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193 lines
7.9 KiB
193 lines
7.9 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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"# データの分析\n",
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"[レッスン](README.md)で紹介されたPandas関数の例。\n"
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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": 1,
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"source": [
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"import pandas as pd\r\n",
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"import glob\r\n",
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"\r\n",
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"#Loading the dataset\r\n",
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"path = '../../data/emails.csv'\r\n",
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"email_df = pd.read_csv(path)"
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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": 2,
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"source": [
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"# Using Describe on the email dataset\r\n",
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"print(email_df.describe())"
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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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" the to ect and for of \\\n",
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"count 406.000000 406.000000 406.000000 406.000000 406.000000 406.000000 \n",
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"mean 7.022167 6.519704 4.948276 3.059113 3.502463 2.662562 \n",
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"std 10.945522 9.801907 9.293820 6.267806 4.901372 5.443939 \n",
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"min 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 \n",
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"25% 1.000000 1.000000 1.000000 0.000000 1.000000 0.000000 \n",
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"50% 3.000000 3.000000 2.000000 1.000000 2.000000 1.000000 \n",
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"75% 9.000000 7.750000 4.000000 3.000000 4.750000 3.000000 \n",
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"max 99.000000 88.000000 79.000000 69.000000 39.000000 57.000000 \n",
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"\n",
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" a you in on is this \\\n",
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"count 406.000000 406.000000 406.000000 406.000000 406.000000 406.000000 \n",
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"mean 57.017241 2.394089 10.817734 11.591133 5.901478 1.485222 \n",
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"std 78.868243 4.067015 19.050972 16.407175 8.793103 2.912473 \n",
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"min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
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"25% 15.000000 0.000000 1.250000 3.000000 1.000000 0.000000 \n",
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"50% 29.000000 1.000000 5.000000 6.000000 3.000000 0.000000 \n",
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"75% 61.000000 3.000000 12.000000 13.000000 7.000000 2.000000 \n",
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"max 843.000000 31.000000 223.000000 125.000000 61.000000 24.000000 \n",
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"\n",
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" i be that will \n",
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"count 406.000000 406.000000 406.000000 406.000000 \n",
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"mean 47.155172 2.950739 1.034483 0.955665 \n",
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"std 71.043009 4.297865 1.904846 2.042271 \n",
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"min 0.000000 0.000000 0.000000 0.000000 \n",
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"25% 11.000000 1.000000 0.000000 0.000000 \n",
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"50% 24.000000 1.000000 0.000000 0.000000 \n",
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"75% 50.750000 3.000000 1.000000 1.000000 \n",
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"max 754.000000 40.000000 14.000000 24.000000 \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": 5,
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"source": [
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"# Sampling 10 emails\r\n",
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"print(email_df.sample(10))"
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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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" Email No. the to ect and for of a you in on is this i \\\n",
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"150 Email 151 0 1 2 0 3 0 15 0 0 5 0 0 7 \n",
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"380 Email 5147 0 3 2 0 0 0 7 0 1 1 0 0 3 \n",
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"19 Email 20 3 4 11 0 4 2 32 1 1 3 9 5 25 \n",
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"300 Email 301 2 1 1 0 1 1 15 2 2 3 2 0 8 \n",
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"307 Email 308 0 0 1 0 0 0 1 0 1 0 0 0 2 \n",
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"167 Email 168 2 2 2 1 5 1 24 2 5 6 4 0 30 \n",
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"320 Email 321 10 12 4 6 8 6 187 5 26 28 23 2 171 \n",
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"61 Email 62 0 1 1 0 4 1 15 4 4 3 3 0 19 \n",
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"26 Email 27 5 4 1 1 4 4 51 0 8 6 6 2 44 \n",
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"73 Email 74 0 0 1 0 0 0 7 0 4 3 0 0 6 \n",
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"\n",
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" be that will \n",
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"150 1 0 0 \n",
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"380 0 0 0 \n",
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"19 3 0 1 \n",
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"300 0 0 0 \n",
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"307 0 0 0 \n",
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"167 2 0 0 \n",
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"320 5 1 1 \n",
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"61 2 0 0 \n",
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"26 6 0 0 \n",
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"73 0 0 0 \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": 14,
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"source": [
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"# Returns rows where there are more occurrences of \"to\" than \"the\"\r\n",
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"print(email_df.query('the < to'))"
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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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" Email No. the to ect and for of a you in on is this i \\\n",
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"1 Email 2 8 13 24 6 6 2 102 1 18 21 13 0 61 \n",
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"3 Email 4 0 5 22 0 5 1 51 2 1 5 9 2 16 \n",
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"5 Email 6 4 5 1 4 2 3 45 1 16 12 8 1 52 \n",
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"7 Email 8 0 2 2 3 1 2 21 6 2 6 2 0 28 \n",
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"13 Email 14 4 5 7 1 5 1 37 1 8 8 6 1 43 \n",
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".. ... ... .. ... ... ... .. ... ... .. .. .. ... .. \n",
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"390 Email 5157 4 13 1 0 3 1 48 2 8 26 9 1 45 \n",
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"393 Email 5160 2 13 1 0 2 1 38 2 7 24 6 1 34 \n",
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"396 Email 5163 2 3 1 2 1 2 32 0 7 3 2 0 26 \n",
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"404 Email 5171 2 7 1 0 2 1 28 2 8 11 7 1 39 \n",
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"405 Email 5172 22 24 5 1 6 5 148 8 23 13 5 4 99 \n",
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"\n",
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" be that will \n",
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"1 4 2 0 \n",
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"3 2 0 0 \n",
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"5 2 0 0 \n",
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"7 1 0 1 \n",
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"13 1 0 1 \n",
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".. .. ... ... \n",
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"390 1 0 0 \n",
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"393 1 0 0 \n",
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"396 3 0 0 \n",
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"404 1 0 0 \n",
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"405 6 4 1 \n",
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"\n",
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"[169 rows x 17 columns]\n"
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]
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}
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"metadata": {}
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n---\n\n**免責事項**: \nこの文書は、AI翻訳サービス [Co-op Translator](https://github.com/Azure/co-op-translator) を使用して翻訳されています。正確性を追求しておりますが、自動翻訳には誤りや不正確な部分が含まれる可能性があることをご承知ください。元の言語で記載された文書が正式な情報源とみなされるべきです。重要な情報については、専門の人間による翻訳を推奨します。この翻訳の使用に起因する誤解や誤解釈について、当方は一切の責任を負いません。\n"
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]
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