{
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    "colab": {
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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!date\n",
        "!python --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0laTR9rlNGnK",
        "outputId": "cdbdc8dd-c5d1-4e28-ea03-afe81374eb0a"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Apr 11 03:29:22 AM UTC 2025\n",
            "Python 3.11.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ScZhDqvHxWm"
      },
      "source": [
        "# 数値データに対する前処理コード例\n",
        "- 変更履歴\n",
        "  - 2024年5月: quilt 周りで不具合あり。そこでquiltは使用せず、別途データセットをファイルとして用意して利用するように修正。**本コードを実行する際には、別に用意している ``youtuber.xlsx`` をダウンロードしてから実行してください。**\n",
        "  - 2025年4月11日: matplotlib => plotlyに変更。\n",
        "- ref.\n",
        "    - preprocess methods\n",
        "        - [機械学習のための特徴量エンジニアリング](https://www.oreilly.co.jp/books/9784873118680/)\n",
        "        - [5.3. Preprocessing data](https://scikit-learn.org/stable/modules/preprocessing.html#normalization)\n",
        "    - data: [YouTuberデータセット公開してみた](https://qiita.com/myaun/items/7e0dd7f3f9d9d2fef497)\n",
        "- 全体の流れ\n",
        "    - データセットの準備。\n",
        "    - 数値データに対する前処理の例\n",
        "        - 手法1：バイナリ化\n",
        "        - 手法2：アドホックな離散化\n",
        "        - 手法3：統計的な離散化\n",
        "        - 手法4：ログスケール化\n",
        "            - デフォルトとログスケールとの比較\n",
        "        - 手法5：標準化\n",
        "        - 手法6：min-maxスケーリング\n",
        "    - 特徴ベクトルに対する前処理の例\n",
        "        - 手法7：正規化\n",
        "        - 手法8：正規分布への写像\n",
        "            - デフォルトとbox-cox写像との比較"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1n_M-w2iHxWs"
      },
      "source": [
        "## 環境構築"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RWL_14XPHxWs"
      },
      "source": [
        "#!pip install quilt\n",
        "#!quilt install haradai1262/YouTuber"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "216bw7OVHxWt"
      },
      "source": [
        "## データセットの準備"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "nw8dF89KHxWt",
        "outputId": "7e1d0c43-571e-43c5-9ec7-492d7b9bd87e"
      },
      "source": [
        "#from quilt.data.haradai1262 import YouTuber\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "#import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "#df = YouTuber.channels.UUUM()\n",
        "#df = YouTuber.channel_videos.UUUM_videos()\n",
        "df = pd.read_excel(\"youtuber.xlsx\")\n",
        "\n",
        "# check the descriptive statistics of numerical data\n",
        "df.describe()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "          viewCount      likeCount  favoriteCount   dislikeCount  \\\n",
              "count  6.627900e+04   64256.000000        66289.0   64256.000000   \n",
              "mean   4.545539e+05    3233.703390            0.0     296.430014   \n",
              "std    1.328105e+06    9768.090605            0.0    1633.734833   \n",
              "min    0.000000e+00       0.000000            0.0       0.000000   \n",
              "25%    3.907100e+04     266.000000            0.0      23.000000   \n",
              "50%    1.214190e+05     776.000000            0.0      67.000000   \n",
              "75%    3.512260e+05    2260.000000            0.0     201.000000   \n",
              "max    8.664236e+07  630051.000000            0.0  213677.000000   \n",
              "\n",
              "        commentCount  TopicIds           idx  \n",
              "count   65997.000000       0.0  66289.000000  \n",
              "mean      533.418807       NaN    235.730136  \n",
              "std      2253.437482       NaN    142.868881  \n",
              "min         0.000000       NaN      1.000000  \n",
              "25%        55.000000       NaN    111.000000  \n",
              "50%       150.000000       NaN    229.000000  \n",
              "75%       394.000000       NaN    357.000000  \n",
              "max    227598.000000       NaN    501.000000  "
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"viewCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30516553.4322789,\n        \"min\": 0.0,\n        \"max\": 86642355.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          454553.940690113,\n          121419.0,\n          66279.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"likeCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 219767.34013593014,\n        \"min\": 0.0,\n        \"max\": 630051.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3233.703389566733,\n          776.0,\n          64256.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"favoriteCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23436.700709037523,\n        \"min\": 0.0,\n        \"max\": 66289.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          66289.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dislikeCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 75572.76695256456,\n        \"min\": 0.0,\n        \"max\": 213677.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          296.4300143177291,\n          67.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"commentCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80300.58998613172,\n        \"min\": 0.0,\n        \"max\": 227598.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          533.4188069154659,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TopicIds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"idx\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23357.520374911255,\n        \"min\": 1.0,\n        \"max\": 66289.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          235.73013622169591\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iIYWGb9ZHxWu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 960
        },
        "outputId": "b4f6f32e-d934-4f78-c945-7636639775d2"
      },
      "source": [
        "# the description of data frame\n",
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            id                                          title  \\\n",
              "0  R7V5d94XkGQ  【大食い】超高級寿司店で３人で食べ放題したらいくらかかるの!?【大トロ1カン2,000円】   \n",
              "1  2R9_bkcWNd4       【女王集結】女性YouTuberたちと飲みながら本音トークしてみたら爆笑www   \n",
              "2  EU8S-zxS9PI                【悪質】偽物ヒカキン許さねぇ…注意してください！【なりすまし】   \n",
              "3  5wnfkIfw0jE                ツイッターのヒカキンシンメトリーBotが面白すぎて爆笑www   \n",
              "4  -6duBsde_XM    【放送事故】酒飲みながら東海オンエア×ヒカキンで質問コーナーやったらヤバかったwww   \n",
              "\n",
              "                                         description liveBroadcastContent  \\\n",
              "0  提供：ポコロンダンジョンズ\\n\\n\\n\\niOS：https://bit.ly/2sGgOR...                 none   \n",
              "1  しばなんチャンネルの動画\\n\\n\\n\\nhttps://www.youtube.com/wa...                 none   \n",
              "2  ◆チャンネル登録はこちら↓\\n\\n\\n\\nhttp://www.youtube.com/us...                 none   \n",
              "3  ◆チャンネル登録はこちら↓\\n\\n\\n\\nhttp://www.youtube.com/us...                 none   \n",
              "4  提供：モンスターストライク\\n\\n\\n\\n▼キャンペーンサイトはこちら\\n\\n\\n\\nhtt...                 none   \n",
              "\n",
              "                                                tags  \\\n",
              "0  ['ヒカキン', 'ヒカキンtv', 'hikakintv', 'hikakin', 'ひか...   \n",
              "1  ['ヒカキン', 'ヒカキンtv', 'hikakintv', 'hikakin', 'ひか...   \n",
              "2  ['ヒカキン', 'ヒカキンtv', 'hikakintv', 'hikakin', 'ひか...   \n",
              "3  ['ヒカキン', 'ヒカキンtv', 'hikakintv', 'hikakin', 'ひか...   \n",
              "4  ['ヒカキン', 'ヒカキンtv', 'hikakintv', 'hikakin', 'ひか...   \n",
              "\n",
              "                publishedAt                                      thumbnails  \\\n",
              "0  2018-06-30T04:00:01.000Z  https://i.ytimg.com/vi/R7V5d94XkGQ/default.jpg   \n",
              "1  2018-06-29T08:00:01.000Z  https://i.ytimg.com/vi/2R9_bkcWNd4/default.jpg   \n",
              "2  2018-06-27T08:38:55.000Z  https://i.ytimg.com/vi/EU8S-zxS9PI/default.jpg   \n",
              "3  2018-06-25T07:46:07.000Z  https://i.ytimg.com/vi/5wnfkIfw0jE/default.jpg   \n",
              "4  2018-06-21T08:00:00.000Z  https://i.ytimg.com/vi/-6duBsde_XM/default.jpg   \n",
              "\n",
              "   viewCount  likeCount  favoriteCount  ...  commentCount  caption  \\\n",
              "0  2244205.0    27703.0              0  ...        8647.0    False   \n",
              "1  1869268.0    30889.0              0  ...        8859.0    False   \n",
              "2  1724625.0    33038.0              0  ...       11504.0    False   \n",
              "3  1109029.0    25986.0              0  ...        6852.0    False   \n",
              "4  1759797.0    33923.0              0  ...        4517.0    False   \n",
              "\n",
              "   definition dimension  duration   projection TopicIds  \\\n",
              "0          hd        2d  PT21M16S  rectangular      NaN   \n",
              "1          hd        2d  PT18M38S  rectangular      NaN   \n",
              "2          hd        2d   PT6M12S  rectangular      NaN   \n",
              "3          hd        2d   PT6M31S  rectangular      NaN   \n",
              "4          hd        2d   PT27M7S  rectangular      NaN   \n",
              "\n",
              "                                    relevantTopicIds idx  \\\n",
              "0  ['/m/02wbm', '/m/019_rr', '/m/019_rr', '/m/02w...   1   \n",
              "1               ['/m/04rlf', '/m/02jjt', '/m/02jjt']   2   \n",
              "2               ['/m/04rlf', '/m/02jjt', '/m/02jjt']   3   \n",
              "3               ['/m/04rlf', '/m/02jjt', '/m/02jjt']   4   \n",
              "4  ['/m/098wr', '/m/019_rr', '/m/02wbm', '/m/019_...   5   \n",
              "\n",
              "                         cid  \n",
              "0  UCZf__ehlCEBPop___sldpBUQ  \n",
              "1  UCZf__ehlCEBPop___sldpBUQ  \n",
              "2  UCZf__ehlCEBPop___sldpBUQ  \n",
              "3  UCZf__ehlCEBPop___sldpBUQ  \n",
              "4  UCZf__ehlCEBPop___sldpBUQ  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-124cec00-84d7-46ce-b063-6047d4dd3f58\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>title</th>\n",
              "      <th>description</th>\n",
              "      <th>liveBroadcastContent</th>\n",
              "      <th>tags</th>\n",
              "      <th>publishedAt</th>\n",
              "      <th>thumbnails</th>\n",
              "      <th>viewCount</th>\n",
              "      <th>likeCount</th>\n",
              "      <th>favoriteCount</th>\n",
              "      <th>...</th>\n",
              "      <th>commentCount</th>\n",
              "      <th>caption</th>\n",
              "      <th>definition</th>\n",
              "      <th>dimension</th>\n",
              "      <th>duration</th>\n",
              "      <th>projection</th>\n",
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              "      <th>relevantTopicIds</th>\n",
              "      <th>idx</th>\n",
              "      <th>cid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>R7V5d94XkGQ</td>\n",
              "      <td>【大食い】超高級寿司店で３人で食べ放題したらいくらかかるの!?【大トロ1カン2,000円】</td>\n",
              "      <td>提供：ポコロンダンジョンズ\\n\\n\\n\\niOS：https://bit.ly/2sGgOR...</td>\n",
              "      <td>none</td>\n",
              "      <td>['ヒカキン', 'ヒカキンtv', 'hikakintv', 'hikakin', 'ひか...</td>\n",
              "      <td>2018-06-30T04:00:01.000Z</td>\n",
              "      <td>https://i.ytimg.com/vi/R7V5d94XkGQ/default.jpg</td>\n",
              "      <td>2244205.0</td>\n",
              "      <td>27703.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>8647.0</td>\n",
              "      <td>False</td>\n",
              "      <td>hd</td>\n",
              "      <td>2d</td>\n",
              "      <td>PT21M16S</td>\n",
              "      <td>rectangular</td>\n",
              "      <td>NaN</td>\n",
              "      <td>['/m/02wbm', '/m/019_rr', '/m/019_rr', '/m/02w...</td>\n",
              "      <td>1</td>\n",
              "      <td>UCZf__ehlCEBPop___sldpBUQ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2R9_bkcWNd4</td>\n",
              "      <td>【女王集結】女性YouTuberたちと飲みながら本音トークしてみたら爆笑www</td>\n",
              "      <td>しばなんチャンネルの動画\\n\\n\\n\\nhttps://www.youtube.com/wa...</td>\n",
              "      <td>none</td>\n",
              "      <td>['ヒカキン', 'ヒカキンtv', 'hikakintv', 'hikakin', 'ひか...</td>\n",
              "      <td>2018-06-29T08:00:01.000Z</td>\n",
              "      <td>https://i.ytimg.com/vi/2R9_bkcWNd4/default.jpg</td>\n",
              "      <td>1869268.0</td>\n",
              "      <td>30889.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>8859.0</td>\n",
              "      <td>False</td>\n",
              "      <td>hd</td>\n",
              "      <td>2d</td>\n",
              "      <td>PT18M38S</td>\n",
              "      <td>rectangular</td>\n",
              "      <td>NaN</td>\n",
              "      <td>['/m/04rlf', '/m/02jjt', '/m/02jjt']</td>\n",
              "      <td>2</td>\n",
              "      <td>UCZf__ehlCEBPop___sldpBUQ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>EU8S-zxS9PI</td>\n",
              "      <td>【悪質】偽物ヒカキン許さねぇ…注意してください！【なりすまし】</td>\n",
              "      <td>◆チャンネル登録はこちら↓\\n\\n\\n\\nhttp://www.youtube.com/us...</td>\n",
              "      <td>none</td>\n",
              "      <td>['ヒカキン', 'ヒカキンtv', 'hikakintv', 'hikakin', 'ひか...</td>\n",
              "      <td>2018-06-27T08:38:55.000Z</td>\n",
              "      <td>https://i.ytimg.com/vi/EU8S-zxS9PI/default.jpg</td>\n",
              "      <td>1724625.0</td>\n",
              "      <td>33038.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>11504.0</td>\n",
              "      <td>False</td>\n",
              "      <td>hd</td>\n",
              "      <td>2d</td>\n",
              "      <td>PT6M12S</td>\n",
              "      <td>rectangular</td>\n",
              "      <td>NaN</td>\n",
              "      <td>['/m/04rlf', '/m/02jjt', '/m/02jjt']</td>\n",
              "      <td>3</td>\n",
              "      <td>UCZf__ehlCEBPop___sldpBUQ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5wnfkIfw0jE</td>\n",
              "      <td>ツイッターのヒカキンシンメトリーBotが面白すぎて爆笑www</td>\n",
              "      <td>◆チャンネル登録はこちら↓\\n\\n\\n\\nhttp://www.youtube.com/us...</td>\n",
              "      <td>none</td>\n",
              "      <td>['ヒカキン', 'ヒカキンtv', 'hikakintv', 'hikakin', 'ひか...</td>\n",
              "      <td>2018-06-25T07:46:07.000Z</td>\n",
              "      <td>https://i.ytimg.com/vi/5wnfkIfw0jE/default.jpg</td>\n",
              "      <td>1109029.0</td>\n",
              "      <td>25986.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>6852.0</td>\n",
              "      <td>False</td>\n",
              "      <td>hd</td>\n",
              "      <td>2d</td>\n",
              "      <td>PT6M31S</td>\n",
              "      <td>rectangular</td>\n",
              "      <td>NaN</td>\n",
              "      <td>['/m/04rlf', '/m/02jjt', '/m/02jjt']</td>\n",
              "      <td>4</td>\n",
              "      <td>UCZf__ehlCEBPop___sldpBUQ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-6duBsde_XM</td>\n",
              "      <td>【放送事故】酒飲みながら東海オンエア×ヒカキンで質問コーナーやったらヤバかったwww</td>\n",
              "      <td>提供：モンスターストライク\\n\\n\\n\\n▼キャンペーンサイトはこちら\\n\\n\\n\\nhtt...</td>\n",
              "      <td>none</td>\n",
              "      <td>['ヒカキン', 'ヒカキンtv', 'hikakintv', 'hikakin', 'ひか...</td>\n",
              "      <td>2018-06-21T08:00:00.000Z</td>\n",
              "      <td>https://i.ytimg.com/vi/-6duBsde_XM/default.jpg</td>\n",
              "      <td>1759797.0</td>\n",
              "      <td>33923.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>4517.0</td>\n",
              "      <td>False</td>\n",
              "      <td>hd</td>\n",
              "      <td>2d</td>\n",
              "      <td>PT27M7S</td>\n",
              "      <td>rectangular</td>\n",
              "      <td>NaN</td>\n",
              "      <td>['/m/098wr', '/m/019_rr', '/m/02wbm', '/m/019_...</td>\n",
              "      <td>5</td>\n",
              "      <td>UCZf__ehlCEBPop___sldpBUQ</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-124cec00-84d7-46ce-b063-6047d4dd3f58')\"\n",
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              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-124cec00-84d7-46ce-b063-6047d4dd3f58');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c152e243-43bf-4e35-9f29-c3f6a8763676 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8pXoHSC0HxWv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "outputId": "4ce80c93-cbe7-42e1-8188-50235ffcaf2b"
      },
      "source": [
        "# column 'viewCount''\n",
        "df['viewCount'].sort_values()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24426    0.0\n",
              "14789    0.0\n",
              "16400    0.0\n",
              "12752    0.0\n",
              "47228    0.0\n",
              "        ... \n",
              "45854    NaN\n",
              "45856    NaN\n",
              "45860    NaN\n",
              "45864    NaN\n",
              "45865    NaN\n",
              "Name: viewCount, Length: 66289, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\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>viewCount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>24426</th>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>45864</th>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>45865</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>66289 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\"NaN\" = Not a Numberの略で直訳すると「数字ではない」。データセットでは欠落しており、データフレームとして読み込むと NaN として保存されることが多い。必要に応じて別途対応が必要。例えばこれらを説明変数として扱うと想定外の動作となることがある。"
      ],
      "metadata": {
        "id": "a26aHmaZVOjO"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ebMtSjgbHxWv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "399b595b-b124-408c-be37-cf2eae50cdb2"
      },
      "source": [
        "# drop samples including NaN & 0 on 'viewCount'\n",
        "\n",
        "print('orig_num = ', len(df))\n",
        "print('num of NaN = ', df['viewCount'].isna().sum())\n",
        "df = df[df['viewCount'].notnull()]\n",
        "print('after_num = ', len(df))\n",
        "\n",
        "df = df[df['viewCount'] != 0]\n",
        "print('after_num2 = ', len(df))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "orig_num =  66289\n",
            "num of NaN =  10\n",
            "after_num =  66279\n",
            "after_num2 =  66231\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4C0kWvqkHxWw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "c08d253c-1036-4f1f-aedb-0c7d0a506704"
      },
      "source": [
        "df['viewCount'].describe()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    6.623100e+04\n",
              "mean     4.548834e+05\n",
              "std      1.328530e+06\n",
              "min      2.000000e+00\n",
              "25%      3.919150e+04\n",
              "50%      1.215850e+05\n",
              "75%      3.515195e+05\n",
              "max      8.664236e+07\n",
              "Name: viewCount, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
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              "      <th>viewCount</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.623100e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.548834e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.328530e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.919150e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.215850e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.515195e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>8.664236e+07</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1枚目: リニアスケール\n",
        "fig1 = go.Figure()\n",
        "fig1.add_trace(go.Histogram(x=df['viewCount'], nbinsx=100))\n",
        "fig1.update_xaxes(title_text='ViewCount')\n",
        "fig1.update_yaxes(title_text='Frequency')\n",
        "fig1.update_layout(\n",
        "    title='histgram of viewCount (linear)',\n",
        "    height=500, width=700\n",
        ")\n",
        "fig1.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "iajcmuI0Zwk3",
        "outputId": "b1bee228-656e-4acf-9045-25e5bb53765b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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of viewCount (linear)\"},\"height\":500,\"width\":700},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2枚目: ログスケール\n",
        "fig2 = go.Figure()\n",
        "fig2.add_trace(go.Histogram(x=df['viewCount'], nbinsx=100))\n",
        "fig2.update_xaxes(title_text='ViewCount')\n",
        "fig2.update_yaxes(title_text='Frequency (log)', type='log')\n",
        "fig2.update_layout(\n",
        "    title='histgram of viewCount (log)',\n",
        "    height=500, width=700\n",
        ")\n",
        "fig2.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "0iYhb5U-aRf8",
        "outputId": "1f766c4a-ab00-44bb-f45c-96903b455142"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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(log)\"},\"type\":\"log\"},\"title\":{\"text\":\"histgram of viewCount (log)\"},\"height\":500,\"width\":700},                        {\"responsive\": true}                    ).then(function(){\n",
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AKATkO81HxWx"
      },
      "source": [
        "## 数値データに対する前処理の例"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WMW474nmHxWx"
      },
      "source": [
        "### 手法1：バイナリ化(binarization)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "gUB5uZw0HxWx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "outputId": "6d86b226-39ee-452c-e9dd-6d3ca8827e20"
      },
      "source": [
        "# preprocess method 1: binarization by mean value\n",
        "\n",
        "THRESHOLD = df['viewCount'].mean()\n",
        "new_column = df['viewCount'] > THRESHOLD\n",
        "new_column = np.where(new_column == True, 1, 0)\n",
        "temp = pd.DataFrame(df['viewCount'])\n",
        "temp['binary'] = new_column\n",
        "temp.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   viewCount  binary\n",
              "0  2244205.0       1\n",
              "1  1869268.0       1\n",
              "2  1724625.0       1\n",
              "3  1109029.0       1\n",
              "4  1759797.0       1"
            ],
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2a5ec4c8-5057-450e-88d1-2ee397594302 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "temp",
              "summary": "{\n  \"name\": \"temp\",\n  \"rows\": 66231,\n  \"fields\": [\n    {\n      \"column\": \"viewCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1328529.7898147323,\n        \"min\": 2.0,\n        \"max\": 86642355.0,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          527058.0,\n          47011.0,\n          61966.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"binary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QrH0ZlFIHxWx"
      },
      "source": [
        "### 手法2：アドホックな離散化(ad-hoc discretization)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p_yNE_bPHxWy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "outputId": "eb396de3-8ef0-448b-cfaa-f78ea1c9fc33"
      },
      "source": [
        "# preprocess method 2: discretization 1, ad-hoc division\n",
        "floor = 10000\n",
        "new_column = np.floor_divide(df['viewCount'], floor)\n",
        "temp['discret_floor'] = new_column\n",
        "temp.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   viewCount  binary  discret_floor\n",
              "0  2244205.0       1          224.0\n",
              "1  1869268.0       1          186.0\n",
              "2  1724625.0       1          172.0\n",
              "3  1109029.0       1          110.0\n",
              "4  1759797.0       1          175.0"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>viewCount</th>\n",
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              "      <td>2244205.0</td>\n",
              "      <td>1</td>\n",
              "      <td>224.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1869268.0</td>\n",
              "      <td>1</td>\n",
              "      <td>186.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1724625.0</td>\n",
              "      <td>1</td>\n",
              "      <td>172.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1109029.0</td>\n",
              "      <td>1</td>\n",
              "      <td>110.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>1</td>\n",
              "      <td>175.0</td>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-05cc5638-fa81-49e8-a0ec-1439bb67a03e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-05cc5638-fa81-49e8-a0ec-1439bb67a03e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a6d9ceaa-fa61-41c5-a0dd-6792ee2f5d65')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
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              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a6d9ceaa-fa61-41c5-a0dd-6792ee2f5d65 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "temp",
              "summary": "{\n  \"name\": \"temp\",\n  \"rows\": 66231,\n  \"fields\": [\n    {\n      \"column\": \"viewCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1328529.7898147323,\n        \"min\": 2.0,\n        \"max\": 86642355.0,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          527058.0,\n          47011.0,\n          61966.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"binary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discret_floor\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 132.85438744813106,\n        \"min\": 0.0,\n        \"max\": 8664.0,\n        \"num_unique_values\": 985,\n        \"samples\": [\n          56.0,\n          624.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# default (logスケール)\n",
        "fig3 = go.Figure()\n",
        "fig3.add_trace(go.Histogram(x=temp['viewCount'], nbinsx=100))\n",
        "fig3.update_xaxes(title_text='viewCounts')\n",
        "fig3.update_yaxes(title_text='Freqency (log)', type='log')\n",
        "fig3.update_layout(\n",
        "    title='default',\n",
        "    height=500, width=700\n",
        ")\n",
        "fig3.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "cKmIVVURap6O",
        "outputId": "2b026d1d-b997-47f7-fac4-e64885100c0c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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(log)\"},\"type\":\"log\"},\"title\":{\"text\":\"default\"},\"height\":500,\"width\":700},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "\n",
              "// Listen for the removal of the full notebook cells\n",
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              "    x.observe(notebookContainer, {childList: true});\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# discret_floor (logスケール)\n",
        "fig4 = go.Figure()\n",
        "fig4.add_trace(go.Histogram(x=temp['discret_floor'], nbinsx=100))\n",
        "fig4.update_xaxes(title_text='discret_floor')\n",
        "fig4.update_yaxes(title_text='Freqency (log)', type='log')\n",
        "fig4.update_layout(\n",
        "    title='discret_floor',\n",
        "    height=500, width=700\n",
        ")\n",
        "fig4.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "yzC-Jh1za0vg",
        "outputId": "4d6b5c17-bae3-4191-c00a-7242e00925d8"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2DGEK_AoHxWy"
      },
      "source": [
        "### 手法3：統計的な離散化"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3qhyXhOHxWy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "81313636-dba4-429d-de43-06ed32b4c376"
      },
      "source": [
        "# preprocess method 3: discretization 2, quantilzation\n",
        "discret_num = 4\n",
        "ranges = np.linspace(0, 1, discret_num)\n",
        "data = df['viewCount'].quantile(ranges)\n",
        "data"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.000000    2.000000e+00\n",
              "0.333333    5.913133e+04\n",
              "0.666667    2.408843e+05\n",
              "1.000000    8.664236e+07\n",
              "Name: viewCount, dtype: float64"
            ],
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>viewCount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.000000</th>\n",
              "      <td>2.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.333333</th>\n",
              "      <td>5.913133e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.666667</th>\n",
              "      <td>2.408843e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.000000</th>\n",
              "      <td>8.664236e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "abTAiICgHxWz",
        "colab": {
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          "height": 413
        },
        "outputId": "27248e52-1ceb-4636-d915-243e9f6c59a9"
      },
      "source": [
        "new_column = pd.qcut(df['viewCount'], discret_num, labels=False)\n",
        "temp['discret_quantile'] = new_column\n",
        "temp"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       viewCount  binary  discret_floor  discret_quantile\n",
              "0      2244205.0       1          224.0                 3\n",
              "1      1869268.0       1          186.0                 3\n",
              "2      1724625.0       1          172.0                 3\n",
              "3      1109029.0       1          110.0                 3\n",
              "4      1759797.0       1          175.0                 3\n",
              "...          ...     ...            ...               ...\n",
              "66284   131489.0       0           13.0                 2\n",
              "66285    13271.0       0            1.0                 0\n",
              "66286    76266.0       0            7.0                 1\n",
              "66287   282447.0       0           28.0                 2\n",
              "66288     6900.0       0            0.0                 0\n",
              "\n",
              "[66231 rows x 4 columns]"
            ],
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>viewCount</th>\n",
              "      <th>binary</th>\n",
              "      <th>discret_floor</th>\n",
              "      <th>discret_quantile</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2244205.0</td>\n",
              "      <td>1</td>\n",
              "      <td>224.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1869268.0</td>\n",
              "      <td>1</td>\n",
              "      <td>186.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1724625.0</td>\n",
              "      <td>1</td>\n",
              "      <td>172.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1109029.0</td>\n",
              "      <td>1</td>\n",
              "      <td>110.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1759797.0</td>\n",
              "      <td>1</td>\n",
              "      <td>175.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66284</th>\n",
              "      <td>131489.0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66285</th>\n",
              "      <td>13271.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66286</th>\n",
              "      <td>76266.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66287</th>\n",
              "      <td>282447.0</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66288</th>\n",
              "      <td>6900.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>66231 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-03cf1ef5-3da8-4743-9582-c1ddfb453eff button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-03cf1ef5-3da8-4743-9582-c1ddfb453eff');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0518038b-6e2a-453e-8f21-ab8da856a804 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_b7debc42-6500-44ca-9c96-2b4646175568\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('temp')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b7debc42-6500-44ca-9c96-2b4646175568 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('temp');\n",
              "      }\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "temp",
              "summary": "{\n  \"name\": \"temp\",\n  \"rows\": 66231,\n  \"fields\": [\n    {\n      \"column\": \"viewCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1328529.7898147323,\n        \"min\": 2.0,\n        \"max\": 86642355.0,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          527058.0,\n          47011.0,\n          61966.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"binary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discret_floor\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 132.85438744813106,\n        \"min\": 0.0,\n        \"max\": 8664.0,\n        \"num_unique_values\": 985,\n        \"samples\": [\n          56.0,\n          624.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discret_quantile\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# default (logスケール)\n",
        "fig5 = go.Figure()\n",
        "fig5.add_trace(go.Histogram(x=temp['viewCount'], nbinsx=100))\n",
        "fig5.update_xaxes(title_text='viewCounts')\n",
        "fig5.update_yaxes(title_text='Freqency (log)', type='log')\n",
        "fig5.update_layout(\n",
        "    title='default',\n",
        "    height=500, width=700\n",
        ")\n",
        "fig5.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "GQyjww7YblBz",
        "outputId": "f6560440-787f-4ee3-de59-109c5fb92415"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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(log)\"},\"type\":\"log\"},\"title\":{\"text\":\"default\"},\"height\":500,\"width\":700},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('44295db1-838a-458a-be75-7c6f2ff333a1');\n",
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              "\n",
              "// Listen for the removal of the full notebook cells\n",
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              "    x.observe(notebookContainer, {childList: true});\n",
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              "var outputEl = gd.closest('.output');\n",
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              "</body>\n",
              "</html>"
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# discret_quantile (logスケール)\n",
        "fig6 = go.Figure()\n",
        "fig6.add_trace(go.Histogram(x=temp['discret_quantile'], nbinsx=discret_num*2))\n",
        "fig6.update_xaxes(title_text='viewCounts (discret_quantile={})'.format(discret_num))\n",
        "fig6.update_yaxes(title_text='Freqency (log)', type='log')\n",
        "fig6.update_layout(\n",
        "    title='discret_quantile',\n",
        "    height=500, width=700\n",
        ")\n",
        "fig6.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "yPzR3VCbbpDC",
        "outputId": "1352d469-6be2-46d7-eda0-399d69b44300"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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3,2,2,2,2,2,3,1,1,2,1,2,2,2,2,1,1,2,1,1,1,1,2,2,1,2,3,2,2,2,2,2,2,0,2,2,1,2,2,1,2,2,2,2,3,2,1,2,1,2,2,2,2,2,2,3,1,2,1,1,2,2,2,2,2,2,2,3,2,2,1,1,2,1,2,1,2,2,2,1,2,2,2,2,2,2,2,2,1,2,2,1,2,2,2,3,1,1,1,0,2,2,2,2,1,1,1,1,1,2,2,2,1,2,3,2,1,0,2,1,2,2,2,2,1,2,1,3,2,1,1,2,2,2,2,2,1,2,2,2,1,2,2,2,3,0,2,1,3,2,1,3,2,0,1,2,1,1,2,2,2,0,2,2,1,2,1,2,2,1,2,2,0,2,2,3,1,2,1,1,1,0,2,1,1,1,3,0,0,2,1,1,2,1,1,2,1,2,2,2,2,1,1,2,2,2,2,2,2,2,2,2,1,2,2,2,3,3,3,1,3,3,1,2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,2,1,3,3,2,2,1,3,1,2,2,3,2,2,2,1,2,2,2,3,1,2,2,1,3,2,3,3,2,2,3,2,2,3,2,2,3,1,2,2,3,2,3,2,2,3,2,2,2,2,2,1,2,3,2,2,2,3,2,2,2,2,3,1,3,3,2,2,2,3,2,2,2,2,3,3,2,2,2,2,3,2,2,3,3,3,2,3,2,2,2,2,2,2,2,3,2,3,2,2,3,2,2,2,2,1,3,3,3,2,2,3,2,3,3,3,3,3,2,3,3,2,2,2,3,3,2,2,2,3,2,2,3,1,3,3,2,2,3,2,3,1,3,2,2,3,3,2,3,3,3,2,3,2,3,2,3,2,3,2,3,3,2,2,3,2,2,3,2,3,3,3,2,2,3,2,3,3,3,3,3,3,3,2,3,2,2,3,3,3,3,2,3,1,1,2,2,2,3,1,2,3,3,2,3,2,3,1,2,1,2,1,2,3,3,1,3,3,0,3,1,1,1,3,1,1,3,1,1,1,3,2,2,2,1,1,2,3,2,1,1,1,3,3,2,3,2,3,1,3,1,2,2,1,1,1,2,2,3,0,3,2,2,2,2,1,2,1,2,2,3,1,1,1,2,2,2,2,1,0,3,2,1,2,2,2,1,2,3,2,0,3,1,3,1,1,1,1,1,1,2,1,3,3,1,2,1,1,1,1,1,1,1,3,2,1,1,1,0,0,0,1,2,1,1,2,1,0,1,1,1,1,3,0,1,2,2,1,0,1,1,1,1,2,1,1,1,0,1,1,0,0,1,2,1,0,0,1,0,1,1,2,3,0,0,2,2,1,0,2,0,0,0,0,2,0,0,0,0,2,2,0,0,2,0,0,0,2,2,0,0,0,0,3,0,0,2,3,1,2,1,0,1,0,2,0,3,1,0,0,0,1,2,0,0,0,2,1,1,0,1,1,2,0,3,2,1,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,1,0,0,1,1,2,1,1,1,0,3,3,2,2,2,2,2,1,2,2,1,2,3,1,1,0,2,1,3,1,3,3,1,0,2,1,2,1,3,1,1,1,1,1,1,1,1,1,1,1,3,2,1,2,2,1,2,1,2,2,0,2,3,1,1,2,2,1,1,1,1,1,2,2,2,1,1,1,1,1,0,1,1,1,1,1,1,1,1,2,1,1,3,3,0,3,1,2,1,1,1,1,1,2,1,1,2,1,1,1,3,1,2,0,3,1,2,1,1,1,1,2,2,2,1,1,2,2,2,2,1,3,1,2,1,1,2,1,2,1,3,1,3,2,2,3,2,2,2,2,2,2,3,2,3,2,3,1,0,2,1,1,3,3,1,2,3,3,1,2,1,1,2,1,3,1,2,0,2,1,2,3,3,2,2,1,1,2,2,2,1,1,2,2,2,3,1,2,3,1,2,3,2,3,3,3,3,3,1,2,2,1,3,2,3,1,1,3,3,2,3,1,2,1,3,2,2,2,2,1,3,2,2,2,2,2,2,1,3,3,2,3,1,1,2,1,1,1,2,3,3,2,3,2,2,3,1,3,2,3,1,3,3,2,2,2,2,2,2,3,1,3,3,2,2,1,3,1,2,1,1,3,1,2,2,1,3,3,0,1,1,0,1,0,2,1,1,2,1,2,2,1,1,1,3,1,1,2,2,2,1,0,2,1,2,0,3,0,0,1,3,3,3,0,1,0,3,0,1,1,2,2,1,2,2,3,1,3,1,3,3,0,2,2,3,3,3,0,2,2,1,2,3,0,2,3,2,0,2,2,0,0,0,2,1,3,0,0,0,2,0,2,2,2,1,2,1,0,3,0,2,1,1,0,1,0,0,3,1,1,2,1,1,0,1,2,2,3,2,2,2,0,0,1,2,1,0,2,1,2,1,0,3,2,0,3,3,2,1,1,0,0,0,2,0,2,0,0,0,0,2,1,2,1,1,2,0,1,2,1,1,0,0,0,1,0,3,1,0,1,2,0,0,0,3,3,3,3,0,0,1,1,2,0,2,1,3,3,0,0,0,0,0,0,1,0,2,0,3,1,0,3,1,2,3,3,3,0,0,0,0,0,2,0,0,1,1,1,1,0,1,2,0,1,1,1,1,1,0,0,0,1,1,1,1,1,1,0,2,2,1,2,2,2,1,1,1,0,0,0,1,0,1,1,1,0,2,1,1,1,3,2,0,2,1,1,2,0,2,1,1,1,1,2,1,1,2,2,1,1,1,2,1,1,1,1,1,2,1,1,0,1,2,1,1,1,1,2,1,1,2,2,1,3,3,2,1,0,3,2,0,3,1,2,1,0,3,1,1,2,3,2,1,2,1,3,0,1,1,1,1,0,0,1,2,2,1,1,3,2,2,1,2,2,1,3,0,1,1,1,1,2,2,2,0,2,0,0,2,1,1,1,2,2,2,3,3,3,2,2,1,2,3,3,3,0,2,2,2,1,2,2,2,3,3,0,2,3,1,1,0,2,2,3,0,2,1,2,2,2,0,1,2,2,1,2,3,3,2,2,1,1,1,2,1,3,2,3,1,3,3,3,2,2,0,1,3,1,2,2,2,3,3,2,1,0,2,2,1,1,1,3,2,3,1,1,3,2,2,2,1,2,3,2,2,2,2,1,2,3,1,3,3,2,1,3,0,2,3,1,0,2,2,2,2,3,2,0,1,2,2,2,1,2,1,3,2,2,3,3,2,2,2,1,1,2,0,2,2,1,3,2,2,2,1,2,1,1,2,0,3,3,2,2,2,1,2,3,2,1,1,0,2,1,2,3,2,0,0,2,2,2,1,1,1,1,2,2,1,1,2,0,2,1,0,2,3,1,3,3,2,1,3,2,2,1,1,2,2,3,2,3,0,1,1,3,0,1,2,1,3,3,3,3,3,2,2,0,0,2,0,1,1,1,2,1,1,2,2,2,0,1,3,2,2,1,2,2,1,3,2,1,1,1,0,1,2,0,2,2,2,3,1,1,1,2,2,1,1,0,2,3,3,3,0,2,3,2,2,0,2,1,0,2,3,0,1,2,0,3,1,2,1,1,2,1,1,0,0,0,0,0,0,1,1,0,1,3,0,3,2,2,2,1,2,1,1,1,1,3,3,2,2,2,1,2,1,0,0,0,1,1,2,1,0,0,2,0,0,2,0,1,1,0,0,0,0,1,2,1,1,0,1,1,0,1,1,1,2,1,2,1,1,3,2,2,1,1,0,1,1,2,2,2,0,0,0,0,0,1,0,0,1,1,0,2,1,1,0,1,0,1,1,2,0,2,1,0,0,1,1,0,0,3,2,0,1,1,1,1,0,0,1,0,2,1,0,1,2,0,0,0,2,2,2,2,1,2,1,1,0,1,0,0,1,1,1,1,1,3,3,1,2,2,2,1,1,3,1,2,1,1,1,1,1,0,2,2,2,2,1,1,0,1,0,3,1,2,0,0,2,1,3,2,1,2,0,2,1,1,0,0,3,3,2,2,2,3,0,1,1,1,1,0,2,0,2,1,2,0,3,1,0,2,3,0,0,3,1,1,0,2,1,1,1,2,0,2,2,2,2,3,0,1,2,3,1,1,2,0,0,1,1,1,1,1,2,1,1,2,0,2,3,2,0,2,2,0,2,2,2,0,1,1,0,0,1,2,2,1,2,2,3,2,0,3,2,3,0,2,0,2,2,0,0,0,2,0,1,0,1,2,0,2,2,1,1,0,2,0,1,3,1,1,2,1,1,2,0,2,2,2,1,3,2,2,2,1,2,3,1,3,2,1,2,1,0,2,2,1,0,2,1,2,2,2,1,0,1,2,1,2,1,2,1,2,1,2,1,1,1,0,1,1,2,1,1,1,1,2,1,2,2,1,2,2,0,2,2,1,3,3,1,2,2,0,2,1,0,0,1,2,2,3,1,3,2,3,2,1,3,3,1,2,1,3,0,1,3,1,2,3,2,3,1,2,1,2,2,1,2,1,3,0,2,2,3,2,2,3,1,2,1,3,2,3,0,2,0,3,2,2,2,2,2,3,2,1,1,1,2,3,1,3,1,2,2,3,3,1,3,2,1,1,2,2,3,3,1,1,3,2,1,3,1,0,1,1,1,2,2,2,2,1,3,2,0,1,1,1,0,0,0,0,1,2,3,0,3,1,0,3,0,1,0,0,0,0,1,2,2,0,0,1,0,0,0,3,3,0,1,2,0,1,0,1,1,0,1,0,1,2,2,2,2,0,1,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,1,2,0,0,0,0,0,1,0,1,0,0,0,2,1,2,2,0,0,0,0,1,1,0,0,2,0,1,0,2,0,0,1,0,0,0,0,0,1,0,1,0,0,0,1,1,2,1,1,1,1,0,2,1,1,0,0,1,1,2,1,0,2,0,2,1,2,2,3,0,0,2,0,2,1,2,0,1,1,1,1,0,0,1,1,2,1,1,1,1,2,1,2,1,2,2,3,0,0,2,2,2,2,1,1,0,1,2,1,1,1,1,2,2,1,2,0,3,1,1,1,1,3,1,2,2,0,2,1,2,3,1,1,3,2,2,1,0,2,0,1,3,2,1,0,0,1,1,1,2,3,0,3,2,1,2,1,3,0,2,0,1,3,3,2,2,3,3,1,2,1,3,2,2,2,2,3,1,3,3,2,2,3,3,3,3,1,3,3,1,2,2,2,3,2,3,2,2,2,2,2,2,2,3,1,2,3,3,3,3,3,2,3,3,2,3,3,3,3,3,2,3,1,3,2,1,1,1,3,2,2,2,2,3,2,3,1,3,3,3,1,3,2,2,1,3,1,3,0,3,2,1,1,2,1,3,1,2,1,3,1,1,2,1,3,3,1,2,3,3,3,1,1,3,1,2,1,2,2,3,2,2,1,1,3,1,2,1,3,3,3,3,1,3,0,2,2,3,2,1,2,3,1,2,1,2,2,1,3,3,2,1,3,3,3,0,3,1,3,3,3,3,1,2,1,2,1,3,3,1,2,3,2,1,3,1,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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EseLksGcHxW0"
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      "source": [
        "### 手法4：ログスケール化(log-scaling)"
      ]
    },
    {
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          "height": 201
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        "outputId": "7a99fe59-3edd-435e-ff81-cb6eb63e6093"
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      "source": [
        "# preprocess method 4: log-scaling\n",
        "new_column = np.log10(df['viewCount'] + 1)\n",
        "temp['log10'] = new_column\n",
        "temp.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   viewCount  binary  discret_floor  discret_quantile     log10\n",
              "0  2244205.0       1          224.0                 3  6.351063\n",
              "1  1869268.0       1          186.0                 3  6.271672\n",
              "2  1724625.0       1          172.0                 3  6.236695\n",
              "3  1109029.0       1          110.0                 3  6.044943\n",
              "4  1759797.0       1          175.0                 3  6.245463"
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4a204fe4-3e80-4f19-9a7a-e1c0a5fdb20a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4a204fe4-3e80-4f19-9a7a-e1c0a5fdb20a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0999c3f7-8b18-4e8d-993e-b659c9d1382f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0999c3f7-8b18-4e8d-993e-b659c9d1382f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0999c3f7-8b18-4e8d-993e-b659c9d1382f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "temp",
              "summary": "{\n  \"name\": \"temp\",\n  \"rows\": 66231,\n  \"fields\": [\n    {\n      \"column\": \"viewCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1328529.7898147323,\n        \"min\": 2.0,\n        \"max\": 86642355.0,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          527058.0,\n          47011.0,\n          61966.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"binary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discret_floor\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 132.85438744813106,\n        \"min\": 0.0,\n        \"max\": 8664.0,\n        \"num_unique_values\": 985,\n        \"samples\": [\n          56.0,\n          624.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discret_quantile\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"log10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7204346083099966,\n        \"min\": 0.47712125471966244,\n        \"max\": 7.9377302532189224,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          5.721859233694926,\n          4.672208727480268\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q4NH3TbwHxW0"
      },
      "source": [
        "### デフォルトとログスケールとの比較"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# default\n",
        "fig7 = go.Figure()\n",
        "fig7.add_trace(go.Histogram(x=temp['viewCount'], nbinsx=100))\n",
        "fig7.update_xaxes(title_text='viewCounts')\n",
        "fig7.update_yaxes(title_text='Freqency')\n",
        "fig7.update_layout(\n",
        "    title='default',\n",
        "    height=500, width=700\n",
        ")\n",
        "fig7.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "DzqXKG-pb662",
        "outputId": "0c3415c9-bf2e-474f-8825-32c3177f3097"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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                       {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "\n",
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              "</body>\n",
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# log10\n",
        "fig8 = go.Figure()\n",
        "fig8.add_trace(go.Histogram(x=temp['log10'], nbinsx=100))\n",
        "fig8.update_xaxes(title_text='viewCounts')\n",
        "fig8.update_yaxes(title_text='Freqency')\n",
        "fig8.update_layout(\n",
        "    title='log10',\n",
        "    height=500, width=700\n",
        ")\n",
        "fig8.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "ioOso3bCcAL9",
        "outputId": "dc091400-5659-48d7-865c-78fe7bb65b6f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "<head><meta charset=\"utf-8\" /></head>\n",
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              "</body>\n",
              "</html>"
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "34FHK1_5HxW1"
      },
      "source": [
        "### 手法5：標準化(standardization)\n",
        "- [5.3.1. Standardization, or mean removal and variance scaling](https://scikit-learn.org/stable/modules/preprocessing.html)\n",
        "- [sklearn.preprocessing.StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BgF53riYHxW1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "outputId": "66e5b504-01bc-411d-e4a6-1b166a4e6400"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "\n",
        "data = np.array(df['viewCount'].values, dtype='float64')\n",
        "data = data.reshape(len(data), 1)\n",
        "new_column = preprocessing.scale(data)\n",
        "temp['standardization'] = new_column\n",
        "temp.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   viewCount  binary  discret_floor  discret_quantile     log10  \\\n",
              "0  2244205.0       1          224.0                 3  6.351063   \n",
              "1  1869268.0       1          186.0                 3  6.271672   \n",
              "2  1724625.0       1          172.0                 3  6.236695   \n",
              "3  1109029.0       1          110.0                 3  6.044943   \n",
              "4  1759797.0       1          175.0                 3  6.245463   \n",
              "\n",
              "   standardization  \n",
              "0         1.346854  \n",
              "1         1.064632  \n",
              "2         0.955757  \n",
              "3         0.492387  \n",
              "4         0.982231  "
            ],
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1714529b-0ab5-4fb1-8960-71b7cb5b8a0e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "temp",
              "summary": "{\n  \"name\": \"temp\",\n  \"rows\": 66231,\n  \"fields\": [\n    {\n      \"column\": \"viewCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1328529.7898147323,\n        \"min\": 2.0,\n        \"max\": 86642355.0,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          527058.0,\n          47011.0,\n          61966.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"binary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discret_floor\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 132.85438744813106,\n        \"min\": 0.0,\n        \"max\": 8664.0,\n        \"num_unique_values\": 985,\n        \"samples\": [\n          56.0,\n          624.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discret_quantile\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"log10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7204346083099966,\n        \"min\": 0.47712125471966244,\n        \"max\": 7.9377302532189224,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          5.721859233694926,\n          4.672208727480268\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"standardization\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000075494203953,\n        \"min\": -0.3423971445288709,\n        \"max\": 64.87481346211443,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          0.054327101331522486,\n          -0.3070126501418395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D4yz6nZwHxW1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "outputId": "a47da5d4-7a1d-4fd9-8ecc-79b0a0959cef"
      },
      "source": [
        "mean = np.mean(new_column)\n",
        "var = np.var(new_column)\n",
        "print('mean = ', mean, ', var = ', var)\n",
        "\n",
        "temp['standardization'].describe()\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mean =  1.2873900181367037e-17 , var =  1.0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    6.623100e+04\n",
              "mean     1.287390e-17\n",
              "std      1.000008e+00\n",
              "min     -3.423971e-01\n",
              "25%     -3.128985e-01\n",
              "50%     -2.508795e-01\n",
              "75%     -7.780379e-02\n",
              "max      6.487481e+01\n",
              "Name: standardization, dtype: float64"
            ],
            "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>standardization</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.623100e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.287390e-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.000008e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-3.423971e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-3.128985e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-2.508795e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-7.780379e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.487481e+01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# default (logスケール)\n",
        "fig9 = go.Figure()\n",
        "fig9.add_trace(go.Histogram(x=temp['viewCount'], nbinsx=100))\n",
        "fig9.update_yaxes(type='log')\n",
        "fig9.update_xaxes(title_text='viewCounts')\n",
        "fig9.update_yaxes(title_text='Freqency')\n",
        "fig9.update_layout(\n",
        "    title='default',\n",
        "    height=500, width=700\n",
        ")\n",
        "fig9.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "WheDMgx5cYNU",
        "outputId": "4614ca65-e3aa-48a2-b0c9-82ff6077871e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
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                       {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('a066fee4-1fe9-49d1-9e81-28c084bbbf0e');\n",
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              "            Plotly.purge(gd);\n",
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              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
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              "    x.observe(notebookContainer, {childList: true});\n",
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              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# standardization (logスケール)\n",
        "fig10 = go.Figure()\n",
        "fig10.add_trace(go.Histogram(x=temp['standardization'], nbinsx=100))\n",
        "fig10.update_yaxes(type='log')\n",
        "fig10.update_xaxes(title_text='viewCounts')\n",
        "fig10.update_yaxes(title_text='Freqency (log)')\n",
        "fig10.update_layout(\n",
        "    title='standaridization',\n",
        "    height=500, width=700\n",
        ")\n",
        "fig10.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "9GzxoAq5cajH",
        "outputId": "61262278-c39e-45cb-824f-b25f9091cc6b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "arbdfRHDHxW2"
      },
      "source": [
        "### 手法6：min-maxスケーリング(Min-Max scalering)\n",
        "- [min-max scaler](https://scikit-learn.org/stable/modules/preprocessing.html#scaling-features-to-a-range)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SmQVz5gHxW2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "ca92ca79-0836-4651-e11b-689b06aa26b0"
      },
      "source": [
        "\"\"\"\n",
        "min = data.min(axis=0)\n",
        "max = data.max(axis=0)\n",
        "new_column = (data - min) / (max - min)\n",
        "temp['min-max'] = new_column\n",
        "temp.head()\n",
        "\"\"\"\n",
        "\n",
        "new_column = preprocessing.minmax_scale(df['viewCount'])\n",
        "temp['min-max'] = new_column\n",
        "temp.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   viewCount  binary  discret_floor  discret_quantile     log10  \\\n",
              "0  2244205.0       1          224.0                 3  6.351063   \n",
              "1  1869268.0       1          186.0                 3  6.271672   \n",
              "2  1724625.0       1          172.0                 3  6.236695   \n",
              "3  1109029.0       1          110.0                 3  6.044943   \n",
              "4  1759797.0       1          175.0                 3  6.245463   \n",
              "\n",
              "   standardization   min-max  \n",
              "0         1.346854  0.025902  \n",
              "1         1.064632  0.021575  \n",
              "2         0.955757  0.019905  \n",
              "3         0.492387  0.012800  \n",
              "4         0.982231  0.020311  "
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              "      <th></th>\n",
              "      <th>viewCount</th>\n",
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              "      <th>discret_floor</th>\n",
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              "      <th>0</th>\n",
              "      <td>2244205.0</td>\n",
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              "      <td>224.0</td>\n",
              "      <td>3</td>\n",
              "      <td>6.351063</td>\n",
              "      <td>1.346854</td>\n",
              "      <td>0.025902</td>\n",
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              "      <th>1</th>\n",
              "      <td>1869268.0</td>\n",
              "      <td>1</td>\n",
              "      <td>186.0</td>\n",
              "      <td>3</td>\n",
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              "      <td>1.064632</td>\n",
              "      <td>0.021575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1724625.0</td>\n",
              "      <td>1</td>\n",
              "      <td>172.0</td>\n",
              "      <td>3</td>\n",
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              "      <td>0.955757</td>\n",
              "      <td>0.019905</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1109029.0</td>\n",
              "      <td>1</td>\n",
              "      <td>110.0</td>\n",
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              "      <td>0.492387</td>\n",
              "      <td>0.012800</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1759797.0</td>\n",
              "      <td>1</td>\n",
              "      <td>175.0</td>\n",
              "      <td>3</td>\n",
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              "      <td>0.982231</td>\n",
              "      <td>0.020311</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "    60% {\n",
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              "    80% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-60ab8c1c-5e41-4ac9-92db-0c8081e055ee button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "temp",
              "summary": "{\n  \"name\": \"temp\",\n  \"rows\": 66231,\n  \"fields\": [\n    {\n      \"column\": \"viewCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1328529.7898147323,\n        \"min\": 2.0,\n        \"max\": 86642355.0,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          527058.0,\n          47011.0,\n          61966.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"binary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discret_floor\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 132.85438744813106,\n        \"min\": 0.0,\n        \"max\": 8664.0,\n        \"num_unique_values\": 985,\n        \"samples\": [\n          56.0,\n          624.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discret_quantile\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"log10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7204346083099966,\n        \"min\": 0.47712125471966244,\n        \"max\": 7.9377302532189224,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          5.721859233694926,\n          4.672208727480268\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"standardization\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000075494203953,\n        \"min\": -0.3423971445288709,\n        \"max\": 64.87481346211443,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          0.054327101331522486,\n          -0.3070126501418395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min-max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015333491575589274,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          0.006083121957687368,\n          0.0005425637505481874\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# default\n",
        "fig11 = go.Figure()\n",
        "fig11.add_trace(go.Histogram(x=temp['viewCount'], nbinsx=100))\n",
        "fig11.update_yaxes(type='log')\n",
        "fig11.update_xaxes(title_text='viewCounts')\n",
        "fig11.update_yaxes(title_text='Freqency (log)')\n",
        "fig11.update_layout(\n",
        "    title='default',\n",
        "    height=500, width=700\n",
        ")\n",
        "fig11.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "zn_ki3TmcgsU",
        "outputId": "e5f738ee-bab6-470a-9e0b-5588869bf976"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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mbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"yaxis\":{\"type\":\"log\",\"title\":{\"text\":\"Freqency (log)\"}},\"xaxis\":{\"title\":{\"text\":\"viewCounts\"}},\"title\":{\"text\":\"default\"},\"height\":500,\"width\":700},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('4782007d-116d-462c-b0a5-b0fe34419d99');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
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              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# min-max\n",
        "fig12 = go.Figure()\n",
        "fig12.add_trace(go.Histogram(x=temp['min-max'], nbinsx=100))\n",
        "fig12.update_yaxes(type='log')\n",
        "fig12.update_xaxes(title_text='viewCounts (min-max)')\n",
        "fig12.update_yaxes(title_text='Freqency (log)')\n",
        "fig12.update_layout(\n",
        "    title='min-max',\n",
        "    height=500, width=700\n",
        ")\n",
        "fig12.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "ocV1C7etclcg",
        "outputId": "01deaf0e-6ee9-4272-883c-b96a71909ac1"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yYCu0qBoHxW3"
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      "source": [
        "### 手法7：正規分布への写像(Mapping to a Gaussian distribution)\n",
        "- [Mapping to a Gaussian distribution](https://scikit-learn.org/stable/modules/preprocessing.html#mapping-to-a-gaussian-distribution)"
      ]
    },
    {
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          "height": 238
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        "outputId": "b39847e8-6aa8-4c88-f637-0ac06eaeb039"
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      "source": [
        "pt = preprocessing.PowerTransformer(method='box-cox', standardize=False)\n",
        "orig = df['viewCount'].values.reshape(-1,1)\n",
        "new_column = pt.fit_transform(orig)\n",
        "temp['box-cox'] = new_column\n",
        "temp.head()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   viewCount  binary  discret_floor  discret_quantile     log10  \\\n",
              "0  2244205.0       1          224.0                 3  6.351063   \n",
              "1  1869268.0       1          186.0                 3  6.271672   \n",
              "2  1724625.0       1          172.0                 3  6.236695   \n",
              "3  1109029.0       1          110.0                 3  6.044943   \n",
              "4  1759797.0       1          175.0                 3  6.245463   \n",
              "\n",
              "   standardization   min-max    box-cox  \n",
              "0         1.346854  0.025902  15.286493  \n",
              "1         1.064632  0.021575  15.086986  \n",
              "2         0.955757  0.019905  14.999159  \n",
              "3         0.492387  0.012800  14.518429  \n",
              "4         0.982231  0.020311  15.021171  "
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ad350604-dfb7-48f9-8c23-afe05fd8b1ae button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ad350604-dfb7-48f9-8c23-afe05fd8b1ae');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f4eda7c5-d511-4ebf-88f6-00d80492bfd9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "temp",
              "summary": "{\n  \"name\": \"temp\",\n  \"rows\": 66231,\n  \"fields\": [\n    {\n      \"column\": \"viewCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1328529.7898147323,\n        \"min\": 2.0,\n        \"max\": 86642355.0,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          527058.0,\n          47011.0,\n          61966.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"binary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discret_floor\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 132.85438744813106,\n        \"min\": 0.0,\n        \"max\": 8664.0,\n        \"num_unique_values\": 985,\n        \"samples\": [\n          56.0,\n          624.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discret_quantile\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"log10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7204346083099966,\n        \"min\": 0.47712125471966244,\n        \"max\": 7.9377302532189224,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          5.721859233694926,\n          4.672208727480268\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"standardization\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000075494203953,\n        \"min\": -0.3423971445288709,\n        \"max\": 64.87481346211443,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          0.054327101331522486,\n          -0.3070126501418395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min-max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015333491575589274,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          0.006083121957687368,\n          0.0005425637505481874\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"box-cox\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7797910019280336,\n        \"min\": 0.6945945247349017,\n        \"max\": 19.320113295797267,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          13.711324011450849,\n          11.11394181377182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vo_BVRwkHxW5"
      },
      "source": [
        "### デフォルトとBox-Cox写像との比較"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# default\n",
        "fig13 = go.Figure()\n",
        "fig13.add_trace(go.Histogram(x=temp['viewCount'], nbinsx=100))\n",
        "fig13.update_yaxes(type='log')\n",
        "fig13.update_xaxes(title_text='viewCounts')\n",
        "fig13.update_yaxes(title_text='Freqency (log)')\n",
        "fig13.update_layout(\n",
        "    title='default',\n",
        "    height=500, width=700\n",
        ")\n",
        "fig13.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "A3j3z4AGculf",
        "outputId": "927f4c8c-6954-4a65-c53f-a99f86bb15f8"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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(log)\"}},\"xaxis\":{\"title\":{\"text\":\"viewCounts\"}},\"title\":{\"text\":\"default\"},\"height\":500,\"width\":700},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('1df079c5-07f4-4e48-8785-263df6b178f2');\n",
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              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# box-cox\n",
        "fig14 = go.Figure()\n",
        "fig14.add_trace(go.Histogram(x=temp['box-cox'], nbinsx=100))\n",
        "fig14.update_xaxes(title_text='viewCounts')\n",
        "fig14.update_yaxes(title_text='Freqency')\n",
        "fig14.update_layout(\n",
        "    title='box-cox',\n",
        "    height=500, width=700\n",
        ")\n",
        "fig14.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "nts2OPbKcyNj",
        "outputId": "108208f1-a661-4ba9-ff29-3f3fd3b40660"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "                            \n",
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              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
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              "    x.observe(notebookContainer, {childList: true});\n",
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              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rY_NZkJCfUyE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "outputId": "bc65e8a3-f0a0-4bd2-c596-e7f69574b6e4"
      },
      "source": [
        "# 似ている分布、log-scaledとの比較\n",
        "\n",
        "# log10\n",
        "fig15 = go.Figure()\n",
        "fig15.add_trace(go.Histogram(x=temp['log10'], nbinsx=100))\n",
        "fig15.update_xaxes(title_text='viewCounts (log-scaled)')\n",
        "fig15.update_yaxes(title_text='Freqency')\n",
        "fig15.update_layout(\n",
        "    title='log10',\n",
        "    height=500, width=700\n",
        ")\n",
        "fig15.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XCFVlj6T6rz3"
      },
      "source": [
        "## 特徴ベクトルに対する前処理の例"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NRP5-kd7HxW2"
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      "source": [
        "### 手法8：正規化(normalization)\n",
        "- 正規化という考え方自体は特徴量（データセットを表と見た時の列）に対しても適用できる。ここでは特徴ベクトル（行）に対して適用した際の値を観察する。\n",
        "- NOTE: the process target is NOT one feature value (one column). **The target of normalization is \"feature vector (one row)\"**.\n",
        "- [5.3.3. Normalization](https://scikit-learn.org/stable/modules/preprocessing.html#normalization)"
      ]
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    {
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          "height": 238
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        "outputId": "6463c04d-a7dc-440b-a979-45a48c7a1bd5"
      },
      "source": [
        "temp.head()"
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      "execution_count": 32,
      "outputs": [
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              "   viewCount  binary  discret_floor  discret_quantile     log10  \\\n",
              "0  2244205.0       1          224.0                 3  6.351063   \n",
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              "3  1109029.0       1          110.0                 3  6.044943   \n",
              "4  1759797.0       1          175.0                 3  6.245463   \n",
              "\n",
              "   standardization   min-max    box-cox  \n",
              "0         1.346854  0.025902  15.286493  \n",
              "1         1.064632  0.021575  15.086986  \n",
              "2         0.955757  0.019905  14.999159  \n",
              "3         0.492387  0.012800  14.518429  \n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bbdc6345-eb76-4dc8-82da-e13fab8a22ee button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "temp",
              "summary": "{\n  \"name\": \"temp\",\n  \"rows\": 66231,\n  \"fields\": [\n    {\n      \"column\": \"viewCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1328529.7898147323,\n        \"min\": 2.0,\n        \"max\": 86642355.0,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          527058.0,\n          47011.0,\n          61966.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"binary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discret_floor\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 132.85438744813106,\n        \"min\": 0.0,\n        \"max\": 8664.0,\n        \"num_unique_values\": 985,\n        \"samples\": [\n          56.0,\n          624.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discret_quantile\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"log10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7204346083099966,\n        \"min\": 0.47712125471966244,\n        \"max\": 7.9377302532189224,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          5.721859233694926,\n          4.672208727480268\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"standardization\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000075494203953,\n        \"min\": -0.3423971445288709,\n        \"max\": 64.87481346211443,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          0.054327101331522486,\n          -0.3070126501418395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min-max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015333491575589274,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          0.006083121957687368,\n          0.0005425637505481874\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"box-cox\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7797910019280336,\n        \"min\": 0.6945945247349017,\n        \"max\": 19.320113295797267,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          13.711324011450849,\n          11.11394181377182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0QBpNZJHxW3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "42d5b7f8-ceab-4301-9fe7-439184a295d6"
      },
      "source": [
        "normalized_l2 = preprocessing.normalize(temp, norm='l2')\n",
        "normalized_l2 = pd.DataFrame(normalized_l2, columns=temp.columns)\n",
        "normalized_l2.head()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   viewCount        binary  discret_floor  discret_quantile     log10  \\\n",
              "0        1.0  4.455921e-07       0.000100          0.000001  0.000003   \n",
              "1        1.0  5.349688e-07       0.000100          0.000002  0.000003   \n",
              "2        1.0  5.798362e-07       0.000100          0.000002  0.000004   \n",
              "3        1.0  9.016897e-07       0.000099          0.000003  0.000005   \n",
              "4        1.0  5.682474e-07       0.000099          0.000002  0.000004   \n",
              "\n",
              "   standardization       min-max   box-cox  \n",
              "0     6.001473e-07  1.154169e-08  0.000007  \n",
              "1     5.695449e-07  1.154169e-08  0.000008  \n",
              "2     5.541823e-07  1.154169e-08  0.000009  \n",
              "3     4.439801e-07  1.154168e-08  0.000013  \n",
              "4     5.581503e-07  1.154169e-08  0.000009  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-032e4ada-e99b-4072-a445-40403a89e244\" class=\"colab-df-container\">\n",
              "    <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>viewCount</th>\n",
              "      <th>binary</th>\n",
              "      <th>discret_floor</th>\n",
              "      <th>discret_quantile</th>\n",
              "      <th>log10</th>\n",
              "      <th>standardization</th>\n",
              "      <th>min-max</th>\n",
              "      <th>box-cox</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>4.455921e-07</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.000001</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>6.001473e-07</td>\n",
              "      <td>1.154169e-08</td>\n",
              "      <td>0.000007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>5.349688e-07</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>5.695449e-07</td>\n",
              "      <td>1.154169e-08</td>\n",
              "      <td>0.000008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>5.798362e-07</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>5.541823e-07</td>\n",
              "      <td>1.154169e-08</td>\n",
              "      <td>0.000009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>9.016897e-07</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>4.439801e-07</td>\n",
              "      <td>1.154168e-08</td>\n",
              "      <td>0.000013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>5.682474e-07</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>5.581503e-07</td>\n",
              "      <td>1.154169e-08</td>\n",
              "      <td>0.000009</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-032e4ada-e99b-4072-a445-40403a89e244')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-032e4ada-e99b-4072-a445-40403a89e244 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-032e4ada-e99b-4072-a445-40403a89e244');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f8c678e4-4646-4516-a7d5-d44b8807db0a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f8c678e4-4646-4516-a7d5-d44b8807db0a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "normalized_l2",
              "summary": "{\n  \"name\": \"normalized_l2\",\n  \"rows\": 66231,\n  \"fields\": [\n    {\n      \"column\": \"viewCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0006598383258048837,\n        \"min\": 0.9102820090353079,\n        \"max\": 0.9999999950167688,\n        \"num_unique_values\": 60464,\n        \"samples\": [\n          0.9999999935939771,\n          0.999999994873716,\n          0.9999999921152997\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"binary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.014191400847861e-07,\n        \"min\": 0.0,\n        \"max\": 2.1983433165121587e-06,\n        \"num_unique_values\": 13253,\n        \"samples\": [\n          2.9203790622816377e-07,\n          2.0803255179428315e-06,\n          1.102162768421603e-06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discret_floor\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.5274030548754216e-05,\n        \"min\": 0.0,\n        \"max\": 9.999999949848078e-05,\n        \"num_unique_values\": 56987,\n        \"samples\": [\n          9.999352900632422e-05,\n          9.121902693557661e-05,\n          8.805271239551444e-05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discret_quantile\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.515152222312064e-06,\n        \"min\": 0.0,\n        \"max\": 2.55134571000241e-05,\n        \"num_unique_values\": 47100,\n        \"samples\": [\n          1.4215449053244911e-05,\n          1.41829886279131e-05,\n          9.094050612203903e-06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"log10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0015773256437109453,\n        \"min\": 9.161489451123492e-08,\n        \"max\": 0.2171574471498306,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          1.0856223040861412e-05,\n          9.938543225564822e-05,\n          7.7335318602493e-05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"standardization\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0009100472660510793,\n        \"min\": -0.1558389803048466,\n        \"max\": 7.487655793490133e-07,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          1.0307613402044993e-07,\n          -6.530655354251332e-06,\n          -4.772871468572203e-06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min-max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.885237033575822e-11,\n        \"min\": 0.0,\n        \"max\": 1.154169914933593e-08,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          1.1541655615804099e-08,\n          1.1541208028083552e-08,\n          1.1541326670430922e-08\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"box-cox\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0028790743197711216,\n        \"min\": 2.2298693519117712e-07,\n        \"max\": 0.33705197615935056,\n        \"num_unique_values\": 60505,\n        \"samples\": [\n          2.601482937176456e-05,\n          0.0002364115080581019,\n          0.0001841148343749819\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_S96YIwHxW3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cdbeaa2b-c4d4-479c-fe66-1222b31f704d"
      },
      "source": [
        "sum = 0\n",
        "for item in normalized_l2.values[0]:\n",
        "    sum += item ** 2\n",
        "print('L2 norm = ', sum)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "L2 norm =  0.9999999999999999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 演習\n",
        "1. dislikeCountの出現頻度をヒストグラムとして描画してみよう。数値はそのまま用いるものとする。ビン数は50とすること。\n",
        "1. 1のヒストグラムにおいて、ビン数を10〜100の間で変更し、描画されるグラフへの影響を確認してみよう。\n",
        "1. 1のヒストグラムにおいて、カウント数をlogスケールで描画してみよう。\n",
        "1. 1のヒストグラムにおいて、カウント数を標準化して描画してみよう。\n",
        "1. 1のヒストグラムにおいて、カウント数をBox-Cox写像して描画してみよう。\n",
        "1. 前処理なし、logスケール、標準化、Box-Cox写像、各々によるヒストグラム上の違いを確認してみよう。"
      ],
      "metadata": {
        "id": "J2lk5avzkZof"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "329eSZL8fuCD"
      },
      "source": [],
      "execution_count": 34,
      "outputs": []
    }
  ]
}