{ "cells": [ { "cell_type": "code", "source": [ "!date\n", "!python --version" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uUS6510pkOy4", "outputId": "5053c0d7-3701-4e2e-8c3e-f799f16f88ba" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Fri Apr 25 01:33:25 AM UTC 2025\n", "Python 3.11.12\n" ] } ] }, { "cell_type": "markdown", "source": [ "更新履歴\n", "- 2025年4月25日: df.describe()後の指定例追加。" ], "metadata": { "id": "H6447RelkRDh" } }, { "cell_type": "markdown", "metadata": { "id": "OTEIqhB7dwPq" }, "source": [ "# Pandas, Matplotlib, Plotlyを用いたデータ分析例\n", "POSレジ購買データ（疑似データ）を例に代表的な使い方を例示します。なおこれらの細かい使い方は膨大なため、今回は一例として紹介するに留めます。より詳細は参考サイトを参照ください（=調べながら使えるようになりましょう。個別相談には応じます）。\n", "- **大前提**\n", " - 今回は全体の流れを確認しながら一部コード修正して実行する練習をしていきます。より詳細なプログラムの書き方や実行方法はその都度説明しますので、今回で全てを覚える必要はありません。\n", "- 疑似データ\n", " - [data.csv](../assets/data.csv) を使います。\n", " - step 1: Google Colabratoryで新規ノートブックを用意してください。\n", " - step 2: 授業資料フォルダを開き、data.csvを用意してください。\n", " - step 3: data.csvをノートブックにアップロードしてください。\n", "- 今回のデータ説明\n", " - purchase_date: 購入日（yyyy-mm-dd hh:mm:ss）\n", " - year: 購入年 (yyyy)\n", " - month: 購入月 (mm)\n", " - day_of_week: 曜日 (str)\n", " - time_of_day: 購入日（hh:mm:ss）\n", " - weather: 天候 (str)\n", " - age_group: 年代 (str)\n", " - gender: 性別 (str), 10代〜40代、50代以上\n", " - persona: 顧客属性 (str), 社会人、学生\n", " - purchase_tendency: 購入傾向 (dict)\n", " - product_name: 購入カテゴリ (list[str])\n", " - price: 購入金額 (float)\n", " - **疑似データ**です。同じ時間帯に天候が変わってたり、秒単位で同じ履歴があったり、金額に小数点があったりします。夏に雪がふることもあります。世界中に展開してるコンビニチェーン店全ログぐらいに捉えてください。\n", "\n", "- より深く学ぶための参考サイト\n", " - pandas: [ [公式チュートリアル](https://pandas.pydata.org/docs/user_guide/10min.html) | [100 pandas puzzles](https://notebook.community/prashantas/MyDataScience/Python/100-pandas-puzzles-with-solutions) | [cheat sheet](https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf) ]\n", " - plotly: [ [公式チュートリアル](https://plotly.com/python/getting-started/) | [basic charts](https://plotly.com/python/basic-charts/) | [cheat sheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) ]\n", " - matplotlib: [ [公式チュートリアル](https://matplotlib.org/stable/tutorials/index.html) | [examples](https://matplotlib.org/stable/gallery/index.html) | [cheat sheet](https://matplotlib.org/cheatsheets/) ]\n", "- 具体的なデータを利用したチュートリアル\n", " - [マンガと学ぶデータビジュアライゼーション](https://kakeami.github.io/viz-madb/index.html)" ] }, { "cell_type": "markdown", "metadata": { "id": "SiRvcR9EdwPt" }, "source": [ "## データ読み込み〜例示\n", "pd.read_csv等で読み込んだデータはpd.DataFrame型（データフレーム型）と呼ばれる。ノートブック内でデータフレーム形式で保存した変数名を書くと、きれいな表敬式で出力してくれる。通常の.pyファイルでprint関数により出力することもできるが、ノートブックと比べると見づらい。\n", "- [pd.read_csv](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html): CSVファイル読み込み用の関数。\n", "- [pd.read_excel](https://pandas.pydata.org/docs/reference/api/pandas.read_excel.html): Excelファイルを読み込むこともできる（今回は未使用）" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZI50vX6JeGuN", "outputId": "b8430b49-af76-441a-fc80-28e992dc9c4a" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: japanize_matplotlib in /usr/local/lib/python3.11/dist-packages (1.1.3)\n", "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from japanize_matplotlib) (3.10.0)\n", "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->japanize_matplotlib) (1.3.2)\n", "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->japanize_matplotlib) (0.12.1)\n", "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->japanize_matplotlib) (4.57.0)\n", "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->japanize_matplotlib) (1.4.8)\n", "Requirement already satisfied: numpy>=1.23 in /usr/local/lib/python3.11/dist-packages (from matplotlib->japanize_matplotlib) (2.0.2)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->japanize_matplotlib) (24.2)\n", "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib->japanize_matplotlib) (11.1.0)\n", "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->japanize_matplotlib) (3.2.3)\n", "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib->japanize_matplotlib) (2.8.2)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib->japanize_matplotlib) (1.17.0)\n" ] } ], "source": [ "# matplotlibで日本語を使うためのライブラリをインストール\n", "!pip install japanize_matplotlib" ] }, { "cell_type": "markdown", "metadata": { "id": "JypqX3iuypGS" }, "source": [ "### ライブラリの読み込み\n", "プログラミングにおける「ライブラリ」とは、特定の機能をまとめたものを指すことが多いです。\n", "\n", "日常生活で例えると、例えば英文を読んでいるときに分からない単語が出てきたら書籍やアプリにより英文辞書を用意し、調べるでしょう。これに対し数学を勉強しているときに英文辞書は不要なので片付けておくでしょう。このように必要なときに取り出し、不要なときには片付けておくことで卓上スペースを有効活用することができます。\n", "\n", "プログラムにおける「ライブラリの読み込み」は、同じように効率良く使うための工夫です。一般的にPCでの処理はストレージに保存されているものをメモリに読み込み、CPUやGPUにより処理されます。メモリはストレージに対して高速に動作しますがその分高価なので容量が小さいです。このためメモリには必要な情報だけを置いておきたいです。これを実現しているのが「ライブラリの読み込み」です。\n", "\n", "- 今回利用するライブラリ\n", " - pandasは表形式（正確にはデータフレーム形式）のデータを処理するためのライブラリです。``as pd``と書いているのは、プログラム中で「pd」と省略して書くために別称を設定しています。\n", " - plotly, matplotlibは、グラフ描画のためのライブラリです。\n", " - ``%matplotlib inline``は、ノートブック固有の記述です。ノートブック中にグラフを描画するために書いています。\n", " - japanize_matplotlibは、matplotlibで日本語フォントを利用するためのライブラリです。\n", "\n", "- 参考\n", " - [IT用語辞典 > ソフトウェア > プログラミング > 開発環境 > ライブラリとは](https://www.sophia-it.com/content/%E3%83%A9%E3%82%A4%E3%83%96%E3%83%A9%E3%83%AA)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dXhUaMwxykHo" }, "outputs": [], "source": [ "# ライブラリの読み込み\n", "import pandas as pd\n", "import plotly.express as px\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import japanize_matplotlib" ] }, { "cell_type": "markdown", "metadata": { "id": "GvMjRdwi1Tkt" }, "source": [ "### データの読み込み\n", "- ``pd.read_csv()`` は、「pdライブラリの中にある関数read_csv()を実行せよ」という命令です。カッコ内でCSVファイルの名前を指定することで、該当ファイルを読み込み、データフレームとして利用することができます。\n", "- ``df = pd.read_csv()`` は、「pd.read_csv()の実行結果を変数dfに保存せよ」という命令です。実行結果を変数に保存しておかないと、参照する（利用する）ことができません。\n", "- ``df`` は、「変数dfの中身を出力しろ」という命令です。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 779 }, "id": "DY3YU2C_dwPt", "outputId": "c9e60a77-a954-4527-d1a3-5c523aa35864" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " purchase_date year month day_of_week time_of_day weather \\\n", "0 2022-01-01 10:47:25 2022 1 Saturday 10:47:25 晴れ \n", "1 2022-01-01 11:20:06 2022 1 Saturday 11:20:06 雨 \n", "2 2022-01-01 11:43:07 2022 1 Saturday 11:43:07 雨 \n", "3 2022-01-01 11:58:43 2022 1 Saturday 11:58:43 晴れ \n", "4 2022-01-01 12:11:03 2022 1 Saturday 12:11:03 晴れ \n", "... ... ... ... ... ... ... \n", "9995 2022-12-31 21:34:22 2022 12 Saturday 21:34:22 晴れ \n", "9996 2022-12-31 21:55:54 2022 12 Saturday 21:55:54 晴れ \n", "9997 2022-12-31 22:03:36 2022 12 Saturday 22:03:36 晴れ \n", "9998 2022-12-31 22:25:55 2022 12 Saturday 22:25:55 晴れ \n", "9999 2022-12-31 22:29:17 2022 12 Saturday 22:29:17 曇り \n", "\n", " age_group gender persona \\\n", "0 10代 男性 学生 \n", "1 10代 男性 社会人 \n", "2 30代 女性 社会人 \n", "3 20代 女性 学生 \n", "4 40代 男性 学生 \n", "... ... ... ... \n", "9995 50代以上 男性 学生 \n", "9996 10代 男性 社会人 \n", "9997 50代以上 女性 学生 \n", "9998 30代 男性 学生 \n", "9999 30代 男性 学生 \n", "\n", " purchase_tendency product_name price \n", "0 {'おにぎり': 0.1, '野菜飲料': 0.05, 'エナジードリンク': 0.1500... ['お菓子'] 158.39 \n", "1 {'おにぎり': 0.1, '野菜飲料': 0.05, 'エナジードリンク': 0.05, ... ['傘'] 1422.74 \n", "2 {'おにぎり': 0.2, '野菜飲料': 0.2, 'エナジードリンク': 0.25, '... ['エナジードリンク'] 483.69 \n", "3 {'おにぎり': 0.2, '野菜飲料': 0.2, 'エナジードリンク': 0.35, '... ['お菓子'] 173.22 \n", "4 {'おにぎり': 0.2, '野菜飲料': 0.25, 'エナジードリンク': 0.1500... ['アイス'] 248.47 \n", "... ... ... ... \n", "9995 {'おにぎり': 0.3, '野菜飲料': 0.25, 'エナジードリンク': 0.1500... ['清涼飲料水'] 784.93 \n", "9996 {'おにぎり': 0.1, '野菜飲料': 0.05, 'エナジードリンク': 0.05, ... ['お菓子'] 152.61 \n", "9997 {'おにぎり': 0.3, '野菜飲料': 0.4, 'エナジードリンク': 0.15000... ['お菓子'] 131.72 \n", "9998 {'おにぎり': 0.2, '野菜飲料': 0.05, 'エナジードリンク': 0.35, ... ['おにぎり'] 1227.61 \n", "9999 {'おにぎり': 0.2, '野菜飲料': 0.05, 'エナジードリンク': 0.35, ... ['ビッグ弁当'] 552.63 \n", "\n", "[10000 rows x 12 columns]" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
purchase_dateyearmonthday_of_weektime_of_dayweatherage_groupgenderpersonapurchase_tendencyproduct_nameprice
02022-01-01 10:47:2520221Saturday10:47:25晴れ10代男性学生{'おにぎり': 0.1, '野菜飲料': 0.05, 'エナジードリンク': 0.1500...['お菓子']158.39
12022-01-01 11:20:0620221Saturday11:20:06雨10代男性社会人{'おにぎり': 0.1, '野菜飲料': 0.05, 'エナジードリンク': 0.05, ...['傘']1422.74
22022-01-01 11:43:0720221Saturday11:43:07雨30代女性社会人{'おにぎり': 0.2, '野菜飲料': 0.2, 'エナジードリンク': 0.25, '...['エナジードリンク']483.69
32022-01-01 11:58:4320221Saturday11:58:43晴れ20代女性学生{'おにぎり': 0.2, '野菜飲料': 0.2, 'エナジードリンク': 0.35, '...['お菓子']173.22
42022-01-01 12:11:0320221Saturday12:11:03晴れ40代男性学生{'おにぎり': 0.2, '野菜飲料': 0.25, 'エナジードリンク': 0.1500...['アイス']248.47
.......................................
99952022-12-31 21:34:22202212Saturday21:34:22晴れ50代以上男性学生{'おにぎり': 0.3, '野菜飲料': 0.25, 'エナジードリンク': 0.1500...['清涼飲料水']784.93
99962022-12-31 21:55:54202212Saturday21:55:54晴れ10代男性社会人{'おにぎり': 0.1, '野菜飲料': 0.05, 'エナジードリンク': 0.05, ...['お菓子']152.61
99972022-12-31 22:03:36202212Saturday22:03:36晴れ50代以上女性学生{'おにぎり': 0.3, '野菜飲料': 0.4, 'エナジードリンク': 0.15000...['お菓子']131.72
99982022-12-31 22:25:55202212Saturday22:25:55晴れ30代男性学生{'おにぎり': 0.2, '野菜飲料': 0.05, 'エナジードリンク': 0.35, ...['おにぎり']1227.61
99992022-12-31 22:29:17202212Saturday22:29:17曇り30代男性学生{'おにぎり': 0.2, '野菜飲料': 0.05, 'エナジードリンク': 0.35, ...['ビッグ弁当']552.63
\n", "

10000 rows × 12 columns

\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", " \n", " \n", " \n", "
\n", "\n", "
\n", "
\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df", "summary": "{\n \"name\": \"df\",\n \"rows\": 10000,\n \"fields\": [\n {\n \"column\": \"purchase_date\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 9997,\n \"samples\": [\n \"2022-06-02 17:31:37\",\n \"2022-05-31 20:10:15\",\n \"2022-03-08 11:19:42\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 2022,\n \"max\": 2022,\n \"num_unique_values\": 1,\n \"samples\": [\n 2022\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"month\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3,\n \"min\": 1,\n \"max\": 12,\n \"num_unique_values\": 12,\n \"samples\": [\n 11\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"day_of_week\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"Saturday\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"time_of_day\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 9034,\n \"samples\": [\n \"15:06:51\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"weather\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"\\u96e8\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"age_group\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"30\\u4ee3\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gender\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"\\u5973\\u6027\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"persona\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"\\u793e\\u4f1a\\u4eba\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"purchase_tendency\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 981,\n \"samples\": [\n \"{'\\u304a\\u306b\\u304e\\u308a': 0.1, '\\u91ce\\u83dc\\u98f2\\u6599': 0.2, '\\u30a8\\u30ca\\u30b8\\u30fc\\u30c9\\u30ea\\u30f3\\u30af': 0.15000000000000002, '\\u6e05\\u6dbc\\u98f2\\u6599\\u6c34': 0.25, '\\u30d3\\u30c3\\u30b0\\u5f01\\u5f53': 0.30000000000000004, '\\u304a\\u83d3\\u5b50': 0.15000000000000002, '\\u30a2\\u30a4\\u30b9': 0.8, '\\u5098': 0.0}\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"product_name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 8,\n \"samples\": [\n \"['\\u5098']\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 688.3052663343537,\n \"min\": 50.08,\n \"max\": 2998.88,\n \"num_unique_values\": 9694,\n \"samples\": [\n 231.15\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 4 } ], "source": [ "# データ読み込み\n", "# assets/data.csvをアップロードしてから実行する。\n", "df = pd.read_csv(\"data.csv\")\n", "df" ] }, { "cell_type": "markdown", "metadata": { "id": "R_pFGBptdwPu" }, "source": [ "## データフレームの概要\n", "列ごとのNullじゃない要素数や、数値データかどうかを確認する際には pd.info() が便利です。``Null``とは「欠落や欠損」を意味します。世の中には一部が欠落しているデータが良くあります。\n", "- [pd.info](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.info.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Jzvvs_aBdwPv", "outputId": "16498c69-05b9-4deb-d592-93168467a8ad" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "RangeIndex: 10000 entries, 0 to 9999\n", "Data columns (total 12 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 purchase_date 10000 non-null object \n", " 1 year 10000 non-null int64 \n", " 2 month 10000 non-null int64 \n", " 3 day_of_week 10000 non-null object \n", " 4 time_of_day 10000 non-null object \n", " 5 weather 10000 non-null object \n", " 6 age_group 10000 non-null object \n", " 7 gender 10000 non-null object \n", " 8 persona 10000 non-null object \n", " 9 purchase_tendency 10000 non-null object \n", " 10 product_name 10000 non-null object \n", " 11 price 10000 non-null float64\n", "dtypes: float64(1), int64(2), object(9)\n", "memory usage: 937.6+ KB\n" ] } ], "source": [ "# 各列の基本情報\n", "df.info()" ] }, { "cell_type": "markdown", "metadata": { "id": "4Y2HIO1EdwPv" }, "source": [ "## 数値データに関する基本統計量\n", "今回のデータでは year, month, price が数値データになっている。これらに対する基本統計量を見るには pd.descriobe() を使おう\n", "- [pd.describe](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 297 }, "id": "l0TnKhkNdwPv", "outputId": "a1018f35-fec0-43fd-d3be-98498f5266ad" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " year month price\n", "count 10000.0 10000.000000 10000.000000\n", "mean 2022.0 6.583700 889.433363\n", "std 0.0 3.441334 688.305266\n", "min 2022.0 1.000000 50.080000\n", "25% 2022.0 4.000000 276.595000\n", "50% 2022.0 7.000000 767.475000\n", "75% 2022.0 10.000000 1263.915000\n", "max 2022.0 12.000000 2998.880000" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
yearmonthprice
count10000.010000.00000010000.000000
mean2022.06.583700889.433363
std0.03.441334688.305266
min2022.01.00000050.080000
25%2022.04.000000276.595000
50%2022.07.000000767.475000
75%2022.010.0000001263.915000
max2022.012.0000002998.880000
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", "
\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"df\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3007.199124007017,\n \"min\": 0.0,\n \"max\": 10000.0,\n \"num_unique_values\": 3,\n \"samples\": [\n 10000.0,\n 2022.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"month\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3533.3120910200128,\n \"min\": 1.0,\n \"max\": 10000.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 6.5837,\n 7.0,\n 10000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3309.6333461438303,\n \"min\": 50.08,\n \"max\": 10000.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 889.4333630000001,\n 767.475,\n 10000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 6 } ], "source": [ "# 数値データに関する基本統計量\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "a_SWG6sV4zAo", "outputId": "3287aa69-bb31-43e4-d96d-b243bb709b0c" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "np.float64(2998.88)" ] }, "metadata": {}, "execution_count": 7 } ], "source": [ "# 特定の値、例えば price列 の max を抜き出すには次のように書く。\n", "# 書式：df['列名'].describe()['行名']\n", "df['price'].describe()['max']" ] }, { "cell_type": "code", "source": [ "# もしくは次のように書くこともできる\n", "df['price'].max()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yDFg5P7tkDMB", "outputId": "a07de705-b70d-4016-946e-96412b68fdd4" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "2998.88" ] }, "metadata": {}, "execution_count": 8 } ] }, { "cell_type": "markdown", "metadata": { "id": "viQAHatDdwPv" }, "source": [ "## 数値データの出現分布 => ヒストグラム\n", "ヒストグラムを描画するには matplotlib か plotly を使うことが多い。どちらかというとplotlyの方がスタイリッシュに見えやすくインタラクティブな図も作成しやすいことから、ここではplotlyを使ってみよう。\n", "\n", "描画ライブラリは指定できる引数が極めて多く、最低限データフレームとその列名を与える必要がある。\n", "- [px.histogram](https://plotly.com/python/histograms/)\n", " - 代表的な引数\n", " - x: データフレームにおける列名。\n", " - nbins: ビン数。\n", " - marginal: \"box\"と書くと箱ひげ図も追加。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 542 }, "id": "u6UqRKfBdwPw", "outputId": "d410f046-0836-4ccb-a5ce-9228c0a3f61b" }, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ], "source": [ "# 数値データに関するヒストグラム\n", "px.histogram(df, x=\"price\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 542 }, "id": "s9O0H3LodwPw", "outputId": "4f8215b1-d8bd-4a46-8e0f-f2691bf7bd97" }, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ], "source": [ "# パラメータ指定によりカスタマイズできる\n", "px.histogram(df, x=\"price\", nbins=20, marginal=\"box\")" ] }, { "cell_type": "markdown", "metadata": { "id": "i5WAVSzedwPw" }, "source": [ "## カテゴリデータの出現回数\n", "product_nameには購入カテゴリ名が書かれている。各カテゴリが出現した回数をカウントするには df[\"カテゴリ名\"].value_counts() という形式で確認することができる。カテゴリ名を省略して実行することもできるが、この場合には参照しづらいこともあるので使い分けよう。\n", "- [pd.value_counts](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.value_counts.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 331 }, "id": "kkyH6WQOdwPw", "outputId": "b730d815-cc84-4af4-bf50-cac7fa747464" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "day_of_week\n", "Monday 1530\n", "Friday 1464\n", "Tuesday 1451\n", "Wednesday 1444\n", "Saturday 1418\n", "Sunday 1368\n", "Thursday 1325\n", "Name: count, dtype: int64" ], "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
count
day_of_week
Monday1530
Friday1464
Tuesday1451
Wednesday1444
Saturday1418
Sunday1368
Thursday1325
\n", "

" ] }, "metadata": {}, "execution_count": 11 } ], "source": [ "# カテゴリデータに関する出現回数\n", "df.value_counts(\"day_of_week\")" ] }, { "cell_type": "markdown", "metadata": { "id": "QtRutPFCdwPx" }, "source": [ "## 演習3.1: カテゴリデータの出現回数\n", "他のカテゴリデータ（age_group, gender, product_name）についても出現回数を確認してみよう。" ] }, { "cell_type": "markdown", "metadata": { "id": "577B322wdwPx" }, "source": [ "## クロス分析\n", "今回のデータには age_group（顧客の年代カテゴリ）も含まれている。そこで「10代の購入したカテゴリ集計」「20代の購入したカテゴリ集計」というように年代カテゴリごとに集計を見てみたい、というような2変数以上の組み合わせを考慮することを「クロス分析」と呼ぶ。このような場合には pd.crosstab か pd.groupby を使おう。以下では両方のコード例を示しているが、直感的に使いやすいのはcrosstabの方だろう。\n", "- [pd.crosstab](https://pandas.pydata.org/docs/reference/api/pandas.crosstab.html)\n", "- [pd.groupby](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 252 }, "id": "4YB6C5_sdwPx", "outputId": "f0ac6242-e74c-4951-f725-dbc73127d32d" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "product_name ['おにぎり'] ['お菓子'] ['アイス'] ['エナジードリンク'] ['ビッグ弁当'] ['傘'] \\\n", "age_group \n", "10代 78 161 186 76 301 101 \n", "20代 183 183 205 261 145 128 \n", "30代 220 189 220 303 187 128 \n", "40代 225 192 243 98 77 161 \n", "50代以上 712 448 513 242 184 354 \n", "\n", "product_name ['清涼飲料水'] ['野菜飲料'] \n", "age_group \n", "10代 255 103 \n", "20代 185 125 \n", "30代 202 148 \n", "40代 341 365 \n", "50代以上 762 810 " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
product_name['おにぎり']['お菓子']['アイス']['エナジードリンク']['ビッグ弁当']['傘']['清涼飲料水']['野菜飲料']
age_group
10代7816118676301101255103
20代183183205261145128185125
30代220189220303187128202148
40代2251922439877161341365
50代以上712448513242184354762810
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", " \n", " \n", " \n", "
\n", "\n", "
\n", "
\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "cross", "summary": "{\n \"name\": \"cross\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"age_group\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"20\\u4ee3\",\n \"50\\u4ee3\\u4ee5\\u4e0a\",\n \"30\\u4ee3\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u304a\\u306b\\u304e\\u308a']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 246,\n \"min\": 78,\n \"max\": 712,\n \"num_unique_values\": 5,\n \"samples\": [\n 183,\n 712,\n 220\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u304a\\u83d3\\u5b50']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 119,\n \"min\": 161,\n \"max\": 448,\n \"num_unique_values\": 5,\n \"samples\": [\n 183,\n 448,\n 189\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u30a2\\u30a4\\u30b9']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 135,\n \"min\": 186,\n \"max\": 513,\n \"num_unique_values\": 5,\n \"samples\": [\n 205,\n 513,\n 220\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u30a8\\u30ca\\u30b8\\u30fc\\u30c9\\u30ea\\u30f3\\u30af']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 102,\n \"min\": 76,\n \"max\": 303,\n \"num_unique_values\": 5,\n \"samples\": [\n 261,\n 242,\n 303\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u30d3\\u30c3\\u30b0\\u5f01\\u5f53']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 81,\n \"min\": 77,\n \"max\": 301,\n \"num_unique_values\": 5,\n \"samples\": [\n 145,\n 184,\n 187\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u5098']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 102,\n \"min\": 101,\n \"max\": 354,\n \"num_unique_values\": 4,\n \"samples\": [\n 128,\n 354,\n 101\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u6e05\\u6dbc\\u98f2\\u6599\\u6c34']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 238,\n \"min\": 185,\n \"max\": 762,\n \"num_unique_values\": 5,\n \"samples\": [\n 185,\n 762,\n 202\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u91ce\\u83dc\\u98f2\\u6599']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 298,\n \"min\": 103,\n \"max\": 810,\n \"num_unique_values\": 5,\n \"samples\": [\n 125,\n 810,\n 148\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 12 } ], "source": [ "# クロス分析 (pd.groupbyの例)\n", "labels = [\"age_group\", \"product_name\"]\n", "data = df[labels]\n", "cross = data.groupby(labels).size().unstack() # 組み合わせの頻度を計算\n", "cross" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 252 }, "id": "-pd5eMu130DN", "outputId": "10f78512-25e8-422c-dbff-bcc143ee157f" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "product_name ['おにぎり'] ['お菓子'] ['アイス'] ['エナジードリンク'] ['ビッグ弁当'] ['傘'] \\\n", "age_group \n", "10代 78 161 186 76 301 101 \n", "20代 183 183 205 261 145 128 \n", "30代 220 189 220 303 187 128 \n", "40代 225 192 243 98 77 161 \n", "50代以上 712 448 513 242 184 354 \n", "\n", "product_name ['清涼飲料水'] ['野菜飲料'] \n", "age_group \n", "10代 255 103 \n", "20代 185 125 \n", "30代 202 148 \n", "40代 341 365 \n", "50代以上 762 810 " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
product_name['おにぎり']['お菓子']['アイス']['エナジードリンク']['ビッグ弁当']['傘']['清涼飲料水']['野菜飲料']
age_group
10代7816118676301101255103
20代183183205261145128185125
30代220189220303187128202148
40代2251922439877161341365
50代以上712448513242184354762810
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", " \n", " \n", " \n", "
\n", "\n", "
\n", "
\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "cross", "summary": "{\n \"name\": \"cross\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"age_group\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"20\\u4ee3\",\n \"50\\u4ee3\\u4ee5\\u4e0a\",\n \"30\\u4ee3\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u304a\\u306b\\u304e\\u308a']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 246,\n \"min\": 78,\n \"max\": 712,\n \"num_unique_values\": 5,\n \"samples\": [\n 183,\n 712,\n 220\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u304a\\u83d3\\u5b50']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 119,\n \"min\": 161,\n \"max\": 448,\n \"num_unique_values\": 5,\n \"samples\": [\n 183,\n 448,\n 189\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u30a2\\u30a4\\u30b9']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 135,\n \"min\": 186,\n \"max\": 513,\n \"num_unique_values\": 5,\n \"samples\": [\n 205,\n 513,\n 220\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u30a8\\u30ca\\u30b8\\u30fc\\u30c9\\u30ea\\u30f3\\u30af']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 102,\n \"min\": 76,\n \"max\": 303,\n \"num_unique_values\": 5,\n \"samples\": [\n 261,\n 242,\n 303\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u30d3\\u30c3\\u30b0\\u5f01\\u5f53']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 81,\n \"min\": 77,\n \"max\": 301,\n \"num_unique_values\": 5,\n \"samples\": [\n 145,\n 184,\n 187\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u5098']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 102,\n \"min\": 101,\n \"max\": 354,\n \"num_unique_values\": 4,\n \"samples\": [\n 128,\n 354,\n 101\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u6e05\\u6dbc\\u98f2\\u6599\\u6c34']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 238,\n \"min\": 185,\n \"max\": 762,\n \"num_unique_values\": 5,\n \"samples\": [\n 185,\n 762,\n 202\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u91ce\\u83dc\\u98f2\\u6599']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 298,\n \"min\": 103,\n \"max\": 810,\n \"num_unique_values\": 5,\n \"samples\": [\n 125,\n 810,\n 148\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 13 } ], "source": [ "# クロス分析（pd.crosstabの例）\n", "cross = pd.crosstab(index=df[\"age_group\"], columns=df[\"product_name\"])\n", "cross\n" ] }, { "cell_type": "markdown", "metadata": { "id": "1hnvOcj7dwPx" }, "source": [ "## クロス分析結果を描画\n", "plotlyの棒グラフで描画するなら px.bar を使おう。\n", "- [px.bar](https://plotly.com/python/bar-charts/)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 542 }, "id": "X3cZWLIUdwPx", "outputId": "2c587079-95b9-4b24-fc83-1c86eb265fae" }, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ], "source": [ "# プロット作成（カウント）\n", "fig = px.bar(cross, barmode=\"stack\", text=\"product_name\")\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "AhPIdRV4dwPx" }, "source": [ "## カウントではなく割合で見たい場合\n", "pd.sumで総和を取り、pd.divで割り算することで割合を求めた上で描画する。\n", "- [pd.sum](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sum.html)\n", "- [pd.div](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.div.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 542 }, "id": "W-R2yOe6dwPy", "outputId": "759a96b9-fa67-4d36-821f-f9e7b0955a66" }, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ], "source": [ "# プロット作成（割合）\n", "# グループごとの合計値で各値を割る\n", "cross_ratio = cross.div(cross.sum(axis=1), axis=0)\n", "fig = px.bar(cross_ratio, barmode=\"stack\", text=\"product_name\")\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "jJnwCd-FdwPy" }, "source": [ "## 演習3.2: クロス集計の描画\n", "以下のクロス集計を積み上げ棒グラフで作成し、気づいたことを報告しよう。\n", "- クロス1: gender x product_name\n", "- クロス2: persona x product_name\n", "- クロス3: month x product_name" ] }, { "cell_type": "markdown", "metadata": { "id": "fuZ-OAS4dwPy" }, "source": [ "## 3変数のクロス集計\n", "今回のデータには gender（性別）もある。「年代 x 性別」毎の集計を取るには、indexで複数指定する必要がある。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 407 }, "id": "ItpqCyBF5pGm", "outputId": "0324db06-463d-4e5c-8689-4e031b1b6054" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "product_name ['おにぎり'] ['お菓子'] ['アイス'] ['エナジードリンク'] ['ビッグ弁当'] ['傘'] \\\n", "age_group gender \n", "10代 女性 33 69 80 36 125 47 \n", " 男性 45 92 106 40 176 54 \n", "20代 女性 97 92 89 124 44 73 \n", " 男性 86 91 116 137 101 55 \n", "30代 女性 104 90 116 139 55 65 \n", " 男性 116 99 104 164 132 63 \n", "40代 女性 126 89 132 53 0 83 \n", " 男性 99 103 111 45 77 78 \n", "50代以上 女性 341 241 255 120 0 183 \n", " 男性 371 207 258 122 184 171 \n", "\n", "product_name ['清涼飲料水'] ['野菜飲料'] \n", "age_group gender \n", "10代 女性 127 83 \n", " 男性 128 20 \n", "20代 女性 104 103 \n", " 男性 81 22 \n", "30代 女性 87 122 \n", " 男性 115 26 \n", "40代 女性 153 217 \n", " 男性 188 148 \n", "50代以上 女性 399 505 \n", " 男性 363 305 " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
product_name['おにぎり']['お菓子']['アイス']['エナジードリンク']['ビッグ弁当']['傘']['清涼飲料水']['野菜飲料']
age_groupgender
10代女性336980361254712783
男性4592106401765412820
20代女性9792891244473104103
男性8691116137101558122
30代女性10490116139556587122
男性116991041641326311526
40代女性1268913253083153217
男性99103111457778188148
50代以上女性3412412551200183399505
男性371207258122184171363305
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", " \n", " \n", " \n", "
\n", "\n", "
\n", "
\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "cross3", "summary": "{\n \"name\": \"cross3\",\n \"rows\": 10,\n \"fields\": [\n {\n \"column\": \"['\\u304a\\u306b\\u304e\\u308a']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 116,\n \"min\": 33,\n \"max\": 371,\n \"num_unique_values\": 10,\n \"samples\": [\n 341,\n 45,\n 116\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u304a\\u83d3\\u5b50']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 57,\n \"min\": 69,\n \"max\": 241,\n \"num_unique_values\": 9,\n \"samples\": [\n 241,\n 92,\n 89\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u30a2\\u30a4\\u30b9']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 64,\n \"min\": 80,\n \"max\": 258,\n \"num_unique_values\": 9,\n \"samples\": [\n 255,\n 106,\n 132\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u30a8\\u30ca\\u30b8\\u30fc\\u30c9\\u30ea\\u30f3\\u30af']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 48,\n \"min\": 36,\n \"max\": 164,\n \"num_unique_values\": 10,\n \"samples\": [\n 120,\n 40,\n 164\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u30d3\\u30c3\\u30b0\\u5f01\\u5f53']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 65,\n \"min\": 0,\n \"max\": 184,\n \"num_unique_values\": 9,\n \"samples\": [\n 77,\n 176,\n 132\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u5098']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 48,\n \"min\": 47,\n \"max\": 183,\n \"num_unique_values\": 10,\n \"samples\": [\n 183,\n 54,\n 63\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u6e05\\u6dbc\\u98f2\\u6599\\u6c34']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 113,\n \"min\": 81,\n \"max\": 399,\n \"num_unique_values\": 10,\n \"samples\": [\n 399,\n 128,\n 115\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"['\\u91ce\\u83dc\\u98f2\\u6599']\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 152,\n \"min\": 20,\n \"max\": 505,\n \"num_unique_values\": 10,\n \"samples\": [\n 505,\n 20,\n 26\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 16 } ], "source": [ "cross3 = pd.crosstab(index=[df[\"age_group\"],df[\"gender\"]], columns=df[\"product_name\"])\n", "cross3" ] }, { "cell_type": "markdown", "metadata": { "id": "r3hMhHQpdwPy" }, "source": [ "## 3変数クロス分析の描画\n", "残念ながら3変数クロス分析結果をplotlyでそのまま描画することはできない。工夫するとできるが、ここでは簡易さの観点から matplotlib を使ってみよう。\n", "- [plt.plot](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 559 }, "id": "P6lfm_K8dwPy", "outputId": "501480ad-d248-4325-9108-855d2c808518" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ], "source": [ "# クロス集計結果のカテゴリ順序を指定する\n", "age_order = sorted(list(df[\"age_group\"].unique()))\n", "cross3 = cross3.reindex(age_order, level='age_group')\n", "\n", "# バープロットを作成する\n", "ax = cross3.plot(kind='bar', stacked=True)\n", "\n", "# グラフの設定\n", "plt.xlabel('Age Group')\n", "plt.ylabel('Count')\n", "plt.title('Cross Tabulation')\n", "\n", "# 凡例を表示\n", "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", "\n", "# プロットを表示する\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "wCwgSbxL3Jcx" }, "source": [ "## 月毎の集計\n", "- [px.line](https://plotly.com/python/line-charts/)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 419 }, "id": "1VqzXmRr3Jcy", "outputId": "9526543b-d1bb-4dbb-a0e1-cd712789558a" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " month product_name purchase_count\n", "0 1 ['おにぎり'] 104\n", "1 1 ['お菓子'] 180\n", "2 1 ['アイス'] 46\n", "3 1 ['エナジードリンク'] 70\n", "4 1 ['ビッグ弁当'] 84\n", ".. ... ... ...\n", "91 12 ['エナジードリンク'] 72\n", "92 12 ['ビッグ弁当'] 70\n", "93 12 ['傘'] 67\n", "94 12 ['清涼飲料水'] 146\n", "95 12 ['野菜飲料'] 135\n", "\n", "[96 rows x 3 columns]" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
monthproduct_namepurchase_count
01['おにぎり']104
11['お菓子']180
21['アイス']46
31['エナジードリンク']70
41['ビッグ弁当']84
............
9112['エナジードリンク']72
9212['ビッグ弁当']70
9312['傘']67
9412['清涼飲料水']146
9512['野菜飲料']135
\n", "

96 rows × 3 columns

\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "\n", "
\n", " \n", " \n", " \n", "
\n", "\n", "
\n", "
\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "monthly_counts", "summary": "{\n \"name\": \"monthly_counts\",\n \"rows\": 96,\n \"fields\": [\n {\n \"column\": \"month\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3,\n \"min\": 1,\n \"max\": 12,\n \"num_unique_values\": 12,\n \"samples\": [\n 11,\n 10,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"product_name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 8,\n \"samples\": [\n \"['\\u304a\\u83d3\\u5b50']\",\n \"['\\u5098']\",\n \"['\\u304a\\u306b\\u304e\\u308a']\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"purchase_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 39,\n \"min\": 39,\n \"max\": 229,\n \"num_unique_values\": 68,\n \"samples\": [\n 107,\n 126,\n 84\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 18 } ], "source": [ "# 商品別の購買数を月ごとに集計\n", "monthly_counts = df.groupby(['month', 'product_name']).size().reset_index(name='purchase_count')\n", "monthly_counts" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 542 }, "id": "ne_3jNUS3Jcy", "outputId": "6308ddeb-8e95-47e0-c1f4-88e3eb790223" }, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ], "source": [ "# 購買数の集計結果をplotlyで描画\n", "fig = px.line(monthly_counts, x='month', y='purchase_count', color='product_name', title='Monthly Purchase Counts by Product')\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "XuRQx5yc3Jcy" }, "outputs": [], "source": [] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "dm", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 0 }