNQ1jmPK2E8i2tzLx6OP54DU3M3ziLKBzda2aOZeIDWfkCWehDBMrEmfChdcSO/ZdBGb5eXlA0xws+Q6goZbzYMg3Fuvf8/qtRukK3wl6ySWXMG7cOK655hps22bZsmVcdtllLFy4kPvuu69fno4czCthu3Q+rq/wvGCvBaVtGwQB+H7n5ZxdX4Pg+7pf808mE7TuSOP5etDdKGyaEHFMCj4UCp2rsIahMA3VYz67jnFnX3b23f84DENhKAg0+zw2hqEwDPAPcM6+OBg+QVuWAVrj+eVJ79p/ftD3fWKaqvMDjVd62fNgyHl3pmmg6D7//nCw5TsQhlrOgyFfWQnrWcUfwbr11lu5+eabOeecczBNk9raWm688UbmzJnDfffdV+npB40g2Hch5bqlf1S0Lm/rL65XmXErzfchlS29BN6bfVva98DjCALdzZ1PlZ1zsNmzSNpdX/bfnnxf0929gAebXR+mhBBDV8VXwgbCW2El7GAy1PKFoZfzUMsXhl7OQy1fGHo5D4Z8ZSWsZ/J1zUIIIYQQVSBFmBBCCCFEFUgRJoQQQghRBVKECSGEEEJUgRRhQgghhBBVIEWYEEIIIUQVSBEmhBBCCFEFUoQJIYQQQlSBFGFCCCGEEFUgRZgQQgghRBVIESaEEEIIUQVShAkhhBBCVMGAFGHpdJprr72WM888k/e85z189KMf5bnnngPgggsu4B//+Eex75/+9CdOO+001qxZMxChiYOAZRmEQiamaRBywLLUPvorQiEDw+i5X2dfCDkapfbdd3+YBoRsH6OCP0lKQcjysM2gcpMIIYQYcFalJ9Bac9lllzF9+nQWLVqE4zgsX76cSy+9lIULF5b0feihh7jpppv45S9/yaGHHlrp0ESVKaWorTUxzQwGoL0WlLeFQCXw1DjaUw5al25TWwOW0YRBFj9SR76QIJ3Zy9jRDozCqxheB35oCjk9hmy+/075mnAHVvp5jI43CaKHUwgfSSof77fxAUJmjmjhVczND6OtGvyR82nXEwiCyhSVQgghBk7Fi7AlS5awadMm7rzzzuJqxIwZM/jkJz/JbbfdVuz317/+lZtuuok77riDcePGVToscRBIJExsuxW0AYWtKG8jAIZO4dBEPDqbjrRZ7B+Pg2OsBXwATLYSdvIU3CSuW1qtJSIZrNQjXX2z/yQSyZM3DifohwWlaCiPvf1ulLejM+a27YRyjbg155F3++fHSimI5l/GXH9752vAal9OYspXaGNMv8whhBCieip+OXLp0qXMnDmz7HLQ7NmzWbp0KQCPPvoon//855k/f74UYEOIafqAhsAAb9Me73qYRukSl2W47CqqdjFoJRzaY7kMMHVbed/cciKOd+CBA3awo1iA7aLy63FUe7+MD+CYBcxtf96jNcDMrKRCV1eFEEIMoIqvhCml8H2/rN113eL/H3vsMX7+85+zYMEC3vGOdzBr1qw+zTFiRP9eAupOMpmo+BwHk4HJN9v5j4LOzwOl54ltWaVxBO17dgEU4ZBNOBIuadW57eXTKZtINEQ0Ee02mr7krLMt3bQqQqEQ4dr+2Xfaz4IRKWs3rAj19Qc+x1A7p2Ho5TzU8oWhl/NQy/etpuJF2FFHHcVdd92F67rYtl1sf+mll5g1axZBEPD1r3+dE044gSuvvJIrr7ySe++9l1gs1us5mptTBEH5akh/SSYTNDV1VGz8g81A5RuPm4TDBkr5YE8E943ie5oY2VyIdLYrjmjEIBoKo8gV23waaGv18P3SeOORGkJGFBV0rab54WNo3RGgdXlufc05bCeIhidh5FYX24LEsbRnI3gd/bfv6ka/D2v1TV0NZoxCaDLtB3h8hto5DUMv56GWLwy9nAdDvlIk9qzilyOPO+44Jk+ezHe+853i6teyZcv47//+bz71qU8B4DgOAOeffz4TJ07km9/8ZqXDEgeBVMonn6/FD6IEZhLtHENgTsAzp5PjSNLZ0tMzk9Xk3HF4egwB9Xh6AulsDb5fXoCnsg6F8Dz86PEE4Rl4sXmk3FHoPe/030851yZXcxZ+/bkENXPwk+8nEz4Rz+vfH6kONRl38jUEI+cTjPkIhUlX0+En+3UOIYQQ1VHxlTCAW2+9lZtvvplzzjkH0zSpra3lxhtvZM6cOdx6660lfb/97W9zzjnn8OCDDzJ//vyBCE9UUUeHh1IKpSyCIIZhxHtc1UylNRDBMKI7++29b0c2hFLjUUoR5Pt/pTRTiJBhCoZ1OIFbmZVYPzBpYwJG4lC01uj+uaVNCCHEQUDp/loaqCK5HNm/hlq+MPRyHmr5wtDLeajlC0Mv58GQr1yO7Jl8Y74QQgghRBVIESaEEEIIUQVShAkhhBBCVIEUYUIIIYQQVSBFmBBCCCFEFUgRJoQQQghRBVKECSGEEEJUgRRhQgghhBBVIEWYEEIIIUQVSBEmhBBCCFEFUoQJIYQQQlSBFGFCCCGEEFUgRZgQQgghRBVYlZ4gnU7z/e9/n6effppIJEI8Hufyyy9n7ty5XHXVVSxevJja2loAstksdXV1XH/99UyaNKnSoQ0opRSRcABocnmDIOi+n2lC2PbwtUEuX1ojG4YiHAIU5HIQBHofc0IkYqKUIpv19jpnb4R1CsPPUbCHE2AQ1jvQWORUAt1zGGUswyNECo8w+SBc8p5hQNhMoTHJeRG0BlP52PkWAitCwYgD4FgBtlnAC2zyrlk6htJY2e1gOrhObZ/jM4M8KteGDiXwzUiPfQ0vi851oKK1BEao2K78PF6mFTNaC2a4hxEGOb9AId2KHUmg7J73lRBCiFIVLcK01lx22WVMnz6dRYsW4TgOy5cv59JLL2XhwoUALFiwgPPOO6+4zbe//W1uueUWfvCDH1QytAHl2JpYeAdW8AZon3BsIhk3SS5XWmTFIgVC/kqM7BtoM0Y4dhwd2eH4AYQciEXTmGwDNGFnJJlsjFy++wrDtg0SCY1ptgOaUChKNmuTzfp9it1QAfHmF/AevQ5aNxCZdR7W1NMwn/k6WFHCx32WVPJkXB3a92BAwtiGveZOjG3PohOHEZn6SdrMyWitiVhpIh1PYWx7GIww4TEfouA2UHjyJ6T/dTfm8PFE3/strAnTsdqfwMitRjuHEB52Ju354WgNdnYr2b/fzra//hSrpp76j16PnnwavrJ7l2/LG6y/6+u0vPg3aqbO5tD/vAFGH1FWyCkF7sZXeem2q9i+/J+Mmj2PIy+5FiM5mezmlfz9R19m/QtPM/64t/P2T19HZPTUPu33wSC1dQ1/vvWbLH/qr4yfeSzv/cL1jDh0Vp+LXiGEGKoqejlyyZIlbNq0iauvvhrHcQCYMWMGn/zkJ7ntttvK+hcKBZqamoorY28V0XAGy38VdB7wMP1VROwdKNXVxzQh7K/EyK8EfJTfjpV6nFg43TlGxMVkE+ABPiabiETye50zFlM7C7DO1TfTTBOJeKjdJ+1N7Kk1eHdfBNvfAC+P/+L/4b3yALpmAmS2Yf79GmIdr/VqrJCZx3nthxhbnoTARbWtxHr+KuJsRSlFOPMCxub7wM+CuwNj618pPPYDcs/dCW4Of+vrdPzPR1BbXsDIvg7aR+U3YG/7P6JOBqUg9+yv2fHgTeh8GrdpHZt/+FHMrct6FZ9ZaOP1711A85I/o90CbcueYfm152J0bC7rG7Rt5skvvpdtLz9D4BbY9OxDPPvNCwnaNnLfFR/kzSWPE3guby5+lPuvOB8/3dyn/X6w83Pt/Oqai3nlsT/huQXWvLiYn1z2PrItm6odmhBCDBoVLcKWLl3KzJkzy/7wz549m6VLlwLwox/9iPe85z28/e1v593vfjejR4/mC1/4QiXDGlCmqTD19rJ2S2/Etrv2S9hxUflVe/TSmLoNyzIw1Y7ysWnBssyydsNQGEahm1hyOE7fDrlqWQ26dPXMf+1h9OgTuubb9ByGse/izvGbUTteKW30cxjZjThmHnP7YyVvBfYkcv/6Q2n/wMdr2rBHWxYraMUq7KD98f8tmze/9oV9xgbgN68ns2FFSZvb3kxh65qyvtkta8m3lRZW7W+uoG3DKjq2bixt39pIavObvYphsGjf1kjjiqUlbZn2VrZvWF2liIQQYvCp6OVIpRS+X375y3Xd4v93XY5cs2YNF110EXPmzCEej/dpnhEj+tZ/fySTif3fuBDtXJDanYpSWxMF1VkU6SALuSgEqZJuph1mWDwGXhh0e+kQKsywYdG9TJrups2kpiZMb2rvXfl6LbXseQRVogGVbyu+NmoP6dUx0Ok4mJHOla7d2JEa7NoaCDVArmslReksRqKBoK10JcoIledsh6IkQnW01I/Da91S8p4zbCR1vTh+kdo6lGmhfa+0vW4YtXts37R1WNn2yrSI1NR1Xqvc/ZqcUsSHDTuwc6gCDiQer6MWy3bw3NJiv2b48IMuz90dzLFVwlDLF4ZezkMt37eaihZhRx11FHfddReu62LbXffkvPTSS8yaNauk72GHHcaVV17JNddcw1/+8hcSid6fWM3NqX3epH4gkskETU0d+719TXwYIRxg1x8sA5dxtG7vKpSUgrrIbMz048U2bY4gW0iQae+gJpEgZDZDsSQyKPi1tO3oPq5EwiIUMlCqq/pz3Sitrd0VZ6V2zzdSezjGmGPQG1/cFSnO3ItQy3/RGWN0JIURs+noxf4xVA21Uy/FXP7DYlsw8hQyajS55hx1Dedgtb8KurNIV96bJM77Fm2/vKRY1FgTZ2MeMhXSa7vGiB9NKpeg4MGID36Djd87F3YWUvboKahxx+zz+CWTCfLRQ5jwsa/y5p1fL7aPOvsSvNqJZdurEROY/J5LeOOBnxfbjrzoK0QPmcrxF3yWJXfeXGyfc+HnMOvGHtA51N8O9Jy2EqM5Z8HXuX/hl4ttJ7z3AuLJQw+qPHd3oDkPNkMtXxh6OQ+GfKVI7JnSurK30V5yySWMGzeOa665Btu2WbZsGZdddhkLFy7kvvvu4/jjjy+5Mf9DH/oQxx57LF/60pd6PcfBXoQBJGIelpEGNL6O0ZF22HPXmwbEwykM3Y5WDoWglkyus3hVCuIxsKw8aPCCEKkUPd4EHY+b2LbunNM3SKX8Xj0huWe+Ea8Zq/k1yLfBsMNQ0QRmy4rOpw9rp5IyR/Z6P9iqQNRbh5HdhHaGkQsdRk53rqIZCuLmVsz8RlAWrjOejBcj1LIS3fQGKlKHHjWTIFpHxGjG8FvRRpwcSXJu5z2HCo29fQXe5hWoUBRz7JEUoqN7nbPhpfEal5Hf+ibOiDHY42YShOq63yjbRmb9UrJNG4mOOpTo+JloJ0aQ66B17VJSWxuJjxxL3cSZGOGaXu+jgdAf57RfSLN97XJaNr5JTf0oGibPwo7uZV8dBAbDH6z+NNTyhaGX82DIV4qwnlW8CMvlctx888088cQTmKZJbW0tCxYsKH5FxZ5F2PPPP8/HP/5x/vznPzNu3LhezTEYirDBZKjlC0Mv56GWLwy9nIdavjD0ch4M+UoR1rOKF2EDQYqw/jXU8oWhl/NQyxeGXs5DLV8YejkPhnylCOuZfGO+EEIIIUQVSBEmhBBCCFEFUoQJIYQQQlSBFGFCCCGEEFUgRZgQQgghRBVIESaEEEIIUQVShAkhhBBCVIEUYUIIIYQQVSBFmBBCCCFEFUgRJoQQQghRBVKECSGEEEJUgRRhQgghhBBVIEWYEEIIIUQVWAMxSWNjI2eddRaTJk1CKYXrujQ0NHD99dczatQoAN7//veTTCb5yU9+MhAhHfQsCxIxH9DkXZt+HOcJAAAgAElEQVRMRmNZikjEBCCbDfC8oNfjRUI+llHA1zbZvIXW/RtvSOVwCk0EZpis1UAQ9PMEOymliNpZDJXH1TFyhe5PYcsICOvtKDTaSaC8FJ6RIOtG+jEWCHs7MPKtBOFh+B07wLTxEmMItOqMAxc7vQkMk3z0EAJ9cH3uMYMcunUTWA7UjmVfh83QHl5LIxgG1rCxBIP5c5yXJde8CTuWwHVd3HyeeP0YlBWqdmRCiCFiQIowgIaGBv74xz8WXy9cuJDrrruOH//4x6xcuRLbtlmxYgWbN29m9OjRAxXWQSkagaizFeWuBgJMYwThusNRJhhGOwCOEyGbDZHJ+D2OpRTURjswM8+gghQYUZzoSbRl6tD9VInV+BuxF38LtfUFcOI4c75ER8M78LTdL+PvYhqaGmsd5vZFEGRwnJGEhs+nLVdX0i+sUkQ33Yux7l7QGsa8A2pG4qSWE5rwX7T5Yw+4CFUKEs3P4y/6AnraubS88i9SLz6MskMMf981OCdegOVn8P52I6nn7wHDIjJvAcz+DwpW7YFN3k/MjkbW//Iamhc/iOGEmfCxr1F76gUEdrzb/rpjK6/9+ruseeguUAbTP/wZDj33UxAZNsCRH7hC8zoevfFzaA2102fz6K/+m0I2wxFv/zfOvfK7REeMq3aIQoghoGofY4877jjefPNNAO69915OOukk5s2bx913312tkA4akVAa5a4COle6VNCM4W/AwCv2MYwsoZCPUj2PFQ25WOknOwswgCCDlX6cWDjfL7E6ysV64YedBRhAIYX51FeJZdf0y/i7i9ltmNvugSADgCpsxWp5kLDjlfSLpF7BePP3oH0ggI2PgqdRXhpr7S1ErNQBxxLNb8K797/QVoiOjRtJvfgwANrN03z311GNL6KXPUj+X7/vLAR9l+xfF2JufP6A5+4POgho+svPaV78IABBIcfa/70Gb91L3fZXSrH1uQdZvegOdBCgfY/lv15I66vPDGTY/UJpl8U//w7r//kkY+acwUP/s5BCtvOcevXvf+Hvv/oxit6vMgshxP6qShHmui4PPfQQxx57LK7r8sADD3D22Wdz9tlnc8899+B53r4HeQsrFky7t/nbALOkzTTzWJZZ1rekj8qCzpU2ag+T9IGGCYATtGFs+HtZu9GxoV/GLxnTbwVKl7BUYSu26srFNA3U9mfLN97+CkQngNuK5e844FhU+0Zws6jRR9Px8uNl7+feWIK78rGydm/V3zGMfVTOA8Bt307TU/eUtadWP99tYW/gse5v5R+QNi9+GNMcXJckvdQOXn/8AaxQmPYdzWXvv/jXeymkDvwcEUKIfRmwy5Hbtm3j3HPPBaBQKHDkkUdyxRVX8OSTT5JMJpk8eTJaawzD4PHHH+fMM8/s9dgjRnR/+aQ/JZOJis9R5LXBnnWoEaesAFEOdXXRHofSng8ZE9j9sqXCDsVIxveeU2/z1Tkf6iZCa+nKl5lI9vs+09ma8kYzRiiSIFzTNZdumQ6bnyztl5gAhU2gbJxoHclIeWx9idf3GsihoH0jobFTcZveLHk/PGYqSqVwV5WuFNljZ1I3AOfrvvh5i8TkY8hv31jSHhszmfr68v2gtSY5ay7Nr/2rpL1++tsYPjxW0Vj7UzKZIBv2SU4+gi0rXiKWKL80PHb6UYwYmSQUHTx57c2A/t46SAy1nIdavm81VbsnbJc//OEPbN68mdNPPx2AVCrFb3/72z4VYc3NqYrdCA6dJ3lTU0fFxt9TbU0C2xiGCnZ9GrfQ9mRQLrsWKbQ2KRRs2tt7jsswTGqjszEzi4ttQeQY2jscPL/7bfuWr0ndCV/GeuRT4Hde4gwmnkUmfBi5ft5njlVDPHE8RseSnS0G/oh309pmonXXXPG64wnFFqHSO1fjwvWdheKWl/HHX0RbJkGQKo2tr8fYsg4hctqV+E/cyPBTriHz+hKCdOfxCk+Zgzp0Nta4IzBe/hNBR1PnNmNmoQ89aUDPpb1JJhOM/dDVtC59Ci/dBkDNzJNxJh631/gmnHUB6x7/A9mmTZ39D51G/bHzDop8eqPrGJuc/vkbufvy+eSaGpl87Am88ULnz0c4luDdl3+N9nQA6cGR194M9O+tg8FQy3kw5CtFYs+U7q+7s3vQ2NjIhRdeyGOPlV6e2b59O6effjqPPPIII0eOBGDDhg2cddZZPPzww4wb17ubY99qRRhAIq5xrAxon4AYbR0mkYiJbXfm6boG6XTvLtuapiYWymDoDIGKkM1Hcf29X0Lqa76Ggpi3EbNjPdpJkI9OJEdlVhEcyyNi7EDpDIFRR6pQU3wScXdRox0ntx7wUZERaLedwBlOOkjiB+WXcPfnGFvkCafWojLNBKHh5LdvBjuCGjUV1xkOQCS/BZpWgWmj6w8nZw/fr7z7WzKZYPv2Doy2DeQ3vY7hRLAPmY4f3sdN9h1bSK1fgTJNYuOmQ6x+YALuB7sfY6UUuW1raWt8g8jwBlIdHRQKOZLjpxBLju/3p4erYTD8ge5vQy3nwZCvFGE9G7CVsO488MADnHrqqcUCDGDcuHGcfvrp/O53v+PKK6+sYnTV1ZFSsEchk07vfkmx9zcO+76iPRMrG6+/BBo6zDFQN6Yi4++u4FkUSO6zXyaoIePM7Hzh03n3Yz/faugRIhWfBruuLtZOL+uTDY2CsaP6d+J+ojX4NeOwajo/7PT8nO1OiVHEjzg48+kLrTWh5KE0JA8FIFHyXlVCEkIMQQNShI0dO7ZsFQzgoosu6rb/LbfcUumQhBBCCCGqanA91iSEEEII8RYhRZgQQgghRBVIESaEEEIIUQVShAkhhBBCVIEUYUIIIYQQVSBFmBBCCCFEFUgRJoQQQghRBVKECSGEEEJUgRRhQgghhBBVIEWYEEIIIUQVSBEmhBBCCFEFUoQJIYQQQlSBFGFCCCGEEFVgDcQkDz/8MD/72c/wPA+tNeeeey6XXHIJF1xwAVu2bCEajeL7Po7j8JnPfIZTTz11IMLqF6apiIZdDOWiDIdAK0CRzRl4niYatbCsgCBQZDIBvq9LtncsTdjJgPZxgxjZvNmreZWCaLAdM7MFHaolY4/B1/uuqcM6hdWxHgyTQmw8BRUped8wNLpjPTXZbQQdrfjRkeTj4wjoXVyVZuBjta0jSLVgDBuDGxuN1uX9Qrodf9sbELhEkiPxw/VkvHi3Y6p8O9teWYYbKMINEwnsaL/Fq3NtdGxei2nZxEZNhH4cu8d5vTxtm9fg5rPUjT4UOzZ8QOYVQgjRexUvwrZu3coNN9zAvffey7Bhw0in01xwwQVMnDgRgG9961vMmTMHgKVLl3LJJZfw61//msmTJ1c6tANmmlAbbcb0XwOtIbAgNA30DuxYHYEagWW1AQEAth2nvb2zOAOIhFwiwTKM9OrO9806rOjJdGTC+5y7Nvsa1qOfgXwbGBbW8VfSMebdeNrZ6zaxwmb4y5cIGp8HwJkyD+u0r5Kxkp35KJ+a1D8obN9C6++uRWdawbCIvfc63JkfwFd7H3sgmEEe/1+/ZeOvrwLfw4wPZ9Rn/4/CqGNL+oWyjaz56RU0//OvANTOmMP0iy6jdtxc2ryRJX31jvX84zuX0LziXwCMO/W9zLrsuxBvOOB4C83reejrF7PltRcAmHbmeZz0/76NlTjwsXviZnbw6M9v4Onf/RyAkROn8J8Lf0185GEVnVcIIUTfVPxy5I4dO3Bdl1wuB0AsFuO73/1ut0XWrFmzOPvss/n9739f6bD6RSxSwPSXA7uWYjworAJqMGjGNHLsKsAATDNFLNa1y8PmDozC6uJr5bdie6sw93FUIrRj/v3LnQUYQOBhLv4u0dz6vW5jGApj1Z/ROwswgGDVo5gbnkWpnfnorbDtJdruX9hZgO0cO33v1Thta/axNyrPbH6DpjuvBN8DwE+1sO1nl2IXdhT7GAa0v/S3YgEG0Lb8H2x7cSlW04M4lldsVwrWP/KbYgEGsOHJ+2lZ+vQBx2oY8NqiXxULMIAVj9zLlleePeCx92XzypeKBRjA1rWrePR/b0Jpv+JzCyGE6L2KF2HTpk1j3rx5nHHGGXzgAx/gxhtvJAgCJkyY0G3/KVOmsGZN9f/g94ZBvrxR54HOqkbpQvH/uyjVWZQZhkL5LeVjuhuxraCsfXeW24ZKbSzfNrN1r9uYKkCvfqw83PWLMYzO00DltxMQJdjRWD5A26YeYxoIXkt5XO62dahM1360bYuWl54o67f9hWfRbgZbZYttyi+w6bmHy/o2LX0W01Rl7X2h3Ryrn/1LWfumVxZjGAc2dk+Ugq2rXytrX/nc47iZ9orNK4QQou8G5J6wa6+9lk996lM8/fTTPP3005x//vl8//vf77avUopweN+X43Y3YkT39/r0p2QyUd7oB5Dbo005FFfGlAN7FGq27ZBMdl7W07n68u2dMcQTCRJq7/WxzjRAfAzsUYhZdWO6j3OnwpTTCTa+VLrNxLkMHx7rHLdtFMGmDMawMQQ7Ssd2kuOJ9zD2QMi0lhfudnIC0eRoauu6Yht+1Kk0Pf3Hkn71x8zFsKNEEsOImp33wWmtGXPiWex445WSvqOOOpHhww/snNI6zuST/o2mVctK2scdM7fi5+uYw2eUtU094TSSh4zEckLFtp7OlbeqoZbzUMsXhl7OQy3ft5qKF2FPPPEEmUyGd73rXbz//e/n/e9/P3fffTf33HNPt/1XrlzJpEmT+jRHc3OKIOjm7ux+kkwmaGrqKGs3TUVtdDqmv4LOwssC53DQOwgYThCEsYwCu4oy34/T0eHiup2FWSRUR8SZiFFYC4A2aymYh9OxPb2PiELUnXId1qOfhUI7KJPg+CtoNw7B6ybOXWKT3oVa9Th644sAqMNOozB6Lq07tzHVCGoajqT23Ctp/d030Nk2MEyi536TVGQ8fg9jDwSzZiLJf/8eTb+5GgIfI1ZHw6U/pdWNwm6x1RxzBsPfdgYtz/+t8/W04xh57JG4ybm0tXhAV9+xZ3yUjf94hB0rO/fJ2FPmU3PEyd0e776a9q6PsWbxo2xd0Vn4Tp33XpIz5vbL2D1JTjqKkz54Ec/8/vbO1+MPY95FV7CjrQAUOtv2ck6/lQ21nIdavjD0ch4M+UqR2DOldXfPlvWfxYsX8+Uvf5k77riDsWPHorXm+uuvJxKJ8MILL/DpT3+6eGP+K6+8wqWXXso999zD2LFjez1HtYow6Lw5Pxr2MFQBZYTpDGP3pyNNLEvv/elIWxO29/PpSL8JM7sF7dSRcfr4dKQyKMTHU1ClT+uZhmaY00qhZStBaufTkYnxBPogejqy9U2CdAvGsLF7fToyrNvxtq6CwCXcMJIgnOzh6cg2/Jb1eL4iNHIi2o71W7w610bHpjUYlk1s9ERUP47dk8DN0b5lLYVclmGHlD8dORh+efe3oZbzUMsXhl7OgyFfKcJ6VvEiDOC+++7jF7/4Ba7rAnDKKafwxS9+kYsvvrj4FRVKKSKRCAsWLGDu3Ll9Gr+aRdhb0VDLF4ZezkMtXxh6OQ+1fGHo5TwY8pUirGcDck/Y+973Pt73vveVtd91110DMb0QQgghxEFHvjFfCCGEEKIKpAgTQgghhKgCKcKEEEIIIapAijAhhBBCiCqQIkwIIYQQogqkCBNCCCGEqAIpwoQQQgghqkCKMCGEEEKIKpAiTAghhBCiCqQIE0IIIYSoAinChBBCCCGqQIowIYQQQogqkCJMCCGEEKIKrEpPcO211/LCCy/gui7r169n0qRJAFx44YV873vfY9GiRdTX1wPQ2NjIhRdeyGOPPVbpsMQgYJoQi2oM5WKYDoHu/MxQKCgyGb+kr21pok4GiwzaS+GrBJ5Rh2W5KO3h6wipjI3Wutu5tNtKrXoTtItvjybt1bKXrj0KkSGceRNVaMNPjCdtjSHYOY6pfGLBZoz8VgKnnqw1GjdwehxPKYgUtmDsWIOywrh1k8gZtX0PDAgXtqO2rwLAt44Aanrsbxd2oLesQHt5zFFTKURH79e8B8rMbKOwcQUohfP/2bvzOLmqMvH/n3O32nvv7J09gCQhgmASEkQIwTAiMqBfkXXQcRkVEQeYgfE1yG/cEGeGQXAdHREZUQEhRCcSFsEQJEASCIFskL07nU6vtVfd5fdHke5UV6fTS1VXQj/vv9Knzj3nee69Vf30ufdWJpyEE6wf8LZKgXNwB/F9b+HW1qPXT8ezIiWMtrQ0TaNj9+u07NhKoKKKMTNmY4QHvj+EEMeWkhdht912G9BTYD322GMAPPLII8TjcW677TbuvffeUochjjO6rqiMJNDZl2vwQNfGgaZhGApNCxOL5Qox0/CIGDvQsgloeQKFiwYYVQtQoUqU14mJhh45jY4uf8FcIaMLtv0MM7r5nfHC6NNvptMeO6iY/V6M4Mb/Qtu+HABDt9CX3ktH8BQ05VERfR799e/lkgGMEz9LV+0F2O6R34bh+Hac31yNm+rM7ZeG9xNc9l0SxuB+8QYTe0j8z9U4B98GIFk7heC195MITemzvxVv4uBPPk36rZdy81bUM/6mR0lXnzCoeYdLa9/Bm9+4lNT+HQAEJs7ixFt/h1vVd9y9Jbev5c83fxQ3kwZg2rIreM+nvgGBoRWy5da48Tl+/OVPkk0lAZh37of56D9+C6tqYpkjE0IMRVkvR37oQx9i165dPP744+UMQxyDggEPncb8Rnc/eCZK2ViWjVK55oAVR3Nj0Pos4HZ3Vx1/BTdwaGMMZwt+X+FcVuYteKcAA8COobf8AUMf3FKYL769uwADwMmgr/k3AipGkIPob/wXhwowAG3LTwk6+484nqFsvL/+AN4pwAC8PWvR928YVFyaprBf/0N3AQbgtO7Cfv1xNE31uU12+/PdBRiA09VC1xM/QFdun/1LQdMUbc8/3F2AAST3baPjxceOGPfhVLqTl/79uu4CDGDHygdI7NlUknhLzU208vB3b+kuwABeffoPNG15tYxRCSGGo+QrYf0xTZNvf/vbfO5zn2PhwoVDHqe2NlzEqPpWX3/8XsIYirLn68bA6asIyhUBuu5QV1cNgJfuAFsDN9lH9zTouX8qL0YkrBOpCOZ18fY0F2ymxd+iappCmQPfD157R0Gb6tpNyMyCkwA303sLLGJH3NduvJVU08bC2Dp3Un/q4I5P266XCtqct1+k9sJb+uy/v/HNgrbU9rWMD3jooZE7N3a++XxBW3Tzi8y48ujv+ejeA0T3bCtot7sOlv/8HoLGrbvZ//aWgvZoW8sR8zke8xyu0ZbzaMv33aasRRjA3Llz+fjHP85tt93GLbf0/QvhaFpbY7juEG7gGaD6+ggtLdGSjX+sORbyDYUgaOrA4fd+aRxavLVtk/b2XIyRoB8fGTBrIdua318PAu0AuFo9XV0O2Wx+blX+aQVvBLd6AZ1d4LgD3w+VgQmYvdq8CQuIZkMYSiPoq4F0W8+Lup+UVkvsCPta1ywC7/kw3os/zY9t7CmDOj5Kge+Uj5B548m8dvO9H+XgwWif975Zs84E7s5rCy/4OB0pAzcxMueGUlC/+GN0bPhzXnvtwosHlL9mVDL+jPNoeik/b/+4qWU/v4fCjNRx8qLz2PSXJ/La6xqm95nPsfA+HmmjLefjIV8pEvt3TDwd+cUvfpFdu3axYsWKcocijhGJBGS9KdBd1pigN4CWwXWDJJM9p2486ccxpkLtB3KFGIAWhHGXALnVKU9VkWUG2Wwfc+lTYeLHQeXmcitPI1N1Fs4gr7zF/dNxzvoGWLlVGq9uDtkzbiLjWSS9Sux5/4oXGJfr7KvBOfV24hz53i7HBW/uZWizluQadB/62TeSrp09qLg8D7xpZ+Ff9ClQGiiNwOJr8WZ88IgPH6gpp1Pzt7eAntsn4fl/S2DhJ0r6x05fcYfmLWX8BZ8BTUNpOhM+8g+E5pwzoO1d3ce8L3yH2vecDoAZjLDglp/gG39SKcMuHSPIhV/6GlNPyeXjC4X5f7d8lzGz3lvmwIQQQ6W8Iz0uVmS9n3x85JFHWLt2Ld/5zncA2LhxI1dddRU1NTWDfjpSVsKK61jJVykI+BW67uKho5QCck9GOr0uVSoFAV8WU8ugvAxZx0/KCRLw2SjlksmapHtfDTxMXV2IVMdeFA5pqsnY+pBi1jRFwD6A5iTJWPWkvUDe6wEthuF04ugRkl7FgJ7ANMlgpZrwNJO0bzyOd/T7ofqiKxsrlrvPLjRpJgfb+9khgKZcjK694Nq4FZOwVf9PcpaK7mXxOt55QKNqEq4a3AK+lo2RaWsiXF1Nxl8/ooVkKbjJdjqbdmIFI0QmzMQ9wh8Lx8r7eCSNtpyPh3xlJax/Zb8cecjcuXO55ppr5CZ90c3zIJH0AMXhN9wfsW/KJLdyFjrUSiyh031TWD+U0ojZ1cMLGHBdj7hWn1tj7uN3fdINgwofLZ08WSyy/neeBhxG/eB4BsnQZADChg/ovwhzPY1MZPLQJywSR5lQPXXI27tmGGPsLCLHwS+sgdAC1VRPz52rRyrAhBDHhxFbCSslWQkrrtGWL4y+nEdbvjD6ch5t+cLoy/l4yFdWwvp3TNwTJoQQQggx2kgRJoQQQghRBlKECSGEEEKUgRRhQgghhBBlIEWYEEIIIUQZSBEmhBBCCFEGUoQJIYQQQpSBFGFCCCGEEGUgRZgQQgghRBlIESaEEEIIUQZShAkhhBBClIEUYUIIIYQQZSBFmBBCCCFEGRgjMcntt9/OunXryGaz7N69mxkzZgCwdOlS1q5dy3333YdSCs/z+Id/+AcWLVrEVVddNRKhDUso6GDpCcDB8YLEEhauC6GQjmV5gIeGC24c7C48DNJuFfGkVfRYgl47VtcWVKYLt3IWrgO0bgXNxKk/mYQ5Lq+/lW3HaNkE0Sb0qjFo1WNJ+yaR8sLDiiPkS2C5B8DL4uj1xDJVuN6whsxjptvwGjfidLVgjJ2BO+ZkHM2Hnu4gu+c17PZ9BMaMIzSxgYx/Aik72O94SinsA9t565U38NComH4KqrqheAEPgJdN0rVrE52NOwjVjKVq+lz0YHVJ5kp3NtO07TW2JePUTTmB6oaTQMnfYkIIUQ4jUoTddtttAOzdu5err76axx57DADHcfjLX/7CL37xC6699lp+/vOfo2nacVGAhYMOfl5DOXEADBR6+DQyTphAIIZSLjgKz05A53OAiwL8WhgVOIdY0le0WIJeB4E1t6Ka1+UaTr+J7J++DXYq93NkHOGP30fMNwkAy4nh/PE2khse6x6j4qKbCYyP4E74BBAZUhxhXxxf229Q2TYAdHQqxl5BR3rMkHM7nJntpPPXNxJ/+fHutrF/fy/GnA+z71dfo/npB7rbZ336a0w4fSpUX0jKOfK+zuzdyDNfWYadSgDgrxnLB//jj2i104oS89FoCrY99SBP3nljd9ucD1/OmV/8Nso3vIK4t3RnM//z1U+yd/Nrubl1nc//8BEmzF5U1HmEEEIMTFn/BNZ1nTvvvJMf//jHLF++nF//+td861vfKmdIA2bqMRTxw1o8dGc7PstDKRtcC8+NQmIz4Hb3Um4Mk5aixmJFt3UXYN6YedhbnukpwACi+1G716BU7ke9bRuZwwowgOjKeyHtELD3DTkO02nsLsByHPTOZ7FM94jbDErzlrwCDKDlgX/GaXw9rwADeOv+75HusvF5+484nKY8tj50b3cBBpBqa6Z57RPd+6rUUq17+PPdX8tre/0P/0t037aiz9W4ZUN3AQbgOg6P3nkLbjpW9LmEEEIc3YishPVn8uTJXH/99dx000384he/oKqqatBj1NYWd8WgL/X1vVaHsq3g5DcpL4mu9So43Di96aQKxxsGr+OwOfy1uM1bC/qo6D7q6nJzppoKf+l6mTiup2OSBPrIdyBxtBXmquwOKsIGyggNerzeunYUxu0mo7iJzsL2TAon6xLU0tTX9p2LnUoS3VNY7MT37+jeV6XWdDCNnU4VtLvpWFHPEYDN8a6CtrbGPViGS3WR5zpWFXufHutGW74w+nIebfm+25S9CAN4+eWXqaur47nnnmPhwoWD3r61NYZbzBuPeqmvj9DSEs1rqwyH6H1nl6tPxM7qWD5Aubn73PzTIP5aXr+sqqOz13jDURGcgqV08BzUgXWYJ3yEzIHNeX28KYu7cwhUTkFZQbxMzwqQOfV96O5BUsY4AlCQ70BU+iZg9mpzw6fS0enhusPP11c7DWX68bI9RUvg5LMxx5+AHozgJHrmiMyYhz+QIkUdsSPkohTM+MinaNuyLq99/PwLhpT/UBiRsYx7z2nsf7MnBisUITR2WtFjqJ96Yve9l4csvPQaHL3w/H436ut9/G422vKF0Zfz8ZCvFIn9K/sduQ8//DA7duzgoYce4rHHHuOFF14od0gDEkv6sfV5oPyAhqtNIeWMJxZ3se1KPBzQa8AaC4H3AAZoAdzQYhLpyuLG4p+KvfQevIopkImjaiZhnPVlsEIQqkP/8HdJ1czp7p8KTSHy2QcxJs0FTcc35zwqln0Gd9rfEvdqhxxH3K7Fqb8EjApQJm7FmaSs2UUrkDOV05hw86P4Js8F3SA8/xJqr/p3nKqpzLl9OZETTkdpOnXvP5+Tv/AvqInnEXeOnI/nQf0Zy5j3ma9jBiP4q+t5/z/9kPCsM4oS74D4Iiy77cfMPPvDKF1n3Mnv4+N3P4pVO6noU9VOOZnP3P0baidNxbB8fODyz7PoE5/DY4SuvQohhMijvMP/LC6xQzfmP/300wC8/fbbXHnllfzqV79i+vTprFq1im984xssX76cysqBFyrlWAmD3EqK3+ehaR6ptI7j9MQQCOhomsK2XUzDQ1NZsllFKqNTqj3uV3F0L01aq8JFw5dtA6WT0qvo6zBbXhzTjqJ8PrIqSPqdG9iH89eVUuA3M2jKJZX14xTpdrDDmU4MLZPACVRjH4nmkqwAACAASURBVLb2ZtgJtGwXZjiArQKk7YE9haoUBNxOkikHgrV97qtSU24GO96B7gvliudSzaMgm+zEp3u4ZuWoKsCOh1WDYhpt+cLoy/l4yFdWwvpXtpWwTCbDV7/6Va6//nqmT58O5L6yYtGiRd1PUx7rPA+SKUU8oeUVYADJpEM8bpNOu8TiHl0xg2S6dAUYQMoLEacG29VwXUjqNSS1yiMWFRkVIm6OI+ZWdxdgw+V5kMxYxNOlKcAAsnqYdGBMXgEGYBtBMoFxxJ3KARdgkIs5NGYSXqCmLAUYgKdZ6JExJS3AIJer4a+kZkLDqCrAhBDiWDSi94RNmjSpexXMsiweffTRgj7Hy9ORQgghhBDDUfZ7woQQQgghRiMpwoQQQgghykCKMCGEEEKIMpAiTAghhBCiDKQIE0IIIYQoAynChBBCCCHKQIowIYQQQogykCJMCCGEEKIMpAgTQgghhCgDKcKEEEIIIcpAijAhhBBCiDKQIkwIIYQQogykCCsyTVMo1Ve7hqbl9ysnpfJjOFLcA3H4OEoNfZzDKZUbq/f4wx/36PH1nq/3vhrJWI6k3OePEEKI4TNGYpK9e/dy9dVX8/TTT+e1n3jiiZx33nns3buXRCLBwYMHmTx5MgA33ngjZ5111kiEVxSGAeFAEo02wIftVdEVM7BMiASikGkEzQfmOFw0NBK4BEhl/CST3ojGGrH3YjSvRaXacSadhZeM4+1aA6ExeA0LgfcMaJywOojZ9ToqsRen5n3YcZvs1tUo3UKfdRbJyhPxvMHlphRE9AMY8TdQbgoiM/GcTlyjmrQ2gWTGP4SMQXk23r7X6NjwNHqokopTzsGrm8nh4XmdjbS9vpquXVuon7eY8MzT8aUOkt7yF+yO/fhnn4M7/hQczTekGLpjwSO2+3V2vfQMmm4w+YxzCE4c2D7PRFvYs3EtuzatY+rc05k4+wx8kbphxSOEEKI8RqQI68+9994LwIsvvsg999zD/fffX+aIhiYSiGI4r3b/rGMRCZ2GpUVRnU/1dFQWeuWZKNWBBgR9VThOPZnMyBRiEWcfvic+DakOMEN4rkn6yTt6wgvV41zzO6C+33GCWge+125HxXYCCjvlp+uXN4Pr5DqYASJffIxE5YmDi09vxtr+LXBTuYYmhZp2LVr7o2ihk3AC55OxB3/aOm//lddvu4hDVZcRqmTOt5/Eq50JQLxlHy/efiVt2zYA8Oav/5P3fvbrhN9aSXr7S7lBlt/J+Ot/DbPOG/T8h+vasYEHP78M17Zzsfj8XPbjJwg1zO53OzeTYMVdX2Pdyoe72+Z/9Ao+/NU70IyhFadCCCHKRy5HFoHf56E723u1ZjCNNCrxan6zl4FsJ4d2vUYHfp87InECGAdfzRVggDflXDKvPJgfXrwFt/HVvjbNYyV3vVOAgVd5EomXV/YUYADZJPbrfxzUZTOlFEbs9Z4CLDc6HHwB/A1o8Tfw650DHu8Q3U2z58Fvc/iylx3vpOv1Z7svB3a89Xp3AXbIxvu+g3nCB/LaWh/6OqYTH3QM3bHoig2/+0l3AQZgp1NsefLho+6rjqYdeQUYwIuPPUBX044hxyOEEKJ8yr4SVgy1teGSz1FfHznyi24aUjb0WszSlAdetrC/ZwNW948+S6O+vvQ5AHhNmZ4f9ABeOlbYyU71ny/gNR5WcOk+3FS0sFOqc9DHxtudKmx0kqBMACwD6iv6j623bNzFjncUvpCOU1eXG2v3G4XzOpk0nm7mtbmJKCG/hlk1uBgO8VyXZFdbYSjR9qPuq45dTp/tGs5Rj1dfhrLN8W605Tza8oXRl/Noy/fdZkSKME0rXHDzPK/7xuvham2N4bqlu5xXXx+hpaWPIuMdmqaoCk1Bd7Ye1qrI2j6MwMmo2At57Zg1QG5Fx8NPPAHJ5JHHL6bKmtmYSgfPQe19DnPe35J94b97Omgm2rg5/eYLELEa8BlBsBOojjcInv5ROrf/Na+PMfcjRx2nIL7wXEweJ6+irVsA8Y14Zh2JbITkIMdUSmPixdez9b8+d3gj4ZPP6o6vavpsrIpqMl3t3V2mLf0E9vbn88aq/psv0+UEcQcZw+He+7HPsuOFJ/PaTjr//x11X4XrGxg/4ySa3trc3dbwnvcSqm8Y9H4+2jn9bjTach5t+cLoy/l4yFeKxP6NSBFWUVFBNJp/orS2tlJZWTkS05ec63okMvUELR3d3YOnfNjaNKIxk6B/PL7wmajkm7kb84Nz8ZRC4cMlQsapGtEb86P+mVQs+yn6qz9FJVvQpi3AiEzCWfcAqnIi2oJ/QBs/Gw72f8ktRj3a++5E3/VbtOjbmGMbqLj6RySe/gHK9OM/7yukx5wy6Phi3iQiM29C378cnARqzDngxXDD88iGTiOZso4+SC+eB6H3LuPEG/6bvb+/C7OiloZP/BNq4tzuUq+iYSbn/Psf2PKbu2jbuoGpSz/BxHMvwxdvov3xO3Ha9lGx9HOYc/6G7DAL/jGzz+SjdzzA2vv+Hc0wWXDtTVTPOu2o2xnBav7ue7/i+d/9lM1rnmb2B85nwSWfQve/O95HQggx2ihvsI+vDdHHPvYxPvOZz/ChD30IyN2Qv23bNu666y5geDfml3sl7BClwDLB9RTZbE88mgYBv8J1FcmUi64rDANsGxxnZJ+MPMTUbDQcMl7uST/TS+Epg6xnDOqvK0P30MmQ9fy4rodJGpQi6w2+WDqcZTgoPGzXwNAdbEfHGeatc0qBbifxNB1H5cd3KGcNF7JJ8IW7zymDLMq1sfUAxXy3KDcNgDfIpy2V8nDSCXQriMfQVpOPh7+gi2205Tza8oXRl/PxkK+shPVvxO4Ju/POO/n617/OvffeSzab5cQTT+Rf//VfR2r6EeF5kM5A75vDXBfiCa+73XE8nL5v7xkxWdfg8MOfwV9wT9tA2I7CxsehjbP4hjRObxlb7/634+r99Bw4zwNbD/Tbx0UDMwSHFfU2JmhmUfLKi2eIX3XheQrNChU7HCGEECNsxIqwadOmcd999x3x9fnz5zN//vyRCkcIIYQQoqzkKyqEEEIIIcpAijAhhBBCiDKQIkwIIYQQogykCBNCCCGEKAMpwoQQQgghykCKMCGEEEKIMpAiTAghhBCiDKQIE0IIIYQoAynChBBCCCHKQIowIYQQQogykCJMCCGEEKIMpAgTQgghhCiDEfkPvC+//HIuv/xyLrzwwu62RCLB2WefzdixY9F1nd27d1NXV0cwGGTSpEnce++9IxFaSWmaQtcVtu3heV53u1IKwyhsHw7DyNXTtu32+bpSCkNXOK6H6xZnzt5MQ8P1PBznyOMbhoahKzJZB7fvUMvC8zwMQzvi/hsOXc8dG8cZ+YQ1TaFrCttx6X2qGYaG00d7MebUNDXosQ1DwzvK+TPUsUX/ZL8KUR4jUoRdcsklrFixIq8Ie+KJJ1i4cCF33303AFdddRVf+tKXmD9//kiEVHKRMJhGF7qK4bgVpLMh4gkIBx0s/SC6dxDHX0fGqSOW0Ic8j6ZBRYWOrqdQysO2A8RiLrbd80ka8mewaES39+Ba48moBmJJXzHSBMDvRQm0rkXb9hhe9UycGRfRZU0t+DCvcnbhbVyBs/tlfCeehzf9XLq08UWLYyhMLUMovQXWPk6FEcSdeCFdxgxcTw17bN1L4Wt6hfSa+1D+CMGF15CsnYvH8MceiIjRitn1V7T4VpyqBaQDc0jYEfTOXbz9p9/Tvv5pahdeTMX7LsAJF+c4WNFdJF96hNSbzxE6/WLMucvIBsf2u42TaKdp/bO8vuJ+6mfO4eS/uYLghBMKzp94yy42/Okhtr/0F977oUs46czz8VWNK0rco5nTsp0dq35N25ZXmHr+5dSeugSCteUOS4hRYUSKsAsuuIDvfve7dHR0UFVVBcDy5cu55pprRmL6ERcMKHzGHhQp8EBXcfxWBagx+NmMctoB0GnHrw5i+04mlR7aL+aKCgPTbO/+2TTTRCKVtLfnfoP5TA+f/Qpadh8AWrYZn7EHx3c2yfTwr0ZrCoK7V6C9fBcAqulFtO2PE77gPqL6hJ44OUj24evwWt8CwN29Fv3k9QSWfJOkXbyCcLBCqTcx1n0NyF2b15r+TOSM/6BTmz7ssX371hL92VXdP6fX/57Il5aTqJ4z7LGPJmRGsXb8JyrTAoAe3Uyg7oNk/Ut5847LSex6A4COjX+hfvHzTPrcvTiaf1hzWulWWr5/JZnGzQAk3lxNZOGLhD/5n9ha38dY0+DN//sVz/3g6wDsWvtnXl/xAJ/876exaid398vEWvn5DZfRvGMbANteXs3pF36Ci2/+D9DLd/4c77zOfTx744Wk2g4A0LzuOU6+4h+Z9slb8Dy5W0WIUhuRd1koFGLJkiWsXLkSgObmZnbs2MFZZ501EtOPONO0cwXYYTS68JsplNee1668dnxmZkjz5C53pgvadT2JaeYOrc+Idxdg3XParZhabEhz9hbw2tFe/Ul+Y6YLvXNbfqzt27sLsEOcN/4PK767KHEMhWm46Dt/l9/oOegtL6Bpw1utMjWH1J9/kN/oOthvrBr22ANhZJu6C7BD1MFnsRs3dxdgh7SsfgS3dfjHwT2wrbsAOyT6wu/QOvcccZtMZzMv/M9389pSXe20vv16XtvBXVu7C7BDXl7xG6Ite4cZ9egW3fVGdwF2yObffh+3o6lMEQkxuozIShjApZdeyl133cVll13G448/zkUXXYSmFacGrK0NF2Wc/tTXRwbe2Y2BU9h8pHwtyxjc+Hk6C1qUgqqqIKDwsmlI9DWnTn34yHMONB4vFoc+Lq8ZRn5OdldfuSt0XRtG7sPjORnQCi8Fa7o+7HPKy6Zp6+N4a4YxIuer19HXW1uh6X2fgz6fSWSYxyHe3MecSuHzm1QeYexOuwOlCmMye50/B63CsZVS+Ab53inXuVYuR8s3afZ9zAJ+a9jnQ7nIMRbHkxErwk4//XRaWlpoampi+fLl3HPPPUUbu7U1VrKbzSF3kre0RAfcPxBQhHxB1GHVj0slqZRFQNWgvLaedlVHLKaTzgx8/MNVVZmYZn5bNhukoyO30mWZJmFrMlqmZ6XDM8YQTwZIdfY952Dy1bQwVad+AW3tnT2N/mpSoRnEDhujonIGasxJeAd6Vkr0uR8l4WsgNYh9W2xVUz6GcXBdT4PSydYuoLMIMQU/+CWy257vadBNtBOWDOpcGvLcxhgCvvGodM+Khlu/BM13IuGZpxLbvr67fcw5l5MNTxh2XGb1DHxTTiG967XutoqzriTpH0/0CGMrs4ozP3Mrf/6vW3tir6mnYsp78uKpHD+diSfMYd/WnhWyBZdcg1U1fsBxD/Z9fLwbSL7+iScRHDORxIGe1fKTL/9HMr6643JfyTE+9kiR2D/lFevxvAH40Y9+RGNjIzt27OD+++/Pe204N+Yfa0UYQDgElhFHU3EcL0ImGyCegFDQwae3o3mtuKqWtFNNfNg35hvoegrwcBw/0aib94RZyJ/FUvvR7EZcfSxZNYFY0jrimIPN10eMYNt6tB0rcaum40xZSpcxuaBfpb0b3noKd886tFkfxJu8iC5V3hurDWUTzm7HaHoCVw/gjD+PqDa1SDfmZ/Ad2EDmpd+g/BWY77uUZM1svCKMPRARow0zth6V2IZbeTop30kk7TB61x6i61fS/tpz1JxxAeG55+GExhRlTiu2h/Sr/0dyyxpCp16AcdI5ZAP9j+2mOmne+Dyb//QQdTPew6xz/xb/2JkF/RKte3jjuT/y9itrmHPOh5lxxjn4KuoHHNvx8AurmAaar3PwbRr/8hjt2zYw+ZxLqZpzFgSqRyDC4pNjfOyRIqx/I1qE7d+/nyVLlvDNb36Tiy++OO+1d1sRBrnLJbpe+Ni3UrmvLSjm4+BH+xqEwcw51HwNQ8M9yldgaJqGaWpks/Yx9RUV9fUR2triJfkaidyx6f+rF0rl0DnY+6s36usjtLfHS/KVBEc6749mIOfPUMeG4+MXVjENbkU79xUVpfiKlpEkx/jYI0VY/0bsciTAuHHj2LRpU5+v9V4ZezfwPC/vqyJ62o/8fV5DdbTioRRz9jaQ8V3XJZ0+Nj/oS/U9XuX4frBDjnQOQunOh/7m7M9A4hnq2KJ/Ryt+hRClIc8gCyGEEEKUgRRhQgghhBBlIEWYEEIIIUQZSBEmhBBCCFEGUoQJIYQQQpSBFGFCCCGEEGUgRZgQQgghRBlIESaEEEIIUQZShAkhhBBClIEUYUIIIYQQZSBFmBBCCCFEGUgRJoQQQghRBlKECSGEEEKUQcmLsMsvv5wVK1bktSUSCebPn09bWxsAl156KZ///OdLHYooA01T+P0Ky1ID6AsBn4NpHLmPz6fj9+sodfTxBhYfBPweeE5RxjvE0D0CRhJD94o6bl98poPfsou2T3ozdZuAlkAb4KeFT88S8VqxdLck8RzOdBP43S58bgxNK03+oniUAi0bQ7OTQ9pe08BId6J72SJHNnzKs7GTnShK/54fCKXAS3Wi3Ey5QxH96OfXXXFccsklrFixggsvvLC77YknnmD+/PnU1NSwZcsWTNNk8+bNNDU1MX78+FKHJEZIIAABXxSdNjwMnMA4OqMabh+/m0P+DD7vbbTMDjy9Cjt0Cl2JEN47n2e6rqio0ND1OEq5BINBEgmdVGroxVMo6OLTm9HdfZAKUVUxnc6oH88b3odohXkQ4+Cf0KKv40bmYNctoytbO6wx+6JrLhGjCb3zWZSbIhBZQNKYSSprFWV8pRSV7g70rfejYjsJTFhKesz5xL2aI25TnXgT+4Uf4zZtxDfjAwRPu5IO34yixHM43UnBW8/R+vtv4TlZqs++Aqt2AkxbRMpXX/T5xPCpTJTWl//EGw98D93nZ87f/QsVsz+Ap/sGtL0ebeTg0/dx4Jn/JTR1Lg2X3Qrj5zDMt+uwKQUde7aw6r+/y66Nr/De8y9m4cf+nmDtpLLFZHc28cYff8WmFQ9QN+NkFv79LUSmnDLszzZRfMor8VGJx+Occ845PPHEE1RVVQHwqU99imuuuYazzz6bb3/724TDYTo6OqioqOD6668f9BytrTFct3Rp1NdHaGmJlmz8Y00x8tU0qKqIotOc1552ZtAVzV+xMAyoUOvQMm/3NCqDdGAZ0aQfgMpKHcvqyNvOcSK0t6shfbAYBlT6d6G5ew6PmrR2Bl0xc9DjHRI0EgR23YFK7+9u8/wTSEy5mWQ2MORx+1LpO4DZ/Mu8Nqf2Qjrck476i2kgxziimvGtvQ6cnlULd+L5dE39IrZTuCxWkd2D/esrIX6wu02bdDreR75PnIoBZDRw5q7nabzz4ry2cZffju6lsRd/BdcrXBWT93F5dbz8OGtuvyav7Zz/+COBWQuOuq3uZdn331/mwJ8f7GkLhJn7vb/gVk3tbitHzqn2Rv7zyg8S72jrbjt58VI++c2fo4zivud76ytf5dk8f/dNbFze89lgBoJc8T/P4aufVtJ4+lJfHxnxOY8nJb8cGQqFWLJkCStXrgSgubmZHTt2cNZZZ5HNZlm+fDkXXHABF1xwAQ899BC2bZc6JDEC/H6FzsGCdl1LF/Y102iZHfmNno1O7sNFKdD1wvNC1xP4fEM7hf0+F83d26vVRVeJIY13iOG25BVgACrViOm0DGvc3pRS6OmdBe169EUsoziXVvXE7rwCDEDb9yR+t63P/lrb9rwCDMDd+zJGrDDOYcWla8T++ruC9q71T2Lv2YAvXXjeifLSlcO23/+ooH3vc4+i60e/jOx1NnLg2d/ktTnJGOl9W4oW41C17N6WV4ABvLF6FdEDvT9fRkamYz+vr/hVXls2maBt5+ayxCP6V/LLkZC75+uuu+7isssu4/HHH+eiiy5C0zSefvpp6uvrmTlzJp7noWkazzzzDEuXLh3U+LW14RJF3mO0VfPDztezwdaB/ILAMIyCsT0HSOq5bQ7va1rUhw/1jfcxiUYk4icSGUIh5qYgaQD595YYhjms3L1YsM92yx+kPlTcc8hr72MuPUykIkiFdvRLkkfL02vuY3zDjz8YIBAs3NbpKx7NQLcCRX//ZGvGFbTp4SrAIRAJE67oez55H5eHa9sEawuPWbB2LDU1R//8TjohdH8IJxnLa7dCYep65TjSObeGC+M3TItQJDwisfSeo9MOYQXDpGNdee2B8MjEIwZnRIqw008/nZaWFpqamli+fDn33HMPAA8//DBNTU2ce+65AMRiMR588MFBF2FyObK4ipGvUlBVMR5D7epu8zyTVMogFs8fW9MUlYHT0BNre/rqNSTTIRJdub6RiIHfrwE9N5TZdoj29r6Ks4HEp6gKn4DhbOqZkzDJtJ94+9Bzt/RqwrUfQGt9rrvNrT2bWLqKTKK451CFbzKWFgD30GqVwq5YREdrGihccTzcQI5x0JhEoGIWqmtbd5tzwt/TmQzjxgu3DVfNRJu2CHfH891txhlXEw9MI13k94//tItRK3+El84df2VYVMw9G71yDO2ZAF4f88n7uLxmXfol9vxlBa6d+8PHDEYYu+BvBhSjMuqYevX/x1s//mp3W+SE09HGnpi3fTlyrhw/gxPmf5CtL/65u23Z5/8JIzK25LH0la9m1XD2df/GE9/uubVnzInziDScVJbzQQq//pX8nrBDfvSjH9HY2MiOHTu4//77OXjwIOeeey6rVq1i7NixAOzZs4dly5axcuVKGhoaBjy2FGHFVax8DSN387uuJfA8A9vxE4313dcyXIJWJ5rbiqdCZLxa4qn81ZxIxMAwbJTycByDWMzFcYZ+3E3DIxRIodGJrgdIZsLEEvqQxzvEb8TxZ3eipfbi+htImVNJ2X2vkA1XxNeFkd2L8jI4VgPRbG2fDz70NtBjHNLasKJvolLNuBUnkfDNIOMe+UbqCnsv2v5X8VrfRo07mWzdKcT14t8orxSYbVvJbn0eL5PAP+lEtIqxpGtOxFF9rwLK+7i8FB6Zxjdo3fgCmmlRO2chxthZA76xXssmsHevJ7rtJfxjpxKcNR8nnP8gV7lyTnc2s3fTyxzYtZ2Gk09l/ImnovtHZhWsz3yzSdrf2kDTppeoGDeFcXPej1FZnofepAjr34gVYfv372fJkiV885vf5OKLL+bnP/8569ev5/vf/35ev+uuu44pU6Zw4403DnhsKcKKq9j5KsWAP2gH0ncw4w10zrq64h/jYsdZzLkGe4wHO76mMaBisBgOfTNHMR5GeDc5VvMd7vuiv+3LnfNIvufh6PmOdDx9kSKsfyNyORJg3LhxbNrUc+nnU5/6VJ/9ehdl4vg3mA+BgfQt9odKqT6kRvLDr9RzDXb8kSrAoPy/ZMTgDPd4HcvH+1iL7ViLRxSSb8wXQgghhCgDKcKEEEIIIcpAijAhhBBCiDKQIkwIIYQQogykCBNCCCGEKAMpwoQQQgghykCKMCGEEEKIMpAiTAghhBCiDKQIE0IIIYQoAynChBBCCCHKQIowIYQQQogykCJMCCGEEKIMpAgTQgghhCiDkhRh1157LatWrer++Y477uDUU08lk8l0ty1evJg9e/YAcOmll/L5z3++FKGMCL/fIxxyCfghHFIEgwqlFACWpREOGwQCer9jBHwO4UAGyxj5//be0FzCWhtBLYpSCi/VQdhtwafSJZ9bKQi4HUSyTYTS+/CTJORLErRSaJoq6ly65hE2uwiasbyxNeWRObALK9VSMKdSCpVoRbXvwojvR3dTRY2p2JRnY3c04ibaUMXdfWWllCIbbyXV3ojynHKHI4QQRWGUYtCFCxeybt06li5dCsCaNWuYN28er7zyCgsXLmTXrl0Eg0EaGhrYsmULpmmyefNmmpqaGD9+fClCKgmlFJWRJIb9JsqL4xnVKLMBvDb81ngcL4RpxlEqg+fp+P0ROjsdXLdnDE1BZbADLfFXlBvDMieRDZ5KNOEbkRxCWhu+Xb9Ga1wFwYkEZlwLT/8n/vbt+Madjv/9N9NpTCnJ3DoOoQNr8TobiT3zU+zGNzGmvo+Kj3wZ09yMr+ps4vosMs7wT9OgEcXfsQqt5UnQ/PgmfoK47zS8eCfJ537GW//3fZTho/bj/4px2qXYRhjlpml/eSXr7rmZdFcbU8+5mEnvXUjV3A/g1Mwswh4ormzbXv76s2/y5hMPEa4bx3k3/wdj552Dp5XkbT5iPDvN9rWrePg7NxPvaGXBxVdxzrVfJVA9odyhCSHEsJRkJWzBggWsX78egObmZizLYtmyZaxevRqAl19+mTPPPBOARx55hEWLFrFkyRJ++9vfliKckgkHs5j2OhRxAJTbDpm3QYXQ2YNppFAqt/qnlINhdBAO5/9CDAeS6LGnUW4MAC27FyvzMpZZ+hUxXfPw7XsMbd9K8ByoPxP11M3Qvj0X8/6XMZ79ZwIqVpL5A7HtuLtfpPPRf8NufBMAe+crdPzqVlzVgN76B4L6gWHPo2kKf2wt2oE/5fJ04ui7f07Q20t240ral38PL5vGTXbR8ssbYddLAKR2v86af7uWVHsLnuOw48mH2b/5NXY9cDt6NjrsuIpJeTZr7/sub6z8LZ7rEj3QyO9v+iTRvW+WO7RhO7hzE7+46e+Ith7AdRzWPPwL1vz2JyhGftVYCCGKqSRF2OzZs9m9ezfpdJrVq1ezaNEiFi1alFeELV68mGw2y/Lly7ngggu44IILeOihh7BtuxQhlYSuUoCb3+jFwMsVWsorvHSlafmXUjQvWjCGyjbhM0p/KdBHFG3fEz0NrgfZeH4snW9jJveXZH7V/jaeMvHi7Xntbvs+7FhuP2npncO+LGlqabTW5wrnTzYTfe6+gvbEq39C1xUd218reG336j9iVU/Abd83rJiKzY62sun/Hsxv9Dzad28rT0BF1LRtU0Hbi489QDrWVoZohBCieEpynULXdebNm8fGjRtZ8GE+4gAAIABJREFUvXo1V1xxBQ0NDaRSKTo7O1m/fj233norzz77LPX19cycORPP89A0jWeeeab7MuZA1daGS5FGnvr6SGGjk4WCOksHBXiAMoBs3qumaVJf33Op0ctEIdFrCOXDFwjgD5c2Ly+rIDQBOrfmGnSrsJNuYUWqqK/qI/9hsttryLbvKXxBM9B8uX2k+WqprRzefvDcLAQmQDq/mNR8EXwNs0nt2JDX7pt4AjU1YTrrCy+Nh8dNxo61EayqJtjXOVEmCSNN1YSptO95K689XF3b97nLEc7pY1DVmLEFbfWTp1NTV00gMrgcjpeci2W05QujL+fRlu+7TcluFjl0X9hrr73GnXfe2d321FNPUVVVRSQS4eGHH6apqYlzzz0XgFgsxoMPPjjoIqy1NYbrlu7SRH19hJaWwstPlmUQtiaiu4etipgzwO3CUwE8L4B2WBHmun7icZtUqucBBb8VJGhNRsvs7m5zgu+nox08r/SXvKpO+CzGy/8Mng2dG/FO+Chq62M9sZzxVbqcOpw+8h8uf8UJGMmnCZx5Bck1D3S3hy/4MrrRiKdqSKuJxIowd1X9hRhdm8DNrTB6vnGkjQlElnyO6EvLcZO5OYy6Bsz3LKGlJUpo2jxqTjiVtq25S+uaaXHShVdg+QMkfGOJl2CfDJ2P8276dx664VI8J7eK2PC+D1AxZXaf5+6Rzulj0diZ85g85zR2v74OAN00+egN3yCW0oilBp7D8ZRzMYy2fGH05Xw85CtFYv+U53klqV62bt3KDTfcwOTJk/nhD38IwKpVq7j77rs5//zz+eQnP8m5557LqlWrGDs295funj17WLZsGStXrqShoWHAc5WrCAPw+zz8VhJFBk3343oaHops1iKVhnBYQ9NcPE+RSilSqcInu/yWjd+IorwUjooQT4Vw3JF5tE1THmGvES2xG/QArn8SVqoZJ7ofNzSRRGAaWUr3kIDfacPs2o0Xb8VNdKHXTkarq0EZGlmtnng6WJR5lFKE9Rb0TCMok6w5kbhdgVJgdu6E5i04aOgTZ5MJHnbDd+wAsR2vYkfbCdaNxVdVD3Uz8LSReXBiMBQu8X1baN+9DV+4kupps9HDdX32PR4+vA+X6Wph/1uvk0nEqJ92IpUTZpFbch644y3n4Rpt+cLoy/l4yFeKsP6VrAgDWLRoEV/4whe44oorAIhGoyxYsID777+fDRs2sH79er7//e/nbXPdddcxZcoUbrzxxgHPU84i7N1otOULoy/n0ZYvjL6cR1u+MPpyPh7ylSKsfyV9dv3555/P+zkSibBpU+4m29NOO63PbXoXZUIIIYQQ70byjflCCCGEEGUgRZgQQgghRBlIESaEEEIIUQZShAkhhBBClIEUYUIIIYQQZSBFmBBCCCFEGUgRJoQQQghRBlKECSGEEEKUgRRhQgghhBBlIEWYEEIIIUQZSBEmhBBCCFEGUoQJIYQQQpSBFGFCCCGEEGVQkiLs2muvZdWqVd0/33HHHZx66qlkMpnutsWLF/PpT3+6YNtbbrmFX/7yl6UISxSRYSgiYYiEPXw+1d2u64pIRCcS0fH5jnx6+SyIhDJEQja6Xvi6piDsNhNJv01AxbAMh4ivk4ivC0P3+o3Np6WoZA/VRhMVgSSW0X//45Vl2ET0A0SMgxiaO+jt/W4nodg2gtlmNE0VvK4rh0B8J4Gu7ZikCl5XCqxMK+bBN7AS+1GqcIxi0jSF19VEdt8mSLQWbVylXNIHd5HYtxmycXQ7jjqwGa19J5oa/H4tBaUg2baP9t1vYCc6yh2OEKJIjFIMunDhQtatW8fSpUsBWLNmDfPmzeOVV15h4cKF7Nq1C8uy2LBhA62trdTW1gKQTCZ55plnuPnmm0sRligSv18R8rejcRAAnxHBNMeSzWqEQja63pVr95lYVpho1M7bPhJ28HmbUV47oDCD04mnx5HO5H6JG2SJND+J/tc7wE5iVU2HxTeiEs8CHmbkdJL+BSSzvoLYIuoA1pt3odo3gjLQZ1yKMf400v6ZxFNWSffLSAobnVj7/xetawOgMMecT6JqGSk7OKDtnX0b0B7+El7HbrDChJd9g/jEc3HIVcRWtgPW/JjYcz8G18E6aQmBj36DpH8CkCsKzP0baL7377Db9qGFqhj7mR/izFyCR/GLMYVL5/on+et3Pkc21klwzCQWff1+zIZ5wxrXS8d4Y8XPef4n38K1s0w8ZT6LP3Ylu3/wRZRhMuWTt1K15O9xzXCRMhlCjG6Gzc89zu+++VXSiThjp87kmu/eR8XEE8sWkxCiOEqyErZgwQLWr18PQHNzM5ZlsWzZMlavXg3Ayy+/zAc+8AGWLl3KH//4x+7tnnzySRYsWEB1dXUpwhJFEvCluwswAEUUy+giEABdj/e0qyyWlULXD18pA4u97xRgAB668xYBX6K7Tyi1A33118FO5sbpeBv14g/ANw3w0KIv4fP2FsSlax7mnodzBRiAZ8P236DFW7FoLPlKzUjRNIUVXfNOAQbgoR34E4HsWwPa3u9FST96A3TszjVkYtjLbyCQ2NndR298heSffwCuk+uy+SmcV37DoV1oplppvuca7LZ9ALjxDpq+fzVW505KwW7ZwerbriQb6wQgcWAvz3/9qmGviLW//Rp/+cHtuHYWgH2vvcjG554kPOt9eHaWnfffTnbXhqOMUlode7bxq3/5HOlE7r3VvHM7v/76l3DTsbLGJYQYvpIUYbNnz2b37t2k02lWr17NokWLWLRoUV4RtnjxYi655BJWrFjRvd2jjz7KpZdeWoqQRJFomkJTiYJ2nU60Pi6JaVoay+o5zXymh+YdKOxHtPsXvIrvK5y45XWgoqd/age6nn/6+lQM7cCawm3jTejZvRh9XPY8HplaFq3jpYJ2FdvS52XF3vTUQbzW3gWbh+rK7XdNU9g7CsfPvLYC08kde6+zCbu9Mb+Dk8Vp2zOwJAYpeWA3npO/opo4sJdsR/Owxm3d8WZB29trnyMw64yeufcU9hlJbY27Ctr2vLGeRGdLGaIRQhRTSS5H6rrOvHnz2LhxI6tXr+aKK66goaGBVCpFZ2cn69ev59ZbbyUcDtPe3s6ePXvw+/3s3LmTRYsWDXq+2trSXyqor4+UfI5jSb/5OmnoXW+pELrW1+lkEQ4HCIffKQ48B9KV4OQXYroRoa4uN6eXGVs4TEUDqJ77krTAJGqqQnldPMeEqvfAgRfytw3UgVVPVbj/8+R4Ocae50LHSZDML1a18LQBvRfcrnpSoXq8eP4vcbNqXPc+SEyeU3AXmDVjIRV1tSjdIOOOQwtU4Ca7ejoohb9uAoES7Ec1dkJBmxWpIlI/lsgg5ut9jNsaphX0GXfiHDKN27p/Dk2cXtZzo2P8xIK2mgmTqRkzhsqjxHW8nNPFNNpyHm35vtuUpAiDnvvCXnvtNe68887utqeeeoqqqioikdyJc/HFF7NixQr8fj8XXXQRmjb4xbnW1hiuW7qbr+vrI7S0REs2/rHmaPmGgj4CVghF7vKI55lk3RqyaY9AwELTDj2AoZHJBOjszL9sUhmZhkUHkOvnanUkkwESqdycljWV8MlXoL3xQG4DIwALroN07rKQZ00kpU0m3keMldOvwux4AzK5y1aMeT9eqIqMmkK0n5yOt2NcUX0uZscGVCZ3Oc4Ln0jKnNXnPulNqTBVF91J+refBSd3DPQzv0TUN5nsO9v7J74fc8Yism89D4BWOQHjzE9xsC13iVgz6hn7mR/QdO/fwTsrVHWXfYNkeDKxEuxHVT2V937+G2z40dfemd9k/j/9iLRVR2qA8/V1jCunz+OEcz/K1qcfAyBQWcPpf3Mp+35yPQB1Cy9Cb5hX1nMjMmEmS679Ck/9z10AmD4/l99+L1kV6jeu4+2cLobRlvPxkK8Uif1TnueVpHrZunUrN9xwA5MnT+aHP/whAKtWreLuu+/m/PPP57rrrgOgqamJL37xi5imyfe+9z0aGhoGPZcUYcU1kHz9foXPsgEP2zaJJ7x32nV8Pt5pV8TjTp/bh4IOhpYCNNJZP6l0/mU0S6XwJ3ejMp04oUl4wRoMtxWURoYakpkj32Qf0tqw0vvQDAMnUE+GKhKp/v/eOB6PcciMYmSbQemk9XGk7MCAt62rC5HctxnVtRcCtaTCU7DJf9DBZ3eitb0FTgavdgYpqz7vdQ0Xo2MHbtsetMqxONXTcbTChyWKRTlpMs1vkW4/QGBsA0bd9EE9BHCkY+ylo3Tt3Uo2maCqYQY+XZFp2o7mD2KMnYVjVfQx2shysyk69m0j3tFGzcQphMdM5Wif3MfjOT1coy3n4yFfKcL6V7KVsBNOOIGOjg4uv/zy7rYFCxbwla98Je+S4/jx46mursZ13SEVYKI8UimPVOrQTVbeYe0OqcJvMygQT+hA6IivZzw/Gf8J4H+nIQ1QeEmqz7HdGuJmzTsBDWiT41I8GwEiud1vH613PqU04r4GqD/yey5tVMKY0474uotGpmoGVM0Y3ORD5Ok+zAknY044OfdzkcZVvgiVM97X/bMD6LPGd//7WKCZfmqmzuWds/qoBZgQ4vhQsiIM4Pnnn8/7ORKJsGnTpoJ+P/vZz0oZhhBCCCHEMUe+MV8IIYQQogykCBNCCCGEKAMpwoQQQgghykCKMCGEEEKIMpAiTAghhBCiDKQIE0IIIYQoAynChBBCCCHKQIowIYQQQogykCJMCCGEEKIMpAgTQgghhCgDKcKEEEIIIcpAijAhhBBCiDKQIkwIIYQQogyMkZro2muv5fLLL2fp0qUA3HHHHTz44IO8+OKLWJYFwOLFi1m0aBHz58/nkksuGanQRpRhQCSURSONi59o3AAUwaCGUh6OoxGPO3ied8QxdA1C/iTKS+ESJJ7y4R65e8kpBYHsAbSuPWi+EJgWKhsjG2wgoaoBsLQMfnsfyklh+8bjpGxU+04wA9hV08hqwQHNZeguQaML5SZwtArimUi/+6o3v5HC57bgeTa2MQ67swP34E6S0Tr00GQczVewjeZm8Vp3YHe1YNZOwqueQn9TKlzsgztJtTbhrx2PUTcVrwR/77jxVroa30YzTCITZqB8kSGP5dkpOhrfJhXtpHrCVAI14/vNUQghxPCNWBG2cOFC1q1b112ErVmzhv+/vXuNr6q80z7+W2sfs5OdhIQkKKIioiIKKqggtYgUpQKDSq2tPmWUHrTFE7Uqoq2tFBsoT63IU1tt6zNtmRmttaJIO1qqM5+ARUFUQORUBIQYIOScfV73vAhsiAkkwYRldq7vK/e9Dvf/v+4gF2utDUOHDmX16tWMHDmS7du3EwqFsCzreJV03Hm9kJ9TjRVfDxhsLPJzBuOQh8dXe2AvC683j+rqVKvn8NiG3MAuPA0rAQN48GZfSnWkqENhpDOF6zaSfG4aTqQaB/AOvRavN4K3agvesQuI+woJ7fg37I/+AoB1yjepf7YUp3oXAIFhUwhc+SAxX8FR5/F5UoSdd7ErXgMMPsuPt/jL1MT7tCswZHtrCXz8e+za98DykvJNpPzxO3EaqgDoNfG7BL9wO0lvTvoY24lTV/YHtj51DzgOdiCLs2c9g2fgpa3OaWGoevMl/vGTb+EkE9heHyNmPUWvCydh6Lyf7djebbw48wb2f7gJgAGfu5LL7n4Ub25Jh8+VjNbxP7/7GX//t8cByM4v4Jb/9ycKTj230+oVEZGWjtvjyBEjRrBmzRoAKioq8Pv9jB8/nrKyMgBWrVrFJZdccrzKcUU4O4kVf5+m8ARgsOLv47GTh+1l8HrrCIU8rZ4jOxjF03gwgAGk8DSuIBSMdV3hR+GnkdTfHoJIdXos+e7zOAVDsBrK8a5/mqzk7nQAM3lnU//GknQAA4it/hN2+bttzhXyVGNX/Z107yaOt/JFsnzx9tUa29QUwIBUaCjli0rTAQygasnPsCo2NDvG7N3C1l/dDY4DgBOLsPHRr2M37Gl1jlTldv4x91acZKJp/2SCf5TeQmr/jnbV2B62BWsX/zYdwAC2lv0XH7+34pjOt2/b++kABtBQvZ8//eRunHjDp65VRESO7LjdCRs8eDA7duwgFotRVlbGqFGjGDVqFLfddhv33HMPq1atYuzYsfztb3/r8LkLC3Pa3ulTKio69kc9ackKwPnEoAOmeYiwrBTZ2T6ys1s+ojOxGg4FsIODcbL8KUI5nVDjAe3t16muJlK+tsW4SUQBsMtXwaB/ObR/8CQS219reaLKrRQN+5eW44efs25ny8FkLaFgkuy8wjZrNdu2pf87RQGxne+33Knu42a9792yt8Uuieq9eOM1FJwyoMW28h37ceLNA7ETj2EaKik685w2a2yPWEMd2998vcV4xYa3Of+aqe0+z8E+t5R93GLb9nVv43Ei9C7qc8x1fhZ1yq/jbqSn9Qs9r+ee1m+mOW4hzOPxMHToUNauXUtZWRk33ngj/fr1IxqNUlNTw5o1a5g1a9YxhbDKynqcLnwpqqgozN69dZ/6PL3yAnjxAIc/avSA5Qei6RFjvNTXJ4hGo588BeEsPwEsmgUxK0Ak5qWh5tPXCB3r108QX7+LMTtXNhu3/FkAOCddiuMNp3/Q7MZ/Ehg4gsjqxc1PVHxWm3PmBnLwf2LM+HrRGPURqW273vzQQLw0/Xx5zB6yTh9OZMuq5jvlndSsDivvBLDt9J0wgEDvvqQCvVqt1xMuwhsMkYw2pse8WdlY4aJO+RmCpnfwTv/8BPZtbR4iTzj34nbPcfga5xb3bbH99OGfw/HmdFrNnwWd9eu4u+hp/ULP67k79KuQeHTH9duRB98Le++99zjvvPPSY8uWLSM/P59wOLMXq7beg/Gfw6Hs68X4zyXl+A7byyaZDBONtv5OWEM0SCr7c2AdOIcVIJl9KY0xX6v7d7U4QeyxD0L+yU0DthffiGnYFf/AFJxFYtD/odHTF2fAjYCNVbeF0PBxePsOPlC/RXD0raRKhrQ5V0Myj1ThxEO9e3JIFV5NJN6+3mP+gTiFn286tHEtJV/9Hr7eB+r2+Oj91Tk4xWc1P6hoAGfO+DV2oClU+vJ6c+b3/j+pUO9W57AL+jHqh7/Hl53btH92LqN++Ds8vfq1q8b2MAYGXXUjJ50/Kj02ZPJNlJwz4pjOV3jq2UyeMRvb23Rde/frz7X3zsXyBjulXhERaZ1ljuPb3Js2bWLGjBmcfPLJPPHEEwC8+uqrLFiwgCuuuILbb7+dmTNnctFFF3Xo25Hd5U4YgG3b5IUTWMQxBKip82JZhlDIg2UZkklobGw9gKXPYUEoGMMmTsoEaYz5OvWbbMfSb9CpwVu/GwIhLK8PK9FAPOtEoiYbAK+dIsupwHJiJHwlOPEUds0O8AWJh08mRfuClG1DyFePbaIkySGSCHaod78nSdDaDyZJzOqNaazHVO0kmFdIQ9aJOKbln0ssDFb1TlL1+/EWnIiTXXz0b0daFk71LuLVe/DnF2Pn9+2SL02YeD0NFdvxeH1kFZ8Cnpbf7DyST66xhUNdxXZiDXXklvTDl92r0+t1W3e4a9CZelq/0PN67g796k7Y0R23x5EAZ5xxBtXV1dxwww3psREjRnDXXXcxatSooxyZORzHoarGA2QdHAGgri55xGNanMNAfSQAtP833a4WtfMgN+/QgIdmT0yTjoc6Tmy695o6sL1gcIfncRyoj+UAx/YeYDzlJU7xoQF/AZQUkF8Upu4I/zMzWJj8k7HyT256kNxGnjLGYOWdSCDvxPTnrmD5c8jp1/Fr2BqDTU5J/2O8qiIiciyOawgDWL58ebPP4XCY9evXpz+XlpYe75JEREREjjv9jfkiIiIiLlAIExEREXGBQpiIiIiICxTCRERERFygECYiIiLiAoUwERERERcohImIiIi4QCFMRERExAUKYSIiIiIuUAgTERERcYFCmIiIiIgLFMJEREREXKAQJiIiIuKCLglhN998M6+++mr689y5czn//POJx+PpsSFDhnDhhRdSUVGRHotGo4wbN47XXnutK8rqMh4P5ObEyQ/XUJAXoVdekvzcODnZYFkQzoqRn7WX/NB+ckIp8vI85Odb5OV58Pk6fwksC3JSH5NfvYL82rcImf1H2dciJ7qDcPnr5NauI7t8OZE1i8lq+BDLOvo8QbuRvOQH5EfeJJePsG3TyZ00F/JHyfftIt/7IeFAPZYF/sZyPFv+jnfra/gbP8ayLMLeSvLNOnJim2FbGfG1f8Gu3NJmP0djOQkSu9+nevXLRLetgnh9h4430VqqN71J+ZtLadz9AZjUMdXhJKJU/vM9tqx4mcpt72GS0WM6j4iIuM/bFScdOXIkb7/9NuPGjQNgxYoVDB06lNWrVzNy5Ei2b99Onz59GDt2LLNnz2bhwoUAPPbYYwwfPpwxY8Z0RVldwrYt8rKr8CTXNQ2kAE9f8ATx2TH84SLs2r+BSQDg8RRgBS7G8joHPudQU2OTSnVegMmNb8P3X9+CeG3THPmnYV/2c+rtkhb7hmvWknxmKvbga2jY8i6JbW81bfAGyL3lWRp6n9fqHEG7kdCHT2LvXtY0YHnIO/+HVAfOw3RBFssONBKs+jNWvLypPMuPY4/jo3nXk6w6MFZ4Ev2+t4hA5S+J+Yaw8ZmXqVz9elN5Pj/n/uhFOOXiDs9t2xaVK1/mjR9PS4+ded1tnP7VmRhfqM3jnWgt//jVQ6x98XdNtdg2k+f+O32GjevQtTJOklWLn+bFR7+fHrv2vlIumHQzWJ72n0hERD4TuuRO2IgRI1izZg0AFRUV+P1+xo8fT1lZGQCrVq3ikksu4c4772Tz5s0sW7aMDRs28Oqrr/LAAw90RUldJhRM4kl+0HwwtQusIAY/VuPadAADsFL7IVGd/uzx1BMKdd4yeG2DZ8OidAADsKr/iW/PWy3uBPmJknp9LiRjmFDJoQAGkIzR+OJD+E1jq/MEYtsPBTAAk8Kz/udkWXWd1kuzWlO70wEMAE+IuuXPpgMYQLLyI+re/HPTtv3+dAADMIk4W351N95Ex+tLVe1i1c/ubDa28Y8LiZZvbtfxtTs/SAcwAOM4vPKT20nW7e1QHXUfb+Olxx5qNvbC/32QuooPO3QeERH5bOiSO2GDBw9mx44dxGIxysrKGDVqFKNGjeK2227jnnvuYdWqVYwdO5ZgMMicOXO4//77KSwsZPbs2eTk5HR4vsLCjh/TUUVF4dY3pGogmmw5blKABytV3XJbqh7IB5ruhgWDHoLBtu+otIeJN0Dlhhbjntpt9D63eQ9ObT2RfZvBsjHxSMsyKzbTy5fC06tl72ZXK4/jYpWEAkmyc45wrT4FU1nTfMCbS2TbX1rsF/1wA5wzhERdy/oiH20ky5MgeIS1PNIaV+5vJNHYMryZxuoj/1wcZu+aqhZjjfv34jGxdh1/0P6tdRjHaTbmJJMko/UdOs9Bx3JMd9fTeu5p/ULP67mn9ZtpuiSEeTwehg4dytq1aykrK+PGG2+kX79+RKNRampqWLNmDbNmzQJg+PDhDB8+HGMMI0eOPKb5KivrcZyuex+pqCjM3r2t30HJClpk29lYNBw2aoPlBacBE+iPFVnb/CBfIQcDGNg0Njo0NHTOHSTbtsgfMAl71aPNxpMlF1H9iR48VojQ2ZNIrfl37KyWv5D9F1xNTSpEqpXec3198GMf1geYwvOoi2cTP8K1+jTy/Sc0/2GNfUzeyKtpXP8/zeu66IsQ+Qeh4ktbnKPo0ik02mHqWqnvaGtsZxeT238QtdsOhVvb58fX++QjHnO4nD79sTweTOrQe2B9h46AYEG7jj8oVHgiobxeNNYcCnXhgiJCBX06dB44er+Zqqf13NP6hZ7Xc3foVyHx6Lrs25EH3wt77733OO+889Jjy5YtIz8/n3D40MKceOKJ9O3bt6tK6VKRqEXCcw7Gym0asIIQOBucGoyVR9J7Gk5gAGCB5cVkXwjebACM8ZBI5NHY2MqdtGPkOIbYSeMwZ36p6T0hbxDnwhk05p7TYt+UsXEumIZ95njM1r8RnnQ/VlYeAP5zvojv89NJmdbfNWqw+5I6/yHw92rqpddQEmdMJ57yd1ovh2t0ikkVXAmWH7Bwss8kcO44ek2YAR4fltdPr8n34h80BifvAnICmzj7rvl4wwUAFFz4Rfp+eRYpfB2e2wnkMvKB31Jw1jAAQsUnMbr0T3h7n9qu40MnnM41P32GnN59AOh3/ijGzVwA7Xif7HBZvfpwy8I/UtJ/IAB9BpzJNxc8QzCv5bt+IiLy2WcZ0xWvUcOmTZuYMWMGJ598Mk888QQAr776KgsWLOCKK67g9ttvT+/7+OOPAzQb6wg374QdFPCD35fEMR4su+nlq0jEIpUyeD0Q9Mcw2ESiPgJBD14vJBIQjR7bt+Ta4rUdgok9GMtDxFvEJ55iNeOzEvije8AXwkkmCXhT1NsFJDl6oLIsyLJq8ThR4nY+MadrAthBtgVZ/ggWKaKpbJJJC9syeOt3A5DMORHHWPg8KQJ2LcbyEatubHrUmtuHlB044rnbs8Z2op5k7T48oVwIFXTopXrLgmTdPpKRegK9SsCb1f6DPyEZqSFWV0UgtwBvMPeYztEd/gTd2Xpazz2tX+h5PXeHfnUn7Oi65HEkwBlnnEF1dTU33HBDemzEiBHcddddjBo1qqumdU0sDrH4Jy9n0+/SyRTURw4FgEika4LX4ZKOTb2n6c4LRwlgAAnjIxE4cCfSDzlFYZLt+IVtDDSaXCC3zTk6g2OgIZb1iTGLePaB2g+EokTKQyLVdIeOUA50zut2OL4c7MKcpmk6mPmNAU9Obzw5vT91Hd6sPLwH7liKiEj31WUhDGD58uXNPofDYdavX99iv2O9AyYiIiLSXelvzBcRERFxgUKYiIiIiAsUwkRERERcoBAmIiIi4gKFMBEREREXKISJiIiIuEAhTERERMSk+KrIAAAOgElEQVQFCmEiIiIiLlAIExEREXGBQpiIiIiICxTCRERERFygECYiIiLiAoUwERERERd4j8ckf/3rX3nyySdJJpMYY5g8eTLf+MY3AHjhhRf4/e9/TzKZxHEcrrvuOqZOnXo8yuo2PDbkBOuxTRUGH0kKqI/43S6r0wR9MYLWXqxkFcZbSMT0JpbInP66gm1BjudjPNHtYPlIBk6lLlngdlkiItIBXR7CKioqmDt3Ls8//zy9evWioaGBr33ta/Tv3599+/bxn//5n/zqV7+iuLiY2tpapk2bRlZWFtddd11Xl9ZthLP2461/DTAAeOwwZI3JiCDm9yYJRcqw69ekx7LzLiHlHUEypRu1R5Jr78S7uRRMEgCPNwynzaQu2dvlykREpL26/He5qqoqEokE0WgUgOzsbEpLSzn99NN54oknmDlzJsXFxQDk5uYyd+5czjjjjK4uq9sI+A2eyNscDGAAllOHl/3uFdWJgp6aZgEMwK55gyxvnUsVffb5vAbPnqXpAAZAsg5v43osy726RESkY7r8TthZZ53F2LFj+cIXvsCgQYO4+OKLmTRpEuFwmPLycoYOHdps/wEDBnR4jsLCnM4q94iKisJdPkdrTKoR9kdajHvtZJfWdLz6NY37Whsl4DMEc4/vNXdrjTvKpKLwUcvr5klW07tv+3voLv12pp7Wc0/rF3pezz2t30xzXN4J+9GPfsR3vvMdysrKKCsr48tf/jLz588HwBjTxtFtq6ysx3E+/XmOpKgozN697tyZsS3ID5yJHX2v2XjC5FPTRTUdz35D/hxCnjCkDs1nfL1pjGcTqT9+19zNNe4oy4L83l/As+O3zcaT2edS3c4eulO/naWn9dzT+oWe13N36Fch8ei6/HHk66+/ztKlSykpKWHKlCk8+uijPPjggzz33HP069ePdevWNdv/zTffTAc0AcdAlP44wSFgBTCefJI5l1Mf7fq7f8dDYzxIougrOFkDwQ7iZA8iUTiFSNzndmmfWcZAJDAE56QbwJcPgWJS/afTYJ/sdmkiItIBXX4nLBgMMnv2bIYMGcJJJ52EMYYtW7YwaNAgxowZQ2lpKb/85S8pKipi//79lJaW8tWvfrWry+pWGmM+otYZBIMDSDk2sYbMevGnJpqHL3sSvtwE8ZSfZDSz+usK0VQW8eBlBPpfhLFsYskAJuV2VSIi0hFdHsJGjBjBbbfdxq233koikQDg0ksvZfr06fj9fhKJBNOmTcOyLIwxXH/99fpmZCscA41Rj9tldJlE0iaRDLhdRrfiOIaIk+V2GSIicows0xkvZbksk98Jc0NP6xd6Xs89rV/oeT33tH6h5/XcHfrVO2FHp7+ISURERMQFCmEiIiIiLlAIExEREXGBQpiIiIiICxTCRERERFygECYiIiLiAoUwERERERcohImIiIi4QCFMRERExAUKYSIiIiIuUAgTERERcYFCmIiIiIgLFMJEREREXKAQJiIiIuIChTARERERFyiEiYiIiLhAIUxERETEBQphIiIiIi5QCBMRERFxgUKYiIiIiAsUwkRERERcoBAmIiIi4gKFMBEREREXKISJiIiIuEAhTERERMQFCmEiIiIiLlAIExEREXGBQpiIiIiICxTCRERERFygECYiIiLiAoUwERERERcohImIiIi4QCFMRERExAUKYSIiIiIuUAgTERERcYFCmIiIiIgLFMJEREREXKAQJiIiIuIChTARERERFyiEiYiIiLjA63YBncG2rYyY47Okp/ULPa/nntYv9Lyee1q/0PN67mn9ZhrLGGPcLkJERESkp9HjSBEREREXKISJiIiIuEAhTERERMQFCmEiIiIiLlAIExEREXGBQpiIiIiICxTCRERERFygECYiIiLiAoUwERERERcohB3mpZde4qqrrmLcuHEsWrSoxfaFCxcyZswYJk+ezOTJk1vdp7upr69n4sSJfPTRRy22bdiwgSlTpnDllVfywAMPkEwmXaiwcx2t30xc34ULFzJhwgQmTJjAvHnzWmzPtDVuq99MXOPHHnuMq666igkTJvD000+32J5pawxt95yJ6wwwd+5cZs6c2WJ89+7d3HjjjYwfP55vf/vbNDQ0uFCdHBMjxhhjPv74YzNmzBhTVVVlGhoazKRJk8zmzZub7XPLLbeYt99+26UKO98777xjJk6caAYPHmx27tzZYvuECRPMmjVrjDHG3H///WbRokXHu8RO1Va/mba+y5cvN9dff72JxWImHo+bqVOnmldeeaXZPpm0xu3pN9PWeOXKleYrX/mKSSQSJhKJmDFjxpitW7c22yeT1tiY9vWcaetsjDErVqwwF198sbnvvvtabPvWt75llixZYowxZuHChWbevHnHuzw5RroTdsCKFSsYMWIE+fn5hEIhrrzySv76178222fdunU89dRTTJo0iYcffphYLOZStZ3j2Wef5aGHHqK4uLjFtl27dhGNRjnvvPMAuPbaa1tcj+7maP1C5q1vUVERM2fOxO/34/P5GDBgALt3705vz7Q1bqtfyLw1vuiii/jd736H1+ulsrKSVCpFKBRKb8+0NYa2e4bMW+fq6moeffRRbr311hbbEokEb731FldeeSWQGWvckyiEHbBnzx6KiorSn4uLi6moqEh/bmhoYNCgQdx33338+c9/pra2ll/84hdulNpp5syZw/Dhw1vd9snrUVRU1Ox6dEdH6zcT13fgwIHp33w//PBDli5dyujRo9PbM22N2+o3E9cYwOfzsWDBAiZMmMDIkSMpKSlJb8u0NT7oaD1n4jr/4Ac/YMaMGeTm5rbYVlVVRU5ODl6vF8icNe4pFMIOMMa0GLMsK/3f2dnZPPXUU5xyyil4vV6mTZvGf//3fx/PEo+rtq5Hpsnk9d28eTPTpk3jvvvu49RTT02PZ+oaH6nfTF7jO+64gzfeeIPy8nKeffbZ9HimrjEcuedMW+c//vGPnHDCCYwcObLV7Zm8xj2BQtgBJSUl7Nu3L/15z549zR5b7d69m+eeey792RiT/pNHJvrk9di7d+8RH+Nlgkxd39WrV3PTTTdx9913c8011zTblolrfLR+M3GNt27dyoYNGwDIysriiiuuYOPGjentmbjGbfWcaeu8dOlSli9fzuTJk1mwYAF///vfeeSRR9LbCwoKqK+vJ5VKAZmxxj2JQtgBl1xyCW+88Qb79+8nEonwyiuv8PnPfz69PRgM8tOf/pSdO3dijGHRokWMGzfOxYq7Vt++fQkEAqxevRqAF154odn1yDSZuL7l5eVMnz6d+fPnM2HChBbbM22N2+o3E9f4o48+4sEHHyQejxOPx1m2bBnDhg1Lb8+0NYa2e860dX766adZsmQJixcv5o477uDyyy9n1qxZ6e0+n4/hw4ezdOlSIDPWuCdRCDugpKSEGTNmMHXqVK6++momTpzIkCFD+OY3v8natWspKCjg4Ycf5tvf/jbjx4/HGMPNN9/sdtmd7mC/APPnz+cnP/kJX/ziF4lEIkydOtXl6jpfJq/vb37zG2KxGKWlpemv6v/Hf/x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" 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" 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" + }, + "metadata": {} + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "建立雙折線圖（此解決方案由 Kedar Ghule 提出：https://kedar.hashnode.dev/how-to-combine-two-line-charts-in-seaborn-and-python）\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 200, + "source": [ + "fig, ax = plt.subplots(figsize=(12,6))\n", + "lineplot = sns.lineplot(x=honey['year'], y=honey['numcol'], data=honey, \n", + " label = 'Number of bee colonies', legend=False)\n", + "sns.despine()\n", + "plt.ylabel('# colonies')\n", + "plt.title('Honey Production Year over Year');\n", + "\n", + "ax2 = ax.twinx()\n", + "lineplot2 = sns.lineplot(x=honey['year'], y=honey['yieldpercol'], ax=ax2, color=\"r\", \n", + " label ='Yield per colony', legend=False) \n", + "sns.despine(right=False)\n", + "plt.ylabel('colony yield')\n", + "ax.figure.legend();" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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" + }, + "metadata": {} + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**免責聲明**： \n此文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。 \n" + ] + } + ], + "metadata": { + "orig_nbformat": 4, + "language_info": { + "name": "python", + "version": "3.7.0", + "mimetype": "text/x-python", + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "pygments_lexer": "ipython3", + "nbconvert_exporter": "python", + "file_extension": ".py" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3.7.0 64-bit" + }, + "interpreter": { + "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" + }, + "coopTranslator": { + "original_hash": "6dc6d959b7a74ab98a06ca0c23248709", + "translation_date": "2025-09-02T09:04:30+00:00", + "source_file": "3-Data-Visualization/12-visualization-relationships/solution/notebook.ipynb", + "language_code": "hk" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/13-meaningful-visualizations/README.md b/translations/zh-HK/3-Data-Visualization/13-meaningful-visualizations/README.md new file mode 100644 index 00000000..31846d9a --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/13-meaningful-visualizations/README.md @@ -0,0 +1,173 @@ +# 製作有意義的視覺化圖表 + +|![由 [(@sketchthedocs)](https://sketchthedocs.dev) 繪製的速記筆記](../../sketchnotes/13-MeaningfulViz.png)| +|:---:| +| 有意義的視覺化圖表 - _由 [@nitya](https://twitter.com/nitya) 繪製的速記筆記_ | + +> 「如果你對數據折磨得夠久，它會承認任何事情」-- [Ronald Coase](https://en.wikiquote.org/wiki/Ronald_Coase) + +作為一名數據科學家，基本技能之一就是能夠創建有意義的數據視覺化，幫助回答你可能提出的問題。在進行數據視覺化之前，你需要確保數據已經像之前課程中所教的那樣進行清理和準備。之後，你就可以開始決定如何最好地呈現數據。 + +在本課中，你將學習： + +1. 如何選擇合適的圖表類型 +2. 如何避免誤導性的圖表 +3. 如何使用顏色 +4. 如何設計圖表以提高可讀性 +5. 如何構建動畫或3D圖表解決方案 +6. 如何創建創意視覺化 + +## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/24) + +## 選擇合適的圖表類型 + +在之前的課程中，你已經嘗試使用 Matplotlib 和 Seaborn 創建各種有趣的數據視覺化圖表。通常，你可以使用以下表格選擇[合適的圖表類型](https://chartio.com/learn/charts/how-to-select-a-data-vizualization/)來回答你的問題： + +| 你的需求： | 你應該使用： | +| -------------------------- | ------------------------------- | +| 展示隨時間變化的數據趨勢 | 折線圖 | +| 比較類別 | 柱狀圖、餅圖 | +| 比較總量 | 餅圖、堆疊柱狀圖 | +| 展示關係 | 散點圖、折線圖、分面圖、雙折線圖 | +| 展示分佈 | 散點圖、直方圖、箱型圖 | +| 展示比例 | 餅圖、甜甜圈圖、華夫圖 | + +> ✅ 根據數據的組成，你可能需要將其從文本轉換為數字，以支持某些圖表類型。 + +## 避免誤導 + +即使數據科學家謹慎選擇了合適的圖表類型，仍然有許多方法可以以誤導的方式展示數據，通常是為了證明某個觀點，卻犧牲了數據的真實性。有許多誤導性圖表和信息圖的例子！ + +[![Alberto Cairo 的《How Charts Lie》](../../../../3-Data-Visualization/13-meaningful-visualizations/images/tornado.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") + +> 🎥 點擊上方圖片觀看有關誤導性圖表的會議演講 + +這張圖表反轉了 X 軸，根據日期顯示了與事實相反的內容： + +![糟糕的圖表 1](../../../../3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-1.png) + +[這張圖表](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) 更加誤導，因為視覺上吸引人注意的是右側，讓人誤以為隨著時間推移，各縣的 COVID 病例數量下降。事實上，如果仔細查看日期，你會發現日期被重新排列以製造這種下降趨勢。 + +![糟糕的圖表 2](../../../../3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-2.jpg) + +這個臭名昭著的例子使用顏色和反轉的 Y 軸來誤導：原本應該得出槍支死亡率在槍支友好立法通過後激增的結論，卻讓人誤以為情況正好相反： + +![糟糕的圖表 3](../../../../3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-3.jpg) + +這張奇怪的圖表展示了比例如何被操控，效果令人捧腹： + +![糟糕的圖表 4](../../../../3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-4.jpg) + +比較不可比的事物是另一種不正當的手段。有一個[精彩的網站](https://tylervigen.com/spurious-correlations)專門展示「虛假的相關性」，例如緬因州的離婚率與人造奶油的消耗量之間的「事實」相關性。一個 Reddit 群組也收集了[糟糕的數據使用](https://www.reddit.com/r/dataisugly/top/?t=all)。 + +了解眼睛如何容易被誤導性圖表欺騙是很重要的。即使數據科學家的意圖是好的，選擇不合適的圖表類型，例如顯示過多類別的餅圖，也可能具有誤導性。 + +## 顏色 + +你在上面「佛羅里達槍支暴力」的圖表中看到，顏色可以為圖表提供額外的意義層次，尤其是那些未使用 Matplotlib 和 Seaborn 等庫設計的圖表，這些庫自帶各種經過驗證的顏色庫和調色板。如果你手動製作圖表，可以稍微研究一下[顏色理論](https://colormatters.com/color-and-design/basic-color-theory)。 + +> ✅ 在設計圖表時，請注意可訪問性是視覺化的重要方面。一些用戶可能是色盲——你的圖表是否能為視覺障礙用戶良好顯示？ + +選擇圖表顏色時要小心，因為顏色可能傳達你未曾預料的含義。上面「身高」圖表中的「粉紅女士」傳達了一種明顯的「女性化」含義，這增加了圖表本身的怪異感。 + +雖然[顏色的含義](https://colormatters.com/color-symbolism/the-meanings-of-colors)可能因地區而異，並且通常根據色調而改變，但一般來說，顏色的含義包括： + +| 顏色 | 含義 | +| ------ | ------------------- | +| 紅色 | 力量 | +| 藍色 | 信任、忠誠 | +| 黃色 | 快樂、警告 | +| 綠色 | 生態、幸運、嫉妒 | +| 紫色 | 快樂 | +| 橙色 | 活力 | + +如果你需要使用自定義顏色構建圖表，請確保你的圖表既可訪問又符合你想要傳達的含義。 + +## 設計圖表以提高可讀性 + +如果圖表不可讀，那就沒有意義！花點時間考慮調整圖表的寬度和高度，使其能與數據良好匹配。如果需要顯示一個變量（例如所有50個州），請盡可能垂直顯示在 Y 軸上，以避免水平滾動的圖表。 + +標記你的軸，必要時提供圖例，並提供工具提示以更好地理解數據。 + +如果你的數據在 X 軸上是文本且冗長，可以將文本角度調整以提高可讀性。[Matplotlib](https://matplotlib.org/stable/tutorials/toolkits/mplot3d.html) 提供了 3D 繪圖功能，如果你的數據支持它。使用 `mpl_toolkits.mplot3d` 可以生成更高級的數據視覺化。 + +![3D 圖表](../../../../3-Data-Visualization/13-meaningful-visualizations/images/3d.png) + +## 動畫和3D圖表顯示 + +如今一些最好的數據視覺化是動畫的。Shirley Wu 使用 D3 創作了令人驚嘆的作品，例如「[電影花朵](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)」，每朵花都是一部電影的視覺化。另一個例子是《衛報》的「Bussed Out」，這是一個結合 Greensock 和 D3 的互動體驗，並採用滾動敘事文章格式，展示紐約市如何通過將無家可歸者送出城市來處理其無家可歸問題。 + +![Bussed Out](../../../../3-Data-Visualization/13-meaningful-visualizations/images/busing.png) + +> 「Bussed Out: How America Moves its Homeless」來自[衛報](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study)。視覺化由 Nadieh Bremer 和 Shirley Wu 創作 + +雖然本課程不足以深入教授這些強大的視覺化庫，但你可以嘗試在 Vue.js 應用中使用 D3，展示一本書《危險關係》的動畫社交網絡視覺化。 + +> 《Les Liaisons Dangereuses》（危險關係）是一部書信體小說，或以信件形式呈現的小說。由 Choderlos de Laclos 於1782年撰寫，講述了18世紀法國貴族中兩位主角 Vicomte de Valmont 和 Marquise de Merteuil 的惡毒、道德敗壞的社交操縱。他們最終都遭遇了悲劇，但在此之前造成了巨大的社會損害。小說以一系列信件展開，這些信件寫給他們圈子中的各種人，策劃復仇或僅僅是製造麻煩。創建這些信件的視覺化，探索敘事中的主要角色。 + +你將完成一個網頁應用，顯示這個社交網絡的動畫視圖。它使用了一個庫，該庫旨在使用 Vue.js 和 D3 創建[網絡視覺化](https://github.com/emiliorizzo/vue-d3-network)。當應用運行時，你可以在屏幕上拖動節點來重新排列數據。 + +![危險關係](../../../../3-Data-Visualization/13-meaningful-visualizations/images/liaisons.png) + +## 項目：使用 D3.js 構建一個展示網絡的圖表 + +> 本課程文件夾包含一個 `solution` 文件夾，你可以在其中找到完整的項目作為參考。 + +1. 按照起始文件夾根目錄中的 README.md 文件中的指示操作。確保你的機器上已安裝 NPM 和 Node.js，並運行項目依賴。 + +2. 打開 `starter/src` 文件夾。你會發現一個 `assets` 文件夾，其中有一個 .json 文件，包含小說中的所有信件，編號並附有「to」和「from」標註。 + +3. 完成 `components/Nodes.vue` 中的代碼以啟用視覺化。找到名為 `createLinks()` 的方法，並添加以下嵌套循環。 + +循環遍歷 .json 對象以捕獲信件的「to」和「from」數據，並構建 `links` 對象，以便視覺化庫可以使用： + +```javascript +//loop through letters + let f = 0; + let t = 0; + for (var i = 0; i < letters.length; i++) { + for (var j = 0; j < characters.length; j++) { + + if (characters[j] == letters[i].from) { + f = j; + } + if (characters[j] == letters[i].to) { + t = j; + } + } + this.links.push({ sid: f, tid: t }); + } + ``` + +從終端運行你的應用（npm run serve），享受視覺化效果！ + +## 🚀 挑戰 + +在互聯網上探索誤導性視覺化。作者如何欺騙用戶，這是故意的嗎？嘗試修正這些視覺化，展示它們應有的樣子。 + +## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/25) + +## 回顧與自學 + +以下是一些有關誤導性數據視覺化的文章： + +https://gizmodo.com/how-to-lie-with-data-visualization-1563576606 + +http://ixd.prattsi.org/2017/12/visual-lies-usability-in-deceptive-data-visualizations/ + +看看這些有趣的歷史資產和文物視覺化： + +https://handbook.pubpub.org/ + +閱讀這篇文章，了解動畫如何提升你的視覺化效果： + +https://medium.com/@EvanSinar/use-animation-to-supercharge-data-visualization-cd905a882ad4 + +## 作業 + +[創建你自己的自定義視覺化](assignment.md) + +--- + +**免責聲明**： +此文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議使用專業的人工作翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/13-meaningful-visualizations/assignment.md b/translations/zh-HK/3-Data-Visualization/13-meaningful-visualizations/assignment.md new file mode 100644 index 00000000..530f6928 --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/13-meaningful-visualizations/assignment.md @@ -0,0 +1,14 @@ +# 建立你自己的自定義視覺化 + +## 指引 + +使用此專案中的代碼範例來創建一個社交網絡，模擬你自己的社交互動數據。你可以繪製你的社交媒體使用情況，或者製作一個家庭成員的圖表。創建一個有趣的網頁應用程式，展示一個獨特的社交網絡視覺化。 + +## 評分標準 + +卓越 | 合格 | 需要改進 +--- | --- | --- | +提供一個 GitHub 儲存庫，其中的代碼能正常運行（嘗試將其部署為靜態網頁應用程式），並附有註解清晰的 README 文件解釋專案 | 儲存庫無法正常運行或文檔不夠完善 | 儲存庫無法正常運行且文檔不夠完善 + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/zh-HK/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb new file mode 100644 index 00000000..0686c14d --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb @@ -0,0 +1,100 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "b1e38707", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "data = {\n", + " 'Math': [80, 85, 90, 95, 100],\n", + " 'Physics': [82, 88, 91, 97, 99],\n", + " 'Chemistry': [78, 83, 88, 90, 94],\n", + " 'Biology': [76, 81, 85, 89, 92]\n", + "}\n", + "\n", + "df = pd.DataFrame(data)\n", + "corr = df.corr()\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "sns.heatmap(corr, annot=True, cmap='YlGnBu')\n", + "plt.title(\"Pearson Correlation Matrix\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "da3c9cb7", + "metadata": {}, + "source": [ + "## 🔍 皮爾遜相關性分析\n", + "\n", + "此筆記本增強了數據可視化模組，展示如何使用皮爾遜相關性分析多個特徵之間的關係。\n", + "\n", + "皮爾遜相關性衡量兩個連續變數之間**線性關係**的強度和方向。它返回一個介於 -1 和 1 之間的值：\n", + "- **+1** → 完美正相關 \n", + "- **0** → 無線性關係 \n", + "- **-1** → 完美負相關\n", + "\n", + "在這裡，我們計算了數學、物理、化學和生物學的學生成績的相關矩陣。生成的**熱圖**幫助我們直觀地了解每個科目成績之間的相關性強度。\n", + "\n", + "這種相關性分析在**特徵工程和預處理**中至關重要，尤其是在建立機器學習模型之前。它有助於識別：\n", + "- 冗餘特徵\n", + "- 強預測因子\n", + "- 潛在的多重共線性\n", + "\n", + "在分析真實世界數據集時，這是一個有助於生成有意義可視化的實用工具。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**免責聲明**： \n本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + }, + "coopTranslator": { + "original_hash": "8ad076c4d4df20aa5939d32b7a50baff", + "translation_date": "2025-09-06T19:37:36+00:00", + "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb", + "language_code": "hk" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/13-meaningful-visualizations/solution/README.md b/translations/zh-HK/3-Data-Visualization/13-meaningful-visualizations/solution/README.md new file mode 100644 index 00000000..1fc007e7 --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/13-meaningful-visualizations/solution/README.md @@ -0,0 +1,29 @@ +# 危險關係數據可視化項目 + +要開始使用，請確保你的電腦已安裝 NPM 和 Node。安裝依賴項（npm install），然後在本地運行項目（npm run serve）： + +## 項目設置 +``` +npm install +``` + +### 編譯並熱重載以進行開發 +``` +npm run serve +``` + +### 編譯並壓縮以進行生產環境 +``` +npm run build +``` + +### 檢查並修復文件 +``` +npm run lint +``` + +### 自定義配置 +請參閱 [配置參考](https://cli.vuejs.org/config/)。 + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/13-meaningful-visualizations/starter/README.md b/translations/zh-HK/3-Data-Visualization/13-meaningful-visualizations/starter/README.md new file mode 100644 index 00000000..00532356 --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/13-meaningful-visualizations/starter/README.md @@ -0,0 +1,29 @@ +# 危險關係數據可視化項目 + +要開始使用，請確保你的電腦已安裝並運行 NPM 和 Node。安裝依賴項（npm install），然後在本地運行項目（npm run serve）： + +## 項目設置 +``` +npm install +``` + +### 編譯並啟用熱重載以進行開發 +``` +npm run serve +``` + +### 編譯並壓縮以進行生產環境使用 +``` +npm run build +``` + +### 檢查並修復文件 +``` +npm run lint +``` + +### 自定義配置 +請參閱[配置參考](https://cli.vuejs.org/config/)。 + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原文檔的母語版本作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤詮釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/zh-HK/3-Data-Visualization/R/09-visualization-quantities/README.md new file mode 100644 index 00000000..a62e012a --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -0,0 +1,221 @@ +# 視覺化數量 +|![由 [(@sketchthedocs)](https://sketchthedocs.dev) 繪製的速記筆記](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/09-Visualizing-Quantities.png)| +|:---:| +| 視覺化數量 - _由 [@nitya](https://twitter.com/nitya) 繪製的速記筆記_ | + +在這節課中，你將探索如何使用一些 R 套件庫來學習如何圍繞數量概念創建有趣的視覺化。使用一個關於明尼蘇達州鳥類的清理過的數據集，你可以了解許多關於當地野生動物的有趣事實。 +## [課前測驗](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/16) + +## 使用 ggplot2 觀察翼展 +一個非常出色的庫是 [ggplot2](https://cran.r-project.org/web/packages/ggplot2/index.html)，它可以用來創建各種簡單和複雜的圖表。一般來說，使用這些庫繪製數據的過程包括：識別你想要針對的數據框部分，對數據進行必要的轉換，分配 x 和 y 軸的值，決定要顯示的圖表類型，然後顯示圖表。 + +`ggplot2` 是一個基於圖形語法（Grammar of Graphics）聲明式創建圖形的系統。[圖形語法](https://en.wikipedia.org/wiki/Ggplot2) 是一種數據視覺化的通用方案，它將圖表分解為語義組件，例如比例和層次。換句話說，`ggplot2` 使得用少量代碼為單變量或多變量數據創建圖表和圖形變得非常容易，因此成為 R 中最受歡迎的視覺化套件。用戶告訴 `ggplot2` 如何將變量映射到美學屬性，使用哪些圖形原語，然後 `ggplot2` 負責其餘部分。 + +> ✅ 圖表 = 數據 + 美學 + 幾何 +> - 數據指的是數據集 +> - 美學表示要研究的變量（x 和 y 變量） +> - 幾何指的是圖表類型（折線圖、柱狀圖等） + +根據你的數據和你想通過圖表講述的故事，選擇最合適的幾何（圖表類型）。 + +> - 分析趨勢：折線圖、柱狀圖 +> - 比較數值：條形圖、柱狀圖、餅圖、散點圖 +> - 顯示部分與整體的關係：餅圖 +> - 顯示數據分佈：散點圖、柱狀圖 +> - 顯示數值之間的關係：折線圖、散點圖、氣泡圖 + +✅ 你也可以查看這份描述性的 [ggplot2 速查表](https://nyu-cdsc.github.io/learningr/assets/data-visualization-2.1.pdf)。 + +## 建立鳥類翼展值的折線圖 + +打開 R 控制台並導入數據集。 +> 注意：數據集存儲在此倉庫的 `/data` 文件夾中。 + +讓我們導入數據集並觀察數據的頭部（前 5 行）。 + +```r +birds <- read.csv("../../data/birds.csv",fileEncoding="UTF-8-BOM") +head(birds) +``` +數據的頭部包含文本和數字的混合： + +| | 名稱 | 學名 | 類別 | 目 | 科 | 屬 | 保育狀況 | 最小長度 | 最大長度 | 最小體重 | 最大體重 | 最小翼展 | 最大翼展 | +| ---: | :--------------------------- | :--------------------- | :-------------------- | :----------- | :------- | :---------- | :----------------- | --------: | --------: | ----------: | ----------: | ----------: | ----------: | +| 0 | 黑腹吹哨鴨 | Dendrocygna autumnalis | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 吹哨鴨屬 | LC | 47 | 56 | 652 | 1020 | 76 | 94 | +| 1 | 棕吹哨鴨 | Dendrocygna bicolor | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 吹哨鴨屬 | LC | 45 | 53 | 712 | 1050 | 85 | 93 | +| 2 | 雪鵝 | Anser caerulescens | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 雁屬 | LC | 64 | 79 | 2050 | 4050 | 135 | 165 | +| 3 | 羅斯鵝 | Anser rossii | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 雁屬 | LC | 57.3 | 64 | 1066 | 1567 | 113 | 116 | +| 4 | 大白額鵝 | Anser albifrons | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 雁屬 | LC | 64 | 81 | 1930 | 3310 | 130 | 165 | + +讓我們開始使用基本折線圖繪製一些數字數據。假設你想查看這些有趣鳥類的最大翼展。 + +```r +install.packages("ggplot2") +library("ggplot2") +ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + + geom_line() +``` +在這裡，你安裝了 `ggplot2` 套件，然後使用 `library("ggplot2")` 命令將其導入工作空間。要在 ggplot 中繪製任何圖表，使用 `ggplot()` 函數並指定數據集、x 和 y 變量作為屬性。在這種情況下，我們使用 `geom_line()` 函數，因為我們的目標是繪製折線圖。 + +![MaxWingspan-lineplot](../../../../../translated_images/zh-HK/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) + +你立即注意到什麼？似乎至少有一個異常值——那是一個相當大的翼展！2000+ 厘米的翼展超過 20 米——明尼蘇達州有翼龍在飛嗎？讓我們調查一下。 + +雖然你可以在 Excel 中快速排序以找到那些異常值（可能是輸入錯誤），但繼續從圖表內部進行視覺化處理。 + +為 x 軸添加標籤以顯示涉及哪些鳥類： + +```r +ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + + geom_line() + + theme(axis.text.x = element_text(angle = 45, hjust=1))+ + xlab("Birds") + + ylab("Wingspan (CM)") + + ggtitle("Max Wingspan in Centimeters") +``` +我們在 `theme` 中指定角度，並在 `xlab()` 和 `ylab()` 中分別指定 x 和 y 軸標籤。`ggtitle()` 為圖表/圖形命名。 + +![MaxWingspan-lineplot-improved](../../../../../translated_images/zh-HK/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) + +即使將標籤的旋轉設置為 45 度，仍然有太多標籤難以閱讀。讓我們嘗試另一種策略：僅標記那些異常值並在圖表內設置標籤。你可以使用散點圖來為標籤留出更多空間： + +```r +ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + + geom_point() + + geom_text(aes(label=ifelse(MaxWingspan>500,as.character(Name),'')),hjust=0,vjust=0) + + theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank()) + ylab("Wingspan (CM)") + + ggtitle("Max Wingspan in Centimeters") + +``` +這裡發生了什麼？你使用 `geom_point()` 函數繪製散點。通過這個，你為 `MaxWingspan > 500` 的鳥類添加了標籤，並隱藏了 x 軸上的標籤以減少圖表的混亂。 + +你發現了什麼？ + +![MaxWingspan-scatterplot](../../../../../translated_images/zh-HK/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) + +## 篩選數據 + +禿鷹和草原隼，雖然可能是非常大的鳥類，但似乎被錯誤標記了，最大翼展多了一個 0。遇到翼展 25 米的禿鷹的可能性不大，但如果真的遇到，請告訴我們！讓我們創建一個新的數據框，去掉這兩個異常值： + +```r +birds_filtered <- subset(birds, MaxWingspan < 500) + +ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + + geom_point() + + ylab("Wingspan (CM)") + + xlab("Birds") + + ggtitle("Max Wingspan in Centimeters") + + geom_text(aes(label=ifelse(MaxWingspan>500,as.character(Name),'')),hjust=0,vjust=0) + + theme(axis.text.x=element_blank(), axis.ticks.x=element_blank()) +``` +我們創建了一個新的數據框 `birds_filtered`，然後繪製了一個散點圖。通過篩選掉異常值，你的數據現在更加一致且易於理解。 + +![MaxWingspan-scatterplot-improved](../../../../../translated_images/zh-HK/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) + +現在我們至少在翼展方面有了一個更乾淨的數據集，讓我們了解更多關於這些鳥類的信息。 + +雖然折線圖和散點圖可以顯示數據值及其分佈的信息，但我們想要思考這個數據集中固有的數值。你可以創建視覺化來回答以下關於數量的問題： + +> 有多少類別的鳥類？它們的數量是多少？ +> 有多少鳥類是滅絕的、瀕危的、稀有的或常見的？ +> 根據林奈的術語，有多少屬和目？ + +## 探索條形圖 + +當你需要顯示數據分組時，條形圖非常實用。讓我們探索這個數據集中存在的鳥類類別，看看哪一類最常見。 + +讓我們在篩選後的數據上創建一個條形圖。 + +```r +install.packages("dplyr") +install.packages("tidyverse") + +library(lubridate) +library(scales) +library(dplyr) +library(ggplot2) +library(tidyverse) + +birds_filtered %>% group_by(Category) %>% + summarise(n=n(), + MinLength = mean(MinLength), + MaxLength = mean(MaxLength), + MinBodyMass = mean(MinBodyMass), + MaxBodyMass = mean(MaxBodyMass), + MinWingspan=mean(MinWingspan), + MaxWingspan=mean(MaxWingspan)) %>% + gather("key", "value", - c(Category, n)) %>% + ggplot(aes(x = Category, y = value, group = key, fill = key)) + + geom_bar(stat = "identity") + + scale_fill_manual(values = c("#D62728", "#FF7F0E", "#8C564B","#2CA02C", "#1F77B4", "#9467BD")) + + xlab("Category")+ggtitle("Birds of Minnesota") + +``` +在以下代碼片段中，我們安裝了 [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) 和 [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) 套件，以幫助操作和分組數據以繪製堆疊條形圖。首先，你按鳥類的 `Category` 分組數據，然後總結 `MinLength`、`MaxLength`、`MinBodyMass`、`MaxBodyMass`、`MinWingspan`、`MaxWingspan` 列。然後，使用 `ggplot2` 套件繪製條形圖並指定不同類別的顏色和標籤。 + +![堆疊條形圖](../../../../../translated_images/zh-HK/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) + +然而，這個條形圖難以閱讀，因為有太多未分組的數據。你需要選擇你想要繪製的數據，所以讓我們看看基於鳥類類別的鳥類長度。 + +篩選數據以僅包含鳥類的類別。 + +由於有許多類別，你可以垂直顯示此圖表並調整其高度以容納所有數據： + +```r +birds_count<-dplyr::count(birds_filtered, Category, sort = TRUE) +birds_count$Category <- factor(birds_count$Category, levels = birds_count$Category) +ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() +``` +你首先計算 `Category` 列中的唯一值，然後將它們排序到一個新的數據框 `birds_count` 中。這些排序後的數據在相同層次中進行分級，以便按排序方式繪製。使用 `ggplot2`，你然後在條形圖中繪製數據。`coord_flip()` 繪製水平條形圖。 + +![類別-長度](../../../../../translated_images/zh-HK/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) + +這個條形圖很好地展示了每個類別中鳥類的數量。一眼就能看出，在這個地區最多的鳥類是鴨/鵝/水禽類別。明尼蘇達州是“萬湖之地”，所以這並不令人驚訝！ + +✅ 嘗試對此數據集進行其他計數。有什麼讓你感到驚訝嗎？ + +## 比較數據 + +你可以通過創建新的軸嘗試不同的分組數據比較。嘗試比較基於鳥類類別的最大長度： + +```r +birds_grouped <- birds_filtered %>% + group_by(Category) %>% + summarise( + MaxLength = max(MaxLength, na.rm = T), + MinLength = max(MinLength, na.rm = T) + ) %>% + arrange(Category) + +ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_flip() +``` +我們按 `Category` 分組 `birds_filtered` 數據，然後繪製條形圖。 + +![比較數據](../../../../../translated_images/zh-HK/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) + +這裡沒有什麼令人驚訝的：蜂鳥的最大長度比鵜鶘或鵝要小得多。當數據符合邏輯時，這是件好事！ + +你可以通過疊加數據創建更有趣的條形圖視覺化。讓我們在給定的鳥類類別上疊加最小和最大長度： + +```r +ggplot(data=birds_grouped, aes(x=Category)) + + geom_bar(aes(y=MaxLength), stat="identity", position ="identity", fill='blue') + + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ + coord_flip() +``` +![疊加值](../../../../../translated_images/zh-HK/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) + +## 🚀 挑戰 + +這個鳥類數據集提供了大量關於特定生態系統中不同類型鳥類的信息。在網上搜索，看看你是否能找到其他與鳥類相關的數據集。練習圍繞這些鳥類構建圖表和圖形，發現你之前未曾意識到的事實。 +## [課後測驗](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/17) + +## 回顧與自學 + +這第一節課提供了一些關於如何使用 `ggplot2` 視覺化數量的信息。進行一些研究，了解其他方法來處理數據集進行視覺化。研究並尋找可以使用其他套件（如 [Lattice](https://stat.ethz.ch/R-manual/R-devel/library/lattice/html/Lattice.html) 和 [Plotly](https://github.com/plotly/plotly.R#readme)）進行視覺化的數據集。 + +## 作業 +[折線圖、散點圖和條形圖](assignment.md) + +**免責聲明**： +本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為權威來源。對於重要資訊，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/R/09-visualization-quantities/assignment.md b/translations/zh-HK/3-Data-Visualization/R/09-visualization-quantities/assignment.md new file mode 100644 index 00000000..b57d2565 --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/R/09-visualization-quantities/assignment.md @@ -0,0 +1,14 @@ +# 折線圖、散點圖與柱狀圖 + +## 指引 + +在這節課中，你學習了如何使用折線圖、散點圖和柱狀圖來展示這個數據集中的有趣事實。在這次作業中，深入挖掘數據集，發現關於某種特定鳥類的事實。例如，創建一個腳本，將你能找到的所有關於雪雁的有趣數據可視化。使用上述三種圖表，在你的筆記本中講述一個故事。 + +## 評分標準 + +優秀 | 合格 | 需要改進 +--- | --- | -- | +腳本包含良好的註解、完整的故事敘述以及吸引人的圖表 | 腳本缺少其中一個元素 | 腳本缺少其中兩個元素 + +**免責聲明**： +本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為具權威性的來源。對於重要資訊，建議使用專業的人力翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/zh-HK/3-Data-Visualization/R/10-visualization-distributions/README.md new file mode 100644 index 00000000..6c544b9b --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -0,0 +1,174 @@ +# 視覺化分佈 + +|![由 [(@sketchthedocs)](https://sketchthedocs.dev) 繪製的手繪筆記](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/10-Visualizing-Distributions.png)| +|:---:| +| 視覺化分佈 - _手繪筆記由 [@nitya](https://twitter.com/nitya) 提供_ | + +在上一課中，你學到了一些關於明尼蘇達州鳥類數據集的有趣事實。通過視覺化異常值，你發現了一些錯誤的數據，並比較了不同鳥類分類的最大長度差異。 + +## [課前測驗](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/18) +## 探索鳥類數據集 + +另一種深入了解數據的方法是查看其分佈，即數據如何沿著某個軸排列。例如，你可能想了解這個數據集中鳥類的最大翼展或最大體重的整體分佈。 + +讓我們來發掘一些關於這個數據集中分佈的事實。在你的 R 控制台中，導入 `ggplot2` 和數據庫。像上一個主題一樣，從數據庫中移除異常值。 + +```r +library(ggplot2) + +birds <- read.csv("../../data/birds.csv",fileEncoding="UTF-8-BOM") + +birds_filtered <- subset(birds, MaxWingspan < 500) +head(birds_filtered) +``` +| | 名稱 | 學名 | 分類 | 目 | 科 | 屬 | 保育狀況 | 最小長度 | 最大長度 | 最小體重 | 最大體重 | 最小翼展 | 最大翼展 | +| ---: | :--------------------------- | :--------------------- | :-------------------- | :----------- | :------- | :---------- | :----------------- | --------: | --------: | ----------: | ----------: | ----------: | ----------: | +| 0 | 黑腹樹鴨 | Dendrocygna autumnalis | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 樹鴨屬 | LC | 47 | 56 | 652 | 1020 | 76 | 94 | +| 1 | 赤樹鴨 | Dendrocygna bicolor | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 樹鴨屬 | LC | 45 | 53 | 712 | 1050 | 85 | 93 | +| 2 | 雪鵝 | Anser caerulescens | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 鵝屬 | LC | 64 | 79 | 2050 | 4050 | 135 | 165 | +| 3 | 羅氏鵝 | Anser rossii | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 鵝屬 | LC | 57.3 | 64 | 1066 | 1567 | 113 | 116 | +| 4 | 大白額鵝 | Anser albifrons | 鴨/鵝/水禽 | 雁形目 | 鴨科 | 鵝屬 | LC | 64 | 81 | 1930 | 3310 | 130 | 165 | + +通常，你可以通過像上一課中那樣使用散點圖快速查看數據的分佈方式： + +```r +ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + + geom_point() + + ggtitle("Max Length per order") + coord_flip() +``` +![每目最大長度](../../../../../translated_images/zh-HK/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) + +這提供了每個鳥類目身體長度分佈的概覽，但這並不是顯示真實分佈的最佳方式。這個任務通常通過創建直方圖來完成。 + +## 使用直方圖 + +`ggplot2` 提供了非常好的方法來使用直方圖視覺化數據分佈。這種類型的圖表類似於條形圖，通過條形的升降可以看到分佈情況。要構建直方圖，你需要數值數據。構建直方圖時，可以將圖表類型定義為 'hist'。這個圖表顯示了整個數據集範圍內最大體重的分佈。通過將數據分為更小的區間（bins），它可以顯示數據值的分佈： + +```r +ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + + geom_histogram(bins=10)+ylab('Frequency') +``` +![整個數據集的分佈](../../../../../translated_images/zh-HK/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) + +如你所見，這個數據集中大多數 400 多種鳥類的最大體重都在 2000 以下。通過將 `bins` 參數設置為更高的數值（例如 30），可以獲得更多的數據洞察： + +```r +ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') +``` + +![30 個區間的分佈](../../../../../translated_images/zh-HK/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) + +這個圖表以更細緻的方式顯示了分佈。通過僅選擇給定範圍內的數據，可以創建一個不那麼偏向左側的圖表： + +篩選數據以僅獲取體重低於 60 的鳥類，並顯示 30 個 `bins`： + +```r +birds_filtered_1 <- subset(birds_filtered, MaxBodyMass > 1 & MaxBodyMass < 60) +ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + + geom_histogram(bins=30)+ylab('Frequency') +``` + +![篩選後的直方圖](../../../../../translated_images/zh-HK/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) + +✅ 試試其他篩選條件和數據點。若要查看數據的完整分佈，移除 `['MaxBodyMass']` 篩選條件以顯示帶標籤的分佈。 + +直方圖還提供了一些不錯的顏色和標籤增強功能可以嘗試： + +創建一個 2D 直方圖來比較兩個分佈之間的關係。我們來比較 `MaxBodyMass` 和 `MaxLength`。`ggplot2` 提供了一種內建方式，通過更亮的顏色顯示匯聚點： + +```r +ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + + geom_bin2d() +scale_fill_continuous(type = "viridis") +``` +可以看到這兩個元素之間沿著預期軸線存在預期的相關性，並且有一個特別強的匯聚點： + +![2D 圖表](../../../../../translated_images/zh-HK/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) + +直方圖對於數值數據效果很好。如果需要查看基於文本數據的分佈該怎麼辦？ + +## 使用文本數據探索數據集的分佈 + +這個數據集還包括關於鳥類分類、屬、種、科以及保育狀況的良好信息。讓我們深入了解這些保育信息。鳥類根據其保育狀況的分佈是什麼樣的？ + +> ✅ 在數據集中，使用了一些縮寫來描述保育狀況。這些縮寫來自 [IUCN 紅色名錄分類](https://www.iucnredlist.org/)，該組織記錄了物種的狀況。 +> +> - CR: 極危 +> - EN: 瀕危 +> - EX: 滅絕 +> - LC: 無危 +> - NT: 近危 +> - VU: 易危 + +這些是基於文本的值，因此你需要進行轉換以創建直方圖。使用篩選後的 `filteredBirds` 數據框，顯示其保育狀況與最小翼展。你看到了什麼？ + +```r +birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'EX'] <- 'x1' +birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'CR'] <- 'x2' +birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'EN'] <- 'x3' +birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'NT'] <- 'x4' +birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'VU'] <- 'x5' +birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'LC'] <- 'x6' + +ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + + geom_histogram(position = "identity", alpha = 0.4, bins = 20) + + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) +``` + +![翼展與保育狀況的對比](../../../../../translated_images/zh-HK/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) + +最小翼展與保育狀況之間似乎沒有明顯的相關性。使用這種方法測試數據集的其他元素。你也可以嘗試不同的篩選條件。你發現了任何相關性嗎？ + +## 密度圖 + +你可能已經注意到，我們目前看到的直方圖是“階梯式”的，並未以平滑的弧線呈現。若要顯示更平滑的密度圖，可以嘗試密度圖。 + +現在讓我們來使用密度圖！ + +```r +ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + + geom_density() +``` +![密度圖](../../../../../translated_images/zh-HK/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) + +你可以看到這個圖表反映了之前的最小翼展數據，只是更平滑了一些。如果你想重新查看第二個圖表中那條不平滑的最大體重線，可以使用這種方法將其非常平滑地重現： + +```r +ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + + geom_density() +``` +![體重密度](../../../../../translated_images/zh-HK/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) + +如果你想要一條平滑但不過於平滑的線，可以編輯 `adjust` 參數： + +```r +ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + + geom_density(adjust = 1/5) +``` +![較少平滑的體重線](../../../../../translated_images/zh-HK/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) + +✅ 閱讀此類圖表可用的參數並進行實驗！ + +這種類型的圖表提供了非常具有解釋性的視覺化。例如，只需幾行代碼，你就可以顯示每個鳥類目最大體重的密度： + +```r +ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + + geom_density(alpha=0.5) +``` +![每目體重密度](../../../../../translated_images/zh-HK/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) + +## 🚀 挑戰 + +直方圖比基本的散點圖、條形圖或折線圖更為複雜。上網搜索一些使用直方圖的好例子。它們是如何使用的？它們展示了什麼？它們通常在哪些領域或研究範疇中使用？ + +## [課後測驗](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/19) + +## 回顧與自學 + +在這一課中，你使用了 `ggplot2` 並開始製作更為複雜的圖表。研究一下 `geom_density_2d()`，這是一種“在一維或多維中顯示連續概率密度曲線”的方法。閱讀 [文檔](https://ggplot2.tidyverse.org/reference/geom_density_2d.html) 以了解其工作原理。 + +## 作業 + +[應用你的技能](assignment.md) + +**免責聲明**： +本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，請注意自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要資訊，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/R/10-visualization-distributions/assignment.md b/translations/zh-HK/3-Data-Visualization/R/10-visualization-distributions/assignment.md new file mode 100644 index 00000000..f8fcea77 --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/R/10-visualization-distributions/assignment.md @@ -0,0 +1,14 @@ +# 運用你的技能 + +## 指引 + +到目前為止，你已經使用了明尼蘇達州的鳥類數據集，探索了有關鳥類數量和種群密度的信息。現在，嘗試運用這些技術，選擇一個不同的數據集來練習，或許可以從 [Kaggle](https://www.kaggle.com/) 獲取數據。撰寫一個 R 腳本，講述這個數據集的故事，並確保在討論中使用直方圖。 + +## 評分標準 + +卓越 | 合格 | 需要改進 +--- | --- | --- | +提供了一個腳本，包含有關數據集的註解（包括其來源），並使用至少 5 個直方圖來發掘數據的相關信息。 | 提供了一個腳本，但註解不完整或存在錯誤。 | 提供了一個腳本，但缺乏註解且包含錯誤。 + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/zh-HK/3-Data-Visualization/R/11-visualization-proportions/README.md new file mode 100644 index 00000000..c1bff39a --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -0,0 +1,189 @@ +# 視覺化比例 + +|![由 [(@sketchthedocs)](https://sketchthedocs.dev) 繪製的速記筆記](../../../sketchnotes/11-Visualizing-Proportions.png)| +|:---:| +|視覺化比例 - _速記筆記由 [@nitya](https://twitter.com/nitya) 繪製_ | + +在這節課中，你將使用一個以自然為主題的數據集來視覺化比例，例如在一個關於蘑菇的數據集中有多少不同種類的真菌。讓我們使用一個來自 Audubon 的數據集來探索這些迷人的真菌，該數據集列出了 Agaricus 和 Lepiota 家族中 23 種有鰓蘑菇的詳細信息。你將嘗試一些有趣的視覺化方式，例如： + +- 圓餅圖 🥧 +- 甜甜圈圖 🍩 +- 華夫圖 🧇 + +> 💡 微軟研究的一個非常有趣的項目 [Charticulator](https://charticulator.com) 提供了一個免費的拖放界面來進行數據視覺化。在他們的一個教程中，他們也使用了這個蘑菇數據集！因此，你可以同時探索數據並學習這個工具庫：[Charticulator 教程](https://charticulator.com/tutorials/tutorial4.html)。 + +## [課前測驗](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/20) + +## 認識你的蘑菇 🍄 + +蘑菇非常有趣。讓我們導入一個數據集來研究它們： + +```r +mushrooms = read.csv('../../data/mushrooms.csv') +head(mushrooms) +``` +一個表格被打印出來，包含一些很棒的分析數據： + +| 類別 | 菌蓋形狀 | 菌蓋表面 | 菌蓋顏色 | 是否有瘀傷 | 氣味 | 鰓附著方式 | 鰓間距 | 鰓大小 | 鰓顏色 | 菌柄形狀 | 菌柄根部 | 菌柄表面（環上方） | 菌柄表面（環下方） | 菌柄顏色（環上方） | 菌柄顏色（環下方） | 菌膜類型 | 菌膜顏色 | 環數量 | 環類型 | 孢子印顏色 | 分布 | 棲息地 | +| --------- | --------- | --------- | --------- | --------- | ------- | ----------- | ----------- | --------- | --------- | ----------- | ----------- | ------------------ | ------------------ | ------------------ | ------------------ | --------- | --------- | ----------- | --------- | ----------------- | --------- | ------- | +| 有毒 | 凸形 | 光滑 | 棕色 | 有瘀傷 | 刺鼻 | 自由 | 緊密 | 狹窄 | 黑色 | 擴大 | 等長 | 光滑 | 光滑 | 白色 | 白色 | 部分 | 白色 | 一個 | 垂懸 | 黑色 | 分散 | 城市 | +| 可食用 | 凸形 | 光滑 | 黃色 | 有瘀傷 | 杏仁 | 自由 | 緊密 | 寬大 | 黑色 | 擴大 | 棍狀 | 光滑 | 光滑 | 白色 | 白色 | 部分 | 白色 | 一個 | 垂懸 | 棕色 | 多數 | 草地 | +| 可食用 | 鐘形 | 光滑 | 白色 | 有瘀傷 | 茴香 | 自由 | 緊密 | 寬大 | 棕色 | 擴大 | 棍狀 | 光滑 | 光滑 | 白色 | 白色 | 部分 | 白色 | 一個 | 垂懸 | 棕色 | 多數 | 草原 | +| 有毒 | 凸形 | 鱗片狀 | 白色 | 有瘀傷 | 刺鼻 | 自由 | 緊密 | 狹窄 | 棕色 | 擴大 | 等長 | 光滑 | 光滑 | 白色 | 白色 | 部分 | 白色 | 一個 | 垂懸 | 黑色 | 分散 | 城市 | +| 可食用 | 凸形 | 光滑 | 綠色 | 無瘀傷 | 無氣味 | 自由 | 擁擠 | 寬大 | 黑色 | 錐形 | 等長 | 光滑 | 光滑 | 白色 | 白色 | 部分 | 白色 | 一個 | 消失 | 棕色 | 豐富 | 草地 | +| 可食用 | 凸形 | 鱗片狀 | 黃色 | 有瘀傷 | 杏仁 | 自由 | 緊密 | 寬大 | 棕色 | 擴大 | 棍狀 | 光滑 | 光滑 | 白色 | 白色 | 部分 | 白色 | 一個 | 垂懸 | 黑色 | 多數 | 草地 | + +你會立刻注意到所有的數據都是文本格式。你需要將這些數據轉換為可以用於圖表的格式。事實上，大部分數據是以對象形式表示的： + +```r +names(mushrooms) +``` + +輸出結果為： + +```output +[1] "class" "cap.shape" + [3] "cap.surface" "cap.color" + [5] "bruises" "odor" + [7] "gill.attachment" "gill.spacing" + [9] "gill.size" "gill.color" +[11] "stalk.shape" "stalk.root" +[13] "stalk.surface.above.ring" "stalk.surface.below.ring" +[15] "stalk.color.above.ring" "stalk.color.below.ring" +[17] "veil.type" "veil.color" +[19] "ring.number" "ring.type" +[21] "spore.print.color" "population" +[23] "habitat" +``` +將這些數據中的「類別」列轉換為分類： + +```r +library(dplyr) +grouped=mushrooms %>% + group_by(class) %>% + summarise(count=n()) +``` + +現在，如果你打印出蘑菇數據，你會看到它已根據有毒/可食用類別分組： + +```r +View(grouped) +``` + +| 類別 | 數量 | +| --------- | --------- | +| 可食用 | 4208 | +| 有毒 | 3916 | + +如果你按照這個表格中呈現的順序來創建類別標籤，你可以製作一個圓餅圖。 + +## 圓餅圖！ + +```r +pie(grouped$count,grouped$class, main="Edible?") +``` +完成，一個圓餅圖展示了根據這兩類蘑菇的比例數據。正確排列標籤的順序非常重要，尤其是在這裡，因此請務必核對標籤數組的構建順序！ + +![圓餅圖](../../../../../translated_images/zh-HK/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) + +## 甜甜圈圖！ + +一種更具視覺吸引力的圓餅圖是甜甜圈圖，它是一個中間有洞的圓餅圖。讓我們用這種方法來查看數據。 + +看看蘑菇生長的各種棲息地： + +```r +library(dplyr) +habitat=mushrooms %>% + group_by(habitat) %>% + summarise(count=n()) +View(habitat) +``` +輸出結果為： + +| 棲息地 | 數量 | +| --------- | --------- | +| 草地 | 2148 | +| 樹葉 | 832 | +| 草原 | 292 | +| 小徑 | 1144 | +| 城市 | 368 | +| 廢棄地 | 192 | +| 樹木 | 3148 | + +在這裡，你將數據按棲息地分組。共有 7 種棲息地，因此使用這些作為甜甜圈圖的標籤： + +```r +library(ggplot2) +library(webr) +PieDonut(habitat, aes(habitat, count=count)) +``` + +![甜甜圈圖](../../../../../translated_images/zh-HK/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) + +這段代碼使用了兩個庫——ggplot2 和 webr。使用 webr 庫的 PieDonut 函數，我們可以輕鬆創建甜甜圈圖！ + +在 R 中僅使用 ggplot2 庫也可以製作甜甜圈圖。你可以在[這裡](https://www.r-graph-gallery.com/128-ring-or-donut-plot.html)了解更多並自己嘗試。 + +現在你知道如何分組數據並將其顯示為圓餅圖或甜甜圈圖，你可以探索其他類型的圖表。試試華夫圖，它是一種不同的方式來探索數量。 + +## 華夫圖！ + +「華夫」類型的圖表是一種以 2D 方格陣列視覺化數量的方式。試著視覺化這個數據集中蘑菇菌蓋顏色的不同數量。為此，你需要安裝一個名為 [waffle](https://cran.r-project.org/web/packages/waffle/waffle.pdf) 的輔助庫，並使用它來生成你的視覺化： + +```r +install.packages("waffle", repos = "https://cinc.rud.is") +``` + +選擇數據的一部分進行分組： + +```r +library(dplyr) +cap_color=mushrooms %>% + group_by(cap.color) %>% + summarise(count=n()) +View(cap_color) +``` + +通過創建標籤並分組數據來製作華夫圖： + +```r +library(waffle) +names(cap_color$count) = paste0(cap_color$cap.color) +waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual(values=c("brown", "#F0DC82", "#D2691E", "green", + "pink", "purple", "red", "grey", + "yellow","white")) +``` + +使用華夫圖，你可以清楚地看到這個蘑菇數據集中菌蓋顏色的比例。有趣的是，有許多綠色菌蓋的蘑菇！ + +![華夫圖](../../../../../translated_images/zh-HK/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) + +在這節課中，你學到了三種視覺化比例的方法。首先，你需要將數據分組到分類中，然後決定哪種方式最適合展示數據——圓餅圖、甜甜圈圖或華夫圖。這些方法都很有趣，能讓用戶快速了解數據集。 + +## 🚀 挑戰 + +試著在 [Charticulator](https://charticulator.com) 中重現這些有趣的圖表。 + +## [課後測驗](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/21) + +## 回顧與自學 + +有時候，什麼時候使用圓餅圖、甜甜圈圖或華夫圖並不明顯。以下是一些相關文章供你閱讀： + +https://www.beautiful.ai/blog/battle-of-the-charts-pie-chart-vs-donut-chart + +https://medium.com/@hypsypops/pie-chart-vs-donut-chart-showdown-in-the-ring-5d24fd86a9ce + +https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c6.htm + +https://medium.datadriveninvestor.com/data-visualization-done-the-right-way-with-tableau-waffle-chart-fdf2a19be402 + +進行一些研究以了解更多關於這個選擇的資訊。 + +## 作業 + +[在 Excel 中試試看](assignment.md) + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/zh-HK/3-Data-Visualization/R/12-visualization-relationships/README.md new file mode 100644 index 00000000..7042f2ed --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -0,0 +1,168 @@ +# 視覺化關係：關於蜂蜜 🍯 + +|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/12-Visualizing-Relationships.png)| +|:---:| +|視覺化關係 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | + +延續我們研究的自然主題，讓我們探索一些有趣的視覺化方式，展示不同種類蜂蜜之間的關係。這些數據來自[美國農業部](https://www.nass.usda.gov/About_NASS/index.php)的資料集。 + +這個包含約600項的資料集展示了美國多個州的蜂蜜生產情況。例如，您可以查看每個州在1998年至2012年間的蜂群數量、每群產量、總生產量、庫存、每磅價格以及蜂蜜的生產價值，每年每州一行數據。 + +我們可以視覺化某州每年的生產量與該州蜂蜜價格之間的關係。或者，您也可以視覺化各州每群蜂蜜產量之間的關係。這段時間涵蓋了2006年首次出現的毀滅性“蜂群崩潰症”（CCD，Colony Collapse Disorder）（http://npic.orst.edu/envir/ccd.html），因此這是一個值得研究的數據集。🐝 + +## [課前測驗](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/22) + +在本課中，您可以使用 ggplot2，這是一個您之前使用過的優秀庫，用於視覺化變量之間的關係。特別有趣的是使用 ggplot2 的 `geom_point` 和 `qplot` 函數，這些函數可以快速生成散點圖和折線圖，視覺化“[統計關係](https://ggplot2.tidyverse.org/)”，幫助數據科學家更好地理解變量之間的關聯。 + +## 散點圖 + +使用散點圖展示蜂蜜價格每年每州的變化。ggplot2 的 `ggplot` 和 `geom_point` 可以方便地將州的數據分組，並顯示分類和數值數據的數據點。 + +讓我們先導入數據和 Seaborn： + +```r +honey=read.csv('../../data/honey.csv') +head(honey) +``` +您會注意到蜂蜜數據中有幾個有趣的列，包括年份和每磅價格。讓我們探索按美國州分組的數據： + +| 州 | 蜂群數量 | 每群產量 | 總生產量 | 庫存 | 每磅價格 | 生產價值 | 年份 | +| ----- | -------- | -------- | -------- | -------- | ---------- | --------- | ---- | +| AL | 16000 | 71 | 1136000 | 159000 | 0.72 | 818000 | 1998 | +| AZ | 55000 | 60 | 3300000 | 1485000 | 0.64 | 2112000 | 1998 | +| AR | 53000 | 65 | 3445000 | 1688000 | 0.59 | 2033000 | 1998 | +| CA | 450000 | 83 | 37350000 | 12326000 | 0.62 | 23157000 | 1998 | +| CO | 27000 | 72 | 1944000 | 1594000 | 0.7 | 1361000 | 1998 | +| FL | 230000 | 98 | 22540000 | 4508000 | 0.64 | 14426000 | 1998 | + +創建一個基本的散點圖，展示蜂蜜每磅價格與其來源州之間的關係。讓 `y` 軸足夠高以顯示所有州： + +```r +library(ggplot2) +ggplot(honey, aes(x = priceperlb, y = state)) + + geom_point(colour = "blue") +``` +![scatterplot 1](../../../../../translated_images/zh-HK/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) + +現在，使用蜂蜜色彩方案展示價格隨年份的變化。您可以通過添加 'scale_color_gradientn' 參數來顯示每年的變化： + +> ✅ 了解更多關於 [scale_color_gradientn](https://www.rdocumentation.org/packages/ggplot2/versions/0.9.1/topics/scale_colour_gradientn) 的信息 - 試試美麗的彩虹色方案！ + +```r +ggplot(honey, aes(x = priceperlb, y = state, color=year)) + + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) +``` +![scatterplot 2](../../../../../translated_images/zh-HK/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) + +使用這種色彩方案，您可以看到蜂蜜每磅價格隨年份的明顯增長趨勢。事實上，如果您查看數據中的樣本集（例如選擇亞利桑那州），您可以看到價格每年逐漸上漲，僅有少數例外： + +| 州 | 蜂群數量 | 每群產量 | 總生產量 | 庫存 | 每磅價格 | 生產價值 | 年份 | +| ----- | -------- | -------- | -------- | -------- | ---------- | --------- | ---- | +| AZ | 55000 | 60 | 3300000 | 1485000 | 0.64 | 2112000 | 1998 | +| AZ | 52000 | 62 | 3224000 | 1548000 | 0.62 | 1999000 | 1999 | +| AZ | 40000 | 59 | 2360000 | 1322000 | 0.73 | 1723000 | 2000 | +| AZ | 43000 | 59 | 2537000 | 1142000 | 0.72 | 1827000 | 2001 | +| AZ | 38000 | 63 | 2394000 | 1197000 | 1.08 | 2586000 | 2002 | +| AZ | 35000 | 72 | 2520000 | 983000 | 1.34 | 3377000 | 2003 | +| AZ | 32000 | 55 | 1760000 | 774000 | 1.11 | 1954000 | 2004 | +| AZ | 36000 | 50 | 1800000 | 720000 | 1.04 | 1872000 | 2005 | +| AZ | 30000 | 65 | 1950000 | 839000 | 0.91 | 1775000 | 2006 | +| AZ | 30000 | 64 | 1920000 | 902000 | 1.26 | 2419000 | 2007 | +| AZ | 25000 | 64 | 1600000 | 336000 | 1.26 | 2016000 | 2008 | +| AZ | 20000 | 52 | 1040000 | 562000 | 1.45 | 1508000 | 2009 | +| AZ | 24000 | 77 | 1848000 | 665000 | 1.52 | 2809000 | 2010 | +| AZ | 23000 | 53 | 1219000 | 427000 | 1.55 | 1889000 | 2011 | +| AZ | 22000 | 46 | 1012000 | 253000 | 1.79 | 1811000 | 2012 | + +另一種視覺化這種趨勢的方法是使用大小而非顏色。對於色盲用戶，這可能是一個更好的選擇。編輯您的視覺化，通過點的直徑大小展示價格的增長： + +```r +ggplot(honey, aes(x = priceperlb, y = state)) + + geom_point(aes(size = year),colour = "blue") + + scale_size_continuous(range = c(0.25, 3)) +``` +您可以看到點的大小逐漸增大。 + +![scatterplot 3](../../../../../translated_images/zh-HK/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) + +這是否是一個簡單的供需問題？由於氣候變化和蜂群崩潰等因素，是否每年可供購買的蜂蜜減少，導致價格上漲？ + +為了探索數據集中某些變量之間的相關性，讓我們研究一些折線圖。 + +## 折線圖 + +問題：蜂蜜每磅價格是否每年明顯上漲？您可以通過創建一個單一折線圖來最簡單地發現這一點： + +```r +qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab = "priceperlb") +``` +答案：是的，但在2003年左右有一些例外： + +![line chart 1](../../../../../translated_images/zh-HK/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) + +問題：那麼在2003年，我們是否也能看到蜂蜜供應的激增？如果您查看每年的總生產量呢？ + +```python +qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") +``` + +![line chart 2](../../../../../translated_images/zh-HK/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) + +答案：並不完全。如果您查看總生產量，實際上在那一年似乎有所增加，儘管總體而言蜂蜜的生產量在這些年中呈下降趨勢。 + +問題：在這種情況下，2003年蜂蜜價格的激增可能是什麼原因？ + +為了探索這一點，您可以使用分面網格。 + +## 分面網格 + +分面網格可以選擇數據集的一個方面（在我們的例子中，您可以選擇“年份”，以避免生成過多的分面）。Seaborn 可以根據您選擇的 x 和 y 坐標為每個分面生成一個圖表，方便進行視覺比較。2003年是否在這種比較中顯得突出？ + +使用 [ggplot2 的文檔](https://ggplot2.tidyverse.org/reference/facet_wrap.html)推薦的 `facet_wrap` 創建分面網格。 + +```r +ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + + geom_line() + facet_wrap(vars(year)) +``` +在此視覺化中，您可以比較每群產量和蜂群數量每年每州的變化，並將列數設置為3： + +![facet grid](../../../../../translated_images/zh-HK/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) + +對於此數據集，關於蜂群數量和每群產量每年每州的變化，並未有特別突出的地方。是否有其他方式可以找到這兩個變量之間的相關性？ + +## 雙折線圖 + +嘗試使用 R 的 `par` 和 `plot` 函數，通過疊加兩個折線圖來創建多折線圖。我們將在 x 軸上繪製年份，並顯示兩個 y 軸。展示每群產量和蜂群數量，疊加在一起： + +```r +par(mar = c(5, 4, 4, 4) + 0.3) +plot(honey$year, honey$numcol, pch = 16, col = 2,type="l") +par(new = TRUE) +plot(honey$year, honey$yieldpercol, pch = 17, col = 3, + axes = FALSE, xlab = "", ylab = "",type="l") +axis(side = 4, at = pretty(range(y2))) +mtext("colony yield", side = 4, line = 3) +``` +![superimposed plots](../../../../../translated_images/zh-HK/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) + +雖然在2003年並未有明顯的異常，但這讓我們可以以一個稍微樂觀的結論結束本課：儘管蜂群數量總體上在下降，但蜂群數量正在穩定，即使每群產量在減少。 + +加油，蜜蜂們！ + +🐝❤️ +## 🚀 挑戰 + +在本課中，您學到了更多關於散點圖和折線網格的其他用途，包括分面網格。挑戰自己使用不同的數據集（可能是您之前使用過的數據集）創建分面網格。注意它們的生成時間以及需要小心處理的分面數量。 + +## [課後測驗](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/23) + +## 回顧與自學 + +折線圖可以是簡單的，也可以是非常複雜的。閱讀 [ggplot2 文檔](https://ggplot2.tidyverse.org/reference/geom_path.html#:~:text=geom_line()%20connects%20them%20in,which%20cases%20are%20connected%20together)，了解構建折線圖的各種方法。嘗試使用文檔中列出的其他方法來增強您在本課中構建的折線圖。 + +## 作業 + +[深入蜂巢](assignment.md) + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/zh-HK/3-Data-Visualization/R/13-meaningful-vizualizations/README.md new file mode 100644 index 00000000..dc7b66d9 --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -0,0 +1,171 @@ +# 製作有意義的視覺化圖表 + +|![由 [(@sketchthedocs)](https://sketchthedocs.dev) 繪製的速寫筆記](../../../sketchnotes/13-MeaningfulViz.png)| +|:---:| +| 有意義的視覺化圖表 - _速寫筆記由 [@nitya](https://twitter.com/nitya) 提供_ | + +> 「如果你對數據施加足夠的壓力，它會承認任何事情」-- [Ronald Coase](https://en.wikiquote.org/wiki/Ronald_Coase) + +作為一名數據科學家，基本技能之一就是能夠創建有意義的數據視覺化，幫助回答你可能提出的問題。在進行數據視覺化之前，你需要確保數據已經像之前課程中所教的那樣進行清理和準備。之後，你就可以開始決定如何最好地呈現數據。 + +在本課中，你將學習： + +1. 如何選擇合適的圖表類型 +2. 如何避免誤導性的圖表 +3. 如何使用顏色 +4. 如何設計圖表以提高可讀性 +5. 如何構建動畫或3D圖表解決方案 +6. 如何創建創意視覺化 + +## [課前測驗](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/24) + +## 選擇合適的圖表類型 + +在之前的課程中，你已經使用 Matplotlib 和 Seaborn 嘗試構建各種有趣的數據視覺化圖表。通常，你可以根據這張表格選擇[合適的圖表類型](https://chartio.com/learn/charts/how-to-select-a-data-vizualization/)來回答你的問題： + +| 你的需求是： | 你應該使用： | +| -------------------------- | ---------------------------- | +| 展示隨時間變化的數據趨勢 | 折線圖 | +| 比較不同類別 | 柱狀圖、餅圖 | +| 比較總量 | 餅圖、堆疊柱狀圖 | +| 展示關係 | 散點圖、折線圖、分面圖、雙折線圖 | +| 展示分佈 | 散點圖、直方圖、箱型圖 | +| 展示比例 | 餅圖、甜甜圈圖、華夫圖 | + +> ✅ 根據數據的組成，你可能需要將其從文本轉換為數字，以支持某些圖表。 + +## 避免誤導 + +即使數據科學家謹慎地為正確的數據選擇了合適的圖表，仍然有許多方法可以以誤導的方式展示數據，通常是為了證明某個觀點，卻犧牲了數據的真實性。有許多誤導性圖表和信息圖的例子！ + +[![Alberto Cairo 的《How Charts Lie》](../../../../../translated_images/zh-HK/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") + +> 🎥 點擊上方圖片觀看關於誤導性圖表的會議演講 + +這張圖表反轉了 X 軸，根據日期顯示了與事實相反的內容： + +![糟糕的圖表 1](../../../../../translated_images/zh-HK/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) + +[這張圖表](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) 更加誤導，因為視覺上吸引人注意的是右側，讓人得出隨時間推移各縣的 COVID 病例數下降的結論。事實上，如果仔細查看日期，你會發現它們被重新排列以製造出誤導性的下降趨勢。 + +![糟糕的圖表 2](../../../../../translated_images/zh-HK/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) + +這個臭名昭著的例子使用顏色和反轉的 Y 軸來誤導：原本應該得出槍支友好立法通過後槍支死亡率激增的結論，卻讓人誤以為情況正好相反： + +![糟糕的圖表 3](../../../../../translated_images/zh-HK/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) + +這張奇怪的圖表展示了比例如何被操控，效果令人捧腹： + +![糟糕的圖表 4](../../../../../translated_images/zh-HK/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) + +比較不可比的事物是另一種不正當的手段。有一個[精彩的網站](https://tylervigen.com/spurious-correlations)專門展示「虛假的相關性」，例如顯示緬因州的離婚率與人造奶油的消耗量之間的「事實」相關性。一個 Reddit 群組也收集了[糟糕的數據使用](https://www.reddit.com/r/dataisugly/top/?t=all)。 + +理解眼睛如何容易被誤導性圖表欺騙是很重要的。即使數據科學家的意圖是好的，選擇了糟糕的圖表類型，例如顯示過多類別的餅圖，也可能具有誤導性。 + +## 顏色 + +你在上面提到的「佛羅里達槍支暴力」圖表中看到，顏色可以為圖表提供額外的意義，尤其是那些未使用 ggplot2 和 RColorBrewer 等庫設計的圖表，這些庫提供了各種經過驗證的顏色庫和調色板。如果你是手動製作圖表，可以稍微研究一下[顏色理論](https://colormatters.com/color-and-design/basic-color-theory)。 + +> ✅ 在設計圖表時，請注意可訪問性是視覺化的重要方面。一些用戶可能是色盲——你的圖表是否能為視覺障礙者良好顯示？ + +選擇圖表顏色時要小心，因為顏色可能傳達你未曾預料的含義。上面「身高」圖表中的「粉紅女士」傳達了一種明顯的「女性化」含義，這增加了圖表本身的怪異感。 + +雖然[顏色的含義](https://colormatters.com/color-symbolism/the-meanings-of-colors)可能因地區而異，並且根據色調的不同而改變，但一般來說，顏色的含義包括： + +| 顏色 | 含義 | +| ------ | -------------------- | +| 紅色 | 力量 | +| 藍色 | 信任、忠誠 | +| 黃色 | 快樂、警告 | +| 綠色 | 生態、幸運、嫉妒 | +| 紫色 | 快樂 | +| 橙色 | 活力 | + +如果你需要使用自定義顏色構建圖表，請確保你的圖表既可訪問又符合你想要傳達的含義。 + +## 設計圖表以提高可讀性 + +如果圖表不可讀，它就沒有意義！花點時間考慮調整圖表的寬度和高度，使其能與數據良好匹配。如果需要顯示一個變量（例如所有 50 個州），請盡可能在 Y 軸上垂直顯示，以避免水平滾動的圖表。 + +標記你的軸，必要時提供圖例，並提供工具提示以便更好地理解數據。 + +如果你的數據在 X 軸上是文本且冗長，可以將文本角度調整以提高可讀性。[plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) 提供了 3D 繪圖功能，如果你的數據支持它，可以使用它來生成更高級的數據視覺化。 + +![3D 圖表](../../../../../translated_images/zh-HK/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) + +## 動畫和 3D 圖表展示 + +如今一些最好的數據視覺化是動畫化的。Shirley Wu 使用 D3 創作了令人驚嘆的作品，例如「[電影之花](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)」，每朵花都是一部電影的視覺化。另一個例子是《衛報》的「Bussed Out」，這是一個結合 Greensock 和 D3 的視覺化與滾動敘事文章格式的互動體驗，展示了紐約市如何通過將無家可歸者送出城市來處理其無家可歸問題。 + +![Bussed Out](../../../../../translated_images/zh-HK/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) + +> 「Bussed Out: How America Moves its Homeless」來自[衛報](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study)。視覺化由 Nadieh Bremer 和 Shirley Wu 創作 + +雖然本課程不足以深入教授這些強大的視覺化庫，但你可以嘗試在 Vue.js 應用中使用 D3，展示一本書《危險關係》的動畫社交網絡視覺化。 + +> 《危險關係》是一部書信體小說，即以一系列信件形式呈現的小說。由 Choderlos de Laclos 於 1782 年撰寫，講述了 18 世紀晚期法國貴族中兩位主角 Vicomte de Valmont 和 Marquise de Merteuil 的惡毒、道德敗壞的社交手段。兩人最終都遭遇了悲劇，但在此之前造成了大量社會損害。小說以寫給圈內各人的信件形式展開，策劃復仇或僅僅是製造麻煩。創建這些信件的視覺化，探索敘事中的主要角色，並以視覺方式呈現。 + +你將完成一個網頁應用，展示這個社交網絡的動畫視圖。它使用了一個庫來創建[網絡視覺化](https://github.com/emiliorizzo/vue-d3-network)，基於 Vue.js 和 D3。當應用運行時，你可以在屏幕上拖動節點，重新排列數據。 + +![危險關係](../../../../../translated_images/zh-HK/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) + +## 項目：使用 D3.js 構建一個展示網絡的圖表 + +> 本課程文件夾包含一個 `solution` 文件夾，你可以在其中找到完整的項目供參考。 + +1. 按照起始文件夾根目錄中的 README.md 文件中的指示操作。確保你的機器上已安裝 NPM 和 Node.js，並運行它們以安裝項目依賴。 + +2. 打開 `starter/src` 文件夾。你會發現一個 `assets` 文件夾，其中包含一個 .json 文件，列出了所有信件，並帶有「to」和「from」的註釋。 + +3. 完成 `components/Nodes.vue` 中的代碼以啟用視覺化。找到名為 `createLinks()` 的方法，並添加以下嵌套循環。 + +循環遍歷 .json 對象以捕獲信件的「to」和「from」數據，並構建 `links` 對象，以便視覺化庫可以使用它： + +```javascript +//loop through letters + let f = 0; + let t = 0; + for (var i = 0; i < letters.length; i++) { + for (var j = 0; j < characters.length; j++) { + + if (characters[j] == letters[i].from) { + f = j; + } + if (characters[j] == letters[i].to) { + t = j; + } + } + this.links.push({ sid: f, tid: t }); + } + ``` + +從終端運行你的應用（npm run serve），享受視覺化效果！ + +## 🚀 挑戰 + +瀏覽互聯網，發現誤導性的視覺化圖表。作者如何欺騙用戶？這是故意的嗎？嘗試修正這些視覺化圖表，展示它們應有的樣子。 + +## [課後測驗](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/25) + +## 回顧與自學 + +以下是一些關於誤導性數據視覺化的文章： + +https://gizmodo.com/how-to-lie-with-data-visualization-1563576606 + +http://ixd.prattsi.org/2017/12/visual-lies-usability-in-deceptive-data-visualizations/ + +看看這些有趣的歷史資產和文物視覺化： + +https://handbook.pubpub.org/ + +閱讀這篇文章，了解動畫如何增強你的視覺化效果： + +https://medium.com/@EvanSinar/use-animation-to-supercharge-data-visualization-cd905a882ad4 + +## 作業 + +[創建你自己的自定義視覺化](assignment.md) + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/3-Data-Visualization/README.md b/translations/zh-HK/3-Data-Visualization/README.md new file mode 100644 index 00000000..d68fed03 --- /dev/null +++ b/translations/zh-HK/3-Data-Visualization/README.md @@ -0,0 +1,31 @@ +# 視覺化 + +![一隻蜜蜂停在薰衣草花上](../../../translated_images/zh-HK/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) +> 照片由 Jenna Lee 提供，來源於 Unsplash + +視覺化數據是數據科學家最重要的任務之一。圖片勝過千言萬語，視覺化可以幫助你識別數據中的各種有趣部分，例如峰值、異常值、分組、趨勢等等，這些都能幫助你理解數據背後的故事。 + +在這五節課中，你將探索來自自然的數據，並使用各種技術創建有趣且美麗的視覺化。 + +| 主題編號 | 主題 | 相關課程 | 作者 | +| :-----------: | :--: | :-----------: | :----: | +| 1. | 數量視覺化 |
  • [Python](09-visualization-quantities/README.md)
  • [R](../../../3-Data-Visualization/R/09-visualization-quantities)
|
  • [Jen Looper](https://twitter.com/jenlooper)
  • [Vidushi Gupta](https://github.com/Vidushi-Gupta)
  • [Jasleen Sondhi](https://github.com/jasleen101010)
| +| 2. | 分佈視覺化 |
  • [Python](10-visualization-distributions/README.md)
  • [R](../../../3-Data-Visualization/R/10-visualization-distributions)
|
  • [Jen Looper](https://twitter.com/jenlooper)
  • [Vidushi Gupta](https://github.com/Vidushi-Gupta)
  • [Jasleen Sondhi](https://github.com/jasleen101010)
| +| 3. | 比例視覺化 |
  • [Python](11-visualization-proportions/README.md)
  • [R](../../../3-Data-Visualization)
|
  • [Jen Looper](https://twitter.com/jenlooper)
  • [Vidushi Gupta](https://github.com/Vidushi-Gupta)
  • [Jasleen Sondhi](https://github.com/jasleen101010)
| +| 4. | 關係視覺化 |
  • [Python](12-visualization-relationships/README.md)
  • [R](../../../3-Data-Visualization)
|
  • [Jen Looper](https://twitter.com/jenlooper)
  • [Vidushi Gupta](https://github.com/Vidushi-Gupta)
  • [Jasleen Sondhi](https://github.com/jasleen101010)
| +| 5. | 創建有意義的視覺化 |
  • [Python](13-meaningful-visualizations/README.md)
  • [R](../../../3-Data-Visualization)
|
  • [Jen Looper](https://twitter.com/jenlooper)
  • [Vidushi Gupta](https://github.com/Vidushi-Gupta)
  • [Jasleen Sondhi](https://github.com/jasleen101010)
| + +### 致謝 + +這些視覺化課程由 [Jen Looper](https://twitter.com/jenlooper)、[Jasleen Sondhi](https://github.com/jasleen101010) 和 [Vidushi Gupta](https://github.com/Vidushi-Gupta) 用 🌸 精心編寫。 + +🍯 美國蜂蜜生產數據來源於 Jessica Li 在 [Kaggle](https://www.kaggle.com/jessicali9530/honey-production) 上的項目。該 [數據](https://usda.library.cornell.edu/concern/publications/rn301137d) 來自 [美國農業部](https://www.nass.usda.gov/About_NASS/index.php)。 + +🍄 蘑菇數據同樣來源於 [Kaggle](https://www.kaggle.com/hatterasdunton/mushroom-classification-updated-dataset)，由 Hatteras Dunton 修訂。該數據集包括描述假設樣本，涵蓋 Agaricus 和 Lepiota 家族中 23 種有鰓蘑菇的特徵。蘑菇數據摘自《Audubon Society Field Guide to North American Mushrooms》（1981）。該數據集於 1987 年捐贈給 UCI ML 27。 + +🦆 明尼蘇達州鳥類數據來自 [Kaggle](https://www.kaggle.com/hannahcollins/minnesota-birds)，由 Hannah Collins 從 [Wikipedia](https://en.wikipedia.org/wiki/List_of_birds_of_Minnesota) 抓取。 + +所有這些數據集均以 [CC0: Creative Commons](https://creativecommons.org/publicdomain/zero/1.0/) 授權。 + +**免責聲明**： +本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為具權威性的來源。對於重要資訊，建議使用專業的人類翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/zh-HK/4-Data-Science-Lifecycle/14-Introduction/README.md new file mode 100644 index 00000000..70431734 --- /dev/null +++ b/translations/zh-HK/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -0,0 +1,111 @@ +# 數據科學生命周期簡介 + +|![ 由 [(@sketchthedocs)](https://sketchthedocs.dev) 繪製的手繪筆記 ](../../sketchnotes/14-DataScience-Lifecycle.png)| +|:---:| +| 數據科學生命周期簡介 - _手繪筆記由 [@nitya](https://twitter.com/nitya) 繪製_ | + +## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/26) + +到目前為止，你可能已經意識到數據科學是一個過程。這個過程可以分為五個階段： + +- 捕獲 +- 處理 +- 分析 +- 溝通 +- 維護 + +本課程將重點介紹生命周期中的三個部分：捕獲、處理和維護。 + +![數據科學生命周期圖](../../../../translated_images/zh-HK/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) +> 圖片來源：[Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) + +## 捕獲 + +生命周期的第一階段非常重要，因為接下來的階段都依賴於它。這實際上是兩個階段的結合：獲取數據以及定義需要解決的目的和問題。 +定義項目的目標需要對問題或問題有更深入的背景了解。首先，我們需要識別並獲取那些需要解決問題的人。這些可能是企業的利益相關者或項目的贊助者，他們可以幫助確定誰或什麼將從該項目中受益，以及他們需要什麼和為什麼需要它。一個定義良好的目標應該是可測量且可量化的，以便定義可接受的結果。 + +數據科學家可能會問的問題： +- 這個問題以前是否被解決過？發現了什麼？ +- 所有參與者是否都理解目的和目標？ +- 是否存在模糊性？如何減少模糊性？ +- 有哪些限制？ +- 最終結果可能是什麼樣子？ +- 有多少資源（時間、人員、計算能力）可用？ + +接下來是識別、收集，最後探索為實現這些定義目標所需的數據。在這個獲取階段，數據科學家還必須評估數據的數量和質量。這需要一些數據探索來確認所獲取的數據是否能支持達到預期結果。 + +數據科學家可能會問的數據相關問題： +- 我已經擁有哪些數據？ +- 誰擁有這些數據？ +- 有哪些隱私問題？ +- 我是否擁有足夠的數據來解決這個問題？ +- 這些數據的質量是否適合解決這個問題？ +- 如果通過這些數據發現了額外的信息，我們是否應該考慮更改或重新定義目標？ + +## 處理 + +生命周期的處理階段專注於發現數據中的模式以及建模。在處理階段使用的一些技術需要統計方法來揭示模式。通常，對於大型數據集來說，這是一項繁瑣的任務，需要依賴計算機來完成繁重的工作以加快過程。這一階段也是數據科學與機器學習交叉的地方。正如你在第一課中學到的，機器學習是構建模型以理解數據的過程。模型是數據中變量之間關係的表示，有助於預測結果。 + +本階段常用的技術在《機器學習初學者》課程中有介紹。點擊以下鏈接了解更多： + +- [分類](https://github.com/microsoft/ML-For-Beginners/tree/main/4-Classification)：將數據組織到類別中以提高使用效率。 +- [聚類](https://github.com/microsoft/ML-For-Beginners/tree/main/5-Clustering)：將數據分組到相似的群組中。 +- [回歸](https://github.com/microsoft/ML-For-Beginners/tree/main/2-Regression)：確定變量之間的關係以預測或預測值。 + +## 維護 + +在生命周期的圖表中，你可能注意到維護位於捕獲和處理之間。維護是一個持續的過程，涉及在項目過程中管理、存儲和保護數據，並且應在整個項目中加以考慮。 + +### 存儲數據 +數據存儲的方式和位置會影響存儲成本以及數據訪問的速度。這些決策通常不會由數據科學家單獨做出，但他們可能需要根據數據的存儲方式來選擇如何處理數據。 + +以下是現代數據存儲系統的一些方面，可能會影響這些選擇： + +**本地存儲 vs 非本地存儲 vs 公有雲或私有雲** + +本地存儲是指在自己的設備上管理數據，例如擁有一台存儲數據的服務器；而非本地存儲依賴於你不擁有的設備，例如數據中心。公有雲是一種流行的數據存儲選擇，無需了解數據的具體存儲位置或方式，其中“公有”指的是所有使用雲服務的人共享統一的基礎設施。一些組織有嚴格的安全政策，要求完全訪問存儲數據的設備，這時會選擇提供專屬雲服務的私有雲。你將在[後續課程](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/5-Data-Science-In-Cloud)中學到更多關於雲端數據的內容。 + +**冷數據 vs 熱數據** + +在訓練模型時，你可能需要更多的訓練數據。如果你對模型感到滿意，仍然會有更多數據到來以支持模型的用途。無論如何，隨著數據的積累，存儲和訪問數據的成本將會增加。將很少使用的數據（稱為冷數據）與經常訪問的數據（稱為熱數據）分開存儲，通過硬件或軟件服務可以是一種更便宜的存儲選擇。如果需要訪問冷數據，可能會比熱數據花費更長的時間。 + +### 管理數據 +在處理數據時，你可能會發現一些數據需要使用[數據準備](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/2-Working-With-Data/08-data-preparation)課程中介紹的技術進行清理，以構建準確的模型。當新數據到來時，也需要應用相同的技術來保持質量的一致性。一些項目會使用自動化工具來進行清理、聚合和壓縮，然後將數據移動到最終位置。Azure Data Factory 就是一個這樣的工具。 + +### 保護數據 +保護數據的主要目標之一是確保數據的收集和使用處於控制之中。保持數據安全包括限制只有需要的人才能訪問數據，遵守當地法律和法規，以及維持[道德標準](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/1-Introduction/02-ethics)。 + +以下是團隊可能採取的一些安全措施： +- 確保所有數據都已加密 +- 向客戶提供有關其數據使用方式的信息 +- 移除已離開項目人員的數據訪問權限 +- 僅允許特定項目成員更改數據 + +## 🚀 挑戰 + +數據科學生命周期有許多不同的版本，每個版本的步驟名稱和階段數量可能不同，但都包含本課程中提到的相同過程。 + +探索[團隊數據科學過程生命周期](https://docs.microsoft.com/en-us/azure/architecture/data-science-process/lifecycle)和[跨行業數據挖掘標準過程](https://www.datascience-pm.com/crisp-dm-2/)。列出兩者的三個相似點和不同點。 + +|團隊數據科學過程 (TDSP)|跨行業數據挖掘標準過程 (CRISP-DM)| +|--|--| +|![團隊數據科學生命周期](../../../../translated_images/zh-HK/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![數據科學過程聯盟圖片](../../../../translated_images/zh-HK/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | +| 圖片來源：[Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | 圖片來源：[Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | + +## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/27) + +## 回顧與自學 + +應用數據科學生命周期涉及多種角色和任務，其中一些可能專注於每個階段的特定部分。團隊數據科學過程提供了一些資源，解釋了某人在項目中可能擔任的角色和任務。 + +* [團隊數據科學過程中的角色和任務](https://docs.microsoft.com/en-us/azure/architecture/data-science-process/roles-tasks) +* [執行數據科學任務：探索、建模和部署](https://docs.microsoft.com/en-us/azure/architecture/data-science-process/execute-data-science-tasks) + +## 作業 + +[評估數據集](assignment.md) + +--- + +**免責聲明**： +此文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業的人類翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。 \ No newline at end of file diff --git a/translations/zh-HK/4-Data-Science-Lifecycle/14-Introduction/assignment.md b/translations/zh-HK/4-Data-Science-Lifecycle/14-Introduction/assignment.md new file mode 100644 index 00000000..1485c6f9 --- /dev/null +++ b/translations/zh-HK/4-Data-Science-Lifecycle/14-Introduction/assignment.md @@ -0,0 +1,26 @@ +# 評估數據集 + +一位客戶向你的團隊尋求幫助，調查紐約市計程車乘客的季節性消費習慣。 + +他們想知道：**紐約市的黃計程車乘客在冬季或夏季是否給司機更多小費？** + +你的團隊目前處於數據科學生命周期的[捕捉](Readme.md#Capturing)階段，而你負責處理數據集。你已獲得一個筆記本和[數據](../../../../data/taxi.csv)供你探索。 + +在此目錄中有一個[筆記本](../../../../4-Data-Science-Lifecycle/14-Introduction/notebook.ipynb)，使用 Python 從[紐約市計程車與豪華車委員會](https://docs.microsoft.com/en-us/azure/open-datasets/dataset-taxi-yellow?tabs=azureml-opendatasets)載入黃計程車行程數據。 +你也可以使用文字編輯器或像 Excel 這樣的電子表格軟件打開計程車數據文件。 + +## 指引 + +- 評估此數據集中的數據是否能幫助回答問題。 +- 探索[紐約市開放數據目錄](https://data.cityofnewyork.us/browse?sortBy=most_accessed&utf8=%E2%9C%93)。識別一個可能有助於回答客戶問題的額外數據集。 +- 撰寫三個問題，向客戶提出以獲得更多澄清並更好地理解問題。 + +參考[數據集字典](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf)和[使用指南](https://www1.nyc.gov/assets/tlc/downloads/pdf/trip_record_user_guide.pdf)以獲取更多關於數據的信息。 + +## 評分標準 + +卓越 | 合格 | 需要改進 +--- | --- | --- | + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，請注意自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/4-Data-Science-Lifecycle/14-Introduction/notebook.ipynb b/translations/zh-HK/4-Data-Science-Lifecycle/14-Introduction/notebook.ipynb new file mode 100644 index 00000000..8959f0bc --- /dev/null +++ b/translations/zh-HK/4-Data-Science-Lifecycle/14-Introduction/notebook.ipynb @@ -0,0 +1,140 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# 冬季和夏季的紐約市計程車數據\n", + "\n", + "請參考[數據字典](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf)，了解提供的欄位詳細資訊。\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "#Install the pandas library\r\n", + "!pip install pandas" + ], + "outputs": [], + "metadata": { + "scrolled": true + } + }, + { + "cell_type": "code", + "execution_count": 7, + "source": [ + "import pandas as pd\r\n", + "\r\n", + "path = '../../data/taxi.csv'\r\n", + "\r\n", + "#Load the csv file into a dataframe\r\n", + "df = pd.read_csv(path)\r\n", + "\r\n", + "#Print the dataframe\r\n", + "print(df)\r\n" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " VendorID tpep_pickup_datetime tpep_dropoff_datetime passenger_count \\\n", + "0 2.0 2019-07-15 16:27:53 2019-07-15 16:44:21 3.0 \n", + "1 2.0 2019-07-17 20:26:35 2019-07-17 20:40:09 6.0 \n", + "2 2.0 2019-07-06 16:01:08 2019-07-06 16:10:25 1.0 \n", + "3 1.0 2019-07-18 22:32:23 2019-07-18 22:35:08 1.0 \n", + "4 2.0 2019-07-19 14:54:29 2019-07-19 15:19:08 1.0 \n", + ".. ... ... ... ... \n", + "195 2.0 2019-01-18 08:42:15 2019-01-18 08:56:57 1.0 \n", + "196 1.0 2019-01-19 04:34:45 2019-01-19 04:43:44 1.0 \n", + "197 2.0 2019-01-05 10:37:39 2019-01-05 10:42:03 1.0 \n", + "198 2.0 2019-01-23 10:36:29 2019-01-23 10:44:34 2.0 \n", + "199 2.0 2019-01-30 06:55:58 2019-01-30 07:07:02 5.0 \n", + "\n", + " trip_distance RatecodeID store_and_fwd_flag PULocationID DOLocationID \\\n", + "0 2.02 1.0 N 186 233 \n", + "1 1.59 1.0 N 141 161 \n", + "2 1.69 1.0 N 246 249 \n", + "3 0.90 1.0 N 229 141 \n", + "4 4.79 1.0 N 237 107 \n", + ".. ... ... ... ... ... \n", + "195 1.18 1.0 N 43 237 \n", + "196 2.30 1.0 N 148 234 \n", + "197 0.83 1.0 N 237 263 \n", + "198 1.12 1.0 N 144 113 \n", + "199 2.41 1.0 N 209 107 \n", + "\n", + " payment_type fare_amount extra mta_tax tip_amount tolls_amount \\\n", + "0 1.0 12.0 1.0 0.5 4.08 0.0 \n", + "1 2.0 10.0 0.5 0.5 0.00 0.0 \n", + "2 2.0 8.5 0.0 0.5 0.00 0.0 \n", + "3 1.0 4.5 3.0 0.5 1.65 0.0 \n", + "4 1.0 19.5 0.0 0.5 5.70 0.0 \n", + ".. ... ... ... ... ... ... \n", + "195 1.0 10.0 0.0 0.5 2.16 0.0 \n", + "196 1.0 9.5 0.5 0.5 2.15 0.0 \n", + "197 1.0 5.0 0.0 0.5 1.16 0.0 \n", + "198 2.0 7.0 0.0 0.5 0.00 0.0 \n", + "199 1.0 10.5 0.0 0.5 1.00 0.0 \n", + "\n", + " improvement_surcharge total_amount congestion_surcharge \n", + "0 0.3 20.38 2.5 \n", + "1 0.3 13.80 2.5 \n", + "2 0.3 11.80 2.5 \n", + "3 0.3 9.95 2.5 \n", + "4 0.3 28.50 2.5 \n", + ".. ... ... ... \n", + "195 0.3 12.96 0.0 \n", + "196 0.3 12.95 0.0 \n", + "197 0.3 6.96 0.0 \n", + "198 0.3 7.80 0.0 \n", + "199 0.3 12.30 0.0 \n", + "\n", + "[200 rows x 18 columns]\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**免責聲明**： \n此文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤詮釋概不負責。\n" + ] + } + ], + "metadata": { + "kernelspec": { + "name": "python3", + "display_name": "Python 3.9.7 64-bit ('venv': venv)" + }, + "language_info": { + "mimetype": "text/x-python", + "name": "python", + "pygments_lexer": "ipython3", + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "version": "3.9.7", + "nbconvert_exporter": "python", + "file_extension": ".py" + }, + "name": "04-nyc-taxi-join-weather-in-pandas", + "notebookId": 1709144033725344, + "interpreter": { + "hash": "6b9b57232c4b57163d057191678da2030059e733b8becc68f245de5a75abe84e" + }, + "coopTranslator": { + "original_hash": "3bd4c20c4e8f3158f483f0f1cc543bb1", + "translation_date": "2025-09-02T08:36:34+00:00", + "source_file": "4-Data-Science-Lifecycle/14-Introduction/notebook.ipynb", + "language_code": "hk" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/zh-HK/4-Data-Science-Lifecycle/15-analyzing/README.md b/translations/zh-HK/4-Data-Science-Lifecycle/15-analyzing/README.md new file mode 100644 index 00000000..c11a7c36 --- /dev/null +++ b/translations/zh-HK/4-Data-Science-Lifecycle/15-analyzing/README.md @@ -0,0 +1,54 @@ +# 數據科學生命周期：分析 + +|![由 [(@sketchthedocs)](https://sketchthedocs.dev) 繪製的手繪筆記](../../sketchnotes/15-Analyzing.png)| +|:---:| +| 數據科學生命周期：分析 - _手繪筆記由 [@nitya](https://twitter.com/nitya) 繪製_ | + +## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/28) + +在數據生命周期中的分析階段，確認數據是否能回答所提出的問題或解決特定問題。這一步驟還可以用來確認模型是否正確地解決了這些問題。本課重點介紹探索性數據分析（Exploratory Data Analysis，簡稱 EDA），這是一種用於定義數據特徵和關係的技術，並可用於為建模做準備。 + +我們將使用來自 [Kaggle](https://www.kaggle.com/balaka18/email-spam-classification-dataset-csv/version/1) 的示例數據集，展示如何使用 Python 和 Pandas 庫應用這些技術。該數據集包含一些電子郵件中常見詞彙的計數，這些電子郵件的來源是匿名的。請使用此目錄中的 [notebook](notebook.ipynb) 跟隨學習。 + +## 探索性數據分析 + +在生命周期的數據捕獲階段，我們獲取數據並確定問題和相關問題，但我們如何知道這些數據能否支持最終結果？ +回想一下，數據科學家在獲取數據時可能會問以下問題： +- 我有足夠的數據來解決這個問題嗎？ +- 這些數據的質量是否能滿足這個問題的需求？ +- 如果通過這些數據發現了額外的信息，我們是否應該考慮改變或重新定義目標？ + +探索性數據分析是一個了解數據的過程，可以用來回答這些問題，並識別處理數據集時可能面臨的挑戰。讓我們來看看一些用於實現這些目標的技術。 + +## 數據剖析、描述性統計和 Pandas + +我們如何評估是否有足夠的數據來解決這個問題？數據剖析可以通過描述性統計技術總結並收集數據集的一些整體信息。數據剖析幫助我們了解手頭的數據，而描述性統計幫助我們了解數據的數量。 + +在之前的一些課程中，我們使用 Pandas 的 [`describe()` 函數](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html) 提供了一些描述性統計。它可以對數值數據提供計數、最大值和最小值、平均值、標準差和分位數等信息。使用像 `describe()` 這樣的描述性統計函數可以幫助你評估數據量是否足夠，或者是否需要更多數據。 + +## 抽樣與查詢 + +探索大型數據集中的所有內容可能非常耗時，通常這是交給計算機完成的任務。然而，抽樣是一種理解數據的有用工具，能幫助我們更好地了解數據集的內容及其代表的意義。通過抽樣，你可以應用概率和統計來對數據得出一些一般性的結論。雖然沒有明確的規則規定應該抽取多少數據，但需要注意的是，抽樣的數據越多，對數據的概括就越精確。 + +Pandas 提供了 [`sample()` 函數](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sample.html)，你可以傳遞一個參數來指定希望獲取的隨機樣本數量並使用它們。 + +對數據進行一般性查詢可以幫助你回答一些普遍的問題和假設。與抽樣不同，查詢允許你控制並專注於數據中你感興趣的特定部分。Pandas 庫中的 [`query()` 函數](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html) 允許你選擇列並通過檢索的行獲得關於數據的簡單答案。 + +## 使用可視化進行探索 + +你不需要等到數據徹底清理和分析後才開始創建可視化。事實上，在探索過程中使用可視化可以幫助識別數據中的模式、關係和問題。此外，可視化還能為那些未參與數據管理的人提供一種溝通方式，並且是一個分享和澄清在捕獲階段未解決的額外問題的機會。請參考[可視化部分](../../../../../../../../../3-Data-Visualization) 了解更多關於可視化探索的流行方法。 + +## 探索以識別不一致 + +本課中的所有主題都可以幫助識別缺失或不一致的值，而 Pandas 提供了一些函數來檢查這些問題。[isna() 或 isnull()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.isna.html) 可以檢查缺失值。在數據中探索這些值的一個重要部分是了解它們為什麼會出現。這可以幫助你決定採取哪些[措施來解決它們](/2-Working-With-Data/08-data-preparation/notebook.ipynb)。 + +## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/29) + +## 作業 + +[探索答案](assignment.md) + +--- + +**免責聲明**： +此文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業的人類翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。 \ No newline at end of file diff --git a/translations/zh-HK/4-Data-Science-Lifecycle/15-analyzing/assignment.ipynb b/translations/zh-HK/4-Data-Science-Lifecycle/15-analyzing/assignment.ipynb new file mode 100644 index 00000000..6a24515f --- /dev/null +++ b/translations/zh-HK/4-Data-Science-Lifecycle/15-analyzing/assignment.ipynb @@ -0,0 +1,154 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# 冬季和夏季的紐約市計程車數據\n", + "\n", + "請參考[數據字典](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf)，了解提供的欄位詳細資訊。\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "#Install the pandas library\r\n", + "!pip install pandas" + ], + "outputs": [], + "metadata": { + "scrolled": true + } + }, + { + "cell_type": "code", + "execution_count": 7, + "source": [ + "import pandas as pd\r\n", + "\r\n", + "path = '../../data/taxi.csv'\r\n", + "\r\n", + "#Load the csv file into a dataframe\r\n", + "df = pd.read_csv(path)\r\n", + "\r\n", + "#Print the dataframe\r\n", + "print(df)\r\n" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " VendorID tpep_pickup_datetime tpep_dropoff_datetime passenger_count \\\n", + "0 2.0 2019-07-15 16:27:53 2019-07-15 16:44:21 3.0 \n", + "1 2.0 2019-07-17 20:26:35 2019-07-17 20:40:09 6.0 \n", + "2 2.0 2019-07-06 16:01:08 2019-07-06 16:10:25 1.0 \n", + "3 1.0 2019-07-18 22:32:23 2019-07-18 22:35:08 1.0 \n", + "4 2.0 2019-07-19 14:54:29 2019-07-19 15:19:08 1.0 \n", + ".. ... ... ... ... \n", + "195 2.0 2019-01-18 08:42:15 2019-01-18 08:56:57 1.0 \n", + "196 1.0 2019-01-19 04:34:45 2019-01-19 04:43:44 1.0 \n", + "197 2.0 2019-01-05 10:37:39 2019-01-05 10:42:03 1.0 \n", + "198 2.0 2019-01-23 10:36:29 2019-01-23 10:44:34 2.0 \n", + "199 2.0 2019-01-30 06:55:58 2019-01-30 07:07:02 5.0 \n", + "\n", + " trip_distance RatecodeID store_and_fwd_flag PULocationID DOLocationID \\\n", + "0 2.02 1.0 N 186 233 \n", + "1 1.59 1.0 N 141 161 \n", + "2 1.69 1.0 N 246 249 \n", + "3 0.90 1.0 N 229 141 \n", + "4 4.79 1.0 N 237 107 \n", + ".. ... ... ... ... ... \n", + "195 1.18 1.0 N 43 237 \n", + "196 2.30 1.0 N 148 234 \n", + "197 0.83 1.0 N 237 263 \n", + "198 1.12 1.0 N 144 113 \n", + "199 2.41 1.0 N 209 107 \n", + "\n", + " payment_type fare_amount extra mta_tax tip_amount tolls_amount \\\n", + "0 1.0 12.0 1.0 0.5 4.08 0.0 \n", + "1 2.0 10.0 0.5 0.5 0.00 0.0 \n", + "2 2.0 8.5 0.0 0.5 0.00 0.0 \n", + "3 1.0 4.5 3.0 0.5 1.65 0.0 \n", + "4 1.0 19.5 0.0 0.5 5.70 0.0 \n", + ".. ... ... ... ... ... ... \n", + "195 1.0 10.0 0.0 0.5 2.16 0.0 \n", + "196 1.0 9.5 0.5 0.5 2.15 0.0 \n", + "197 1.0 5.0 0.0 0.5 1.16 0.0 \n", + "198 2.0 7.0 0.0 0.5 0.00 0.0 \n", + "199 1.0 10.5 0.0 0.5 1.00 0.0 \n", + "\n", + " improvement_surcharge total_amount congestion_surcharge \n", + "0 0.3 20.38 2.5 \n", + "1 0.3 13.80 2.5 \n", + "2 0.3 11.80 2.5 \n", + "3 0.3 9.95 2.5 \n", + "4 0.3 28.50 2.5 \n", + ".. ... ... ... \n", + "195 0.3 12.96 0.0 \n", + "196 0.3 12.95 0.0 \n", + "197 0.3 6.96 0.0 \n", + "198 0.3 7.80 0.0 \n", + "199 0.3 12.30 0.0 \n", + "\n", + "[200 rows x 18 columns]\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# 使用以下單元格進行您的探索性數據分析\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**免責聲明**： \n本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議使用專業的人類翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。\n" + ] + } + ], + "metadata": { + "kernelspec": { + "name": "python3", + "display_name": "Python 3.9.7 64-bit ('venv': venv)" + }, + "language_info": { + "mimetype": "text/x-python", + "name": "python", + "pygments_lexer": "ipython3", + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "version": "3.9.7", + "nbconvert_exporter": "python", + "file_extension": ".py" + }, + "name": "04-nyc-taxi-join-weather-in-pandas", + "notebookId": 1709144033725344, + "interpreter": { + "hash": "6b9b57232c4b57163d057191678da2030059e733b8becc68f245de5a75abe84e" + }, + "coopTranslator": { + "original_hash": "7bca1c1abc1e55842817b62e44e1a963", + "translation_date": "2025-09-02T08:34:06+00:00", + "source_file": "4-Data-Science-Lifecycle/15-analyzing/assignment.ipynb", + "language_code": "hk" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/zh-HK/4-Data-Science-Lifecycle/15-analyzing/assignment.md b/translations/zh-HK/4-Data-Science-Lifecycle/15-analyzing/assignment.md new file mode 100644 index 00000000..b42769ba --- /dev/null +++ b/translations/zh-HK/4-Data-Science-Lifecycle/15-analyzing/assignment.md @@ -0,0 +1,25 @@ +# 探索答案 + +這是上一課[作業](../14-Introduction/assignment.md)的延續，我們之前簡單地查看了數據集。現在，我們將更深入地分析這些數據。 + +再次重申，客戶想知道的問題是：**紐約市的黃色計程車乘客在冬季還是夏季給司機的小費更多？** + +您的團隊目前處於數據科學生命周期的[分析](README.md)階段，負責對數據集進行探索性數據分析。您已獲得一個筆記本和數據集，其中包含2019年1月和7月的200筆計程車交易記錄。 + +## 指引 + +在此目錄中，有一個[筆記本](../../../../4-Data-Science-Lifecycle/15-analyzing/assignment.ipynb)和來自[計程車與豪華轎車委員會](https://docs.microsoft.com/en-us/azure/open-datasets/dataset-taxi-yellow?tabs=azureml-opendatasets)的數據。請參考[數據集字典](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf)和[用戶指南](https://www1.nyc.gov/assets/tlc/downloads/pdf/trip_record_user_guide.pdf)以獲取更多關於數據的信息。 + +使用本課中的一些技術，在筆記本中進行自己的探索性數據分析（如有需要可新增單元格），並回答以下問題： + +- 數據中還有哪些其他因素可能影響小費金額？ +- 哪些欄位最有可能不需要用來回答客戶的問題？ +- 根據目前提供的數據，是否有任何證據顯示季節性的小費行為？ + +## 評分標準 + +優秀 | 合格 | 需要改進 +--- | --- | --- + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/4-Data-Science-Lifecycle/15-analyzing/notebook.ipynb b/translations/zh-HK/4-Data-Science-Lifecycle/15-analyzing/notebook.ipynb new file mode 100644 index 00000000..1d35e98d --- /dev/null +++ b/translations/zh-HK/4-Data-Science-Lifecycle/15-analyzing/notebook.ipynb @@ -0,0 +1,193 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# 分析數據\n", + "[課程](README.md)中提到的 Pandas 函數示例。\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 1, + "source": [ + "import pandas as pd\r\n", + "import glob\r\n", + "\r\n", + "#Loading the dataset\r\n", + "path = '../../data/emails.csv'\r\n", + "email_df = pd.read_csv(path)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 2, + "source": [ + "# Using Describe on the email dataset\r\n", + "print(email_df.describe())" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " the to ect and for of \\\n", + "count 406.000000 406.000000 406.000000 406.000000 406.000000 406.000000 \n", + "mean 7.022167 6.519704 4.948276 3.059113 3.502463 2.662562 \n", + "std 10.945522 9.801907 9.293820 6.267806 4.901372 5.443939 \n", + "min 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 \n", + "25% 1.000000 1.000000 1.000000 0.000000 1.000000 0.000000 \n", + "50% 3.000000 3.000000 2.000000 1.000000 2.000000 1.000000 \n", + "75% 9.000000 7.750000 4.000000 3.000000 4.750000 3.000000 \n", + "max 99.000000 88.000000 79.000000 69.000000 39.000000 57.000000 \n", + "\n", + " a you in on is this \\\n", + "count 406.000000 406.000000 406.000000 406.000000 406.000000 406.000000 \n", + "mean 57.017241 2.394089 10.817734 11.591133 5.901478 1.485222 \n", + "std 78.868243 4.067015 19.050972 16.407175 8.793103 2.912473 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 15.000000 0.000000 1.250000 3.000000 1.000000 0.000000 \n", + "50% 29.000000 1.000000 5.000000 6.000000 3.000000 0.000000 \n", + "75% 61.000000 3.000000 12.000000 13.000000 7.000000 2.000000 \n", + "max 843.000000 31.000000 223.000000 125.000000 61.000000 24.000000 \n", + "\n", + " i be that will \n", + "count 406.000000 406.000000 406.000000 406.000000 \n", + "mean 47.155172 2.950739 1.034483 0.955665 \n", + "std 71.043009 4.297865 1.904846 2.042271 \n", + "min 0.000000 0.000000 0.000000 0.000000 \n", + "25% 11.000000 1.000000 0.000000 0.000000 \n", + "50% 24.000000 1.000000 0.000000 0.000000 \n", + "75% 50.750000 3.000000 1.000000 1.000000 \n", + "max 754.000000 40.000000 14.000000 24.000000 \n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 5, + "source": [ + "# Sampling 10 emails\r\n", + "print(email_df.sample(10))" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Email No. the to ect and for of a you in on is this i \\\n", + "150 Email 151 0 1 2 0 3 0 15 0 0 5 0 0 7 \n", + "380 Email 5147 0 3 2 0 0 0 7 0 1 1 0 0 3 \n", + "19 Email 20 3 4 11 0 4 2 32 1 1 3 9 5 25 \n", + "300 Email 301 2 1 1 0 1 1 15 2 2 3 2 0 8 \n", + "307 Email 308 0 0 1 0 0 0 1 0 1 0 0 0 2 \n", + "167 Email 168 2 2 2 1 5 1 24 2 5 6 4 0 30 \n", + "320 Email 321 10 12 4 6 8 6 187 5 26 28 23 2 171 \n", + "61 Email 62 0 1 1 0 4 1 15 4 4 3 3 0 19 \n", + "26 Email 27 5 4 1 1 4 4 51 0 8 6 6 2 44 \n", + "73 Email 74 0 0 1 0 0 0 7 0 4 3 0 0 6 \n", + "\n", + " be that will \n", + "150 1 0 0 \n", + "380 0 0 0 \n", + "19 3 0 1 \n", + "300 0 0 0 \n", + "307 0 0 0 \n", + "167 2 0 0 \n", + "320 5 1 1 \n", + "61 2 0 0 \n", + "26 6 0 0 \n", + "73 0 0 0 \n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 14, + "source": [ + "# Returns rows where there are more occurrences of \"to\" than \"the\"\r\n", + "print(email_df.query('the < to'))" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Email No. the to ect and for of a you in on is this i \\\n", + "1 Email 2 8 13 24 6 6 2 102 1 18 21 13 0 61 \n", + "3 Email 4 0 5 22 0 5 1 51 2 1 5 9 2 16 \n", + "5 Email 6 4 5 1 4 2 3 45 1 16 12 8 1 52 \n", + "7 Email 8 0 2 2 3 1 2 21 6 2 6 2 0 28 \n", + "13 Email 14 4 5 7 1 5 1 37 1 8 8 6 1 43 \n", + ".. ... ... .. ... ... ... .. ... ... .. .. .. ... .. \n", + "390 Email 5157 4 13 1 0 3 1 48 2 8 26 9 1 45 \n", + "393 Email 5160 2 13 1 0 2 1 38 2 7 24 6 1 34 \n", + "396 Email 5163 2 3 1 2 1 2 32 0 7 3 2 0 26 \n", + "404 Email 5171 2 7 1 0 2 1 28 2 8 11 7 1 39 \n", + "405 Email 5172 22 24 5 1 6 5 148 8 23 13 5 4 99 \n", + "\n", + " be that will \n", + "1 4 2 0 \n", + "3 2 0 0 \n", + "5 2 0 0 \n", + "7 1 0 1 \n", + "13 1 0 1 \n", + ".. .. ... ... \n", + "390 1 0 0 \n", + "393 1 0 0 \n", + "396 3 0 0 \n", + "404 1 0 0 \n", + "405 6 4 1 \n", + "\n", + "[169 rows x 17 columns]\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**免責聲明**： \n此文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤詮釋概不負責。\n" + ] + } + ], + "metadata": { + "orig_nbformat": 4, + "language_info": { + "name": "python", + "version": "3.9.7", + "mimetype": "text/x-python", + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "pygments_lexer": "ipython3", + "nbconvert_exporter": "python", + "file_extension": ".py" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3.9.7 64-bit ('venv': venv)" + }, + "interpreter": { + "hash": "6b9b57232c4b57163d057191678da2030059e733b8becc68f245de5a75abe84e" + }, + "coopTranslator": { + "original_hash": "9d102c8c3cdbc8ea4e92fc32593462c6", + "translation_date": "2025-09-02T08:30:56+00:00", + "source_file": "4-Data-Science-Lifecycle/15-analyzing/notebook.ipynb", + "language_code": "hk" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/zh-HK/4-Data-Science-Lifecycle/16-communication/README.md b/translations/zh-HK/4-Data-Science-Lifecycle/16-communication/README.md new file mode 100644 index 00000000..1b942dec --- /dev/null +++ b/translations/zh-HK/4-Data-Science-Lifecycle/16-communication/README.md @@ -0,0 +1,213 @@ +# 數據科學生命周期：溝通 + +|![由 [(@sketchthedocs)](https://sketchthedocs.dev) 繪製的手繪筆記](../../sketchnotes/16-Communicating.png)| +|:---:| +| 數據科學生命周期：溝通 - _手繪筆記由 [@nitya](https://twitter.com/nitya) 提供_ | + +## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/30) + +透過以上的課前測驗來檢視你對即將學習內容的了解程度吧！ + +# 簡介 + +### 什麼是溝通？ +讓我們從定義溝通的含義開始這節課。**溝通是傳遞或交換信息的過程。** 信息可以是想法、思考、感受、訊息、隱秘信號、數據——任何一個**_發送者_**（傳遞信息的人）希望**_接收者_**（接收信息的人）理解的內容。在這節課中，我們將把發送者稱為溝通者，而接收者稱為受眾。 + +### 數據溝通與故事講述 +我們知道，溝通的目的是傳遞或交換信息。但在進行數據溝通時，你的目標不應該僅僅是向受眾傳遞數字。你的目標應該是通過數據講述一個故事——有效的數據溝通與故事講述是密不可分的。受眾更有可能記住你講述的故事，而不是你提供的數字。在這節課的後面，我們將討論一些方法，幫助你更有效地利用故事講述來進行數據溝通。 + +### 溝通的類型 +在這節課中，我們將討論兩種不同的溝通類型：單向溝通和雙向溝通。 + +**單向溝通** 是指發送者向接收者傳遞信息，但沒有任何反饋或回應。我們每天都能看到單向溝通的例子——例如群發郵件、新聞報導最新事件，或者電視廣告告訴你他們的產品有多好。在這些情況下，發送者並不尋求信息的交換，他們只是想傳遞或提供信息。 + +**雙向溝通** 是指所有參與方既是發送者也是接收者。發送者首先向接收者溝通，接收者則提供反饋或回應。雙向溝通是我們通常所認為的溝通方式。我們通常會想到人們進行的對話——無論是面對面、電話、社交媒體還是短信。 + +在進行數據溝通時，有些情況下你會使用單向溝通（例如在會議或大型群體中進行演講，之後不會直接回答問題），而有些情況下你會使用雙向溝通（例如用數據說服幾位利益相關者支持某個提案，或者說服團隊成員投入時間和精力去開發某個新項目）。 + +# 有效溝通 + +### 作為溝通者的責任 +在溝通時，你的責任是確保接收者能夠理解你希望他們理解的信息。在進行數據溝通時，你不僅希望接收者記住數字，還希望他們能夠理解由數據支撐的故事。一個好的數據溝通者也是一個好的故事講述者。 + +那麼，如何用數據講述故事呢？方法無窮無盡——但以下是我們在這節課中將討論的六種方法： +1. 理解你的受眾、溝通渠道和溝通方式 +2. 以終為始 +3. 像講述一個真正的故事一樣進行溝通 +4. 使用有意義的詞語和短語 +5. 善用情感 + +以下將詳細解釋這些策略。 + +### 1. 理解你的受眾、溝通渠道和溝通方式 +你與家人溝通的方式可能與你與朋友溝通的方式不同。你可能會使用不同的詞語和短語，讓對方更容易理解。同樣的道理，在進行數據溝通時，你也應該採取這種方式。思考你正在與誰溝通，思考他們的目標以及他們對你所解釋情況的背景了解。 + +你可以將大多數受眾歸類到某個類別中。在 _哈佛商業評論_ 的文章《[如何用數據講故事](http://blogs.hbr.org/2013/04/how-to-tell-a-story-with-data/)》中，戴爾的執行策略師 Jim Stikeleather 將受眾分為五類： + +- **新手**：第一次接觸該主題，但不希望內容過於簡化 +- **普通受眾**：對該主題有所了解，但希望獲得概述和主要主題 +- **管理者**：對細節和相互關係有深入、可操作的理解，並能獲取詳細信息 +- **專家**：更注重探索和發現，較少需要故事講述，並希望獲得詳細信息 +- **高管**：只關注權重概率的意義和結論 + +這些類別可以幫助你決定如何向受眾展示數據。 + +除了考慮受眾的類別，你還應該考慮與受眾溝通的渠道。如果你是寫備忘錄或電子郵件，與開會或在會議上演講的方式應該略有不同。 + +在了解受眾的基礎上，知道你將如何與他們溝通（使用單向溝通還是雙向溝通）也至關重要。 + +如果你的受眾主要是新手，並且你使用的是單向溝通，那麼你必須首先教育受眾，為他們提供適當的背景知識。然後，你需要向他們展示數據，並解釋數據的含義以及為什麼數據很重要。在這種情況下，你可能需要專注於提高清晰度，因為你的受眾無法直接向你提問。 + +如果你的受眾主要是管理者，並且你使用的是雙向溝通，那麼你可能不需要教育受眾或提供太多背景知識。你可以直接進入討論你收集的數據及其重要性。然而，在這種情況下，你應該專注於掌控時間和演示內容。當使用雙向溝通時（尤其是面對尋求“對細節和相互關係的可操作理解”的管理者受眾），可能會出現一些問題，將討論引向與你試圖講述的故事無關的方向。當這種情況發生時，你可以採取行動，將討論拉回到你的故事主題上。 + +### 2. 以終為始 +以終為始的意思是，在開始與受眾溝通之前，先明確你希望他們獲得的關鍵信息。提前思考你希望受眾獲得的內容，可以幫助你構建一個他們能夠理解的故事。以終為始適用於單向溝通和雙向溝通。 + +如何以終為始？在溝通數據之前，先寫下你的關鍵信息。然後，在準備你想用數據講述的故事的每一步時，問自己：“這如何融入我正在講述的故事？” + +需要注意的是——雖然以終為始是理想的，但你不應該只溝通支持你結論的數據。這種做法被稱為“挑選性呈現”，即溝通者只呈現支持自己觀點的數據，而忽略其他數據。 + +如果你收集的所有數據都明確支持你的結論，那很好。但如果你收集的數據中有不支持你的結論的部分，甚至支持與你的結論相反的觀點，你也應該溝通這些數據。如果出現這種情況，坦誠地告訴受眾，並解釋為什麼即使所有數據並不完全支持你的結論，你仍然選擇堅持自己的故事。 + +### 3. 像講述一個真正的故事一樣進行溝通 +一個傳統的故事分為五個階段。你可能聽說過這些階段被表述為：背景介紹、情節發展、高潮、情節緩和和結局。或者更容易記住的表述：背景、衝突、高潮、結尾和結論。在溝通你的數據和故事時，你可以採取類似的方法。 + +你可以從背景開始，設置場景，確保受眾都在同一個起點。然後引入衝突——為什麼你需要收集這些數據？你試圖解決什麼問題？接下來是高潮——數據是什麼？數據的含義是什麼？數據告訴我們需要哪些解決方案？然後是結尾——你可以重申問題和建議的解決方案。最後是結論——總結你的關鍵信息以及你建議團隊採取的下一步行動。 + +### 4. 使用有意義的詞語和短語 +如果我們一起合作開發一個產品，我對你說：“我們的用戶在我們的平台上花了很長時間完成註冊流程。”你會估計“很長時間”是多久？一小時？一週？很難知道。如果我對整個受眾說這句話呢？每個人可能會對“很長時間”有不同的理解。 + +但如果我說：“我們的用戶平均花了3分鐘完成註冊流程。” + +這樣的表述就更清晰了。在溝通數據時，很容易認為受眾的想法與你一致。但事實並非總是如此。清晰地傳達數據及其含義是你作為溝通者的責任。如果數據或你的故事不清晰，受眾將難以理解，並且他們理解你的關鍵信息的可能性會降低。 + +你可以通過使用有意義的詞語和短語，而不是模糊的表述來更清晰地溝通數據。以下是一些例子： + +- 我們有一個*令人印象深刻*的年度！ + - 一個人可能認為“令人印象深刻”意味著收入增長2%-3%，而另一個人可能認為是50%-60%的增長。 +- 我們用戶的成功率*顯著*提高了。 + - “顯著”提高是多大幅度的提高？ +- 這項工作將需要*大量*的努力。 + - “大量”努力是多大的努力？ + +使用模糊的詞語可能在引入更多數據或總結故事時有用。但請考慮確保你的演示的每個部分對受眾都是清晰的。 + +### 5. 善用情感 +情感是故事講述的關鍵。在用數據講述故事時，情感更為重要。當你溝通數據時，一切都圍繞著你希望受眾獲得的關鍵信息。當你激發受眾的情感時，可以幫助他們產生共鳴，並更有可能採取行動。情感還能增加受眾記住你信息的可能性。 + +你可能在電視廣告中遇到過這種情況。有些廣告非常沉重，利用悲傷的情感與受眾建立聯繫，讓他們對所呈現的數據印象深刻。或者，有些廣告非常歡快，讓你將他們的數據與快樂的感覺聯繫起來。 + +那麼，如何在溝通數據時使用情感呢？以下是幾種方法： + +- 使用見證和個人故事 + - 在收集數據時，嘗試收集定量數據和定性數據，並在溝通時整合這兩種類型的數據。如果你的數據主要是定量的，尋找個人故事來了解更多關於他們與數據相關的經歷。 +- 使用圖像 + - 圖像可以幫助受眾將自己代入某個情境。當你使用圖像時，可以引導受眾產生你希望他們對數據產生的情感。 +- 使用顏色 + - 不同的顏色會激發不同的情感。以下是一些常見顏色及其通常引發的情感。需要注意的是，不同文化中顏色可能有不同的含義。 + - 藍色通常引發平靜和信任的情感 + - 綠色通常與自然和環境相關 + - 紅色通常代表激情和興奮 + - 黃色通常代表樂觀和快樂 + +# 溝通案例研究 +Emerson 是一款移動應用的產品經理。他發現用戶在週末提交的投訴和錯誤報告比平時多42%。Emerson 還發現，如果用戶提交的投訴在48小時內未得到回應，他們給應用打1星或2星評分的可能性會增加32%。 + +經過研究，Emerson 提出了幾個解決方案來解決這個問題。他安排了一個30分鐘的會議，與公司內的3位領導溝通數據和建議的解決方案。 + +在這次會議中，Emerson 的目標是讓公司領導理解以下兩個解決方案可以提高應用的評分，這可能會轉化為更高的收入。 + +**解決方案1.** 雇用客服人員在週末工作 + +**解決方案2.** 購買一個新的客服工單系統，讓客服人員能夠輕鬆識別哪些投訴在隊列中等待的時間最長——以便他們能夠優先處理。 + +在會議中，Emerson 花了5分鐘解釋為什麼應用商店中的低評分會帶來負面影響，10分鐘解釋研究過程以及如何識別趨勢，10分鐘分析一些近期的用戶投訴，最後5分鐘簡略介紹了兩個潛在的解決方案。 +Emerson在這次會議中的溝通方式是否有效？ + +在會議中，一位公司主管只專注於Emerson所提到的10分鐘客戶投訴內容。會議結束後，這些投訴成為該主管唯一記得的內容。另一位公司主管主要關注Emerson描述的研究過程。第三位公司主管記得Emerson提出的解決方案，但不確定這些方案如何實施。 + +在上述情況中，可以看到Emerson希望公司主管們從會議中獲得的重點與他們實際記住的內容之間存在顯著差距。以下是Emerson可以考慮的另一種方法。 + +Emerson如何改進這種方法？ +背景、衝突、高潮、結尾、結論 +**背景** - Emerson可以花前5分鐘介紹整個情況，確保公司主管們了解問題如何影響公司關鍵指標，例如收入。 + +可以這樣表述：「目前，我們的應用程式在應用商店的評分是2.5。應用商店的評分對應用商店優化至關重要，這會影響有多少用戶在搜索中看到我們的應用，以及潛在用戶如何看待我們的應用。而且，當然，我們的用戶數量直接與收入掛鉤。」 + +**衝突** Emerson接著可以花5分鐘左右談論衝突。 + +可以這樣表述：「用戶在週末提交的投訴和錯誤報告多了42%。提交投訴後48小時內未得到回覆的客戶，給我們的應用程式評分超過2的可能性降低了32%。將我們的應用程式評分提高到4，將提升20-30%的可見度，我預計這將使收入增加10%。」當然，Emerson應該準備好為這些數據提供合理的解釋。 + +**高潮** 在鋪墊好基礎後，Emerson可以花5分鐘左右進入高潮部分。 + +Emerson可以介紹提出的解決方案，說明這些方案如何解決所列出的問題，如何融入現有的工作流程，解決方案的成本是多少，投資回報率（ROI）如何，甚至可以展示一些解決方案實施後的截圖或線框圖。Emerson還可以分享一些用戶的感言，例如那些投訴超過48小時才得到回覆的用戶的感言，甚至可以分享公司內部客服代表對現有工單系統的評論。 + +**結尾** 接下來，Emerson可以花5分鐘重申公司面臨的問題，重溫提出的解決方案，並回顧為什麼這些方案是正確的選擇。 + +**結論** 由於這是一場與少數利益相關者的會議，會進行雙向溝通，Emerson可以計劃留出10分鐘的時間回答問題，確保主管們在會議結束前能夠澄清任何困惑的地方。 + +如果Emerson採用方法#2，主管們更有可能從會議中獲得Emerson希望他們記住的重點——即投訴和錯誤的處理方式可以改進，並且有兩個解決方案可以實施以實現這一改進。這種方法將更有效地傳達Emerson希望溝通的數據和故事。 + +# 結論 +### 主要要點摘要 +- 溝通是傳遞或交換信息。 +- 在溝通數據時，目標不應僅僅是向觀眾傳遞數字，而是要傳達一個由數據支持的故事。 +- 溝通有兩種類型：單向溝通（信息傳遞無需回應）和雙向溝通（信息來回交流）。 +- 有許多策略可以用來講述數據故事，我們討論了以下五種策略： + - 了解你的觀眾、媒介和溝通方式 + - 從結果開始思考 + - 像講述真正的故事一樣進行 + - 使用有意義的詞語和短語 + - 善用情感 + +### 自學推薦資源 +[The Five C's of Storytelling - Articulate Persuasion](http://articulatepersuasion.com/the-five-cs-of-storytelling/) + +[1.4 Your Responsibilities as a Communicator – Business Communication for Success (umn.edu)](https://open.lib.umn.edu/businesscommunication/chapter/1-4-your-responsibilities-as-a-communicator/) + +[How to Tell a Story with Data (hbr.org)](https://hbr.org/2013/04/how-to-tell-a-story-with-data) + +[Two-Way Communication: 4 Tips for a More Engaged Workplace (yourthoughtpartner.com)](https://www.yourthoughtpartner.com/blog/bid/59576/4-steps-to-increase-employee-engagement-through-two-way-communication) + +[6 succinct steps to great data storytelling - BarnRaisers, LLC (barnraisersllc.com)](https://barnraisersllc.com/2021/05/02/6-succinct-steps-to-great-data-storytelling/) + +[How to Tell a Story With Data | Lucidchart Blog](https://www.lucidchart.com/blog/how-to-tell-a-story-with-data) + +[6 Cs of Effective Storytelling on Social Media | Cooler Insights](https://coolerinsights.com/2018/06/effective-storytelling-social-media/) + +[The Importance of Emotions In Presentations | Ethos3 - A Presentation Training and Design Agency](https://ethos3.com/2015/02/the-importance-of-emotions-in-presentations/) + +[Data storytelling: linking emotions and rational decisions (toucantoco.com)](https://www.toucantoco.com/en/blog/data-storytelling-dataviz) + +[Emotional Advertising: How Brands Use Feelings to Get People to Buy (hubspot.com)](https://blog.hubspot.com/marketing/emotions-in-advertising-examples) + +[Choosing Colors for Your Presentation Slides | Think Outside The Slide](https://www.thinkoutsidetheslide.com/choosing-colors-for-your-presentation-slides/) + +[How To Present Data [10 Expert Tips] | ObservePoint](https://resources.observepoint.com/blog/10-tips-for-presenting-data) + +[Microsoft Word - Persuasive Instructions.doc (tpsnva.org)](https://www.tpsnva.org/teach/lq/016/persinstr.pdf) + +[The Power of Story for Your Data (thinkhdi.com)](https://www.thinkhdi.com/library/supportworld/2019/power-story-your-data.aspx) + +[Common Mistakes in Data Presentation (perceptualedge.com)](https://www.perceptualedge.com/articles/ie/data_presentation.pdf) + +[Infographic: Here are 15 Common Data Fallacies to Avoid (visualcapitalist.com)](https://www.visualcapitalist.com/here-are-15-common-data-fallacies-to-avoid/) + +[Cherry Picking: When People Ignore Evidence that They Dislike – Effectiviology](https://effectiviology.com/cherry-picking/#How_to_avoid_cherry_picking) + +[Tell Stories with Data: Communication in Data Science | by Sonali Verghese | Towards Data Science](https://towardsdatascience.com/tell-stories-with-data-communication-in-data-science-5266f7671d7) + +[1. Communicating Data - Communicating Data with Tableau [Book] (oreilly.com)](https://www.oreilly.com/library/view/communicating-data-with/9781449372019/ch01.html) + +## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/31) + +使用上方的課後測驗來回顧你剛剛學到的內容！ + +## 作業 + +[市場研究](assignment.md) + +--- + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為權威來源。對於重要資訊，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/4-Data-Science-Lifecycle/16-communication/assignment.md b/translations/zh-HK/4-Data-Science-Lifecycle/16-communication/assignment.md new file mode 100644 index 00000000..51620c93 --- /dev/null +++ b/translations/zh-HK/4-Data-Science-Lifecycle/16-communication/assignment.md @@ -0,0 +1,15 @@ +# 講述一個故事 + +## 指引 + +數據科學的核心在於講故事。選擇任何一個數據集，撰寫一篇短文，講述你可以從中挖掘出的故事。你希望你的數據集能揭示什麼？如果它的揭示結果令人困擾，你會怎樣處理？如果你的數據無法輕易解開它的秘密，你又會怎樣應對？思考你的數據集可能呈現的情境，並將它們記錄下來。 + +## 評分標準 + +卓越 | 合格 | 需要改進 +--- | --- | -- | + +一篇完整的文章以 .doc 格式呈現，清楚解釋、記錄並標註數據集，並以數據中的詳細例子講述一個連貫的故事。| 一篇較短的文章，細節較少 | 文章在上述某些細節方面有所欠缺。 + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，請注意自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋不承擔責任。 \ No newline at end of file diff --git a/translations/zh-HK/4-Data-Science-Lifecycle/README.md b/translations/zh-HK/4-Data-Science-Lifecycle/README.md new file mode 100644 index 00000000..8da213f4 --- /dev/null +++ b/translations/zh-HK/4-Data-Science-Lifecycle/README.md @@ -0,0 +1,19 @@ +# 數據科學生命周期 + +![communication](../../../translated_images/zh-HK/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) +> 圖片由 Headway 提供，來自 Unsplash + +在這些課程中，你將探索數據科學生命周期的一些方面，包括數據的分析和溝通。 + +### 主題 + +1. [簡介](14-Introduction/README.md) +2. [分析](15-analyzing/README.md) +3. [溝通](16-communication/README.md) + +### 致謝 + +這些課程由 [Jalen McGee](https://twitter.com/JalenMCG) 和 [Jasmine Greenaway](https://twitter.com/paladique) 用 ❤️ 編寫。 + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/5-Data-Science-In-Cloud/17-Introduction/README.md b/translations/zh-HK/5-Data-Science-In-Cloud/17-Introduction/README.md new file mode 100644 index 00000000..4d7b6e17 --- /dev/null +++ b/translations/zh-HK/5-Data-Science-In-Cloud/17-Introduction/README.md @@ -0,0 +1,107 @@ +# 雲端中的數據科學簡介 + +|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/17-DataScience-Cloud.png)| +|:---:| +| 雲端中的數據科學：簡介 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | + +在這節課中，你將學習雲端的基本原則，了解為什麼使用雲端服務來執行數據科學項目可能對你有吸引力，並且我們會看看一些在雲端中運行的數據科學項目示例。 + +## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/32) + +## 什麼是雲端？ + +雲端，或稱雲端計算，是通過互聯網提供的按需付費的多種計算服務，這些服務基於一個基礎設施。服務包括存儲、數據庫、網絡、軟件、分析以及智能服務等解決方案。 + +我們通常將公有雲、私有雲和混合雲區分如下： + +* 公有雲：公有雲由第三方雲端服務提供商擁有和運營，並通過互聯網向公眾提供其計算資源。 +* 私有雲：指專門由單一企業或組織使用的雲端計算資源，服務和基礎設施維護在私有網絡上。 +* 混合雲：混合雲是一種結合公有雲和私有雲的系統。用戶選擇使用內部數據中心，同時允許數據和應用程序在一個或多個公有雲上運行。 + +大多數雲端計算服務分為三類：基礎設施即服務（IaaS）、平台即服務（PaaS）和軟件即服務（SaaS）。 + +* 基礎設施即服務（IaaS）：用戶租用IT基礎設施，例如伺服器和虛擬機（VM）、存儲、網絡、操作系統。 +* 平台即服務（PaaS）：用戶租用一個開發、測試、交付和管理軟件應用程序的環境。用戶不需要擔心設置或管理開發所需的伺服器、存儲、網絡和數據庫的基礎設施。 +* 軟件即服務（SaaS）：用戶通過互聯網按需訪問軟件應用程序，通常是基於訂閱的方式。用戶不需要擔心托管和管理軟件應用程序、基礎設施或維護，例如軟件升級和安全修補。 + +一些最大的雲端提供商包括 Amazon Web Services、Google Cloud Platform 和 Microsoft Azure。 + +## 為什麼選擇雲端進行數據科學？ + +開發者和IT專業人士選擇使用雲端的原因有很多，包括以下幾點： + +* 創新：你可以通過將雲端提供商創建的創新服務直接整合到你的應用程序中來提升應用程序的能力。 +* 靈活性：你只需支付所需的服務費用，並可以從多種服務中選擇。通常是按需付費，並根據不斷變化的需求調整服務。 +* 預算：你不需要進行初期投資來購買硬件和軟件，設置和運行本地數據中心，只需支付你使用的部分。 +* 可擴展性：你的資源可以根據項目的需求進行擴展，這意味著你的應用程序可以根據外部因素在任何時候使用更多或更少的計算能力、存儲和帶寬。 +* 生產力：你可以專注於你的業務，而不是花時間在可以由其他人管理的任務上，例如管理數據中心。 +* 可靠性：雲端計算提供多種方式來持續備份你的數據，並且你可以設置災難恢復計劃，即使在危機時期也能保持你的業務和服務運行。 +* 安全性：你可以受益於加強項目安全性的政策、技術和控制措施。 + +以上是人們選擇使用雲端服務的一些常見原因。現在我們對雲端有了更好的理解，也了解了它的主要優勢，接下來我們將更具體地探討數據科學家和處理數據的開發者的工作，以及雲端如何幫助他們解決可能面臨的幾個挑戰： + +* 存儲大量數據：與其購買、管理和保護大型伺服器，你可以直接將數據存儲在雲端，例如使用 Azure Cosmos DB、Azure SQL Database 和 Azure Data Lake Storage。 +* 執行數據整合：數據整合是數據科學的重要部分，它讓你從數據收集過渡到採取行動。通過雲端提供的數據整合服務，你可以從多個來源收集、轉換和整合數據到一個單一的數據倉庫，例如使用 Data Factory。 +* 處理數據：處理大量數據需要大量的計算能力，而並非每個人都能獲得足夠強大的機器，因此許多人選擇直接利用雲端的巨大計算能力來運行和部署解決方案。 +* 使用數據分析服務：雲端服務如 Azure Synapse Analytics、Azure Stream Analytics 和 Azure Databricks 可以幫助你將數據轉化為可行的洞察。 +* 使用機器學習和數據智能服務：與其從零開始，你可以使用雲端提供商提供的機器學習算法，例如 AzureML。你還可以使用認知服務，例如語音轉文字、文字轉語音、計算機視覺等。 + +## 雲端中的數據科學示例 + +讓我們通過幾個場景來使這些概念更具體化。 + +### 實時社交媒體情感分析 + +我們先從一個常見的機器學習入門場景開始：實時社交媒體情感分析。 + +假設你運營一個新聞媒體網站，並希望利用即時數據來了解讀者可能感興趣的內容。為了更好地了解這一點，你可以構建一個程序，對 Twitter 上的相關主題的數據進行實時情感分析。 + +你需要關注的關鍵指標是特定主題（標籤）的推文數量和情感，情感是通過分析工具對指定主題進行情感分析得出的。 + +創建此項目所需的步驟如下： + +* 創建一個事件中心來流式輸入，收集來自 Twitter 的數據 +* 配置並啟動 Twitter 客戶端應用程序，調用 Twitter Streaming APIs +* 創建一個流分析作業 +* 指定作業輸入和查詢 +* 創建輸出接收器並指定作業輸出 +* 啟動作業 + +查看完整過程，請參考[文檔](https://docs.microsoft.com/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends?WT.mc_id=academic-77958-bethanycheum&ocid=AID30411099)。 + +### 科學論文分析 + +讓我們看另一個由本課程作者之一 [Dmitry Soshnikov](http://soshnikov.com) 創建的項目示例。 + +Dmitry 創建了一個分析 COVID 論文的工具。通過審視這個項目，你可以了解如何創建一個工具，從科學論文中提取知識，獲得洞察，並幫助研究人員高效地瀏覽大量論文。 + +以下是使用的不同步驟： + +* 使用 [Text Analytics for Health](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 提取和預處理信息 +* 使用 [Azure ML](https://azure.microsoft.com/services/machine-learning?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 進行並行處理 +* 使用 [Cosmos DB](https://azure.microsoft.com/services/cosmos-db?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 存儲和查詢信息 +* 使用 Power BI 創建交互式儀表板進行數據探索和可視化 + +查看完整過程，請訪問 [Dmitry 的博客](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/)。 + +如你所見，我們可以以多種方式利用雲端服務來進行數據科學。 + +## 備註 + +來源： +* https://azure.microsoft.com/overview/what-is-cloud-computing?ocid=AID3041109 +* https://docs.microsoft.com/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends?ocid=AID3041109 +* https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/ + +## 課後測驗 + +## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/33) + +## 作業 + +[市場研究](assignment.md) + +--- + +**免責聲明**： +此文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議使用專業的人類翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/5-Data-Science-In-Cloud/17-Introduction/assignment.md b/translations/zh-HK/5-Data-Science-In-Cloud/17-Introduction/assignment.md new file mode 100644 index 00000000..95327043 --- /dev/null +++ b/translations/zh-HK/5-Data-Science-In-Cloud/17-Introduction/assignment.md @@ -0,0 +1,14 @@ +# 市場調查 + +## 指引 + +在這節課中，你學到有幾個重要的雲端服務供應商。進行一些市場調查，了解每個供應商能為數據科學家提供什麼服務。這些服務是否具有可比性？撰寫一篇文章，描述三個或以上這些雲端供應商的服務內容。 + +## 評分標準 + +優秀 | 合格 | 需改進 +--- | --- | -- | +一篇一頁的文章描述了三個雲端供應商的數據科學服務，並區分它們之間的差異。 | 提交了一篇較短的文章 | 提交了一篇文章，但未完成分析 + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原文文件作為權威來源。如涉及關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/5-Data-Science-In-Cloud/18-Low-Code/README.md b/translations/zh-HK/5-Data-Science-In-Cloud/18-Low-Code/README.md new file mode 100644 index 00000000..18331812 --- /dev/null +++ b/translations/zh-HK/5-Data-Science-In-Cloud/18-Low-Code/README.md @@ -0,0 +1,339 @@ +# 雲端中的數據科學：「低代碼/無代碼」方式 + +|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/18-DataScience-Cloud.png)| +|:---:| +| 雲端中的數據科學：低代碼 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | + +目錄： + +- [雲端中的數據科學：「低代碼/無代碼」方式](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [課前測驗](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [1. 簡介](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [1.1 什麼是 Azure Machine Learning？](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [1.2 心臟衰竭預測項目：](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [1.3 心臟衰竭數據集：](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [2. 在 Azure ML Studio 中進行低代碼/無代碼模型訓練](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [2.1 創建 Azure ML 工作區](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [2.2 計算資源](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [2.2.1 選擇合適的計算資源選項](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [2.2.2 創建計算集群](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [2.3 加載數據集](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [2.4 使用 AutoML 進行低代碼/無代碼訓練](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [3. 低代碼/無代碼模型部署及端點使用](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [3.1 模型部署](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [3.2 端點使用](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [🚀 挑戰](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [課後測驗](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [回顧與自學](../../../../5-Data-Science-In-Cloud/18-Low-Code) + - [作業](../../../../5-Data-Science-In-Cloud/18-Low-Code) + +## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/34) + +## 1. 簡介 +### 1.1 什麼是 Azure Machine Learning？ + +Azure 雲端平台包含超過 200 種產品和雲端服務，旨在幫助您實現創新解決方案。數據科學家通常需要花費大量精力來探索和預處理數據，並嘗試各種模型訓練算法以生成準確的模型。這些任務耗時且可能導致昂貴的計算硬件使用效率低下。 + +[Azure ML](https://docs.microsoft.com/azure/machine-learning/overview-what-is-azure-machine-learning?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 是一個基於雲端的平台，用於在 Azure 中構建和運行機器學習解決方案。它提供了多種功能和能力，幫助數據科學家準備數據、訓練模型、發布預測服務並監控其使用情況。最重要的是，它通過自動化許多與模型訓練相關的耗時任務來提高效率；並且它能夠使用可有效擴展的雲端計算資源來處理大量數據，僅在實際使用時產生成本。 + +Azure ML 提供了開發者和數據科學家所需的所有工具，用於機器學習工作流程，包括： + +- **Azure Machine Learning Studio**：Azure Machine Learning 的網頁入口，提供低代碼和無代碼選項，用於模型訓練、部署、自動化、跟蹤和資產管理。Studio 與 Azure Machine Learning SDK 集成，提供無縫體驗。 +- **Jupyter Notebooks**：快速原型設計和測試 ML 模型。 +- **Azure Machine Learning Designer**：允許通過拖放模塊來構建實驗，並在低代碼環境中部署管道。 +- **自動化機器學習界面 (AutoML)**：自動化機器學習模型開發的迭代任務，能以高效和高生產力的方式構建 ML 模型，同時保持模型質量。 +- **數據標籤**：一種輔助 ML 工具，用於自動標籤數據。 +- **Visual Studio Code 的機器學習擴展**：提供完整的開發環境，用於構建和管理 ML 項目。 +- **機器學習 CLI**：提供命令行管理 Azure ML 資源的功能。 +- **與開源框架集成**：如 PyTorch、TensorFlow、Scikit-learn 等，用於訓練、部署和管理端到端的機器學習過程。 +- **MLflow**：一個開源庫，用於管理機器學習實驗的生命周期。**MLFlow Tracking** 是 MLflow 的一個組件，用於記錄和跟蹤訓練運行的指標和模型工件，無論實驗環境如何。 + +### 1.2 心臟衰竭預測項目： + +毫無疑問，製作和構建項目是檢驗技能和知識的最佳方式。在本課程中，我們將探索兩種不同的方式來構建一個心臟衰竭攻擊預測的數據科學項目，分別是通過低代碼/無代碼方式和通過 Azure ML SDK，如下圖所示： + +![project-schema](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/project-schema.PNG) + +每種方式都有其優缺點。低代碼/無代碼方式更容易入門，因為它涉及與 GUI（圖形用戶界面）交互，無需事先了解代碼。此方法能快速測試項目的可行性並創建 POC（概念驗證）。然而，隨著項目規模的擴大並需要進入生產階段，通過 GUI 創建資源的方式將不再可行。我們需要以編程方式自動化所有內容，從資源的創建到模型的部署。在這種情況下，了解如何使用 Azure ML SDK 就變得至關重要。 + +| | 低代碼/無代碼 | Azure ML SDK | +|-------------------|------------------|---------------------------| +| 代碼專業知識 | 不需要 | 需要 | +| 開發時間 | 快速且簡單 | 取決於代碼專業知識 | +| 生產準備 | 否 | 是 | + +### 1.3 心臟衰竭數據集： + +心血管疾病（CVDs）是全球死亡的首要原因，佔全球死亡人數的 31%。環境和行為風險因素，如吸煙、不健康飲食和肥胖、缺乏運動以及有害的酒精使用，可以用作估算模型的特徵。能夠估算 CVD 發展的概率對於預防高風險人群的攻擊非常有用。 + +Kaggle 提供了一個[心臟衰竭數據集](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data)，我們將在此項目中使用該數據集。您現在可以下載該數據集。這是一個包含 13 列（12 個特徵和 1 個目標變量）和 299 行的表格數據集。 + +| | 變量名稱 | 類型 | 描述 | 示例 | +|----|---------------------------|-----------------|---------------------------------------------------------|-------------------| +| 1 | age | 數值型 | 患者年齡 | 25 | +| 2 | anaemia | 布爾型 | 紅細胞或血紅蛋白減少 | 0 或 1 | +| 3 | creatinine_phosphokinase | 數值型 | 血液中 CPK 酶的水平 | 542 | +| 4 | diabetes | 布爾型 | 患者是否患有糖尿病 | 0 或 1 | +| 5 | ejection_fraction | 數值型 | 每次心臟收縮時血液流出的百分比 | 45 | +| 6 | high_blood_pressure | 布爾型 | 患者是否患有高血壓 | 0 或 1 | +| 7 | platelets | 數值型 | 血液中的血小板數量 | 149000 | +| 8 | serum_creatinine | 數值型 | 血液中的血清肌酐水平 | 0.5 | +| 9 | serum_sodium | 數值型 | 血液中的血清鈉水平 | jun | +| 10 | sex | 布爾型 | 女性或男性 | 0 或 1 | +| 11 | smoking | 布爾型 | 患者是否吸煙 | 0 或 1 | +| 12 | time | 數值型 | 隨訪期（天） | 4 | +|----|---------------------------|-----------------|---------------------------------------------------------|-------------------| +| 21 | DEATH_EVENT [目標] | 布爾型 | 患者是否在隨訪期內死亡 | 0 或 1 | + +獲取數據集後，我們就可以在 Azure 中開始項目了。 + +## 2. 在 Azure ML Studio 中進行低代碼/無代碼模型訓練 +### 2.1 創建 Azure ML 工作區 +要在 Azure ML 中訓練模型，首先需要創建 Azure ML 工作區。工作區是 Azure Machine Learning 的頂級資源，提供了一個集中式位置，用於管理您使用 Azure Machine Learning 創建的所有工件。工作區會保留所有訓練運行的歷史記錄，包括日誌、指標、輸出以及腳本的快照。您可以使用這些信息來確定哪次訓練運行生成了最佳模型。[了解更多](https://docs.microsoft.com/azure/machine-learning/concept-workspace?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) + +建議使用與您的操作系統兼容的最新瀏覽器。支持以下瀏覽器： + +- Microsoft Edge（最新版本的新 Microsoft Edge，不是 Microsoft Edge 遺留版） +- Safari（最新版本，僅限 Mac） +- Chrome（最新版本） +- Firefox（最新版本） + +要使用 Azure Machine Learning，請在您的 Azure 訂閱中創建工作區。然後，您可以使用此工作區來管理數據、計算資源、代碼、模型以及與機器學習工作負載相關的其他工件。 + +> **_注意：_** 您的 Azure 訂閱將因數據存儲而產生少量費用，只要 Azure Machine Learning 工作區存在於您的訂閱中。因此，我們建議在不再使用工作區時刪除它。 + +1. 使用與您的 Azure 訂閱相關聯的 Microsoft 賬戶登錄 [Azure 入口網站](https://ms.portal.azure.com/)。 +2. 選擇 **＋創建資源** + + ![workspace-1](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-1.PNG) + + 搜索 Machine Learning 並選擇 Machine Learning 磚塊 + + ![workspace-2](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-2.PNG) + + 點擊創建按鈕 + + ![workspace-3](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-3.PNG) + + 按以下方式填寫設置： + - 訂閱：您的 Azure 訂閱 + - 資源組：創建或選擇一個資源組 + - 工作區名稱：輸入工作區的唯一名稱 + - 地區：選擇離您最近的地理區域 + - 存儲帳戶：注意將為您的工作區創建的默認新存儲帳戶 + - 密鑰保管庫：注意將為您的工作區創建的默認新密鑰保管庫 + - 應用洞察：注意將為您的工作區創建的默認新應用洞察資源 + - 容器註冊表：無（第一次將模型部署到容器時會自動創建） + + ![workspace-4](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-4.PNG) + + - 點擊創建 + 審核，然後點擊創建按鈕 +3. 等待您的工作區創建完成（這可能需要幾分鐘）。然後在入口網站中找到它。您可以通過 Machine Learning Azure 服務找到它。 +4. 在工作區的概覽頁面中，啟動 Azure Machine Learning Studio（或打開新的瀏覽器標籤並導航到 https://ml.azure.com），並使用您的 Microsoft 賬戶登錄 Azure Machine Learning Studio。如果提示，選擇您的 Azure 目錄和訂閱，以及您的 Azure Machine Learning 工作區。 + +![workspace-5](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-5.PNG) + +5. 在 Azure Machine Learning Studio 中，切換左上角的 ☰ 圖標以查看界面中的各個頁面。您可以使用這些頁面來管理工作區中的資源。 + +![workspace-6](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-6.PNG) + +您可以使用 Azure 入口網站管理您的工作區，但對於數據科學家和機器學習運營工程師來說，Azure Machine Learning Studio 提供了一個更專注的用戶界面，用於管理工作區資源。 + +### 2.2 計算資源 + +計算資源是基於雲端的資源，用於運行模型訓練和數據探索過程。您可以創建四種類型的計算資源： + +- **計算實例**：數據科學家用於處理數據和模型的開發工作站。這涉及創建虛擬機（VM）並啟動筆記本實例。然後，您可以通過筆記本調用計算集群來訓練模型。 +- **計算集群**：可擴展的虛擬機集群，用於按需處理實驗代碼。訓練模型時需要使用計算集群。計算集群還可以使用專門的 GPU 或 CPU 資源。 +- **推理集群**：用於部署使用您訓練模型的預測服務的目標。 +- **附加計算資源**：連結到現有的 Azure 計算資源，例如虛擬機器或 Azure Databricks 群集。 + +#### 2.2.1 為您的計算資源選擇合適的選項 + +在創建計算資源時需要考慮一些關鍵因素，這些選擇可能是至關重要的決策。 + +**您需要 CPU 還是 GPU？** + +CPU（中央處理器）是執行計算機程序指令的電子電路。GPU（圖形處理器）是一種專門的電子電路，可以以非常高的速度執行與圖形相關的代碼。 + +CPU 和 GPU 架構的主要區別在於，CPU 設計用於快速處理廣泛的任務（以 CPU 時鐘速度衡量），但在同時運行的任務數量上有限。GPU 則設計用於並行計算，因此在深度學習任務中表現更佳。 + +| CPU | GPU | +|-----------------------------------------|-----------------------------| +| 價格較低 | 價格較高 | +| 並行性較低 | 並行性較高 | +| 深度學習模型訓練速度較慢 | 深度學習的最佳選擇 | + +**群集大小** + +較大的群集成本更高，但響應速度更快。因此，如果您有時間但預算有限，應該選擇小型群集。相反，如果您有預算但時間有限，應該選擇大型群集。 + +**虛擬機器大小** + +根據您的時間和預算限制，您可以調整 RAM、磁碟、核心數量和時鐘速度的大小。增加這些參數會提高成本，但性能也會更好。 + +**專用或低優先級實例？** + +低優先級實例意味著它是可中斷的：基本上，Microsoft Azure 可以將這些資源分配給其他任務，從而中斷作業。專用實例（不可中斷）意味著作業不會在未經您允許的情況下被終止。這是另一個時間與金錢的考量，因為可中斷實例比專用實例便宜。 + +#### 2.2.2 創建計算群集 + +在我們之前創建的 [Azure ML 工作區](https://ml.azure.com/) 中，進入計算資源頁面，您將能看到我們剛剛討論的不同計算資源（例如計算實例、計算群集、推理群集和附加計算資源）。在這個項目中，我們需要一個計算群集來進行模型訓練。在 Studio 中，點擊 "Compute" 菜單，然後選擇 "Compute cluster" 標籤，點擊 "+ New" 按鈕以創建計算群集。 + +![22](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/cluster-1.PNG) + +1. 選擇您的選項：專用 vs 低優先級、CPU 或 GPU、虛擬機器大小和核心數量（您可以保留此項目的默認設置）。 +2. 點擊 "Next" 按鈕。 + +![23](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/cluster-2.PNG) + +3. 為群集命名。 +4. 選擇您的選項：最小/最大節點數量、閒置秒數後縮減、SSH 訪問。注意，如果最小節點數量為 0，當群集閒置時您可以節省成本。注意，最大節點數量越高，訓練時間越短。建議的最大節點數量為 3。 +5. 點擊 "Create" 按鈕。此步驟可能需要幾分鐘。 + +![29](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/cluster-3.PNG) + +太棒了！現在我們有了一個計算群集，接下來需要將數據加載到 Azure ML Studio。 + +### 2.3 加載數據集 + +1. 在我們之前創建的 [Azure ML 工作區](https://ml.azure.com/) 中，點擊左側菜單中的 "Datasets"，然後點擊 "+ Create dataset" 按鈕以創建數據集。選擇 "From local files" 選項並選擇我們之前下載的 Kaggle 數據集。 + + ![24](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/dataset-1.PNG) + +2. 為您的數據集命名、選擇類型並添加描述。點擊 "Next"。從文件中上傳數據。點擊 "Next"。 + + ![25](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/dataset-2.PNG) + +3. 在 Schema 中，將以下特徵的數據類型更改為 Boolean：anaemia、diabetes、高血壓、性別、吸煙和 DEATH_EVENT。點擊 "Next" 並點擊 "Create"。 + + ![26](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/dataset-3.PNG) + +太棒了！現在數據集已準備好，計算群集也已創建，我們可以開始訓練模型了！ + +### 2.4 使用 AutoML 進行低代碼/無代碼訓練 + +傳統的機器學習模型開發資源密集，需要大量的領域知識和時間來生成並比較多個模型。自動化機器學習（AutoML）是一種自動化機器學習模型開發中耗時且迭代任務的過程。它使數據科學家、分析師和開發者能夠以高效和高生產力的方式構建 ML 模型，同時保持模型質量。它減少了生成可投入生產的 ML 模型所需的時間，並且操作簡便高效。[了解更多](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) + +1. 在我們之前創建的 [Azure ML 工作區](https://ml.azure.com/) 中，點擊左側菜單中的 "Automated ML"，選擇您剛剛上傳的數據集。點擊 "Next"。 + + ![27](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/aml-1.PNG) + +2. 輸入新的實驗名稱、目標列（DEATH_EVENT）以及我們創建的計算群集。點擊 "Next"。 + + ![28](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/aml-2.PNG) + +3. 選擇 "Classification" 並點擊 "Finish"。此步驟可能需要 30 分鐘到 1 小時，具體取決於您的計算群集大小。 + + ![30](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/aml-3.PNG) + +4. 一旦運行完成，點擊 "Automated ML" 標籤，點擊您的運行，然後在 "Best model summary" 卡片中點擊算法。 + + ![31](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/aml-4.PNG) + +在這裡，您可以看到 AutoML 生成的最佳模型的詳細描述。您還可以在 "Models" 標籤中探索其他生成的模型。花幾分鐘時間探索 "Explanations (preview)" 按鈕中的模型。一旦您選擇了要使用的模型（在這裡我們選擇 AutoML 選擇的最佳模型），我們將了解如何部署它。 + +## 3. 低代碼/無代碼模型部署及端點使用 +### 3.1 模型部署 + +自動化機器學習界面允許您將最佳模型部署為網絡服務，僅需幾步。部署是模型的集成，使其能夠基於新數據進行預測並識別潛在的機會領域。對於此項目，部署到網絡服務意味著醫療應用程序將能夠使用該模型進行患者心臟病風險的即時預測。 + +在最佳模型描述中，點擊 "Deploy" 按鈕。 + +![deploy-1](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/deploy-1.PNG) + +15. 為其命名、添加描述、選擇計算類型（Azure Container Instance），啟用身份驗證並點擊 "Deploy"。此步驟可能需要約 20 分鐘完成。部署過程包括註冊模型、生成資源並配置它們以供網絡服務使用。部署狀態下會顯示狀態消息。定期點擊 "Refresh" 檢查部署狀態。當狀態顯示 "Healthy" 時，表示已部署並正在運行。 + +![deploy-2](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/deploy-2.PNG) + +16. 部署完成後，點擊 "Endpoint" 標籤並點擊您剛剛部署的端點。在這裡，您可以找到有關端點的所有詳細信息。 + +![deploy-3](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/deploy-3.PNG) + +太棒了！現在我們已經部署了模型，可以開始使用端點。 + +### 3.2 端點使用 + +點擊 "Consume" 標籤。在這裡，您可以找到 REST 端點以及消耗選項中的 Python 腳本。花些時間閱讀 Python 代碼。 + +此腳本可以直接從您的本地機器運行，並將使用您的端點。 + +![35](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/consumption-1.PNG) + +花些時間檢查以下兩行代碼： + +```python +url = 'http://98e3715f-xxxx-xxxx-xxxx-9ec22d57b796.centralus.azurecontainer.io/score' +api_key = '' # Replace this with the API key for the web service +``` +`url` 變量是消耗標籤中找到的 REST 端點，而 `api_key` 變量是消耗標籤中找到的主密鑰（僅在啟用了身份驗證的情況下）。這就是腳本如何使用端點。 + +18. 運行腳本，您應該看到以下輸出： + ```python + b'"{\\"result\\": [true]}"' + ``` +這意味著給定數據的心臟衰竭預測為真。這是合理的，因為如果您仔細查看腳本中自動生成的數據，所有值默認為 0 和 false。您可以使用以下輸入樣本更改數據： + +```python +data = { + "data": + [ + { + 'age': "0", + 'anaemia': "false", + 'creatinine_phosphokinase': "0", + 'diabetes': "false", + 'ejection_fraction': "0", + 'high_blood_pressure': "false", + 'platelets': "0", + 'serum_creatinine': "0", + 'serum_sodium': "0", + 'sex': "false", + 'smoking': "false", + 'time': "0", + }, + { + 'age': "60", + 'anaemia': "false", + 'creatinine_phosphokinase': "500", + 'diabetes': "false", + 'ejection_fraction': "38", + 'high_blood_pressure': "false", + 'platelets': "260000", + 'serum_creatinine': "1.40", + 'serum_sodium': "137", + 'sex': "false", + 'smoking': "false", + 'time': "130", + }, + ], +} +``` +腳本應返回： + ```python + b'"{\\"result\\": [true, false]}"' + ``` + +恭喜！您剛剛使用 Azure ML 訓練並部署了模型，並成功使用了端點！ + +> **_注意：_** 完成項目後，請記得刪除所有資源。 +## 🚀 挑戰 + +仔細查看 AutoML 為頂級模型生成的模型解釋和詳細信息。嘗試理解為什麼最佳模型比其他模型更好。比較了哪些算法？它們之間有什麼差異？為什麼在這種情況下最佳模型表現更好？ + +## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/35) + +## 回顧與自學 + +在本課中，您學習了如何在雲端以低代碼/無代碼方式訓練、部署和使用模型來預測心臟衰竭風險。如果您尚未完成，請深入研究 AutoML 為頂級模型生成的模型解釋，並嘗試理解為什麼最佳模型比其他模型更好。 + +您可以通過閱讀此 [文檔](https://docs.microsoft.com/azure/machine-learning/tutorial-first-experiment-automated-ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 進一步了解低代碼/無代碼 AutoML。 + +## 作業 + +[基於 Azure ML 的低代碼/無代碼數據科學項目](assignment.md) + +--- + +**免責聲明**： +此文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議使用專業的人工作翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/5-Data-Science-In-Cloud/18-Low-Code/assignment.md b/translations/zh-HK/5-Data-Science-In-Cloud/18-Low-Code/assignment.md new file mode 100644 index 00000000..acbc99bd --- /dev/null +++ b/translations/zh-HK/5-Data-Science-In-Cloud/18-Low-Code/assignment.md @@ -0,0 +1,14 @@ +# Azure ML 上的低代碼/無代碼數據科學項目 + +## 指引 + +我們已經學習了如何使用 Azure ML 平台以低代碼/無代碼的方式進行模型的訓練、部署和使用。現在，請尋找一些可以用來訓練其他模型的數據，並將其部署和使用。你可以在 [Kaggle](https://kaggle.com) 和 [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 上尋找數據集。 + +## 評分標準 + +| 卓越 | 合格 | 需要改進 | +|------|------|----------| +|在上傳數據時，你注意到了是否需要更改特徵的類型。如果需要，你也清理了數據。你使用 AutoML 在數據集上進行了訓練，並檢查了模型解釋。你部署了最佳模型並成功使用了它。 | 在上傳數據時，你注意到了是否需要更改特徵的類型。你使用 AutoML 在數據集上進行了訓練，部署了最佳模型並成功使用了它。 | 你部署了由 AutoML 訓練的最佳模型並成功使用了它。 | + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/5-Data-Science-In-Cloud/19-Azure/README.md b/translations/zh-HK/5-Data-Science-In-Cloud/19-Azure/README.md new file mode 100644 index 00000000..83856a07 --- /dev/null +++ b/translations/zh-HK/5-Data-Science-In-Cloud/19-Azure/README.md @@ -0,0 +1,303 @@ +# 雲端中的數據科學：Azure ML SDK 的方法 + +|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/19-DataScience-Cloud.png)| +|:---:| +| 雲端中的數據科學：Azure ML SDK - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | + +目錄： + +- [雲端中的數據科學：Azure ML SDK 的方法](../../../../5-Data-Science-In-Cloud/19-Azure) + - [課前測驗](../../../../5-Data-Science-In-Cloud/19-Azure) + - [1. 簡介](../../../../5-Data-Science-In-Cloud/19-Azure) + - [1.1 什麼是 Azure ML SDK？](../../../../5-Data-Science-In-Cloud/19-Azure) + - [1.2 心臟衰竭預測項目及數據集介紹](../../../../5-Data-Science-In-Cloud/19-Azure) + - [2. 使用 Azure ML SDK 訓練模型](../../../../5-Data-Science-In-Cloud/19-Azure) + - [2.1 創建 Azure ML 工作區](../../../../5-Data-Science-In-Cloud/19-Azure) + - [2.2 創建計算實例](../../../../5-Data-Science-In-Cloud/19-Azure) + - [2.3 加載數據集](../../../../5-Data-Science-In-Cloud/19-Azure) + - [2.4 創建筆記本](../../../../5-Data-Science-In-Cloud/19-Azure) + - [2.5 訓練模型](../../../../5-Data-Science-In-Cloud/19-Azure) + - [2.5.1 設置工作區、實驗、計算集群和數據集](../../../../5-Data-Science-In-Cloud/19-Azure) + - [2.5.2 AutoML 配置和訓練](../../../../5-Data-Science-In-Cloud/19-Azure) + - [3. 使用 Azure ML SDK 部署模型及消費端點](../../../../5-Data-Science-In-Cloud/19-Azure) + - [3.1 保存最佳模型](../../../../5-Data-Science-In-Cloud/19-Azure) + - [3.2 模型部署](../../../../5-Data-Science-In-Cloud/19-Azure) + - [3.3 消費端點](../../../../5-Data-Science-In-Cloud/19-Azure) + - [🚀 挑戰](../../../../5-Data-Science-In-Cloud/19-Azure) + - [課後測驗](../../../../5-Data-Science-In-Cloud/19-Azure) + - [回顧與自學](../../../../5-Data-Science-In-Cloud/19-Azure) + - [作業](../../../../5-Data-Science-In-Cloud/19-Azure) + +## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/36) + +## 1. 簡介 + +### 1.1 什麼是 Azure ML SDK？ + +數據科學家和人工智能開發者使用 Azure Machine Learning SDK 與 Azure Machine Learning 服務一起構建和運行機器學習工作流。您可以在任何 Python 環境中與該服務交互，包括 Jupyter Notebooks、Visual Studio Code 或您喜愛的 Python IDE。 + +SDK 的主要功能包括： + +- 探索、準備和管理機器學習實驗中使用的數據集的生命周期。 +- 管理雲端資源以進行監控、日誌記錄和組織機器學習實驗。 +- 在本地或使用雲端資源（包括 GPU 加速的模型訓練）訓練模型。 +- 使用自動化機器學習，該功能接受配置參數和訓練數據，並自動迭代算法和超參數設置以找到最佳模型進行預測。 +- 部署網絡服務，將訓練好的模型轉換為可在任何應用程序中使用的 RESTful 服務。 + +[了解更多關於 Azure Machine Learning SDK 的信息](https://docs.microsoft.com/python/api/overview/azure/ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) + +在[上一課](../18-Low-Code/README.md)中，我們學習了如何以低代碼/無代碼的方式訓練、部署和使用模型。我們使用了心臟衰竭數據集來生成心臟衰竭預測模型。在本課中，我們將使用 Azure Machine Learning SDK 完成相同的任務。 + +![項目架構](../../../../5-Data-Science-In-Cloud/19-Azure/images/project-schema.PNG) + +### 1.2 心臟衰竭預測項目及數據集介紹 + +查看[這裡](../18-Low-Code/README.md)了解心臟衰竭預測項目及數據集介紹。 + +## 2. 使用 Azure ML SDK 訓練模型 +### 2.1 創建 Azure ML 工作區 + +為了簡化操作，我們將在 Jupyter Notebook 中工作。這意味著您已經擁有一個工作區和一個計算實例。如果您已經擁有工作區，可以直接跳到 2.3 筆記本創建部分。 + +如果沒有，請按照[上一課](../18-Low-Code/README.md)中 **2.1 創建 Azure ML 工作區** 的指示創建工作區。 + +### 2.2 創建計算實例 + +在我們之前創建的 [Azure ML 工作區](https://ml.azure.com/) 中，進入計算菜單，您將看到不同的計算資源。 + +![計算實例-1](../../../../5-Data-Science-In-Cloud/19-Azure/images/compute-instance-1.PNG) + +讓我們創建一個計算實例來提供 Jupyter Notebook。 +1. 點擊 + New 按鈕。 +2. 為您的計算實例命名。 +3. 選擇您的選項：CPU 或 GPU、虛擬機大小和核心數量。 +4. 點擊 Create 按鈕。 + +恭喜，您已成功創建計算實例！我們將在[創建筆記本部分](../../../../5-Data-Science-In-Cloud/19-Azure)中使用此計算實例。 + +### 2.3 加載數據集 +如果您尚未上傳數據集，請參考[上一課](../18-Low-Code/README.md)中的 **2.3 加載數據集** 部分。 + +### 2.4 創建筆記本 + +> **_注意:_** 接下來的步驟，您可以選擇從頭創建一個新的筆記本，或者上傳我們之前創建的 [筆記本](../../../../5-Data-Science-In-Cloud/19-Azure/notebook.ipynb) 到您的 Azure ML Studio。要上傳，只需點擊 "Notebook" 菜單並上傳筆記本。 + +筆記本是數據科學過程中非常重要的一部分。它們可以用於進行探索性數據分析（EDA）、調用計算集群訓練模型、調用推理集群部署端點。 + +要創建筆記本，我們需要一個提供 Jupyter Notebook 實例的計算節點。返回 [Azure ML 工作區](https://ml.azure.com/) 並點擊計算實例。在計算實例列表中，您應該看到[我們之前創建的計算實例](../../../../5-Data-Science-In-Cloud/19-Azure)。 + +1. 在 Applications 部分，點擊 Jupyter 選項。 +2. 勾選 "Yes, I understand" 框並點擊 Continue 按鈕。 +![筆記本-1](../../../../5-Data-Science-In-Cloud/19-Azure/images/notebook-1.PNG) +3. 這將在瀏覽器中打開一個新的標籤頁，顯示您的 Jupyter Notebook 實例。點擊 "New" 按鈕創建筆記本。 + +![筆記本-2](../../../../5-Data-Science-In-Cloud/19-Azure/images/notebook-2.PNG) + +現在我們有了一個筆記本，可以開始使用 Azure ML SDK 訓練模型。 + +### 2.5 訓練模型 + +首先，如果您有任何疑問，請參考 [Azure ML SDK 文檔](https://docs.microsoft.com/python/api/overview/azure/ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)。它包含了理解我們在本課中將看到的模塊所需的所有信息。 + +#### 2.5.1 設置工作區、實驗、計算集群和數據集 + +您需要使用以下代碼從配置文件加載 `workspace`： + +```python +from azureml.core import Workspace +ws = Workspace.from_config() +``` + +這將返回一個表示工作區的 `Workspace` 類型的對象。接著，您需要使用以下代碼創建一個 `experiment`： + +```python +from azureml.core import Experiment +experiment_name = 'aml-experiment' +experiment = Experiment(ws, experiment_name) +``` +要從工作區獲取或創建實驗，您需要使用實驗名稱請求實驗。實驗名稱必須是 3-36 個字符，並以字母或數字開頭，只能包含字母、數字、下劃線和連字符。如果在工作區中找不到實驗，則會創建一個新的實驗。 + +現在，您需要使用以下代碼創建一個訓練用的計算集群。請注意，此步驟可能需要幾分鐘。 + +```python +from azureml.core.compute import AmlCompute + +aml_name = "heart-f-cluster" +try: + aml_compute = AmlCompute(ws, aml_name) + print('Found existing AML compute context.') +except: + print('Creating new AML compute context.') + aml_config = AmlCompute.provisioning_configuration(vm_size = "Standard_D2_v2", min_nodes=1, max_nodes=3) + aml_compute = AmlCompute.create(ws, name = aml_name, provisioning_configuration = aml_config) + aml_compute.wait_for_completion(show_output = True) + +cts = ws.compute_targets +compute_target = cts[aml_name] +``` + +您可以通過數據集名稱從工作區獲取數據集，如下所示： + +```python +dataset = ws.datasets['heart-failure-records'] +df = dataset.to_pandas_dataframe() +df.describe() +``` +#### 2.5.2 AutoML 配置和訓練 + +要設置 AutoML 配置，請使用 [AutoMLConfig 類](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig(class)?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)。 + +如文檔所述，您可以使用許多參數進行配置。對於本項目，我們將使用以下參數： + +- `experiment_timeout_minutes`：實驗允許運行的最大時間（以分鐘為單位），超過此時間後實驗將自動停止並生成結果。 +- `max_concurrent_iterations`：實驗允許的最大並發訓練迭代次數。 +- `primary_metric`：用於確定實驗狀態的主要指標。 +- `compute_target`：運行自動化機器學習實驗的 Azure Machine Learning 計算目標。 +- `task`：要運行的任務類型。值可以是 'classification'、'regression' 或 'forecasting'，具體取決於要解決的自動化機器學習問題類型。 +- `training_data`：實驗中使用的訓練數據。它應包含訓練特徵和標籤列（可選的樣本權重列）。 +- `label_column_name`：標籤列的名稱。 +- `path`：Azure Machine Learning 項目文件夾的完整路徑。 +- `enable_early_stopping`：是否啟用早期終止，如果短期內分數沒有改善則終止。 +- `featurization`：指示是否應自動完成特徵化步驟，或者是否使用自定義特徵化。 +- `debug_log`：用於寫入調試信息的日誌文件。 + +```python +from azureml.train.automl import AutoMLConfig + +project_folder = './aml-project' + +automl_settings = { + "experiment_timeout_minutes": 20, + "max_concurrent_iterations": 3, + "primary_metric" : 'AUC_weighted' +} + +automl_config = AutoMLConfig(compute_target=compute_target, + task = "classification", + training_data=dataset, + label_column_name="DEATH_EVENT", + path = project_folder, + enable_early_stopping= True, + featurization= 'auto', + debug_log = "automl_errors.log", + **automl_settings + ) +``` +現在您已設置好配置，可以使用以下代碼訓練模型。此步驟可能需要長達一小時，具體取決於集群大小。 + +```python +remote_run = experiment.submit(automl_config) +``` +您可以運行 RunDetails 小部件來顯示不同的實驗。 +```python +from azureml.widgets import RunDetails +RunDetails(remote_run).show() +``` +## 3. 使用 Azure ML SDK 部署模型及消費端點 + +### 3.1 保存最佳模型 + +`remote_run` 是 [AutoMLRun](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.run.automlrun?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 類型的對象。此對象包含 `get_output()` 方法，該方法返回最佳運行及相應的擬合模型。 + +```python +best_run, fitted_model = remote_run.get_output() +``` +您可以通過打印 fitted_model 查看最佳模型使用的參數，並使用 [get_properties()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.run(class)?view=azure-ml-py#azureml_core_Run_get_properties?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 方法查看最佳模型的屬性。 + +```python +best_run.get_properties() +``` + +現在使用 [register_model](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.run.automlrun?view=azure-ml-py#register-model-model-name-none--description-none--tags-none--iteration-none--metric-none-?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 方法註冊模型。 +```python +model_name = best_run.properties['model_name'] +script_file_name = 'inference/score.py' +best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py') +description = "aml heart failure project sdk" +model = best_run.register_model(model_name = model_name, + model_path = './outputs/', + description = description, + tags = None) +``` +### 3.2 模型部署 + +保存最佳模型後，我們可以使用 [InferenceConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py?ocid=AID3041109) 類進行部署。InferenceConfig 表示用於部署的自定義環境的配置設置。[AciWebservice](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.aciwebservice?view=azure-ml-py) 類表示部署為 Azure 容器實例上的網絡服務端點的機器學習模型。部署的服務由模型、腳本和相關文件創建。生成的網絡服務是一個負載均衡的 HTTP 端點，具有 REST API。您可以向此 API 發送數據並接收模型返回的預測。 + +模型使用 [deploy](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model(class)?view=azure-ml-py#deploy-workspace--name--models--inference-config-none--deployment-config-none--deployment-target-none--overwrite-false--show-output-false-?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 方法進行部署。 + +```python +from azureml.core.model import InferenceConfig, Model +from azureml.core.webservice import AciWebservice + +inference_config = InferenceConfig(entry_script=script_file_name, environment=best_run.get_environment()) + +aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, + memory_gb = 1, + tags = {'type': "automl-heart-failure-prediction"}, + description = 'Sample service for AutoML Heart Failure Prediction') + +aci_service_name = 'automl-hf-sdk' +aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig) +aci_service.wait_for_deployment(True) +print(aci_service.state) +``` +此步驟可能需要幾分鐘。 + +### 3.3 消費端點 + +您可以通過創建樣本輸入來使用您的端點： + +```python +data = { + "data": + [ + { + 'age': "60", + 'anaemia': "false", + 'creatinine_phosphokinase': "500", + 'diabetes': "false", + 'ejection_fraction': "38", + 'high_blood_pressure': "false", + 'platelets': "260000", + 'serum_creatinine': "1.40", + 'serum_sodium': "137", + 'sex': "false", + 'smoking': "false", + 'time': "130", + }, + ], +} + +test_sample = str.encode(json.dumps(data)) +``` +然後，您可以將此輸入發送到您的模型進行預測： +```python +response = aci_service.run(input_data=test_sample) +response +``` +這應該輸出 `'{"result": [false]}'`。這表示我們傳送到端點的病人輸入生成了預測結果 `false`，即這個人不太可能會有心臟病發作。 + +恭喜！你剛剛使用 Azure ML SDK 成功消耗了在 Azure ML 上部署和訓練的模型！ + +> **_NOTE:_** 完成專案後，記得刪除所有資源。 + +## 🚀 挑戰 + +透過 SDK 還有許多其他事情可以做，但很遺憾，我們無法在這節課中全部涵蓋。不過好消息是，學會如何快速瀏覽 SDK 文件可以讓你在學習上走得更遠。查看 Azure ML SDK 文件，找到允許你建立管道的 `Pipeline` 類別。管道是一系列可以作為工作流程執行的步驟集合。 + +**提示：** 前往 [SDK 文件](https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)，在搜尋欄中輸入關鍵字如 "Pipeline"。你應該可以在搜尋結果中找到 `azureml.pipeline.core.Pipeline` 類別。 + +## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/37) + +## 回顧與自學 + +在這節課中，你學會了如何使用 Azure ML SDK 在雲端訓練、部署和消耗模型來預測心臟衰竭風險。查看這份 [文件](https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 以獲取更多關於 Azure ML SDK 的資訊。試著使用 Azure ML SDK 建立你自己的模型。 + +## 作業 + +[使用 Azure ML SDK 的數據科學專案](assignment.md) + +--- + +**免責聲明**： +此文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤詮釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/5-Data-Science-In-Cloud/19-Azure/assignment.md b/translations/zh-HK/5-Data-Science-In-Cloud/19-Azure/assignment.md new file mode 100644 index 00000000..d43025df --- /dev/null +++ b/translations/zh-HK/5-Data-Science-In-Cloud/19-Azure/assignment.md @@ -0,0 +1,14 @@ +# 使用 Azure ML SDK 的數據科學項目 + +## 指引 + +我們已經學習了如何使用 Azure ML 平台通過 Azure ML SDK 進行模型的訓練、部署和使用。現在，請尋找一些可以用來訓練其他模型的數據，並將其部署和使用。你可以在 [Kaggle](https://kaggle.com) 和 [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 上尋找數據集。 + +## 評分標準 + +| 卓越 | 合格 | 需要改進 | +|------|------|----------| +|在進行 AutoML 配置時，你查閱了 SDK 文檔以了解可以使用的參數。你通過 Azure ML SDK 使用 AutoML 在數據集上進行了訓練，並檢查了模型解釋。你部署了最佳模型，並能通過 Azure ML SDK 使用該模型。 | 你通過 Azure ML SDK 使用 AutoML 在數據集上進行了訓練，並檢查了模型解釋。你部署了最佳模型，並能通過 Azure ML SDK 使用該模型。 | 你通過 Azure ML SDK 使用 AutoML 在數據集上進行了訓練。你部署了最佳模型，並能通過 Azure ML SDK 使用該模型。 | + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，請注意自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋不承擔責任。 \ No newline at end of file diff --git a/translations/zh-HK/5-Data-Science-In-Cloud/19-Azure/notebook.ipynb b/translations/zh-HK/5-Data-Science-In-Cloud/19-Azure/notebook.ipynb new file mode 100644 index 00000000..e2e4cee8 --- /dev/null +++ b/translations/zh-HK/5-Data-Science-In-Cloud/19-Azure/notebook.ipynb @@ -0,0 +1,323 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# 雲端中的數據科學：「Azure ML SDK」方法\n", + "\n", + "## 簡介\n", + "\n", + "在這份筆記中，我們將學習如何使用 Azure ML SDK 來訓練、部署及使用模型，通過 Azure ML 平台完成。\n", + "\n", + "前置條件：\n", + "1. 你已建立 Azure ML 工作區。\n", + "2. 你已將 [心臟衰竭數據集](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data) 加載到 Azure ML。\n", + "3. 你已將這份筆記上傳到 Azure ML Studio。\n", + "\n", + "接下來的步驟是：\n", + "\n", + "1. 在現有的工作區中建立一個實驗。\n", + "2. 建立一個計算叢集。\n", + "3. 加載數據集。\n", + "4. 使用 AutoMLConfig 配置 AutoML。\n", + "5. 執行 AutoML 實驗。\n", + "6. 探索結果並獲取最佳模型。\n", + "7. 註冊最佳模型。\n", + "8. 部署最佳模型。\n", + "9. 使用端點。\n", + "\n", + "## Azure Machine Learning SDK 特定的導入\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "from azureml.core import Workspace, Experiment\n", + "from azureml.core.compute import AmlCompute\n", + "from azureml.train.automl import AutoMLConfig\n", + "from azureml.widgets import RunDetails\n", + "from azureml.core.model import InferenceConfig, Model\n", + "from azureml.core.webservice import AciWebservice" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## 初始化工作區\n", + "從已保存的配置中初始化一個工作區物件。請確保配置文件存在於 .\\config.json\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "ws = Workspace.from_config()\n", + "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## 建立 Azure ML 實驗\n", + "\n", + "讓我們在剛剛初始化的工作區中建立一個名為「aml-experiment」的實驗。\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "experiment_name = 'aml-experiment'\n", + "experiment = Experiment(ws, experiment_name)\n", + "experiment" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## 建立計算叢集 \n", + "你需要為你的 AutoML 執行建立一個[計算目標](https://docs.microsoft.com/azure/machine-learning/concept-azure-machine-learning-architecture#compute-target)。 \n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "aml_name = \"heart-f-cluster\"\n", + "try:\n", + " aml_compute = AmlCompute(ws, aml_name)\n", + " print('Found existing AML compute context.')\n", + "except:\n", + " print('Creating new AML compute context.')\n", + " aml_config = AmlCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\", min_nodes=1, max_nodes=3)\n", + " aml_compute = AmlCompute.create(ws, name = aml_name, provisioning_configuration = aml_config)\n", + " aml_compute.wait_for_completion(show_output = True)\n", + "\n", + "cts = ws.compute_targets\n", + "compute_target = cts[aml_name]" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## 數據\n", + "請確保你已將數據集上載到 Azure ML，並且鍵的名稱與數據集的名稱相同。\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "key = 'heart-failure-records'\n", + "dataset = ws.datasets[key]\n", + "df = dataset.to_pandas_dataframe()\n", + "df.describe()" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## 自動機器學習配置\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "automl_settings = {\n", + " \"experiment_timeout_minutes\": 20,\n", + " \"max_concurrent_iterations\": 3,\n", + " \"primary_metric\" : 'AUC_weighted'\n", + "}\n", + "\n", + "automl_config = AutoMLConfig(compute_target=compute_target,\n", + " task = \"classification\",\n", + " training_data=dataset,\n", + " label_column_name=\"DEATH_EVENT\",\n", + " enable_early_stopping= True,\n", + " featurization= 'auto',\n", + " debug_log = \"automl_errors.log\",\n", + " **automl_settings\n", + " )" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## 自動機器學習運行\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "remote_run = experiment.submit(automl_config)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "RunDetails(remote_run).show()" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "best_run, fitted_model = remote_run.get_output()" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "best_run.get_properties()" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "model_name = best_run.properties['model_name']\n", + "script_file_name = 'inference/score.py'\n", + "best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py')\n", + "description = \"aml heart failure project sdk\"\n", + "model = best_run.register_model(model_name = model_name,\n", + " description = description,\n", + " tags = None)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## 部署最佳模型\n", + "\n", + "執行以下程式碼以部署最佳模型。你可以在 Azure ML 入口網站中查看部署的狀態。此步驟可能需要幾分鐘時間。\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "inference_config = InferenceConfig(entry_script=script_file_name, environment=best_run.get_environment())\n", + "\n", + "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1,\n", + " memory_gb = 1,\n", + " tags = {'type': \"automl-heart-failure-prediction\"},\n", + " description = 'Sample service for AutoML Heart Failure Prediction')\n", + "\n", + "aci_service_name = 'automl-hf-sdk'\n", + "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n", + "aci_service.wait_for_deployment(True)\n", + "print(aci_service.state)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## 使用端點\n", + "你可以為以下的輸入範例添加輸入內容。\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "data = {\n", + " \"data\":\n", + " [\n", + " {\n", + " 'age': \"60\",\n", + " 'anaemia': \"false\",\n", + " 'creatinine_phosphokinase': \"500\",\n", + " 'diabetes': \"false\",\n", + " 'ejection_fraction': \"38\",\n", + " 'high_blood_pressure': \"false\",\n", + " 'platelets': \"260000\",\n", + " 'serum_creatinine': \"1.40\",\n", + " 'serum_sodium': \"137\",\n", + " 'sex': \"false\",\n", + " 'smoking': \"false\",\n", + " 'time': \"130\",\n", + " },\n", + " ],\n", + "}\n", + "\n", + "test_sample = str.encode(json.dumps(data))" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "response = aci_service.run(input_data=test_sample)\n", + "response" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**免責聲明**： \n此文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議使用專業的人工作業翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。\n" + ] + } + ], + "metadata": { + "orig_nbformat": 4, + "language_info": { + "name": "python" + }, + "coopTranslator": { + "original_hash": "af42669556d5dc19fc4cc3866f7d2597", + "translation_date": "2025-09-02T05:44:56+00:00", + "source_file": "5-Data-Science-In-Cloud/19-Azure/notebook.ipynb", + "language_code": "hk" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/zh-HK/5-Data-Science-In-Cloud/19-Azure/solution/notebook.ipynb b/translations/zh-HK/5-Data-Science-In-Cloud/19-Azure/solution/notebook.ipynb new file mode 100644 index 00000000..e69de29b diff --git a/translations/zh-HK/5-Data-Science-In-Cloud/README.md b/translations/zh-HK/5-Data-Science-In-Cloud/README.md new file mode 100644 index 00000000..22ff63a5 --- /dev/null +++ b/translations/zh-HK/5-Data-Science-In-Cloud/README.md @@ -0,0 +1,23 @@ +# 雲端中的數據科學 + +![cloud-picture](../../../translated_images/zh-HK/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) + +> 圖片來源：[Jelleke Vanooteghem](https://unsplash.com/@ilumire) 來自 [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) + +當涉及到使用大數據進行數據科學時，雲端可以成為改變遊戲規則的關鍵。在接下來的三節課中，我們將了解什麼是雲端以及為什麼它非常有用。我們還將探索一個心臟衰竭數據集，並建立一個模型來幫助評估某人發生心臟衰竭的可能性。我們將利用雲端的強大功能來訓練、部署和以兩種不同的方式使用模型。一種方式是僅使用用戶界面，以低代碼/無代碼的方式進行；另一種方式是使用 Azure 機器學習軟件開發工具包 (Azure ML SDK)。 + +![project-schema](../../../translated_images/zh-HK/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) + +### 主題 + +1. [為什麼在數據科學中使用雲端？](17-Introduction/README.md) +2. [雲端中的數據科學：「低代碼/無代碼」方式](18-Low-Code/README.md) +3. [雲端中的數據科學：「Azure ML SDK」方式](19-Azure/README.md) + +### 鳴謝 +這些課程由 [Maud Levy](https://twitter.com/maudstweets) 和 [Tiffany Souterre](https://twitter.com/TiffanySouterre) 帶著 ☁️ 和 💕 編寫。 + +心臟衰竭預測項目的數據來源於 [Kaggle](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data) 上的 [Larxel](https://www.kaggle.com/andrewmvd)。該數據根據 [Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) 授權。 + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為權威來源。對於重要信息，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/zh-HK/6-Data-Science-In-Wild/20-Real-World-Examples/README.md new file mode 100644 index 00000000..dff94755 --- /dev/null +++ b/translations/zh-HK/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -0,0 +1,147 @@ +# 數據科學在現實世界中的應用 + +| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-RealWorld.png) | +| :--------------------------------------------------------------------------------------------------------------: | +| 數據科學在現實世界中的應用 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | + +我們的學習旅程即將結束！ + +我們從數據科學和倫理的定義開始，探索了各種數據分析和可視化的工具與技術，回顧了數據科學的生命周期，並研究了如何利用雲端計算服務擴展和自動化數據科學工作流程。所以，你可能會想：_「如何將這些學到的知識應用到現實世界的情境中？」_ + +在這節課中，我們將探討數據科學在各行業中的現實應用，並深入研究在科研、數字人文和可持續性方面的具體例子。我們還會介紹學生項目機會，並提供一些有用的資源，幫助你繼續你的學習旅程！ + +## 課前測驗 + +## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/38) + +## 數據科學 + 行業 + +隨著人工智能的普及化，開發者現在更容易設計和整合基於人工智能的決策和數據驅動的洞察到用戶體驗和開發工作流程中。以下是數據科學在各行業中的一些「現實應用」例子： + + * [Google Flu Trends](https://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/) 使用數據科學將搜索詞與流感趨勢相關聯。雖然這種方法存在缺陷，但它提高了人們對數據驅動的醫療預測可能性（以及挑戰）的認識。 + + * [UPS 路線預測](https://www.technologyreview.com/2018/11/21/139000/how-ups-uses-ai-to-outsmart-bad-weather/) - 解釋了 UPS 如何利用數據科學和機器學習來預測最佳配送路線，考慮到天氣條件、交通模式、配送截止時間等因素。 + + * [紐約市出租車路線可視化](http://chriswhong.github.io/nyctaxi/) - 使用[信息自由法](https://chriswhong.com/open-data/foil_nyc_taxi/)收集的數據幫助可視化紐約市出租車一天的運作情況，讓我們了解它們如何在繁忙的城市中穿梭、賺取的收入以及每24小時內行程的持續時間。 + + * [Uber 數據科學工作台](https://eng.uber.com/dsw/) - 利用每天從數百萬次 Uber 行程中收集的數據（如接送地點、行程時長、偏好路線等），構建數據分析工具，用於定價、安全、欺詐檢測和導航決策。 + + * [體育分析](https://towardsdatascience.com/scope-of-analytics-in-sports-world-37ed09c39860) - 專注於_預測分析_（團隊和球員分析，例如[Moneyball](https://datasciencedegree.wisconsin.edu/blog/moneyball-proves-importance-big-data-big-ideas/)）和_數據可視化_（團隊和球迷儀表板、比賽等），應用於人才選拔、體育博彩和場地管理。 + + * [數據科學在銀行業的應用](https://data-flair.training/blogs/data-science-in-banking/) - 強調數據科學在金融行業的價值，應用包括風險建模和欺詐檢測、客戶分群、實時預測和推薦系統。預測分析還推動了關鍵指標，如[信用評分](https://dzone.com/articles/using-big-data-and-predictive-analytics-for-credit)。 + + * [數據科學在醫療保健中的應用](https://data-flair.training/blogs/data-science-in-healthcare/) - 強調應用包括醫學影像（例如 MRI、X光、CT掃描）、基因組學（DNA測序）、藥物開發（風險評估、成功預測）、預測分析（患者護理和供應物流）、疾病追蹤和預防等。 + +![數據科學在現實世界中的應用](../../../../translated_images/zh-HK/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) 圖片來源：[Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) + +該圖展示了其他領域和應用數據科學技術的例子。想探索更多應用？查看下面的[回顧與自學](../../../../6-Data-Science-In-Wild/20-Real-World-Examples)部分。 + +## 數據科學 + 科研 + +| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-Research.png) | +| :---------------------------------------------------------------------------------------------------------------: | +| 數據科學與科研 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | + +雖然現實世界的應用通常專注於行業中的大規模使用案例，_科研_應用和項目可以從兩個角度提供價值： + +* _創新機會_ - 探索先進概念的快速原型設計以及下一代應用的用戶體驗測試。 +* _部署挑戰_ - 調查數據科學技術在現實世界中的潛在危害或意外後果。 + +對於學生來說，這些科研項目既能提供學習和合作的機會，又能幫助你加深對主題的理解，並擴展你與相關領域的專家或團隊的交流和參與。那麼，科研項目是什麼樣的？它們如何產生影響？ + +讓我們看一個例子——[MIT Gender Shades Study](http://gendershades.org/overview.html)，由 Joy Buolamwini（MIT Media Labs）主導，並與 Timnit Gebru（當時在 Microsoft Research）共同撰寫了一篇[重要的研究論文](http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf)，該研究聚焦於： + + * **研究內容：** 該研究項目的目的是_評估基於性別和膚色的自動面部分析算法和數據集中的偏差_。 + * **研究原因：** 面部分析被應用於執法、機場安檢、招聘系統等領域——在這些情境中，由於偏差導致的不準確分類可能對受影響的個人或群體造成潛在的經濟和社會損害。理解（並消除或減輕）偏差是使用公平性的關鍵。 + * **研究方法：** 研究人員發現現有的基準主要使用膚色較淺的受試者，並策劃了一個新的數據集（1000多張圖片），該數據集在性別和膚色方面更加平衡。該數據集被用於評估三個性別分類產品（來自 Microsoft、IBM 和 Face++）的準確性。 + +研究結果顯示，雖然整體分類準確性良好，但不同子群體之間的錯誤率存在顯著差異——其中**性別錯誤分類**在女性或膚色較深的人群中更高，表明存在偏差。 + +**主要成果：** 提高了人們對數據科學需要更多_代表性數據集_（平衡的子群體）和更多_包容性團隊_（多樣化背景）的認識，以便在人工智能解決方案中更早地識別並消除或減輕這些偏差。像這樣的研究努力對許多組織制定負責任的人工智能原則和實踐以改善其人工智能產品和流程的公平性也至關重要。 + +**想了解 Microsoft 的相關研究工作？** + +* 查看 [Microsoft Research Projects](https://www.microsoft.com/research/research-area/artificial-intelligence/?facet%5Btax%5D%5Bmsr-research-area%5D%5B%5D=13556&facet%5Btax%5D%5Bmsr-content-type%5D%5B%5D=msr-project) 中的人工智能研究項目。 +* 探索 [Microsoft Research Data Science Summer School](https://www.microsoft.com/en-us/research/academic-program/data-science-summer-school/) 的學生項目。 +* 查看 [Fairlearn](https://fairlearn.org/) 項目和 [Responsible AI](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1%3aprimaryr6) 的相關倡議。 + +## 數據科學 + 人文 + +| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-Humanities.png) | +| :---------------------------------------------------------------------------------------------------------------: | +| 數據科學與數字人文 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | + +數字人文[被定義為](https://digitalhumanities.stanford.edu/about-dh-stanford)「結合計算方法與人文研究的一系列實踐和方法」。[斯坦福項目](https://digitalhumanities.stanford.edu/projects)如_「重啟歷史」_和_「詩意思考」_展示了[數字人文與數據科學](https://digitalhumanities.stanford.edu/digital-humanities-and-data-science)之間的聯繫——強調網絡分析、信息可視化、空間和文本分析等技術，幫助我們重新審視歷史和文學數據集，從中獲得新的洞察和視角。 + +*想探索並擴展這方面的項目？* + +查看 ["Emily Dickinson and the Meter of Mood"](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671)——這是一個來自 [Jen Looper](https://twitter.com/jenlooper) 的精彩例子，探討如何利用數據科學重新審視熟悉的詩歌，並在新的背景下重新評估其意義及作者的貢獻。例如，_我們能否通過分析詩歌的語氣或情感來預測詩歌創作的季節_——這又能告訴我們作者在相關時期的心理狀態？ + +為了回答這個問題，我們遵循數據科學生命周期的步驟： + * [`數據獲取`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#acquiring-the-dataset) - 收集相關數據集進行分析。選項包括使用 API（例如 [Poetry DB API](https://poetrydb.org/index.html)）或使用工具（如 [Scrapy](https://scrapy.org/)）抓取網頁（例如 [Project Gutenberg](https://www.gutenberg.org/files/12242/12242-h/12242-h.htm)）。 + * [`數據清理`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#clean-the-data) - 解釋如何使用基本工具（如 Visual Studio Code 和 Microsoft Excel）格式化、清理和簡化文本。 + * [`數據分析`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#working-with-the-data-in-a-notebook) - 解釋如何將數據集導入「筆記本」進行分析，使用 Python 包（如 pandas、numpy 和 matplotlib）組織和可視化數據。 + * [`情感分析`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#sentiment-analysis-using-cognitive-services) - 解釋如何使用低代碼工具（如 [Power Automate](https://flow.microsoft.com/en-us/)）集成雲服務（如文本分析）進行自動化數據處理工作流程。 + +通過這個工作流程，我們可以探索季節對詩歌情感的影響，並幫助我們形成自己對作者的看法。試試看，然後擴展筆記本以提出其他問題或以新的方式可視化數據！ + +> 你可以使用 [Digital Humanities toolkit](https://github.com/Digital-Humanities-Toolkit) 中的一些工具來進行這些研究。 + +## 數據科學 + 可持續性 + +| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-Sustainability.png) | +| :---------------------------------------------------------------------------------------------------------------: | +| 數據科學與可持續性 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | + +[2030可持續發展議程](https://sdgs.un.org/2030agenda)——由所有聯合國成員於2015年通過——確定了17個目標，其中包括專注於**保護地球**免受退化和氣候變化影響的目標。[Microsoft Sustainability](https://www.microsoft.com/en-us/sustainability)倡議支持這些目標，探索技術解決方案如何支持並構建更可持續的未來，並專注於[四個目標](https://dev.to/azure/a-visual-guide-to-sustainable-software-engineering-53hh)——到2030年實現碳負、正水、零廢物和生物多樣性。 + +以可擴展和及時的方式應對這些挑戰需要雲端規模的思維——以及大規模數據。[Planetary Computer](https://planetarycomputer.microsoft.com/)倡議提供了四個組件，幫助數據科學家和開發者應對這些挑戰： + + * [數據目錄](https://planetarycomputer.microsoft.com/catalog) - 提供地球系統數據的PB級數據（免費且托管於Azure）。 + * [Planetary API](https://planetarycomputer.microsoft.com/docs/reference/stac/) - 幫助用戶在空間和時間上搜索相關數據。 + * [Hub](https://planetarycomputer.microsoft.com/docs/overview/environment/) - 為科學家提供處理大規模地理空間數據集的管理環境。 + * [應用](https://planetarycomputer.microsoft.com/applications) - 展示可持續性洞察的使用案例和工具。 +**Planetary Computer Project 目前處於預覽階段（截至 2021 年 9 月）** - 以下是如何開始使用數據科學為可持續發展解決方案作出貢獻。 + +* [申請訪問權限](https://planetarycomputer.microsoft.com/account/request)，開始探索並與同行交流。 +* [探索文件](https://planetarycomputer.microsoft.com/docs/overview/about)，了解支持的數據集和 API。 +* 探索像 [生態系統監測](https://analytics-lab.org/ecosystemmonitoring/) 這樣的應用程式，尋找應用靈感。 + +思考如何利用數據可視化揭示或放大與氣候變化和森林砍伐等領域相關的洞察力。或者思考如何利用洞察力創造新的用戶體驗，激勵行為改變以實現更可持續的生活。 + +## 數據科學 + 學生 + +我們已經討論了行業和研究中的實際應用，並探索了數字人文和可持續發展中的數據科學應用範例。那麼，作為數據科學初學者，你如何提升技能並分享專業知識？ + +以下是一些數據科學學生項目範例，供你參考。 + + * [MSR 數據科學夏季學校](https://www.microsoft.com/en-us/research/academic-program/data-science-summer-school/#!projects) 的 GitHub [項目](https://github.com/msr-ds3)，探索以下主題： + - [警察使用武力中的種族偏見](https://www.microsoft.com/en-us/research/video/data-science-summer-school-2019-replicating-an-empirical-analysis-of-racial-differences-in-police-use-of-force/) | [Github](https://github.com/msr-ds3/stop-question-frisk) + - [紐約地鐵系統的可靠性](https://www.microsoft.com/en-us/research/video/data-science-summer-school-2018-exploring-the-reliability-of-the-nyc-subway-system/) | [Github](https://github.com/msr-ds3/nyctransit) + * [數字化物質文化：探索 Sirkap 的社會經濟分佈](https://claremont.maps.arcgis.com/apps/Cascade/index.html?appid=bdf2aef0f45a4674ba41cd373fa23afc) - 由 [Ornella Altunyan](https://twitter.com/ornelladotcom) 和 Claremont 團隊使用 [ArcGIS StoryMaps](https://storymaps.arcgis.com/) 完成。 + +## 🚀 挑戰 + +搜尋推薦適合初學者的數據科學項目文章，例如 [這 50 個主題領域](https://www.upgrad.com/blog/data-science-project-ideas-topics-beginners/)、[這 21 個項目想法](https://www.intellspot.com/data-science-project-ideas) 或 [這 16 個帶有源代碼的項目](https://data-flair.training/blogs/data-science-project-ideas/)，你可以拆解並重新組合。別忘了記錄你的學習旅程，並與我們分享你的洞察力。 + +## 課後測驗 + +## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/39) + +## 回顧與自學 + +想探索更多用例？以下是一些相關文章： + * [17 個數據科學應用及範例](https://builtin.com/data-science/data-science-applications-examples) - 2021 年 7 月 + * [11 個令人驚嘆的數據科學實際應用](https://myblindbird.com/data-science-applications-real-world/) - 2021 年 5 月 + * [現實世界中的數據科學](https://towardsdatascience.com/data-science-in-the-real-world/home) - 文章合集 + * [12 個帶有範例的現實世界數據科學應用](https://www.scaler.com/blog/data-science-applications/) - 2024 年 5 月 + * 數據科學在以下領域的應用：[教育](https://data-flair.training/blogs/data-science-in-education/)、[農業](https://data-flair.training/blogs/data-science-in-agriculture/)、[金融](https://data-flair.training/blogs/data-science-in-finance/)、[電影](https://data-flair.training/blogs/data-science-at-movies/)、[醫療保健](https://onlinedegrees.sandiego.edu/data-science-health-care/) 等。 + +## 作業 + +[探索 Planetary Computer 數據集](assignment.md) + +--- + +**免責聲明**： +此文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議使用專業的人工作翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/zh-HK/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md new file mode 100644 index 00000000..525706f2 --- /dev/null +++ b/translations/zh-HK/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -0,0 +1,39 @@ +# 探索行星電腦數據集 + +## 指引 + +在這節課中，我們討論了多個數據科學應用領域，並深入探討了與研究、可持續性和數字人文相關的例子。在這次作業中，你將更詳細地探索其中一個例子，並應用你在數據可視化和分析方面的學習，從可持續性數據中獲取洞察。 + +[行星電腦](https://planetarycomputer.microsoft.com/)項目提供了數據集和API，這些可以通過註冊帳戶來訪問——如果你想嘗試作業的額外步驟，可以申請一個帳戶。該網站還提供了一個[Explorer](https://planetarycomputer.microsoft.com/explore)功能，即使不創建帳戶也可以使用。 + +`步驟：` +Explorer界面（如下圖所示）允許你選擇一個數據集（從提供的選項中），一個預設查詢（用於篩選數據）和一個渲染選項（用於創建相關的可視化）。在這次作業中，你的任務是： + + 1. 閱讀[Explorer文檔](https://planetarycomputer.microsoft.com/docs/overview/explorer/)——了解選項。 + 2. 探索數據集[目錄](https://planetarycomputer.microsoft.com/catalog)——了解每個數據集的用途。 + 3. 使用Explorer——選擇一個感興趣的數據集，選擇相關的查詢和渲染選項。 + +![行星電腦Explorer](../../../../translated_images/zh-HK/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) + +`你的任務：` +現在，研究瀏覽器中渲染的可視化，並回答以下問題： + * 該數據集有哪些_特徵_？ + * 該可視化提供了哪些_洞察_或結果？ + * 這些洞察對於該項目的可持續性目標有什麼_影響_？ + * 該可視化的_局限性_是什麼（即，你未能獲得哪些洞察）？ + * 如果你能獲取原始數據，你會創建哪些_替代可視化_，為什麼？ + +`額外加分：` +申請一個帳戶——並在獲批後登錄。 + * 使用 _Launch Hub_ 選項在Notebook中打開原始數據。 + * 交互式地探索數據，並實現你想到的替代可視化。 + * 現在分析你的自定義可視化——你是否能夠獲得之前錯過的洞察？ + +## 評分標準 + +優秀 | 合格 | 需要改進 +--- | --- | -- | +回答了所有五個核心問題。學生清楚地指出了當前和替代可視化如何提供對可持續性目標或結果的洞察。| 學生詳細回答了至少前三個問題，表明他們對Explorer有實際操作經驗。| 學生未能回答多個問題，或提供的細節不足——表明未對該項目進行有意義的嘗試 | + +**免責聲明**： +本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，請注意自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要資訊，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/6-Data-Science-In-Wild/README.md b/translations/zh-HK/6-Data-Science-In-Wild/README.md new file mode 100644 index 00000000..edb6c67e --- /dev/null +++ b/translations/zh-HK/6-Data-Science-In-Wild/README.md @@ -0,0 +1,14 @@ +# 野外數據科學 + +數據科學在各行業中的實際應用。 + +### 主題 + +1. [現實世界中的數據科學](20-Real-World-Examples/README.md) + +### 致謝 + +由 [Nitya Narasimhan](https://twitter.com/nitya) 用 ❤️ 撰寫 + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/AGENTS.md b/translations/zh-HK/AGENTS.md new file mode 100644 index 00000000..8891fe0b --- /dev/null +++ b/translations/zh-HK/AGENTS.md @@ -0,0 +1,366 @@ +# AGENTS.md + +## 項目概覽 + +《Data Science for Beginners》是一個由 Microsoft Azure Cloud Advocates 創建的全面 10 週、20 課的課程。此資源庫是一個學習資源，通過基於項目的課程（包括 Jupyter 筆記本、互動測驗和實踐作業）教授數據科學的基礎概念。 + +**主要技術：** +- **Jupyter 筆記本**：使用 Python 3 作為主要學習媒介 +- **Python 庫**：pandas、numpy、matplotlib 用於數據分析和可視化 +- **Vue.js 2**：測驗應用程式（quiz-app 資料夾） +- **Docsify**：用於離線訪問的文檔站點生成器 +- **Node.js/npm**：JavaScript 組件的包管理 +- **Markdown**：所有課程內容和文檔 + +**架構：** +- 多語言教育資源庫，提供廣泛的翻譯 +- 按課程模組結構化（1-Introduction 到 6-Data-Science-In-Wild） +- 每節課包括 README、筆記本、作業和測驗 +- 獨立的 Vue.js 測驗應用程式，用於課前/課後評估 +- 支援 GitHub Codespaces 和 VS Code 開發容器 + +## 設置指令 + +### 資源庫設置 +```bash +# Clone the repository (if not already cloned) +git clone https://github.com/microsoft/Data-Science-For-Beginners.git +cd Data-Science-For-Beginners +``` + +### Python 環境設置 +```bash +# Create a virtual environment (recommended) +python -m venv venv +source venv/bin/activate # On Windows: venv\Scripts\activate + +# Install common data science libraries (no requirements.txt exists) +pip install jupyter pandas numpy matplotlib seaborn scikit-learn +``` + +### 測驗應用程式設置 +```bash +# Navigate to quiz app +cd quiz-app + +# Install dependencies +npm install + +# Start development server +npm run serve + +# Build for production +npm run build + +# Lint and fix files +npm run lint +``` + +### Docsify 文檔伺服器 +```bash +# Install Docsify globally +npm install -g docsify-cli + +# Serve documentation locally +docsify serve + +# Documentation will be available at localhost:3000 +``` + +### 可視化項目設置 +針對如 meaningful-visualizations（第 13 課）這樣的可視化項目： +```bash +# Navigate to starter or solution folder +cd 3-Data-Visualization/13-meaningful-visualizations/starter + +# Install dependencies +npm install + +# Start development server +npm run serve + +# Build for production +npm run build + +# Lint files +npm run lint +``` + + +## 開發工作流程 + +### 使用 Jupyter 筆記本 +1. 在資源庫根目錄啟動 Jupyter：`jupyter notebook` +2. 導航到所需的課程資料夾 +3. 打開 `.ipynb` 文件以完成練習 +4. 筆記本是自包含的，包含解釋和代碼單元 +5. 大多數筆記本使用 pandas、numpy 和 matplotlib——確保這些庫已安裝 + +### 課程結構 +每節課通常包含： +- `README.md` - 包含理論和示例的主要課程內容 +- `notebook.ipynb` - 實踐 Jupyter 筆記本練習 +- `assignment.ipynb` 或 `assignment.md` - 練習作業 +- `solution/` 資料夾 - 解答筆記本和代碼 +- `images/` 資料夾 - 支援的視覺材料 + +### 測驗應用程式開發 +- Vue.js 2 應用程式，開發期間支持熱加載 +- 測驗存儲在 `quiz-app/src/assets/translations/` +- 每種語言都有自己的翻譯資料夾（en, fr, es 等） +- 測驗編號從 0 開始，共 40 個測驗 + +### 添加翻譯 +- 翻譯存放在資源庫根目錄的 `translations/` 資料夾中 +- 每種語言的課程結構與英文完全對應 +- 通過 GitHub Actions 自動翻譯（co-op-translator.yml） + +## 測試說明 + +### 測驗應用程式測試 +```bash +cd quiz-app + +# Run lint checks +npm run lint + +# Test build process +npm run build + +# Manual testing: Start dev server and verify quiz functionality +npm run serve +``` + +### 筆記本測試 +- 筆記本沒有自動化測試框架 +- 手動驗證：按順序運行所有單元以確保無錯誤 +- 驗證數據文件是否可訪問並正確生成輸出 +- 檢查可視化是否正確渲染 + +### 文檔測試 +```bash +# Verify Docsify renders correctly +docsify serve + +# Check for broken links manually by navigating through content +# Verify all lesson links work in the rendered documentation +``` + +### 代碼質量檢查 +```bash +# Vue.js projects (quiz-app and visualization projects) +cd quiz-app # or visualization project folder +npm run lint + +# Python notebooks - manual verification recommended +# Ensure imports work and cells execute without errors +``` + + +## 代碼風格指南 + +### Python（Jupyter 筆記本） +- 遵循 PEP 8 風格指南 +- 使用清晰的變量名稱，說明所分析的數據 +- 在代碼單元之前添加帶有解釋的 Markdown 單元 +- 保持代碼單元專注於單一概念或操作 +- 使用 pandas 進行數據操作，matplotlib 進行可視化 +- 常見的導入模式： + ```python + import pandas as pd + import numpy as np + import matplotlib.pyplot as plt + ``` + + +### JavaScript/Vue.js +- 遵循 Vue.js 2 風格指南和最佳實踐 +- ESLint 配置在 `quiz-app/package.json` +- 使用 Vue 單文件組件（.vue 文件） +- 維持基於組件的架構 +- 提交更改前運行 `npm run lint` + +### Markdown 文檔 +- 使用清晰的標題層次結構（# ## ### 等） +- 包含帶有語言標識的代碼塊 +- 為圖片添加替代文字 +- 鏈接到相關課程和資源 +- 保持合理的行長以提高可讀性 + +### 文件組織 +- 課程內容存放在編號資料夾中（01-defining-data-science 等） +- 解答存放在專用的 `solution/` 子資料夾中 +- 翻譯與英文結構對應，存放在 `translations/` 資料夾中 +- 數據文件存放在 `data/` 或課程專用資料夾中 + +## 構建與部署 + +### 測驗應用程式部署 +```bash +cd quiz-app + +# Build production version +npm run build + +# Output is in dist/ folder +# Deploy dist/ folder to static hosting (Azure Static Web Apps, Netlify, etc.) +``` + +### Azure 靜態 Web 應用部署 +測驗應用程式可部署到 Azure 靜態 Web 應用： +1. 創建 Azure 靜態 Web 應用資源 +2. 連接到 GitHub 資源庫 +3. 配置構建設置： + - 應用位置：`quiz-app` + - 輸出位置：`dist` +4. GitHub Actions 工作流會在推送時自動部署 + +### 文檔站點 +```bash +# Build PDF from Docsify (optional) +npm run convert + +# Docsify documentation is served directly from markdown files +# No build step required for deployment +# Deploy repository to static hosting with Docsify +``` + +### GitHub Codespaces +- 資源庫包含開發容器配置 +- Codespaces 自動設置 Python 和 Node.js 環境 +- 通過 GitHub UI 打開資源庫的 Codespace +- 所有依賴項自動安裝 + +## 拉取請求指南 + +### 提交前 +```bash +# For Vue.js changes in quiz-app +cd quiz-app +npm run lint +npm run build + +# Test changes locally +npm run serve +``` + +### PR 標題格式 +- 使用清晰、描述性的標題 +- 格式：`[組件] 簡要描述` +- 示例： + - `[Lesson 7] 修復 Python 筆記本導入錯誤` + - `[Quiz App] 添加德語翻譯` + - `[Docs] 更新 README，添加新前置條件` + +### 必要檢查 +- 確保所有代碼運行無錯誤 +- 驗證筆記本完全執行 +- 確認 Vue.js 應用成功構建 +- 檢查文檔鏈接是否有效 +- 測試修改後的測驗應用程式 +- 確保翻譯結構一致 + +### 貢獻指南 +- 遵循現有代碼風格和模式 +- 為複雜邏輯添加解釋性註釋 +- 更新相關文檔 +- 如果適用，測試更改在不同課程模組中的效果 +- 查看 CONTRIBUTING.md 文件 + +## 附加說明 + +### 常用庫 +- **pandas**：數據操作和分析 +- **numpy**：數值計算 +- **matplotlib**：數據可視化和繪圖 +- **seaborn**：統計數據可視化（部分課程） +- **scikit-learn**：機器學習（進階課程） + +### 使用數據文件 +- 數據文件位於 `data/` 資料夾或課程專用目錄中 +- 大多數筆記本預期數據文件位於相對路徑 +- CSV 文件是主要數據格式 +- 部分課程使用 JSON 作為非關係數據示例 + +### 多語言支持 +- 通過 GitHub Actions 提供 40 多種語言翻譯 +- 翻譯工作流位於 `.github/workflows/co-op-translator.yml` +- 翻譯存放在 `translations/` 資料夾中，使用語言代碼命名 +- 測驗翻譯存放在 `quiz-app/src/assets/translations/` + +### 開發環境選項 +1. **本地開發**：本地安裝 Python、Jupyter、Node.js +2. **GitHub Codespaces**：基於雲的即時開發環境 +3. **VS Code 開發容器**：基於容器的本地開發 +4. **Binder**：在雲中啟動筆記本（如果已配置） + +### 課程內容指南 +- 每節課是獨立的，但建立在之前的概念之上 +- 課前測驗測試先前知識 +- 課後測驗加強學習 +- 作業提供實踐練習 +- 手繪筆記提供視覺摘要 + +### 常見問題排查 + +**Jupyter 核心問題：** +```bash +# Ensure correct kernel is installed +python -m ipykernel install --user --name=datascience +``` + +**npm 安裝失敗：** +```bash +# Clear npm cache and retry +npm cache clean --force +rm -rf node_modules package-lock.json +npm install +``` + +**筆記本導入錯誤：** +- 確保已安裝所有必要的庫 +- 檢查 Python 版本兼容性（建議使用 Python 3.7+） +- 確保虛擬環境已啟動 + +**Docsify 無法加載：** +- 確保從資源庫根目錄提供服務 +- 檢查 `index.html` 是否存在 +- 確保網絡訪問正常（端口 3000） + +### 性能考量 +- 大型數據集可能需要較長時間加載到筆記本中 +- 複雜圖表的可視化渲染可能較慢 +- Vue.js 開發伺服器支持熱加載，便於快速迭代 +- 生產構建已優化並壓縮 + +### 安全注意事項 +- 不應提交敏感數據或憑據 +- 在雲課程中使用環境變量存儲 API 密鑰 +- 與 Azure 相關的課程可能需要 Azure 帳戶憑據 +- 保持依賴項更新以獲取安全補丁 + +## 貢獻翻譯 +- 通過 GitHub Actions 管理自動翻譯 +- 歡迎手動修正以提高翻譯準確性 +- 遵循現有翻譯資料夾結構 +- 更新測驗鏈接以包含語言參數：`?loc=fr` +- 測試翻譯課程以確保正確渲染 + +## 相關資源 +- 主課程：https://aka.ms/datascience-beginners +- Microsoft Learn：https://docs.microsoft.com/learn/ +- 學生中心：https://docs.microsoft.com/learn/student-hub +- 討論論壇：https://github.com/microsoft/Data-Science-For-Beginners/discussions +- 其他 Microsoft 課程：ML for Beginners, AI for Beginners, Web Dev for Beginners + +## 項目維護 +- 定期更新以保持內容最新 +- 歡迎社區貢獻 +- 問題在 GitHub 上跟蹤 +- PR 由課程維護者審核 +- 每月進行內容審查和更新 + +--- + +**免責聲明**： +此文件已使用AI翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/CODE_OF_CONDUCT.md b/translations/zh-HK/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..22c2ee09 --- /dev/null +++ b/translations/zh-HK/CODE_OF_CONDUCT.md @@ -0,0 +1,12 @@ +# Microsoft 開源行為準則 + +此項目已採用 [Microsoft 開源行為準則](https://opensource.microsoft.com/codeofconduct/)。 + +資源： + +- [Microsoft 開源行為準則](https://opensource.microsoft.com/codeofconduct/) +- [Microsoft 行為準則常見問題](https://opensource.microsoft.com/codeofconduct/faq/) +- 如有疑問或關注，請聯絡 [opencode@microsoft.com](mailto:opencode@microsoft.com) + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/CONTRIBUTING.md b/translations/zh-HK/CONTRIBUTING.md new file mode 100644 index 00000000..b6d419da --- /dev/null +++ b/translations/zh-HK/CONTRIBUTING.md @@ -0,0 +1,353 @@ +# 貢獻《初學者數據科學》 + +感謝您對《初學者數據科學》課程的貢獻感興趣！我們歡迎社群的貢獻。 + +## 目錄 + +- [行為準則](../..) +- [我可以如何貢獻？](../..) +- [入門指南](../..) +- [貢獻指南](../..) +- [拉取請求流程](../..) +- [風格指南](../..) +- [貢獻者授權協議](../..) + +## 行為準則 + +此專案採用了 [Microsoft 開源行為準則](https://opensource.microsoft.com/codeofconduct/)。 +欲了解更多資訊，請參閱 [行為準則 FAQ](https://opensource.microsoft.com/codeofconduct/faq/) 或聯絡 [opencode@microsoft.com](mailto:opencode@microsoft.com) 提出其他問題或意見。 + +## 我可以如何貢獻？ + +### 回報錯誤 + +在建立錯誤報告之前，請檢查現有的問題以避免重複。當您建立錯誤報告時，請盡可能提供詳細資訊： + +- **使用清晰且描述性的標題** +- **描述重現問題的具體步驟** +- **提供具體範例**（程式碼片段、截圖） +- **描述您觀察到的行為以及預期的行為** +- **包含您的環境細節**（作業系統、Python版本、瀏覽器） + +### 建議改進 + +我們歡迎改進建議！提出建議時： + +- **使用清晰且描述性的標題** +- **提供詳細的建議描述** +- **解釋此改進的用途** +- **列出其他專案中類似的功能（如果適用）** + +### 貢獻文件 + +文件改進始終受到歡迎： + +- **修正拼寫和語法錯誤** +- **提高解釋的清晰度** +- **補充缺失的文件** +- **更新過時的資訊** +- **添加範例或使用案例** + +### 貢獻程式碼 + +我們歡迎以下程式碼貢獻： + +- **新增課程或練習** +- **修正錯誤** +- **改進現有的筆記本** +- **新增數據集或範例** +- **改進測驗應用程式** + +## 入門指南 + +### 先決條件 + +在貢獻之前，請確保您已具備以下條件： + +1. 一個 GitHub 帳戶 +2. 您的系統已安裝 Git +3. 安裝了 Python 3.7+ 和 Jupyter +4. 安裝了 Node.js 和 npm（針對測驗應用程式的貢獻） +5. 熟悉課程結構 + +請參閱 [INSTALLATION.md](INSTALLATION.md) 以獲取詳細的設置指導。 + +### Fork 和 Clone + +1. **在 GitHub 上 Fork 此倉庫** +2. **將您的 Fork 本地克隆**： + ```bash + git clone https://github.com/YOUR-USERNAME/Data-Science-For-Beginners.git + cd Data-Science-For-Beginners + ``` +3. **添加上游遠端**： + ```bash + git remote add upstream https://github.com/microsoft/Data-Science-For-Beginners.git + ``` + +### 建立分支 + +為您的工作建立新分支： + +```bash +git checkout -b feature/your-feature-name +# or +git checkout -b fix/your-bug-fix +``` + +分支命名規範： +- `feature/` - 新功能或課程 +- `fix/` - 錯誤修正 +- `docs/` - 文件更改 +- `refactor/` - 程式碼重構 + +## 貢獻指南 + +### 關於課程內容 + +在貢獻課程或修改現有課程時： + +1. **遵循現有結構**： + - README.md 包含課程內容 + - Jupyter 筆記本包含練習 + - 作業（如果適用） + - 連結到前測和後測 + +2. **包含以下元素**： + - 清晰的學習目標 + - 步驟式解釋 + - 帶註解的程式碼範例 + - 練習題以供練習 + - 其他資源的連結 + +3. **確保可訪問性**： + - 使用清晰、簡單的語言 + - 為圖片提供替代文字 + - 包含程式碼註解 + - 考慮不同的學習風格 + +### 關於 Jupyter 筆記本 + +1. **在提交之前清除所有輸出**： + ```bash + jupyter nbconvert --clear-output --inplace notebook.ipynb + ``` + +2. **包含帶解釋的 Markdown 單元格** + +3. **使用一致的格式**： + ```python + # Import libraries at the top + import pandas as pd + import numpy as np + import matplotlib.pyplot as plt + + # Use meaningful variable names + # Add comments for complex operations + # Follow PEP 8 style guidelines + ``` + +4. **在提交之前完整測試您的筆記本** + +### 關於 Python 程式碼 + +遵循 [PEP 8](https://www.python.org/dev/peps/pep-0008/) 風格指南： + +```python +# Good practices +import pandas as pd + +def calculate_mean(data): + """Calculate the mean of a dataset. + + Args: + data (list): List of numerical values + + Returns: + float: Mean of the dataset + """ + return sum(data) / len(data) +``` + +### 關於測驗應用程式的貢獻 + +在修改測驗應用程式時： + +1. **本地測試**： + ```bash + cd quiz-app + npm install + npm run serve + ``` + +2. **運行 linter**： + ```bash + npm run lint + ``` + +3. **成功構建**： + ```bash + npm run build + ``` + +4. **遵循 Vue.js 風格指南**及現有模式 + +### 關於翻譯 + +在新增或更新翻譯時： + +1. 遵循 `translations/` 資料夾中的結構 +2. 使用語言代碼作為資料夾名稱（例如，法語使用 `fr`） +3. 保持與英文版本相同的檔案結構 +4. 更新測驗連結以包含語言參數：`?loc=fr` +5. 測試所有連結和格式 + +## 拉取請求流程 + +### 提交之前 + +1. **使用最新更改更新您的分支**： + ```bash + git fetch upstream + git rebase upstream/main + ``` + +2. **測試您的更改**： + - 運行所有修改過的筆記本 + - 測試測驗應用程式（如果已修改） + - 驗證所有連結是否有效 + - 檢查拼寫和語法錯誤 + +3. **提交您的更改**： + ```bash + git add . + git commit -m "Brief description of changes" + ``` + + 撰寫清晰的提交訊息： + - 使用現在時態（例如 "Add feature" 而非 "Added feature"） + - 使用命令式語氣（例如 "Move cursor to..." 而非 "Moves cursor to..."） + - 第一行限制在 72 個字元內 + - 在相關時引用問題和拉取請求 + +4. **推送到您的 Fork**： + ```bash + git push origin feature/your-feature-name + ``` + +### 建立拉取請求 + +1. 前往 [倉庫](https://github.com/microsoft/Data-Science-For-Beginners) +2. 點擊 "Pull requests" → "New pull request" +3. 點擊 "compare across forks" +4. 選擇您的 Fork 和分支 +5. 點擊 "Create pull request" + +### PR 標題格式 + +使用清晰、描述性的標題，遵循以下格式： + +``` +[Component] Brief description +``` + +範例： +- `[Lesson 7] 修正 Python 筆記本導入錯誤` +- `[Quiz App] 添加德語翻譯` +- `[Docs] 更新 README，新增先決條件` +- `[Fix] 修正可視化課程中的數據路徑` + +### PR 描述 + +在您的 PR 描述中包含： + +- **內容**：您做了哪些更改？ +- **原因**：為什麼需要這些更改？ +- **方法**：您如何實現這些更改？ +- **測試**：您如何測試這些更改？ +- **截圖**：對於視覺更改，請包含截圖 +- **相關問題**：連結到相關問題（例如 "Fixes #123"） + +### 審核流程 + +1. **自動檢查**將在您的 PR 上運行 +2. **維護者將審核**您的貢獻 +3. **根據反饋進行修改**，提交額外的更改 +4. 一旦獲得批准，**維護者將合併**您的 PR + +### PR 合併後 + +1. 刪除您的分支： + ```bash + git branch -d feature/your-feature-name + git push origin --delete feature/your-feature-name + ``` + +2. 更新您的 Fork： + ```bash + git checkout main + git pull upstream main + git push origin main + ``` + +## 風格指南 + +### Markdown + +- 使用一致的標題層級 +- 在各部分之間包含空行 +- 使用帶語言指定的程式碼塊： + ````markdown + ```python + import pandas as pd + ``` + ```` +- 為圖片添加替代文字：`![Alt text](../../translated_images/zh-HK/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` +- 保持合理的行長（約 80-100 字元） + +### Python + +- 遵循 PEP 8 風格指南 +- 使用有意義的變數名稱 +- 為函數添加文檔字符串 +- 在適當的地方包含類型提示： + ```python + def process_data(df: pd.DataFrame) -> pd.DataFrame: + """Process the input dataframe.""" + return df + ``` + +### JavaScript/Vue.js + +- 遵循 Vue.js 2 風格指南 +- 使用提供的 ESLint 配置 +- 撰寫模組化、可重用的元件 +- 為複雜邏輯添加註解 + +### 檔案組織 + +- 將相關檔案放在一起 +- 使用描述性的檔案名稱 +- 遵循現有的目錄結構 +- 不要提交不必要的檔案（例如 .DS_Store、.pyc、node_modules 等） + +## 貢獻者授權協議 + +此專案歡迎貢獻和建議。大多數貢獻需要您同意貢獻者授權協議 (CLA)，聲明您有權並實際授予我們使用您的貢獻的權利。欲了解詳情，請訪問 https://cla.microsoft.com。 + +當您提交拉取請求時，CLA 機器人將自動判斷您是否需要提供 CLA 並適當地標記 PR（例如，標籤、評論）。只需按照機器人提供的指示操作即可。您只需在所有使用我們 CLA 的倉庫中執行一次此操作。 + +## 有問題嗎？ + +- 查看我們的 [Discord 頻道 #data-science-for-beginners](https://aka.ms/ds4beginners/discord) +- 加入我們的 [Discord 社群](https://aka.ms/ds4beginners/discord) +- 查看現有的 [問題](https://github.com/microsoft/Data-Science-For-Beginners/issues) 和 [拉取請求](https://github.com/microsoft/Data-Science-For-Beginners/pulls) + +## 感謝！ + +您的貢獻使這個課程對所有人都更好。感謝您花時間貢獻！ + +--- + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於關鍵資訊，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/INSTALLATION.md b/translations/zh-HK/INSTALLATION.md new file mode 100644 index 00000000..b34f59ca --- /dev/null +++ b/translations/zh-HK/INSTALLATION.md @@ -0,0 +1,252 @@ +# 安裝指南 + +本指南將幫助您設置環境，以使用《初學者數據科學課程》。 + +## 目錄 + +- [先決條件](../..) +- [快速開始選項](../..) +- [本地安裝](../..) +- [驗證您的安裝](../..) + +## 先決條件 + +在開始之前，您應該具備以下條件： + +- 基本的命令行/終端操作知識 +- 一個 GitHub 帳戶（免費） +- 穩定的網絡連接以完成初始設置 + +## 快速開始選項 + +### 選項 1：GitHub Codespaces（推薦給初學者） + +最簡單的開始方式是使用 GitHub Codespaces，它提供了一個完整的開發環境，直接在瀏覽器中使用。 + +1. 前往 [倉庫](https://github.com/microsoft/Data-Science-For-Beginners) +2. 點擊 **Code** 下拉菜單 +3. 選擇 **Codespaces** 標籤 +4. 點擊 **Create codespace on main** +5. 等待環境初始化（2-3 分鐘） + +您的環境現在已準備好，所有依賴項都已預先安裝！ + +### 選項 2：本地開發 + +如果您希望在自己的電腦上工作，請按照以下詳細說明進行操作。 + +## 本地安裝 + +### 步驟 1：安裝 Git + +Git 是用於克隆倉庫和跟蹤更改的必要工具。 + +**Windows:** +- 從 [git-scm.com](https://git-scm.com/download/win) 下載 +- 使用默認設置運行安裝程序 + +**macOS:** +- 使用 Homebrew 安裝：`brew install git` +- 或從 [git-scm.com](https://git-scm.com/download/mac) 下載 + +**Linux:** +```bash +# Debian/Ubuntu +sudo apt-get update +sudo apt-get install git + +# Fedora +sudo dnf install git + +# Arch +sudo pacman -S git +``` + +### 步驟 2：克隆倉庫 + +```bash +# Clone the repository +git clone https://github.com/microsoft/Data-Science-For-Beginners.git + +# Navigate to the directory +cd Data-Science-For-Beginners +``` + +### 步驟 3：安裝 Python 和 Jupyter + +數據科學課程需要 Python 3.7 或更高版本。 + +**Windows:** +1. 從 [python.org](https://www.python.org/downloads/) 下載 Python +2. 安裝過程中勾選 "Add Python to PATH" +3. 驗證安裝： +```bash +python --version +``` + +**macOS:** +```bash +# Using Homebrew +brew install python3 + +# Verify installation +python3 --version +``` + +**Linux:** +```bash +# Most Linux distributions come with Python pre-installed +python3 --version + +# If not installed: +# Debian/Ubuntu +sudo apt-get install python3 python3-pip + +# Fedora +sudo dnf install python3 python3-pip +``` + +### 步驟 4：設置 Python 環境 + +建議使用虛擬環境來隔離依賴項。 + +```bash +# Create a virtual environment +python -m venv venv + +# Activate the virtual environment +# On Windows: +venv\Scripts\activate + +# On macOS/Linux: +source venv/bin/activate +``` + +### 步驟 5：安裝 Python 套件 + +安裝所需的數據科學庫： + +```bash +pip install jupyter pandas numpy matplotlib seaborn scikit-learn +``` + +### 步驟 6：安裝 Node.js 和 npm（用於測驗應用） + +測驗應用需要 Node.js 和 npm。 + +**Windows/macOS:** +- 從 [nodejs.org](https://nodejs.org/) 下載（推薦 LTS 版本） +- 運行安裝程序 + +**Linux:** +```bash +# Debian/Ubuntu +# WARNING: Piping scripts from the internet directly into bash can be a security risk. +# It is recommended to review the script before running it: +# curl -fsSL https://deb.nodesource.com/setup_lts.x -o setup_lts.x +# less setup_lts.x +# Then run: +# sudo -E bash setup_lts.x +# +# Alternatively, you can use the one-liner below at your own risk: +curl -fsSL https://deb.nodesource.com/setup_lts.x | sudo -E bash - +sudo apt-get install -y nodejs + +# Fedora +sudo dnf install nodejs + +# Verify installation +node --version +npm --version +``` + +### 步驟 7：安裝測驗應用依賴項 + +```bash +# Navigate to quiz app directory +cd quiz-app + +# Install dependencies +npm install + +# Return to root directory +cd .. +``` + +### 步驟 8：安裝 Docsify（可選） + +用於離線訪問文檔： + +```bash +npm install -g docsify-cli +``` + +## 驗證您的安裝 + +### 測試 Python 和 Jupyter + +```bash +# Activate your virtual environment if not already activated +# On Windows: +venv\Scripts\activate +# On macOS/Linux: +source venv/bin/activate + +# Start Jupyter Notebook +jupyter notebook +``` + +您的瀏覽器應打開 Jupyter 界面。您現在可以導航到任何課程的 `.ipynb` 文件。 + +### 測試測驗應用 + +```bash +# Navigate to quiz app +cd quiz-app + +# Start development server +npm run serve +``` + +測驗應用應可在 `http://localhost:8080`（如果 8080 端口被佔用，則使用其他端口）訪問。 + +### 測試文檔服務器 + +```bash +# From the root directory of the repository +docsify serve +``` + +文檔應可在 `http://localhost:3000` 訪問。 + +## 使用 VS Code Dev Containers + +如果您已安裝 Docker，可以使用 VS Code Dev Containers： + +1. 安裝 [Docker Desktop](https://www.docker.com/products/docker-desktop) +2. 安裝 [Visual Studio Code](https://code.visualstudio.com/) +3. 安裝 [Remote - Containers 擴展](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers) +4. 在 VS Code 中打開倉庫 +5. 按 `F1` 並選擇 "Remote-Containers: Reopen in Container" +6. 等待容器構建（僅首次需要） + +## 下一步 + +- 探索 [README.md](README.md) 以了解課程概覽 +- 閱讀 [USAGE.md](USAGE.md) 以了解常見工作流程和示例 +- 如果遇到問題，查看 [TROUBLESHOOTING.md](TROUBLESHOOTING.md) +- 如果您想貢獻，請查看 [CONTRIBUTING.md](CONTRIBUTING.md) + +## 獲取幫助 + +如果您遇到問題： + +1. 查看 [TROUBLESHOOTING.md](TROUBLESHOOTING.md) 指南 +2. 搜索現有的 [GitHub Issues](https://github.com/microsoft/Data-Science-For-Beginners/issues) +3. 加入我們的 [Discord 社群](https://aka.ms/ds4beginners/discord) +4. 創建一個新問題，並詳細描述您的問題 + +--- + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為權威來源。對於關鍵信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/README.md b/translations/zh-HK/README.md new file mode 100644 index 00000000..c8666b31 --- /dev/null +++ b/translations/zh-HK/README.md @@ -0,0 +1,251 @@ +# Data Science for Beginners - 一個課程大綱 + +[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198) + +[![GitHub license](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE) +[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/) +[![GitHub issues](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/) +[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/) +[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) + +[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/Data-Science-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/Data-Science-For-Beginners/watchers/) +[![GitHub forks](https://img.shields.io/github/forks/microsoft/Data-Science-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/Data-Science-For-Beginners/network/) +[![GitHub stars](https://img.shields.io/github/stars/microsoft/Data-Science-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/Data-Science-For-Beginners/stargazers/) + + +[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) + +[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) + +微軟 Azure Cloud Advocates 很高興呈獻一個長達 10 週，共 20 課的數據科學課程。每一課包括課前和課後測驗、完成課程的文字指示、解決方案和作業。我們以專案為本的教學法讓你在實作中學習，這是一種經證實能讓新技能「牢記於心」的學習方式。 + +**衷心感謝我們的作者：** [Jasmine Greenaway](https://www.twitter.com/paladique), [Dmitry Soshnikov](http://soshnikov.com), [Nitya Narasimhan](https://twitter.com/nitya), [Jalen McGee](https://twitter.com/JalenMcG), [Jen Looper](https://twitter.com/jenlooper), [Maud Levy](https://twitter.com/maudstweets), [Tiffany Souterre](https://twitter.com/TiffanySouterre), [Christopher Harrison](https://www.twitter.com/geektrainer)。 + +**🙏 特別感謝 🙏 我們的 [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) 作者、審閱者及內容貢獻者，** 特別是 Aaryan Arora、[Aditya Garg](https://github.com/AdityaGarg00)、[Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/)、[Ankita Singh](https://www.linkedin.com/in/ankitasingh007)、[Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/)、[Arpita Das](https://www.linkedin.com/in/arpitadas01/)、ChhailBihari Dubey、[Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor)、[Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb)、[Majd Safi](https://www.linkedin.com/in/majd-s/)、[Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/)、[Miguel Correa](https://www.linkedin.com/in/miguelmque/)、[Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119)、[Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum)、[Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/)、[Rohit Yadav](https://www.linkedin.com/in/rty2423)、Samridhi Sharma、[Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), +[Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/)、[Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/)、Yogendrasingh Pawar 、[Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/)、[Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) + +|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/zh-HK/00-Title.8af36cd35da1ac55.webp)| +|:---:| +| Data Science For Beginners - _手繪筆記由 [@nitya](https://twitter.com/nitya) 製作_ | + +### 🌐 多語言支援 + +#### 透過 GitHub Action 支援（自動且始終保持最新） + + +[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](./README.md) | [Chinese (Traditional, Macau)](../zh-MO/README.md) | [Chinese (Traditional, Taiwan)](../zh-TW/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../pt-BR/README.md) | [Portuguese (Portugal)](../pt-PT/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) + +> **偏好本地克隆？** + +> 本倉庫包含 50 多種語言的翻譯，這大幅增加下載大小。若想不含翻譯檔案克隆，請使用 sparse checkout： +> ```bash +> git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.git +> cd Data-Science-For-Beginners +> git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' +> ``` +> 這樣可以讓您用更快的速度獲得完成課程所需的一切。 + + +**如需其他翻譯語言支援列表，請參閱[此處](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** + +#### 加入我們的社群 +[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) + +我們正在舉辦 Discord Learn with AI 系列，詳細瞭解並於 2025 年 9 月 18 日至 30 日加入我們，詳情見 [Learn with AI Series](https://aka.ms/learnwithai/discord)。你將獲得如何使用 GitHub Copilot 進行數據科學的技巧與竅門。 + +![Learn with AI series](../../translated_images/zh-HK/1.2b28cdc6205e26fe.webp) + +# 你是學生嗎？ + +開始使用以下資源： + +- [學生中心頁面](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) 在此頁面，您會找到初學者資源、學生包甚至獲取免費認證券的方法。這是一個你會想收藏並不時查看的頁面，因為我們每月至少更新一次內容。 +- [Microsoft Learn 學生大使](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) 加入全球學生大使社群，這可能是你進入微軟的途徑。 + +# 入門指引 + +## 📚 文件 + +- **[安裝指南](INSTALLATION.md)** - 初學者逐步設置指引 +- **[使用指南](USAGE.md)** - 範例和常用工作流程 +- **[疑難排解](TROUBLESHOOTING.md)** - 常見問題解決 +- **[貢獻指南](CONTRIBUTING.md)** - 如何為本專案做出貢獻 +- **[給教師參考](for-teachers.md)** - 教學指導與課堂資源 + +## 👨‍🎓 給學生 +> **徹底初學者**：對數據科學陌生？請由我們的[初學者範例](examples/README.md)開始！這些簡單且充分註解的範例有助你理解基礎，然後再深入整個課程。 +> **[學生](https://aka.ms/student-page)**：要自行使用本課程，請 fork 整個倉庫並自行完成練習，從課前小測開始。然後閱讀課程並完成剩餘活動。嘗試理解課堂內容來創建專案，而不是直接複製解決方案代碼；不過這些代碼可以在每個以專案為導向的課程之 /solutions 資料夾找到。另一個方法是與朋友組成學習小組，一起完成內容。進一步學習建議參考 [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum)。 + +**快速開始步驟：** +1. 查看[安裝指南](INSTALLATION.md)來設置你的環境 +2. 閱讀[使用指南](USAGE.md)學習課程的使用方式 +3. 從第 1 課開始，依序進行 +4. 加入我們的[Discord 社群](https://aka.ms/ds4beginners/discord)尋求支援 + +## 👩‍🏫 給教師 + +> **教師們**：我們提供了[一些建議](for-teachers.md)介紹如何使用本課程。歡迎您在[討論論壇](https://github.com/microsoft/Data-Science-For-Beginners/discussions)提供回饋！ +## 介紹團隊 + +[![宣傳短片](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "宣傳短片") + +**動圖由** [Mohit Jaisal](https://www.linkedin.com/in/mohitjaisal) 製作 + +> 🎥 點擊上面圖片觀看關於本專案及其創建者的影片！ + +## 教學法 + +我們在設計此課程時選擇了兩個教學原則：確保課程以專案為基礎，並包含頻繁的小測驗。在本系列課程結束時，學生將學會資料科學的基本原理，包括倫理概念、資料準備、資料處理的不同方法、資料視覺化、資料分析、資料科學的實際應用案例等。 + +此外，課前的低壓力測驗能設定學生學習主題的意圖，課後的另一個測驗則確保持續記憶。此課程設計靈活且有趣，可全程或分段學習。專案由淺入深，於10週循環結束時逐漸變得複雜。 + +> 查看我們的[行為守則](CODE_OF_CONDUCT.md)、[貢獻指南](CONTRIBUTING.md)、[翻譯指南](TRANSLATIONS.md)。我們歡迎您的建設性反饋！ + +## 每課包含： + +- 選擇性的手繪筆記 +- 選擇性的補充影片 +- 課前暖身測驗 +- 書面課程 +- 對專案課程，附詳細的逐步專案建置指引 +- 知識檢核 +- 挑戰任務 +- 補充閱讀 +- 作業 +- [課後測驗](https://ff-quizzes.netlify.app/en/) + +> **關於測驗的一點說明**：所有測驗均收錄於 Quiz-App 資料夾，共40個測驗，每個測驗包含三題問題。它們在課程中有連結，但測驗應用程式可於本機執行或部署至 Azure；請參照 `quiz-app` 資料夾中的說明。測驗正逐步本地化中。 + +## 🎓 初學者友善範例 + +**資料科學新手？** 我們建立了特別的[範例目錄](examples/README.md)，提供簡單且詳細註解的程式碼，助您快速入門： + +- 🌟 **Hello World** - 您的第一個資料科學程式 +- 📂 **載入資料** - 學習讀取與探索資料集 +- 📊 **簡易分析** - 計算統計數據並發掘規律 +- 📈 **基本視覺化** - 製作圖表 +- 🔬 **實務專案** - 從頭到尾完成工作流程 + +每個範例都有詳細註解，解釋每一步驟，適合完全初學者！ + +👉 **[從範例開始](examples/README.md)** 👈 + +## 課程 + +|![ 由 @sketchthedocs 繪製手繪筆記 https://sketchthedocs.dev](../../translated_images/zh-HK/00-Roadmap.4905d6567dff4753.webp)| +|:---:| +| 資料科學初學者路線圖 - _手繪筆記由 [@nitya](https://twitter.com/nitya) 製作_ | + +| 課程編號 | 主題 | 課程分類 | 學習目標 | 連結課程 | 作者 | +| :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: | +| 01 | 定義資料科學 | [介紹](1-Introduction/README.md) | 了解資料科學的基本概念及其與人工智能、機器學習和大數據的關係。 | [課程](1-Introduction/01-defining-data-science/README.md) [影片](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) | +| 02 | 資料科學倫理學 | [介紹](1-Introduction/README.md) | 資料倫理概念、挑戰和框架。 | [課程](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) | +| 03 | 定義資料 | [介紹](1-Introduction/README.md) | 資料如何分類及其常見來源。 | [課程](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) | +| 04 | 統計與機率入門 | [介紹](1-Introduction/README.md) | 介紹機率與統計的數學技巧，用以理解資料。 | [課程](1-Introduction/04-stats-and-probability/README.md) [影片](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) | +| 05 | 使用關聯式資料庫 | [資料操作](2-Working-With-Data/README.md) | 介紹關聯式資料及結構化查詢語言（SQL，發音為“see-quell”） 基本探索與分析技巧。 | [課程](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | | +| 06 | 使用 NoSQL 資料 | [資料操作](2-Working-With-Data/README.md) | 介紹非關聯式資料及其各種型態，以及文件資料庫的探索與分析基礎。 | [課程](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)| +| 07 | 使用 Python | [資料操作](2-Working-With-Data/README.md) | 使用 Python 及 Pandas 等函式庫進行資料探索的基礎。建議具備 Python 程式設計基礎。 | [課程](2-Working-With-Data/07-python/README.md) [影片](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) | +| 08 | 資料準備 | [資料操作](2-Working-With-Data/README.md) | 資料清理與轉換技巧，處理缺失、不準確或不完整資料的挑戰。 | [課程](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) | +| 09 | 視覺化數量 | [資料視覺化](3-Data-Visualization/README.md) | 使用 Matplotlib 視覺化鳥類資料 🦆 | [課程](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) | +| 10 | 視覺化資料分布 | [資料視覺化](3-Data-Visualization/README.md) | 視覺化觀察值及趨勢於區間內。 | [課程](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) | +| 11 | 視覺化比例 | [資料視覺化](3-Data-Visualization/README.md) | 視覺化離散及群組百分比。 | [課程](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) | +| 12 | 視覺化關係 | [資料視覺化](3-Data-Visualization/README.md) | 視覺化資料及其變數間的連結及相關性。 | [課程](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) | +| 13 | 有意義的視覺化 | [資料視覺化](3-Data-Visualization/README.md) | 運用技巧與指導，使視覺化對於有效問題解決與洞察有價值。 | [課程](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) | +| 14 | 資料科學生命週期導論 | [生命週期](4-Data-Science-Lifecycle/README.md) | 介紹資料科學生命週期及其第一步──資料獲取與萃取。 | [課程](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) | +| 15 | 資料分析 | [生命週期](4-Data-Science-Lifecycle/README.md) | 生命週期中著重於資料分析的技術。 | [課程](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | | +| 16 | 溝通呈現 | [生命週期](4-Data-Science-Lifecycle/README.md) | 著重呈現資料洞察，以方便決策者理解資料。 | [課程](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | | +| 17 | 雲端資料科學 | [雲端資料](5-Data-Science-In-Cloud/README.md) | 介紹雲端資料科學及其優點。 | [課程](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) 及 [Maud](https://twitter.com/maudstweets) | +| 18 | 雲端資料科學 | [雲端資料](5-Data-Science-In-Cloud/README.md) | 使用低程式碼工具訓練模型。 |[課程](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) 及 [Maud](https://twitter.com/maudstweets) | +| 19 | 雲端資料科學 | [雲端資料](5-Data-Science-In-Cloud/README.md) | 使用 Azure Machine Learning Studio 部署模型。 | [課程](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) 及 [Maud](https://twitter.com/maudstweets) | +| 20 | 實務資料科學 | [實務應用](6-Data-Science-In-Wild/README.md) | 真實世界中由資料科學驅動的專案。 | [課程](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) | + +## GitHub Codespaces + +請依照以下步驟在 Codespace 中開啟此範例： +1. 點擊「Code」下拉選單，選擇「Open with Codespaces」。 +2. 在右側窗格底部選擇「+ New codespace」。 +更多資訊請參考 [GitHub 文件](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace)。 + +## VSCode 遠端 - 容器 +請依照以下步驟，使用本機電腦與 VSCode 以及 VS Code Remote - Containers 擴充功能，在容器中開啟此儲存庫： + +1. 如是第一次使用開發容器，請確定系統符合前置需求（例如安裝 Docker），詳見[快速入門文件](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started)。 + +使用此儲存庫，可選擇於獨立 Docker 卷中開啟儲存庫： + +**注意**：底層會執行 Remote-Containers: **Clone Repository in Container Volume...** 指令，將原始碼複製到 Docker 卷而非本機檔案系統。[卷](https://docs.docker.com/storage/volumes/) 是持久化容器資料的推薦方式。 + +或開啟本機已克隆或下載版本的儲存庫： + +- 將此儲存庫克隆到本機檔案系統。 +- 按 F1，選擇 **Remote-Containers: Open Folder in Container...** 指令。 +- 選擇克隆後的資料夾，等待容器啟動，開始操作。 + +## 離線存取 + +您可使用 [Docsify](https://docsify.js.org/#/) 離線瀏覽此文件。請先 fork 此儲存庫，[安裝 Docsify](https://docsify.js.org/#/quickstart) 至本機，然後在此儲存庫根目錄輸入 `docsify serve`。網站會在本機的 3000 埠提供服務：`localhost:3000`。 + +> 注意，使用 Docsify 不會呈現筆記本檔案，需時請另以 VS Code 執行 Python 核心來執行筆記本。 + +## 其他課程 + +我們團隊還有其他課程！請參考： + + +### LangChain +[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) +[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) + +--- + +### Azure / Edge / MCP / 代理人 +[![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AI Agents for Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) + +--- + +### 生成式人工智能系列 +[![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) +[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) +[![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) + +--- + +### 核心學習 +[![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) +[![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) +[![Cybersecurity for Beginners](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) +[![Web Dev for Beginners](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) +[![IoT for Beginners](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) +[![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) + +--- + +### Copilot 系列 +[![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) +[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) + + +## 獲取幫助 + +**遇到問題？** 請查看我們的 [疑難排解指南](TROUBLESHOOTING.md)，了解常見問題的解決方案。 + +如果您遇到困難或對構建 AI 應用有任何疑問，歡迎加入與其他學習者及有經驗的開發人員一起討論 MCP 的社群。在這裡，問題被歡迎，並且知識自由分享。 + +[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) + +如果您在開發過程中有產品反饋或遇到錯誤，請訪問： + +[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) + +--- + + +**免責聲明**： +此文件經由 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。儘管我們致力於確保準確性，但請注意自動翻譯可能包含錯誤或不準確之處。原始文件的本地語言版本應被視為權威來源。對於重要資訊，建議採用專業人工翻譯。本公司不對因使用此翻譯而引起的任何誤解或誤釋承擔責任。 + \ No newline at end of file diff --git a/translations/zh-HK/SECURITY.md b/translations/zh-HK/SECURITY.md new file mode 100644 index 00000000..3b96f67d --- /dev/null +++ b/translations/zh-HK/SECURITY.md @@ -0,0 +1,40 @@ +## 安全性 + +Microsoft 非常重視我們軟件產品和服務的安全性，包括所有透過我們 GitHub 組織管理的原始碼庫，這些組織包括 [Microsoft](https://github.com/Microsoft)、[Azure](https://github.com/Azure)、[DotNet](https://github.com/dotnet)、[AspNet](https://github.com/aspnet)、[Xamarin](https://github.com/xamarin) 以及 [我們的 GitHub 組織](https://opensource.microsoft.com/)。 + +如果您認為在任何 Microsoft 擁有的原始碼庫中發現了符合 [Microsoft 安全漏洞定義](https://docs.microsoft.com/en-us/previous-versions/tn-archive/cc751383(v=technet.10)) 的安全漏洞，請按照以下描述向我們報告。 + +## 報告安全問題 + +**請勿透過公開的 GitHub 問題報告安全漏洞。** + +相反，請透過 Microsoft Security Response Center (MSRC) 報告安全漏洞：[https://msrc.microsoft.com/create-report](https://msrc.microsoft.com/create-report)。 + +如果您希望在不登入的情況下提交報告，請發送電子郵件至 [secure@microsoft.com](mailto:secure@microsoft.com)。如果可能，請使用我們的 PGP 密鑰加密您的訊息；您可以從 [Microsoft Security Response Center PGP Key 頁面](https://www.microsoft.com/en-us/msrc/pgp-key-msrc) 下載密鑰。 + +您應該在 24 小時內收到回覆。如果因某些原因未收到回覆，請透過電子郵件跟進，以確保我們收到您的原始訊息。更多資訊可參考 [microsoft.com/msrc](https://www.microsoft.com/msrc)。 + +請提供以下所需資訊（盡可能提供完整），以幫助我們更好地了解問題的性質和範圍： + + * 問題類型（例如：緩衝區溢出、SQL 注入、跨站腳本攻擊等） + * 與問題表現相關的原始碼文件完整路徑 + * 受影響原始碼的位置（標籤/分支/提交或直接 URL） + * 重現問題所需的任何特殊配置 + * 重現問題的逐步指引 + * 概念驗證或漏洞利用代碼（如果可能） + * 問題的影響，包括攻擊者可能如何利用該問題 + +這些資訊將幫助我們更快速地處理您的報告。 + +如果您是為漏洞賞金計劃報告，提供更完整的報告可能會獲得更高的賞金獎勵。請訪問我們的 [Microsoft Bug Bounty Program](https://microsoft.com/msrc/bounty) 頁面，了解更多有關我們現行計劃的詳情。 + +## 優先語言 + +我們希望所有的溝通均使用英文。 + +## 政策 + +Microsoft 遵循 [協調漏洞披露](https://www.microsoft.com/en-us/msrc/cvd) 的原則。 + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為權威來源。對於重要信息，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/SUPPORT.md b/translations/zh-HK/SUPPORT.md new file mode 100644 index 00000000..dc5e7813 --- /dev/null +++ b/translations/zh-HK/SUPPORT.md @@ -0,0 +1,13 @@ +# 支援 +## 如何提交問題和獲取幫助 + +此項目使用 GitHub Issues 來追蹤錯誤和功能請求。在提交新問題之前，請先搜尋現有問題以避免重複。對於新問題，請將您的錯誤或功能請求提交為一個新問題。 + +如需有關使用此項目的幫助和問題，請提交一個問題。 + +## Microsoft 支援政策 + +對於此存儲庫的支援僅限於上述列出的資源。 + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為具權威性的來源。對於重要資訊，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋不承擔責任。 \ No newline at end of file diff --git a/translations/zh-HK/TROUBLESHOOTING.md b/translations/zh-HK/TROUBLESHOOTING.md new file mode 100644 index 00000000..714ac4ed --- /dev/null +++ b/translations/zh-HK/TROUBLESHOOTING.md @@ -0,0 +1,618 @@ +# 疑難排解指南 + +本指南提供了解決在使用《初學者數據科學》課程時可能遇到的常見問題的方法。 + +## 目錄 + +- [Python 和 Jupyter 問題](../..) +- [套件和依賴問題](../..) +- [Jupyter Notebook 問題](../..) +- [測驗應用程式問題](../..) +- [Git 和 GitHub 問題](../..) +- [Docsify 文件問題](../..) +- [數據和檔案問題](../..) +- [效能問題](../..) +- [尋求額外幫助](../..) + +## Python 和 Jupyter 問題 + +### 找不到 Python 或版本錯誤 + +**問題：** `python: command not found` 或 Python 版本錯誤 + +**解決方法：** + +```bash +# Check Python version +python --version +python3 --version + +# If Python 3 is installed as 'python3', create an alias +# On macOS/Linux, add to ~/.bashrc or ~/.zshrc: +alias python=python3 +alias pip=pip3 + +# Or use python3 explicitly +python3 -m pip install jupyter +``` + +**Windows 解決方法：** +1. 從 [python.org](https://www.python.org/) 重新安裝 Python +2. 安裝過程中勾選 "Add Python to PATH" +3. 重啟終端/命令提示符 + +### 虛擬環境啟動問題 + +**問題：** 虛擬環境無法啟動 + +**解決方法：** + +**Windows：** +```bash +# If you get execution policy error +Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser + +# Then activate +venv\Scripts\activate +``` + +**macOS/Linux：** +```bash +# Ensure the activate script is executable +chmod +x venv/bin/activate + +# Then activate +source venv/bin/activate +``` + +**驗證啟動：** +```bash +# Your prompt should show (venv) +# Check Python location +which python # Should point to venv +``` + +### Jupyter 核心問題 + +**問題：** "Kernel not found" 或 "Kernel keeps dying" + +**解決方法：** + +```bash +# Reinstall kernel +python -m ipykernel install --user --name=datascience --display-name="Python (Data Science)" + +# Or use the default kernel +python -m ipykernel install --user + +# Restart Jupyter +jupyter notebook +``` + +**問題：** Jupyter 中的 Python 版本錯誤 + +**解決方法：** +```bash +# Install Jupyter in your virtual environment +source venv/bin/activate # Activate first +pip install jupyter ipykernel + +# Register the kernel +python -m ipykernel install --user --name=venv --display-name="Python (venv)" + +# In Jupyter, select Kernel -> Change kernel -> Python (venv) +``` + +## 套件和依賴問題 + +### 匯入錯誤 + +**問題：** `ModuleNotFoundError: No module named 'pandas'`（或其他套件） + +**解決方法：** + +```bash +# Ensure virtual environment is activated +source venv/bin/activate # macOS/Linux +venv\Scripts\activate # Windows + +# Install missing package +pip install pandas + +# Install all common packages +pip install jupyter pandas numpy matplotlib seaborn scikit-learn + +# Verify installation +python -c "import pandas; print(pandas.__version__)" +``` + +### Pip 安裝失敗 + +**問題：** `pip install` 因權限錯誤失敗 + +**解決方法：** + +```bash +# Use --user flag +pip install --user package-name + +# Or use virtual environment (recommended) +python -m venv venv +source venv/bin/activate +pip install package-name +``` + +**問題：** `pip install` 因 SSL 憑證錯誤失敗 + +**解決方法：** + +```bash +# Update pip first +python -m pip install --upgrade pip + +# Try installing with trusted host (temporary workaround) +pip install --trusted-host pypi.org --trusted-host files.pythonhosted.org package-name +``` + +### 套件版本衝突 + +**問題：** 套件版本不兼容 + +**解決方法：** + +```bash +# Create fresh virtual environment +python -m venv venv-new +source venv-new/bin/activate # or venv-new\Scripts\activate on Windows + +# Install packages with specific versions if needed +pip install pandas==1.3.0 +pip install numpy==1.21.0 + +# Or let pip resolve dependencies +pip install jupyter pandas numpy matplotlib seaborn scikit-learn +``` + +## Jupyter Notebook 問題 + +### Jupyter 無法啟動 + +**問題：** `jupyter notebook` 命令未找到 + +**解決方法：** + +```bash +# Install Jupyter +pip install jupyter + +# Or use python -m +python -m jupyter notebook + +# Add to PATH if needed (macOS/Linux) +export PATH="$HOME/.local/bin:$PATH" +``` + +### Notebook 無法加載或保存 + +**問題：** "Notebook failed to load" 或保存錯誤 + +**解決方法：** + +1. 檢查檔案權限 +```bash +# Make sure you have write permissions +ls -l notebook.ipynb +chmod 644 notebook.ipynb # If needed +``` + +2. 檢查檔案是否損壞 +```bash +# Try opening in text editor to check JSON structure +# Copy content to new notebook if corrupted +``` + +3. 清除 Jupyter 快取 +```bash +jupyter notebook --clear-cache +``` + +### Cell 無法執行 + +**問題：** Cell 停留在 "In [*]" 或執行時間過長 + +**解決方法：** + +1. **中斷核心**：點擊 "Interrupt" 按鈕或按 `I, I` +2. **重啟核心**：Kernel 菜單 → Restart +3. **檢查代碼中的無限循環** +4. **清除輸出**：Cell → All Output → Clear + +### 圖表無法顯示 + +**問題：** `matplotlib` 圖表未在 Notebook 中顯示 + +**解決方法：** + +```python +# Add magic command at the top of notebook +%matplotlib inline + +import matplotlib.pyplot as plt + +# Create plot +plt.plot([1, 2, 3, 4]) +plt.show() # Make sure to call show() +``` + +**互動式圖表的替代方法：** +```python +%matplotlib notebook +# Or +%matplotlib widget +``` + +## 測驗應用程式問題 + +### npm install 失敗 + +**問題：** `npm install` 過程中出現錯誤 + +**解決方法：** + +```bash +# Clear npm cache +npm cache clean --force + +# Remove node_modules and package-lock.json +rm -rf node_modules package-lock.json + +# Reinstall +npm install + +# If still failing, try with legacy peer deps +npm install --legacy-peer-deps +``` + +### 測驗應用程式無法啟動 + +**問題：** `npm run serve` 失敗 + +**解決方法：** + +```bash +# Check Node.js version +node --version # Should be 12.x or higher + +# Reinstall dependencies +cd quiz-app +rm -rf node_modules package-lock.json +npm install + +# Try different port +npm run serve -- --port 8081 +``` + +### 埠已被佔用 + +**問題：** "Port 8080 is already in use" + +**解決方法：** + +```bash +# Find and kill process on port 8080 +# macOS/Linux: +lsof -ti:8080 | xargs kill -9 + +# Windows: +netstat -ano | findstr :8080 +taskkill /PID /F + +# Or use a different port +npm run serve -- --port 8081 +``` + +### 測驗無法加載或顯示空白頁面 + +**問題：** 測驗應用程式加載但顯示空白頁面 + +**解決方法：** + +1. 檢查瀏覽器控制台中的錯誤（F12） +2. 清除瀏覽器快取和 Cookie +3. 嘗試使用其他瀏覽器 +4. 確保 JavaScript 已啟用 +5. 檢查是否有廣告攔截器干擾 + +```bash +# Rebuild the app +npm run build +npm run serve +``` + +## Git 和 GitHub 問題 + +### Git 未被識別 + +**問題：** `git: command not found` + +**解決方法：** + +**Windows：** +- 從 [git-scm.com](https://git-scm.com/) 安裝 Git +- 安裝後重啟終端 + +**macOS：** + +> **注意：** 如果尚未安裝 Homebrew，請按照 [https://brew.sh/](https://brew.sh/) 的指示進行安裝。 +```bash +# Install via Homebrew +brew install git + +# Or install Xcode Command Line Tools +xcode-select --install +``` + +**Linux：** +```bash +sudo apt-get install git # Debian/Ubuntu +sudo dnf install git # Fedora +``` + +### Clone 失敗 + +**問題：** `git clone` 因身份驗證錯誤失敗 + +**解決方法：** + +```bash +# Use HTTPS URL +git clone https://github.com/microsoft/Data-Science-For-Beginners.git + +# If you have 2FA enabled on GitHub, use Personal Access Token +# Create token at: https://github.com/settings/tokens +# Use token as password when prompted +``` + +### 權限被拒絕（publickey） + +**問題：** SSH 密鑰身份驗證失敗 + +**解決方法：** + +```bash +# Generate SSH key +ssh-keygen -t ed25519 -C "your_email@example.com" + +# Add key to ssh-agent +eval "$(ssh-agent -s)" +ssh-add ~/.ssh/id_ed25519 + +# Add public key to GitHub +# Copy key: cat ~/.ssh/id_ed25519.pub +# Add at: https://github.com/settings/keys +``` + +## Docsify 文件問題 + +### Docsify 命令未找到 + +**問題：** `docsify: command not found` + +**解決方法：** + +```bash +# Install globally +npm install -g docsify-cli + +# If permission error on macOS/Linux +sudo npm install -g docsify-cli + +# Verify installation +docsify --version + +# If still not found, add npm global path +# Find npm global path +npm config get prefix + +# Add to PATH (add to ~/.bashrc or ~/.zshrc) +export PATH="$PATH:/usr/local/bin" +``` + +### 文件無法加載 + +**問題：** Docsify 啟動但內容未加載 + +**解決方法：** + +```bash +# Ensure you're in the repository root +cd Data-Science-For-Beginners + +# Check for index.html +ls index.html + +# Serve with specific port +docsify serve --port 3000 + +# Check browser console for errors (F12) +``` + +### 圖片無法顯示 + +**問題：** 圖片顯示為斷鏈圖標 + +**解決方法：** + +1. 檢查圖片路徑是否為相對路徑 +2. 確保圖片檔案存在於倉庫中 +3. 清除瀏覽器快取 +4. 驗證檔案擴展名是否匹配（某些系統對大小寫敏感） + +## 數據和檔案問題 + +### 檔案未找到錯誤 + +**問題：** 加載數據時出現 `FileNotFoundError` + +**解決方法：** + +```python +import os + +# Check current working directory +print(os.getcwd()) + +# Use absolute path +data_path = os.path.join(os.getcwd(), 'data', 'filename.csv') +df = pd.read_csv(data_path) + +# Or use relative path from notebook location +df = pd.read_csv('../data/filename.csv') + +# Verify file exists +print(os.path.exists('data/filename.csv')) +``` + +### CSV 讀取錯誤 + +**問題：** 讀取 CSV 檔案時出現錯誤 + +**解決方法：** + +```python +import pandas as pd + +# Try different encodings +df = pd.read_csv('file.csv', encoding='utf-8') +# or +df = pd.read_csv('file.csv', encoding='latin-1') +# or +df = pd.read_csv('file.csv', encoding='ISO-8859-1') + +# Handle missing values +df = pd.read_csv('file.csv', na_values=['NA', 'N/A', '']) + +# Specify delimiter if not comma +df = pd.read_csv('file.csv', delimiter=';') +``` + +### 大型數據集的記憶體錯誤 + +**問題：** 加載大型檔案時出現 `MemoryError` + +**解決方法：** + +```python +# Read in chunks +chunk_size = 10000 +chunks = [] +for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size): + # Process chunk + chunks.append(chunk) +df = pd.concat(chunks) + +# Or read specific columns only +df = pd.read_csv('file.csv', usecols=['col1', 'col2']) + +# Use more efficient data types +df = pd.read_csv('file.csv', dtype={'column_name': 'int32'}) +``` + +## 效能問題 + +### Notebook 效能緩慢 + +**問題：** Notebook 運行速度非常慢 + +**解決方法：** + +1. **重啟核心並清除輸出** + - Kernel → Restart & Clear Output + +2. **關閉未使用的 Notebook** + +3. **優化代碼：** +```python +# Use vectorized operations instead of loops +# Bad: +result = [] +for x in data: + result.append(x * 2) + +# Good: +result = data * 2 # NumPy/Pandas vectorization +``` + +4. **抽樣大型數據集：** +```python +# Work with sample during development +df_sample = df.sample(n=1000) # or df.head(1000) +``` + +### 瀏覽器崩潰 + +**問題：** 瀏覽器崩潰或無響應 + +**解決方法：** + +1. 關閉未使用的標籤 +2. 清除瀏覽器快取 +3. 增加瀏覽器記憶體（Chrome：`chrome://settings/system`） +4. 使用 JupyterLab 替代： +```bash +pip install jupyterlab +jupyter lab +``` + +## 尋求額外幫助 + +### 在尋求幫助之前 + +1. 檢查本疑難排解指南 +2. 搜索 [GitHub Issues](https://github.com/microsoft/Data-Science-For-Beginners/issues) +3. 查看 [INSTALLATION.md](INSTALLATION.md) 和 [USAGE.md](USAGE.md) +4. 嘗試在線搜索錯誤信息 + +### 如何尋求幫助 + +在創建問題或尋求幫助時，請提供以下信息： + +1. **操作系統**：Windows、macOS 或 Linux（哪個版本） +2. **Python 版本**：運行 `python --version` +3. **錯誤信息**：複製完整的錯誤信息 +4. **重現步驟**：描述錯誤發生前的操作 +5. **已嘗試的解決方法**：列出已嘗試的解決方法 + +**範例：** +``` +**Operating System:** macOS 12.0 +**Python Version:** 3.9.7 +**Error Message:** ModuleNotFoundError: No module named 'pandas' +**Steps to Reproduce:** +1. Activated virtual environment +2. Started Jupyter notebook +3. Tried to import pandas + +**What I've Tried:** +- Ran pip install pandas +- Restarted Jupyter +``` + +### 社群資源 + +- **GitHub Issues**：[創建問題](https://github.com/microsoft/Data-Science-For-Beginners/issues/new) +- **Discord**：[加入我們的社群](https://aka.ms/ds4beginners/discord) +- **討論區**：[GitHub Discussions](https://github.com/microsoft/Data-Science-For-Beginners/discussions) +- **Microsoft Learn**：[問答論壇](https://docs.microsoft.com/answers/) + +### 相關文件 + +- [INSTALLATION.md](INSTALLATION.md) - 安裝指南 +- [USAGE.md](USAGE.md) - 如何使用課程 +- [CONTRIBUTING.md](CONTRIBUTING.md) - 如何貢獻 +- [README.md](README.md) - 專案概述 + +--- + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/USAGE.md b/translations/zh-HK/USAGE.md new file mode 100644 index 00000000..a4562599 --- /dev/null +++ b/translations/zh-HK/USAGE.md @@ -0,0 +1,365 @@ +# 使用指南 + +本指南提供使用「初學者的數據科學」課程的範例和常見工作流程。 + +## 目錄 + +- [如何使用此課程](../..) +- [使用課程內容](../..) +- [使用 Jupyter Notebook](../..) +- [使用測驗應用程式](../..) +- [常見工作流程](../..) +- [自學者的提示](../..) +- [教師的提示](../..) + +## 如何使用此課程 + +此課程設計靈活，可用於多種方式： + +- **自學**：按自己的速度獨立完成課程 +- **課堂教學**：作為結構化課程進行指導教學 +- **學習小組**：與同伴合作學習 +- **工作坊形式**：短期密集學習 + +## 使用課程內容 + +每節課遵循一致的結構以最大化學習效果： + +### 課程結構 + +1. **課前測驗**：測試現有知識 +2. **手繪筆記**（可選）：關鍵概念的視覺摘要 +3. **影片**（可選）：補充影片內容 +4. **書面課程**：核心概念和解釋 +5. **Jupyter Notebook**：動手編碼練習 +6. **作業**：練習所學內容 +7. **課後測驗**：鞏固理解 + +### 課程範例工作流程 + +```bash +# 1. Navigate to the lesson directory +cd 1-Introduction/01-defining-data-science + +# 2. Read the README.md +# Open README.md in your browser or editor + +# 3. Take the pre-lesson quiz +# Click the quiz link in the README + +# 4. Open the Jupyter notebook (if available) +jupyter notebook + +# 5. Complete the exercises in the notebook + +# 6. Work on the assignment + +# 7. Take the post-lesson quiz +``` + +## 使用 Jupyter Notebook + +### 啟動 Jupyter + +```bash +# Activate your virtual environment +source venv/bin/activate # On macOS/Linux +# OR +venv\Scripts\activate # On Windows + +# Start Jupyter from the repository root +jupyter notebook +``` + +### 執行 Notebook 的單元格 + +1. **執行單元格**：按 `Shift + Enter` 或點擊「執行」按鈕 +2. **執行所有單元格**：從選單中選擇「Cell」→「Run All」 +3. **重啟核心**：如果遇到問題，選擇「Kernel」→「Restart」 + +### 範例：在 Notebook 中處理數據 + +```python +# Import required libraries +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + +# Load a dataset +df = pd.read_csv('data/sample.csv') + +# Explore the data +df.head() +df.info() +df.describe() + +# Create a visualization +plt.figure(figsize=(10, 6)) +plt.plot(df['column_name']) +plt.title('Sample Visualization') +plt.xlabel('X-axis Label') +plt.ylabel('Y-axis Label') +plt.show() +``` + +### 保存您的工作 + +- Jupyter 會定期自動保存 +- 手動保存：按 `Ctrl + S`（macOS 上為 `Cmd + S`） +- 您的進度會保存到 `.ipynb` 文件中 + +## 使用測驗應用程式 + +### 本地運行測驗應用程式 + +```bash +# Navigate to quiz app directory +cd quiz-app + +# Start the development server +npm run serve + +# Access at http://localhost:8080 +``` + +### 進行測驗 + +1. 課前測驗鏈接位於每節課的頂部 +2. 課後測驗鏈接位於每節課的底部 +3. 每個測驗有 3 個問題 +4. 測驗旨在鞏固學習，而非全面測試 + +### 測驗編號 + +- 測驗編號為 0-39（共 40 個測驗） +- 每節課通常有課前和課後測驗 +- 測驗 URL 包含測驗編號：`https://ff-quizzes.netlify.app/en/ds/quiz/0` + +## 常見工作流程 + +### 工作流程 1：完全初學者路徑 + +```bash +# 1. Set up your environment (see INSTALLATION.md) + +# 2. Start with Lesson 1 +cd 1-Introduction/01-defining-data-science + +# 3. For each lesson: +# - Take pre-lesson quiz +# - Read the lesson content +# - Work through the notebook +# - Complete the assignment +# - Take post-lesson quiz + +# 4. Progress through all 20 lessons sequentially +``` + +### 工作流程 2：特定主題學習 + +如果您對某個特定主題感興趣： + +```bash +# Example: Focus on Data Visualization +cd 3-Data-Visualization + +# Explore lessons 9-13: +# - Lesson 9: Visualizing Quantities +# - Lesson 10: Visualizing Distributions +# - Lesson 11: Visualizing Proportions +# - Lesson 12: Visualizing Relationships +# - Lesson 13: Meaningful Visualizations +``` + +### 工作流程 3：基於項目的學習 + +```bash +# 1. Review the Data Science Lifecycle lessons (14-16) +cd 4-Data-Science-Lifecycle + +# 2. Work through a real-world example (Lesson 20) +cd ../6-Data-Science-In-Wild/20-Real-World-Examples + +# 3. Apply concepts to your own project +``` + +### 工作流程 4：基於雲端的數據科學 + +```bash +# Learn about cloud data science (Lessons 17-19) +cd 5-Data-Science-In-Cloud + +# 17: Introduction to Cloud Data Science +# 18: Low-Code ML Tools +# 19: Azure Machine Learning Studio +``` + +## 自學者的提示 + +### 保持有條理 + +```bash +# Create a learning journal +mkdir my-learning-journal + +# For each lesson, create notes +echo "# Lesson 1 Notes" > my-learning-journal/lesson-01-notes.md +``` + +### 定期練習 + +- 每天或每週安排固定的學習時間 +- 每週至少完成一節課 +- 定期回顧之前的課程 + +### 與社群互動 + +- 加入 [Discord 社群](https://aka.ms/ds4beginners/discord) +- 參與 Discord 的 #Data-Science-for-Beginners 頻道 [Discord 討論](https://aka.ms/ds4beginners/discord) +- 分享您的進度並提出問題 + +### 建立自己的項目 + +完成課程後，將概念應用於個人項目： + +```python +# Example: Analyze your own dataset +import pandas as pd + +# Load your own data +my_data = pd.read_csv('my-project/data.csv') + +# Apply techniques learned +# - Data cleaning (Lesson 8) +# - Exploratory data analysis (Lesson 7) +# - Visualization (Lessons 9-13) +# - Analysis (Lesson 15) +``` + +## 教師的提示 + +### 課堂設置 + +1. 查看 [for-teachers.md](for-teachers.md) 以獲取詳細指導 +2. 設置共享環境（GitHub Classroom 或 Codespaces） +3. 建立溝通渠道（Discord、Slack 或 Teams） + +### 課程規劃 + +**建議的 10 週時間表：** + +- **第 1-2 週**：介紹（第 1-4 節課） +- **第 3-4 週**：數據處理（第 5-8 節課） +- **第 5-6 週**：數據可視化（第 9-13 節課） +- **第 7-8 週**：數據科學生命周期（第 14-16 節課） +- **第 9 週**：雲端數據科學（第 17-19 節課） +- **第 10 週**：實際應用與最終項目（第 20 節課） + +### 運行 Docsify 以離線訪問 + +```bash +# Serve documentation locally for classroom use +docsify serve + +# Students can access at localhost:3000 +# No internet required after initial setup +``` + +### 作業評分 + +- 查看學生的 Notebook 是否完成練習 +- 通過測驗分數檢查理解程度 +- 使用數據科學生命周期原則評估最終項目 + +### 創建作業 + +```python +# Example custom assignment template +""" +Assignment: [Topic] + +Objective: [Learning goal] + +Dataset: [Provide or have students find one] + +Tasks: +1. Load and explore the dataset +2. Clean and prepare the data +3. Create at least 3 visualizations +4. Perform analysis +5. Communicate findings + +Deliverables: +- Jupyter notebook with code and explanations +- Written summary of findings +""" +``` + +## 離線使用 + +### 下載資源 + +```bash +# Clone the entire repository +git clone https://github.com/microsoft/Data-Science-For-Beginners.git + +# Download datasets in advance +# Most datasets are included in the repository +``` + +### 本地運行文檔 + +```bash +# Serve with Docsify +docsify serve + +# Access at localhost:3000 +``` + +### 本地運行測驗應用程式 + +```bash +cd quiz-app +npm run serve +``` + +## 訪問翻譯內容 + +翻譯版本提供超過 40 種語言： + +```bash +# Access translated lessons +cd translations/fr # French +cd translations/es # Spanish +cd translations/de # German +# ... and many more +``` + +每個翻譯版本的結構與英文版保持一致。 + +## 其他資源 + +### 繼續學習 + +- [Microsoft Learn](https://docs.microsoft.com/learn/) - 額外的學習路徑 +- [Student Hub](https://docs.microsoft.com/learn/student-hub) - 學生資源 +- [Azure AI Foundry](https://aka.ms/foundry/forum) - 社群論壇 + +### 相關課程 + +- [AI for Beginners](https://aka.ms/ai-beginners) +- [ML for Beginners](https://aka.ms/ml-beginners) +- [Web Dev for Beginners](https://aka.ms/webdev-beginners) +- [Generative AI for Beginners](https://aka.ms/genai-beginners) + +## 獲取幫助 + +- 查看 [TROUBLESHOOTING.md](TROUBLESHOOTING.md) 以解決常見問題 +- 搜索 [GitHub Issues](https://github.com/microsoft/Data-Science-For-Beginners/issues) +- 加入我們的 [Discord](https://aka.ms/ds4beginners/discord) +- 查看 [CONTRIBUTING.md](CONTRIBUTING.md) 以報告問題或貢獻內容 + +--- + +**免責聲明**： +此文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原文文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。 \ No newline at end of file diff --git a/translations/zh-HK/docs/_sidebar.md b/translations/zh-HK/docs/_sidebar.md new file mode 100644 index 00000000..3eef4a39 --- /dev/null +++ b/translations/zh-HK/docs/_sidebar.md @@ -0,0 +1,29 @@ +- 介紹 + - [定義數據科學](../1-Introduction/01-defining-data-science/README.md) + - [數據科學的倫理](../1-Introduction/02-ethics/README.md) + - [定義數據](../1-Introduction/03-defining-data/README.md) + - [概率與統計](../1-Introduction/04-stats-and-probability/README.md) +- 數據處理 + - [關聯式數據庫](../2-Working-With-Data/05-relational-databases/README.md) + - [非關聯式數據庫](../2-Working-With-Data/06-non-relational/README.md) + - [Python](../2-Working-With-Data/07-python/README.md) + - [數據準備](../2-Working-With-Data/08-data-preparation/README.md) +- 數據可視化 + - [可視化數量](../3-Data-Visualization/09-visualization-quantities/README.md) + - [可視化分佈](../3-Data-Visualization/10-visualization-distributions/README.md) + - [可視化比例](../3-Data-Visualization/11-visualization-proportions/README.md) + - [可視化關係](../3-Data-Visualization/12-visualization-relationships/README.md) + - [有意義的可視化](../3-Data-Visualization/13-meaningful-visualizations/README.md) +- 數據科學生命周期 + - [介紹](../4-Data-Science-Lifecycle/14-Introduction/README.md) + - [分析](../4-Data-Science-Lifecycle/15-analyzing/README.md) + - [溝通](../4-Data-Science-Lifecycle/16-communication/README.md) +- 雲端中的數據科學 + - [介紹](../5-Data-Science-In-Cloud/17-Introduction/README.md) + - [低代碼](../5-Data-Science-In-Cloud/18-Low-Code/README.md) + - [Azure](../5-Data-Science-In-Cloud/19-Azure/README.md) +- 野外的數據科學 + - [野外的數據科學](../6-Data-Science-In-Wild/README.md) + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原文文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。 \ No newline at end of file diff --git a/translations/zh-HK/examples/README.md b/translations/zh-HK/examples/README.md new file mode 100644 index 00000000..d0a14d0c --- /dev/null +++ b/translations/zh-HK/examples/README.md @@ -0,0 +1,136 @@ +# 初學者友善的數據科學範例 + +歡迎來到範例目錄！這些簡單且附有詳細註解的範例旨在幫助您開始學習數據科學，即使您是完全的初學者也能輕鬆上手。 + +## 📚 您會在這裡找到什麼 + +每個範例都是獨立的，並包含： +- **清晰的註解**，解釋每一步驟 +- **簡單易讀的程式碼**，一次展示一個概念 +- **真實世界的背景**，幫助您理解何時以及為什麼使用這些技術 +- **預期輸出**，讓您知道應該看到什麼結果 + +## 🚀 開始使用 + +### 先決條件 +在執行這些範例之前，請確保您已經： +- 安裝了 Python 3.7 或更高版本 +- 基本了解如何執行 Python 腳本 + +### 安裝所需的庫 +```bash +pip install pandas numpy matplotlib +``` + +## 📖 範例概覽 + +### 1. Hello World - 數據科學風格 +**檔案：** `01_hello_world_data_science.py` + +您的第一個數據科學程式！學習如何： +- 加載一個簡單的數據集 +- 顯示數據的基本資訊 +- 輸出您的第一個數據科學結果 + +非常適合想要看到第一個數據科學程式運行的絕對初學者。 + +--- + +### 2. 加載和探索數據 +**檔案：** `02_loading_data.py` + +學習處理數據的基本知識： +- 從 CSV 文件讀取數據 +- 查看數據集的前幾行 +- 獲取數據的基本統計資訊 +- 理解數據類型 + +這通常是任何數據科學項目的第一步！ + +--- + +### 3. 簡單數據分析 +**檔案：** `03_simple_analysis.py` + +進行您的第一次數據分析： +- 計算基本統計數據（平均值、中位數、眾數） +- 找出最大值和最小值 +- 計算值的出現次數 +- 根據條件篩選數據 + +看看如何回答關於數據的簡單問題。 + +--- + +### 4. 數據可視化基礎 +**檔案：** `04_basic_visualization.py` + +創建您的第一個可視化： +- 繪製簡單的柱狀圖 +- 創建折線圖 +- 生成餅圖 +- 將可視化保存為圖片 + +學習如何以視覺方式傳達您的發現！ + +--- + +### 5. 使用真實數據 +**檔案：** `05_real_world_example.py` + +將所有內容結合在一起，完成一個完整的範例： +- 從資料庫加載真實數據 +- 清理並準備數據 +- 進行分析 +- 創建有意義的可視化 +- 得出結論 + +此範例展示了從頭到尾的完整工作流程。 + +--- + +## 🎯 如何使用這些範例 + +1. **從頭開始**：範例按難度排序編號。從 `01_hello_world_data_science.py` 開始，逐步完成。 +2. **閱讀註解**：每個檔案都有詳細的註解，解釋程式碼的作用及原因。仔細閱讀！ +3. **嘗試修改**：嘗試修改程式碼。改變某個值會發生什麼？打破程式並修復它——這是學習的方式！ +4. **執行程式碼**：執行每個範例並觀察輸出。與您的預期結果進行比較。 +5. **擴展範例**：理解範例後，嘗試用自己的想法擴展它。 + +## 💡 初學者提示 + +- **不要急於求成**：在進入下一個範例之前，花時間理解每個範例 +- **自己輸入程式碼**：不要只是複製貼上。自己輸入有助於學習和記憶 +- **查詢不熟悉的概念**：如果看到不理解的內容，請在線搜索或查看主要課程 +- **提出問題**：如果需要幫助，請加入 [討論論壇](https://github.com/microsoft/Data-Science-For-Beginners/discussions) +- **定期練習**：每天嘗試編寫一些程式碼，而不是每週一次的長時間學習 + +## 🔗 下一步 + +完成這些範例後，您可以： +- 學習主要課程的內容 +- 嘗試每個課程文件夾中的作業 +- 探索 Jupyter 筆記本以進一步深入學習 +- 創建自己的數據科學項目 + +## 📚 其他資源 + +- [主要課程](../README.md) - 完整的 20 節課程 +- [給教師的指南](../for-teachers.md) - 在課堂中使用此課程 +- [Microsoft Learn](https://docs.microsoft.com/learn/) - 免費的在線學習資源 +- [Python 文件](https://docs.python.org/3/) - 官方 Python 參考 + +## 🤝 貢獻 + +發現錯誤或有新範例的想法？我們歡迎您的貢獻！請參閱 [貢獻指南](../CONTRIBUTING.md)。 + +--- + +**祝您學習愉快！🎉** + +記住：每位專家曾經都是初學者。一步一步來，不要害怕犯錯——它們是學習過程的一部分！ + +--- + +**免責聲明**： +此文件已使用AI翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤詮釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/for-teachers.md b/translations/zh-HK/for-teachers.md new file mode 100644 index 00000000..d7809efd --- /dev/null +++ b/translations/zh-HK/for-teachers.md @@ -0,0 +1,67 @@ +## 給教育工作者 + +想在課堂上使用這套課程嗎？請隨意使用！ + +事實上，你可以直接在 GitHub 上使用這套課程，透過 GitHub Classroom 來進行。 + +要做到這一點，請 fork 此 repo。你需要為每一課建立一個 repo，因此需要將每個文件夾提取到一個獨立的 repo。這樣，[GitHub Classroom](https://classroom.github.com/classrooms) 就可以分別處理每一課。 + +這些[完整指引](https://github.blog/2020-03-18-set-up-your-digital-classroom-with-github-classroom/)可以幫助你了解如何設置你的課堂。 + +## 按原樣使用此 repo + +如果你希望直接使用目前的 repo，而不使用 GitHub Classroom，也可以做到。你需要與學生溝通，告訴他們一起學習哪一課。 + +在網上教學形式（例如 Zoom、Teams 或其他平台）中，你可以為測驗設置分組討論室，並指導學生準備學習。然後邀請學生參加測驗，並在指定時間以 "issues" 的形式提交答案。如果你希望學生公開合作完成作業，也可以採用相同的方式。 + +如果你更喜歡私密的形式，可以要求學生逐課 fork 課程到他們自己的 GitHub 私人 repo，並授予你訪問權限。然後他們可以私下完成測驗和作業，並通過 classroom repo 的 issues 提交給你。 + +在網上課堂中有很多方法可以使這套課程運作起來。請告訴我們哪種方式最適合你！ + +## 課程內容包括： + +20 課、40 個測驗和 20 個作業。課程配有手繪筆記，適合視覺型學習者。許多課程提供 Python 和 R 版本，可以使用 VS Code 中的 Jupyter notebooks 完成。了解更多關於如何設置課堂以使用這些技術堆疊：https://code.visualstudio.com/docs/datascience/jupyter-notebooks。 + +所有手繪筆記，包括一張大幅海報，都在[此文件夾](../../sketchnotes)中。 + +你也可以使用 [Docsify](https://docsify.js.org/#/) 將這套課程作為獨立的離線友好型網站運行。[安裝 Docsify](https://docsify.js.org/#/quickstart) 到你的本地機器，然後在本地 repo 的根文件夾中輸入 `docsify serve`。網站將在本地端口 3000 上運行：`localhost:3000`。 + +課程的離線友好版本將以獨立網頁形式打開：https://localhost:3000 + +課程分為 6 部分： + +- 1: 簡介 + - 1: 定義數據科學 + - 2: 倫理 + - 3: 定義數據 + - 4: 概率與統計概述 +- 2: 處理數據 + - 5: 關聯式數據庫 + - 6: 非關聯式數據庫 + - 7: Python + - 8: 數據準備 +- 3: 數據可視化 + - 9: 數量的可視化 + - 10: 分佈的可視化 + - 11: 比例的可視化 + - 12: 關係的可視化 + - 13: 有意義的可視化 +- 4: 數據科學生命周期 + - 14: 簡介 + - 15: 分析 + - 16: 溝通 +- 5: 雲端中的數據科學 + - 17: 簡介 + - 18: 低代碼選項 + - 19: Azure +- 6: 真實世界中的數據科學 + - 20: 概述 + +## 請分享你的想法！ + +我們希望這套課程能夠滿足你和你的學生的需求。請在討論區提供反饋！歡迎在討論區為你的學生創建一個課堂專區。 + +--- + +**免責聲明**： +此文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原文文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。 \ No newline at end of file diff --git a/translations/zh-HK/quiz-app/README.md b/translations/zh-HK/quiz-app/README.md new file mode 100644 index 00000000..9cb002c7 --- /dev/null +++ b/translations/zh-HK/quiz-app/README.md @@ -0,0 +1,128 @@ +# 測驗 + +這些測驗是數據科學課程的課前和課後測驗，課程網址：https://aka.ms/datascience-beginners + +## 添加翻譯的測驗集 + +要添加測驗翻譯，請在 `assets/translations` 文件夾中創建相應的測驗結構。原始測驗位於 `assets/translations/en`。測驗分為幾個組別。請確保編號與正確的測驗部分對齊。整個課程共有 40 個測驗，編號從 0 開始。 + +編輯翻譯後，請編輯翻譯文件夾中的 index.js 文件，按照 `en` 中的約定導入所有文件。 + +接著，編輯 `assets/translations` 中的 `index.js` 文件以導入新的翻譯文件。 + +然後，編輯此應用中的 `App.vue` 文件中的下拉選單，添加您的語言。將本地化縮寫與您的語言文件夾名稱匹配。 + +最後，編輯翻譯課程中的所有測驗鏈接（如果存在），以包含本地化查詢參數，例如：`?loc=fr`。 + +## 項目設置 + +``` +npm install +``` + +### 編譯並熱重載以進行開發 + +``` +npm run serve +``` + +### 編譯並壓縮以進行生產環境 + +``` +npm run build +``` + +### 檢查並修復文件 + +``` +npm run lint +``` + +### 自定義配置 + +請參閱 [配置參考](https://cli.vuejs.org/config/)。 + +致謝：感謝此測驗應用的原始版本：https://github.com/arpan45/simple-quiz-vue + +## 部署到 Azure + +以下是幫助您開始的逐步指南： + +1. Fork GitHub 儲存庫 +確保您的靜態網頁應用程式代碼在您的 GitHub 儲存庫中。Fork 此儲存庫。 + +2. 創建 Azure 靜態網頁應用 +- 創建 [Azure 帳戶](http://azure.microsoft.com) +- 前往 [Azure 入口網站](https://portal.azure.com) +- 點擊“創建資源”，然後搜索“靜態網頁應用”。 +- 點擊“創建”。 + +3. 配置靜態網頁應用 +- 基本信息： + - 訂閱：選擇您的 Azure 訂閱。 + - 資源組：創建新的資源組或使用現有的資源組。 + - 名稱：為您的靜態網頁應用提供一個名稱。 + - 地區：選擇最接近您的用戶的地區。 + +- #### 部署詳情： + - 資源：選擇“GitHub”。 + - GitHub 帳戶：授權 Azure 訪問您的 GitHub 帳戶。 + - 組織：選擇您的 GitHub 組織。 + - 儲存庫：選擇包含靜態網頁應用的儲存庫。 + - 分支：選擇您要部署的分支。 + +- #### 構建詳情： + - 構建預設：選擇您的應用所使用的框架（例如 React、Angular、Vue 等）。 + - 應用位置：指定包含應用代碼的文件夾（例如，如果在根目錄，則為 /）。 + - API 位置：如果有 API，請指定其位置（可選）。 + - 輸出位置：指定生成構建輸出的文件夾（例如 build 或 dist）。 + +4. 審核並創建 +審核您的設置並點擊“創建”。Azure 會設置必要的資源並在您的儲存庫中創建 GitHub Actions 工作流程。 + +5. GitHub Actions 工作流程 +Azure 會自動在您的儲存庫中創建 GitHub Actions 工作流程文件（.github/workflows/azure-static-web-apps-.yml）。此工作流程將處理構建和部署過程。 + +6. 監控部署 +前往 GitHub 儲存庫中的“Actions”標籤。 +您應該看到一個工作流程正在運行。此工作流程將構建並部署您的靜態網頁應用到 Azure。 +工作流程完成後，您的應用將在提供的 Azure URL 上線。 + +### 示例工作流程文件 + +以下是 GitHub Actions 工作流程文件的示例： +name: Azure Static Web Apps CI/CD +``` +on: + push: + branches: + - main + pull_request: + types: [opened, synchronize, reopened, closed] + branches: + - main + +jobs: + build_and_deploy_job: + runs-on: ubuntu-latest + name: Build and Deploy Job + steps: + - uses: actions/checkout@v2 + - name: Build And Deploy + id: builddeploy + uses: Azure/static-web-apps-deploy@v1 + with: + azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN }} + repo_token: ${{ secrets.GITHUB_TOKEN }} + action: "upload" + app_location: "quiz-app" # App source code path + api_location: ""API source code path optional + output_location: "dist" #Built app content directory - optional +``` + +### 其他資源 +- [Azure 靜態網頁應用文檔](https://learn.microsoft.com/azure/static-web-apps/getting-started) +- [GitHub Actions 文檔](https://docs.github.com/actions/use-cases-and-examples/deploying/deploying-to-azure-static-web-app) + +**免責聲明**： +本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，請注意自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為權威來源。對於重要資訊，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file diff --git a/translations/zh-HK/sketchnotes/README.md b/translations/zh-HK/sketchnotes/README.md new file mode 100644 index 00000000..1d59c9c4 --- /dev/null +++ b/translations/zh-HK/sketchnotes/README.md @@ -0,0 +1,10 @@ +在這裡可以找到所有的手繪筆記！ + +## 致謝 + +Nitya Narasimhan，藝術家 + +![roadmap sketchnote](../../../translated_images/zh-HK/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) + +**免責聲明**： +本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋不承擔責任。 \ No newline at end of file diff --git a/translations/zh-MO/.co-op-translator.json b/translations/zh-MO/.co-op-translator.json new file mode 100644 index 00000000..d83d7ce3 --- /dev/null +++ b/translations/zh-MO/.co-op-translator.json @@ -0,0 +1,422 @@ +{ + "1-Introduction/01-defining-data-science/README.md": { + "original_hash": "43212cc1ac137b7bb1dcfb37ca06b0f4", + "translation_date": "2025-10-25T18:39:24+00:00", + "source_file": "1-Introduction/01-defining-data-science/README.md", + "language_code": "zh-MO" + }, + "1-Introduction/01-defining-data-science/assignment.md": { + "original_hash": "4e0f1773b9bee1be3b28f9fe2c71b3de", + "translation_date": "2025-08-27T09:03:46+00:00", + "source_file": "1-Introduction/01-defining-data-science/assignment.md", + "language_code": "zh-MO" + }, + "1-Introduction/01-defining-data-science/solution/assignment.md": { + "original_hash": 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:----------------------------------------------------------------------------------------------------: | +| 定義數據科學 - _由 [@nitya](https://twitter.com/nitya) 繪製的手繪筆記_ | + +--- + +[![定義數據科學影片](../../../../translated_images/zh-MO/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) + +## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/0) + +## 什麼是數據？ +在我們的日常生活中，我們無時無刻不被數據所包圍。你現在正在閱讀的文字就是數據。你手機裡朋友的電話號碼列表是數據，你手錶上顯示的當前時間也是數據。作為人類，我們天生就會處理數據，比如數錢或者給朋友寫信。 + +然而，隨著電腦的誕生，數據變得更加重要。電腦的主要功能是進行計算，但它們需要數據來進行操作。因此，我們需要了解電腦如何存儲和處理數據。 + +隨著互聯網的出現，電腦作為數據處理設備的角色變得更加重要。如果你仔細想想，我們現在使用電腦更多的是進行數據處理和通信，而不是實際的計算。當我們給朋友寫電子郵件或在互聯網上搜索信息時，我們實際上是在創建、存儲、傳輸和操作數據。 +> 你能記得上一次你用電腦實際進行計算是什麼時候嗎？ + +## 什麼是數據科學？ + +根據 [維基百科](https://en.wikipedia.org/wiki/Data_science)，**數據科學**被定義為*一個使用科學方法從結構化和非結構化數據中提取知識和洞察力，並將數據中的知識和可行洞察應用於廣泛應用領域的科學領域*。 + +這一定義突出了數據科學的以下重要方面： + +* 數據科學的主要目標是從數據中**提取知識**，換句話說，就是**理解**數據，發現一些隱藏的關係並建立**模型**。 +* 數據科學使用**科學方法**，例如概率和統計。事實上，當*數據科學*這個術語首次被提出時，有些人認為數據科學只是統計學的一個新潮名稱。然而，現在已經很明顯這個領域要廣泛得多。 +* 獲得的知識應用於產生一些**可行的洞察**，即可以應用於實際業務情境的實用洞察。 +* 我們應該能夠處理**結構化**和**非結構化**數據。我們將在課程的後面部分回到這一點，討論不同類型的數據。 +* **應用領域**是一個重要的概念，數據科學家通常需要對問題領域（例如：金融、醫學、行銷等）有一定程度的專業知識。 + +> 數據科學的另一個重要方面是研究如何使用電腦收集、存儲和操作數據。雖然統計學為我們提供了數學基礎，但數據科學將數學概念應用於實際從數據中獲取洞察。 + +根據 [Jim Gray](https://en.wikipedia.org/wiki/Jim_Gray_(computer_scientist)) 的說法，數據科學可以被視為一種獨立的科學範式： +* **經驗科學**，主要依賴觀察和實驗結果 +* **理論科學**，從現有的科學知識中產生新概念 +* **計算科學**，基於一些計算實驗發現新原則 +* **數據驅動科學**，基於發現數據中的關係和模式 + +## 其他相關領域 + +由於數據無處不在，數據科學本身也是一個廣泛的領域，涉及許多其他學科。 + +
+
數據庫
+
+一個關鍵的考量是如何存儲數據，即如何以允許更快處理的方式結構化數據。有不同類型的數據庫可以存儲結構化和非結構化數據，這些我們會在課程中進一步探討。 +
+
大數據
+
+我們經常需要存儲和處理結構相對簡單但數量非常龐大的數據。有專門的方法和工具可以將這些數據分佈式存儲在計算機集群上，並高效地處理它們。 +
+
機器學習
+
+理解數據的一種方法是建立模型，以預測所需的結果。從數據中開發模型被稱為機器學習。你可以參考我們的機器學習入門課程以了解更多。 +
+
人工智慧
+
+機器學習的一個領域稱為人工智慧（AI），它也依賴於數據，並涉及構建模仿人類思維過程的高複雜性模型。AI 方法通常允許我們將非結構化數據（例如自然語言）轉化為結構化洞察。 +
+
可視化
+
+龐大的數據量對人類來說是難以理解的，但一旦我們使用這些數據創建了有用的可視化，我們就能更好地理解數據，並得出一些結論。因此，了解多種可視化信息的方法非常重要——這是我們將在課程的第三部分中涵蓋的內容。相關領域還包括資訊圖表和人機互動。 +
+
+ +## 數據的類型 + +正如我們已經提到的，數據無處不在。我們只需要以正確的方式捕捉它！區分**結構化**和**非結構化**數據是很有用的。前者通常以某種結構化的形式表示，通常是表格或多個表格，而後者則只是文件的集合。有時我們也可以談論**半結構化**數據，它具有某種結構，但結構可能有很大差異。 + +| 結構化數據 | 半結構化數據 | 非結構化數據 | +| -------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- | --------------------------------------- | +| 包含人員及其電話號碼的列表 | 包含鏈接的維基百科頁面 | 《大英百科全書》的文本 | +| 過去 20 年內每分鐘建築物中所有房間的溫度 | 以 JSON 格式存儲的科學論文集合，包括作者、發表日期和摘要 | 包含公司文件的文件共享 | +| 進入建築物的所有人的年齡和性別數據 | 網頁 | 監控攝像頭的原始視頻流 | + +## 從哪裡獲取數據 + +數據的來源有很多種，無法一一列舉！然而，我們可以提到一些典型的數據來源： + +* **結構化數據** + - **物聯網**（IoT），包括來自不同傳感器（如溫度或壓力傳感器）的數據，提供了許多有用的數據。例如，如果一棟辦公樓配備了 IoT 傳感器，我們可以自動控制供暖和照明以降低成本。 + - **調查問卷**，例如我們在用戶購買後或訪問網站後要求他們完成的問卷。 + - **行為分析**，例如可以幫助我們了解用戶如何深入瀏覽網站，以及他們離開網站的典型原因。 +* **非結構化數據** + - **文本**可以是豐富的洞察來源，例如整體的**情感分數**，或提取關鍵詞和語義意義。 + - **圖像**或**視頻**。來自監控攝像頭的視頻可以用於估算道路上的交通流量，並通知人們可能的交通堵塞。 + - 網頁伺服器的**日誌**可以用來了解我們網站的哪些頁面最常被訪問，以及訪問的時長。 +* **半結構化數據** + - **社交網絡**圖表可以是關於用戶個性和信息傳播潛力的數據的絕佳來源。 + - 當我們有一堆派對的照片時，我們可以通過構建人們互相拍照的圖表來嘗試提取**群體動態**數據。 + +通過了解不同的數據來源，你可以嘗試思考不同的場景，看看數據科學技術可以在哪些方面應用，以更好地了解情況並改進業務流程。 + +## 你可以用數據做什麼 + +在數據科學中，我們專注於數據旅程的以下步驟： + +
+
1) 數據獲取
+
+第一步是收集數據。雖然在許多情況下這可能是一個簡單的過程，比如從網頁應用程序進入數據庫的數據，但有時我們需要使用特殊技術。例如，來自 IoT 傳感器的數據可能過於龐大，因此使用像 IoT Hub 這樣的緩衝端點來收集所有數據以便進一步處理是一個好習慣。 +
+
2) 數據存儲
+
+存儲數據可能具有挑戰性，特別是當我們談論大數據時。在決定如何存儲數據時，預測未來你希望如何查詢數據是有意義的。數據可以通過多種方式存儲： +
    +
  • 關聯數據庫存儲表的集合，並使用一種稱為 SQL 的特殊語言來查詢它們。通常，表被組織成不同的組，稱為模式。在許多情況下，我們需要將數據從原始形式轉換為適合模式的形式。
    • +
  • NoSQL 數據庫，例如 CosmosDB，不對數據強制執行模式，並允許存儲更複雜的數據，例如層次結構的 JSON 文檔或圖表。然而，NoSQL 數據庫沒有 SQL 的豐富查詢功能，並且無法強制執行參考完整性，即表結構和表之間關係的規則。
    • +
  • 數據湖存儲用於以原始、非結構化形式存儲大量數據。數據湖通常用於大數據，當所有數據無法容納在一台機器上時，必須由伺服器集群存儲和處理。Parquet 是一種經常與大數據一起使用的數據格式。
    • +
+
+
3) 數據處理
+
+這是數據旅程中最令人興奮的部分，涉及將數據從其原始形式轉換為可用於可視化/模型訓練的形式。當處理非結構化數據（如文本或圖像）時，我們可能需要使用一些 AI 技術從數據中提取特徵，從而將其轉換為結構化形式。 +
+
4) 可視化 / 人類洞察
+
+為了理解數據，我們經常需要對其進行可視化。擁有多種可視化技術，我們可以找到合適的視圖來獲得洞察。通常，數據科學家需要“玩轉數據”，多次對其進行可視化，尋找某些關係。此外，我們還可以使用統計技術來檢驗假設或證明數據之間的相關性。 +
+
5) 訓練預測模型
+
+由於數據科學的最終目標是能夠根據數據做出決策，我們可能希望使用機器學習技術來構建預測模型。然後，我們可以使用這些模型對具有相似結構的新數據集進行預測。 +
+
+ +當然，根據實際數據的不同，有些步驟可能會缺失（例如，當我們已經在數據庫中擁有數據，或者當我們不需要模型訓練時），或者某些步驟可能會重複多次（例如數據處理）。 + +## 數字化與數字化轉型 + +在過去的十年中，許多企業開始意識到數據在業務決策中的重要性。要將數據科學原則應用於經營業務，首先需要收集一些數據，即將業務流程轉化為數字形式，這被稱為**數字化**。將數據科學技術應用於這些數據以指導決策，可以顯著提高生產力（甚至實現業務轉型），這被稱為**數字化轉型**。 + +讓我們來看一個例子。假設我們有一門數據科學課程（比如這門課程），我們在線上向學生提供，並希望使用數據科學來改進它。我們該怎麼做？ + +我們可以從問“什麼可以數字化？”開始。最簡單的方法是測量每位學生完成每個模組所需的時間，並通過在每個模組結束時進行選擇題測試來測量所獲得的知識。通過計算所有學生的平均完成時間，我們可以找出哪些模組對學生來說最具挑戰性，並著手簡化它們。 +> 你可能會認為這種方法並不理想，因為模組的長度可能不同。或許更公平的做法是將時間除以模組的長度（以字元數計算），然後比較這些值。 + +當我們開始分析多選測試的結果時，可以嘗試找出學生難以理解的概念，並利用這些資訊來改進內容。為了達到這個目的，我們需要設計測試，使每個問題都能對應到某個特定的概念或知識塊。 + +如果我們想進一步深入分析，可以將每個模組所花的時間與學生的年齡類別進行對比。我們可能會發現某些年齡層的學生完成模組所需的時間過長，或者在完成之前就中途退出。這可以幫助我們為模組提供年齡建議，並減少因錯誤期望而導致的不滿。 + +## 🚀 挑戰 + +在這個挑戰中，我們將透過分析文本來尋找與資料科學領域相關的概念。我們會選取一篇關於資料科學的維基百科文章，下載並處理文本，然後建立一個像這樣的文字雲： + +![資料科學文字雲](../../../../translated_images/zh-MO/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) + +請訪問 [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') 來閱讀程式碼。你也可以執行程式碼，並即時查看它如何進行所有的資料轉換。 + +> 如果你不知道如何在 Jupyter Notebook 中執行程式碼，可以參考 [這篇文章](https://soshnikov.com/education/how-to-execute-notebooks-from-github/)。 + +## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/1) + +## 作業 + +* **任務 1**：修改上述程式碼，找出與 **大數據** 和 **機器學習** 領域相關的概念 +* **任務 2**：[思考資料科學場景](assignment.md) + +## 致謝 + +這堂課由 [Dmitry Soshnikov](http://soshnikov.com) 用 ♥️ 編寫完成 + +--- + +**免責聲明**： +本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們努力確保翻譯的準確性，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於關鍵信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。 \ No newline at end of file diff --git a/translations/zh-MO/1-Introduction/01-defining-data-science/assignment.md b/translations/zh-MO/1-Introduction/01-defining-data-science/assignment.md new file mode 100644 index 00000000..336f9b41 --- /dev/null +++ b/translations/zh-MO/1-Introduction/01-defining-data-science/assignment.md @@ -0,0 +1,37 @@ +# 作業：資料科學情境 + +在這份作業中，我們希望你思考一些現實生活中的流程或問題，涵蓋不同的問題領域，並探討如何利用資料科學流程來改進它。請思考以下問題： + +1. 你可以收集哪些資料？ +1. 你會如何收集這些資料？ +1. 你會如何儲存這些資料？資料的規模可能有多大？ +1. 從這些資料中你可能獲得哪些洞察？基於這些資料，我們可以做出哪些決策？ + +試著思考三個不同的問題或流程，並針對每個問題領域描述上述的每個要點。 + +以下是一些問題領域和問題，幫助你開始思考： + +1. 如何利用資料來改善學校中兒童的教育流程？ +1. 如何利用資料在疫情期間控制疫苗接種？ +1. 如何利用資料確保自己在工作中保持高效？ + +## 指示 + +填寫以下表格（如果需要，可以替換建議的問題領域為你自己的領域）： + +| 問題領域 | 問題 | 收集哪些資料 | 如何儲存資料 | 我們可以做出的洞察/決策 | +|----------|------|--------------|--------------|--------------------------| +| 教育 | | | | | +| 疫苗接種 | | | | | +| 生產力 | | | | | + +## 評分標準 + +卓越 | 合格 | 需要改進 +--- | --- | -- | +能夠為所有問題領域識別合理的資料來源、儲存方式以及可能的決策/洞察 | 解決方案的某些方面未詳細說明，未討論資料儲存，至少描述了兩個問題領域 | 僅描述部分資料解決方案，僅考慮了一個問題領域。 + +--- + +**免責聲明**： +本文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對於因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。 \ No newline at end of file diff --git a/translations/zh-MO/1-Introduction/01-defining-data-science/notebook.ipynb b/translations/zh-MO/1-Introduction/01-defining-data-science/notebook.ipynb new file mode 100644 index 00000000..5235d5f8 --- /dev/null +++ b/translations/zh-MO/1-Introduction/01-defining-data-science/notebook.ipynb @@ -0,0 +1,431 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# 挑戰：分析有關數據科學的文本\n", + "\n", + "在這個例子中，我們將進行一個簡單的練習，涵蓋傳統數據科學流程的所有步驟。你不需要撰寫任何程式碼，只需點擊下面的單元格執行它們並觀察結果。作為挑戰，鼓勵你使用不同的數據來嘗試這段程式碼。\n", + "\n", + "## 目標\n", + "\n", + "在這節課中，我們討論了與數據科學相關的不同概念。現在讓我們通過進行一些**文本挖掘**來探索更多相關概念。我們將從一段有關數據科學的文本開始，提取其中的關鍵字，然後嘗試將結果可視化。\n", + "\n", + "作為文本，我們將使用維基百科上有關數據科學的頁面：\n" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 62, + "source": [ + "url = 'https://en.wikipedia.org/wiki/Data_science'" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## 第一步：獲取資料\n", + "\n", + "每個資料科學流程的第一步就是獲取資料。我們將使用 `requests` 函式庫來完成這個步驟：\n" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 63, + "source": [ + "import requests\r\n", + "\r\n", + "text = requests.get(url).content.decode('utf-8')\r\n", + "print(text[:1000])" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\n", + "\n", + "Data science - Wikipedia\n", + "