Pythonで視覚化[Dataprep編].ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "gxBzFXzgxEha"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "##**Pythonで視覚化[Dataprep編]**\n",
        "![image.png](data:image/png;base64,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)\\\n",
        "DataPrep.EDAは、Pythonで最も速く、最も簡単なEDA（Exploratory Data Analysis）ツールです。数行のコードで美しいプロファイルレポートを作成することができます。\n",
        "***\n",
        "\n",
        "###**手順**\n",
        "- 「1．インストール」の「▷」をクリックしてください。（インストールが実行されます）。\n",
        "- Select_Datasetのドロップダウンメニュー(dataset:)よりデータセット\n",
        "を選択してください。（任意のcsvデータを読込む場合は Upload を選択してください）\n",
        "- Load dataset の「▷」をクリックしてください。（データセットが読み込まれます）。\n",
        "- DataPrep html Report  の「▷」をクリックしてください。（プロファイルレポートが生成され、HTMLファイルの保存が促されます。）\n",
        "- 2値カテゴリー項目でデータ区分 は、[Category_column_name:] を入力してから、「▷」をクリックしてください。（**[Category_column_name:] が指定できるのは、2値カテゴリーデータのみです。**）\n",
        "\n",
        "***\n",
        "## **csvデータ読み込み時の注意点**\n",
        "- **csvデータの表形式は以下としてください。**\n",
        "\n",
        "|説明変数1|説明変数2|説明変数3|…|説明変数n|…|目的変数|\n",
        "|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n",
        "|data |data|data |…|data |…|data |\n",
        "|data |data|data |…|data |…|data |\n",
        "|･ |･|･ |…|･ |…|･ |\n",
        "|･ |･|･ |…|･ |…|･ |\n",
        "|･ |･|･ |…|･ |…|･ |\n",
        "\n",
        "- [**注意**] csvデータは文字コードを「UTF-8」としてください。\n",
        "\n"
      ],
      "metadata": {
        "id": "299sjPTzkJ3C"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1．インストール**"
      ],
      "metadata": {
        "id": "gxBzFXzgxEha"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install dataprep"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bk2Dp_xd7rgq",
        "outputId": "0330f361-8896-4ca5-ffeb-6e49d19b9e48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting dataprep\n",
            "  Downloading dataprep-0.4.3-py3-none-any.whl (9.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 9.5 MB 4.3 MB/s \n",
            "\u001b[?25hCollecting metaphone<0.7,>=0.6\n",
            "  Downloading Metaphone-0.6.tar.gz (14 kB)\n",
            "Collecting flask_cors<4.0.0,>=3.0.10\n",
            "  Downloading Flask_Cors-3.0.10-py2.py3-none-any.whl (14 kB)\n",
            "Collecting dask[array,dataframe,delayed]<2022.0,>=2021.11\n",
            "  Downloading dask-2021.12.0-py3-none-any.whl (1.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0 MB 42.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pydantic<2.0,>=1.6 in /usr/local/lib/python3.7/dist-packages (from dataprep) (1.10.2)\n",
            "Collecting flask<3,>=2\n",
            "  Downloading Flask-2.2.2-py3-none-any.whl (101 kB)\n",
            "\u001b[K     |████████████████████████████████| 101 kB 8.7 MB/s \n",
            "\u001b[?25hCollecting scipy<=1.7.1\n",
            "  Downloading scipy-1.7.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (28.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 28.5 MB 6.4 MB/s \n",
            "\u001b[?25hCollecting python-crfsuite<0.10.0,>=0.9.7\n",
            "  Downloading python_crfsuite-0.9.8-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (965 kB)\n",
            "\u001b[K     |████████████████████████████████| 965 kB 44.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.21 in /usr/local/lib/python3.7/dist-packages (from dataprep) (1.21.6)\n",
            "Collecting python-Levenshtein<0.13.0,>=0.12.2\n",
            "  Downloading python-Levenshtein-0.12.2.tar.gz (50 kB)\n",
            "\u001b[K     |████████████████████████████████| 50 kB 5.3 MB/s \n",
            "\u001b[?25hCollecting regex<2022.0.0,>=2021.8.3\n",
            "  Downloading regex-2021.11.10-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (749 kB)\n",
            "\u001b[K     |████████████████████████████████| 749 kB 33.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm<5.0,>=4.48 in /usr/local/lib/python3.7/dist-packages (from dataprep) (4.64.1)\n",
            "Collecting jinja2<4,>=3\n",
            "  Downloading Jinja2-3.1.2-py3-none-any.whl (133 kB)\n",
            "\u001b[K     |████████████████████████████████| 133 kB 41.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: ipywidgets<8.0,>=7.5 in /usr/local/lib/python3.7/dist-packages (from dataprep) (7.7.1)\n",
            "Collecting varname<0.9.0,>=0.8.1\n",
            "  Downloading varname-0.8.3-py3-none-any.whl (21 kB)\n",
            "Requirement already satisfied: nltk<4.0.0,>=3.6.7 in /usr/local/lib/python3.7/dist-packages (from dataprep) (3.7)\n",
            "Collecting python-stdnum<2.0,>=1.16\n",
            "  Downloading python_stdnum-1.18-py2.py3-none-any.whl (1.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0 MB 44.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: bokeh<3,>=2 in /usr/local/lib/python3.7/dist-packages (from dataprep) (2.3.3)\n",
            "Requirement already satisfied: pandas<2.0,>=1.1 in /usr/local/lib/python3.7/dist-packages (from dataprep) (1.3.5)\n",
            "Requirement already satisfied: sqlalchemy<2.0.0,>=1.4.32 in /usr/local/lib/python3.7/dist-packages (from dataprep) (1.4.43)\n",
            "Collecting pystache<0.7.0,>=0.6.0\n",
            "  Downloading pystache-0.6.0.tar.gz (78 kB)\n",
            "\u001b[K     |████████████████████████████████| 78 kB 2.9 MB/s \n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: aiohttp<4.0,>=3.6 in /usr/local/lib/python3.7/dist-packages (from dataprep) (3.8.3)\n",
            "Requirement already satisfied: wordcloud<2.0,>=1.8 in /usr/local/lib/python3.7/dist-packages (from dataprep) (1.8.2.2)\n",
            "Collecting jsonpath-ng<2.0,>=1.5\n",
            "  Downloading jsonpath_ng-1.5.3-py3-none-any.whl (29 kB)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp<4.0,>=3.6->dataprep) (1.3.3)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp<4.0,>=3.6->dataprep) (1.8.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp<4.0,>=3.6->dataprep) (22.1.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from aiohttp<4.0,>=3.6->dataprep) (4.1.1)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp<4.0,>=3.6->dataprep) (4.0.2)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp<4.0,>=3.6->dataprep) (6.0.2)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.7/dist-packages (from aiohttp<4.0,>=3.6->dataprep) (1.3.1)\n",
            "Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp<4.0,>=3.6->dataprep) (0.13.0)\n",
            "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp<4.0,>=3.6->dataprep) (2.1.1)\n",
            "Requirement already satisfied: packaging>=16.8 in /usr/local/lib/python3.7/dist-packages (from bokeh<3,>=2->dataprep) (21.3)\n",
            "Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.7/dist-packages (from bokeh<3,>=2->dataprep) (6.0)\n",
            "Requirement already satisfied: tornado>=5.1 in /usr/local/lib/python3.7/dist-packages (from bokeh<3,>=2->dataprep) (6.0.4)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from bokeh<3,>=2->dataprep) (2.8.2)\n",
            "Requirement already satisfied: pillow>=7.1.0 in /usr/local/lib/python3.7/dist-packages (from bokeh<3,>=2->dataprep) (7.1.2)\n",
            "Requirement already satisfied: fsspec>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from dask[array,dataframe,delayed]<2022.0,>=2021.11->dataprep) (2022.10.0)\n",
            "Requirement already satisfied: partd>=0.3.10 in /usr/local/lib/python3.7/dist-packages (from dask[array,dataframe,delayed]<2022.0,>=2021.11->dataprep) (1.3.0)\n",
            "Requirement already satisfied: toolz>=0.8.2 in /usr/local/lib/python3.7/dist-packages (from dask[array,dataframe,delayed]<2022.0,>=2021.11->dataprep) (0.12.0)\n",
            "Requirement already satisfied: cloudpickle>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from dask[array,dataframe,delayed]<2022.0,>=2021.11->dataprep) (1.5.0)\n",
            "Collecting itsdangerous>=2.0\n",
            "  Downloading itsdangerous-2.1.2-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: importlib-metadata>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from flask<3,>=2->dataprep) (4.13.0)\n",
            "Collecting click>=8.0\n",
            "  Downloading click-8.1.3-py3-none-any.whl (96 kB)\n",
            "\u001b[K     |████████████████████████████████| 96 kB 5.8 MB/s \n",
            "\u001b[?25hCollecting Werkzeug>=2.2.2\n",
            "  Downloading Werkzeug-2.2.2-py3-none-any.whl (232 kB)\n",
            "\u001b[K     |████████████████████████████████| 232 kB 67.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: Six in /usr/local/lib/python3.7/dist-packages (from flask_cors<4.0.0,>=3.0.10->dataprep) (1.15.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=3.6.0->flask<3,>=2->dataprep) (3.10.0)\n",
            "Requirement already satisfied: ipython>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets<8.0,>=7.5->dataprep) (7.9.0)\n",
            "Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets<8.0,>=7.5->dataprep) (0.2.0)\n",
            "Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets<8.0,>=7.5->dataprep) (3.0.3)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets<8.0,>=7.5->dataprep) (5.3.4)\n",
            "Requirement already satisfied: traitlets>=4.3.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets<8.0,>=7.5->dataprep) (5.1.1)\n",
            "Requirement already satisfied: widgetsnbextension~=3.6.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets<8.0,>=7.5->dataprep) (3.6.1)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets<8.0,>=7.5->dataprep) (6.1.12)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets<8.0,>=7.5->dataprep) (4.4.2)\n",
            "Collecting jedi>=0.10\n",
            "  Downloading jedi-0.18.1-py2.py3-none-any.whl (1.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.6 MB 63.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets<8.0,>=7.5->dataprep) (2.6.1)\n",
            "Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets<8.0,>=7.5->dataprep) (4.8.0)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets<8.0,>=7.5->dataprep) (0.2.0)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets<8.0,>=7.5->dataprep) (57.4.0)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets<8.0,>=7.5->dataprep) (0.7.5)\n",
            "Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets<8.0,>=7.5->dataprep) (2.0.10)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.7/dist-packages (from jedi>=0.10->ipython>=4.0.0->ipywidgets<8.0,>=7.5->dataprep) (0.8.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.7/dist-packages (from jinja2<4,>=3->dataprep) (2.0.1)\n",
            "Collecting ply\n",
            "  Downloading ply-3.11-py2.py3-none-any.whl (49 kB)\n",
            "\u001b[K     |████████████████████████████████| 49 kB 5.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.7->dataprep) (1.2.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=16.8->bokeh<3,>=2->dataprep) (3.0.9)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0,>=1.1->dataprep) (2022.6)\n",
            "Requirement already satisfied: locket in /usr/local/lib/python3.7/dist-packages (from partd>=0.3.10->dask[array,dataframe,delayed]<2022.0,>=2021.11->dataprep) (1.0.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython>=4.0.0->ipywidgets<8.0,>=7.5->dataprep) (0.2.5)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.7/dist-packages (from sqlalchemy<2.0.0,>=1.4.32->dataprep) (2.0.1)\n",
            "Collecting asttokens<3.0.0,>=2.0.0\n",
            "  Downloading asttokens-2.1.0-py2.py3-none-any.whl (26 kB)\n",
            "Collecting executing<0.9.0,>=0.8.3\n",
            "  Downloading executing-0.8.3-py2.py3-none-any.whl (16 kB)\n",
            "Collecting pure_eval<1.0.0\n",
            "  Downloading pure_eval-0.2.2-py3-none-any.whl (11 kB)\n",
            "Collecting MarkupSafe>=2.0\n",
            "  Downloading MarkupSafe-2.1.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (25 kB)\n",
            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.7/dist-packages (from widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (5.7.16)\n",
            "Requirement already satisfied: prometheus-client in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (0.15.0)\n",
            "Collecting jinja2<4,>=3\n",
            "  Downloading Jinja2-3.0.0-py3-none-any.whl (133 kB)\n",
            "\u001b[K     |████████████████████████████████| 133 kB 69.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyzmq>=17 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (23.2.1)\n",
            "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (1.8.0)\n",
            "Requirement already satisfied: jupyter-core>=4.4.0 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (4.11.2)\n",
            "Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (0.13.3)\n",
            "Requirement already satisfied: nbconvert<6.0 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (5.6.1)\n",
            "Requirement already satisfied: nbformat in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (5.7.0)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from nbconvert<6.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (0.4)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert<6.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (0.6.0)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert<6.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (1.5.0)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert<6.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (5.0.1)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert<6.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (0.7.1)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert<6.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (0.8.4)\n",
            "Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.7/dist-packages (from nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (2.16.2)\n",
            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.7/dist-packages (from nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (4.3.3)\n",
            "Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (5.10.0)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (0.19.2)\n",
            "Requirement already satisfied: ptyprocess in /usr/local/lib/python3.7/dist-packages (from terminado>=0.8.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (0.7.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from wordcloud<2.0,>=1.8->dataprep) (3.2.2)\n",
            "Requirement already satisfied: idna>=2.0 in /usr/local/lib/python3.7/dist-packages (from yarl<2.0,>=1.0->aiohttp<4.0,>=3.6->dataprep) (2.10)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert<6.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (0.5.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->wordcloud<2.0,>=1.8->dataprep) (1.4.4)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->wordcloud<2.0,>=1.8->dataprep) (0.11.0)\n",
            "Building wheels for collected packages: metaphone, pystache, python-Levenshtein\n",
            "  Building wheel for metaphone (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for metaphone: filename=Metaphone-0.6-py3-none-any.whl size=13918 sha256=9e298090cae2edef5394832a524382a53529ae767f40fbcb11d295e435408773\n",
            "  Stored in directory: /root/.cache/pip/wheels/1d/a8/cb/6f8902aa5457bd71344e00665c230e9c45255b3f57f2194a0f\n",
            "  Building wheel for pystache (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pystache: filename=pystache-0.6.0-py3-none-any.whl size=83634 sha256=ec2af508fed05ba895b7db5422341646cfbe04e5b8808a66ff9e94c941a09177\n",
            "  Stored in directory: /root/.cache/pip/wheels/78/87/45/383bd15701a08a94c735e9eaf3ff3299655684aaca63bbad96\n",
            "  Building wheel for python-Levenshtein (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-Levenshtein: filename=python_Levenshtein-0.12.2-cp37-cp37m-linux_x86_64.whl size=149872 sha256=1128dc17d4ad5d3945476c380ad5ca1e7450cdccd7085c7a2b2ec8c3f36ec9e3\n",
            "  Stored in directory: /root/.cache/pip/wheels/05/5f/ca/7c4367734892581bb5ff896f15027a932c551080b2abd3e00d\n",
            "Successfully built metaphone pystache python-Levenshtein\n",
            "Installing collected packages: MarkupSafe, jedi, jinja2, Werkzeug, itsdangerous, click, regex, pure-eval, ply, flask, executing, dask, asttokens, varname, scipy, python-stdnum, python-Levenshtein, python-crfsuite, pystache, metaphone, jsonpath-ng, flask-cors, dataprep\n",
            "  Attempting uninstall: MarkupSafe\n",
            "    Found existing installation: MarkupSafe 2.0.1\n",
            "    Uninstalling MarkupSafe-2.0.1:\n",
            "      Successfully uninstalled MarkupSafe-2.0.1\n",
            "  Attempting uninstall: jinja2\n",
            "    Found existing installation: Jinja2 2.11.3\n",
            "    Uninstalling Jinja2-2.11.3:\n",
            "      Successfully uninstalled Jinja2-2.11.3\n",
            "  Attempting uninstall: Werkzeug\n",
            "    Found existing installation: Werkzeug 1.0.1\n",
            "    Uninstalling Werkzeug-1.0.1:\n",
            "      Successfully uninstalled Werkzeug-1.0.1\n",
            "  Attempting uninstall: itsdangerous\n",
            "    Found existing installation: itsdangerous 1.1.0\n",
            "    Uninstalling itsdangerous-1.1.0:\n",
            "      Successfully uninstalled itsdangerous-1.1.0\n",
            "  Attempting uninstall: click\n",
            "    Found existing installation: click 7.1.2\n",
            "    Uninstalling click-7.1.2:\n",
            "      Successfully uninstalled click-7.1.2\n",
            "  Attempting uninstall: regex\n",
            "    Found existing installation: regex 2022.6.2\n",
            "    Uninstalling regex-2022.6.2:\n",
            "      Successfully uninstalled regex-2022.6.2\n",
            "  Attempting uninstall: flask\n",
            "    Found existing installation: Flask 1.1.4\n",
            "    Uninstalling Flask-1.1.4:\n",
            "      Successfully uninstalled Flask-1.1.4\n",
            "  Attempting uninstall: dask\n",
            "    Found existing installation: dask 2022.2.0\n",
            "    Uninstalling dask-2022.2.0:\n",
            "      Successfully uninstalled dask-2022.2.0\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.7.3\n",
            "    Uninstalling scipy-1.7.3:\n",
            "      Successfully uninstalled scipy-1.7.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "distributed 2022.2.0 requires dask==2022.02.0, but you have dask 2021.12.0 which is incompatible.\u001b[0m\n",
            "Successfully installed MarkupSafe-2.1.1 Werkzeug-2.2.2 asttokens-2.1.0 click-8.1.3 dask-2021.12.0 dataprep-0.4.3 executing-0.8.3 flask-2.2.2 flask-cors-3.0.10 itsdangerous-2.1.2 jedi-0.18.1 jinja2-3.0.0 jsonpath-ng-1.5.3 metaphone-0.6 ply-3.11 pure-eval-0.2.2 pystache-0.6.0 python-Levenshtein-0.12.2 python-crfsuite-0.9.8 python-stdnum-1.18 regex-2021.11.10 scipy-1.7.1 varname-0.8.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from dataprep.eda import *"
      ],
      "metadata": {
        "id": "MkODXfF57hxR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2．データセット読込み**"
      ],
      "metadata": {
        "id": "7avNQxplxTWh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Select_Dataset { run: \"auto\" }\n",
        "#@markdown  **<font color= \"Crimson\">注意</font>：かならず 実行する前に 設定してください。**</font>\n",
        "\n",
        "dataset = 'Loan_prediction :binary' #@param ['Boston_housing :regression', 'Diabetes :regression', 'Breast_cancer :binary','Titanic :binary', 'Titanic(seaborn) :binary', 'Iris :classification', 'Loan_prediction :binary','wine :classification', 'Occupancy_detection :binary', 'Upload']"
      ],
      "metadata": {
        "id": "kYtA-M52ZFGQ",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Load dataset\n",
        "\n",
        "#ライブラリインポート\n",
        "#import numpy as np    #数値計算ライブラリ\n",
        "import pandas as pd   #データを効率的に扱うライブラリ\n",
        "import seaborn as sns #視覚化ライブラリ\n",
        "import warnings       #警告を表示させないライブラリ\n",
        "warnings.simplefilter('ignore')\n",
        "\n",
        "'''\n",
        "dataset（ドロップダウンメニュー）で選択したデータセットを読込み、データフレーム（df）に格納。\n",
        "目的変数は、データフレームの最終列とし、FEATURES、TARGET、X、yを指定した後、データフレーム\n",
        "に関する情報と先頭5列を表示。\n",
        "任意のcsvデータを読込む場合は、datasetで'Upload'を選択。\n",
        "\n",
        "'''\n",
        "\n",
        "#任意のcsvデータ読込み及びデータフレーム格納、\n",
        "if dataset =='Upload':\n",
        "  from google.colab import files\n",
        "  uploaded = files.upload()#Upload\n",
        "  target = list(uploaded.keys())[0]\n",
        "  df = pd.read_csv(target)\n",
        "\n",
        "#Diabetes データセットの読込み及びデータフレーム格納、\n",
        "elif dataset == \"Diabetes :regression\":\n",
        "  from sklearn.datasets import load_diabetes\n",
        "  diabetes = load_diabetes()\n",
        "  df = pd.DataFrame(diabetes.data, columns = diabetes.feature_names)\n",
        "  df['target'] = diabetes.target\n",
        "\n",
        "#Breast_cancer データセットの読込み及びデータフレーム格納、\n",
        "elif dataset == \"Breast_cancer :binary\":\n",
        "  from sklearn.datasets import load_breast_cancer\n",
        "  breast_cancer = load_breast_cancer()\n",
        "  df = pd.DataFrame(breast_cancer.data, columns = breast_cancer.feature_names)\n",
        "  #df['target'] = breast_cancer.target  #目的変数をカテゴリー数値とする時\n",
        "  df['target'] = breast_cancer.target_names[breast_cancer.target]\n",
        "\n",
        "#Titanic データセットの読込み及びデータフレーム格納、\n",
        "elif dataset == \"Titanic :binary\":\n",
        "  data_url = \"https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv\"\n",
        "  df = pd.read_csv(data_url)\n",
        "  #目的変数 Survived をデータフレーム最終列に移動\n",
        "  X = df.drop(['Survived'], axis=1)\n",
        "  y = df['Survived'] \n",
        "  df = pd.concat([X, y], axis=1)    #X,yを結合し、dfに格納\n",
        "\n",
        "#Titanic(seaborn) データセットの読込み及びデータフレーム格納、\n",
        "elif dataset == \"Titanic(seaborn) :binary\":\n",
        "  df = sns.load_dataset('titanic')\n",
        "  #重複データをカットし、目的変数 alive をデータフレーム最終列に移動\n",
        "  X = df.drop(['survived','pclass','embarked','who','adult_male','alive'], axis=1)\n",
        "  y = df['alive']                   #目的変数データ\n",
        "  df = pd.concat([X, y], axis=1)    #X,yを結合し、dfに格納\n",
        "\n",
        "#iris データセットの読込み及びデータフレーム格納、\n",
        "elif dataset == \"Iris :classification\":\n",
        "  from sklearn.datasets import load_iris\n",
        "  iris = load_iris()\n",
        "  df = pd.DataFrame(iris.data, columns = iris.feature_names)\n",
        "  #df['target'] = iris.target  #目的変数をカテゴリー数値とする時\n",
        "  df['target'] = iris.target_names[iris.target]\n",
        "\n",
        "#wine データセットの読込み及びデータフレーム格納、\n",
        "elif dataset == \"wine :classification\":\n",
        "  from sklearn.datasets import load_wine\n",
        "  wine = load_wine()\n",
        "  df = pd.DataFrame(wine.data, columns = wine.feature_names)\n",
        "  #df['target'] = wine.target  #目的変数をカテゴリー数値とする時\n",
        "  df['target'] = wine.target_names[wine.target]\n",
        "\n",
        "#Loan_prediction データセットの読込み及びデータフレーム格納、 \n",
        "elif dataset == \"Loan_prediction :binary\":\n",
        "  data_url = \"https://github.com/shrikant-temburwar/Loan-Prediction-Dataset/raw/master/train.csv\"\n",
        "  df = pd.read_csv(data_url)\n",
        "\n",
        "#Occupancy_detection データセットの読込み及びデータフレーム格納、 \n",
        "elif dataset =='Occupancy_detection :binary':\n",
        "  data_url = 'https://raw.githubusercontent.com/hima2b4/Auto_Profiling/main/Occupancy-detection-datatest.csv'\n",
        "  df = pd.read_csv(data_url)\n",
        "  df['date'] = pd.to_datetime(df['date'])    #[date]のデータ型をdatetime型に変更\n",
        "\n",
        "#Boston データセットの読込み及びデータフレーム格納 \n",
        "else:\n",
        "  from sklearn.datasets import load_boston\n",
        "  boston = load_boston()\n",
        "  df = pd.DataFrame(boston.data, columns = boston.feature_names)\n",
        "  df['target'] = boston.target\n",
        "\n",
        "#FEATURES、TARGET、X、yを指定 \n",
        "FEATURES = df.columns[:-1]    #説明変数のデータ項目を指定\n",
        "TARGET = df.columns[-1]       #目的変数のデータ項目を指定\n",
        "X = df.loc[:, FEATURES]       #FEATURESのすべてのデータをXに格納\n",
        "y = df.loc[:, TARGET]         #TARGETのすべてのデータをyに格納\n",
        "\n",
        "#データフレーム表示\n",
        "df.info(verbose=True)         #データフレーム情報表示（verbose=Trueで表示数制限カット）\n",
        "df.head()                     #データフレーム先頭5行表示"
      ],
      "metadata": {
        "id": "rwv_xazpmwbC",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **データ項目一覧**\n",
        "#@markdown **※データ項目一覧を表示します。以後のデータ項目の入力は、表示された項目をコピーアンドペーストすると確実です。**\n",
        "print('データ項目名：',df.columns.values)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "Hsq9dRszfpTG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3．Dataprep実行**"
      ],
      "metadata": {
        "id": "oaEpMteP8RDT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title DataPrep html Report\n",
        "report = create_report(df)\n",
        "report.save('DataPrep_Report')\n",
        "\n",
        "from google.colab import files\n",
        "files.download( \"/content/DataPrep_Report.html\" ) "
      ],
      "metadata": {
        "cellView": "form",
        "id": "N2PajFgj8c7Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 2値カテゴリー項目でデータ区分\n",
        "Category_column_name =  'xxx'#@param {type:\"raw\"}\n",
        "\n",
        "#カテゴリー項目のクラス名をclass1,2に格納\n",
        "class1 = df[Category_column_name].unique().tolist()[0]\n",
        "class2 = df[Category_column_name].unique().tolist()[1]\n",
        "\n",
        "#class1,2データをdf1,2に振り分け\n",
        "df1 = df[df[Category_column_name] == class1]\n",
        "df2 = df[df[Category_column_name] == class2]\n",
        "\n",
        "\n",
        "print('───────────────────────────')\n",
        "print(' df1:',Category_column_name,'=',class1)\n",
        "print(' df2:',Category_column_name,'=',class2)\n",
        "print('───────────────────────────')\n",
        "\n",
        "plot_diff([df1, df2])"
      ],
      "metadata": {
        "id": "dP5qVbF9AAsT",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **参考**\n",
        "\n",
        "- [**公式のGithub**](https://github.com/sfu-db/dataprep)\n",
        "- [**Demo in Colab**](https://colab.research.google.com/drive/1U_-pAMcne3hK1HbMB3kuEt-093Np_7Uk?usp=sharing)"
      ],
      "metadata": {
        "id": "hS721EClUYOg"
      }
    }
  ]
}