{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# データサイエンス100本ノック（構造化データ加工編） - Python\n# for AzureNotebook"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## 【注意】オリジナル版との変更点\n1. Azure Notebook ではDockerを使えないので、2020.06.18時点の 100knocks-preprocess/docker/work/data にあるCSVファイルをPostgreSQLから入手する代わりに使っています。\n2. オリジナルのCSVデータ, geocode.csvの'latitude'列名の初めにスペースが入っていたため、それを削除しました。\n\n   オリジナル(100knocks-preprocess ver.1.0): ' latitude' --> 'latitude'\n   \n   \n3. 本環境下において、いくつかのオリジナル解答例にエラーが確認されましたので、オリジナルのスクリプトを最大限尊重しコードを訂正しました。\n4. オリジナルの解答例に必要のないlibraryはimportせず、そして必要なlibraryをAzureNotebookでインストールするように最初のセルを改変してあります。\n5. また、SQLではなく上記CSVからデータを読み込むように'はじめに'の最初のセルを改変してあります。"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## 【Azure Notebook】動かす前に\n1. 本スクリプトはPython3.6で動作検証しました。メニュ-->Kernel-->Change Kernelから、Python 3.6を選んでください。Python3ではlibraryをインストールする際にエラーが発生します。"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## はじめに\n- 初めに以下のセルを実行してください\n- 必要なライブラリのインポートと~~データベース（PostgreSQL）~~ 100knocks-preprocess/docker/work/data にあるCSVファイルからのデータ読み込みを行います。geocode.csvに変更を加えたため、またgit cloneをするとAzure Notebooksの簡便さを損なうため、筆者(noguhiro2002)のgithubレポジトリから直接読み込みます。\n- pandas等、利用が想定されるライブラリは以下セルでインポートしています\n- ~~その他利用したいライブラリがあれば適宜インストールしてください（\"!pip install ライブラリ名\"でインストールも可能）~~\n- オリジナルの解答例を元に、必要なライブラリーをpipでインストールします。\n- 処理は複数回に分けても構いません\n- 名前、住所等はダミーデータであり、実在するものではありません"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# pipでオリジナルの解答に必要なライブラリーをインストール\n!pip install --upgrade pip\n!pip install -U pandas numpy scikit-learn\n!pip install imbalanced-learn\n\n\n# pipでオリジナルの解答に必要なライブラリーをインポート\nimport os\nimport pandas as pd\nimport numpy as np\nfrom datetime import datetime, date\nfrom dateutil.relativedelta import relativedelta\nimport math\nfrom sklearn import preprocessing\nfrom sklearn.model_selection import train_test_split\nfrom imblearn.under_sampling import RandomUnderSampler\n\n\n# データを github/noguhiro2002/100knocks-preprocess/work/data フォルダよりDataframe形式でRead\ndf_customer = pd.read_csv('https://raw.githubusercontent.com/The-Japan-DataScientist-Society/100knocks-preprocess/master/docker/work/data/customer.csv')\ndf_category = pd.read_csv('https://raw.githubusercontent.com/The-Japan-DataScientist-Society/100knocks-preprocess/master/docker/work/data/category.csv')\ndf_product = pd.read_csv('https://raw.githubusercontent.com/The-Japan-DataScientist-Society/100knocks-preprocess/master/docker/work/data/product.csv')\ndf_receipt = pd.read_csv('https://raw.githubusercontent.com/The-Japan-DataScientist-Society/100knocks-preprocess/master/docker/work/data/receipt.csv')\ndf_store = pd.read_csv('https://raw.githubusercontent.com/The-Japan-DataScientist-Society/100knocks-preprocess/master/docker/work/data/store.csv')\ndf_geocode = pd.read_csv('https://raw.githubusercontent.com/noguhiro2002/100knocks-preprocess_ForColab-AzureNotebook/master/data/geocode.csv')",

      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Requirement already up-to-date: pip in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (20.1.1)\nRequirement already up-to-date: pandas in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (1.0.4)\nRequirement already up-to-date: numpy in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (1.18.5)\nRequirement already up-to-date: scikit-learn in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (0.23.1)\nRequirement already satisfied, skipping upgrade: python-dateutil>=2.6.1 in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (from pandas) (2.8.1)\nRequirement already satisfied, skipping upgrade: pytz>=2017.2 in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (from pandas) (2019.3)\nRequirement already satisfied, skipping upgrade: threadpoolctl>=2.0.0 in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (from scikit-learn) (2.1.0)\nRequirement already satisfied, skipping upgrade: scipy>=0.19.1 in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (from scikit-learn) (1.1.0)\nRequirement already satisfied, skipping upgrade: joblib>=0.11 in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (from scikit-learn) (0.14.0)\nRequirement already satisfied, skipping upgrade: six>=1.5 in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (from python-dateutil>=2.6.1->pandas) (1.11.0)\nRequirement already satisfied: imbalanced-learn in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (0.7.0)\nRequirement already satisfied: scikit-learn>=0.23 in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (from imbalanced-learn) (0.23.1)\nRequirement already satisfied: scipy>=0.19.1 in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (from imbalanced-learn) (1.1.0)\nRequirement already satisfied: numpy>=1.13.3 in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (from imbalanced-learn) (1.18.5)\nRequirement already satisfied: joblib>=0.11 in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (from imbalanced-learn) (0.14.0)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /home/nbuser/anaconda3_501/lib/python3.6/site-packages (from scikit-learn>=0.23->imbalanced-learn) (2.1.0)\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "/home/nbuser/anaconda3_501/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3020: DtypeWarning: Columns (4) have mixed types.Specify dtype option on import or set low_memory=False.\n  interactivity=interactivity, compiler=compiler, result=result)\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# 演習問題"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-001: レシート明細のデータフレーム（df_receipt）から全項目の先頭10件を表示し、どのようなデータを保有しているか目視で確認せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_receipt.head(10)",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sales_ymd</th>\n      <th>sales_epoch</th>\n      <th>store_cd</th>\n      <th>receipt_no</th>\n      <th>receipt_sub_no</th>\n      <th>customer_id</th>\n      <th>product_cd</th>\n      <th>quantity</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>20181103</td>\n      <td>1257206400</td>\n      <td>S14006</td>\n      <td>112</td>\n      <td>1</td>\n      <td>CS006214000001</td>\n      <td>P070305012</td>\n      <td>1</td>\n      <td>158</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>20181118</td>\n      <td>1258502400</td>\n      <td>S13008</td>\n      <td>1132</td>\n      <td>2</td>\n      <td>CS008415000097</td>\n      <td>P070701017</td>\n      <td>1</td>\n      <td>81</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>20170712</td>\n      <td>1215820800</td>\n      <td>S14028</td>\n      <td>1102</td>\n      <td>1</td>\n      <td>CS028414000014</td>\n      <td>P060101005</td>\n      <td>1</td>\n      <td>170</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20190205</td>\n      <td>1265328000</td>\n      <td>S14042</td>\n      <td>1132</td>\n      <td>1</td>\n      <td>ZZ000000000000</td>\n      <td>P050301001</td>\n      <td>1</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>20180821</td>\n      <td>1250812800</td>\n      <td>S14025</td>\n      <td>1102</td>\n      <td>2</td>\n      <td>CS025415000050</td>\n      <td>P060102007</td>\n      <td>1</td>\n      <td>90</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>20190605</td>\n      <td>1275696000</td>\n      <td>S13003</td>\n      <td>1112</td>\n      <td>1</td>\n      <td>CS003515000195</td>\n      <td>P050102002</td>\n      <td>1</td>\n      <td>138</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>20181205</td>\n      <td>1259971200</td>\n      <td>S14024</td>\n      <td>1102</td>\n      <td>2</td>\n      <td>CS024514000042</td>\n      <td>P080101005</td>\n      <td>1</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>20190922</td>\n      <td>1285113600</td>\n      <td>S14040</td>\n      <td>1102</td>\n      <td>1</td>\n      <td>CS040415000178</td>\n      <td>P070501004</td>\n      <td>1</td>\n      <td>128</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>20170504</td>\n      <td>1209859200</td>\n      <td>S13020</td>\n      <td>1112</td>\n      <td>2</td>\n      <td>ZZ000000000000</td>\n      <td>P071302010</td>\n      <td>1</td>\n      <td>770</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>20191010</td>\n      <td>1286668800</td>\n      <td>S14027</td>\n      <td>1102</td>\n      <td>1</td>\n      <td>CS027514000015</td>\n      <td>P071101003</td>\n      <td>1</td>\n      <td>680</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   sales_ymd  sales_epoch store_cd  receipt_no  receipt_sub_no  \\\n0   20181103   1257206400   S14006         112               1   \n1   20181118   1258502400   S13008        1132               2   \n2   20170712   1215820800   S14028        1102               1   \n3   20190205   1265328000   S14042        1132               1   \n4   20180821   1250812800   S14025        1102               2   \n5   20190605   1275696000   S13003        1112               1   \n6   20181205   1259971200   S14024        1102               2   \n7   20190922   1285113600   S14040        1102               1   \n8   20170504   1209859200   S13020        1112               2   \n9   20191010   1286668800   S14027        1102               1   \n\n      customer_id  product_cd  quantity  amount  \n0  CS006214000001  P070305012         1     158  \n1  CS008415000097  P070701017         1      81  \n2  CS028414000014  P060101005         1     170  \n3  ZZ000000000000  P050301001         1      25  \n4  CS025415000050  P060102007         1      90  \n5  CS003515000195  P050102002         1     138  \n6  CS024514000042  P080101005         1      30  \n7  CS040415000178  P070501004         1     128  \n8  ZZ000000000000  P071302010         1     770  \n9  CS027514000015  P071101003         1     680  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-002: レシート明細のデータフレーム（df_receipt）から売上日（sales_ymd）、顧客ID（customer_id）、商品コード（product_cd）、売上金額（amount）の順に列を指定し、10件表示させよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_receipt[['sales_ymd', 'customer_id', 'product_cd', 'amount']].head(10)",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sales_ymd</th>\n      <th>customer_id</th>\n      <th>product_cd</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>20181103</td>\n      <td>CS006214000001</td>\n      <td>P070305012</td>\n      <td>158</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>20181118</td>\n      <td>CS008415000097</td>\n      <td>P070701017</td>\n      <td>81</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>20170712</td>\n      <td>CS028414000014</td>\n      <td>P060101005</td>\n      <td>170</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20190205</td>\n      <td>ZZ000000000000</td>\n      <td>P050301001</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>20180821</td>\n      <td>CS025415000050</td>\n      <td>P060102007</td>\n      <td>90</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>20190605</td>\n      <td>CS003515000195</td>\n      <td>P050102002</td>\n      <td>138</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>20181205</td>\n      <td>CS024514000042</td>\n      <td>P080101005</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>20190922</td>\n      <td>CS040415000178</td>\n      <td>P070501004</td>\n      <td>128</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>20170504</td>\n      <td>ZZ000000000000</td>\n      <td>P071302010</td>\n      <td>770</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>20191010</td>\n      <td>CS027514000015</td>\n      <td>P071101003</td>\n      <td>680</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   sales_ymd     customer_id  product_cd  amount\n0   20181103  CS006214000001  P070305012     158\n1   20181118  CS008415000097  P070701017      81\n2   20170712  CS028414000014  P060101005     170\n3   20190205  ZZ000000000000  P050301001      25\n4   20180821  CS025415000050  P060102007      90\n5   20190605  CS003515000195  P050102002     138\n6   20181205  CS024514000042  P080101005      30\n7   20190922  CS040415000178  P070501004     128\n8   20170504  ZZ000000000000  P071302010     770\n9   20191010  CS027514000015  P071101003     680"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-003: レシート明細のデータフレーム（df_receipt）から売上日（sales_ymd）、顧客ID（customer_id）、商品コード（product_cd）、売上金額（amount）の順に列を指定し、10件表示させよ。ただし、sales_ymdはsales_dateに項目名を変更しながら抽出すること。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_receipt[['sales_ymd', 'customer_id', 'product_cd', 'amount']].rename(columns={'sales_ymd': 'sales_date'}).head(10)",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sales_date</th>\n      <th>customer_id</th>\n      <th>product_cd</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>20181103</td>\n      <td>CS006214000001</td>\n      <td>P070305012</td>\n      <td>158</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>20181118</td>\n      <td>CS008415000097</td>\n      <td>P070701017</td>\n      <td>81</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>20170712</td>\n      <td>CS028414000014</td>\n      <td>P060101005</td>\n      <td>170</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20190205</td>\n      <td>ZZ000000000000</td>\n      <td>P050301001</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>20180821</td>\n      <td>CS025415000050</td>\n      <td>P060102007</td>\n      <td>90</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>20190605</td>\n      <td>CS003515000195</td>\n      <td>P050102002</td>\n      <td>138</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>20181205</td>\n      <td>CS024514000042</td>\n      <td>P080101005</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>20190922</td>\n      <td>CS040415000178</td>\n      <td>P070501004</td>\n      <td>128</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>20170504</td>\n      <td>ZZ000000000000</td>\n      <td>P071302010</td>\n      <td>770</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>20191010</td>\n      <td>CS027514000015</td>\n      <td>P071101003</td>\n      <td>680</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   sales_date     customer_id  product_cd  amount\n0    20181103  CS006214000001  P070305012     158\n1    20181118  CS008415000097  P070701017      81\n2    20170712  CS028414000014  P060101005     170\n3    20190205  ZZ000000000000  P050301001      25\n4    20180821  CS025415000050  P060102007      90\n5    20190605  CS003515000195  P050102002     138\n6    20181205  CS024514000042  P080101005      30\n7    20190922  CS040415000178  P070501004     128\n8    20170504  ZZ000000000000  P071302010     770\n9    20191010  CS027514000015  P071101003     680"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-004: レシート明細のデータフレーム（df_receipt）から売上日（sales_ymd）、顧客ID（customer_id）、商品コード（product_cd）、売上金額（amount）の順に列を指定し、以下の条件を満たすデータを抽出せよ。\n> - 顧客ID（customer_id）が\"CS018205000001\""
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_receipt[['sales_ymd', 'customer_id', 'product_cd', 'amount']].query('customer_id == \"CS018205000001\"')",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sales_ymd</th>\n      <th>customer_id</th>\n      <th>product_cd</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>36</th>\n      <td>20180911</td>\n      <td>CS018205000001</td>\n      <td>P071401012</td>\n      <td>2200</td>\n    </tr>\n    <tr>\n      <th>9843</th>\n      <td>20180414</td>\n      <td>CS018205000001</td>\n      <td>P060104007</td>\n      <td>600</td>\n    </tr>\n    <tr>\n      <th>21110</th>\n      <td>20170614</td>\n      <td>CS018205000001</td>\n      <td>P050206001</td>\n      <td>990</td>\n    </tr>\n    <tr>\n      <th>27673</th>\n      <td>20170614</td>\n      <td>CS018205000001</td>\n      <td>P060702015</td>\n      <td>108</td>\n    </tr>\n    <tr>\n      <th>27840</th>\n      <td>20190216</td>\n      <td>CS018205000001</td>\n      <td>P071005024</td>\n      <td>102</td>\n    </tr>\n    <tr>\n      <th>28757</th>\n      <td>20180414</td>\n      <td>CS018205000001</td>\n      <td>P071101002</td>\n      <td>278</td>\n    </tr>\n    <tr>\n      <th>39256</th>\n      <td>20190226</td>\n      <td>CS018205000001</td>\n      <td>P070902035</td>\n      <td>168</td>\n    </tr>\n    <tr>\n      <th>58121</th>\n      <td>20190924</td>\n      <td>CS018205000001</td>\n      <td>P060805001</td>\n      <td>495</td>\n    </tr>\n    <tr>\n      <th>68117</th>\n      <td>20190226</td>\n      <td>CS018205000001</td>\n      <td>P071401020</td>\n      <td>2200</td>\n    </tr>\n    <tr>\n      <th>72254</th>\n      <td>20180911</td>\n      <td>CS018205000001</td>\n      <td>P071401005</td>\n      <td>1100</td>\n    </tr>\n    <tr>\n      <th>88508</th>\n      <td>20190216</td>\n      <td>CS018205000001</td>\n      <td>P040101002</td>\n      <td>218</td>\n    </tr>\n    <tr>\n      <th>91525</th>\n      <td>20190924</td>\n      <td>CS018205000001</td>\n      <td>P091503001</td>\n      <td>280</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       sales_ymd     customer_id  product_cd  amount\n36      20180911  CS018205000001  P071401012    2200\n9843    20180414  CS018205000001  P060104007     600\n21110   20170614  CS018205000001  P050206001     990\n27673   20170614  CS018205000001  P060702015     108\n27840   20190216  CS018205000001  P071005024     102\n28757   20180414  CS018205000001  P071101002     278\n39256   20190226  CS018205000001  P070902035     168\n58121   20190924  CS018205000001  P060805001     495\n68117   20190226  CS018205000001  P071401020    2200\n72254   20180911  CS018205000001  P071401005    1100\n88508   20190216  CS018205000001  P040101002     218\n91525   20190924  CS018205000001  P091503001     280"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-005: レシート明細のデータフレーム（df_receipt）から売上日（sales_ymd）、顧客ID（customer_id）、商品コード（product_cd）、売上金額（amount）の順に列を指定し、以下の条件を満たすデータを抽出せよ。\n> - 顧客ID（customer_id）が\"CS018205000001\"\n> - 売上金額（amount）が1,000以上"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_receipt[['sales_ymd', 'customer_id', 'product_cd', 'amount']] \\\n            .query('customer_id == \"CS018205000001\" & amount >= 1000')",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sales_ymd</th>\n      <th>customer_id</th>\n      <th>product_cd</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>36</th>\n      <td>20180911</td>\n      <td>CS018205000001</td>\n      <td>P071401012</td>\n      <td>2200</td>\n    </tr>\n    <tr>\n      <th>68117</th>\n      <td>20190226</td>\n      <td>CS018205000001</td>\n      <td>P071401020</td>\n      <td>2200</td>\n    </tr>\n    <tr>\n      <th>72254</th>\n      <td>20180911</td>\n      <td>CS018205000001</td>\n      <td>P071401005</td>\n      <td>1100</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       sales_ymd     customer_id  product_cd  amount\n36      20180911  CS018205000001  P071401012    2200\n68117   20190226  CS018205000001  P071401020    2200\n72254   20180911  CS018205000001  P071401005    1100"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-006: レシート明細データフレーム「df_receipt」から売上日（sales_ymd）、顧客ID（customer_id）、商品コード（product_cd）、売上数量（quantity）、売上金額（amount）の順に列を指定し、以下の条件を満たすデータを抽出せよ。\n> - 顧客ID（customer_id）が\"CS018205000001\"\n> - 売上金額（amount）が1,000以上または売上数量（quantity）が5以上"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_receipt[['sales_ymd', 'customer_id', 'product_cd', 'quantity', 'amount']].query('customer_id == \"CS018205000001\" & (amount >= 1000 | quantity >=5)')",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sales_ymd</th>\n      <th>customer_id</th>\n      <th>product_cd</th>\n      <th>quantity</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>36</th>\n      <td>20180911</td>\n      <td>CS018205000001</td>\n      <td>P071401012</td>\n      <td>1</td>\n      <td>2200</td>\n    </tr>\n    <tr>\n      <th>9843</th>\n      <td>20180414</td>\n      <td>CS018205000001</td>\n      <td>P060104007</td>\n      <td>6</td>\n      <td>600</td>\n    </tr>\n    <tr>\n      <th>21110</th>\n      <td>20170614</td>\n      <td>CS018205000001</td>\n      <td>P050206001</td>\n      <td>5</td>\n      <td>990</td>\n    </tr>\n    <tr>\n      <th>68117</th>\n      <td>20190226</td>\n      <td>CS018205000001</td>\n      <td>P071401020</td>\n      <td>1</td>\n      <td>2200</td>\n    </tr>\n    <tr>\n      <th>72254</th>\n      <td>20180911</td>\n      <td>CS018205000001</td>\n      <td>P071401005</td>\n      <td>1</td>\n      <td>1100</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       sales_ymd     customer_id  product_cd  quantity  amount\n36      20180911  CS018205000001  P071401012         1    2200\n9843    20180414  CS018205000001  P060104007         6     600\n21110   20170614  CS018205000001  P050206001         5     990\n68117   20190226  CS018205000001  P071401020         1    2200\n72254   20180911  CS018205000001  P071401005         1    1100"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-007: レシート明細のデータフレーム（df_receipt）から売上日（sales_ymd）、顧客ID（customer_id）、商品コード（product_cd）、売上金額（amount）の順に列を指定し、以下の条件を満たすデータを抽出せよ。\n> - 顧客ID（customer_id）が\"CS018205000001\"\n> - 売上金額（amount）が1,000以上2,000以下"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_receipt[['sales_ymd', 'customer_id', 'product_cd', 'amount']] \\\n    .query('customer_id == \"CS018205000001\" & 1000 <= amount <= 2000')",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sales_ymd</th>\n      <th>customer_id</th>\n      <th>product_cd</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>72254</th>\n      <td>20180911</td>\n      <td>CS018205000001</td>\n      <td>P071401005</td>\n      <td>1100</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       sales_ymd     customer_id  product_cd  amount\n72254   20180911  CS018205000001  P071401005    1100"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-008: レシート明細のデータフレーム（df_receipt）から売上日（sales_ymd）、顧客ID（customer_id）、商品コード（product_cd）、売上金額（amount）の順に列を指定し、以下の条件を満たすデータを抽出せよ。\n> - 顧客ID（customer_id）が\"CS018205000001\"\n> - 商品コード（product_cd）が\"P071401019\"以外"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_receipt[['sales_ymd', 'customer_id', 'product_cd', 'amount']] \\\n    .query('customer_id == \"CS018205000001\" & product_cd != \"P071401019\"')",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sales_ymd</th>\n      <th>customer_id</th>\n      <th>product_cd</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>36</th>\n      <td>20180911</td>\n      <td>CS018205000001</td>\n      <td>P071401012</td>\n      <td>2200</td>\n    </tr>\n    <tr>\n      <th>9843</th>\n      <td>20180414</td>\n      <td>CS018205000001</td>\n      <td>P060104007</td>\n      <td>600</td>\n    </tr>\n    <tr>\n      <th>21110</th>\n      <td>20170614</td>\n      <td>CS018205000001</td>\n      <td>P050206001</td>\n      <td>990</td>\n    </tr>\n    <tr>\n      <th>27673</th>\n      <td>20170614</td>\n      <td>CS018205000001</td>\n      <td>P060702015</td>\n      <td>108</td>\n    </tr>\n    <tr>\n      <th>27840</th>\n      <td>20190216</td>\n      <td>CS018205000001</td>\n      <td>P071005024</td>\n      <td>102</td>\n    </tr>\n    <tr>\n      <th>28757</th>\n      <td>20180414</td>\n      <td>CS018205000001</td>\n      <td>P071101002</td>\n      <td>278</td>\n    </tr>\n    <tr>\n      <th>39256</th>\n      <td>20190226</td>\n      <td>CS018205000001</td>\n      <td>P070902035</td>\n      <td>168</td>\n    </tr>\n    <tr>\n      <th>58121</th>\n      <td>20190924</td>\n      <td>CS018205000001</td>\n      <td>P060805001</td>\n      <td>495</td>\n    </tr>\n    <tr>\n      <th>68117</th>\n      <td>20190226</td>\n      <td>CS018205000001</td>\n      <td>P071401020</td>\n      <td>2200</td>\n    </tr>\n    <tr>\n      <th>72254</th>\n      <td>20180911</td>\n      <td>CS018205000001</td>\n      <td>P071401005</td>\n      <td>1100</td>\n    </tr>\n    <tr>\n      <th>88508</th>\n      <td>20190216</td>\n      <td>CS018205000001</td>\n      <td>P040101002</td>\n      <td>218</td>\n    </tr>\n    <tr>\n      <th>91525</th>\n      <td>20190924</td>\n      <td>CS018205000001</td>\n      <td>P091503001</td>\n      <td>280</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       sales_ymd     customer_id  product_cd  amount\n36      20180911  CS018205000001  P071401012    2200\n9843    20180414  CS018205000001  P060104007     600\n21110   20170614  CS018205000001  P050206001     990\n27673   20170614  CS018205000001  P060702015     108\n27840   20190216  CS018205000001  P071005024     102\n28757   20180414  CS018205000001  P071101002     278\n39256   20190226  CS018205000001  P070902035     168\n58121   20190924  CS018205000001  P060805001     495\n68117   20190226  CS018205000001  P071401020    2200\n72254   20180911  CS018205000001  P071401005    1100\n88508   20190216  CS018205000001  P040101002     218\n91525   20190924  CS018205000001  P091503001     280"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-009: 以下の処理において、出力結果を変えずにORをANDに書き換えよ。\n\n`df_store.query('not(prefecture_cd == \"13\" | floor_area > 900)')`"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_store.query('prefecture_cd != \"13\" & floor_area <= 900')",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>store_cd</th>\n      <th>store_name</th>\n      <th>prefecture_cd</th>\n      <th>prefecture</th>\n      <th>address</th>\n      <th>address_kana</th>\n      <th>tel_no</th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>floor_area</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>18</th>\n      <td>S14046</td>\n      <td>北山田店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市都筑区北山田一丁目</td>\n      <td>カナガワケンヨコハマシツヅキクキタヤマタイッチョウメ</td>\n      <td>045-123-4049</td>\n      <td>139.5916</td>\n      <td>35.56189</td>\n      <td>831.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>S14011</td>\n      <td>日吉本町店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市港北区日吉本町四丁目</td>\n      <td>カナガワケンヨコハマシコウホククヒヨシホンチョウヨンチョウメ</td>\n      <td>045-123-4033</td>\n      <td>139.6316</td>\n      <td>35.54655</td>\n      <td>890.0</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>S12013</td>\n      <td>習志野店</td>\n      <td>12</td>\n      <td>千葉県</td>\n      <td>千葉県習志野市芝園一丁目</td>\n      <td>チバケンナラシノシシバゾノイッチョウメ</td>\n      <td>047-123-4002</td>\n      <td>140.0220</td>\n      <td>35.66122</td>\n      <td>808.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   store_cd store_name  prefecture_cd prefecture            address  \\\n18   S14046       北山田店             14       神奈川県   神奈川県横浜市都筑区北山田一丁目   \n20   S14011      日吉本町店             14       神奈川県  神奈川県横浜市港北区日吉本町四丁目   \n38   S12013       習志野店             12        千葉県       千葉県習志野市芝園一丁目   \n\n                      address_kana        tel_no  longitude  latitude  \\\n18      カナガワケンヨコハマシツヅキクキタヤマタイッチョウメ  045-123-4049   139.5916  35.56189   \n20  カナガワケンヨコハマシコウホククヒヨシホンチョウヨンチョウメ  045-123-4033   139.6316  35.54655   \n38             チバケンナラシノシシバゾノイッチョウメ  047-123-4002   140.0220  35.66122   \n\n    floor_area  \n18       831.0  \n20       890.0  \n38       808.0  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-010: 店舗データフレーム（df_store）から、店舗コード（store_cd）が\"S14\"で始まるものだけ全項目抽出し、10件だけ表示せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_store.query(\"store_cd.str.startswith('S14')\", engine='python').head(10)",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>store_cd</th>\n      <th>store_name</th>\n      <th>prefecture_cd</th>\n      <th>prefecture</th>\n      <th>address</th>\n      <th>address_kana</th>\n      <th>tel_no</th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>floor_area</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>S14010</td>\n      <td>菊名店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市港北区菊名一丁目</td>\n      <td>カナガワケンヨコハマシコウホククキクナイッチョウメ</td>\n      <td>045-123-4032</td>\n      <td>139.6326</td>\n      <td>35.50049</td>\n      <td>1732.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>S14033</td>\n      <td>阿久和店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市瀬谷区阿久和西一丁目</td>\n      <td>カナガワケンヨコハマシセヤクアクワニシイッチョウメ</td>\n      <td>045-123-4043</td>\n      <td>139.4961</td>\n      <td>35.45918</td>\n      <td>1495.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>S14036</td>\n      <td>相模原中央店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県相模原市中央二丁目</td>\n      <td>カナガワケンサガミハラシチュウオウニチョウメ</td>\n      <td>042-123-4045</td>\n      <td>139.3716</td>\n      <td>35.57327</td>\n      <td>1679.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>S14040</td>\n      <td>長津田店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市緑区長津田みなみ台五丁目</td>\n      <td>カナガワケンヨコハマシミドリクナガツタミナミダイゴチョウメ</td>\n      <td>045-123-4046</td>\n      <td>139.4994</td>\n      <td>35.52398</td>\n      <td>1548.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>S14050</td>\n      <td>阿久和西店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市瀬谷区阿久和西一丁目</td>\n      <td>カナガワケンヨコハマシセヤクアクワニシイッチョウメ</td>\n      <td>045-123-4053</td>\n      <td>139.4961</td>\n      <td>35.45918</td>\n      <td>1830.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>S14028</td>\n      <td>二ツ橋店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市瀬谷区二ツ橋町</td>\n      <td>カナガワケンヨコハマシセヤクフタツバシチョウ</td>\n      <td>045-123-4042</td>\n      <td>139.4963</td>\n      <td>35.46304</td>\n      <td>1574.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>S14012</td>\n      <td>本牧和田店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市中区本牧和田</td>\n      <td>カナガワケンヨコハマシナカクホンモクワダ</td>\n      <td>045-123-4034</td>\n      <td>139.6582</td>\n      <td>35.42156</td>\n      <td>1341.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>S14046</td>\n      <td>北山田店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市都筑区北山田一丁目</td>\n      <td>カナガワケンヨコハマシツヅキクキタヤマタイッチョウメ</td>\n      <td>045-123-4049</td>\n      <td>139.5916</td>\n      <td>35.56189</td>\n      <td>831.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>S14022</td>\n      <td>逗子店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県逗子市逗子一丁目</td>\n      <td>カナガワケンズシシズシイッチョウメ</td>\n      <td>046-123-4036</td>\n      <td>139.5789</td>\n      <td>35.29642</td>\n      <td>1838.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>S14011</td>\n      <td>日吉本町店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市港北区日吉本町四丁目</td>\n      <td>カナガワケンヨコハマシコウホククヒヨシホンチョウヨンチョウメ</td>\n      <td>045-123-4033</td>\n      <td>139.6316</td>\n      <td>35.54655</td>\n      <td>890.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   store_cd store_name  prefecture_cd prefecture              address  \\\n2    S14010        菊名店             14       神奈川県      神奈川県横浜市港北区菊名一丁目   \n3    S14033       阿久和店             14       神奈川県    神奈川県横浜市瀬谷区阿久和西一丁目   \n4    S14036     相模原中央店             14       神奈川県        神奈川県相模原市中央二丁目   \n7    S14040       長津田店             14       神奈川県  神奈川県横浜市緑区長津田みなみ台五丁目   \n9    S14050      阿久和西店             14       神奈川県    神奈川県横浜市瀬谷区阿久和西一丁目   \n12   S14028       二ツ橋店             14       神奈川県       神奈川県横浜市瀬谷区二ツ橋町   \n16   S14012      本牧和田店             14       神奈川県        神奈川県横浜市中区本牧和田   \n18   S14046       北山田店             14       神奈川県     神奈川県横浜市都筑区北山田一丁目   \n19   S14022        逗子店             14       神奈川県         神奈川県逗子市逗子一丁目   \n20   S14011      日吉本町店             14       神奈川県    神奈川県横浜市港北区日吉本町四丁目   \n\n                      address_kana        tel_no  longitude  latitude  \\\n2        カナガワケンヨコハマシコウホククキクナイッチョウメ  045-123-4032   139.6326  35.50049   \n3        カナガワケンヨコハマシセヤクアクワニシイッチョウメ  045-123-4043   139.4961  35.45918   \n4           カナガワケンサガミハラシチュウオウニチョウメ  042-123-4045   139.3716  35.57327   \n7    カナガワケンヨコハマシミドリクナガツタミナミダイゴチョウメ  045-123-4046   139.4994  35.52398   \n9        カナガワケンヨコハマシセヤクアクワニシイッチョウメ  045-123-4053   139.4961  35.45918   \n12          カナガワケンヨコハマシセヤクフタツバシチョウ  045-123-4042   139.4963  35.46304   \n16            カナガワケンヨコハマシナカクホンモクワダ  045-123-4034   139.6582  35.42156   \n18      カナガワケンヨコハマシツヅキクキタヤマタイッチョウメ  045-123-4049   139.5916  35.56189   \n19               カナガワケンズシシズシイッチョウメ  046-123-4036   139.5789  35.29642   \n20  カナガワケンヨコハマシコウホククヒヨシホンチョウヨンチョウメ  045-123-4033   139.6316  35.54655   \n\n    floor_area  \n2       1732.0  \n3       1495.0  \n4       1679.0  \n7       1548.0  \n9       1830.0  \n12      1574.0  \n16      1341.0  \n18       831.0  \n19      1838.0  \n20       890.0  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-011: 顧客データフレーム（df_customer）から顧客ID（customer_id）の末尾が1のものだけ全項目抽出し、10件だけ表示せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_customer.query(\"customer_id.str.endswith('1')\", engine='python').head(10)",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>customer_name</th>\n      <th>gender_cd</th>\n      <th>gender</th>\n      <th>birth_day</th>\n      <th>age</th>\n      <th>postal_cd</th>\n      <th>address</th>\n      <th>application_store_cd</th>\n      <th>application_date</th>\n      <th>status_cd</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>CS037613000071</td>\n      <td>六角 雅彦</td>\n      <td>9</td>\n      <td>不明</td>\n      <td>1952-04-01</td>\n      <td>66</td>\n      <td>136-0076</td>\n      <td>東京都江東区南砂**********</td>\n      <td>S13037</td>\n      <td>20150414</td>\n      <td>0-00000000-0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS028811000001</td>\n      <td>堀井 かおり</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1933-03-27</td>\n      <td>86</td>\n      <td>245-0016</td>\n      <td>神奈川県横浜市泉区和泉町**********</td>\n      <td>S14028</td>\n      <td>20160115</td>\n      <td>0-00000000-0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>CS040412000191</td>\n      <td>川井 郁恵</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1977-01-05</td>\n      <td>42</td>\n      <td>226-0021</td>\n      <td>神奈川県横浜市緑区北八朔町**********</td>\n      <td>S14040</td>\n      <td>20151101</td>\n      <td>1-20091025-4</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>CS028314000011</td>\n      <td>小菅 あおい</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1983-11-26</td>\n      <td>35</td>\n      <td>246-0038</td>\n      <td>神奈川県横浜市瀬谷区宮沢**********</td>\n      <td>S14028</td>\n      <td>20151123</td>\n      <td>1-20080426-5</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>CS039212000051</td>\n      <td>藤島 恵梨香</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1997-02-03</td>\n      <td>22</td>\n      <td>166-0001</td>\n      <td>東京都杉並区阿佐谷北**********</td>\n      <td>S13039</td>\n      <td>20171121</td>\n      <td>1-20100215-4</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>CS015412000111</td>\n      <td>松居 奈月</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1972-10-04</td>\n      <td>46</td>\n      <td>136-0071</td>\n      <td>東京都江東区亀戸**********</td>\n      <td>S13015</td>\n      <td>20150629</td>\n      <td>0-00000000-0</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>CS004702000041</td>\n      <td>野島 洋</td>\n      <td>0</td>\n      <td>男性</td>\n      <td>1943-08-24</td>\n      <td>75</td>\n      <td>176-0022</td>\n      <td>東京都練馬区向山**********</td>\n      <td>S13004</td>\n      <td>20170218</td>\n      <td>0-00000000-0</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>CS041515000001</td>\n      <td>栗田 千夏</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1967-01-02</td>\n      <td>52</td>\n      <td>206-0001</td>\n      <td>東京都多摩市和田**********</td>\n      <td>S13041</td>\n      <td>20160422</td>\n      <td>E-20100803-F</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>CS029313000221</td>\n      <td>北条 ひかり</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1987-06-19</td>\n      <td>31</td>\n      <td>279-0011</td>\n      <td>千葉県浦安市美浜**********</td>\n      <td>S12029</td>\n      <td>20180810</td>\n      <td>0-00000000-0</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>CS034312000071</td>\n      <td>望月 奈央</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1980-09-20</td>\n      <td>38</td>\n      <td>213-0026</td>\n      <td>神奈川県川崎市高津区久末**********</td>\n      <td>S14034</td>\n      <td>20160106</td>\n      <td>0-00000000-0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "        customer_id customer_name  gender_cd gender   birth_day  age  \\\n1    CS037613000071         六角 雅彦          9     不明  1952-04-01   66   \n3    CS028811000001        堀井 かおり          1     女性  1933-03-27   86   \n14   CS040412000191         川井 郁恵          1     女性  1977-01-05   42   \n31   CS028314000011        小菅 あおい          1     女性  1983-11-26   35   \n56   CS039212000051        藤島 恵梨香          1     女性  1997-02-03   22   \n59   CS015412000111         松居 奈月          1     女性  1972-10-04   46   \n63   CS004702000041          野島 洋          0     男性  1943-08-24   75   \n74   CS041515000001         栗田 千夏          1     女性  1967-01-02   52   \n85   CS029313000221        北条 ひかり          1     女性  1987-06-19   31   \n102  CS034312000071         望月 奈央          1     女性  1980-09-20   38   \n\n    postal_cd                  address application_store_cd  application_date  \\\n1    136-0076       東京都江東区南砂**********               S13037          20150414   \n3    245-0016   神奈川県横浜市泉区和泉町**********               S14028          20160115   \n14   226-0021  神奈川県横浜市緑区北八朔町**********               S14040          20151101   \n31   246-0038   神奈川県横浜市瀬谷区宮沢**********               S14028          20151123   \n56   166-0001     東京都杉並区阿佐谷北**********               S13039          20171121   \n59   136-0071       東京都江東区亀戸**********               S13015          20150629   \n63   176-0022       東京都練馬区向山**********               S13004          20170218   \n74   206-0001       東京都多摩市和田**********               S13041          20160422   \n85   279-0011       千葉県浦安市美浜**********               S12029          20180810   \n102  213-0026   神奈川県川崎市高津区久末**********               S14034          20160106   \n\n        status_cd  \n1    0-00000000-0  \n3    0-00000000-0  \n14   1-20091025-4  \n31   1-20080426-5  \n56   1-20100215-4  \n59   0-00000000-0  \n63   0-00000000-0  \n74   E-20100803-F  \n85   0-00000000-0  \n102  0-00000000-0  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-012: 店舗データフレーム（df_store）から横浜市の店舗だけ全項目表示せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_store.query(\"address.str.contains('横浜市')\", engine='python')",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>store_cd</th>\n      <th>store_name</th>\n      <th>prefecture_cd</th>\n      <th>prefecture</th>\n      <th>address</th>\n      <th>address_kana</th>\n      <th>tel_no</th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>floor_area</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>S14010</td>\n      <td>菊名店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市港北区菊名一丁目</td>\n      <td>カナガワケンヨコハマシコウホククキクナイッチョウメ</td>\n      <td>045-123-4032</td>\n      <td>139.6326</td>\n      <td>35.50049</td>\n      <td>1732.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>S14033</td>\n      <td>阿久和店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市瀬谷区阿久和西一丁目</td>\n      <td>カナガワケンヨコハマシセヤクアクワニシイッチョウメ</td>\n      <td>045-123-4043</td>\n      <td>139.4961</td>\n      <td>35.45918</td>\n      <td>1495.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>S14040</td>\n      <td>長津田店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市緑区長津田みなみ台五丁目</td>\n      <td>カナガワケンヨコハマシミドリクナガツタミナミダイゴチョウメ</td>\n      <td>045-123-4046</td>\n      <td>139.4994</td>\n      <td>35.52398</td>\n      <td>1548.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>S14050</td>\n      <td>阿久和西店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市瀬谷区阿久和西一丁目</td>\n      <td>カナガワケンヨコハマシセヤクアクワニシイッチョウメ</td>\n      <td>045-123-4053</td>\n      <td>139.4961</td>\n      <td>35.45918</td>\n      <td>1830.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>S14028</td>\n      <td>二ツ橋店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市瀬谷区二ツ橋町</td>\n      <td>カナガワケンヨコハマシセヤクフタツバシチョウ</td>\n      <td>045-123-4042</td>\n      <td>139.4963</td>\n      <td>35.46304</td>\n      <td>1574.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>S14012</td>\n      <td>本牧和田店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市中区本牧和田</td>\n      <td>カナガワケンヨコハマシナカクホンモクワダ</td>\n      <td>045-123-4034</td>\n      <td>139.6582</td>\n      <td>35.42156</td>\n      <td>1341.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>S14046</td>\n      <td>北山田店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市都筑区北山田一丁目</td>\n      <td>カナガワケンヨコハマシツヅキクキタヤマタイッチョウメ</td>\n      <td>045-123-4049</td>\n      <td>139.5916</td>\n      <td>35.56189</td>\n      <td>831.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>S14011</td>\n      <td>日吉本町店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市港北区日吉本町四丁目</td>\n      <td>カナガワケンヨコハマシコウホククヒヨシホンチョウヨンチョウメ</td>\n      <td>045-123-4033</td>\n      <td>139.6316</td>\n      <td>35.54655</td>\n      <td>890.0</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>S14048</td>\n      <td>中川中央店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市都筑区中川中央二丁目</td>\n      <td>カナガワケンヨコハマシツヅキクナカガワチュウオウニチョウメ</td>\n      <td>045-123-4051</td>\n      <td>139.5758</td>\n      <td>35.54912</td>\n      <td>1657.0</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>S14042</td>\n      <td>新山下店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市中区新山下二丁目</td>\n      <td>カナガワケンヨコハマシナカクシンヤマシタニチョウメ</td>\n      <td>045-123-4047</td>\n      <td>139.6593</td>\n      <td>35.43894</td>\n      <td>1044.0</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>S14006</td>\n      <td>葛が谷店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市都筑区葛が谷</td>\n      <td>カナガワケンヨコハマシツヅキククズガヤ</td>\n      <td>045-123-4031</td>\n      <td>139.5633</td>\n      <td>35.53573</td>\n      <td>1886.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   store_cd store_name  prefecture_cd prefecture              address  \\\n2    S14010        菊名店             14       神奈川県      神奈川県横浜市港北区菊名一丁目   \n3    S14033       阿久和店             14       神奈川県    神奈川県横浜市瀬谷区阿久和西一丁目   \n7    S14040       長津田店             14       神奈川県  神奈川県横浜市緑区長津田みなみ台五丁目   \n9    S14050      阿久和西店             14       神奈川県    神奈川県横浜市瀬谷区阿久和西一丁目   \n12   S14028       二ツ橋店             14       神奈川県       神奈川県横浜市瀬谷区二ツ橋町   \n16   S14012      本牧和田店             14       神奈川県        神奈川県横浜市中区本牧和田   \n18   S14046       北山田店             14       神奈川県     神奈川県横浜市都筑区北山田一丁目   \n20   S14011      日吉本町店             14       神奈川県    神奈川県横浜市港北区日吉本町四丁目   \n26   S14048      中川中央店             14       神奈川県    神奈川県横浜市都筑区中川中央二丁目   \n40   S14042       新山下店             14       神奈川県      神奈川県横浜市中区新山下二丁目   \n52   S14006       葛が谷店             14       神奈川県        神奈川県横浜市都筑区葛が谷   \n\n                      address_kana        tel_no  longitude  latitude  \\\n2        カナガワケンヨコハマシコウホククキクナイッチョウメ  045-123-4032   139.6326  35.50049   \n3        カナガワケンヨコハマシセヤクアクワニシイッチョウメ  045-123-4043   139.4961  35.45918   \n7    カナガワケンヨコハマシミドリクナガツタミナミダイゴチョウメ  045-123-4046   139.4994  35.52398   \n9        カナガワケンヨコハマシセヤクアクワニシイッチョウメ  045-123-4053   139.4961  35.45918   \n12          カナガワケンヨコハマシセヤクフタツバシチョウ  045-123-4042   139.4963  35.46304   \n16            カナガワケンヨコハマシナカクホンモクワダ  045-123-4034   139.6582  35.42156   \n18      カナガワケンヨコハマシツヅキクキタヤマタイッチョウメ  045-123-4049   139.5916  35.56189   \n20  カナガワケンヨコハマシコウホククヒヨシホンチョウヨンチョウメ  045-123-4033   139.6316  35.54655   \n26   カナガワケンヨコハマシツヅキクナカガワチュウオウニチョウメ  045-123-4051   139.5758  35.54912   \n40       カナガワケンヨコハマシナカクシンヤマシタニチョウメ  045-123-4047   139.6593  35.43894   \n52             カナガワケンヨコハマシツヅキククズガヤ  045-123-4031   139.5633  35.53573   \n\n    floor_area  \n2       1732.0  \n3       1495.0  \n7       1548.0  \n9       1830.0  \n12      1574.0  \n16      1341.0  \n18       831.0  \n20       890.0  \n26      1657.0  \n40      1044.0  \n52      1886.0  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-013: 顧客データフレーム（df_customer）から、ステータスコード（status_cd）の先頭がアルファベットのA〜Fで始まるデータを全項目抽出し、10件だけ表示せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_customer.query(\"status_cd.str.contains('^[A-F]', regex=True)\", engine='python').head(10)",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>customer_name</th>\n      <th>gender_cd</th>\n      <th>gender</th>\n      <th>birth_day</th>\n      <th>age</th>\n      <th>postal_cd</th>\n      <th>address</th>\n      <th>application_store_cd</th>\n      <th>application_date</th>\n      <th>status_cd</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>CS031415000172</td>\n      <td>宇多田 貴美子</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1976-10-04</td>\n      <td>42</td>\n      <td>151-0053</td>\n      <td>東京都渋谷区代々木**********</td>\n      <td>S13031</td>\n      <td>20150529</td>\n      <td>D-20100325-C</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS015414000103</td>\n      <td>奥野 陽子</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1977-08-09</td>\n      <td>41</td>\n      <td>136-0073</td>\n      <td>東京都江東区北砂**********</td>\n      <td>S13015</td>\n      <td>20150722</td>\n      <td>B-20100609-B</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>CS011215000048</td>\n      <td>芦田 沙耶</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1992-02-01</td>\n      <td>27</td>\n      <td>223-0062</td>\n      <td>神奈川県横浜市港北区日吉本町**********</td>\n      <td>S14011</td>\n      <td>20150228</td>\n      <td>C-20100421-9</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>CS029415000023</td>\n      <td>梅田 里穂</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1976-01-17</td>\n      <td>43</td>\n      <td>279-0043</td>\n      <td>千葉県浦安市富士見**********</td>\n      <td>S12029</td>\n      <td>20150610</td>\n      <td>D-20100918-E</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>CS035415000029</td>\n      <td>寺沢 真希</td>\n      <td>9</td>\n      <td>不明</td>\n      <td>1977-09-27</td>\n      <td>41</td>\n      <td>158-0096</td>\n      <td>東京都世田谷区玉川台**********</td>\n      <td>S13035</td>\n      <td>20141220</td>\n      <td>F-20101029-F</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>CS031415000106</td>\n      <td>宇野 由美子</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1970-02-26</td>\n      <td>49</td>\n      <td>151-0053</td>\n      <td>東京都渋谷区代々木**********</td>\n      <td>S13031</td>\n      <td>20150201</td>\n      <td>F-20100511-E</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>CS029215000025</td>\n      <td>石倉 美帆</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1993-09-28</td>\n      <td>25</td>\n      <td>279-0022</td>\n      <td>千葉県浦安市今川**********</td>\n      <td>S12029</td>\n      <td>20150708</td>\n      <td>B-20100820-C</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>CS033605000005</td>\n      <td>猪股 雄太</td>\n      <td>0</td>\n      <td>男性</td>\n      <td>1955-12-05</td>\n      <td>63</td>\n      <td>246-0031</td>\n      <td>神奈川県横浜市瀬谷区瀬谷**********</td>\n      <td>S14033</td>\n      <td>20150425</td>\n      <td>F-20100917-E</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>CS033415000229</td>\n      <td>板垣 菜々美</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1977-11-07</td>\n      <td>41</td>\n      <td>246-0021</td>\n      <td>神奈川県横浜市瀬谷区二ツ橋町**********</td>\n      <td>S14033</td>\n      <td>20150712</td>\n      <td>F-20100326-E</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>CS008415000145</td>\n      <td>黒谷 麻緒</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1977-06-27</td>\n      <td>41</td>\n      <td>157-0067</td>\n      <td>東京都世田谷区喜多見**********</td>\n      <td>S13008</td>\n      <td>20150829</td>\n      <td>F-20100622-F</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       customer_id customer_name  gender_cd gender   birth_day  age postal_cd  \\\n2   CS031415000172       宇多田 貴美子          1     女性  1976-10-04   42  151-0053   \n6   CS015414000103         奥野 陽子          1     女性  1977-08-09   41  136-0073   \n12  CS011215000048         芦田 沙耶          1     女性  1992-02-01   27  223-0062   \n15  CS029415000023         梅田 里穂          1     女性  1976-01-17   43  279-0043   \n21  CS035415000029         寺沢 真希          9     不明  1977-09-27   41  158-0096   \n32  CS031415000106        宇野 由美子          1     女性  1970-02-26   49  151-0053   \n33  CS029215000025         石倉 美帆          1     女性  1993-09-28   25  279-0022   \n40  CS033605000005         猪股 雄太          0     男性  1955-12-05   63  246-0031   \n44  CS033415000229        板垣 菜々美          1     女性  1977-11-07   41  246-0021   \n53  CS008415000145         黒谷 麻緒          1     女性  1977-06-27   41  157-0067   \n\n                     address application_store_cd  application_date  \\\n2        東京都渋谷区代々木**********               S13031          20150529   \n6         東京都江東区北砂**********               S13015          20150722   \n12  神奈川県横浜市港北区日吉本町**********               S14011          20150228   \n15       千葉県浦安市富士見**********               S12029          20150610   \n21      東京都世田谷区玉川台**********               S13035          20141220   \n32       東京都渋谷区代々木**********               S13031          20150201   \n33        千葉県浦安市今川**********               S12029          20150708   \n40    神奈川県横浜市瀬谷区瀬谷**********               S14033          20150425   \n44  神奈川県横浜市瀬谷区二ツ橋町**********               S14033          20150712   \n53      東京都世田谷区喜多見**********               S13008          20150829   \n\n       status_cd  \n2   D-20100325-C  \n6   B-20100609-B  \n12  C-20100421-9  \n15  D-20100918-E  \n21  F-20101029-F  \n32  F-20100511-E  \n33  B-20100820-C  \n40  F-20100917-E  \n44  F-20100326-E  \n53  F-20100622-F  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-014: 顧客データフレーム（df_customer）から、ステータスコード（status_cd）の末尾が数字の1〜9で終わるデータを全項目抽出し、10件だけ表示せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_customer.query(\"status_cd.str.contains('[1-9]$', regex=True)\", engine='python').head(10)",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>customer_name</th>\n      <th>gender_cd</th>\n      <th>gender</th>\n      <th>birth_day</th>\n      <th>age</th>\n      <th>postal_cd</th>\n      <th>address</th>\n      <th>application_store_cd</th>\n      <th>application_date</th>\n      <th>status_cd</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4</th>\n      <td>CS001215000145</td>\n      <td>田崎 美紀</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1995-03-29</td>\n      <td>24</td>\n      <td>144-0055</td>\n      <td>東京都大田区仲六郷**********</td>\n      <td>S13001</td>\n      <td>20170605</td>\n      <td>6-20090929-2</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS033513000180</td>\n      <td>安斎 遥</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1962-07-11</td>\n      <td>56</td>\n      <td>241-0823</td>\n      <td>神奈川県横浜市旭区善部町**********</td>\n      <td>S14033</td>\n      <td>20150728</td>\n      <td>6-20080506-5</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>CS011215000048</td>\n      <td>芦田 沙耶</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1992-02-01</td>\n      <td>27</td>\n      <td>223-0062</td>\n      <td>神奈川県横浜市港北区日吉本町**********</td>\n      <td>S14011</td>\n      <td>20150228</td>\n      <td>C-20100421-9</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>CS040412000191</td>\n      <td>川井 郁恵</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1977-01-05</td>\n      <td>42</td>\n      <td>226-0021</td>\n      <td>神奈川県横浜市緑区北八朔町**********</td>\n      <td>S14040</td>\n      <td>20151101</td>\n      <td>1-20091025-4</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>CS009315000023</td>\n      <td>皆川 文世</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1980-04-15</td>\n      <td>38</td>\n      <td>154-0012</td>\n      <td>東京都世田谷区駒沢**********</td>\n      <td>S13009</td>\n      <td>20150319</td>\n      <td>5-20080322-1</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>CS015315000033</td>\n      <td>福士 璃奈子</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1983-03-17</td>\n      <td>36</td>\n      <td>135-0043</td>\n      <td>東京都江東区塩浜**********</td>\n      <td>S13015</td>\n      <td>20141024</td>\n      <td>4-20080219-3</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>CS023513000066</td>\n      <td>神戸 そら</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1961-12-17</td>\n      <td>57</td>\n      <td>210-0005</td>\n      <td>神奈川県川崎市川崎区東田町**********</td>\n      <td>S14023</td>\n      <td>20150915</td>\n      <td>5-20100524-9</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>CS035513000134</td>\n      <td>市川 美帆</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1960-03-27</td>\n      <td>59</td>\n      <td>156-0053</td>\n      <td>東京都世田谷区桜**********</td>\n      <td>S13035</td>\n      <td>20150227</td>\n      <td>8-20100711-9</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>CS001515000263</td>\n      <td>高松 夏空</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1962-11-09</td>\n      <td>56</td>\n      <td>144-0051</td>\n      <td>東京都大田区西蒲田**********</td>\n      <td>S13001</td>\n      <td>20160812</td>\n      <td>1-20100804-1</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>CS040314000027</td>\n      <td>鶴田 きみまろ</td>\n      <td>9</td>\n      <td>不明</td>\n      <td>1986-03-26</td>\n      <td>33</td>\n      <td>226-0027</td>\n      <td>神奈川県横浜市緑区長津田**********</td>\n      <td>S14040</td>\n      <td>20150122</td>\n      <td>2-20080426-4</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       customer_id customer_name  gender_cd gender   birth_day  age postal_cd  \\\n4   CS001215000145         田崎 美紀          1     女性  1995-03-29   24  144-0055   \n9   CS033513000180          安斎 遥          1     女性  1962-07-11   56  241-0823   \n12  CS011215000048         芦田 沙耶          1     女性  1992-02-01   27  223-0062   \n14  CS040412000191         川井 郁恵          1     女性  1977-01-05   42  226-0021   \n16  CS009315000023         皆川 文世          1     女性  1980-04-15   38  154-0012   \n22  CS015315000033        福士 璃奈子          1     女性  1983-03-17   36  135-0043   \n23  CS023513000066         神戸 そら          1     女性  1961-12-17   57  210-0005   \n24  CS035513000134         市川 美帆          1     女性  1960-03-27   59  156-0053   \n27  CS001515000263         高松 夏空          1     女性  1962-11-09   56  144-0051   \n28  CS040314000027       鶴田 きみまろ          9     不明  1986-03-26   33  226-0027   \n\n                     address application_store_cd  application_date  \\\n4        東京都大田区仲六郷**********               S13001          20170605   \n9     神奈川県横浜市旭区善部町**********               S14033          20150728   \n12  神奈川県横浜市港北区日吉本町**********               S14011          20150228   \n14   神奈川県横浜市緑区北八朔町**********               S14040          20151101   \n16       東京都世田谷区駒沢**********               S13009          20150319   \n22        東京都江東区塩浜**********               S13015          20141024   \n23   神奈川県川崎市川崎区東田町**********               S14023          20150915   \n24        東京都世田谷区桜**********               S13035          20150227   \n27       東京都大田区西蒲田**********               S13001          20160812   \n28    神奈川県横浜市緑区長津田**********               S14040          20150122   \n\n       status_cd  \n4   6-20090929-2  \n9   6-20080506-5  \n12  C-20100421-9  \n14  1-20091025-4  \n16  5-20080322-1  \n22  4-20080219-3  \n23  5-20100524-9  \n24  8-20100711-9  \n27  1-20100804-1  \n28  2-20080426-4  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-015: 顧客データフレーム（df_customer）から、ステータスコード（status_cd）の先頭がアルファベットのA〜Fで始まり、末尾が数字の1〜9で終わるデータを全項目抽出し、10件だけ表示せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_customer.query(\"status_cd.str.contains('^[A-F].*[1-9]$', regex=True)\", engine='python').head(10)",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>customer_name</th>\n      <th>gender_cd</th>\n      <th>gender</th>\n      <th>birth_day</th>\n      <th>age</th>\n      <th>postal_cd</th>\n      <th>address</th>\n      <th>application_store_cd</th>\n      <th>application_date</th>\n      <th>status_cd</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>12</th>\n      <td>CS011215000048</td>\n      <td>芦田 沙耶</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1992-02-01</td>\n      <td>27</td>\n      <td>223-0062</td>\n      <td>神奈川県横浜市港北区日吉本町**********</td>\n      <td>S14011</td>\n      <td>20150228</td>\n      <td>C-20100421-9</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>CS022513000105</td>\n      <td>島村 貴美子</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1962-03-12</td>\n      <td>57</td>\n      <td>249-0002</td>\n      <td>神奈川県逗子市山の根**********</td>\n      <td>S14022</td>\n      <td>20150320</td>\n      <td>A-20091115-7</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>CS001515000096</td>\n      <td>水野 陽子</td>\n      <td>9</td>\n      <td>不明</td>\n      <td>1960-11-29</td>\n      <td>58</td>\n      <td>144-0053</td>\n      <td>東京都大田区蒲田本町**********</td>\n      <td>S13001</td>\n      <td>20150614</td>\n      <td>A-20100724-7</td>\n    </tr>\n    <tr>\n      <th>122</th>\n      <td>CS013615000053</td>\n      <td>西脇 季衣</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1953-10-18</td>\n      <td>65</td>\n      <td>261-0026</td>\n      <td>千葉県千葉市美浜区幕張西**********</td>\n      <td>S12013</td>\n      <td>20150128</td>\n      <td>B-20100329-6</td>\n    </tr>\n    <tr>\n      <th>144</th>\n      <td>CS020412000161</td>\n      <td>小宮 薫</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1974-05-21</td>\n      <td>44</td>\n      <td>174-0042</td>\n      <td>東京都板橋区東坂下**********</td>\n      <td>S13020</td>\n      <td>20150822</td>\n      <td>B-20081021-3</td>\n    </tr>\n    <tr>\n      <th>178</th>\n      <td>CS001215000097</td>\n      <td>竹中 あさみ</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1990-07-25</td>\n      <td>28</td>\n      <td>146-0095</td>\n      <td>東京都大田区多摩川**********</td>\n      <td>S13001</td>\n      <td>20170315</td>\n      <td>A-20100211-2</td>\n    </tr>\n    <tr>\n      <th>252</th>\n      <td>CS035212000007</td>\n      <td>内村 恵梨香</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1990-12-04</td>\n      <td>28</td>\n      <td>152-0023</td>\n      <td>東京都目黒区八雲**********</td>\n      <td>S13035</td>\n      <td>20151013</td>\n      <td>B-20101018-6</td>\n    </tr>\n    <tr>\n      <th>259</th>\n      <td>CS002515000386</td>\n      <td>野田 コウ</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1963-05-30</td>\n      <td>55</td>\n      <td>185-0013</td>\n      <td>東京都国分寺市西恋ケ窪**********</td>\n      <td>S13002</td>\n      <td>20160410</td>\n      <td>C-20100127-8</td>\n    </tr>\n    <tr>\n      <th>293</th>\n      <td>CS001615000372</td>\n      <td>稲垣 寿々花</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1956-10-29</td>\n      <td>62</td>\n      <td>144-0035</td>\n      <td>東京都大田区南蒲田**********</td>\n      <td>S13001</td>\n      <td>20170403</td>\n      <td>A-20100104-1</td>\n    </tr>\n    <tr>\n      <th>297</th>\n      <td>CS032512000121</td>\n      <td>松井 知世</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1962-09-04</td>\n      <td>56</td>\n      <td>210-0011</td>\n      <td>神奈川県川崎市川崎区富士見**********</td>\n      <td>S13032</td>\n      <td>20150727</td>\n      <td>A-20100103-5</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "        customer_id customer_name  gender_cd gender   birth_day  age  \\\n12   CS011215000048         芦田 沙耶          1     女性  1992-02-01   27   \n68   CS022513000105        島村 貴美子          1     女性  1962-03-12   57   \n71   CS001515000096         水野 陽子          9     不明  1960-11-29   58   \n122  CS013615000053         西脇 季衣          1     女性  1953-10-18   65   \n144  CS020412000161          小宮 薫          1     女性  1974-05-21   44   \n178  CS001215000097        竹中 あさみ          1     女性  1990-07-25   28   \n252  CS035212000007        内村 恵梨香          1     女性  1990-12-04   28   \n259  CS002515000386         野田 コウ          1     女性  1963-05-30   55   \n293  CS001615000372        稲垣 寿々花          1     女性  1956-10-29   62   \n297  CS032512000121         松井 知世          1     女性  1962-09-04   56   \n\n    postal_cd                   address application_store_cd  \\\n12   223-0062  神奈川県横浜市港北区日吉本町**********               S14011   \n68   249-0002      神奈川県逗子市山の根**********               S14022   \n71   144-0053      東京都大田区蒲田本町**********               S13001   \n122  261-0026    千葉県千葉市美浜区幕張西**********               S12013   \n144  174-0042       東京都板橋区東坂下**********               S13020   \n178  146-0095       東京都大田区多摩川**********               S13001   \n252  152-0023        東京都目黒区八雲**********               S13035   \n259  185-0013     東京都国分寺市西恋ケ窪**********               S13002   \n293  144-0035       東京都大田区南蒲田**********               S13001   \n297  210-0011   神奈川県川崎市川崎区富士見**********               S13032   \n\n     application_date     status_cd  \n12           20150228  C-20100421-9  \n68           20150320  A-20091115-7  \n71           20150614  A-20100724-7  \n122          20150128  B-20100329-6  \n144          20150822  B-20081021-3  \n178          20170315  A-20100211-2  \n252          20151013  B-20101018-6  \n259          20160410  C-20100127-8  \n293          20170403  A-20100104-1  \n297          20150727  A-20100103-5  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-016: 店舗データフレーム（df_store）から、電話番号（tel_no）が3桁-3桁-4桁のデータを全項目表示せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_store.query(\"tel_no.str.contains('[0-9]{3}-[0-9]{3}-[0-9]{4}', regex=True)\", engine='python')",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 17,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>store_cd</th>\n      <th>store_name</th>\n      <th>prefecture_cd</th>\n      <th>prefecture</th>\n      <th>address</th>\n      <th>address_kana</th>\n      <th>tel_no</th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>floor_area</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>S12014</td>\n      <td>千草台店</td>\n      <td>12</td>\n      <td>千葉県</td>\n      <td>千葉県千葉市稲毛区千草台一丁目</td>\n      <td>チバケンチバシイナゲクチグサダイイッチョウメ</td>\n      <td>043-123-4003</td>\n      <td>140.1180</td>\n      <td>35.63559</td>\n      <td>1698.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>S13002</td>\n      <td>国分寺店</td>\n      <td>13</td>\n      <td>東京都</td>\n      <td>東京都国分寺市本多二丁目</td>\n      <td>トウキョウトコクブンジシホンダニチョウメ</td>\n      <td>042-123-4008</td>\n      <td>139.4802</td>\n      <td>35.70566</td>\n      <td>1735.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>S14010</td>\n      <td>菊名店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市港北区菊名一丁目</td>\n      <td>カナガワケンヨコハマシコウホククキクナイッチョウメ</td>\n      <td>045-123-4032</td>\n      <td>139.6326</td>\n      <td>35.50049</td>\n      <td>1732.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>S14033</td>\n      <td>阿久和店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市瀬谷区阿久和西一丁目</td>\n      <td>カナガワケンヨコハマシセヤクアクワニシイッチョウメ</td>\n      <td>045-123-4043</td>\n      <td>139.4961</td>\n      <td>35.45918</td>\n      <td>1495.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>S14036</td>\n      <td>相模原中央店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県相模原市中央二丁目</td>\n      <td>カナガワケンサガミハラシチュウオウニチョウメ</td>\n      <td>042-123-4045</td>\n      <td>139.3716</td>\n      <td>35.57327</td>\n      <td>1679.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>S14040</td>\n      <td>長津田店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市緑区長津田みなみ台五丁目</td>\n      <td>カナガワケンヨコハマシミドリクナガツタミナミダイゴチョウメ</td>\n      <td>045-123-4046</td>\n      <td>139.4994</td>\n      <td>35.52398</td>\n      <td>1548.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>S14050</td>\n      <td>阿久和西店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市瀬谷区阿久和西一丁目</td>\n      <td>カナガワケンヨコハマシセヤクアクワニシイッチョウメ</td>\n      <td>045-123-4053</td>\n      <td>139.4961</td>\n      <td>35.45918</td>\n      <td>1830.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>S13052</td>\n      <td>森野店</td>\n      <td>13</td>\n      <td>東京都</td>\n      <td>東京都町田市森野三丁目</td>\n      <td>トウキョウトマチダシモリノサンチョウメ</td>\n      <td>042-123-4030</td>\n      <td>139.4383</td>\n      <td>35.55293</td>\n      <td>1087.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>S14028</td>\n      <td>二ツ橋店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市瀬谷区二ツ橋町</td>\n      <td>カナガワケンヨコハマシセヤクフタツバシチョウ</td>\n      <td>045-123-4042</td>\n      <td>139.4963</td>\n      <td>35.46304</td>\n      <td>1574.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>S14012</td>\n      <td>本牧和田店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市中区本牧和田</td>\n      <td>カナガワケンヨコハマシナカクホンモクワダ</td>\n      <td>045-123-4034</td>\n      <td>139.6582</td>\n      <td>35.42156</td>\n      <td>1341.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>S14046</td>\n      <td>北山田店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市都筑区北山田一丁目</td>\n      <td>カナガワケンヨコハマシツヅキクキタヤマタイッチョウメ</td>\n      <td>045-123-4049</td>\n      <td>139.5916</td>\n      <td>35.56189</td>\n      <td>831.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>S14022</td>\n      <td>逗子店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県逗子市逗子一丁目</td>\n      <td>カナガワケンズシシズシイッチョウメ</td>\n      <td>046-123-4036</td>\n      <td>139.5789</td>\n      <td>35.29642</td>\n      <td>1838.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>S14011</td>\n      <td>日吉本町店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市港北区日吉本町四丁目</td>\n      <td>カナガワケンヨコハマシコウホククヒヨシホンチョウヨンチョウメ</td>\n      <td>045-123-4033</td>\n      <td>139.6316</td>\n      <td>35.54655</td>\n      <td>890.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>S13016</td>\n      <td>小金井店</td>\n      <td>13</td>\n      <td>東京都</td>\n      <td>東京都小金井市本町一丁目</td>\n      <td>トウキョウトコガネイシホンチョウイッチョウメ</td>\n      <td>042-123-4015</td>\n      <td>139.5094</td>\n      <td>35.70018</td>\n      <td>1399.0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>S14034</td>\n      <td>川崎野川店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県川崎市宮前区野川</td>\n      <td>カナガワケンカワサキシミヤマエクノガワ</td>\n      <td>044-123-4044</td>\n      <td>139.5998</td>\n      <td>35.57693</td>\n      <td>1318.0</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>S14048</td>\n      <td>中川中央店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市都筑区中川中央二丁目</td>\n      <td>カナガワケンヨコハマシツヅキクナカガワチュウオウニチョウメ</td>\n      <td>045-123-4051</td>\n      <td>139.5758</td>\n      <td>35.54912</td>\n      <td>1657.0</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>S12007</td>\n      <td>佐倉店</td>\n      <td>12</td>\n      <td>千葉県</td>\n      <td>千葉県佐倉市上志津</td>\n      <td>チバケンサクラシカミシヅ</td>\n      <td>043-123-4001</td>\n      <td>140.1452</td>\n      <td>35.71872</td>\n      <td>1895.0</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>S14026</td>\n      <td>辻堂西海岸店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県藤沢市辻堂西海岸二丁目</td>\n      <td>カナガワケンフジサワシツジドウニシカイガンニチョウメ</td>\n      <td>046-123-4040</td>\n      <td>139.4466</td>\n      <td>35.32464</td>\n      <td>1732.0</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>S13041</td>\n      <td>八王子店</td>\n      <td>13</td>\n      <td>東京都</td>\n      <td>東京都八王子市大塚</td>\n      <td>トウキョウトハチオウジシオオツカ</td>\n      <td>042-123-4026</td>\n      <td>139.4235</td>\n      <td>35.63787</td>\n      <td>810.0</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>S14049</td>\n      <td>川崎大師店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県川崎市川崎区中瀬三丁目</td>\n      <td>カナガワケンカワサキシカワサキクナカゼサンチョウメ</td>\n      <td>044-123-4052</td>\n      <td>139.7327</td>\n      <td>35.53759</td>\n      <td>962.0</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>S14023</td>\n      <td>川崎店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県川崎市川崎区本町二丁目</td>\n      <td>カナガワケンカワサキシカワサキクホンチョウニチョウメ</td>\n      <td>044-123-4037</td>\n      <td>139.7028</td>\n      <td>35.53599</td>\n      <td>1804.0</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>S13018</td>\n      <td>清瀬店</td>\n      <td>13</td>\n      <td>東京都</td>\n      <td>東京都清瀬市松山一丁目</td>\n      <td>トウキョウトキヨセシマツヤマイッチョウメ</td>\n      <td>042-123-4017</td>\n      <td>139.5178</td>\n      <td>35.76885</td>\n      <td>1220.0</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>S14027</td>\n      <td>南藤沢店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県藤沢市南藤沢</td>\n      <td>カナガワケンフジサワシミナミフジサワ</td>\n      <td>046-123-4041</td>\n      <td>139.4896</td>\n      <td>35.33762</td>\n      <td>1521.0</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>S14021</td>\n      <td>伊勢原店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県伊勢原市伊勢原四丁目</td>\n      <td>カナガワケンイセハラシイセハラヨンチョウメ</td>\n      <td>046-123-4035</td>\n      <td>139.3129</td>\n      <td>35.40169</td>\n      <td>962.0</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>S14047</td>\n      <td>相模原店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県相模原市千代田六丁目</td>\n      <td>カナガワケンサガミハラシチヨダロクチョウメ</td>\n      <td>042-123-4050</td>\n      <td>139.3748</td>\n      <td>35.55959</td>\n      <td>1047.0</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>S12013</td>\n      <td>習志野店</td>\n      <td>12</td>\n      <td>千葉県</td>\n      <td>千葉県習志野市芝園一丁目</td>\n      <td>チバケンナラシノシシバゾノイッチョウメ</td>\n      <td>047-123-4002</td>\n      <td>140.0220</td>\n      <td>35.66122</td>\n      <td>808.0</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>S14042</td>\n      <td>新山下店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市中区新山下二丁目</td>\n      <td>カナガワケンヨコハマシナカクシンヤマシタニチョウメ</td>\n      <td>045-123-4047</td>\n      <td>139.6593</td>\n      <td>35.43894</td>\n      <td>1044.0</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>S12030</td>\n      <td>八幡店</td>\n      <td>12</td>\n      <td>千葉県</td>\n      <td>千葉県市川市八幡三丁目</td>\n      <td>チバケンイチカワシヤワタサンチョウメ</td>\n      <td>047-123-4005</td>\n      <td>139.9240</td>\n      <td>35.72318</td>\n      <td>1162.0</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>S14025</td>\n      <td>大和店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県大和市下和田</td>\n      <td>カナガワケンヤマトシシモワダ</td>\n      <td>046-123-4039</td>\n      <td>139.4680</td>\n      <td>35.43414</td>\n      <td>1011.0</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>S14045</td>\n      <td>厚木店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県厚木市中町二丁目</td>\n      <td>カナガワケンアツギシナカチョウニチョウメ</td>\n      <td>046-123-4048</td>\n      <td>139.3651</td>\n      <td>35.44182</td>\n      <td>980.0</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>S12029</td>\n      <td>東野店</td>\n      <td>12</td>\n      <td>千葉県</td>\n      <td>千葉県浦安市東野一丁目</td>\n      <td>チバケンウラヤスシヒガシノイッチョウメ</td>\n      <td>047-123-4004</td>\n      <td>139.8968</td>\n      <td>35.65086</td>\n      <td>1101.0</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>S12053</td>\n      <td>高洲店</td>\n      <td>12</td>\n      <td>千葉県</td>\n      <td>千葉県浦安市高洲五丁目</td>\n      <td>チバケンウラヤスシタカスゴチョウメ</td>\n      <td>047-123-4006</td>\n      <td>139.9176</td>\n      <td>35.63755</td>\n      <td>1555.0</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>S14024</td>\n      <td>三田店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県川崎市多摩区三田四丁目</td>\n      <td>カナガワケンカワサキシタマクミタヨンチョウメ</td>\n      <td>044-123-4038</td>\n      <td>139.5424</td>\n      <td>35.60770</td>\n      <td>972.0</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>S14006</td>\n      <td>葛が谷店</td>\n      <td>14</td>\n      <td>神奈川県</td>\n      <td>神奈川県横浜市都筑区葛が谷</td>\n      <td>カナガワケンヨコハマシツヅキククズガヤ</td>\n      <td>045-123-4031</td>\n      <td>139.5633</td>\n      <td>35.53573</td>\n      <td>1886.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   store_cd store_name  prefecture_cd prefecture              address  \\\n0    S12014       千草台店             12        千葉県      千葉県千葉市稲毛区千草台一丁目   \n1    S13002       国分寺店             13        東京都         東京都国分寺市本多二丁目   \n2    S14010        菊名店             14       神奈川県      神奈川県横浜市港北区菊名一丁目   \n3    S14033       阿久和店             14       神奈川県    神奈川県横浜市瀬谷区阿久和西一丁目   \n4    S14036     相模原中央店             14       神奈川県        神奈川県相模原市中央二丁目   \n7    S14040       長津田店             14       神奈川県  神奈川県横浜市緑区長津田みなみ台五丁目   \n9    S14050      阿久和西店             14       神奈川県    神奈川県横浜市瀬谷区阿久和西一丁目   \n11   S13052        森野店             13        東京都          東京都町田市森野三丁目   \n12   S14028       二ツ橋店             14       神奈川県       神奈川県横浜市瀬谷区二ツ橋町   \n16   S14012      本牧和田店             14       神奈川県        神奈川県横浜市中区本牧和田   \n18   S14046       北山田店             14       神奈川県     神奈川県横浜市都筑区北山田一丁目   \n19   S14022        逗子店             14       神奈川県         神奈川県逗子市逗子一丁目   \n20   S14011      日吉本町店             14       神奈川県    神奈川県横浜市港北区日吉本町四丁目   \n21   S13016       小金井店             13        東京都         東京都小金井市本町一丁目   \n22   S14034      川崎野川店             14       神奈川県         神奈川県川崎市宮前区野川   \n26   S14048      中川中央店             14       神奈川県    神奈川県横浜市都筑区中川中央二丁目   \n27   S12007        佐倉店             12        千葉県            千葉県佐倉市上志津   \n28   S14026     辻堂西海岸店             14       神奈川県      神奈川県藤沢市辻堂西海岸二丁目   \n29   S13041       八王子店             13        東京都            東京都八王子市大塚   \n31   S14049      川崎大師店             14       神奈川県      神奈川県川崎市川崎区中瀬三丁目   \n32   S14023        川崎店             14       神奈川県      神奈川県川崎市川崎区本町二丁目   \n33   S13018        清瀬店             13        東京都          東京都清瀬市松山一丁目   \n35   S14027       南藤沢店             14       神奈川県           神奈川県藤沢市南藤沢   \n36   S14021       伊勢原店             14       神奈川県       神奈川県伊勢原市伊勢原四丁目   \n37   S14047       相模原店             14       神奈川県       神奈川県相模原市千代田六丁目   \n38   S12013       習志野店             12        千葉県         千葉県習志野市芝園一丁目   \n40   S14042       新山下店             14       神奈川県      神奈川県横浜市中区新山下二丁目   \n42   S12030        八幡店             12        千葉県          千葉県市川市八幡三丁目   \n44   S14025        大和店             14       神奈川県           神奈川県大和市下和田   \n45   S14045        厚木店             14       神奈川県         神奈川県厚木市中町二丁目   \n47   S12029        東野店             12        千葉県          千葉県浦安市東野一丁目   \n49   S12053        高洲店             12        千葉県          千葉県浦安市高洲五丁目   \n51   S14024        三田店             14       神奈川県      神奈川県川崎市多摩区三田四丁目   \n52   S14006       葛が谷店             14       神奈川県        神奈川県横浜市都筑区葛が谷   \n\n                      address_kana        tel_no  longitude  latitude  \\\n0           チバケンチバシイナゲクチグサダイイッチョウメ  043-123-4003   140.1180  35.63559   \n1             トウキョウトコクブンジシホンダニチョウメ  042-123-4008   139.4802  35.70566   \n2        カナガワケンヨコハマシコウホククキクナイッチョウメ  045-123-4032   139.6326  35.50049   \n3        カナガワケンヨコハマシセヤクアクワニシイッチョウメ  045-123-4043   139.4961  35.45918   \n4           カナガワケンサガミハラシチュウオウニチョウメ  042-123-4045   139.3716  35.57327   \n7    カナガワケンヨコハマシミドリクナガツタミナミダイゴチョウメ  045-123-4046   139.4994  35.52398   \n9        カナガワケンヨコハマシセヤクアクワニシイッチョウメ  045-123-4053   139.4961  35.45918   \n11             トウキョウトマチダシモリノサンチョウメ  042-123-4030   139.4383  35.55293   \n12          カナガワケンヨコハマシセヤクフタツバシチョウ  045-123-4042   139.4963  35.46304   \n16            カナガワケンヨコハマシナカクホンモクワダ  045-123-4034   139.6582  35.42156   \n18      カナガワケンヨコハマシツヅキクキタヤマタイッチョウメ  045-123-4049   139.5916  35.56189   \n19               カナガワケンズシシズシイッチョウメ  046-123-4036   139.5789  35.29642   \n20  カナガワケンヨコハマシコウホククヒヨシホンチョウヨンチョウメ  045-123-4033   139.6316  35.54655   \n21          トウキョウトコガネイシホンチョウイッチョウメ  042-123-4015   139.5094  35.70018   \n22             カナガワケンカワサキシミヤマエクノガワ  044-123-4044   139.5998  35.57693   \n26   カナガワケンヨコハマシツヅキクナカガワチュウオウニチョウメ  045-123-4051   139.5758  35.54912   \n27                    チバケンサクラシカミシヅ  043-123-4001   140.1452  35.71872   \n28      カナガワケンフジサワシツジドウニシカイガンニチョウメ  046-123-4040   139.4466  35.32464   \n29                トウキョウトハチオウジシオオツカ  042-123-4026   139.4235  35.63787   \n31       カナガワケンカワサキシカワサキクナカゼサンチョウメ  044-123-4052   139.7327  35.53759   \n32      カナガワケンカワサキシカワサキクホンチョウニチョウメ  044-123-4037   139.7028  35.53599   \n33            トウキョウトキヨセシマツヤマイッチョウメ  042-123-4017   139.5178  35.76885   \n35              カナガワケンフジサワシミナミフジサワ  046-123-4041   139.4896  35.33762   \n36           カナガワケンイセハラシイセハラヨンチョウメ  046-123-4035   139.3129  35.40169   \n37           カナガワケンサガミハラシチヨダロクチョウメ  042-123-4050   139.3748  35.55959   \n38             チバケンナラシノシシバゾノイッチョウメ  047-123-4002   140.0220  35.66122   \n40       カナガワケンヨコハマシナカクシンヤマシタニチョウメ  045-123-4047   139.6593  35.43894   \n42              チバケンイチカワシヤワタサンチョウメ  047-123-4005   139.9240  35.72318   \n44                  カナガワケンヤマトシシモワダ  046-123-4039   139.4680  35.43414   \n45            カナガワケンアツギシナカチョウニチョウメ  046-123-4048   139.3651  35.44182   \n47             チバケンウラヤスシヒガシノイッチョウメ  047-123-4004   139.8968  35.65086   \n49               チバケンウラヤスシタカスゴチョウメ  047-123-4006   139.9176  35.63755   \n51          カナガワケンカワサキシタマクミタヨンチョウメ  044-123-4038   139.5424  35.60770   \n52             カナガワケンヨコハマシツヅキククズガヤ  045-123-4031   139.5633  35.53573   \n\n    floor_area  \n0       1698.0  \n1       1735.0  \n2       1732.0  \n3       1495.0  \n4       1679.0  \n7       1548.0  \n9       1830.0  \n11      1087.0  \n12      1574.0  \n16      1341.0  \n18       831.0  \n19      1838.0  \n20       890.0  \n21      1399.0  \n22      1318.0  \n26      1657.0  \n27      1895.0  \n28      1732.0  \n29       810.0  \n31       962.0  \n32      1804.0  \n33      1220.0  \n35      1521.0  \n36       962.0  \n37      1047.0  \n38       808.0  \n40      1044.0  \n42      1162.0  \n44      1011.0  \n45       980.0  \n47      1101.0  \n49      1555.0  \n51       972.0  \n52      1886.0  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-17: 顧客データフレーム（df_customer）を生年月日（birth_day）で高齢順にソートし、先頭10件を全項目表示せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_customer.sort_values('birth_day', ascending=True).head(10)",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 18,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>customer_name</th>\n      <th>gender_cd</th>\n      <th>gender</th>\n      <th>birth_day</th>\n      <th>age</th>\n      <th>postal_cd</th>\n      <th>address</th>\n      <th>application_store_cd</th>\n      <th>application_date</th>\n      <th>status_cd</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>18817</th>\n      <td>CS003813000014</td>\n      <td>村山 菜々美</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1928-11-26</td>\n      <td>90</td>\n      <td>182-0007</td>\n      <td>東京都調布市菊野台**********</td>\n      <td>S13003</td>\n      <td>20160214</td>\n      <td>0-00000000-0</td>\n    </tr>\n    <tr>\n      <th>12328</th>\n      <td>CS026813000004</td>\n      <td>吉村 朝陽</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1928-12-14</td>\n      <td>90</td>\n      <td>251-0043</td>\n      <td>神奈川県藤沢市辻堂元町**********</td>\n      <td>S14026</td>\n      <td>20150723</td>\n      <td>0-00000000-0</td>\n    </tr>\n    <tr>\n      <th>15682</th>\n      <td>CS018811000003</td>\n      <td>熊沢 美里</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1929-01-07</td>\n      <td>90</td>\n      <td>204-0004</td>\n      <td>東京都清瀬市野塩**********</td>\n      <td>S13018</td>\n      <td>20150403</td>\n      <td>0-00000000-0</td>\n    </tr>\n    <tr>\n      <th>15302</th>\n      <td>CS027803000004</td>\n      <td>内村 拓郎</td>\n      <td>0</td>\n      <td>男性</td>\n      <td>1929-01-12</td>\n      <td>90</td>\n      <td>251-0031</td>\n      <td>神奈川県藤沢市鵠沼藤が谷**********</td>\n      <td>S14027</td>\n      <td>20151227</td>\n      <td>0-00000000-0</td>\n    </tr>\n    <tr>\n      <th>1681</th>\n      <td>CS013801000003</td>\n      <td>天野 拓郎</td>\n      <td>0</td>\n      <td>男性</td>\n      <td>1929-01-15</td>\n      <td>90</td>\n      <td>274-0824</td>\n      <td>千葉県船橋市前原東**********</td>\n      <td>S12013</td>\n      <td>20160120</td>\n      <td>0-00000000-0</td>\n    </tr>\n    <tr>\n      <th>7511</th>\n      <td>CS001814000022</td>\n      <td>鶴田 里穂</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1929-01-28</td>\n      <td>90</td>\n      <td>144-0045</td>\n      <td>東京都大田区南六郷**********</td>\n      <td>S13001</td>\n      <td>20161012</td>\n      <td>A-20090415-7</td>\n    </tr>\n    <tr>\n      <th>2378</th>\n      <td>CS016815000002</td>\n      <td>山元 美紀</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1929-02-22</td>\n      <td>90</td>\n      <td>184-0005</td>\n      <td>東京都小金井市桜町**********</td>\n      <td>S13016</td>\n      <td>20150629</td>\n      <td>C-20090923-C</td>\n    </tr>\n    <tr>\n      <th>4680</th>\n      <td>CS009815000003</td>\n      <td>中田 里穂</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1929-04-08</td>\n      <td>89</td>\n      <td>154-0014</td>\n      <td>東京都世田谷区新町**********</td>\n      <td>S13009</td>\n      <td>20150421</td>\n      <td>D-20091021-E</td>\n    </tr>\n    <tr>\n      <th>16070</th>\n      <td>CS005813000015</td>\n      <td>金谷 恵梨香</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1929-04-09</td>\n      <td>89</td>\n      <td>165-0032</td>\n      <td>東京都中野区鷺宮**********</td>\n      <td>S13005</td>\n      <td>20150506</td>\n      <td>0-00000000-0</td>\n    </tr>\n    <tr>\n      <th>6305</th>\n      <td>CS012813000013</td>\n      <td>宇野 南朋</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1929-04-09</td>\n      <td>89</td>\n      <td>231-0806</td>\n      <td>神奈川県横浜市中区本牧町**********</td>\n      <td>S14012</td>\n      <td>20150712</td>\n      <td>0-00000000-0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "          customer_id customer_name  gender_cd gender   birth_day  age  \\\n18817  CS003813000014        村山 菜々美          1     女性  1928-11-26   90   \n12328  CS026813000004         吉村 朝陽          1     女性  1928-12-14   90   \n15682  CS018811000003         熊沢 美里          1     女性  1929-01-07   90   \n15302  CS027803000004         内村 拓郎          0     男性  1929-01-12   90   \n1681   CS013801000003         天野 拓郎          0     男性  1929-01-15   90   \n7511   CS001814000022         鶴田 里穂          1     女性  1929-01-28   90   \n2378   CS016815000002         山元 美紀          1     女性  1929-02-22   90   \n4680   CS009815000003         中田 里穂          1     女性  1929-04-08   89   \n16070  CS005813000015        金谷 恵梨香          1     女性  1929-04-09   89   \n6305   CS012813000013         宇野 南朋          1     女性  1929-04-09   89   \n\n      postal_cd                 address application_store_cd  \\\n18817  182-0007     東京都調布市菊野台**********               S13003   \n12328  251-0043   神奈川県藤沢市辻堂元町**********               S14026   \n15682  204-0004      東京都清瀬市野塩**********               S13018   \n15302  251-0031  神奈川県藤沢市鵠沼藤が谷**********               S14027   \n1681   274-0824     千葉県船橋市前原東**********               S12013   \n7511   144-0045     東京都大田区南六郷**********               S13001   \n2378   184-0005     東京都小金井市桜町**********               S13016   \n4680   154-0014     東京都世田谷区新町**********               S13009   \n16070  165-0032      東京都中野区鷺宮**********               S13005   \n6305   231-0806  神奈川県横浜市中区本牧町**********               S14012   \n\n       application_date     status_cd  \n18817          20160214  0-00000000-0  \n12328          20150723  0-00000000-0  \n15682          20150403  0-00000000-0  \n15302          20151227  0-00000000-0  \n1681           20160120  0-00000000-0  \n7511           20161012  A-20090415-7  \n2378           20150629  C-20090923-C  \n4680           20150421  D-20091021-E  \n16070          20150506  0-00000000-0  \n6305           20150712  0-00000000-0  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-18: 顧客データフレーム（df_customer）を生年月日（birth_day）で若い順にソートし、先頭10件を全項目表示せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_customer.sort_values('birth_day', ascending=False).head(10)",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 19,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>customer_name</th>\n      <th>gender_cd</th>\n      <th>gender</th>\n      <th>birth_day</th>\n      <th>age</th>\n      <th>postal_cd</th>\n      <th>address</th>\n      <th>application_store_cd</th>\n      <th>application_date</th>\n      <th>status_cd</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>15639</th>\n      <td>CS035114000004</td>\n      <td>大村 美里</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>2007-11-25</td>\n      <td>11</td>\n      <td>156-0053</td>\n      <td>東京都世田谷区桜**********</td>\n      <td>S13035</td>\n      <td>20150619</td>\n      <td>6-20091205-6</td>\n    </tr>\n    <tr>\n      <th>7468</th>\n      <td>CS022103000002</td>\n      <td>福山 はじめ</td>\n      <td>9</td>\n      <td>不明</td>\n      <td>2007-10-02</td>\n      <td>11</td>\n      <td>249-0006</td>\n      <td>神奈川県逗子市逗子**********</td>\n      <td>S14022</td>\n      <td>20160909</td>\n      <td>0-00000000-0</td>\n    </tr>\n    <tr>\n      <th>10745</th>\n      <td>CS002113000009</td>\n      <td>柴田 真悠子</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>2007-09-17</td>\n      <td>11</td>\n      <td>184-0014</td>\n      <td>東京都小金井市貫井南町**********</td>\n      <td>S13002</td>\n      <td>20160304</td>\n      <td>0-00000000-0</td>\n    </tr>\n    <tr>\n      <th>19811</th>\n      <td>CS004115000014</td>\n      <td>松井 京子</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>2007-08-09</td>\n      <td>11</td>\n      <td>165-0031</td>\n      <td>東京都中野区上鷺宮**********</td>\n      <td>S13004</td>\n      <td>20161120</td>\n      <td>1-20081231-1</td>\n    </tr>\n    <tr>\n      <th>7039</th>\n      <td>CS002114000010</td>\n      <td>山内 遥</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>2007-06-03</td>\n      <td>11</td>\n      <td>184-0015</td>\n      <td>東京都小金井市貫井北町**********</td>\n      <td>S13002</td>\n      <td>20160920</td>\n      <td>6-20100510-1</td>\n    </tr>\n    <tr>\n      <th>3670</th>\n      <td>CS025115000002</td>\n      <td>小柳 夏希</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>2007-04-18</td>\n      <td>11</td>\n      <td>245-0018</td>\n      <td>神奈川県横浜市泉区上飯田町**********</td>\n      <td>S14025</td>\n      <td>20160116</td>\n      <td>D-20100913-D</td>\n    </tr>\n    <tr>\n      <th>12493</th>\n      <td>CS002113000025</td>\n      <td>広末 まなみ</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>2007-03-30</td>\n      <td>12</td>\n      <td>184-0015</td>\n      <td>東京都小金井市貫井北町**********</td>\n      <td>S13002</td>\n      <td>20171030</td>\n      <td>0-00000000-0</td>\n    </tr>\n    <tr>\n      <th>15977</th>\n      <td>CS033112000003</td>\n      <td>長野 美紀</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>2007-03-22</td>\n      <td>12</td>\n      <td>245-0051</td>\n      <td>神奈川県横浜市戸塚区名瀬町**********</td>\n      <td>S14033</td>\n      <td>20150606</td>\n      <td>0-00000000-0</td>\n    </tr>\n    <tr>\n      <th>5716</th>\n      <td>CS007115000006</td>\n      <td>福岡 瞬</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>2007-03-10</td>\n      <td>12</td>\n      <td>285-0845</td>\n      <td>千葉県佐倉市西志津**********</td>\n      <td>S12007</td>\n      <td>20151118</td>\n      <td>F-20101016-F</td>\n    </tr>\n    <tr>\n      <th>15097</th>\n      <td>CS014113000008</td>\n      <td>矢口 莉緒</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>2007-03-05</td>\n      <td>12</td>\n      <td>260-0041</td>\n      <td>千葉県千葉市中央区東千葉**********</td>\n      <td>S12014</td>\n      <td>20150622</td>\n      <td>3-20091108-6</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "          customer_id customer_name  gender_cd gender   birth_day  age  \\\n15639  CS035114000004         大村 美里          1     女性  2007-11-25   11   \n7468   CS022103000002        福山 はじめ          9     不明  2007-10-02   11   \n10745  CS002113000009        柴田 真悠子          1     女性  2007-09-17   11   \n19811  CS004115000014         松井 京子          1     女性  2007-08-09   11   \n7039   CS002114000010          山内 遥          1     女性  2007-06-03   11   \n3670   CS025115000002         小柳 夏希          1     女性  2007-04-18   11   \n12493  CS002113000025        広末 まなみ          1     女性  2007-03-30   12   \n15977  CS033112000003         長野 美紀          1     女性  2007-03-22   12   \n5716   CS007115000006          福岡 瞬          1     女性  2007-03-10   12   \n15097  CS014113000008         矢口 莉緒          1     女性  2007-03-05   12   \n\n      postal_cd                  address application_store_cd  \\\n15639  156-0053       東京都世田谷区桜**********               S13035   \n7468   249-0006      神奈川県逗子市逗子**********               S14022   \n10745  184-0014    東京都小金井市貫井南町**********               S13002   \n19811  165-0031      東京都中野区上鷺宮**********               S13004   \n7039   184-0015    東京都小金井市貫井北町**********               S13002   \n3670   245-0018  神奈川県横浜市泉区上飯田町**********               S14025   \n12493  184-0015    東京都小金井市貫井北町**********               S13002   \n15977  245-0051  神奈川県横浜市戸塚区名瀬町**********               S14033   \n5716   285-0845      千葉県佐倉市西志津**********               S12007   \n15097  260-0041   千葉県千葉市中央区東千葉**********               S12014   \n\n       application_date     status_cd  \n15639          20150619  6-20091205-6  \n7468           20160909  0-00000000-0  \n10745          20160304  0-00000000-0  \n19811          20161120  1-20081231-1  \n7039           20160920  6-20100510-1  \n3670           20160116  D-20100913-D  \n12493          20171030  0-00000000-0  \n15977          20150606  0-00000000-0  \n5716           20151118  F-20101016-F  \n15097          20150622  3-20091108-6  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-19: レシート明細データフレーム（df_receipt）に対し、1件あたりの売上金額（amount）が高い順にランクを付与し、先頭10件を抽出せよ。項目は顧客ID（customer_id）、売上金額（amount）、付与したランクを表示させること。なお、売上金額（amount）が等しい場合は同一順位を付与するものとする。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = pd.concat([df_receipt[['customer_id', 'amount']] \n                     ,df_receipt['amount'].rank(method='min', ascending=False)], axis=1)\ndf_tmp.columns = ['customer_id', 'amount', 'ranking']\ndf_tmp.sort_values('ranking', ascending=True).head(10)",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 20,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>amount</th>\n      <th>ranking</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1202</th>\n      <td>CS011415000006</td>\n      <td>10925</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>62317</th>\n      <td>ZZ000000000000</td>\n      <td>6800</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>54095</th>\n      <td>CS028605000002</td>\n      <td>5780</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>4632</th>\n      <td>CS015515000034</td>\n      <td>5480</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>72747</th>\n      <td>ZZ000000000000</td>\n      <td>5480</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>10320</th>\n      <td>ZZ000000000000</td>\n      <td>5480</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>97294</th>\n      <td>CS021515000089</td>\n      <td>5440</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>28304</th>\n      <td>ZZ000000000000</td>\n      <td>5440</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>92246</th>\n      <td>CS009415000038</td>\n      <td>5280</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>68553</th>\n      <td>CS040415000200</td>\n      <td>5280</td>\n      <td>9.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "          customer_id  amount  ranking\n1202   CS011415000006   10925      1.0\n62317  ZZ000000000000    6800      2.0\n54095  CS028605000002    5780      3.0\n4632   CS015515000034    5480      4.0\n72747  ZZ000000000000    5480      4.0\n10320  ZZ000000000000    5480      4.0\n97294  CS021515000089    5440      7.0\n28304  ZZ000000000000    5440      7.0\n92246  CS009415000038    5280      9.0\n68553  CS040415000200    5280      9.0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-020: レシート明細データフレーム（df_receipt）に対し、1件あたりの売上金額（amount）が高い順にランクを付与し、先頭10件を抽出せよ。項目は顧客ID（customer_id）、売上金額（amount）、付与したランクを表示させること。なお、売上金額（amount）が等しい場合でも別順位を付与すること。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = pd.concat([df_receipt[['customer_id', 'amount']] \n                     ,df_receipt['amount'].rank(method='first', ascending=False)], axis=1)\ndf_tmp.columns = ['customer_id', 'amount', 'ranking']\ndf_tmp.sort_values('ranking', ascending=True).head(10)",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>amount</th>\n      <th>ranking</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1202</th>\n      <td>CS011415000006</td>\n      <td>10925</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>62317</th>\n      <td>ZZ000000000000</td>\n      <td>6800</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>54095</th>\n      <td>CS028605000002</td>\n      <td>5780</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>4632</th>\n      <td>CS015515000034</td>\n      <td>5480</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>10320</th>\n      <td>ZZ000000000000</td>\n      <td>5480</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>72747</th>\n      <td>ZZ000000000000</td>\n      <td>5480</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th>28304</th>\n      <td>ZZ000000000000</td>\n      <td>5440</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>97294</th>\n      <td>CS021515000089</td>\n      <td>5440</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>596</th>\n      <td>CS015515000083</td>\n      <td>5280</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>11275</th>\n      <td>CS017414000114</td>\n      <td>5280</td>\n      <td>10.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "          customer_id  amount  ranking\n1202   CS011415000006   10925      1.0\n62317  ZZ000000000000    6800      2.0\n54095  CS028605000002    5780      3.0\n4632   CS015515000034    5480      4.0\n10320  ZZ000000000000    5480      5.0\n72747  ZZ000000000000    5480      6.0\n28304  ZZ000000000000    5440      7.0\n97294  CS021515000089    5440      8.0\n596    CS015515000083    5280      9.0\n11275  CS017414000114    5280     10.0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-021: レシート明細データフレーム（df_receipt）に対し、件数をカウントせよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "len(df_receipt)",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 22,
          "data": {
            "text/plain": "104681"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-022: レシート明細データフレーム（df_receipt）の顧客ID（customer_id）に対し、ユニーク件数をカウントせよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "len(df_receipt['customer_id'].unique())",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 23,
          "data": {
            "text/plain": "8307"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-023: レシート明細データフレーム（df_receipt）に対し、店舗コード（store_cd）ごとに売上金額（amount）と売上数量（quantity）を合計せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_receipt.groupby('store_cd').agg({'amount':'sum', 'quantity':'sum'}).reset_index()",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 24,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>store_cd</th>\n      <th>amount</th>\n      <th>quantity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>S12007</td>\n      <td>638761</td>\n      <td>2099</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>S12013</td>\n      <td>787513</td>\n      <td>2425</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>S12014</td>\n      <td>725167</td>\n      <td>2358</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>S12029</td>\n      <td>794741</td>\n      <td>2555</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>S12030</td>\n      <td>684402</td>\n      <td>2403</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>S13001</td>\n      <td>811936</td>\n      <td>2347</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>S13002</td>\n      <td>727821</td>\n      <td>2340</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>S13003</td>\n      <td>764294</td>\n      <td>2197</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>S13004</td>\n      <td>779373</td>\n      <td>2390</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>S13005</td>\n      <td>629876</td>\n      <td>2004</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>S13008</td>\n      <td>809288</td>\n      <td>2491</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>S13009</td>\n      <td>808870</td>\n      <td>2486</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>S13015</td>\n      <td>780873</td>\n      <td>2248</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>S13016</td>\n      <td>793773</td>\n      <td>2432</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>S13017</td>\n      <td>748221</td>\n      <td>2376</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>S13018</td>\n      <td>790535</td>\n      <td>2562</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>S13019</td>\n      <td>827833</td>\n      <td>2541</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>S13020</td>\n      <td>796383</td>\n      <td>2383</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>S13031</td>\n      <td>705968</td>\n      <td>2336</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>S13032</td>\n      <td>790501</td>\n      <td>2491</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>S13035</td>\n      <td>715869</td>\n      <td>2219</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>S13037</td>\n      <td>693087</td>\n      <td>2344</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>S13038</td>\n      <td>708884</td>\n      <td>2337</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>S13039</td>\n      <td>611888</td>\n      <td>1981</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>S13041</td>\n      <td>728266</td>\n      <td>2233</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>S13043</td>\n      <td>587895</td>\n      <td>1881</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>S13044</td>\n      <td>520764</td>\n      <td>1729</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>S13051</td>\n      <td>107452</td>\n      <td>354</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>S13052</td>\n      <td>100314</td>\n      <td>250</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>S14006</td>\n      <td>712839</td>\n      <td>2284</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>S14010</td>\n      <td>790361</td>\n      <td>2290</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>S14011</td>\n      <td>805724</td>\n      <td>2434</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>S14012</td>\n      <td>720600</td>\n      <td>2412</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>S14021</td>\n      <td>699511</td>\n      <td>2231</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>S14022</td>\n      <td>651328</td>\n      <td>2047</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>S14023</td>\n      <td>727630</td>\n      <td>2258</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>S14024</td>\n      <td>736323</td>\n      <td>2417</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>S14025</td>\n      <td>755581</td>\n      <td>2394</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>S14026</td>\n      <td>824537</td>\n      <td>2503</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>S14027</td>\n      <td>714550</td>\n      <td>2303</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>S14028</td>\n      <td>786145</td>\n      <td>2458</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>S14033</td>\n      <td>725318</td>\n      <td>2282</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>S14034</td>\n      <td>653681</td>\n      <td>2024</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>S14036</td>\n      <td>203694</td>\n      <td>635</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>S14040</td>\n      <td>701858</td>\n      <td>2233</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>S14042</td>\n      <td>534689</td>\n      <td>1935</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>S14045</td>\n      <td>458484</td>\n      <td>1398</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>S14046</td>\n      <td>412646</td>\n      <td>1354</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>S14047</td>\n      <td>338329</td>\n      <td>1041</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>S14048</td>\n      <td>234276</td>\n      <td>769</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>S14049</td>\n      <td>230808</td>\n      <td>788</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>S14050</td>\n      <td>167090</td>\n      <td>580</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   store_cd  amount  quantity\n0    S12007  638761      2099\n1    S12013  787513      2425\n2    S12014  725167      2358\n3    S12029  794741      2555\n4    S12030  684402      2403\n5    S13001  811936      2347\n6    S13002  727821      2340\n7    S13003  764294      2197\n8    S13004  779373      2390\n9    S13005  629876      2004\n10   S13008  809288      2491\n11   S13009  808870      2486\n12   S13015  780873      2248\n13   S13016  793773      2432\n14   S13017  748221      2376\n15   S13018  790535      2562\n16   S13019  827833      2541\n17   S13020  796383      2383\n18   S13031  705968      2336\n19   S13032  790501      2491\n20   S13035  715869      2219\n21   S13037  693087      2344\n22   S13038  708884      2337\n23   S13039  611888      1981\n24   S13041  728266      2233\n25   S13043  587895      1881\n26   S13044  520764      1729\n27   S13051  107452       354\n28   S13052  100314       250\n29   S14006  712839      2284\n30   S14010  790361      2290\n31   S14011  805724      2434\n32   S14012  720600      2412\n33   S14021  699511      2231\n34   S14022  651328      2047\n35   S14023  727630      2258\n36   S14024  736323      2417\n37   S14025  755581      2394\n38   S14026  824537      2503\n39   S14027  714550      2303\n40   S14028  786145      2458\n41   S14033  725318      2282\n42   S14034  653681      2024\n43   S14036  203694       635\n44   S14040  701858      2233\n45   S14042  534689      1935\n46   S14045  458484      1398\n47   S14046  412646      1354\n48   S14047  338329      1041\n49   S14048  234276       769\n50   S14049  230808       788\n51   S14050  167090       580"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-024: レシート明細データフレーム（df_receipt）に対し、顧客ID（customer_id）ごとに最も新しい売上日（sales_ymd）を求め、10件表示せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_receipt.groupby('customer_id').sales_ymd.max().reset_index().head(10)",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 25,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>sales_ymd</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS001113000004</td>\n      <td>20190308</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS001114000005</td>\n      <td>20190731</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS001115000010</td>\n      <td>20190405</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS001205000004</td>\n      <td>20190625</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001205000006</td>\n      <td>20190224</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS001211000025</td>\n      <td>20190322</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS001212000027</td>\n      <td>20170127</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS001212000031</td>\n      <td>20180906</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS001212000046</td>\n      <td>20170811</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS001212000070</td>\n      <td>20191018</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id  sales_ymd\n0  CS001113000004   20190308\n1  CS001114000005   20190731\n2  CS001115000010   20190405\n3  CS001205000004   20190625\n4  CS001205000006   20190224\n5  CS001211000025   20190322\n6  CS001212000027   20170127\n7  CS001212000031   20180906\n8  CS001212000046   20170811\n9  CS001212000070   20191018"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-025: レシート明細データフレーム（df_receipt）に対し、顧客ID（customer_id）ごとに最も古い売上日（sales_ymd）を求め、10件表示せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_receipt.groupby('customer_id').agg({'sales_ymd':'min'}).head(10)",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 26,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sales_ymd</th>\n    </tr>\n    <tr>\n      <th>customer_id</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>CS001113000004</th>\n      <td>20190308</td>\n    </tr>\n    <tr>\n      <th>CS001114000005</th>\n      <td>20180503</td>\n    </tr>\n    <tr>\n      <th>CS001115000010</th>\n      <td>20171228</td>\n    </tr>\n    <tr>\n      <th>CS001205000004</th>\n      <td>20170914</td>\n    </tr>\n    <tr>\n      <th>CS001205000006</th>\n      <td>20180207</td>\n    </tr>\n    <tr>\n      <th>CS001211000025</th>\n      <td>20190322</td>\n    </tr>\n    <tr>\n      <th>CS001212000027</th>\n      <td>20170127</td>\n    </tr>\n    <tr>\n      <th>CS001212000031</th>\n      <td>20180906</td>\n    </tr>\n    <tr>\n      <th>CS001212000046</th>\n      <td>20170811</td>\n    </tr>\n    <tr>\n      <th>CS001212000070</th>\n      <td>20191018</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                sales_ymd\ncustomer_id              \nCS001113000004   20190308\nCS001114000005   20180503\nCS001115000010   20171228\nCS001205000004   20170914\nCS001205000006   20180207\nCS001211000025   20190322\nCS001212000027   20170127\nCS001212000031   20180906\nCS001212000046   20170811\nCS001212000070   20191018"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-026: レシート明細データフレーム（df_receipt）に対し、顧客ID（customer_id）ごとに最も新しい売上日（sales_ymd）と古い売上日を求め、両者が異なるデータを10件表示せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = df_receipt.groupby('customer_id').agg({'sales_ymd':['max','min']}).reset_index()\ndf_tmp.columns = [\"_\".join(pair) for pair in df_tmp.columns]\ndf_tmp.query('sales_ymd_max != sales_ymd_min').head(10)",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 27,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id_</th>\n      <th>sales_ymd_max</th>\n      <th>sales_ymd_min</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>CS001114000005</td>\n      <td>20190731</td>\n      <td>20180503</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS001115000010</td>\n      <td>20190405</td>\n      <td>20171228</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS001205000004</td>\n      <td>20190625</td>\n      <td>20170914</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001205000006</td>\n      <td>20190224</td>\n      <td>20180207</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>CS001214000009</td>\n      <td>20190902</td>\n      <td>20170306</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>CS001214000017</td>\n      <td>20191006</td>\n      <td>20180828</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>CS001214000048</td>\n      <td>20190929</td>\n      <td>20171109</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>CS001214000052</td>\n      <td>20190617</td>\n      <td>20180208</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>CS001215000005</td>\n      <td>20181021</td>\n      <td>20170206</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>CS001215000040</td>\n      <td>20171022</td>\n      <td>20170214</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id_  sales_ymd_max  sales_ymd_min\n1   CS001114000005       20190731       20180503\n2   CS001115000010       20190405       20171228\n3   CS001205000004       20190625       20170914\n4   CS001205000006       20190224       20180207\n13  CS001214000009       20190902       20170306\n14  CS001214000017       20191006       20180828\n16  CS001214000048       20190929       20171109\n17  CS001214000052       20190617       20180208\n20  CS001215000005       20181021       20170206\n21  CS001215000040       20171022       20170214"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-027: レシート明細データフレーム（df_receipt）に対し、店舗コード（store_cd）ごとに売上金額（amount）の平均を計算し、降順でTOP5を表示せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_receipt.groupby('store_cd').agg({'amount':'mean'}).reset_index().sort_values('amount', ascending=False).head(5)",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 28,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>store_cd</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>28</th>\n      <td>S13052</td>\n      <td>402.867470</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>S13015</td>\n      <td>351.111960</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>S13003</td>\n      <td>350.915519</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>S14010</td>\n      <td>348.791262</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>S13001</td>\n      <td>348.470386</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   store_cd      amount\n28   S13052  402.867470\n12   S13015  351.111960\n7    S13003  350.915519\n30   S14010  348.791262\n5    S13001  348.470386"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-028: レシート明細データフレーム（df_receipt）に対し、店舗コード（store_cd）ごとに売上金額（amount）の中央値を計算し、降順でTOP5を表示せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_receipt.groupby('store_cd').agg({'amount':'median'}).reset_index().sort_values('amount', ascending=False).head(5)",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 29,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>store_cd</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>28</th>\n      <td>S13052</td>\n      <td>190</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>S14010</td>\n      <td>188</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>S14050</td>\n      <td>185</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>S14040</td>\n      <td>180</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>S13003</td>\n      <td>180</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   store_cd  amount\n28   S13052     190\n30   S14010     188\n51   S14050     185\n44   S14040     180\n7    S13003     180"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-029: レシート明細データフレーム（df_receipt）に対し、店舗コード（store_cd）ごとに商品コード（product_cd）の最頻値を求めよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_receipt.groupby('store_cd').product_cd.apply(lambda x: x.mode()).reset_index()",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 30,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>store_cd</th>\n      <th>level_1</th>\n      <th>product_cd</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>S12007</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>S12013</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>S12014</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>S12029</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>S12030</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>S13001</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>S13002</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>S13003</td>\n      <td>0</td>\n      <td>P071401001</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>S13004</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>S13005</td>\n      <td>0</td>\n      <td>P040503001</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>S13008</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>S13009</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>S13015</td>\n      <td>0</td>\n      <td>P071401001</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>S13016</td>\n      <td>0</td>\n      <td>P071102001</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>S13017</td>\n      <td>0</td>\n      <td>P060101002</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>S13018</td>\n      <td>0</td>\n      <td>P071401001</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>S13019</td>\n      <td>0</td>\n      <td>P071401001</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>S13020</td>\n      <td>0</td>\n      <td>P071401001</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>S13031</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>S13032</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>S13035</td>\n      <td>0</td>\n      <td>P040503001</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>S13037</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>S13038</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>S13039</td>\n      <td>0</td>\n      <td>P071401001</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>S13041</td>\n      <td>0</td>\n      <td>P071401001</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>S13043</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>S13044</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>S13051</td>\n      <td>0</td>\n      <td>P050102001</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>S13051</td>\n      <td>1</td>\n      <td>P071003001</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>S13051</td>\n      <td>2</td>\n      <td>P080804001</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>S13052</td>\n      <td>0</td>\n      <td>P050101001</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>S14006</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>S14010</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>S14011</td>\n      <td>0</td>\n      <td>P060101001</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>S14012</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>S14021</td>\n      <td>0</td>\n      <td>P060101001</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>S14022</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>S14023</td>\n      <td>0</td>\n      <td>P071401001</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>S14024</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>S14025</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>S14026</td>\n      <td>0</td>\n      <td>P071401001</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>S14027</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>S14028</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>S14033</td>\n      <td>0</td>\n      <td>P071401001</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>S14034</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>S14036</td>\n      <td>0</td>\n      <td>P040503001</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>S14036</td>\n      <td>1</td>\n      <td>P060101001</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>S14040</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>S14042</td>\n      <td>0</td>\n      <td>P050101001</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>S14045</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>S14046</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>S14047</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>S14048</td>\n      <td>0</td>\n      <td>P050101001</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>S14049</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>S14050</td>\n      <td>0</td>\n      <td>P060303001</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   store_cd  level_1  product_cd\n0    S12007        0  P060303001\n1    S12013        0  P060303001\n2    S12014        0  P060303001\n3    S12029        0  P060303001\n4    S12030        0  P060303001\n5    S13001        0  P060303001\n6    S13002        0  P060303001\n7    S13003        0  P071401001\n8    S13004        0  P060303001\n9    S13005        0  P040503001\n10   S13008        0  P060303001\n11   S13009        0  P060303001\n12   S13015        0  P071401001\n13   S13016        0  P071102001\n14   S13017        0  P060101002\n15   S13018        0  P071401001\n16   S13019        0  P071401001\n17   S13020        0  P071401001\n18   S13031        0  P060303001\n19   S13032        0  P060303001\n20   S13035        0  P040503001\n21   S13037        0  P060303001\n22   S13038        0  P060303001\n23   S13039        0  P071401001\n24   S13041        0  P071401001\n25   S13043        0  P060303001\n26   S13044        0  P060303001\n27   S13051        0  P050102001\n28   S13051        1  P071003001\n29   S13051        2  P080804001\n30   S13052        0  P050101001\n31   S14006        0  P060303001\n32   S14010        0  P060303001\n33   S14011        0  P060101001\n34   S14012        0  P060303001\n35   S14021        0  P060101001\n36   S14022        0  P060303001\n37   S14023        0  P071401001\n38   S14024        0  P060303001\n39   S14025        0  P060303001\n40   S14026        0  P071401001\n41   S14027        0  P060303001\n42   S14028        0  P060303001\n43   S14033        0  P071401001\n44   S14034        0  P060303001\n45   S14036        0  P040503001\n46   S14036        1  P060101001\n47   S14040        0  P060303001\n48   S14042        0  P050101001\n49   S14045        0  P060303001\n50   S14046        0  P060303001\n51   S14047        0  P060303001\n52   S14048        0  P050101001\n53   S14049        0  P060303001\n54   S14050        0  P060303001"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-030: レシート明細データフレーム（df_receipt）に対し、店舗コード（store_cd）ごとに売上金額（amount）の標本分散を計算し、降順でTOP5を表示せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_receipt.groupby('store_cd').amount.var(ddof=0).reset_index().sort_values('amount', ascending=False).head(5)",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 31,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>store_cd</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>28</th>\n      <td>S13052</td>\n      <td>440088.701311</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>S14011</td>\n      <td>306314.558164</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>S14034</td>\n      <td>296920.081011</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>S13001</td>\n      <td>295431.993329</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>S13015</td>\n      <td>295294.361116</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   store_cd         amount\n28   S13052  440088.701311\n31   S14011  306314.558164\n42   S14034  296920.081011\n5    S13001  295431.993329\n12   S13015  295294.361116"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-031: レシート明細データフレーム（df_receipt）に対し、店舗コード（store_cd）ごとに売上金額（amount）の標本標準偏差を計算し、降順でTOP5を表示せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_receipt.groupby('store_cd').amount.std(ddof=0).reset_index().sort_values('amount', ascending=False).head(5)",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 32,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>store_cd</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>28</th>\n      <td>S13052</td>\n      <td>663.391816</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>S14011</td>\n      <td>553.456916</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>S14034</td>\n      <td>544.903736</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>S13001</td>\n      <td>543.536561</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>S13015</td>\n      <td>543.409938</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   store_cd      amount\n28   S13052  663.391816\n31   S14011  553.456916\n42   S14034  544.903736\n5    S13001  543.536561\n12   S13015  543.409938"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-032: レシート明細データフレーム（df_receipt）の売上金額（amount）について、25％刻みでパーセンタイル値を求めよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# コード例1\nnp.percentile(df_receipt['amount'], q=[25, 50, 75,100])",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 33,
          "data": {
            "text/plain": "array([  102.,   170.,   288., 10925.])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# コード例2\ndf_receipt.amount.quantile(q=np.arange(5)/4)",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 34,
          "data": {
            "text/plain": "0.00       10.0\n0.25      102.0\n0.50      170.0\n0.75      288.0\n1.00    10925.0\nName: amount, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-033: レシート明細データフレーム（df_receipt）に対し、店舗コード（store_cd）ごとに売上金額（amount）の平均を計算し、330以上のものを抽出せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_receipt.groupby('store_cd').amount.mean().reset_index().query('amount >= 330')",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 35,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>store_cd</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>S12013</td>\n      <td>330.194130</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>S13001</td>\n      <td>348.470386</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>S13003</td>\n      <td>350.915519</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>S13004</td>\n      <td>330.943949</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>S13015</td>\n      <td>351.111960</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>S13019</td>\n      <td>330.208616</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>S13020</td>\n      <td>337.879932</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>S13052</td>\n      <td>402.867470</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>S14010</td>\n      <td>348.791262</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>S14011</td>\n      <td>335.718333</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>S14026</td>\n      <td>332.340588</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>S14045</td>\n      <td>330.082073</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>S14047</td>\n      <td>330.077073</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   store_cd      amount\n1    S12013  330.194130\n5    S13001  348.470386\n7    S13003  350.915519\n8    S13004  330.943949\n12   S13015  351.111960\n16   S13019  330.208616\n17   S13020  337.879932\n28   S13052  402.867470\n30   S14010  348.791262\n31   S14011  335.718333\n38   S14026  332.340588\n46   S14045  330.082073\n48   S14047  330.077073"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-034: レシート明細データフレーム（df_receipt）に対し、顧客ID（customer_id）ごとに売上金額（amount）を合計して全顧客の平均を求めよ。ただし、顧客IDが\"Z\"から始まるのものは非会員を表すため、除外して計算すること。\n"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# queryを使わない書き方\ndf_receipt[~df_receipt['customer_id'].str.startswith(\"Z\")].groupby('customer_id').amount.sum().mean()",
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 36,
          "data": {
            "text/plain": "2547.742234529256"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# queryを使う書き方\ndf_receipt.query('not customer_id.str.startswith(\"Z\")', engine='python').groupby('customer_id').amount.sum().mean()",
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 37,
          "data": {
            "text/plain": "2547.742234529256"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-035: レシート明細データフレーム（df_receipt）に対し、顧客ID（customer_id）ごとに売上金額（amount）を合計して全顧客の平均を求め、平均以上に買い物をしている顧客を抽出せよ。ただし、顧客IDが\"Z\"から始まるのものは非会員を表すため、除外して計算すること。なお、データは10件だけ表示させれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "amount_mean = df_receipt[~df_receipt['customer_id'].str.startswith(\"Z\")].groupby('customer_id').amount.sum().mean()\ndf_amount_sum = df_receipt.groupby('customer_id').amount.sum().reset_index()\ndf_amount_sum[df_amount_sum['amount'] >= amount_mean].head(10)",
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 38,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>CS001115000010</td>\n      <td>3044</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001205000006</td>\n      <td>3337</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>CS001214000009</td>\n      <td>4685</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>CS001214000017</td>\n      <td>4132</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>CS001214000052</td>\n      <td>5639</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>CS001215000040</td>\n      <td>3496</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>CS001304000006</td>\n      <td>3726</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>CS001305000005</td>\n      <td>3485</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>CS001305000011</td>\n      <td>4370</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>CS001315000180</td>\n      <td>3300</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       customer_id  amount\n2   CS001115000010    3044\n4   CS001205000006    3337\n13  CS001214000009    4685\n14  CS001214000017    4132\n17  CS001214000052    5639\n21  CS001215000040    3496\n30  CS001304000006    3726\n32  CS001305000005    3485\n33  CS001305000011    4370\n53  CS001315000180    3300"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-036: レシート明細データフレーム（df_receipt）と店舗データフレーム（df_store）を内部結合し、レシート明細データフレームの全項目と店舗データフレームの店舗名（store_name）を10件表示させよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.merge(df_receipt, df_store[['store_cd','store_name']], how='inner', on='store_cd').head(10)",
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 39,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sales_ymd</th>\n      <th>sales_epoch</th>\n      <th>store_cd</th>\n      <th>receipt_no</th>\n      <th>receipt_sub_no</th>\n      <th>customer_id</th>\n      <th>product_cd</th>\n      <th>quantity</th>\n      <th>amount</th>\n      <th>store_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>20181103</td>\n      <td>1257206400</td>\n      <td>S14006</td>\n      <td>112</td>\n      <td>1</td>\n      <td>CS006214000001</td>\n      <td>P070305012</td>\n      <td>1</td>\n      <td>158</td>\n      <td>葛が谷店</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>20181116</td>\n      <td>1258329600</td>\n      <td>S14006</td>\n      <td>112</td>\n      <td>2</td>\n      <td>ZZ000000000000</td>\n      <td>P080401001</td>\n      <td>1</td>\n      <td>48</td>\n      <td>葛が谷店</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>20170118</td>\n      <td>1200614400</td>\n      <td>S14006</td>\n      <td>1162</td>\n      <td>1</td>\n      <td>CS006815000006</td>\n      <td>P050406035</td>\n      <td>1</td>\n      <td>220</td>\n      <td>葛が谷店</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20190524</td>\n      <td>1274659200</td>\n      <td>S14006</td>\n      <td>1192</td>\n      <td>1</td>\n      <td>CS006514000034</td>\n      <td>P060104003</td>\n      <td>1</td>\n      <td>80</td>\n      <td>葛が谷店</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>20190419</td>\n      <td>1271635200</td>\n      <td>S14006</td>\n      <td>112</td>\n      <td>2</td>\n      <td>ZZ000000000000</td>\n      <td>P060501002</td>\n      <td>1</td>\n      <td>148</td>\n      <td>葛が谷店</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>20181119</td>\n      <td>1258588800</td>\n      <td>S14006</td>\n      <td>1152</td>\n      <td>2</td>\n      <td>ZZ000000000000</td>\n      <td>P050701001</td>\n      <td>1</td>\n      <td>88</td>\n      <td>葛が谷店</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>20171211</td>\n      <td>1228953600</td>\n      <td>S14006</td>\n      <td>1132</td>\n      <td>2</td>\n      <td>CS006515000175</td>\n      <td>P090903001</td>\n      <td>1</td>\n      <td>80</td>\n      <td>葛が谷店</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>20191021</td>\n      <td>1287619200</td>\n      <td>S14006</td>\n      <td>1112</td>\n      <td>2</td>\n      <td>CS006415000221</td>\n      <td>P040602001</td>\n      <td>1</td>\n      <td>405</td>\n      <td>葛が谷店</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>20170710</td>\n      <td>1215648000</td>\n      <td>S14006</td>\n      <td>1132</td>\n      <td>2</td>\n      <td>CS006411000036</td>\n      <td>P090301051</td>\n      <td>1</td>\n      <td>330</td>\n      <td>葛が谷店</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>20190805</td>\n      <td>1280966400</td>\n      <td>S14006</td>\n      <td>112</td>\n      <td>1</td>\n      <td>CS006211000012</td>\n      <td>P050104001</td>\n      <td>1</td>\n      <td>115</td>\n      <td>葛が谷店</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   sales_ymd  sales_epoch store_cd  receipt_no  receipt_sub_no  \\\n0   20181103   1257206400   S14006         112               1   \n1   20181116   1258329600   S14006         112               2   \n2   20170118   1200614400   S14006        1162               1   \n3   20190524   1274659200   S14006        1192               1   \n4   20190419   1271635200   S14006         112               2   \n5   20181119   1258588800   S14006        1152               2   \n6   20171211   1228953600   S14006        1132               2   \n7   20191021   1287619200   S14006        1112               2   \n8   20170710   1215648000   S14006        1132               2   \n9   20190805   1280966400   S14006         112               1   \n\n      customer_id  product_cd  quantity  amount store_name  \n0  CS006214000001  P070305012         1     158       葛が谷店  \n1  ZZ000000000000  P080401001         1      48       葛が谷店  \n2  CS006815000006  P050406035         1     220       葛が谷店  \n3  CS006514000034  P060104003         1      80       葛が谷店  \n4  ZZ000000000000  P060501002         1     148       葛が谷店  \n5  ZZ000000000000  P050701001         1      88       葛が谷店  \n6  CS006515000175  P090903001         1      80       葛が谷店  \n7  CS006415000221  P040602001         1     405       葛が谷店  \n8  CS006411000036  P090301051         1     330       葛が谷店  \n9  CS006211000012  P050104001         1     115       葛が谷店  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-037: 商品データフレーム（df_product）とカテゴリデータフレーム（df_category）を内部結合し、商品データフレームの全項目とカテゴリデータフレームの小区分名（category_small_name）を10件表示させよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.merge(df_product\n         , df_category[['category_major_cd', 'category_medium_cd','category_small_cd','category_small_name']]\n         , how='inner', on=['category_major_cd', 'category_medium_cd','category_small_cd']).head(10)",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 40,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>product_cd</th>\n      <th>category_major_cd</th>\n      <th>category_medium_cd</th>\n      <th>category_small_cd</th>\n      <th>unit_price</th>\n      <th>unit_cost</th>\n      <th>category_small_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>P040101001</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>198.0</td>\n      <td>149.0</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>P040101002</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>218.0</td>\n      <td>164.0</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>P040101003</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>230.0</td>\n      <td>173.0</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>P040101004</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>248.0</td>\n      <td>186.0</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>P040101005</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>268.0</td>\n      <td>201.0</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>P040101006</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>298.0</td>\n      <td>224.0</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>P040101007</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>338.0</td>\n      <td>254.0</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>P040101008</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>420.0</td>\n      <td>315.0</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>P040101009</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>498.0</td>\n      <td>374.0</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>P040101010</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>580.0</td>\n      <td>435.0</td>\n      <td>弁当類</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   product_cd  category_major_cd  category_medium_cd  category_small_cd  \\\n0  P040101001                  4                 401              40101   \n1  P040101002                  4                 401              40101   \n2  P040101003                  4                 401              40101   \n3  P040101004                  4                 401              40101   \n4  P040101005                  4                 401              40101   \n5  P040101006                  4                 401              40101   \n6  P040101007                  4                 401              40101   \n7  P040101008                  4                 401              40101   \n8  P040101009                  4                 401              40101   \n9  P040101010                  4                 401              40101   \n\n   unit_price  unit_cost category_small_name  \n0       198.0      149.0                 弁当類  \n1       218.0      164.0                 弁当類  \n2       230.0      173.0                 弁当類  \n3       248.0      186.0                 弁当類  \n4       268.0      201.0                 弁当類  \n5       298.0      224.0                 弁当類  \n6       338.0      254.0                 弁当類  \n7       420.0      315.0                 弁当類  \n8       498.0      374.0                 弁当類  \n9       580.0      435.0                 弁当類  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-038: 顧客データフレーム（df_customer）とレシート明細データフレーム（df_receipt）から、各顧客ごとの売上金額合計を求めよ。ただし、買い物の実績がない顧客については売上金額を0として表示させること。また、顧客は性別コード（gender_cd）が女性（1）であるものを対象とし、非会員（顧客IDが'Z'から始まるもの）は除外すること。なお、結果は10件だけ表示させれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_amount_sum = df_receipt.groupby('customer_id').amount.sum().reset_index()\ndf_tmp = df_customer.query('gender_cd == \"1\" and not customer_id.str.startswith(\"Z\")', engine='python')\npd.merge(df_tmp['customer_id'], df_amount_sum, how='left', on='customer_id').fillna(0).head(10)",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 41,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS021313000114</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS031415000172</td>\n      <td>5088.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS028811000001</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS001215000145</td>\n      <td>875.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS015414000103</td>\n      <td>3122.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS033513000180</td>\n      <td>868.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS035614000014</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS011215000048</td>\n      <td>3444.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS009413000079</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS040412000191</td>\n      <td>210.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id  amount\n0  CS021313000114     0.0\n1  CS031415000172  5088.0\n2  CS028811000001     0.0\n3  CS001215000145   875.0\n4  CS015414000103  3122.0\n5  CS033513000180   868.0\n6  CS035614000014     0.0\n7  CS011215000048  3444.0\n8  CS009413000079     0.0\n9  CS040412000191   210.0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-039: レシート明細データフレーム（df_receipt）から売上日数の多い顧客の上位20件と、売上金額合計の多い顧客の上位20件を抽出し、完全外部結合せよ。ただし、非会員（顧客IDが'Z'から始まるもの）は除外すること。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_sum = df_receipt.groupby('customer_id').amount.sum().reset_index()\ndf_sum = df_sum.query('not customer_id.str.startswith(\"Z\")', engine='python')\ndf_sum = df_sum.sort_values('amount', ascending=False).head(20)\n\ndf_cnt = df_receipt[~df_receipt.duplicated(subset=['customer_id', 'sales_ymd'])]\ndf_cnt = df_cnt.query('not customer_id.str.startswith(\"Z\")', engine='python')\ndf_cnt = df_cnt.groupby('customer_id').sales_ymd.count().reset_index()\ndf_cnt = df_cnt.sort_values('sales_ymd', ascending=False).head(20)\n\npd.merge(df_sum, df_cnt, how='outer', on='customer_id')",
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 42,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>amount</th>\n      <th>sales_ymd</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS017415000097</td>\n      <td>23086.0</td>\n      <td>20.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS015415000185</td>\n      <td>20153.0</td>\n      <td>22.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS031414000051</td>\n      <td>19202.0</td>\n      <td>19.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS028415000007</td>\n      <td>19127.0</td>\n      <td>21.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001605000009</td>\n      <td>18925.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS010214000010</td>\n      <td>18585.0</td>\n      <td>22.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS016415000141</td>\n      <td>18372.0</td>\n      <td>20.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS006515000023</td>\n      <td>18372.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS011414000106</td>\n      <td>18338.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS038415000104</td>\n      <td>17847.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>CS035414000024</td>\n      <td>17615.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>CS021515000089</td>\n      <td>17580.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>CS032414000072</td>\n      <td>16563.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>CS016415000101</td>\n      <td>16348.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>CS011415000006</td>\n      <td>16094.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>CS034415000047</td>\n      <td>16083.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>CS007514000094</td>\n      <td>15735.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>CS009414000059</td>\n      <td>15492.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>CS030415000034</td>\n      <td>15468.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>CS015515000034</td>\n      <td>15300.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>CS040214000008</td>\n      <td>NaN</td>\n      <td>23.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>CS010214000002</td>\n      <td>NaN</td>\n      <td>21.0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>CS014214000023</td>\n      <td>NaN</td>\n      <td>19.0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>CS022515000226</td>\n      <td>NaN</td>\n      <td>19.0</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>CS021515000172</td>\n      <td>NaN</td>\n      <td>19.0</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>CS039414000052</td>\n      <td>NaN</td>\n      <td>19.0</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>CS021514000045</td>\n      <td>NaN</td>\n      <td>19.0</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>CS022515000028</td>\n      <td>NaN</td>\n      <td>18.0</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>CS030214000008</td>\n      <td>NaN</td>\n      <td>18.0</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>CS021515000056</td>\n      <td>NaN</td>\n      <td>18.0</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>CS014415000077</td>\n      <td>NaN</td>\n      <td>18.0</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>CS021515000211</td>\n      <td>NaN</td>\n      <td>18.0</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>CS032415000209</td>\n      <td>NaN</td>\n      <td>18.0</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>CS031414000073</td>\n      <td>NaN</td>\n      <td>18.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       customer_id   amount  sales_ymd\n0   CS017415000097  23086.0       20.0\n1   CS015415000185  20153.0       22.0\n2   CS031414000051  19202.0       19.0\n3   CS028415000007  19127.0       21.0\n4   CS001605000009  18925.0        NaN\n5   CS010214000010  18585.0       22.0\n6   CS016415000141  18372.0       20.0\n7   CS006515000023  18372.0        NaN\n8   CS011414000106  18338.0        NaN\n9   CS038415000104  17847.0        NaN\n10  CS035414000024  17615.0        NaN\n11  CS021515000089  17580.0        NaN\n12  CS032414000072  16563.0        NaN\n13  CS016415000101  16348.0        NaN\n14  CS011415000006  16094.0        NaN\n15  CS034415000047  16083.0        NaN\n16  CS007514000094  15735.0        NaN\n17  CS009414000059  15492.0        NaN\n18  CS030415000034  15468.0        NaN\n19  CS015515000034  15300.0        NaN\n20  CS040214000008      NaN       23.0\n21  CS010214000002      NaN       21.0\n22  CS014214000023      NaN       19.0\n23  CS022515000226      NaN       19.0\n24  CS021515000172      NaN       19.0\n25  CS039414000052      NaN       19.0\n26  CS021514000045      NaN       19.0\n27  CS022515000028      NaN       18.0\n28  CS030214000008      NaN       18.0\n29  CS021515000056      NaN       18.0\n30  CS014415000077      NaN       18.0\n31  CS021515000211      NaN       18.0\n32  CS032415000209      NaN       18.0\n33  CS031414000073      NaN       18.0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-040: 全ての店舗と全ての商品を組み合わせると何件のデータとなるか調査したい。店舗（df_store）と商品（df_product）を直積した件数を計算せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_store_tmp = df_store.copy()\ndf_product_tmp = df_product.copy()\n\ndf_store_tmp['key'] = 0\ndf_product_tmp['key'] = 0\nlen(pd.merge(df_store_tmp, df_product_tmp, how='outer', on='key'))",
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 43,
          "data": {
            "text/plain": "531590"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-041: レシート明細データフレーム（df_receipt）の売上金額（amount）を日付（sales_ymd）ごとに集計し、前日からの売上金額増減を計算せよ。なお、計算結果は10件表示すればよい。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_sales_amount_by_date = df_receipt[['sales_ymd', 'amount']].groupby('sales_ymd').sum().reset_index()\ndf_sales_amount_by_date = pd.concat([df_sales_amount_by_date, df_sales_amount_by_date.shift()], axis=1)\ndf_sales_amount_by_date.columns = ['sales_ymd','amount','lag_ymd','lag_amount']\ndf_sales_amount_by_date['diff_amount'] = df_sales_amount_by_date['amount'] - df_sales_amount_by_date['lag_amount']\ndf_sales_amount_by_date.head(10)",
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 44,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sales_ymd</th>\n      <th>amount</th>\n      <th>lag_ymd</th>\n      <th>lag_amount</th>\n      <th>diff_amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>20170101</td>\n      <td>33723</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>20170102</td>\n      <td>24165</td>\n      <td>20170101.0</td>\n      <td>33723.0</td>\n      <td>-9558.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>20170103</td>\n      <td>27503</td>\n      <td>20170102.0</td>\n      <td>24165.0</td>\n      <td>3338.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20170104</td>\n      <td>36165</td>\n      <td>20170103.0</td>\n      <td>27503.0</td>\n      <td>8662.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>20170105</td>\n      <td>37830</td>\n      <td>20170104.0</td>\n      <td>36165.0</td>\n      <td>1665.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>20170106</td>\n      <td>32387</td>\n      <td>20170105.0</td>\n      <td>37830.0</td>\n      <td>-5443.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>20170107</td>\n      <td>23415</td>\n      <td>20170106.0</td>\n      <td>32387.0</td>\n      <td>-8972.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>20170108</td>\n      <td>24737</td>\n      <td>20170107.0</td>\n      <td>23415.0</td>\n      <td>1322.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>20170109</td>\n      <td>26718</td>\n      <td>20170108.0</td>\n      <td>24737.0</td>\n      <td>1981.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>20170110</td>\n      <td>20143</td>\n      <td>20170109.0</td>\n      <td>26718.0</td>\n      <td>-6575.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   sales_ymd  amount     lag_ymd  lag_amount  diff_amount\n0   20170101   33723         NaN         NaN          NaN\n1   20170102   24165  20170101.0     33723.0      -9558.0\n2   20170103   27503  20170102.0     24165.0       3338.0\n3   20170104   36165  20170103.0     27503.0       8662.0\n4   20170105   37830  20170104.0     36165.0       1665.0\n5   20170106   32387  20170105.0     37830.0      -5443.0\n6   20170107   23415  20170106.0     32387.0      -8972.0\n7   20170108   24737  20170107.0     23415.0       1322.0\n8   20170109   26718  20170108.0     24737.0       1981.0\n9   20170110   20143  20170109.0     26718.0      -6575.0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-042: レシート明細データフレーム（df_receipt）の売上金額（amount）を日付（sales_ymd）ごとに集計し、各日付のデータに対し、１日前、２日前、３日前のデータを結合せよ。結果は10件表示すればよい。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# コード例1:縦持ちケース\ndf_sales_amount_by_date = df_receipt[['sales_ymd', 'amount']].groupby('sales_ymd').sum().reset_index()\nfor i in range(1, 4):\n    if i == 1:\n        df_lag = pd.concat([df_sales_amount_by_date, df_sales_amount_by_date.shift(i)],axis=1)\n    else:\n        df_lag = df_lag.append(pd.concat([df_sales_amount_by_date, df_sales_amount_by_date.shift(i)],axis=1))\ndf_lag.columns = ['sales_ymd', 'amount', 'lag_ymd', 'lag_amount']\ndf_lag.dropna().sort_values(['sales_ymd','lag_ymd']).head(10)",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 45,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sales_ymd</th>\n      <th>amount</th>\n      <th>lag_ymd</th>\n      <th>lag_amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>20170102</td>\n      <td>24165</td>\n      <td>20170101.0</td>\n      <td>33723.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>20170103</td>\n      <td>27503</td>\n      <td>20170101.0</td>\n      <td>33723.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>20170103</td>\n      <td>27503</td>\n      <td>20170102.0</td>\n      <td>24165.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20170104</td>\n      <td>36165</td>\n      <td>20170101.0</td>\n      <td>33723.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20170104</td>\n      <td>36165</td>\n      <td>20170102.0</td>\n      <td>24165.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20170104</td>\n      <td>36165</td>\n      <td>20170103.0</td>\n      <td>27503.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>20170105</td>\n      <td>37830</td>\n      <td>20170102.0</td>\n      <td>24165.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>20170105</td>\n      <td>37830</td>\n      <td>20170103.0</td>\n      <td>27503.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>20170105</td>\n      <td>37830</td>\n      <td>20170104.0</td>\n      <td>36165.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>20170106</td>\n      <td>32387</td>\n      <td>20170103.0</td>\n      <td>27503.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   sales_ymd  amount     lag_ymd  lag_amount\n1   20170102   24165  20170101.0     33723.0\n2   20170103   27503  20170101.0     33723.0\n2   20170103   27503  20170102.0     24165.0\n3   20170104   36165  20170101.0     33723.0\n3   20170104   36165  20170102.0     24165.0\n3   20170104   36165  20170103.0     27503.0\n4   20170105   37830  20170102.0     24165.0\n4   20170105   37830  20170103.0     27503.0\n4   20170105   37830  20170104.0     36165.0\n5   20170106   32387  20170103.0     27503.0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# コード例2:横持ちケース\ndf_sales_amount_by_date = df_receipt[['sales_ymd', 'amount']].groupby('sales_ymd').sum().reset_index()\nfor i in range(1, 4):\n    if i == 1:\n        df_lag = pd.concat([df_sales_amount_by_date, df_sales_amount_by_date.shift(i)],axis=1)\n    else:\n        df_lag = pd.concat([df_lag, df_sales_amount_by_date.shift(i)],axis=1)\ndf_lag.columns = ['sales_ymd', 'amount', 'lag_ymd_1', 'lag_amount_1', 'lag_ymd_2', 'lag_amount_2', 'lag_ymd_3', 'lag_amount_3']\ndf_lag.dropna().sort_values(['sales_ymd']).head(10)",
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 46,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sales_ymd</th>\n      <th>amount</th>\n      <th>lag_ymd_1</th>\n      <th>lag_amount_1</th>\n      <th>lag_ymd_2</th>\n      <th>lag_amount_2</th>\n      <th>lag_ymd_3</th>\n      <th>lag_amount_3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3</th>\n      <td>20170104</td>\n      <td>36165</td>\n      <td>20170103.0</td>\n      <td>27503.0</td>\n      <td>20170102.0</td>\n      <td>24165.0</td>\n      <td>20170101.0</td>\n      <td>33723.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>20170105</td>\n      <td>37830</td>\n      <td>20170104.0</td>\n      <td>36165.0</td>\n      <td>20170103.0</td>\n      <td>27503.0</td>\n      <td>20170102.0</td>\n      <td>24165.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>20170106</td>\n      <td>32387</td>\n      <td>20170105.0</td>\n      <td>37830.0</td>\n      <td>20170104.0</td>\n      <td>36165.0</td>\n      <td>20170103.0</td>\n      <td>27503.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>20170107</td>\n      <td>23415</td>\n      <td>20170106.0</td>\n      <td>32387.0</td>\n      <td>20170105.0</td>\n      <td>37830.0</td>\n      <td>20170104.0</td>\n      <td>36165.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>20170108</td>\n      <td>24737</td>\n      <td>20170107.0</td>\n      <td>23415.0</td>\n      <td>20170106.0</td>\n      <td>32387.0</td>\n      <td>20170105.0</td>\n      <td>37830.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>20170109</td>\n      <td>26718</td>\n      <td>20170108.0</td>\n      <td>24737.0</td>\n      <td>20170107.0</td>\n      <td>23415.0</td>\n      <td>20170106.0</td>\n      <td>32387.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>20170110</td>\n      <td>20143</td>\n      <td>20170109.0</td>\n      <td>26718.0</td>\n      <td>20170108.0</td>\n      <td>24737.0</td>\n      <td>20170107.0</td>\n      <td>23415.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>20170111</td>\n      <td>24287</td>\n      <td>20170110.0</td>\n      <td>20143.0</td>\n      <td>20170109.0</td>\n      <td>26718.0</td>\n      <td>20170108.0</td>\n      <td>24737.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>20170112</td>\n      <td>23526</td>\n      <td>20170111.0</td>\n      <td>24287.0</td>\n      <td>20170110.0</td>\n      <td>20143.0</td>\n      <td>20170109.0</td>\n      <td>26718.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>20170113</td>\n      <td>28004</td>\n      <td>20170112.0</td>\n      <td>23526.0</td>\n      <td>20170111.0</td>\n      <td>24287.0</td>\n      <td>20170110.0</td>\n      <td>20143.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "    sales_ymd  amount   lag_ymd_1  lag_amount_1   lag_ymd_2  lag_amount_2  \\\n3    20170104   36165  20170103.0       27503.0  20170102.0       24165.0   \n4    20170105   37830  20170104.0       36165.0  20170103.0       27503.0   \n5    20170106   32387  20170105.0       37830.0  20170104.0       36165.0   \n6    20170107   23415  20170106.0       32387.0  20170105.0       37830.0   \n7    20170108   24737  20170107.0       23415.0  20170106.0       32387.0   \n8    20170109   26718  20170108.0       24737.0  20170107.0       23415.0   \n9    20170110   20143  20170109.0       26718.0  20170108.0       24737.0   \n10   20170111   24287  20170110.0       20143.0  20170109.0       26718.0   \n11   20170112   23526  20170111.0       24287.0  20170110.0       20143.0   \n12   20170113   28004  20170112.0       23526.0  20170111.0       24287.0   \n\n     lag_ymd_3  lag_amount_3  \n3   20170101.0       33723.0  \n4   20170102.0       24165.0  \n5   20170103.0       27503.0  \n6   20170104.0       36165.0  \n7   20170105.0       37830.0  \n8   20170106.0       32387.0  \n9   20170107.0       23415.0  \n10  20170108.0       24737.0  \n11  20170109.0       26718.0  \n12  20170110.0       20143.0  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-043： レシート明細データフレーム（df_receipt）と顧客データフレーム（df_customer）を結合し、性別（gender）と年代（ageから計算）ごとに売上金額（amount）を合計した売上サマリデータフレーム（df_sales_summary）を作成せよ。性別は0が男性、1が女性、9が不明を表すものとする。\n>\n> ただし、項目構成は年代、女性の売上金額、男性の売上金額、性別不明の売上金額の4項目とすること（縦に年代、横に性別のクロス集計）。また、年代は10歳ごとの階級とすること。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = pd.merge(df_receipt, df_customer, how ='inner', on=\"customer_id\")\ndf_tmp['era'] = df_tmp['age'].apply(lambda x: math.floor(x / 10) * 10)\ndf_sales_summary = pd.pivot_table(df_tmp, index='era', columns='gender_cd', values='amount', aggfunc='sum').reset_index()\ndf_sales_summary.columns = ['era', 'male', 'female', 'unknown']\ndf_sales_summary",
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 47,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>era</th>\n      <th>male</th>\n      <th>female</th>\n      <th>unknown</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10</td>\n      <td>1591.0</td>\n      <td>149836.0</td>\n      <td>4317.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>20</td>\n      <td>72940.0</td>\n      <td>1363724.0</td>\n      <td>44328.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>30</td>\n      <td>177322.0</td>\n      <td>693047.0</td>\n      <td>50441.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>40</td>\n      <td>19355.0</td>\n      <td>9320791.0</td>\n      <td>483512.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>50</td>\n      <td>54320.0</td>\n      <td>6685192.0</td>\n      <td>342923.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>60</td>\n      <td>272469.0</td>\n      <td>987741.0</td>\n      <td>71418.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>70</td>\n      <td>13435.0</td>\n      <td>29764.0</td>\n      <td>2427.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>80</td>\n      <td>46360.0</td>\n      <td>262923.0</td>\n      <td>5111.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>90</td>\n      <td>NaN</td>\n      <td>6260.0</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   era      male     female   unknown\n0   10    1591.0   149836.0    4317.0\n1   20   72940.0  1363724.0   44328.0\n2   30  177322.0   693047.0   50441.0\n3   40   19355.0  9320791.0  483512.0\n4   50   54320.0  6685192.0  342923.0\n5   60  272469.0   987741.0   71418.0\n6   70   13435.0    29764.0    2427.0\n7   80   46360.0   262923.0    5111.0\n8   90       NaN     6260.0       NaN"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-044： 前設問で作成した売上サマリデータフレーム（df_sales_summary）は性別の売上を横持ちさせたものであった。このデータフレームから性別を縦持ちさせ、年代、性別コード、売上金額の3項目に変換せよ。ただし、性別コードは男性を'00'、女性を'01'、不明を'99'とする。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_sales_summary = df_sales_summary.set_index('era'). \\\n        stack().reset_index().replace({'female':'01',\n                                        'male':'00',\n                                        'unknown':'99'}).rename(columns={'level_1':'gender_cd', 0: 'amount'})",
      "execution_count": 48,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_sales_summary",
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 49,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>era</th>\n      <th>gender_cd</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10</td>\n      <td>00</td>\n      <td>1591.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10</td>\n      <td>01</td>\n      <td>149836.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10</td>\n      <td>99</td>\n      <td>4317.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20</td>\n      <td>00</td>\n      <td>72940.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>20</td>\n      <td>01</td>\n      <td>1363724.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>20</td>\n      <td>99</td>\n      <td>44328.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>30</td>\n      <td>00</td>\n      <td>177322.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>30</td>\n      <td>01</td>\n      <td>693047.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>30</td>\n      <td>99</td>\n      <td>50441.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>40</td>\n      <td>00</td>\n      <td>19355.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>40</td>\n      <td>01</td>\n      <td>9320791.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>40</td>\n      <td>99</td>\n      <td>483512.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>50</td>\n      <td>00</td>\n      <td>54320.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>50</td>\n      <td>01</td>\n      <td>6685192.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>50</td>\n      <td>99</td>\n      <td>342923.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>60</td>\n      <td>00</td>\n      <td>272469.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>60</td>\n      <td>01</td>\n      <td>987741.0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>60</td>\n      <td>99</td>\n      <td>71418.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>70</td>\n      <td>00</td>\n      <td>13435.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>70</td>\n      <td>01</td>\n      <td>29764.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>70</td>\n      <td>99</td>\n      <td>2427.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>80</td>\n      <td>00</td>\n      <td>46360.0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>80</td>\n      <td>01</td>\n      <td>262923.0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>80</td>\n      <td>99</td>\n      <td>5111.0</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>90</td>\n      <td>01</td>\n      <td>6260.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "    era gender_cd     amount\n0    10        00     1591.0\n1    10        01   149836.0\n2    10        99     4317.0\n3    20        00    72940.0\n4    20        01  1363724.0\n5    20        99    44328.0\n6    30        00   177322.0\n7    30        01   693047.0\n8    30        99    50441.0\n9    40        00    19355.0\n10   40        01  9320791.0\n11   40        99   483512.0\n12   50        00    54320.0\n13   50        01  6685192.0\n14   50        99   342923.0\n15   60        00   272469.0\n16   60        01   987741.0\n17   60        99    71418.0\n18   70        00    13435.0\n19   70        01    29764.0\n20   70        99     2427.0\n21   80        00    46360.0\n22   80        01   262923.0\n23   80        99     5111.0\n24   90        01     6260.0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-045: 顧客データフレーム（df_customer）の生年月日（birth_day）は日付型（Date）でデータを保有している。これをYYYYMMDD形式の文字列に変換し、顧客ID（customer_id）とともに抽出せよ。データは10件を抽出すれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.concat([df_customer['customer_id'],\n           pd.to_datetime(df_customer['birth_day']).dt.strftime('%Y%m%d')],\n          axis = 1).head(10)",
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 50,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>birth_day</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS021313000114</td>\n      <td>19810429</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS037613000071</td>\n      <td>19520401</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS031415000172</td>\n      <td>19761004</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS028811000001</td>\n      <td>19330327</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001215000145</td>\n      <td>19950329</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS020401000016</td>\n      <td>19740915</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS015414000103</td>\n      <td>19770809</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS029403000008</td>\n      <td>19730817</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS015804000004</td>\n      <td>19310502</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS033513000180</td>\n      <td>19620711</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id birth_day\n0  CS021313000114  19810429\n1  CS037613000071  19520401\n2  CS031415000172  19761004\n3  CS028811000001  19330327\n4  CS001215000145  19950329\n5  CS020401000016  19740915\n6  CS015414000103  19770809\n7  CS029403000008  19730817\n8  CS015804000004  19310502\n9  CS033513000180  19620711"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-046: 顧客データフレーム（df_customer）の申し込み日（application_date）はYYYYMMD形式の文字列型でデータを保有している。これを日付型（dateやdatetime）に変換し、顧客ID（customer_id）とともに抽出せよ。データは10件を抽出すれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.concat([df_customer['customer_id'],pd.to_datetime(df_customer['application_date'])], axis=1).head(10)",
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 51,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>application_date</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS021313000114</td>\n      <td>1970-01-01 00:00:00.020150905</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS037613000071</td>\n      <td>1970-01-01 00:00:00.020150414</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS031415000172</td>\n      <td>1970-01-01 00:00:00.020150529</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS028811000001</td>\n      <td>1970-01-01 00:00:00.020160115</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001215000145</td>\n      <td>1970-01-01 00:00:00.020170605</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS020401000016</td>\n      <td>1970-01-01 00:00:00.020150225</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS015414000103</td>\n      <td>1970-01-01 00:00:00.020150722</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS029403000008</td>\n      <td>1970-01-01 00:00:00.020150515</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS015804000004</td>\n      <td>1970-01-01 00:00:00.020150607</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS033513000180</td>\n      <td>1970-01-01 00:00:00.020150728</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id              application_date\n0  CS021313000114 1970-01-01 00:00:00.020150905\n1  CS037613000071 1970-01-01 00:00:00.020150414\n2  CS031415000172 1970-01-01 00:00:00.020150529\n3  CS028811000001 1970-01-01 00:00:00.020160115\n4  CS001215000145 1970-01-01 00:00:00.020170605\n5  CS020401000016 1970-01-01 00:00:00.020150225\n6  CS015414000103 1970-01-01 00:00:00.020150722\n7  CS029403000008 1970-01-01 00:00:00.020150515\n8  CS015804000004 1970-01-01 00:00:00.020150607\n9  CS033513000180 1970-01-01 00:00:00.020150728"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-047: レシート明細データフレーム（df_receipt）の売上日（sales_ymd）はYYYYMMDD形式の数値型でデータを保有している。これを日付型（dateやdatetime）に変換し、レシート番号(receipt_no)、レシートサブ番号（receipt_sub_no）とともに抽出せよ。データは10件を抽出すれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.concat([df_receipt[['receipt_no', 'receipt_sub_no']],\n           pd.to_datetime(df_receipt['sales_ymd'].astype('str'))],axis=1).head(10)",
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 52,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>receipt_no</th>\n      <th>receipt_sub_no</th>\n      <th>sales_ymd</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>112</td>\n      <td>1</td>\n      <td>2018-11-03</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1132</td>\n      <td>2</td>\n      <td>2018-11-18</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1102</td>\n      <td>1</td>\n      <td>2017-07-12</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1132</td>\n      <td>1</td>\n      <td>2019-02-05</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1102</td>\n      <td>2</td>\n      <td>2018-08-21</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1112</td>\n      <td>1</td>\n      <td>2019-06-05</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1102</td>\n      <td>2</td>\n      <td>2018-12-05</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1102</td>\n      <td>1</td>\n      <td>2019-09-22</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1112</td>\n      <td>2</td>\n      <td>2017-05-04</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1102</td>\n      <td>1</td>\n      <td>2019-10-10</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   receipt_no  receipt_sub_no  sales_ymd\n0         112               1 2018-11-03\n1        1132               2 2018-11-18\n2        1102               1 2017-07-12\n3        1132               1 2019-02-05\n4        1102               2 2018-08-21\n5        1112               1 2019-06-05\n6        1102               2 2018-12-05\n7        1102               1 2019-09-22\n8        1112               2 2017-05-04\n9        1102               1 2019-10-10"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-048: レシート明細データフレーム（df_receipt）の売上エポック秒（sales_epoch）は数値型のUNIX秒でデータを保有している。これを日付型（dateやdatetime）に変換し、レシート番号(receipt_no)、レシートサブ番号（receipt_sub_no）とともに抽出せよ。データは10件を抽出すれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.concat([df_receipt[['receipt_no', 'receipt_sub_no']],\n           pd.to_datetime(df_receipt['sales_epoch'], unit='s')],axis=1).head(10)",
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 53,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>receipt_no</th>\n      <th>receipt_sub_no</th>\n      <th>sales_epoch</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>112</td>\n      <td>1</td>\n      <td>2009-11-03</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1132</td>\n      <td>2</td>\n      <td>2009-11-18</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1102</td>\n      <td>1</td>\n      <td>2008-07-12</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1132</td>\n      <td>1</td>\n      <td>2010-02-05</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1102</td>\n      <td>2</td>\n      <td>2009-08-21</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1112</td>\n      <td>1</td>\n      <td>2010-06-05</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1102</td>\n      <td>2</td>\n      <td>2009-12-05</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1102</td>\n      <td>1</td>\n      <td>2010-09-22</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1112</td>\n      <td>2</td>\n      <td>2008-05-04</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1102</td>\n      <td>1</td>\n      <td>2010-10-10</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   receipt_no  receipt_sub_no sales_epoch\n0         112               1  2009-11-03\n1        1132               2  2009-11-18\n2        1102               1  2008-07-12\n3        1132               1  2010-02-05\n4        1102               2  2009-08-21\n5        1112               1  2010-06-05\n6        1102               2  2009-12-05\n7        1102               1  2010-09-22\n8        1112               2  2008-05-04\n9        1102               1  2010-10-10"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-049: レシート明細データフレーム（df_receipt）の売上エポック秒（sales_epoch）を日付型（timestamp型）に変換し、\"年\"だけ取り出してレシート番号(receipt_no)、レシートサブ番号（receipt_sub_no）とともに抽出せよ。データは10件を抽出すれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.concat([df_receipt[['receipt_no', 'receipt_sub_no']],\n           pd.to_datetime(df_receipt['sales_epoch'], unit='s').dt.year],axis=1).head(10)",
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 54,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>receipt_no</th>\n      <th>receipt_sub_no</th>\n      <th>sales_epoch</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>112</td>\n      <td>1</td>\n      <td>2009</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1132</td>\n      <td>2</td>\n      <td>2009</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1102</td>\n      <td>1</td>\n      <td>2008</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1132</td>\n      <td>1</td>\n      <td>2010</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1102</td>\n      <td>2</td>\n      <td>2009</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1112</td>\n      <td>1</td>\n      <td>2010</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1102</td>\n      <td>2</td>\n      <td>2009</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1102</td>\n      <td>1</td>\n      <td>2010</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1112</td>\n      <td>2</td>\n      <td>2008</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1102</td>\n      <td>1</td>\n      <td>2010</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   receipt_no  receipt_sub_no  sales_epoch\n0         112               1         2009\n1        1132               2         2009\n2        1102               1         2008\n3        1132               1         2010\n4        1102               2         2009\n5        1112               1         2010\n6        1102               2         2009\n7        1102               1         2010\n8        1112               2         2008\n9        1102               1         2010"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-050: レシート明細データフレーム（df_receipt）の売上エポック秒（sales_epoch）を日付型（timestamp型）に変換し、\"月\"だけ取り出してレシート番号(receipt_no)、レシートサブ番号（receipt_sub_no）とともに抽出せよ。なお、\"月\"は0埋め2桁で取り出すこと。データは10件を抽出すれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# dt.monthでも月を取得できるが、ここでは0埋め２桁で取り出すためstrftimeを利用している\npd.concat([df_receipt[['receipt_no', 'receipt_sub_no']],\n           pd.to_datetime(df_receipt['sales_epoch'], unit='s').dt.strftime('%m')],axis=1).head(10)",
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 55,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>receipt_no</th>\n      <th>receipt_sub_no</th>\n      <th>sales_epoch</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>112</td>\n      <td>1</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1132</td>\n      <td>2</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1102</td>\n      <td>1</td>\n      <td>07</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1132</td>\n      <td>1</td>\n      <td>02</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1102</td>\n      <td>2</td>\n      <td>08</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1112</td>\n      <td>1</td>\n      <td>06</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1102</td>\n      <td>2</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1102</td>\n      <td>1</td>\n      <td>09</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1112</td>\n      <td>2</td>\n      <td>05</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1102</td>\n      <td>1</td>\n      <td>10</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   receipt_no  receipt_sub_no sales_epoch\n0         112               1          11\n1        1132               2          11\n2        1102               1          07\n3        1132               1          02\n4        1102               2          08\n5        1112               1          06\n6        1102               2          12\n7        1102               1          09\n8        1112               2          05\n9        1102               1          10"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-051: レシート明細データフレーム（df_receipt）の売上エポック秒（sales_epoch）を日付型（timestamp型）に変換し、\"日\"だけ取り出してレシート番号(receipt_no)、レシートサブ番号（receipt_sub_no）とともに抽出せよ。なお、\"日\"は0埋め2桁で取り出すこと。データは10件を抽出すれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# dt.dayでも日を取得できるが、ここでは0埋め２桁で取り出すためstrftimeを利用している\npd.concat([df_receipt[['receipt_no', 'receipt_sub_no']],\n           pd.to_datetime(df_receipt['sales_epoch'], unit='s').dt.strftime('%d')],axis=1).head(10)",
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 56,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>receipt_no</th>\n      <th>receipt_sub_no</th>\n      <th>sales_epoch</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>112</td>\n      <td>1</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1132</td>\n      <td>2</td>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1102</td>\n      <td>1</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1132</td>\n      <td>1</td>\n      <td>05</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1102</td>\n      <td>2</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1112</td>\n      <td>1</td>\n      <td>05</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1102</td>\n      <td>2</td>\n      <td>05</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1102</td>\n      <td>1</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1112</td>\n      <td>2</td>\n      <td>04</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1102</td>\n      <td>1</td>\n      <td>10</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   receipt_no  receipt_sub_no sales_epoch\n0         112               1          03\n1        1132               2          18\n2        1102               1          12\n3        1132               1          05\n4        1102               2          21\n5        1112               1          05\n6        1102               2          05\n7        1102               1          22\n8        1112               2          04\n9        1102               1          10"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-052: レシート明細データフレーム（df_receipt）の売上金額（amount）を顧客ID（customer_id）ごとに合計の上、売上金額合計に対して2000円以下を0、2000円超を1に2値化し、顧客ID、売上金額合計とともに10件表示せよ。ただし、顧客IDが\"Z\"から始まるのものは非会員を表すため、除外して計算すること。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_sales_amount = df_receipt.query('not customer_id.str.startswith(\"Z\")', engine='python')\ndf_sales_amount = df_sales_amount[['customer_id', 'amount']].groupby('customer_id').sum().reset_index()\ndf_sales_amount['sales_flg'] = df_sales_amount['amount'].apply(lambda x: 1 if x > 2000 else 0)\ndf_sales_amount.head(10)",
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 57,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>amount</th>\n      <th>sales_flg</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS001113000004</td>\n      <td>1298</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS001114000005</td>\n      <td>626</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS001115000010</td>\n      <td>3044</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS001205000004</td>\n      <td>1988</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001205000006</td>\n      <td>3337</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS001211000025</td>\n      <td>456</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS001212000027</td>\n      <td>448</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS001212000031</td>\n      <td>296</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS001212000046</td>\n      <td>228</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS001212000070</td>\n      <td>456</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id  amount  sales_flg\n0  CS001113000004    1298          0\n1  CS001114000005     626          0\n2  CS001115000010    3044          1\n3  CS001205000004    1988          0\n4  CS001205000006    3337          1\n5  CS001211000025     456          0\n6  CS001212000027     448          0\n7  CS001212000031     296          0\n8  CS001212000046     228          0\n9  CS001212000070     456          0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-053: 顧客データフレーム（df_customer）の郵便番号（postal_cd）に対し、東京（先頭3桁が100〜209のもの）を1、それ以外のものを0に２値化せよ。さらにレシート明細データフレーム（df_receipt）と結合し、全期間において買い物実績のある顧客数を、作成した2値ごとにカウントせよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = df_customer[['customer_id', 'postal_cd']].copy()\ndf_tmp['postal_flg'] = df_tmp['postal_cd'].apply(lambda x: 1 if 100 <= int(x[0:3]) <= 209 else 0)\n\npd.merge(df_tmp, df_receipt, how='inner', on='customer_id'). \\\n    groupby('postal_flg').agg({'customer_id':'nunique'})\n\n",
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 58,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n    </tr>\n    <tr>\n      <th>postal_flg</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3906</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4400</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "            customer_id\npostal_flg             \n0                  3906\n1                  4400"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-054: 顧客データデータフレーム（df_customer）の住所（address）は、埼玉県、千葉県、東京都、神奈川県のいずれかとなっている。都道府県毎にコード値を作成し、顧客ID、住所とともに抽出せよ。値は埼玉県を11、千葉県を12、東京都を13、神奈川県を14とすること。結果は10件表示させれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.concat([df_customer[['customer_id', 'address']], df_customer['address'].str[0:3].map({'埼玉県': '11',\n                                                                           '千葉県':'12', \n                                                                           '東京都':'13', \n                                                                           '神奈川':'14'})], axis=1).head(10)",
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 59,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>address</th>\n      <th>address</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS021313000114</td>\n      <td>神奈川県伊勢原市粟窪**********</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS037613000071</td>\n      <td>東京都江東区南砂**********</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS031415000172</td>\n      <td>東京都渋谷区代々木**********</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS028811000001</td>\n      <td>神奈川県横浜市泉区和泉町**********</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001215000145</td>\n      <td>東京都大田区仲六郷**********</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS020401000016</td>\n      <td>東京都板橋区若木**********</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS015414000103</td>\n      <td>東京都江東区北砂**********</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS029403000008</td>\n      <td>千葉県浦安市海楽**********</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS015804000004</td>\n      <td>東京都江東区北砂**********</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS033513000180</td>\n      <td>神奈川県横浜市旭区善部町**********</td>\n      <td>14</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id                 address address\n0  CS021313000114    神奈川県伊勢原市粟窪**********      14\n1  CS037613000071      東京都江東区南砂**********      13\n2  CS031415000172     東京都渋谷区代々木**********      13\n3  CS028811000001  神奈川県横浜市泉区和泉町**********      14\n4  CS001215000145     東京都大田区仲六郷**********      13\n5  CS020401000016      東京都板橋区若木**********      13\n6  CS015414000103      東京都江東区北砂**********      13\n7  CS029403000008      千葉県浦安市海楽**********      12\n8  CS015804000004      東京都江東区北砂**********      13\n9  CS033513000180  神奈川県横浜市旭区善部町**********      14"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-055: レシート明細データフレーム（df_receipt）の売上金額（amount）を顧客ID（customer_id）ごとに合計し、その合計金額の四分位点を求めよ。その上で、顧客ごとの売上金額合計に対して以下の基準でカテゴリ値を作成し、顧客ID、売上金額と合計ともに表示せよ。カテゴリ値は上から順に1〜4とする。結果は10件表示させれば良い。\n>\n> - 最小値以上第一四分位未満\n> - 第一四分位以上第二四分位未満\n> - 第二四分位以上第三四分位未満\n> - 第三四分位以上"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# コード例1\ndf_sales_amount = df_receipt[['customer_id', 'amount']].groupby('customer_id').sum().reset_index()\npct25 = np.quantile(df_sales_amount['amount'], 0.25)\npct50 = np.quantile(df_sales_amount['amount'], 0.5)\npct75 = np.quantile(df_sales_amount['amount'], 0.75)\n\ndef pct_group(x):\n    if x < pct25:\n        return 1\n    elif pct25 <= x < pct50:\n        return 2\n    elif pct50 <= x < pct75:\n        return 3\n    elif pct75 <= x:\n        return 4\n\ndf_sales_amount['pct_group'] = df_sales_amount['amount'].apply(lambda x: pct_group(x))\ndf_sales_amount.head(10)",
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 60,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>amount</th>\n      <th>pct_group</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS001113000004</td>\n      <td>1298</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS001114000005</td>\n      <td>626</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS001115000010</td>\n      <td>3044</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS001205000004</td>\n      <td>1988</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001205000006</td>\n      <td>3337</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS001211000025</td>\n      <td>456</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS001212000027</td>\n      <td>448</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS001212000031</td>\n      <td>296</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS001212000046</td>\n      <td>228</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS001212000070</td>\n      <td>456</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id  amount  pct_group\n0  CS001113000004    1298          2\n1  CS001114000005     626          2\n2  CS001115000010    3044          3\n3  CS001205000004    1988          3\n4  CS001205000006    3337          3\n5  CS001211000025     456          1\n6  CS001212000027     448          1\n7  CS001212000031     296          1\n8  CS001212000046     228          1\n9  CS001212000070     456          1"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# 確認用\nprint('pct25:', pct25)\nprint('pct50:', pct50)\nprint('pct75:', pct75)",
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": "pct25: 548.5\npct50: 1478.0\npct75: 3651.0\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# コード例2\ndf_temp = df_receipt.groupby('customer_id')[['amount']].sum()\ndf_temp['quantile'], bins = pd.qcut(df_receipt.groupby('customer_id')['amount'].sum(), 4, retbins=True)\ndisplay(df_temp.head())\nprint('quantiles:', bins)",
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>amount</th>\n      <th>quantile</th>\n    </tr>\n    <tr>\n      <th>customer_id</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>CS001113000004</th>\n      <td>1298</td>\n      <td>(548.5, 1478.0]</td>\n    </tr>\n    <tr>\n      <th>CS001114000005</th>\n      <td>626</td>\n      <td>(548.5, 1478.0]</td>\n    </tr>\n    <tr>\n      <th>CS001115000010</th>\n      <td>3044</td>\n      <td>(1478.0, 3651.0]</td>\n    </tr>\n    <tr>\n      <th>CS001205000004</th>\n      <td>1988</td>\n      <td>(1478.0, 3651.0]</td>\n    </tr>\n    <tr>\n      <th>CS001205000006</th>\n      <td>3337</td>\n      <td>(1478.0, 3651.0]</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                amount          quantile\ncustomer_id                             \nCS001113000004    1298   (548.5, 1478.0]\nCS001114000005     626   (548.5, 1478.0]\nCS001115000010    3044  (1478.0, 3651.0]\nCS001205000004    1988  (1478.0, 3651.0]\nCS001205000006    3337  (1478.0, 3651.0]"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "quantiles: [7.0000000e+01 5.4850000e+02 1.4780000e+03 3.6510000e+03 1.2395003e+07]\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-056: 顧客データフレーム（df_customer）の年齢（age）をもとに10歳刻みで年代を算出し、顧客ID（customer_id）、生年月日（birth_day）とともに抽出せよ。ただし、60歳以上は全て60歳代とすること。年代を表すカテゴリ名は任意とする。先頭10件を表示させればよい。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# コード例1\ndf_customer_era = pd.concat([df_customer[['customer_id', 'birth_day']],\n                             df_customer['age'].apply(lambda x: min(math.floor(x / 10) * 10, 60))],\n                             axis=1)\n\ndf_customer_era.head(10)",
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 63,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>birth_day</th>\n      <th>age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS021313000114</td>\n      <td>1981-04-29</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS037613000071</td>\n      <td>1952-04-01</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS031415000172</td>\n      <td>1976-10-04</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS028811000001</td>\n      <td>1933-03-27</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001215000145</td>\n      <td>1995-03-29</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS020401000016</td>\n      <td>1974-09-15</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS015414000103</td>\n      <td>1977-08-09</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS029403000008</td>\n      <td>1973-08-17</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS015804000004</td>\n      <td>1931-05-02</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS033513000180</td>\n      <td>1962-07-11</td>\n      <td>50</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id   birth_day  age\n0  CS021313000114  1981-04-29   30\n1  CS037613000071  1952-04-01   60\n2  CS031415000172  1976-10-04   40\n3  CS028811000001  1933-03-27   60\n4  CS001215000145  1995-03-29   20\n5  CS020401000016  1974-09-15   40\n6  CS015414000103  1977-08-09   40\n7  CS029403000008  1973-08-17   40\n8  CS015804000004  1931-05-02   60\n9  CS033513000180  1962-07-11   50"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# コード例2\ndf_customer['age_group'] = pd.cut(df_customer['age'], bins=[0, 10, 20, 30, 40, 50, 60, np.inf], right=False)\ndf_customer[['customer_id', 'birth_day', 'age_group']].head(10)",
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 64,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>birth_day</th>\n      <th>age_group</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS021313000114</td>\n      <td>1981-04-29</td>\n      <td>[30.0, 40.0)</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS037613000071</td>\n      <td>1952-04-01</td>\n      <td>[60.0, inf)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS031415000172</td>\n      <td>1976-10-04</td>\n      <td>[40.0, 50.0)</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS028811000001</td>\n      <td>1933-03-27</td>\n      <td>[60.0, inf)</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001215000145</td>\n      <td>1995-03-29</td>\n      <td>[20.0, 30.0)</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS020401000016</td>\n      <td>1974-09-15</td>\n      <td>[40.0, 50.0)</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS015414000103</td>\n      <td>1977-08-09</td>\n      <td>[40.0, 50.0)</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS029403000008</td>\n      <td>1973-08-17</td>\n      <td>[40.0, 50.0)</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS015804000004</td>\n      <td>1931-05-02</td>\n      <td>[60.0, inf)</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS033513000180</td>\n      <td>1962-07-11</td>\n      <td>[50.0, 60.0)</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id   birth_day     age_group\n0  CS021313000114  1981-04-29  [30.0, 40.0)\n1  CS037613000071  1952-04-01   [60.0, inf)\n2  CS031415000172  1976-10-04  [40.0, 50.0)\n3  CS028811000001  1933-03-27   [60.0, inf)\n4  CS001215000145  1995-03-29  [20.0, 30.0)\n5  CS020401000016  1974-09-15  [40.0, 50.0)\n6  CS015414000103  1977-08-09  [40.0, 50.0)\n7  CS029403000008  1973-08-17  [40.0, 50.0)\n8  CS015804000004  1931-05-02   [60.0, inf)\n9  CS033513000180  1962-07-11  [50.0, 60.0)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-057: 前問題の抽出結果と性別（gender）を組み合わせ、新たに性別×年代の組み合わせを表すカテゴリデータを作成せよ。組み合わせを表すカテゴリの値は任意とする。先頭10件を表示させればよい。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_customer_era['era_gender'] = df_customer['gender_cd'].astype('str') + df_customer_era['age'].astype('str')\ndf_customer_era.head(10)",
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 65,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>birth_day</th>\n      <th>age</th>\n      <th>era_gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS021313000114</td>\n      <td>1981-04-29</td>\n      <td>30</td>\n      <td>130</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS037613000071</td>\n      <td>1952-04-01</td>\n      <td>60</td>\n      <td>960</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS031415000172</td>\n      <td>1976-10-04</td>\n      <td>40</td>\n      <td>140</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS028811000001</td>\n      <td>1933-03-27</td>\n      <td>60</td>\n      <td>160</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001215000145</td>\n      <td>1995-03-29</td>\n      <td>20</td>\n      <td>120</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS020401000016</td>\n      <td>1974-09-15</td>\n      <td>40</td>\n      <td>040</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS015414000103</td>\n      <td>1977-08-09</td>\n      <td>40</td>\n      <td>140</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS029403000008</td>\n      <td>1973-08-17</td>\n      <td>40</td>\n      <td>040</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS015804000004</td>\n      <td>1931-05-02</td>\n      <td>60</td>\n      <td>060</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS033513000180</td>\n      <td>1962-07-11</td>\n      <td>50</td>\n      <td>150</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id   birth_day  age era_gender\n0  CS021313000114  1981-04-29   30        130\n1  CS037613000071  1952-04-01   60        960\n2  CS031415000172  1976-10-04   40        140\n3  CS028811000001  1933-03-27   60        160\n4  CS001215000145  1995-03-29   20        120\n5  CS020401000016  1974-09-15   40        040\n6  CS015414000103  1977-08-09   40        140\n7  CS029403000008  1973-08-17   40        040\n8  CS015804000004  1931-05-02   60        060\n9  CS033513000180  1962-07-11   50        150"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-058: 顧客データフレーム（df_customer）の性別コード（gender_cd）をダミー変数化し、顧客ID（customer_id）とともに抽出せよ。結果は10件表示させれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.get_dummies(df_customer[['customer_id', 'gender_cd']], columns=['gender_cd']).head(10)",
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 66,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>gender_cd_0</th>\n      <th>gender_cd_1</th>\n      <th>gender_cd_9</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS021313000114</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS037613000071</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS031415000172</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS028811000001</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001215000145</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS020401000016</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS015414000103</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS029403000008</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS015804000004</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS033513000180</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id  gender_cd_0  gender_cd_1  gender_cd_9\n0  CS021313000114            0            1            0\n1  CS037613000071            0            0            1\n2  CS031415000172            0            1            0\n3  CS028811000001            0            1            0\n4  CS001215000145            0            1            0\n5  CS020401000016            1            0            0\n6  CS015414000103            0            1            0\n7  CS029403000008            1            0            0\n8  CS015804000004            1            0            0\n9  CS033513000180            0            1            0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-059: レシート明細データフレーム（df_receipt）の売上金額（amount）を顧客ID（customer_id）ごとに合計し、合計した売上金額を平均0、標準偏差1に標準化して顧客ID、売上金額合計とともに表示せよ。標準化に使用する標準偏差は、不偏標準偏差と標本標準偏差のどちらでも良いものとする。ただし、顧客IDが\"Z\"から始まるのものは非会員を表すため、除外して計算すること。結果は10件表示させれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# skleanのpreprocessing.scaleを利用するため、標本標準偏差で計算されている\ndf_sales_amount = df_receipt.query('not customer_id.str.startswith(\"Z\")', engine='python'). \\\n    groupby('customer_id').agg({'amount':'sum'}).reset_index()\ndf_sales_amount['amount_ss'] = preprocessing.scale(df_sales_amount['amount'])\ndf_sales_amount.head(10)",
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 67,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>amount</th>\n      <th>amount_ss</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS001113000004</td>\n      <td>1298</td>\n      <td>-0.459378</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS001114000005</td>\n      <td>626</td>\n      <td>-0.706390</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS001115000010</td>\n      <td>3044</td>\n      <td>0.182413</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS001205000004</td>\n      <td>1988</td>\n      <td>-0.205749</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001205000006</td>\n      <td>3337</td>\n      <td>0.290114</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS001211000025</td>\n      <td>456</td>\n      <td>-0.768879</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS001212000027</td>\n      <td>448</td>\n      <td>-0.771819</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS001212000031</td>\n      <td>296</td>\n      <td>-0.827691</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS001212000046</td>\n      <td>228</td>\n      <td>-0.852686</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS001212000070</td>\n      <td>456</td>\n      <td>-0.768879</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id  amount  amount_ss\n0  CS001113000004    1298  -0.459378\n1  CS001114000005     626  -0.706390\n2  CS001115000010    3044   0.182413\n3  CS001205000004    1988  -0.205749\n4  CS001205000006    3337   0.290114\n5  CS001211000025     456  -0.768879\n6  CS001212000027     448  -0.771819\n7  CS001212000031     296  -0.827691\n8  CS001212000046     228  -0.852686\n9  CS001212000070     456  -0.768879"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-060: レシート明細データフレーム（df_receipt）の売上金額（amount）を顧客ID（customer_id）ごとに合計し、合計した売上金額を最小値0、最大値1に正規化して顧客ID、売上金額合計とともに表示せよ。ただし、顧客IDが\"Z\"から始まるのものは非会員を表すため、除外して計算すること。結果は10件表示させれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# skleanのpreprocessing.scaleを利用するため、標本標準偏差で計算されている\ndf_sales_amount = df_receipt.query('not customer_id.str.startswith(\"Z\")', engine='python'). \\\n    groupby('customer_id').agg({'amount':'sum'}).reset_index()\ndf_sales_amount['amount_mm'] = preprocessing.minmax_scale(df_sales_amount['amount'])\ndf_sales_amount.head(10)",
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 68,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>amount</th>\n      <th>amount_mm</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS001113000004</td>\n      <td>1298</td>\n      <td>0.053354</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS001114000005</td>\n      <td>626</td>\n      <td>0.024157</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS001115000010</td>\n      <td>3044</td>\n      <td>0.129214</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS001205000004</td>\n      <td>1988</td>\n      <td>0.083333</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001205000006</td>\n      <td>3337</td>\n      <td>0.141945</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS001211000025</td>\n      <td>456</td>\n      <td>0.016771</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS001212000027</td>\n      <td>448</td>\n      <td>0.016423</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS001212000031</td>\n      <td>296</td>\n      <td>0.009819</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS001212000046</td>\n      <td>228</td>\n      <td>0.006865</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS001212000070</td>\n      <td>456</td>\n      <td>0.016771</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id  amount  amount_mm\n0  CS001113000004    1298   0.053354\n1  CS001114000005     626   0.024157\n2  CS001115000010    3044   0.129214\n3  CS001205000004    1988   0.083333\n4  CS001205000006    3337   0.141945\n5  CS001211000025     456   0.016771\n6  CS001212000027     448   0.016423\n7  CS001212000031     296   0.009819\n8  CS001212000046     228   0.006865\n9  CS001212000070     456   0.016771"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-061: レシート明細データフレーム（df_receipt）の売上金額（amount）を顧客ID（customer_id）ごとに合計し、合計した売上金額を常用対数化（底=10）して顧客ID、売上金額合計とともに表示せよ。ただし、顧客IDが\"Z\"から始まるのものは非会員を表すため、除外して計算すること。結果は10件表示させれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# skleanのpreprocessing.scaleを利用するため、標本標準偏差で計算されている\ndf_sales_amount = df_receipt.query('not customer_id.str.startswith(\"Z\")', engine='python'). \\\n    groupby('customer_id').agg({'amount':'sum'}).reset_index()\ndf_sales_amount['amount_log10'] = np.log10(df_sales_amount['amount'] + 1)\ndf_sales_amount.head(10)",
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 69,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>amount</th>\n      <th>amount_log10</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS001113000004</td>\n      <td>1298</td>\n      <td>3.113609</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS001114000005</td>\n      <td>626</td>\n      <td>2.797268</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS001115000010</td>\n      <td>3044</td>\n      <td>3.483587</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS001205000004</td>\n      <td>1988</td>\n      <td>3.298635</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001205000006</td>\n      <td>3337</td>\n      <td>3.523486</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS001211000025</td>\n      <td>456</td>\n      <td>2.659916</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS001212000027</td>\n      <td>448</td>\n      <td>2.652246</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS001212000031</td>\n      <td>296</td>\n      <td>2.472756</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS001212000046</td>\n      <td>228</td>\n      <td>2.359835</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS001212000070</td>\n      <td>456</td>\n      <td>2.659916</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id  amount  amount_log10\n0  CS001113000004    1298      3.113609\n1  CS001114000005     626      2.797268\n2  CS001115000010    3044      3.483587\n3  CS001205000004    1988      3.298635\n4  CS001205000006    3337      3.523486\n5  CS001211000025     456      2.659916\n6  CS001212000027     448      2.652246\n7  CS001212000031     296      2.472756\n8  CS001212000046     228      2.359835\n9  CS001212000070     456      2.659916"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-062: レシート明細データフレーム（df_receipt）の売上金額（amount）を顧客ID（customer_id）ごとに合計し、合計した売上金額を自然対数化(底=e）して顧客ID、売上金額合計とともに表示せよ。ただし、顧客IDが\"Z\"から始まるのものは非会員を表すため、除外して計算すること。結果は10件表示させれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# skleanのpreprocessing.scaleを利用するため、標本標準偏差で計算されている\ndf_sales_amount = df_receipt.query('not customer_id.str.startswith(\"Z\")', engine='python'). \\\n    groupby('customer_id').agg({'amount':'sum'}).reset_index()\ndf_sales_amount['amount_loge'] = np.log(df_sales_amount['amount'] + 1)\ndf_sales_amount.head(10)",
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 70,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>amount</th>\n      <th>amount_loge</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS001113000004</td>\n      <td>1298</td>\n      <td>7.169350</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS001114000005</td>\n      <td>626</td>\n      <td>6.440947</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS001115000010</td>\n      <td>3044</td>\n      <td>8.021256</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS001205000004</td>\n      <td>1988</td>\n      <td>7.595387</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001205000006</td>\n      <td>3337</td>\n      <td>8.113127</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS001211000025</td>\n      <td>456</td>\n      <td>6.124683</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS001212000027</td>\n      <td>448</td>\n      <td>6.107023</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS001212000031</td>\n      <td>296</td>\n      <td>5.693732</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS001212000046</td>\n      <td>228</td>\n      <td>5.433722</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS001212000070</td>\n      <td>456</td>\n      <td>6.124683</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id  amount  amount_loge\n0  CS001113000004    1298     7.169350\n1  CS001114000005     626     6.440947\n2  CS001115000010    3044     8.021256\n3  CS001205000004    1988     7.595387\n4  CS001205000006    3337     8.113127\n5  CS001211000025     456     6.124683\n6  CS001212000027     448     6.107023\n7  CS001212000031     296     5.693732\n8  CS001212000046     228     5.433722\n9  CS001212000070     456     6.124683"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-063: 商品データフレーム（df_product）の単価（unit_price）と原価（unit_cost）から、各商品の利益額を算出せよ。結果は10件表示させれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = df_product.copy()\ndf_tmp['unit_profit'] = df_tmp['unit_price'] - df_tmp['unit_cost']\ndf_tmp.head(10)",
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 71,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>product_cd</th>\n      <th>category_major_cd</th>\n      <th>category_medium_cd</th>\n      <th>category_small_cd</th>\n      <th>unit_price</th>\n      <th>unit_cost</th>\n      <th>unit_profit</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>P040101001</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>198.0</td>\n      <td>149.0</td>\n      <td>49.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>P040101002</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>218.0</td>\n      <td>164.0</td>\n      <td>54.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>P040101003</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>230.0</td>\n      <td>173.0</td>\n      <td>57.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>P040101004</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>248.0</td>\n      <td>186.0</td>\n      <td>62.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>P040101005</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>268.0</td>\n      <td>201.0</td>\n      <td>67.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>P040101006</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>298.0</td>\n      <td>224.0</td>\n      <td>74.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>P040101007</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>338.0</td>\n      <td>254.0</td>\n      <td>84.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>P040101008</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>420.0</td>\n      <td>315.0</td>\n      <td>105.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>P040101009</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>498.0</td>\n      <td>374.0</td>\n      <td>124.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>P040101010</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>580.0</td>\n      <td>435.0</td>\n      <td>145.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   product_cd  category_major_cd  category_medium_cd  category_small_cd  \\\n0  P040101001                  4                 401              40101   \n1  P040101002                  4                 401              40101   \n2  P040101003                  4                 401              40101   \n3  P040101004                  4                 401              40101   \n4  P040101005                  4                 401              40101   \n5  P040101006                  4                 401              40101   \n6  P040101007                  4                 401              40101   \n7  P040101008                  4                 401              40101   \n8  P040101009                  4                 401              40101   \n9  P040101010                  4                 401              40101   \n\n   unit_price  unit_cost  unit_profit  \n0       198.0      149.0         49.0  \n1       218.0      164.0         54.0  \n2       230.0      173.0         57.0  \n3       248.0      186.0         62.0  \n4       268.0      201.0         67.0  \n5       298.0      224.0         74.0  \n6       338.0      254.0         84.0  \n7       420.0      315.0        105.0  \n8       498.0      374.0        124.0  \n9       580.0      435.0        145.0  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-064: 商品データフレーム（df_product）の単価（unit_price）と原価（unit_cost）から、各商品の利益率の全体平均を算出せよ。\nただし、単価と原価にはNULLが存在することに注意せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = df_product.copy()\ndf_tmp['unit_profit_rate'] = (df_tmp['unit_price'] - df_tmp['unit_cost']) / df_tmp['unit_price']\ndf_tmp['unit_profit_rate'].mean(skipna=True)",
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 72,
          "data": {
            "text/plain": "0.24911389885176904"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-065: 商品データフレーム（df_product）の各商品について、利益率が30%となる新たな単価を求めよ。ただし、1円未満は切り捨てること。そして結果を10件表示させ、利益率がおよそ30％付近であることを確認せよ。ただし、単価（unit_price）と原価（unit_cost）にはNULLが存在することに注意せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# math.floorはNaNでエラーとなるが、numpy.floorはエラーとならない\ndf_tmp = df_product.copy()\ndf_tmp['new_price'] = df_tmp['unit_cost'].apply(lambda x: np.floor(x / 0.7))\ndf_tmp['new_profit_rate'] = (df_tmp['new_price'] - df_tmp['unit_cost']) / df_tmp['new_price']\ndf_tmp.head(10)",
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 73,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>product_cd</th>\n      <th>category_major_cd</th>\n      <th>category_medium_cd</th>\n      <th>category_small_cd</th>\n      <th>unit_price</th>\n      <th>unit_cost</th>\n      <th>new_price</th>\n      <th>new_profit_rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>P040101001</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>198.0</td>\n      <td>149.0</td>\n      <td>212.0</td>\n      <td>0.297170</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>P040101002</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>218.0</td>\n      <td>164.0</td>\n      <td>234.0</td>\n      <td>0.299145</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>P040101003</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>230.0</td>\n      <td>173.0</td>\n      <td>247.0</td>\n      <td>0.299595</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>P040101004</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>248.0</td>\n      <td>186.0</td>\n      <td>265.0</td>\n      <td>0.298113</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>P040101005</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>268.0</td>\n      <td>201.0</td>\n      <td>287.0</td>\n      <td>0.299652</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>P040101006</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>298.0</td>\n      <td>224.0</td>\n      <td>320.0</td>\n      <td>0.300000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>P040101007</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>338.0</td>\n      <td>254.0</td>\n      <td>362.0</td>\n      <td>0.298343</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>P040101008</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>420.0</td>\n      <td>315.0</td>\n      <td>450.0</td>\n      <td>0.300000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>P040101009</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>498.0</td>\n      <td>374.0</td>\n      <td>534.0</td>\n      <td>0.299625</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>P040101010</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>580.0</td>\n      <td>435.0</td>\n      <td>621.0</td>\n      <td>0.299517</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   product_cd  category_major_cd  category_medium_cd  category_small_cd  \\\n0  P040101001                  4                 401              40101   \n1  P040101002                  4                 401              40101   \n2  P040101003                  4                 401              40101   \n3  P040101004                  4                 401              40101   \n4  P040101005                  4                 401              40101   \n5  P040101006                  4                 401              40101   \n6  P040101007                  4                 401              40101   \n7  P040101008                  4                 401              40101   \n8  P040101009                  4                 401              40101   \n9  P040101010                  4                 401              40101   \n\n   unit_price  unit_cost  new_price  new_profit_rate  \n0       198.0      149.0      212.0         0.297170  \n1       218.0      164.0      234.0         0.299145  \n2       230.0      173.0      247.0         0.299595  \n3       248.0      186.0      265.0         0.298113  \n4       268.0      201.0      287.0         0.299652  \n5       298.0      224.0      320.0         0.300000  \n6       338.0      254.0      362.0         0.298343  \n7       420.0      315.0      450.0         0.300000  \n8       498.0      374.0      534.0         0.299625  \n9       580.0      435.0      621.0         0.299517  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-066: 商品データフレーム（df_product）の各商品について、利益率が30%となる新たな単価を求めよ。今回は、1円未満を四捨五入すること（0.5については偶数方向の丸めで良い）。そして結果を10件表示させ、利益率がおよそ30％付近であることを確認せよ。ただし、単価（unit_price）と原価（unit_cost）にはNULLが存在することに注意せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# 組み込みのroundはNaNでエラーとなるが、numpy.roundはエラーとならない\ndf_tmp = df_product.copy()\ndf_tmp['new_price'] = df_tmp['unit_cost'].apply(lambda x: np.round(x / 0.7))\ndf_tmp['new_profit_rate'] = (df_tmp['new_price'] - df_tmp['unit_cost']) / df_tmp['new_price']\ndf_tmp.head(10)",
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 74,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>product_cd</th>\n      <th>category_major_cd</th>\n      <th>category_medium_cd</th>\n      <th>category_small_cd</th>\n      <th>unit_price</th>\n      <th>unit_cost</th>\n      <th>new_price</th>\n      <th>new_profit_rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>P040101001</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>198.0</td>\n      <td>149.0</td>\n      <td>213.0</td>\n      <td>0.300469</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>P040101002</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>218.0</td>\n      <td>164.0</td>\n      <td>234.0</td>\n      <td>0.299145</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>P040101003</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>230.0</td>\n      <td>173.0</td>\n      <td>247.0</td>\n      <td>0.299595</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>P040101004</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>248.0</td>\n      <td>186.0</td>\n      <td>266.0</td>\n      <td>0.300752</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>P040101005</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>268.0</td>\n      <td>201.0</td>\n      <td>287.0</td>\n      <td>0.299652</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>P040101006</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>298.0</td>\n      <td>224.0</td>\n      <td>320.0</td>\n      <td>0.300000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>P040101007</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>338.0</td>\n      <td>254.0</td>\n      <td>363.0</td>\n      <td>0.300275</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>P040101008</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>420.0</td>\n      <td>315.0</td>\n      <td>450.0</td>\n      <td>0.300000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>P040101009</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>498.0</td>\n      <td>374.0</td>\n      <td>534.0</td>\n      <td>0.299625</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>P040101010</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>580.0</td>\n      <td>435.0</td>\n      <td>621.0</td>\n      <td>0.299517</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   product_cd  category_major_cd  category_medium_cd  category_small_cd  \\\n0  P040101001                  4                 401              40101   \n1  P040101002                  4                 401              40101   \n2  P040101003                  4                 401              40101   \n3  P040101004                  4                 401              40101   \n4  P040101005                  4                 401              40101   \n5  P040101006                  4                 401              40101   \n6  P040101007                  4                 401              40101   \n7  P040101008                  4                 401              40101   \n8  P040101009                  4                 401              40101   \n9  P040101010                  4                 401              40101   \n\n   unit_price  unit_cost  new_price  new_profit_rate  \n0       198.0      149.0      213.0         0.300469  \n1       218.0      164.0      234.0         0.299145  \n2       230.0      173.0      247.0         0.299595  \n3       248.0      186.0      266.0         0.300752  \n4       268.0      201.0      287.0         0.299652  \n5       298.0      224.0      320.0         0.300000  \n6       338.0      254.0      363.0         0.300275  \n7       420.0      315.0      450.0         0.300000  \n8       498.0      374.0      534.0         0.299625  \n9       580.0      435.0      621.0         0.299517  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-067: 商品データフレーム（df_product）の各商品について、利益率が30%となる新たな単価を求めよ。今回は、1円未満を切り上げること。そして結果を10件表示させ、利益率がおよそ30％付近であることを確認せよ。ただし、単価（unit_price）と原価（unit_cost）にはNULLが存在することに注意せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# math.ceilはNaNでエラーとなるが、numpy.ceilはエラーとならない\ndf_tmp = df_product.copy()\ndf_tmp['new_price'] = df_tmp['unit_cost'].apply(lambda x: np.ceil(x / 0.7))\ndf_tmp['new_profit_rate'] = (df_tmp['new_price'] - df_tmp['unit_cost']) / df_tmp['new_price']\ndf_tmp.head(10)",
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 75,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>product_cd</th>\n      <th>category_major_cd</th>\n      <th>category_medium_cd</th>\n      <th>category_small_cd</th>\n      <th>unit_price</th>\n      <th>unit_cost</th>\n      <th>new_price</th>\n      <th>new_profit_rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>P040101001</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>198.0</td>\n      <td>149.0</td>\n      <td>213.0</td>\n      <td>0.300469</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>P040101002</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>218.0</td>\n      <td>164.0</td>\n      <td>235.0</td>\n      <td>0.302128</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>P040101003</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>230.0</td>\n      <td>173.0</td>\n      <td>248.0</td>\n      <td>0.302419</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>P040101004</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>248.0</td>\n      <td>186.0</td>\n      <td>266.0</td>\n      <td>0.300752</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>P040101005</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>268.0</td>\n      <td>201.0</td>\n      <td>288.0</td>\n      <td>0.302083</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>P040101006</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>298.0</td>\n      <td>224.0</td>\n      <td>320.0</td>\n      <td>0.300000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>P040101007</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>338.0</td>\n      <td>254.0</td>\n      <td>363.0</td>\n      <td>0.300275</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>P040101008</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>420.0</td>\n      <td>315.0</td>\n      <td>451.0</td>\n      <td>0.301552</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>P040101009</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>498.0</td>\n      <td>374.0</td>\n      <td>535.0</td>\n      <td>0.300935</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>P040101010</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>580.0</td>\n      <td>435.0</td>\n      <td>622.0</td>\n      <td>0.300643</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   product_cd  category_major_cd  category_medium_cd  category_small_cd  \\\n0  P040101001                  4                 401              40101   \n1  P040101002                  4                 401              40101   \n2  P040101003                  4                 401              40101   \n3  P040101004                  4                 401              40101   \n4  P040101005                  4                 401              40101   \n5  P040101006                  4                 401              40101   \n6  P040101007                  4                 401              40101   \n7  P040101008                  4                 401              40101   \n8  P040101009                  4                 401              40101   \n9  P040101010                  4                 401              40101   \n\n   unit_price  unit_cost  new_price  new_profit_rate  \n0       198.0      149.0      213.0         0.300469  \n1       218.0      164.0      235.0         0.302128  \n2       230.0      173.0      248.0         0.302419  \n3       248.0      186.0      266.0         0.300752  \n4       268.0      201.0      288.0         0.302083  \n5       298.0      224.0      320.0         0.300000  \n6       338.0      254.0      363.0         0.300275  \n7       420.0      315.0      451.0         0.301552  \n8       498.0      374.0      535.0         0.300935  \n9       580.0      435.0      622.0         0.300643  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-068: 商品データフレーム（df_product）の各商品について、消費税率10%の税込み金額を求めよ。 1円未満の端数は切り捨てとし、結果は10件表示すれば良い。ただし、単価（unit_price）にはNULLが存在することに注意せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# math.floorはNaNでエラーとなるが、numpy.floorはエラーとならない\ndf_tmp = df_product.copy()\ndf_tmp['price_tax'] = df_tmp['unit_price'].apply(lambda x: np.floor(x * 1.1))\ndf_tmp.head(10)",
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 76,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>product_cd</th>\n      <th>category_major_cd</th>\n      <th>category_medium_cd</th>\n      <th>category_small_cd</th>\n      <th>unit_price</th>\n      <th>unit_cost</th>\n      <th>price_tax</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>P040101001</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>198.0</td>\n      <td>149.0</td>\n      <td>217.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>P040101002</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>218.0</td>\n      <td>164.0</td>\n      <td>239.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>P040101003</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>230.0</td>\n      <td>173.0</td>\n      <td>253.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>P040101004</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>248.0</td>\n      <td>186.0</td>\n      <td>272.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>P040101005</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>268.0</td>\n      <td>201.0</td>\n      <td>294.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>P040101006</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>298.0</td>\n      <td>224.0</td>\n      <td>327.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>P040101007</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>338.0</td>\n      <td>254.0</td>\n      <td>371.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>P040101008</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>420.0</td>\n      <td>315.0</td>\n      <td>462.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>P040101009</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>498.0</td>\n      <td>374.0</td>\n      <td>547.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>P040101010</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>580.0</td>\n      <td>435.0</td>\n      <td>638.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   product_cd  category_major_cd  category_medium_cd  category_small_cd  \\\n0  P040101001                  4                 401              40101   \n1  P040101002                  4                 401              40101   \n2  P040101003                  4                 401              40101   \n3  P040101004                  4                 401              40101   \n4  P040101005                  4                 401              40101   \n5  P040101006                  4                 401              40101   \n6  P040101007                  4                 401              40101   \n7  P040101008                  4                 401              40101   \n8  P040101009                  4                 401              40101   \n9  P040101010                  4                 401              40101   \n\n   unit_price  unit_cost  price_tax  \n0       198.0      149.0      217.0  \n1       218.0      164.0      239.0  \n2       230.0      173.0      253.0  \n3       248.0      186.0      272.0  \n4       268.0      201.0      294.0  \n5       298.0      224.0      327.0  \n6       338.0      254.0      371.0  \n7       420.0      315.0      462.0  \n8       498.0      374.0      547.0  \n9       580.0      435.0      638.0  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-069: レシート明細データフレーム（df_receipt）と商品データフレーム（df_product）を結合し、顧客毎に全商品の売上金額合計と、カテゴリ大区分（category_major_cd）が\"07\"（瓶詰缶詰）の売上金額合計を計算の上、両者の比率を求めよ。抽出対象はカテゴリ大区分\"07\"（瓶詰缶詰）の購入実績がある顧客のみとし、結果は10件表示させればよい。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# コード例1\ndf_tmp_1 = pd.merge(df_receipt, df_product, \n                    how='inner', on='product_cd').groupby('customer_id').agg({'amount':'sum'}).reset_index()\n\ndf_tmp_2 = pd.merge(df_receipt, df_product.query('category_major_cd == \"07\"'), \n                    how='inner', on='product_cd').groupby('customer_id').agg({'amount':'sum'}).reset_index()\n\ndf_tmp_3 = pd.merge(df_tmp_1, df_tmp_2, how='inner', on='customer_id')\ndf_tmp_3['rate_07'] = df_tmp_3['amount_y'] / df_tmp_3['amount_x']\ndf_tmp_3.head(10)",
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 77,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>amount_x</th>\n      <th>amount_y</th>\n      <th>rate_07</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS001113000004</td>\n      <td>1298</td>\n      <td>1298</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS001114000005</td>\n      <td>626</td>\n      <td>486</td>\n      <td>0.776358</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS001115000010</td>\n      <td>3044</td>\n      <td>2694</td>\n      <td>0.885020</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS001205000004</td>\n      <td>1988</td>\n      <td>346</td>\n      <td>0.174044</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS001205000006</td>\n      <td>3337</td>\n      <td>2004</td>\n      <td>0.600539</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS001212000027</td>\n      <td>448</td>\n      <td>200</td>\n      <td>0.446429</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS001212000031</td>\n      <td>296</td>\n      <td>296</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS001212000046</td>\n      <td>228</td>\n      <td>108</td>\n      <td>0.473684</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS001212000070</td>\n      <td>456</td>\n      <td>308</td>\n      <td>0.675439</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS001213000018</td>\n      <td>243</td>\n      <td>145</td>\n      <td>0.596708</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id  amount_x  amount_y   rate_07\n0  CS001113000004      1298      1298  1.000000\n1  CS001114000005       626       486  0.776358\n2  CS001115000010      3044      2694  0.885020\n3  CS001205000004      1988       346  0.174044\n4  CS001205000006      3337      2004  0.600539\n5  CS001212000027       448       200  0.446429\n6  CS001212000031       296       296  1.000000\n7  CS001212000046       228       108  0.473684\n8  CS001212000070       456       308  0.675439\n9  CS001213000018       243       145  0.596708"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# コード例2\ndf_temp = df_receipt.merge(df_product, how='left', on='product_cd').groupby(['customer_id', 'category_major_cd'])['amount'].sum().unstack()\ndf_temp = df_temp[df_temp[7] > 0]\ndf_temp['sum'] = df_temp.sum(axis=1)\ndf_temp['07_rate'] = df_temp[7] / df_temp['sum']\ndf_temp.head(10)",
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 78,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>category_major_cd</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>sum</th>\n      <th>07_rate</th>\n    </tr>\n    <tr>\n      <th>customer_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>CS001113000004</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1298.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1298.0</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>CS001114000005</th>\n      <td>NaN</td>\n      <td>40.0</td>\n      <td>NaN</td>\n      <td>486.0</td>\n      <td>100.0</td>\n      <td>NaN</td>\n      <td>626.0</td>\n      <td>0.776358</td>\n    </tr>\n    <tr>\n      <th>CS001115000010</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2694.0</td>\n      <td>NaN</td>\n      <td>350.0</td>\n      <td>3044.0</td>\n      <td>0.885020</td>\n    </tr>\n    <tr>\n      <th>CS001205000004</th>\n      <td>100.0</td>\n      <td>128.0</td>\n      <td>286.0</td>\n      <td>346.0</td>\n      <td>368.0</td>\n      <td>760.0</td>\n      <td>1988.0</td>\n      <td>0.174044</td>\n    </tr>\n    <tr>\n      <th>CS001205000006</th>\n      <td>635.0</td>\n      <td>60.0</td>\n      <td>198.0</td>\n      <td>2004.0</td>\n      <td>80.0</td>\n      <td>360.0</td>\n      <td>3337.0</td>\n      <td>0.600539</td>\n    </tr>\n    <tr>\n      <th>CS001212000027</th>\n      <td>248.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>200.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>448.0</td>\n      <td>0.446429</td>\n    </tr>\n    <tr>\n      <th>CS001212000031</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>296.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>296.0</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>CS001212000046</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>108.0</td>\n      <td>NaN</td>\n      <td>120.0</td>\n      <td>228.0</td>\n      <td>0.473684</td>\n    </tr>\n    <tr>\n      <th>CS001212000070</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>148.0</td>\n      <td>308.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>456.0</td>\n      <td>0.675439</td>\n    </tr>\n    <tr>\n      <th>CS001213000018</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>145.0</td>\n      <td>98.0</td>\n      <td>NaN</td>\n      <td>243.0</td>\n      <td>0.596708</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "category_major_cd      4      5      6       7      8      9     sum   07_rate\ncustomer_id                                                                   \nCS001113000004       NaN    NaN    NaN  1298.0    NaN    NaN  1298.0  1.000000\nCS001114000005       NaN   40.0    NaN   486.0  100.0    NaN   626.0  0.776358\nCS001115000010       NaN    NaN    NaN  2694.0    NaN  350.0  3044.0  0.885020\nCS001205000004     100.0  128.0  286.0   346.0  368.0  760.0  1988.0  0.174044\nCS001205000006     635.0   60.0  198.0  2004.0   80.0  360.0  3337.0  0.600539\nCS001212000027     248.0    NaN    NaN   200.0    NaN    NaN   448.0  0.446429\nCS001212000031       NaN    NaN    NaN   296.0    NaN    NaN   296.0  1.000000\nCS001212000046       NaN    NaN    NaN   108.0    NaN  120.0   228.0  0.473684\nCS001212000070       NaN    NaN  148.0   308.0    NaN    NaN   456.0  0.675439\nCS001213000018       NaN    NaN    NaN   145.0   98.0    NaN   243.0  0.596708"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-070: レシート明細データフレーム（df_receipt）の売上日（sales_ymd）に対し、顧客データフレーム（df_customer）の会員申込日（application_date）からの経過日数を計算し、顧客ID（customer_id）、売上日、会員申込日とともに表示せよ。結果は10件表示させれば良い（なお、sales_ymdは数値、application_dateは文字列でデータを保持している点に注意）。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = pd.merge(df_receipt[['customer_id', 'sales_ymd']], df_customer[['customer_id', 'application_date']],\n                 how='inner', on='customer_id')\n\ndf_tmp = df_tmp.drop_duplicates()\n\ndf_tmp['sales_ymd'] = pd.to_datetime(df_tmp['sales_ymd'].astype('str'))\ndf_tmp['application_date'] = pd.to_datetime(df_tmp['application_date'])\ndf_tmp['elapsed_date'] = df_tmp['sales_ymd'] - df_tmp['application_date']\ndf_tmp.head(10)",
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 79,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>sales_ymd</th>\n      <th>application_date</th>\n      <th>elapsed_date</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS006214000001</td>\n      <td>2018-11-03</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>17837 days 23:59:59.979849</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS006214000001</td>\n      <td>2017-05-09</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>17294 days 23:59:59.979849</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS006214000001</td>\n      <td>2017-06-08</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>17324 days 23:59:59.979849</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS006214000001</td>\n      <td>2018-10-28</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>17831 days 23:59:59.979849</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS006214000001</td>\n      <td>2019-09-08</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>18146 days 23:59:59.979849</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS006214000001</td>\n      <td>2018-01-31</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>17561 days 23:59:59.979849</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS006214000001</td>\n      <td>2017-07-05</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>17351 days 23:59:59.979849</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>CS006214000001</td>\n      <td>2018-11-10</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>17844 days 23:59:59.979849</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>CS006214000001</td>\n      <td>2019-04-10</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>17995 days 23:59:59.979849</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>CS006214000001</td>\n      <td>2019-06-01</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>18047 days 23:59:59.979849</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       customer_id  sales_ymd              application_date  \\\n0   CS006214000001 2018-11-03 1970-01-01 00:00:00.020150201   \n1   CS006214000001 2017-05-09 1970-01-01 00:00:00.020150201   \n2   CS006214000001 2017-06-08 1970-01-01 00:00:00.020150201   \n4   CS006214000001 2018-10-28 1970-01-01 00:00:00.020150201   \n7   CS006214000001 2019-09-08 1970-01-01 00:00:00.020150201   \n8   CS006214000001 2018-01-31 1970-01-01 00:00:00.020150201   \n9   CS006214000001 2017-07-05 1970-01-01 00:00:00.020150201   \n10  CS006214000001 2018-11-10 1970-01-01 00:00:00.020150201   \n12  CS006214000001 2019-04-10 1970-01-01 00:00:00.020150201   \n15  CS006214000001 2019-06-01 1970-01-01 00:00:00.020150201   \n\n                 elapsed_date  \n0  17837 days 23:59:59.979849  \n1  17294 days 23:59:59.979849  \n2  17324 days 23:59:59.979849  \n4  17831 days 23:59:59.979849  \n7  18146 days 23:59:59.979849  \n8  17561 days 23:59:59.979849  \n9  17351 days 23:59:59.979849  \n10 17844 days 23:59:59.979849  \n12 17995 days 23:59:59.979849  \n15 18047 days 23:59:59.979849  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-071: レシート明細データフレーム（df_receipt）の売上日（sales_ymd）に対し、顧客データフレーム（df_customer）の会員申込日（application_date）からの経過月数を計算し、顧客ID（customer_id）、売上日、会員申込日とともに表示せよ。結果は10件表示させれば良い（なお、sales_ymdは数値、application_dateは文字列でデータを保持している点に注意）。1ヶ月未満は切り捨てること。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = pd.merge(df_receipt[['customer_id', 'sales_ymd']], df_customer[['customer_id', 'application_date']],\n                 how='inner', on='customer_id')\n\ndf_tmp = df_tmp.drop_duplicates()\n\ndf_tmp['sales_ymd'] = pd.to_datetime(df_tmp['sales_ymd'].astype('str'))\ndf_tmp['application_date'] = pd.to_datetime(df_tmp['application_date'])\n\ndf_tmp['elapsed_date'] = df_tmp[['sales_ymd', 'application_date']].apply(lambda x: \n                                    relativedelta(x[0], x[1]).years * 12 + relativedelta(x[0], x[1]).months, axis=1)\ndf_tmp.sort_values('customer_id').head(10)",
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 80,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>sales_ymd</th>\n      <th>application_date</th>\n      <th>elapsed_date</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>60376</th>\n      <td>CS001113000004</td>\n      <td>2019-03-08</td>\n      <td>1970-01-01 00:00:00.020151105</td>\n      <td>590</td>\n    </tr>\n    <tr>\n      <th>20158</th>\n      <td>CS001114000005</td>\n      <td>2019-07-31</td>\n      <td>1970-01-01 00:00:00.020160412</td>\n      <td>594</td>\n    </tr>\n    <tr>\n      <th>20156</th>\n      <td>CS001114000005</td>\n      <td>2018-05-03</td>\n      <td>1970-01-01 00:00:00.020160412</td>\n      <td>580</td>\n    </tr>\n    <tr>\n      <th>29140</th>\n      <td>CS001115000010</td>\n      <td>2019-04-05</td>\n      <td>1970-01-01 00:00:00.020150417</td>\n      <td>591</td>\n    </tr>\n    <tr>\n      <th>29141</th>\n      <td>CS001115000010</td>\n      <td>2018-07-01</td>\n      <td>1970-01-01 00:00:00.020150417</td>\n      <td>581</td>\n    </tr>\n    <tr>\n      <th>29142</th>\n      <td>CS001115000010</td>\n      <td>2017-12-28</td>\n      <td>1970-01-01 00:00:00.020150417</td>\n      <td>575</td>\n    </tr>\n    <tr>\n      <th>6526</th>\n      <td>CS001205000004</td>\n      <td>2019-06-25</td>\n      <td>1970-01-01 00:00:00.020160615</td>\n      <td>593</td>\n    </tr>\n    <tr>\n      <th>6521</th>\n      <td>CS001205000004</td>\n      <td>2019-03-12</td>\n      <td>1970-01-01 00:00:00.020160615</td>\n      <td>590</td>\n    </tr>\n    <tr>\n      <th>6518</th>\n      <td>CS001205000004</td>\n      <td>2018-08-21</td>\n      <td>1970-01-01 00:00:00.020160615</td>\n      <td>583</td>\n    </tr>\n    <tr>\n      <th>6520</th>\n      <td>CS001205000004</td>\n      <td>2017-09-14</td>\n      <td>1970-01-01 00:00:00.020160615</td>\n      <td>572</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "          customer_id  sales_ymd              application_date  elapsed_date\n60376  CS001113000004 2019-03-08 1970-01-01 00:00:00.020151105           590\n20158  CS001114000005 2019-07-31 1970-01-01 00:00:00.020160412           594\n20156  CS001114000005 2018-05-03 1970-01-01 00:00:00.020160412           580\n29140  CS001115000010 2019-04-05 1970-01-01 00:00:00.020150417           591\n29141  CS001115000010 2018-07-01 1970-01-01 00:00:00.020150417           581\n29142  CS001115000010 2017-12-28 1970-01-01 00:00:00.020150417           575\n6526   CS001205000004 2019-06-25 1970-01-01 00:00:00.020160615           593\n6521   CS001205000004 2019-03-12 1970-01-01 00:00:00.020160615           590\n6518   CS001205000004 2018-08-21 1970-01-01 00:00:00.020160615           583\n6520   CS001205000004 2017-09-14 1970-01-01 00:00:00.020160615           572"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-072: レシート明細データフレーム（df_receipt）の売上日（sales_ymd）に対し、顧客データフレーム（df_customer）の会員申込日（application_date）からの経過年数を計算し、顧客ID（customer_id）、売上日、会員申込日とともに表示せよ。結果は10件表示させれば良い。（なお、sales_ymdは数値、application_dateは文字列でデータを保持している点に注意）。1年未満は切り捨てること。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = pd.merge(df_receipt[['customer_id', 'sales_ymd']], df_customer[['customer_id', 'application_date']],\n                 how='inner', on='customer_id')\n\ndf_tmp['sales_ymd'] = pd.to_datetime(df_tmp['sales_ymd'].astype('str'))\ndf_tmp['application_date'] = pd.to_datetime(df_tmp['application_date'])\n\ndf_tmp['elapsed_date'] = df_tmp[['sales_ymd', 'application_date']].apply(lambda x: \n                                                                         relativedelta(x[0], x[1]).years, axis=1)\ndf_tmp.head(10)",
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 81,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>sales_ymd</th>\n      <th>application_date</th>\n      <th>elapsed_date</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS006214000001</td>\n      <td>2018-11-03</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>48</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS006214000001</td>\n      <td>2017-05-09</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>47</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS006214000001</td>\n      <td>2017-06-08</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>47</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS006214000001</td>\n      <td>2017-06-08</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>47</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS006214000001</td>\n      <td>2018-10-28</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>48</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS006214000001</td>\n      <td>2018-10-28</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>48</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS006214000001</td>\n      <td>2017-05-09</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>47</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS006214000001</td>\n      <td>2019-09-08</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>49</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS006214000001</td>\n      <td>2018-01-31</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>48</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS006214000001</td>\n      <td>2017-07-05</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>47</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id  sales_ymd              application_date  elapsed_date\n0  CS006214000001 2018-11-03 1970-01-01 00:00:00.020150201            48\n1  CS006214000001 2017-05-09 1970-01-01 00:00:00.020150201            47\n2  CS006214000001 2017-06-08 1970-01-01 00:00:00.020150201            47\n3  CS006214000001 2017-06-08 1970-01-01 00:00:00.020150201            47\n4  CS006214000001 2018-10-28 1970-01-01 00:00:00.020150201            48\n5  CS006214000001 2018-10-28 1970-01-01 00:00:00.020150201            48\n6  CS006214000001 2017-05-09 1970-01-01 00:00:00.020150201            47\n7  CS006214000001 2019-09-08 1970-01-01 00:00:00.020150201            49\n8  CS006214000001 2018-01-31 1970-01-01 00:00:00.020150201            48\n9  CS006214000001 2017-07-05 1970-01-01 00:00:00.020150201            47"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-073: レシート明細データフレーム（df_receipt）の売上日（sales_ymd）に対し、顧客データフレーム（df_customer）の会員申込日（application_date）からのエポック秒による経過時間を計算し、顧客ID（customer_id）、売上日、会員申込日とともに表示せよ。結果は10件表示させれば良い（なお、sales_ymdは数値、application_dateは文字列でデータを保持している点に注意）。なお、時間情報は保有していないため各日付は0時0分0秒を表すものとする。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = pd.merge(df_receipt[['customer_id', 'sales_ymd']], df_customer[['customer_id', 'application_date']],\n                 how='inner', on='customer_id')\n\ndf_tmp = df_tmp.drop_duplicates()\n\ndf_tmp['sales_ymd'] = pd.to_datetime(df_tmp['sales_ymd'].astype('str'))\ndf_tmp['application_date'] = pd.to_datetime(df_tmp['application_date'])\n\ndf_tmp['elapsed_date'] = (df_tmp['sales_ymd'].astype(np.int64) / 10**9) - (df_tmp['application_date'].astype(np.int64) / 10**9)\ndf_tmp.head(10)",
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 82,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>sales_ymd</th>\n      <th>application_date</th>\n      <th>elapsed_date</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS006214000001</td>\n      <td>2018-11-03</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>1.541203e+09</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS006214000001</td>\n      <td>2017-05-09</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>1.494288e+09</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS006214000001</td>\n      <td>2017-06-08</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>1.496880e+09</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS006214000001</td>\n      <td>2018-10-28</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>1.540685e+09</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS006214000001</td>\n      <td>2019-09-08</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>1.567901e+09</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS006214000001</td>\n      <td>2018-01-31</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>1.517357e+09</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS006214000001</td>\n      <td>2017-07-05</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>1.499213e+09</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>CS006214000001</td>\n      <td>2018-11-10</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>1.541808e+09</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>CS006214000001</td>\n      <td>2019-04-10</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>1.554854e+09</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>CS006214000001</td>\n      <td>2019-06-01</td>\n      <td>1970-01-01 00:00:00.020150201</td>\n      <td>1.559347e+09</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       customer_id  sales_ymd              application_date  elapsed_date\n0   CS006214000001 2018-11-03 1970-01-01 00:00:00.020150201  1.541203e+09\n1   CS006214000001 2017-05-09 1970-01-01 00:00:00.020150201  1.494288e+09\n2   CS006214000001 2017-06-08 1970-01-01 00:00:00.020150201  1.496880e+09\n4   CS006214000001 2018-10-28 1970-01-01 00:00:00.020150201  1.540685e+09\n7   CS006214000001 2019-09-08 1970-01-01 00:00:00.020150201  1.567901e+09\n8   CS006214000001 2018-01-31 1970-01-01 00:00:00.020150201  1.517357e+09\n9   CS006214000001 2017-07-05 1970-01-01 00:00:00.020150201  1.499213e+09\n10  CS006214000001 2018-11-10 1970-01-01 00:00:00.020150201  1.541808e+09\n12  CS006214000001 2019-04-10 1970-01-01 00:00:00.020150201  1.554854e+09\n15  CS006214000001 2019-06-01 1970-01-01 00:00:00.020150201  1.559347e+09"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-074: レシート明細データフレーム（df_receipt）の売上日（sales_ymd）に対し、当該週の月曜日からの経過日数を計算し、売上日、当該週の月曜日付とともに表示せよ。結果は10件表示させれば良い（なお、sales_ymdは数値でデータを保持している点に注意）。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = df_receipt[['customer_id', 'sales_ymd']]\ndf_tmp = df_tmp.drop_duplicates()\ndf_tmp['sales_ymd'] = pd.to_datetime(df_tmp['sales_ymd'].astype('str'))\ndf_tmp['monday'] = df_tmp['sales_ymd'].apply(lambda x: x - relativedelta(days=x.weekday()))\ndf_tmp['elapsed_weekday'] = df_tmp['sales_ymd'] - df_tmp['monday']\ndf_tmp.head(10)",
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 83,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>sales_ymd</th>\n      <th>monday</th>\n      <th>elapsed_weekday</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS006214000001</td>\n      <td>2018-11-03</td>\n      <td>2018-10-29</td>\n      <td>5 days</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS008415000097</td>\n      <td>2018-11-18</td>\n      <td>2018-11-12</td>\n      <td>6 days</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS028414000014</td>\n      <td>2017-07-12</td>\n      <td>2017-07-10</td>\n      <td>2 days</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ZZ000000000000</td>\n      <td>2019-02-05</td>\n      <td>2019-02-04</td>\n      <td>1 days</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS025415000050</td>\n      <td>2018-08-21</td>\n      <td>2018-08-20</td>\n      <td>1 days</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS003515000195</td>\n      <td>2019-06-05</td>\n      <td>2019-06-03</td>\n      <td>2 days</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS024514000042</td>\n      <td>2018-12-05</td>\n      <td>2018-12-03</td>\n      <td>2 days</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS040415000178</td>\n      <td>2019-09-22</td>\n      <td>2019-09-16</td>\n      <td>6 days</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>ZZ000000000000</td>\n      <td>2017-05-04</td>\n      <td>2017-05-01</td>\n      <td>3 days</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS027514000015</td>\n      <td>2019-10-10</td>\n      <td>2019-10-07</td>\n      <td>3 days</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id  sales_ymd     monday elapsed_weekday\n0  CS006214000001 2018-11-03 2018-10-29          5 days\n1  CS008415000097 2018-11-18 2018-11-12          6 days\n2  CS028414000014 2017-07-12 2017-07-10          2 days\n3  ZZ000000000000 2019-02-05 2019-02-04          1 days\n4  CS025415000050 2018-08-21 2018-08-20          1 days\n5  CS003515000195 2019-06-05 2019-06-03          2 days\n6  CS024514000042 2018-12-05 2018-12-03          2 days\n7  CS040415000178 2019-09-22 2019-09-16          6 days\n8  ZZ000000000000 2017-05-04 2017-05-01          3 days\n9  CS027514000015 2019-10-10 2019-10-07          3 days"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-075: 顧客データフレーム（df_customer）からランダムに1%のデータを抽出し、先頭から10件データを抽出せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_customer.sample(frac=0.01).head(10)",
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 84,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>customer_name</th>\n      <th>gender_cd</th>\n      <th>gender</th>\n      <th>birth_day</th>\n      <th>age</th>\n      <th>postal_cd</th>\n      <th>address</th>\n      <th>application_store_cd</th>\n      <th>application_date</th>\n      <th>status_cd</th>\n      <th>age_group</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>18772</th>\n      <td>CS005515000369</td>\n      <td>春日 翔子</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1964-10-03</td>\n      <td>54</td>\n      <td>167-0031</td>\n      <td>東京都杉並区本天沼**********</td>\n      <td>S13005</td>\n      <td>20180211</td>\n      <td>3-20100517-2</td>\n      <td>[50.0, 60.0)</td>\n    </tr>\n    <tr>\n      <th>11534</th>\n      <td>CS029603000027</td>\n      <td>田村 正義</td>\n      <td>0</td>\n      <td>男性</td>\n      <td>1948-11-01</td>\n      <td>70</td>\n      <td>279-0011</td>\n      <td>千葉県浦安市美浜**********</td>\n      <td>S12029</td>\n      <td>20150128</td>\n      <td>0-00000000-0</td>\n      <td>[60.0, inf)</td>\n    </tr>\n    <tr>\n      <th>992</th>\n      <td>CS018715000013</td>\n      <td>古賀 杏</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1941-02-27</td>\n      <td>78</td>\n      <td>204-0012</td>\n      <td>東京都清瀬市中清戸**********</td>\n      <td>S13018</td>\n      <td>20150801</td>\n      <td>0-00000000-0</td>\n      <td>[60.0, inf)</td>\n    </tr>\n    <tr>\n      <th>13280</th>\n      <td>CS016312000113</td>\n      <td>亀山 れいな</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1987-04-20</td>\n      <td>31</td>\n      <td>187-0011</td>\n      <td>東京都小平市鈴木町**********</td>\n      <td>S13016</td>\n      <td>20150525</td>\n      <td>0-00000000-0</td>\n      <td>[30.0, 40.0)</td>\n    </tr>\n    <tr>\n      <th>12097</th>\n      <td>CS015212000045</td>\n      <td>鶴田 奈央</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1992-01-06</td>\n      <td>27</td>\n      <td>136-0073</td>\n      <td>東京都江東区北砂**********</td>\n      <td>S13015</td>\n      <td>20150722</td>\n      <td>6-20090316-3</td>\n      <td>[20.0, 30.0)</td>\n    </tr>\n    <tr>\n      <th>9797</th>\n      <td>CS019313000127</td>\n      <td>杉本 あさみ</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1987-10-21</td>\n      <td>31</td>\n      <td>173-0036</td>\n      <td>東京都板橋区向原**********</td>\n      <td>S13019</td>\n      <td>20150324</td>\n      <td>0-00000000-0</td>\n      <td>[30.0, 40.0)</td>\n    </tr>\n    <tr>\n      <th>17813</th>\n      <td>CS007615000110</td>\n      <td>岡本 美幸</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1954-05-13</td>\n      <td>64</td>\n      <td>285-0845</td>\n      <td>千葉県佐倉市西志津**********</td>\n      <td>S12007</td>\n      <td>20141019</td>\n      <td>8-20101018-C</td>\n      <td>[60.0, inf)</td>\n    </tr>\n    <tr>\n      <th>19506</th>\n      <td>CS037511000006</td>\n      <td>柴田 陽子</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1966-01-29</td>\n      <td>53</td>\n      <td>136-0071</td>\n      <td>東京都江東区亀戸**********</td>\n      <td>S13037</td>\n      <td>20150630</td>\n      <td>A-20081209-4</td>\n      <td>[50.0, 60.0)</td>\n    </tr>\n    <tr>\n      <th>4257</th>\n      <td>CS006312000096</td>\n      <td>柳川 真帆</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1979-02-03</td>\n      <td>40</td>\n      <td>224-0041</td>\n      <td>神奈川県横浜市都筑区仲町台**********</td>\n      <td>S14006</td>\n      <td>20150129</td>\n      <td>0-00000000-0</td>\n      <td>[40.0, 50.0)</td>\n    </tr>\n    <tr>\n      <th>2686</th>\n      <td>CS028513000046</td>\n      <td>生瀬 さやか</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1961-09-12</td>\n      <td>57</td>\n      <td>246-0031</td>\n      <td>神奈川県横浜市瀬谷区瀬谷**********</td>\n      <td>S14028</td>\n      <td>20150313</td>\n      <td>B-20100927-C</td>\n      <td>[50.0, 60.0)</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "          customer_id customer_name  gender_cd gender   birth_day  age  \\\n18772  CS005515000369         春日 翔子          1     女性  1964-10-03   54   \n11534  CS029603000027         田村 正義          0     男性  1948-11-01   70   \n992    CS018715000013          古賀 杏          1     女性  1941-02-27   78   \n13280  CS016312000113        亀山 れいな          1     女性  1987-04-20   31   \n12097  CS015212000045         鶴田 奈央          1     女性  1992-01-06   27   \n9797   CS019313000127        杉本 あさみ          1     女性  1987-10-21   31   \n17813  CS007615000110         岡本 美幸          1     女性  1954-05-13   64   \n19506  CS037511000006         柴田 陽子          1     女性  1966-01-29   53   \n4257   CS006312000096         柳川 真帆          1     女性  1979-02-03   40   \n2686   CS028513000046        生瀬 さやか          1     女性  1961-09-12   57   \n\n      postal_cd                  address application_store_cd  \\\n18772  167-0031      東京都杉並区本天沼**********               S13005   \n11534  279-0011       千葉県浦安市美浜**********               S12029   \n992    204-0012      東京都清瀬市中清戸**********               S13018   \n13280  187-0011      東京都小平市鈴木町**********               S13016   \n12097  136-0073       東京都江東区北砂**********               S13015   \n9797   173-0036       東京都板橋区向原**********               S13019   \n17813  285-0845      千葉県佐倉市西志津**********               S12007   \n19506  136-0071       東京都江東区亀戸**********               S13037   \n4257   224-0041  神奈川県横浜市都筑区仲町台**********               S14006   \n2686   246-0031   神奈川県横浜市瀬谷区瀬谷**********               S14028   \n\n       application_date     status_cd     age_group  \n18772          20180211  3-20100517-2  [50.0, 60.0)  \n11534          20150128  0-00000000-0   [60.0, inf)  \n992            20150801  0-00000000-0   [60.0, inf)  \n13280          20150525  0-00000000-0  [30.0, 40.0)  \n12097          20150722  6-20090316-3  [20.0, 30.0)  \n9797           20150324  0-00000000-0  [30.0, 40.0)  \n17813          20141019  8-20101018-C   [60.0, inf)  \n19506          20150630  A-20081209-4  [50.0, 60.0)  \n4257           20150129  0-00000000-0  [40.0, 50.0)  \n2686           20150313  B-20100927-C  [50.0, 60.0)  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-076: 顧客データフレーム（df_customer）から性別（gender_cd）の割合に基づきランダムに10%のデータを層化抽出データし、性別ごとに件数を集計せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# sklearn.model_selection.train_test_splitを使用した例\n_, df_tmp = train_test_split(df_customer, test_size=0.1, stratify=df_customer['gender'])\ndf_tmp.groupby('gender_cd').agg({'customer_id' : 'count'})",
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 85,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n    </tr>\n    <tr>\n      <th>gender_cd</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>298</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1793</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>107</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "           customer_id\ngender_cd             \n0                  298\n1                 1793\n9                  107"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp.head(10)",
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 86,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>customer_name</th>\n      <th>gender_cd</th>\n      <th>gender</th>\n      <th>birth_day</th>\n      <th>age</th>\n      <th>postal_cd</th>\n      <th>address</th>\n      <th>application_store_cd</th>\n      <th>application_date</th>\n      <th>status_cd</th>\n      <th>age_group</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>20954</th>\n      <td>CS002413000302</td>\n      <td>細谷 希</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1974-11-22</td>\n      <td>44</td>\n      <td>185-0023</td>\n      <td>東京都国分寺市西元町**********</td>\n      <td>S13002</td>\n      <td>20170201</td>\n      <td>0-00000000-0</td>\n      <td>[40.0, 50.0)</td>\n    </tr>\n    <tr>\n      <th>11786</th>\n      <td>CS001312000436</td>\n      <td>上原 怜奈</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1986-04-26</td>\n      <td>32</td>\n      <td>212-0004</td>\n      <td>神奈川県川崎市幸区小向西町**********</td>\n      <td>S13001</td>\n      <td>20161230</td>\n      <td>0-00000000-0</td>\n      <td>[30.0, 40.0)</td>\n    </tr>\n    <tr>\n      <th>21437</th>\n      <td>CS014312000076</td>\n      <td>戸塚 礼子</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1983-03-29</td>\n      <td>36</td>\n      <td>263-0013</td>\n      <td>千葉県千葉市稲毛区千草台**********</td>\n      <td>S12014</td>\n      <td>20151101</td>\n      <td>0-00000000-0</td>\n      <td>[30.0, 40.0)</td>\n    </tr>\n    <tr>\n      <th>8791</th>\n      <td>CS003113000013</td>\n      <td>日下部 美咲</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>2003-07-07</td>\n      <td>15</td>\n      <td>182-0022</td>\n      <td>東京都調布市国領町**********</td>\n      <td>S13003</td>\n      <td>20160201</td>\n      <td>0-00000000-0</td>\n      <td>[10.0, 20.0)</td>\n    </tr>\n    <tr>\n      <th>15810</th>\n      <td>CS039515000204</td>\n      <td>井田 季衣</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1968-03-11</td>\n      <td>51</td>\n      <td>168-0081</td>\n      <td>東京都杉並区宮前**********</td>\n      <td>S13039</td>\n      <td>20150102</td>\n      <td>D-20100724-E</td>\n      <td>[50.0, 60.0)</td>\n    </tr>\n    <tr>\n      <th>3702</th>\n      <td>CS001713000172</td>\n      <td>中井 愛</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1945-10-16</td>\n      <td>73</td>\n      <td>144-0035</td>\n      <td>東京都大田区南蒲田**********</td>\n      <td>S13001</td>\n      <td>20161221</td>\n      <td>0-00000000-0</td>\n      <td>[60.0, inf)</td>\n    </tr>\n    <tr>\n      <th>11297</th>\n      <td>CS032414000003</td>\n      <td>藤村 みき</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1976-03-04</td>\n      <td>43</td>\n      <td>144-0056</td>\n      <td>東京都大田区西六郷**********</td>\n      <td>S13032</td>\n      <td>20150418</td>\n      <td>F-20101027-E</td>\n      <td>[40.0, 50.0)</td>\n    </tr>\n    <tr>\n      <th>15839</th>\n      <td>CS005713000139</td>\n      <td>五十嵐 結衣</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1946-11-01</td>\n      <td>72</td>\n      <td>167-0021</td>\n      <td>東京都杉並区井草**********</td>\n      <td>S13005</td>\n      <td>20170221</td>\n      <td>0-00000000-0</td>\n      <td>[60.0, inf)</td>\n    </tr>\n    <tr>\n      <th>3082</th>\n      <td>CS020414000070</td>\n      <td>小笠原 千夏</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1970-09-11</td>\n      <td>48</td>\n      <td>114-0001</td>\n      <td>東京都北区東十条**********</td>\n      <td>S13020</td>\n      <td>20150322</td>\n      <td>D-20100901-E</td>\n      <td>[40.0, 50.0)</td>\n    </tr>\n    <tr>\n      <th>14682</th>\n      <td>CS019613000074</td>\n      <td>尾崎 華子</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1955-07-10</td>\n      <td>63</td>\n      <td>176-0002</td>\n      <td>東京都練馬区桜台**********</td>\n      <td>S13019</td>\n      <td>20150918</td>\n      <td>0-00000000-0</td>\n      <td>[60.0, inf)</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "          customer_id customer_name  gender_cd gender   birth_day  age  \\\n20954  CS002413000302          細谷 希          1     女性  1974-11-22   44   \n11786  CS001312000436         上原 怜奈          1     女性  1986-04-26   32   \n21437  CS014312000076         戸塚 礼子          1     女性  1983-03-29   36   \n8791   CS003113000013        日下部 美咲          1     女性  2003-07-07   15   \n15810  CS039515000204         井田 季衣          1     女性  1968-03-11   51   \n3702   CS001713000172          中井 愛          1     女性  1945-10-16   73   \n11297  CS032414000003         藤村 みき          1     女性  1976-03-04   43   \n15839  CS005713000139        五十嵐 結衣          1     女性  1946-11-01   72   \n3082   CS020414000070        小笠原 千夏          1     女性  1970-09-11   48   \n14682  CS019613000074         尾崎 華子          1     女性  1955-07-10   63   \n\n      postal_cd                  address application_store_cd  \\\n20954  185-0023     東京都国分寺市西元町**********               S13002   \n11786  212-0004  神奈川県川崎市幸区小向西町**********               S13001   \n21437  263-0013   千葉県千葉市稲毛区千草台**********               S12014   \n8791   182-0022      東京都調布市国領町**********               S13003   \n15810  168-0081       東京都杉並区宮前**********               S13039   \n3702   144-0035      東京都大田区南蒲田**********               S13001   \n11297  144-0056      東京都大田区西六郷**********               S13032   \n15839  167-0021       東京都杉並区井草**********               S13005   \n3082   114-0001       東京都北区東十条**********               S13020   \n14682  176-0002       東京都練馬区桜台**********               S13019   \n\n       application_date     status_cd     age_group  \n20954          20170201  0-00000000-0  [40.0, 50.0)  \n11786          20161230  0-00000000-0  [30.0, 40.0)  \n21437          20151101  0-00000000-0  [30.0, 40.0)  \n8791           20160201  0-00000000-0  [10.0, 20.0)  \n15810          20150102  D-20100724-E  [50.0, 60.0)  \n3702           20161221  0-00000000-0   [60.0, inf)  \n11297          20150418  F-20101027-E  [40.0, 50.0)  \n15839          20170221  0-00000000-0   [60.0, inf)  \n3082           20150322  D-20100901-E  [40.0, 50.0)  \n14682          20150918  0-00000000-0   [60.0, inf)  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-077: レシート明細データフレーム（df_receipt）の売上金額（amount）を顧客単位に合計し、合計した売上金額の外れ値を抽出せよ。ただし、顧客IDが\"Z\"から始まるのものは非会員を表すため、除外して計算すること。なお、ここでは外れ値を平均から3σ以上離れたものとする。結果は10件表示させれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# skleanのpreprocessing.scaleを利用するため、標本標準偏差で計算されている\ndf_sales_amount = df_receipt.query('not customer_id.str.startswith(\"Z\")', engine='python'). \\\n    groupby('customer_id').agg({'amount':'sum'}).reset_index()\ndf_sales_amount['amount_ss'] = preprocessing.scale(df_sales_amount['amount'])\ndf_sales_amount.query('abs(amount_ss) >= 3').head(10)",
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 87,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>amount</th>\n      <th>amount_ss</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>332</th>\n      <td>CS001605000009</td>\n      <td>18925</td>\n      <td>6.019921</td>\n    </tr>\n    <tr>\n      <th>1755</th>\n      <td>CS006415000147</td>\n      <td>12723</td>\n      <td>3.740202</td>\n    </tr>\n    <tr>\n      <th>1817</th>\n      <td>CS006515000023</td>\n      <td>18372</td>\n      <td>5.816651</td>\n    </tr>\n    <tr>\n      <th>1833</th>\n      <td>CS006515000125</td>\n      <td>12575</td>\n      <td>3.685800</td>\n    </tr>\n    <tr>\n      <th>1841</th>\n      <td>CS006515000209</td>\n      <td>11373</td>\n      <td>3.243972</td>\n    </tr>\n    <tr>\n      <th>1870</th>\n      <td>CS007115000006</td>\n      <td>11528</td>\n      <td>3.300946</td>\n    </tr>\n    <tr>\n      <th>1941</th>\n      <td>CS007514000056</td>\n      <td>13293</td>\n      <td>3.949721</td>\n    </tr>\n    <tr>\n      <th>1943</th>\n      <td>CS007514000094</td>\n      <td>15735</td>\n      <td>4.847347</td>\n    </tr>\n    <tr>\n      <th>1951</th>\n      <td>CS007515000107</td>\n      <td>11188</td>\n      <td>3.175970</td>\n    </tr>\n    <tr>\n      <th>1997</th>\n      <td>CS007615000026</td>\n      <td>11959</td>\n      <td>3.459372</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "         customer_id  amount  amount_ss\n332   CS001605000009   18925   6.019921\n1755  CS006415000147   12723   3.740202\n1817  CS006515000023   18372   5.816651\n1833  CS006515000125   12575   3.685800\n1841  CS006515000209   11373   3.243972\n1870  CS007115000006   11528   3.300946\n1941  CS007514000056   13293   3.949721\n1943  CS007514000094   15735   4.847347\n1951  CS007515000107   11188   3.175970\n1997  CS007615000026   11959   3.459372"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-078: レシート明細データフレーム（df_receipt）の売上金額（amount）を顧客単位に合計し、合計した売上金額の外れ値を抽出せよ。ただし、顧客IDが\"Z\"から始まるのものは非会員を表すため、除外して計算すること。なお、ここでは外れ値を第一四分位と第三四分位の差であるIQRを用いて、「第一四分位数-1.5×IQR」よりも下回るもの、または「第三四分位数+1.5×IQR」を超えるものとする。結果は10件表示させれば良い。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# skleanのpreprocessing.scaleを利用するため、標本標準偏差で計算されている\ndf_sales_amount = df_receipt.query('not customer_id.str.startswith(\"Z\")', engine='python'). \\\n    groupby('customer_id').agg({'amount':'sum'}).reset_index()\n\npct75 = np.percentile(df_sales_amount['amount'], q=75)\npct25 = np.percentile(df_sales_amount['amount'], q=25)\niqr = pct75 - pct25\namount_low = pct25 - (iqr * 1.5)\namount_hight = pct75 + (iqr * 1.5)\ndf_sales_amount.query('amount < @amount_low or @amount_hight < amount').head(10)",
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 88,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>98</th>\n      <td>CS001414000048</td>\n      <td>8584</td>\n    </tr>\n    <tr>\n      <th>332</th>\n      <td>CS001605000009</td>\n      <td>18925</td>\n    </tr>\n    <tr>\n      <th>549</th>\n      <td>CS002415000594</td>\n      <td>9568</td>\n    </tr>\n    <tr>\n      <th>1180</th>\n      <td>CS004414000181</td>\n      <td>9584</td>\n    </tr>\n    <tr>\n      <th>1558</th>\n      <td>CS005415000137</td>\n      <td>8734</td>\n    </tr>\n    <tr>\n      <th>1733</th>\n      <td>CS006414000001</td>\n      <td>9156</td>\n    </tr>\n    <tr>\n      <th>1736</th>\n      <td>CS006414000029</td>\n      <td>9179</td>\n    </tr>\n    <tr>\n      <th>1752</th>\n      <td>CS006415000105</td>\n      <td>10042</td>\n    </tr>\n    <tr>\n      <th>1755</th>\n      <td>CS006415000147</td>\n      <td>12723</td>\n    </tr>\n    <tr>\n      <th>1757</th>\n      <td>CS006415000157</td>\n      <td>10648</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "         customer_id  amount\n98    CS001414000048    8584\n332   CS001605000009   18925\n549   CS002415000594    9568\n1180  CS004414000181    9584\n1558  CS005415000137    8734\n1733  CS006414000001    9156\n1736  CS006414000029    9179\n1752  CS006415000105   10042\n1755  CS006415000147   12723\n1757  CS006415000157   10648"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-079: 商品データフレーム（df_product）の各項目に対し、欠損数を確認せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_product.isnull().sum()",
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 89,
          "data": {
            "text/plain": "product_cd            0\ncategory_major_cd     0\ncategory_medium_cd    0\ncategory_small_cd     0\nunit_price            7\nunit_cost             7\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-080: 商品データフレーム（df_product）のいずれかの項目に欠損が発生しているレコードを全て削除した新たなdf_product_1を作成せよ。なお、削除前後の件数を表示させ、前設問で確認した件数だけ減少していることも確認すること。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_product_1 = df_product.copy()\nprint('削除前:', len(df_product_1))\ndf_product_1.dropna(inplace=True)\nprint('削除後:', len(df_product_1))",
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": "削除前: 10030\n削除後: 10023\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-081: 単価（unit_price）と原価（unit_cost）の欠損値について、それぞれの平均値で補完した新たなdf_product_2を作成せよ。なお、平均値について1円未満は四捨五入とし、0.5については偶数寄せでかまわない。補完実施後、各項目について欠損が生じていないことも確認すること。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_product_2 = df_product.fillna({'unit_price':np.round(np.nanmean(df_product['unit_price'])), \n                                  'unit_cost':np.round(np.nanmean(df_product['unit_cost']))})\ndf_product_2.isnull().sum()",
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 91,
          "data": {
            "text/plain": "product_cd            0\ncategory_major_cd     0\ncategory_medium_cd    0\ncategory_small_cd     0\nunit_price            0\nunit_cost             0\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-082: 単価（unit_price）と原価（unit_cost）の欠損値について、それぞれの中央値で補完した新たなdf_product_3を作成せよ。なお、中央値について1円未満は四捨五入とし、0.5については偶数寄せでかまわない。補完実施後、各項目について欠損が生じていないことも確認すること。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_product_3 = df_product.fillna({'unit_price':np.round(np.nanmedian(df_product['unit_price'])), \n                                  'unit_cost':np.round(np.nanmedian(df_product['unit_cost']))})\ndf_product_3.isnull().sum()",
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 92,
          "data": {
            "text/plain": "product_cd            0\ncategory_major_cd     0\ncategory_medium_cd    0\ncategory_small_cd     0\nunit_price            0\nunit_cost             0\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-083: 単価（unit_price）と原価（unit_cost）の欠損値について、各商品の小区分（category_small_cd）ごとに算出した中央値で補完した新たなdf_product_4を作成せよ。なお、中央値について1円未満は四捨五入とし、0.5については偶数寄せでかまわない。補完実施後、各項目について欠損が生じていないことも確認すること。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = df_product.groupby('category_small_cd').agg({'unit_price':'median', 'unit_cost':'median'}).reset_index()\ndf_tmp.columns = ['category_small_cd', 'median_price', 'median_cost']\n\ndf_product_4 = pd.merge(df_product, df_tmp, how='inner', on='category_small_cd')\n\ndf_product_4['unit_price'] = df_product_4[['unit_price', 'median_price']]. \\\n                    apply(lambda x: np.round(x[1]) if np.isnan(x[0]) else x[0], axis=1)\ndf_product_4['unit_cost'] = df_product_4[['unit_cost', 'median_cost']]. \\\n                    apply(lambda x: np.round(x[1]) if np.isnan(x[0]) else x[0], axis=1)\n\ndf_product_4.isnull().sum()",
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 93,
          "data": {
            "text/plain": "product_cd            0\ncategory_major_cd     0\ncategory_medium_cd    0\ncategory_small_cd     0\nunit_price            0\nunit_cost             0\nmedian_price          0\nmedian_cost           0\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-084: 顧客データフレーム（df_customer）の全顧客に対し、全期間の売上金額に占める2019年売上金額の割合を計算せよ。ただし、販売実績のない場合は0として扱うこと。そして計算した割合が0超のものを抽出せよ。 結果は10件表示させれば良い。また、作成したデータにNAやNANが存在しないことを確認せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp_1 = df_receipt.query('20190101 <= sales_ymd <= 20191231')\ndf_tmp_1 = pd.merge(df_customer['customer_id'], df_tmp_1[['customer_id', 'amount']], how='left', on='customer_id'). \\\n    groupby('customer_id').sum().reset_index().rename(columns={'amount':'amount_2019'})\n\ndf_tmp_2 = pd.merge(df_customer['customer_id'], df_receipt[['customer_id', 'amount']], how='left', on='customer_id'). \\\n    groupby('customer_id').sum().reset_index()\n\ndf_tmp = pd.merge(df_tmp_1, df_tmp_2, how='inner', on='customer_id')\ndf_tmp['amount_rate'] = df_tmp['amount_2019'] / df_tmp['amount']",
      "execution_count": 94,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp.query('amount_rate > 0').head(10)",
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 95,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>amount_2019</th>\n      <th>amount</th>\n      <th>amount_rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8</th>\n      <td>CS001113000004</td>\n      <td>1298.0</td>\n      <td>1298.0</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>CS001114000005</td>\n      <td>188.0</td>\n      <td>626.0</td>\n      <td>0.300319</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>CS001115000010</td>\n      <td>578.0</td>\n      <td>3044.0</td>\n      <td>0.189882</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>CS001205000004</td>\n      <td>702.0</td>\n      <td>1988.0</td>\n      <td>0.353119</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>CS001205000006</td>\n      <td>486.0</td>\n      <td>3337.0</td>\n      <td>0.145640</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>CS001211000025</td>\n      <td>456.0</td>\n      <td>456.0</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>CS001212000070</td>\n      <td>456.0</td>\n      <td>456.0</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>CS001214000009</td>\n      <td>664.0</td>\n      <td>4685.0</td>\n      <td>0.141729</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>CS001214000017</td>\n      <td>2962.0</td>\n      <td>4132.0</td>\n      <td>0.716844</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>CS001214000048</td>\n      <td>1889.0</td>\n      <td>2374.0</td>\n      <td>0.795703</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       customer_id  amount_2019  amount  amount_rate\n8   CS001113000004       1298.0  1298.0     1.000000\n10  CS001114000005        188.0   626.0     0.300319\n12  CS001115000010        578.0  3044.0     0.189882\n17  CS001205000004        702.0  1988.0     0.353119\n18  CS001205000006        486.0  3337.0     0.145640\n23  CS001211000025        456.0   456.0     1.000000\n30  CS001212000070        456.0   456.0     1.000000\n57  CS001214000009        664.0  4685.0     0.141729\n59  CS001214000017       2962.0  4132.0     0.716844\n61  CS001214000048       1889.0  2374.0     0.795703"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_product_4.isnull().sum()",
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 96,
          "data": {
            "text/plain": "product_cd            0\ncategory_major_cd     0\ncategory_medium_cd    0\ncategory_small_cd     0\nunit_price            0\nunit_cost             0\nmedian_price          0\nmedian_cost           0\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-085: 顧客データフレーム（df_customer）の全顧客に対し、郵便番号（postal_cd）を用いて経度緯度変換用データフレーム（df_geocode）を紐付け、新たなdf_customer_1を作成せよ。ただし、複数紐づく場合は経度（longitude）、緯度（latitude）それぞれ平均を算出すること。\n"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_customer_1 = pd.merge(df_customer[['customer_id', 'postal_cd']],\n                         df_geocode[['postal_cd', 'longitude' ,'latitude']],\n                         how='inner', on='postal_cd')\ndf_customer_1 = df_customer_1.groupby('customer_id'). \\\n    agg({'longitude':'mean', 'latitude':'mean'}).reset_index(). \\\n    rename(columns={'longitude':'m_longitude', 'latitude':'m_latitude'})\n\ndf_customer_1 = pd.merge(df_customer, df_customer_1, how='inner', on='customer_id')\ndf_customer_1.head(3)",
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 97,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>customer_name</th>\n      <th>gender_cd</th>\n      <th>gender</th>\n      <th>birth_day</th>\n      <th>age</th>\n      <th>postal_cd</th>\n      <th>address</th>\n      <th>application_store_cd</th>\n      <th>application_date</th>\n      <th>status_cd</th>\n      <th>age_group</th>\n      <th>m_longitude</th>\n      <th>m_latitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS021313000114</td>\n      <td>大野 あや子</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1981-04-29</td>\n      <td>37</td>\n      <td>259-1113</td>\n      <td>神奈川県伊勢原市粟窪**********</td>\n      <td>S14021</td>\n      <td>20150905</td>\n      <td>0-00000000-0</td>\n      <td>[30.0, 40.0)</td>\n      <td>139.31779</td>\n      <td>35.41358</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS037613000071</td>\n      <td>六角 雅彦</td>\n      <td>9</td>\n      <td>不明</td>\n      <td>1952-04-01</td>\n      <td>66</td>\n      <td>136-0076</td>\n      <td>東京都江東区南砂**********</td>\n      <td>S13037</td>\n      <td>20150414</td>\n      <td>0-00000000-0</td>\n      <td>[60.0, inf)</td>\n      <td>139.83502</td>\n      <td>35.67193</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS031415000172</td>\n      <td>宇多田 貴美子</td>\n      <td>1</td>\n      <td>女性</td>\n      <td>1976-10-04</td>\n      <td>42</td>\n      <td>151-0053</td>\n      <td>東京都渋谷区代々木**********</td>\n      <td>S13031</td>\n      <td>20150529</td>\n      <td>D-20100325-C</td>\n      <td>[40.0, 50.0)</td>\n      <td>139.68965</td>\n      <td>35.67374</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id customer_name  gender_cd gender   birth_day  age postal_cd  \\\n0  CS021313000114        大野 あや子          1     女性  1981-04-29   37  259-1113   \n1  CS037613000071         六角 雅彦          9     不明  1952-04-01   66  136-0076   \n2  CS031415000172       宇多田 貴美子          1     女性  1976-10-04   42  151-0053   \n\n                address application_store_cd  application_date     status_cd  \\\n0  神奈川県伊勢原市粟窪**********               S14021          20150905  0-00000000-0   \n1    東京都江東区南砂**********               S13037          20150414  0-00000000-0   \n2   東京都渋谷区代々木**********               S13031          20150529  D-20100325-C   \n\n      age_group  m_longitude  m_latitude  \n0  [30.0, 40.0)    139.31779    35.41358  \n1   [60.0, inf)    139.83502    35.67193  \n2  [40.0, 50.0)    139.68965    35.67374  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-086: 前設問で作成した緯度経度つき顧客データフレーム（df_customer_1）に対し、申込み店舗コード（application_store_cd）をキーに店舗データフレーム（df_store）と結合せよ。そして申込み店舗の緯度（latitude）・経度情報（longitude)と顧客の緯度・経度を用いて距離（km）を求め、顧客ID（customer_id）、顧客住所（address）、店舗住所（address）とともに表示せよ。計算式は簡易式で良いものとするが、その他精度の高い方式を利用したライブラリを利用してもかまわない。結果は10件表示すれば良い。"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "$$\n緯度（ラジアン）：\\phi \\\\\n経度（ラジアン）：\\lambda \\\\\n距離L = 6371 * arccos(sin \\phi_1 * sin \\phi_2\n+ cos \\phi_1 * cos \\phi_2 * cos(\\lambda_1 − \\lambda_2))\n$$"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def calc_distance(x1, y1, x2, y2):\n    distance = 6371 * math.acos(math.sin(math.radians(y1)) * math.sin(math.radians(y2)) \n                       + math.cos(math.radians(y1)) * math.cos(math.radians(y2)) \n                            * math.cos(math.radians(x1) - math.radians(x2)))\n    return distance\n\ndf_tmp = pd.merge(df_customer_1, df_store, how='inner', left_on='application_store_cd', right_on='store_cd')    \n\ndf_tmp['distance'] =   df_tmp[['m_longitude', 'm_latitude','longitude', 'latitude']]. \\\n                                apply(lambda x: calc_distance(x[0], x[1], x[2], x[3]), axis=1)\n\ndf_tmp[['customer_id', 'address_x', 'address_y', 'distance']].head(10)",
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 98,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customer_id</th>\n      <th>address_x</th>\n      <th>address_y</th>\n      <th>distance</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CS021313000114</td>\n      <td>神奈川県伊勢原市粟窪**********</td>\n      <td>神奈川県伊勢原市伊勢原四丁目</td>\n      <td>1.394409</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CS021313000025</td>\n      <td>神奈川県伊勢原市伊勢原**********</td>\n      <td>神奈川県伊勢原市伊勢原四丁目</td>\n      <td>0.474282</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CS021411000096</td>\n      <td>神奈川県伊勢原市高森**********</td>\n      <td>神奈川県伊勢原市伊勢原四丁目</td>\n      <td>2.480155</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CS021415000150</td>\n      <td>神奈川県伊勢原市上粕屋**********</td>\n      <td>神奈川県伊勢原市伊勢原四丁目</td>\n      <td>2.734723</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CS021313000046</td>\n      <td>神奈川県伊勢原市池端**********</td>\n      <td>神奈川県伊勢原市伊勢原四丁目</td>\n      <td>1.111911</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CS021103000002</td>\n      <td>神奈川県伊勢原市西富岡**********</td>\n      <td>神奈川県伊勢原市伊勢原四丁目</td>\n      <td>2.384941</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CS021214000028</td>\n      <td>神奈川県伊勢原市桜台**********</td>\n      <td>神奈川県伊勢原市伊勢原四丁目</td>\n      <td>1.399344</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CS021512000095</td>\n      <td>神奈川県伊勢原市沼目**********</td>\n      <td>神奈川県伊勢原市伊勢原四丁目</td>\n      <td>1.993991</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CS021613000002</td>\n      <td>神奈川県伊勢原市桜台**********</td>\n      <td>神奈川県伊勢原市伊勢原四丁目</td>\n      <td>1.399344</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CS021412000147</td>\n      <td>神奈川県伊勢原市三ノ宮**********</td>\n      <td>神奈川県伊勢原市伊勢原四丁目</td>\n      <td>3.507680</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      customer_id              address_x       address_y  distance\n0  CS021313000114   神奈川県伊勢原市粟窪**********  神奈川県伊勢原市伊勢原四丁目  1.394409\n1  CS021313000025  神奈川県伊勢原市伊勢原**********  神奈川県伊勢原市伊勢原四丁目  0.474282\n2  CS021411000096   神奈川県伊勢原市高森**********  神奈川県伊勢原市伊勢原四丁目  2.480155\n3  CS021415000150  神奈川県伊勢原市上粕屋**********  神奈川県伊勢原市伊勢原四丁目  2.734723\n4  CS021313000046   神奈川県伊勢原市池端**********  神奈川県伊勢原市伊勢原四丁目  1.111911\n5  CS021103000002  神奈川県伊勢原市西富岡**********  神奈川県伊勢原市伊勢原四丁目  2.384941\n6  CS021214000028   神奈川県伊勢原市桜台**********  神奈川県伊勢原市伊勢原四丁目  1.399344\n7  CS021512000095   神奈川県伊勢原市沼目**********  神奈川県伊勢原市伊勢原四丁目  1.993991\n8  CS021613000002   神奈川県伊勢原市桜台**********  神奈川県伊勢原市伊勢原四丁目  1.399344\n9  CS021412000147  神奈川県伊勢原市三ノ宮**********  神奈川県伊勢原市伊勢原四丁目  3.507680"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-087:  顧客データフレーム（df_customer）では、異なる店舗での申込みなどにより同一顧客が複数登録されている。名前（customer_name）と郵便番号（postal_cd）が同じ顧客は同一顧客とみなし、1顧客1レコードとなるように名寄せした名寄顧客データフレーム（df_customer_u）を作成せよ。ただし、同一顧客に対しては売上金額合計が最も高いものを残すものとし、売上金額合計が同一もしくは売上実績の無い顧客については顧客ID（customer_id）の番号が小さいものを残すこととする。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = df_receipt.groupby('customer_id').agg({'amount':sum}).reset_index()\ndf_customer_u = pd.merge(df_customer, df_tmp, how='left', on='customer_id').sort_values(['amount', 'customer_id']\n                                                                                        , ascending=[False, True])\ndf_customer_u.drop_duplicates(subset=['customer_name', 'postal_cd'], keep='first', inplace=True)\n\nprint('減少数: ', len(df_customer) - len(df_customer_u))",
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": "減少数:  30\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-088: 前設問で作成したデータを元に、顧客データフレームに統合名寄IDを付与したデータフレーム（df_customer_n）を作成せよ。ただし、統合名寄IDは以下の仕様で付与するものとする。\n>\n> - 重複していない顧客：顧客ID（customer_id）を設定\n> - 重複している顧客：前設問で抽出したレコードの顧客IDを設定"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_customer_n = pd.merge(df_customer, df_customer_u[['customer_name', 'postal_cd', 'customer_id']],\n                        how='inner', on =['customer_name', 'postal_cd'])\ndf_customer_n.rename(columns={'customer_id_x':'customer_id', 'customer_id_y':'integration_id'}, inplace=True)\n\nprint('ID数の差', len(df_customer_n['customer_id'].unique()) - len(df_customer_n['integration_id'].unique()))",
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "text": "ID数の差 30\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-閑話: df_customer_1, df_customer_nは使わないので削除する。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "del df_customer_1\ndel df_customer_n",
      "execution_count": 101,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-089: 売上実績のある顧客に対し、予測モデル構築のため学習用データとテスト用データに分割したい。それぞれ8:2の割合でランダムにデータを分割せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = pd.merge(df_customer, df_receipt['customer_id'], how='inner', on='customer_id')\ndf_train, df_test = train_test_split(df_tmp, test_size=0.2, random_state=71)\nprint('学習データ割合: ', len(df_train) / len(df_tmp))\nprint('テストデータ割合: ', len(df_test) / len(df_tmp))",
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "text": "学習データ割合:  0.7999908650771901\nテストデータ割合:  0.2000091349228099\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-090: レシート明細データフレーム（df_receipt）は2017年1月1日〜2019年10月31日までのデータを有している。売上金額（amount）を月次で集計し、学習用に12ヶ月、テスト用に6ヶ月のモデル構築用データを3セット作成せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = df_receipt[['sales_ymd', 'amount']].copy()\ndf_tmp['sales_ym'] = df_tmp['sales_ymd'].astype('str').str[0:6]\ndf_tmp = df_tmp.groupby('sales_ym').agg({'amount':'sum'}).reset_index()\n\n# 関数化することで長期間データに対する多数のデータセットもループなどで処理できるようにする\ndef split_data(df, train_size, test_size, slide_window, start_point):\n    train_start = start_point * slide_window\n    test_start = train_start + train_size\n    return df[train_start : test_start], df[test_start : test_start + test_size]\n\ndf_train_1, df_test_1 = split_data(df_tmp, train_size=12, test_size=6, slide_window=6, start_point=0)\ndf_train_2, df_test_2 = split_data(df_tmp, train_size=12, test_size=6, slide_window=6, start_point=1)\ndf_train_3, df_test_3 = split_data(df_tmp, train_size=12, test_size=6, slide_window=6, start_point=2)",
      "execution_count": 103,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_train_1",
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 104,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sales_ym</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>201701</td>\n      <td>902056</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>201702</td>\n      <td>764413</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>201703</td>\n      <td>962945</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>201704</td>\n      <td>847566</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>201705</td>\n      <td>884010</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>201706</td>\n      <td>894242</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>201707</td>\n      <td>959205</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>201708</td>\n      <td>954836</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>201709</td>\n      <td>902037</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>201710</td>\n      <td>905739</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>201711</td>\n      <td>932157</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>201712</td>\n      <td>939654</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   sales_ym  amount\n0    201701  902056\n1    201702  764413\n2    201703  962945\n3    201704  847566\n4    201705  884010\n5    201706  894242\n6    201707  959205\n7    201708  954836\n8    201709  902037\n9    201710  905739\n10   201711  932157\n11   201712  939654"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_test_1",
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 105,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sales_ym</th>\n      <th>amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>12</th>\n      <td>201801</td>\n      <td>944509</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>201802</td>\n      <td>864128</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>201803</td>\n      <td>946588</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>201804</td>\n      <td>937099</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>201805</td>\n      <td>1004438</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>201806</td>\n      <td>1012329</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   sales_ym   amount\n12   201801   944509\n13   201802   864128\n14   201803   946588\n15   201804   937099\n16   201805  1004438\n17   201806  1012329"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-091: 顧客データフレーム（df_customer）の各顧客に対し、売上実績のある顧客数と売上実績のない顧客数が1:1となるようにアンダーサンプリングで抽出せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#unbalancedのubUnderを使った例\ndf_tmp = df_receipt.groupby('customer_id').agg({'amount':'sum'}).reset_index()\ndf_tmp = pd.merge(df_customer, df_tmp, how='left', on='customer_id')\ndf_tmp['buy_flg'] = df_tmp['amount'].apply(lambda x: 0 if np.isnan(x) else 1)\n\nprint('0の件数', len(df_tmp.query('buy_flg == 0')))\nprint('1の件数', len(df_tmp.query('buy_flg == 1')))\n\npositive_count = len(df_tmp.query('buy_flg == 1'))\n\nrs = RandomUnderSampler(random_state=71)\n\ndf_sample, _ = rs.fit_sample(df_tmp, df_tmp.buy_flg)\n\nprint('0の件数', len(df_sample.query('buy_flg == 0')))\nprint('1の件数', len(df_sample.query('buy_flg == 1')))",
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": "0の件数 13665\n1の件数 8306\n0の件数 8306\n1の件数 8306\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-092: 顧客データフレーム（df_customer）では、性別に関する情報が非正規化の状態で保持されている。これを第三正規化せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_gender = df_customer[['gender_cd', 'gender']].drop_duplicates()\ndf_customer_s = df_customer.drop(columns='gender')",
      "execution_count": 107,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-093: 商品データフレーム（df_product）では各カテゴリのコード値だけを保有し、カテゴリ名は保有していない。カテゴリデータフレーム（df_category）と組み合わせて非正規化し、カテゴリ名を保有した新たな商品データフレームを作成せよ。"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_product_full = pd.merge(df_product, df_category[['category_small_cd', \n                                                    'category_major_name',\n                                                    'category_medium_name',\n                                                    'category_small_name']], \n                           how = 'inner', on = 'category_small_cd')",
      "execution_count": 108,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-094: 先に作成したカテゴリ名付き商品データを以下の仕様でファイル出力せよ。なお、出力先のパスはdata配下とする。\n>\n> - ファイル形式はCSV（カンマ区切り）\n> - ヘッダ有り\n> - 文字コードはUTF-8"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "os.makedirs('./data')\ndf_product_full.to_csv('./data/P_df_product_full_UTF-8_header.csv', encoding='UTF-8', index=False)",
      "execution_count": 109,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-095: 先に作成したカテゴリ名付き商品データを以下の仕様でファイル出力せよ。なお、出力先のパスはdata配下とする。\n>\n> - ファイル形式はCSV（カンマ区切り）\n> - ヘッダ有り\n> - 文字コードはCP932"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_product_full.to_csv('./data/P_df_product_full_CP932_header.csv', encoding='CP932', index=False)",
      "execution_count": 110,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-096: 先に作成したカテゴリ名付き商品データを以下の仕様でファイル出力せよ。なお、出力先のパスはdata配下とする。\n>\n> - ファイル形式はCSV（カンマ区切り）\n> - ヘッダ無し\n> - 文字コードはUTF-8"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_product_full.to_csv('./data/P_df_product_full_UTF-8_noh.csv', header=False ,encoding='UTF-8', index=False)",
      "execution_count": 111,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-097: 先に作成した以下形式のファイルを読み込み、データフレームを作成せよ。また、先頭10件を表示させ、正しくとりまれていることを確認せよ。\n>\n> - ファイル形式はCSV（カンマ区切り）\n> - ヘッダ有り\n> - 文字コードはUTF-8"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = pd.read_csv('./data/P_df_product_full_UTF-8_header.csv')\ndf_tmp.head(10)",
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 112,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>product_cd</th>\n      <th>category_major_cd</th>\n      <th>category_medium_cd</th>\n      <th>category_small_cd</th>\n      <th>unit_price</th>\n      <th>unit_cost</th>\n      <th>category_major_name</th>\n      <th>category_medium_name</th>\n      <th>category_small_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>P040101001</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>198.0</td>\n      <td>149.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>P040101002</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>218.0</td>\n      <td>164.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>P040101003</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>230.0</td>\n      <td>173.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>P040101004</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>248.0</td>\n      <td>186.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>P040101005</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>268.0</td>\n      <td>201.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>P040101006</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>298.0</td>\n      <td>224.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>P040101007</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>338.0</td>\n      <td>254.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>P040101008</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>420.0</td>\n      <td>315.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>P040101009</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>498.0</td>\n      <td>374.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>P040101010</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>580.0</td>\n      <td>435.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   product_cd  category_major_cd  category_medium_cd  category_small_cd  \\\n0  P040101001                  4                 401              40101   \n1  P040101002                  4                 401              40101   \n2  P040101003                  4                 401              40101   \n3  P040101004                  4                 401              40101   \n4  P040101005                  4                 401              40101   \n5  P040101006                  4                 401              40101   \n6  P040101007                  4                 401              40101   \n7  P040101008                  4                 401              40101   \n8  P040101009                  4                 401              40101   \n9  P040101010                  4                 401              40101   \n\n   unit_price  unit_cost category_major_name category_medium_name  \\\n0       198.0      149.0                  惣菜                  御飯類   \n1       218.0      164.0                  惣菜                  御飯類   \n2       230.0      173.0                  惣菜                  御飯類   \n3       248.0      186.0                  惣菜                  御飯類   \n4       268.0      201.0                  惣菜                  御飯類   \n5       298.0      224.0                  惣菜                  御飯類   \n6       338.0      254.0                  惣菜                  御飯類   \n7       420.0      315.0                  惣菜                  御飯類   \n8       498.0      374.0                  惣菜                  御飯類   \n9       580.0      435.0                  惣菜                  御飯類   \n\n  category_small_name  \n0                 弁当類  \n1                 弁当類  \n2                 弁当類  \n3                 弁当類  \n4                 弁当類  \n5                 弁当類  \n6                 弁当類  \n7                 弁当類  \n8                 弁当類  \n9                 弁当類  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-098: 先に作成した以下形式のファイルを読み込み、データフレームを作成せよ。また、先頭10件を表示させ、正しくとりまれていることを確認せよ。\n>\n> - ファイル形式はCSV（カンマ区切り）\n> - ヘッダ無し\n> - 文字コードはUTF-8"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = pd.read_csv('./data/P_df_product_full_UTF-8_noh.csv', header=None)\ndf_tmp.head(10)",
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 113,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>P040101001</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>198.0</td>\n      <td>149.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>P040101002</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>218.0</td>\n      <td>164.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>P040101003</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>230.0</td>\n      <td>173.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>P040101004</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>248.0</td>\n      <td>186.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>P040101005</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>268.0</td>\n      <td>201.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>P040101006</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>298.0</td>\n      <td>224.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>P040101007</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>338.0</td>\n      <td>254.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>P040101008</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>420.0</td>\n      <td>315.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>P040101009</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>498.0</td>\n      <td>374.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>P040101010</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>580.0</td>\n      <td>435.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "            0  1    2      3      4      5   6    7    8\n0  P040101001  4  401  40101  198.0  149.0  惣菜  御飯類  弁当類\n1  P040101002  4  401  40101  218.0  164.0  惣菜  御飯類  弁当類\n2  P040101003  4  401  40101  230.0  173.0  惣菜  御飯類  弁当類\n3  P040101004  4  401  40101  248.0  186.0  惣菜  御飯類  弁当類\n4  P040101005  4  401  40101  268.0  201.0  惣菜  御飯類  弁当類\n5  P040101006  4  401  40101  298.0  224.0  惣菜  御飯類  弁当類\n6  P040101007  4  401  40101  338.0  254.0  惣菜  御飯類  弁当類\n7  P040101008  4  401  40101  420.0  315.0  惣菜  御飯類  弁当類\n8  P040101009  4  401  40101  498.0  374.0  惣菜  御飯類  弁当類\n9  P040101010  4  401  40101  580.0  435.0  惣菜  御飯類  弁当類"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-099: 先に作成したカテゴリ名付き商品データを以下の仕様でファイル出力せよ。なお、出力先のパスはdata配下とする。\n>\n> - ファイル形式はTSV（タブ区切り）\n> - ヘッダ有り\n> - 文字コードはUTF-8"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_product_full.to_csv('./data/P_df_product_full_UTF-8_header.tsv', sep='\\t', encoding='UTF-8', index=False)",
      "execution_count": 114,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n> P-100: 先に作成した以下形式のファイルを読み込み、データフレームを作成せよ。また、先頭10件を表示させ、正しくとりまれていることを確認せよ。\n>\n> - ファイル形式はTSV（タブ区切り）\n> - ヘッダ有り\n> - 文字コードはUTF-8"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_tmp = pd.read_table('./data/P_df_product_full_UTF-8_header.tsv', encoding='UTF-8')\ndf_tmp.head(10)",
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 115,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>product_cd</th>\n      <th>category_major_cd</th>\n      <th>category_medium_cd</th>\n      <th>category_small_cd</th>\n      <th>unit_price</th>\n      <th>unit_cost</th>\n      <th>category_major_name</th>\n      <th>category_medium_name</th>\n      <th>category_small_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>P040101001</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>198.0</td>\n      <td>149.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>P040101002</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>218.0</td>\n      <td>164.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>P040101003</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>230.0</td>\n      <td>173.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>P040101004</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>248.0</td>\n      <td>186.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>P040101005</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>268.0</td>\n      <td>201.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>P040101006</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>298.0</td>\n      <td>224.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>P040101007</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>338.0</td>\n      <td>254.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>P040101008</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>420.0</td>\n      <td>315.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>P040101009</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>498.0</td>\n      <td>374.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>P040101010</td>\n      <td>4</td>\n      <td>401</td>\n      <td>40101</td>\n      <td>580.0</td>\n      <td>435.0</td>\n      <td>惣菜</td>\n      <td>御飯類</td>\n      <td>弁当類</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   product_cd  category_major_cd  category_medium_cd  category_small_cd  \\\n0  P040101001                  4                 401              40101   \n1  P040101002                  4                 401              40101   \n2  P040101003                  4                 401              40101   \n3  P040101004                  4                 401              40101   \n4  P040101005                  4                 401              40101   \n5  P040101006                  4                 401              40101   \n6  P040101007                  4                 401              40101   \n7  P040101008                  4                 401              40101   \n8  P040101009                  4                 401              40101   \n9  P040101010                  4                 401              40101   \n\n   unit_price  unit_cost category_major_name category_medium_name  \\\n0       198.0      149.0                  惣菜                  御飯類   \n1       218.0      164.0                  惣菜                  御飯類   \n2       230.0      173.0                  惣菜                  御飯類   \n3       248.0      186.0                  惣菜                  御飯類   \n4       268.0      201.0                  惣菜                  御飯類   \n5       298.0      224.0                  惣菜                  御飯類   \n6       338.0      254.0                  惣菜                  御飯類   \n7       420.0      315.0                  惣菜                  御飯類   \n8       498.0      374.0                  惣菜                  御飯類   \n9       580.0      435.0                  惣菜                  御飯類   \n\n  category_small_name  \n0                 弁当類  \n1                 弁当類  \n2                 弁当類  \n3                 弁当類  \n4                 弁当類  \n5                 弁当類  \n6                 弁当類  \n7                 弁当類  \n8                 弁当類  \n9                 弁当類  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# これで１００本終わりです。おつかれさまでした！"
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python36",
      "display_name": "Python 3.6",
      "language": "python"
    },
    "language_info": {
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
      "name": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6",
      "file_extension": ".py",
      "codemirror_mode": {
        "version": 3,
        "name": "ipython"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}