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143 lines
22 KiB
143 lines
22 KiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Assignment: Evaluating Data from a Form\r\n",
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"\r\n",
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"A client has been testing a [small form](index.html) to gather some basic data about their client-base. They have brought their findings to you to validate the data they have gathered. You can open the `index.html` page in a browser to take a look at the form.\r\n",
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"\r\n",
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"You have been provided a [dataset of csv records](../../data/form.csv) that contain entries from the form as well as some basic visualizations.The client pointed out that some of the visualizations look incorrect but they're unsure about how to resolve them. You can explore it in the [assignment notebook](assignment.ipynb).\r\n",
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"\r\n",
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"## Instructions\r\n",
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"\r\n",
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"Use the techniques in this lesson to make recommendations about the form so it captures accurate and consistent information. "
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [
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"!pip install pandas\r\n",
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"!pip install matplotlib"
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"source": [
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"import pandas as pd\r\n",
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"import matplotlib.pyplot as plt\r\n",
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"\r\n",
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"#Loading the dataset\r\n",
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"path = '../../data/form.csv'\r\n",
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"form_df = pd.read_csv(path)\r\n",
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"print(form_df)"
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],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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" birth_month state pet\n",
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"0 January NaN Cats\n",
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"1 JAN CA Cats\n",
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"2 Sept Hawaii Dog\n",
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"3 january AK Dog\n",
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"4 July RI Cats\n",
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"5 September California Cats\n",
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"6 April CA Dog\n",
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"7 January California Cats\n",
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"8 November FL Dog\n",
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"9 December Florida Cats\n"
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]
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}
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"source": [
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"form_df['state'].value_counts().plot(kind='bar');\r\n",
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"plt.show()"
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],
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"image/png": 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",
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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}
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}
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"source": [
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"form_df['birth_month'].value_counts().plot(kind='bar');\r\n",
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"plt.show()"
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],
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"image/png": 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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/IfAJ26taqONa4EW27xrVPgRcNnqis17JHn3EJCLpeZL+yvY1wDHA+4GHqC48sqLV4gaQpJ3r6X9voOqqPsT2e9sI+drU0SEPj/bTT52oN03QR0wu5wAP1/ePBD4AnE11laL5bRU1iCR9DFgM3A/sb/uDtu9puayHN3HZZknXTcQk0rzilqSzgdW2P1g/vs72QS2WN1AkPUL1bWct65+MJKqDsTu1UNM6Ol9MR8B2tidkrz6jbiImlymStra9FngBMK+xLP+fG2wPXI+F7VbmwM8fRsTkcj7VWZV3AX+muuwikp5BRt3EGNJ1EzHJ1EMpd6MapfFA3fZMYIf6IG3EehL0ERGFG7g+rIiI6K0EfURE4RL0ERGFS9BHRBQuQR8RUbj/D2N5LVp4WgrKAAAAAElFTkSuQmCC",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
}
|
|
}
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [],
|
|
"metadata": {}
|
|
}
|
|
],
|
|
"metadata": {
|
|
"orig_nbformat": 4,
|
|
"language_info": {
|
|
"name": "python",
|
|
"version": "3.9.7",
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"mimetype": "text/x-python",
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|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"pygments_lexer": "ipython3",
|
|
"nbconvert_exporter": "python",
|
|
"file_extension": ".py"
|
|
},
|
|
"kernelspec": {
|
|
"name": "python3",
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"display_name": "Python 3.9.7 64-bit ('venv': venv)"
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},
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"hash": "6b9b57232c4b57163d057191678da2030059e733b8becc68f245de5a75abe84e"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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