bento_demand_prediction_signate.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/hiro106/signate-bento-sales-prediction/blob/main/bento_demand_prediction_signate.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I traced the page below\n",
        "\n",
        "https://signate.jp/competitions/24/tutorials/3"
      ],
      "metadata": {
        "id": "xz3RQgQvPMSt"
      }
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      "cell_type": "code",
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      "outputs": [],
      "source": [
        "# From the middle of this notebook, I checked behaviors of pyplot, \n",
        "# executing the methods in various conditions and combinations."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   「お弁当の需要予測」という小規模なデータセットのコンペを題材に、売上の要因を簡単に分析しながらモデルを組み立てるところまで実施\n",
        "*   線形と非線形のモデル2つを作成し、これを組み合わせて予測を行う手法を今回は試みた\n",
        "*    Trainデータでのクロスバリデーションの結果、RMSEは11前後程度となった\n",
        "\n"
      ],
      "metadata": {
        "id": "otIYhaxfB59X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Introduction**\n",
        "*  「お弁当の需要予測」は実際にお昼に販売されたお弁当の売り上げ数を予測する問題で、回帰問題として分類される\n",
        "*   データサイズはとても小さいが、生のデータであり、データ加工等の工夫が検討できる良いデータセットである\n",
        "*   まずはデータを読み込み、売り上げ数と説明変数との関係を簡単な基礎分析で見ていく\n",
        "\n"
      ],
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/drive')"
      ]
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    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "iLkp04L1FZyY"
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      "source": [
        "from IPython.display import HTML, display\n",
        "\n",
        "def set_css():\n",
        "  display(HTML('''\n",
        "  <style>\n",
        "    pre {\n",
        "        white-space: pre-wrap;\n",
        "    }\n",
        "  </style>\n",
        "  '''))\n",
        "get_ipython().events.register('pre_run_cell', set_css)"
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              "  </style>\n",
              "  "
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      "source": [
        "import pandas as pd"
      ]
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        "id": "ORMzZ8L8EBET",
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              "  "
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              "<module 'pandas' from '/usr/local/lib/python3.7/dist-packages/pandas/__init__.py'>"
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          "execution_count": 5
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      "cell_type": "code",
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        "pd.DataFrame"
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              "  "
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              "pandas.core.frame.DataFrame"
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    {
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              "\n",
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              "  </style>\n",
              "  "
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      "source": [
        "import numpy as np"
      ]
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    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "xK7zMZ7gEE_3",
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              "  "
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          "data": {
            "text/plain": [
              "<module 'numpy' from '/usr/local/lib/python3.7/dist-packages/numpy/__init__.py'>"
            ]
          },
          "metadata": {},
          "execution_count": 8
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      "source": [
        "np"
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    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "72m2QajuEHG_",
        "colab": {
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          "height": 17
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        "outputId": "a1e19a89-8f09-49c0-d93f-992f6144b5f9"
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      "outputs": [
        {
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              "<IPython.core.display.HTML object>"
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              "\n",
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              "    }\n",
              "  </style>\n",
              "  "
            ]
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          "metadata": {}
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "jtSCEmjAEMcI",
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              "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.7/dist-packages/matplotlib/pyplot.py'>"
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          "execution_count": 10
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      "source": [
        "plt"
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        "id": "yqB5UQv5ENcB",
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              "    }\n",
              "  </style>\n",
              "  "
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          "metadata": {}
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      "source": [
        "import seaborn as sns"
      ]
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        "id": "pdR7b1zREPpQ",
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              "<module 'seaborn' from '/usr/local/lib/python3.7/dist-packages/seaborn/__init__.py'>"
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        "id": "VLdpaCRMEPrt",
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      "source": [
        "sns.set(font=\"IPAexGothic\", style=\"white\")"
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      "source": [
        "pd.set_option('max_rows', 100)\n",
        "pd.set_option('min_rows', 20)"
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      "execution_count": 15,
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      "source": [
        "train = pd.read_csv(\"/drive/My Drive/Colab Notebooks/signate/bento/train.csv\")"
      ]
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        "train"
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              "       datetime    y week  soldout           name   kcal          remarks  \\\n",
              "0    2013-11-18   90    月        0       厚切りイカフライ    NaN              NaN   \n",
              "1    2013-11-19  101    火        1        手作りヒレカツ    NaN              NaN   \n",
              "2    2013-11-20  118    水        0     白身魚唐揚げ野菜あん    NaN              NaN   \n",
              "3    2013-11-21  120    木        1         若鶏ピリ辛焼    NaN              NaN   \n",
              "4    2013-11-22  130    金        1       ビッグメンチカツ    NaN              NaN   \n",
              "5    2013-11-25  135    月        1           鶏の唐揚    NaN              NaN   \n",
              "6    2013-11-26  145    火        0       豚のスタミナ炒め    NaN              NaN   \n",
              "7    2013-11-27  140    水        1       ボローニャ風カツ    NaN              NaN   \n",
              "8    2013-11-28  151    木        0          ハンバーグ    NaN              NaN   \n",
              "9    2013-11-29  116    金        0  タルタルinソーセージカツ    NaN              NaN   \n",
              "..          ...  ...  ...      ...            ...    ...              ...   \n",
              "197   2014-9-16   56    火        0       チーズメンチカツ  438.0              NaN   \n",
              "198   2014-9-17   49    水        0       チキンフリカッセ  430.0     料理長のこだわりメニュー   \n",
              "199   2014-9-18   46    木        0    カレイ唐揚げ 甘酢あん  395.0              NaN   \n",
              "200   2014-9-19   45    金        0        厚切イカフライ  400.0              NaN   \n",
              "201   2014-9-22   29    月        0            筑前煮  395.0              NaN   \n",
              "202   2014-9-24   59    水        1    白身魚のマスタード焼き  408.0              NaN   \n",
              "203   2014-9-25   50    木        0        牛カルビ焼き肉  394.0              NaN   \n",
              "204   2014-9-26   45    金        0       ランチビュッフェ    NaN  スペシャルメニュー（800円）   \n",
              "205   2014-9-29   56    月        1       豚肉と玉子の炒め  404.0              NaN   \n",
              "206   2014-9-30   40    火        0   鶏肉とカシューナッツ炒め  398.0              NaN   \n",
              "\n",
              "             event  payday weather precipitation  temperature  \n",
              "0              NaN     NaN      快晴            --         19.8  \n",
              "1              NaN     NaN      快晴            --         17.0  \n",
              "2              NaN     NaN      快晴            --         15.5  \n",
              "3              NaN     NaN      快晴            --         15.2  \n",
              "4              NaN     NaN      快晴            --         16.1  \n",
              "5              NaN     NaN       曇            --         14.6  \n",
              "6              NaN     NaN      快晴            --         17.9  \n",
              "7              NaN     NaN      晴れ            --         14.7  \n",
              "8              NaN     NaN      薄曇            --         17.7  \n",
              "9              NaN     NaN      快晴            --         12.1  \n",
              "..             ...     ...     ...           ...          ...  \n",
              "197            NaN     NaN      晴れ            --         28.8  \n",
              "198           ママの会     NaN       曇            --         25.0  \n",
              "199            NaN     NaN       曇            --         24.1  \n",
              "200            NaN     NaN      薄曇            --         23.6  \n",
              "201            NaN     NaN      晴れ            --         25.2  \n",
              "202            NaN     NaN       曇             0         24.8  \n",
              "203            NaN     NaN       曇             0         25.4  \n",
              "204  キャリアアップ支援セミナー     NaN      晴れ            --         27.1  \n",
              "205            NaN     NaN      快晴            --         26.6  \n",
              "206            NaN     NaN      快晴            --         28.1  \n",
              "\n",
              "[207 rows x 12 columns]"
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              "      <th></th>\n",
              "      <th>datetime</th>\n",
              "      <th>y</th>\n",
              "      <th>week</th>\n",
              "      <th>soldout</th>\n",
              "      <th>name</th>\n",
              "      <th>kcal</th>\n",
              "      <th>remarks</th>\n",
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              "      <th>payday</th>\n",
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              "      <th>0</th>\n",
              "      <td>2013-11-18</td>\n",
              "      <td>90</td>\n",
              "      <td>月</td>\n",
              "      <td>0</td>\n",
              "      <td>厚切りイカフライ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>快晴</td>\n",
              "      <td>--</td>\n",
              "      <td>19.8</td>\n",
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              "      <th>1</th>\n",
              "      <td>2013-11-19</td>\n",
              "      <td>101</td>\n",
              "      <td>火</td>\n",
              "      <td>1</td>\n",
              "      <td>手作りヒレカツ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>快晴</td>\n",
              "      <td>--</td>\n",
              "      <td>17.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013-11-20</td>\n",
              "      <td>118</td>\n",
              "      <td>水</td>\n",
              "      <td>0</td>\n",
              "      <td>白身魚唐揚げ野菜あん</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>快晴</td>\n",
              "      <td>--</td>\n",
              "      <td>15.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2013-11-21</td>\n",
              "      <td>120</td>\n",
              "      <td>木</td>\n",
              "      <td>1</td>\n",
              "      <td>若鶏ピリ辛焼</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>快晴</td>\n",
              "      <td>--</td>\n",
              "      <td>15.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2013-11-22</td>\n",
              "      <td>130</td>\n",
              "      <td>金</td>\n",
              "      <td>1</td>\n",
              "      <td>ビッグメンチカツ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>快晴</td>\n",
              "      <td>--</td>\n",
              "      <td>16.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2013-11-25</td>\n",
              "      <td>135</td>\n",
              "      <td>月</td>\n",
              "      <td>1</td>\n",
              "      <td>鶏の唐揚</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>曇</td>\n",
              "      <td>--</td>\n",
              "      <td>14.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2013-11-26</td>\n",
              "      <td>145</td>\n",
              "      <td>火</td>\n",
              "      <td>0</td>\n",
              "      <td>豚のスタミナ炒め</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>快晴</td>\n",
              "      <td>--</td>\n",
              "      <td>17.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2013-11-27</td>\n",
              "      <td>140</td>\n",
              "      <td>水</td>\n",
              "      <td>1</td>\n",
              "      <td>ボローニャ風カツ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>晴れ</td>\n",
              "      <td>--</td>\n",
              "      <td>14.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2013-11-28</td>\n",
              "      <td>151</td>\n",
              "      <td>木</td>\n",
              "      <td>0</td>\n",
              "      <td>ハンバーグ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>薄曇</td>\n",
              "      <td>--</td>\n",
              "      <td>17.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2013-11-29</td>\n",
              "      <td>116</td>\n",
              "      <td>金</td>\n",
              "      <td>0</td>\n",
              "      <td>タルタルinソーセージカツ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>快晴</td>\n",
              "      <td>--</td>\n",
              "      <td>12.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>2014-9-16</td>\n",
              "      <td>56</td>\n",
              "      <td>火</td>\n",
              "      <td>0</td>\n",
              "      <td>チーズメンチカツ</td>\n",
              "      <td>438.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>晴れ</td>\n",
              "      <td>--</td>\n",
              "      <td>28.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>2014-9-17</td>\n",
              "      <td>49</td>\n",
              "      <td>水</td>\n",
              "      <td>0</td>\n",
              "      <td>チキンフリカッセ</td>\n",
              "      <td>430.0</td>\n",
              "      <td>料理長のこだわりメニュー</td>\n",
              "      <td>ママの会</td>\n",
              "      <td>NaN</td>\n",
              "      <td>曇</td>\n",
              "      <td>--</td>\n",
              "      <td>25.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>2014-9-18</td>\n",
              "      <td>46</td>\n",
              "      <td>木</td>\n",
              "      <td>0</td>\n",
              "      <td>カレイ唐揚げ 甘酢あん</td>\n",
              "      <td>395.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>曇</td>\n",
              "      <td>--</td>\n",
              "      <td>24.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>2014-9-19</td>\n",
              "      <td>45</td>\n",
              "      <td>金</td>\n",
              "      <td>0</td>\n",
              "      <td>厚切イカフライ</td>\n",
              "      <td>400.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>薄曇</td>\n",
              "      <td>--</td>\n",
              "      <td>23.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>2014-9-22</td>\n",
              "      <td>29</td>\n",
              "      <td>月</td>\n",
              "      <td>0</td>\n",
              "      <td>筑前煮</td>\n",
              "      <td>395.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>晴れ</td>\n",
              "      <td>--</td>\n",
              "      <td>25.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>2014-9-24</td>\n",
              "      <td>59</td>\n",
              "      <td>水</td>\n",
              "      <td>1</td>\n",
              "      <td>白身魚のマスタード焼き</td>\n",
              "      <td>408.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>曇</td>\n",
              "      <td>0</td>\n",
              "      <td>24.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>2014-9-25</td>\n",
              "      <td>50</td>\n",
              "      <td>木</td>\n",
              "      <td>0</td>\n",
              "      <td>牛カルビ焼き肉</td>\n",
              "      <td>394.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>曇</td>\n",
              "      <td>0</td>\n",
              "      <td>25.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>2014-9-26</td>\n",
              "      <td>45</td>\n",
              "      <td>金</td>\n",
              "      <td>0</td>\n",
              "      <td>ランチビュッフェ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>スペシャルメニュー（800円）</td>\n",
              "      <td>キャリアアップ支援セミナー</td>\n",
              "      <td>NaN</td>\n",
              "      <td>晴れ</td>\n",
              "      <td>--</td>\n",
              "      <td>27.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>2014-9-29</td>\n",
              "      <td>56</td>\n",
              "      <td>月</td>\n",
              "      <td>1</td>\n",
              "      <td>豚肉と玉子の炒め</td>\n",
              "      <td>404.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>快晴</td>\n",
              "      <td>--</td>\n",
              "      <td>26.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>206</th>\n",
              "      <td>2014-9-30</td>\n",
              "      <td>40</td>\n",
              "      <td>火</td>\n",
              "      <td>0</td>\n",
              "      <td>鶏肉とカシューナッツ炒め</td>\n",
              "      <td>398.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>快晴</td>\n",
              "      <td>--</td>\n",
              "      <td>28.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>207 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-526c30e2-3d29-44c0-9800-72b1a7b10b10')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-526c30e2-3d29-44c0-9800-72b1a7b10b10 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-526c30e2-3d29-44c0-9800-72b1a7b10b10');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
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              "          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",
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          },
          "metadata": {},
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    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
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          "height": 347
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        "outputId": "d0006aa3-a127-46fa-9f43-efb95473392d"
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      "outputs": [
        {
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              "<IPython.core.display.HTML object>"
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              "  </style>\n",
              "  "
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 207 entries, 0 to 206\n",
            "Data columns (total 12 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
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            " 0   datetime       207 non-null    object \n",
            " 1   y              207 non-null    int64  \n",
            " 2   week           207 non-null    object \n",
            " 3   soldout        207 non-null    int64  \n",
            " 4   name           207 non-null    object \n",
            " 5   kcal           166 non-null    float64\n",
            " 6   remarks        21 non-null     object \n",
            " 7   event          14 non-null     object \n",
            " 8   payday         10 non-null     float64\n",
            " 9   weather        207 non-null    object \n",
            " 10  precipitation  207 non-null    object \n",
            " 11  temperature    207 non-null    float64\n",
            "dtypes: float64(3), int64(2), object(7)\n",
            "memory usage: 19.5+ KB\n"
          ]
        }
      ],
      "source": [
        "train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "R-W-iPmdEVId",
        "colab": {
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          "height": 17
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        "outputId": "25f33881-0b80-466f-fe4c-f99ac144fc49"
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      "outputs": [
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              "  "
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      "source": [
        "test = pd.read_csv(\"/drive/My Drive/Colab Notebooks/signate/bento/test.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "HJYweHULEVOi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
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        "outputId": "a9d58725-7215-4e85-d12b-ceb305306949"
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      "outputs": [
        {
          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
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              "  </style>\n",
              "  "
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 40 entries, 0 to 39\n",
            "Data columns (total 11 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
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            " 2   soldout        40 non-null     int64  \n",
            " 3   name           40 non-null     object \n",
            " 4   kcal           36 non-null     float64\n",
            " 5   remarks        7 non-null      object \n",
            " 6   event          3 non-null      object \n",
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            " 8   weather        40 non-null     object \n",
            " 9   precipitation  40 non-null     object \n",
            " 10  temperature    40 non-null     float64\n",
            "dtypes: float64(3), int64(1), object(7)\n",
            "memory usage: 3.6+ KB\n"
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      "source": [
        "test.info()"
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    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "INlxE3BPEPwm",
        "colab": {
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          "height": 17
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        "outputId": "259c7302-79f2-45fe-def9-4b3b4e518c20"
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      "outputs": [
        {
          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
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              "    pre {\n",
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              "  </style>\n",
              "  "
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      "source": [
        "sample = pd.read_csv(\"/drive/My Drive/Colab Notebooks/signate/bento/sample.csv\", header=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
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          "output_type": "display_data",
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              "  "
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 40 entries, 0 to 39\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   0       40 non-null     object\n",
            " 1   1       40 non-null     int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 768.0+ bytes\n"
          ]
        }
      ],
      "source": [
        "sample.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "S6g-ojnrF7WU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9ccaa869-fd88-4b7d-ffdc-65d16551b41e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
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          },
          "metadata": {}
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             0   1\n",
              "0    2014-10-1  24\n",
              "1    2014-10-2  71\n",
              "2    2014-10-3  25\n",
              "3    2014-10-6  32\n",
              "4    2014-10-7  60\n",
              "5    2014-10-8  44\n",
              "6    2014-10-9  64\n",
              "7   2014-10-10  33\n",
              "8   2014-10-14  67\n",
              "9   2014-10-15  28\n",
              "10  2014-10-16  90\n",
              "11  2014-10-17  73\n",
              "12  2014-10-20   9\n",
              "13  2014-10-21  76\n",
              "14  2014-10-22   1\n",
              "15  2014-10-23  99\n",
              "16  2014-10-24  73\n",
              "17  2014-10-27  60\n",
              "18  2014-10-28  99\n",
              "19  2014-10-29  69\n",
              "20  2014-10-30  68\n",
              "21  2014-10-31  35\n",
              "22   2014-11-4   2\n",
              "23   2014-11-5  24\n",
              "24   2014-11-6  10\n",
              "25   2014-11-7  16\n",
              "26  2014-11-10  43\n",
              "27  2014-11-11  20\n",
              "28  2014-11-12  88\n",
              "29  2014-11-13  18\n",
              "30  2014-11-14  63\n",
              "31  2014-11-17  23\n",
              "32  2014-11-18  55\n",
              "33  2014-11-19  83\n",
              "34  2014-11-20  87\n",
              "35  2014-11-21  55\n",
              "36  2014-11-25  47\n",
              "37  2014-11-26  60\n",
              "38  2014-11-27  80\n",
              "39  2014-11-28  77"
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            "text/html": [
              "\n",
              "  <div id=\"df-5e87d1bb-206d-4848-a204-290a562ab78e\">\n",
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              "\n",
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              "      <th>0</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>2014-10-1</td>\n",
              "      <td>24</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-10-2</td>\n",
              "      <td>71</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-10-3</td>\n",
              "      <td>25</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-10-6</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-10-7</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2014-10-8</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2014-10-9</td>\n",
              "      <td>64</td>\n",
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              "      <th>7</th>\n",
              "      <td>2014-10-10</td>\n",
              "      <td>33</td>\n",
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              "      <th>8</th>\n",
              "      <td>2014-10-14</td>\n",
              "      <td>67</td>\n",
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              "      <th>9</th>\n",
              "      <td>2014-10-15</td>\n",
              "      <td>28</td>\n",
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              "      <th>10</th>\n",
              "      <td>2014-10-16</td>\n",
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              "      <th>11</th>\n",
              "      <td>2014-10-17</td>\n",
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              "      <td>2014-10-20</td>\n",
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              "      <td>2014-10-22</td>\n",
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              "      <th>15</th>\n",
              "      <td>2014-10-23</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2014-10-24</td>\n",
              "      <td>73</td>\n",
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              "      <th>17</th>\n",
              "      <td>2014-10-27</td>\n",
              "      <td>60</td>\n",
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              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2014-10-28</td>\n",
              "      <td>99</td>\n",
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2014-10-29</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2014-10-30</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2014-10-31</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2014-11-4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2014-11-5</td>\n",
              "      <td>24</td>\n",
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              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2014-11-6</td>\n",
              "      <td>10</td>\n",
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              "      <th>25</th>\n",
              "      <td>2014-11-7</td>\n",
              "      <td>16</td>\n",
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              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2014-11-10</td>\n",
              "      <td>43</td>\n",
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              "      <th>27</th>\n",
              "      <td>2014-11-11</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2014-11-12</td>\n",
              "      <td>88</td>\n",
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              "      <th>29</th>\n",
              "      <td>2014-11-13</td>\n",
              "      <td>18</td>\n",
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              "      <td>2014-11-14</td>\n",
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              "      <th>31</th>\n",
              "      <td>2014-11-17</td>\n",
              "      <td>23</td>\n",
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              "      <td>2014-11-18</td>\n",
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              "      <td>2014-11-19</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2014-11-20</td>\n",
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              "      <th>35</th>\n",
              "      <td>2014-11-21</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
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              "      <td>2014-11-25</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
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              "      <th>37</th>\n",
              "      <td>2014-11-26</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2014-11-27</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>2014-11-28</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e87d1bb-206d-4848-a204-290a562ab78e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-5e87d1bb-206d-4848-a204-290a562ab78e 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-5e87d1bb-206d-4848-a204-290a562ab78e');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "Hqm3agzpGCE9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "bafd721c-a256-4edf-94cc-3326a17d28ff"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data Shapes\n",
            "Train: (207, 12) Test: (40, 11) Sample: (40, 2)\n"
          ]
        }
      ],
      "source": [
        "print(\"Data Shapes\")\n",
        "print(\"Train:\", train.shape, \"Test:\", test.shape, \"Sample:\", sample.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "M3xVVZXoGCHt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "e240e3e9-b455-49cc-df4a-1633c0503fde"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              datetime    y week  soldout        name  kcal remarks event  \\\n",
              "datetime                                                                    \n",
              "2013-11-18  2013-11-18   90    月        0    厚切りイカフライ   NaN     NaN   NaN   \n",
              "2013-11-19  2013-11-19  101    火        1     手作りヒレカツ   NaN     NaN   NaN   \n",
              "2013-11-20  2013-11-20  118    水        0  白身魚唐揚げ野菜あん   NaN     NaN   NaN   \n",
              "2013-11-21  2013-11-21  120    木        1      若鶏ピリ辛焼   NaN     NaN   NaN   \n",
              "2013-11-22  2013-11-22  130    金        1    ビッグメンチカツ   NaN     NaN   NaN   \n",
              "\n",
              "            payday weather precipitation  temperature  \n",
              "datetime                                               \n",
              "2013-11-18     NaN      快晴            --         19.8  \n",
              "2013-11-19     NaN      快晴            --         17.0  \n",
              "2013-11-20     NaN      快晴            --         15.5  \n",
              "2013-11-21     NaN      快晴            --         15.2  \n",
              "2013-11-22     NaN      快晴            --         16.1  "
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "        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>datetime</th>\n",
              "      <th>y</th>\n",
              "      <th>week</th>\n",
              "      <th>soldout</th>\n",
              "      <th>name</th>\n",
              "      <th>kcal</th>\n",
              "      <th>remarks</th>\n",
              "      <th>event</th>\n",
              "      <th>payday</th>\n",
              "      <th>weather</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>temperature</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>datetime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-11-18</th>\n",
              "      <td>2013-11-18</td>\n",
              "      <td>90</td>\n",
              "      <td>月</td>\n",
              "      <td>0</td>\n",
              "      <td>厚切りイカフライ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>快晴</td>\n",
              "      <td>--</td>\n",
              "      <td>19.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-11-19</th>\n",
              "      <td>2013-11-19</td>\n",
              "      <td>101</td>\n",
              "      <td>火</td>\n",
              "      <td>1</td>\n",
              "      <td>手作りヒレカツ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>快晴</td>\n",
              "      <td>--</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-11-20</th>\n",
              "      <td>2013-11-20</td>\n",
              "      <td>118</td>\n",
              "      <td>水</td>\n",
              "      <td>0</td>\n",
              "      <td>白身魚唐揚げ野菜あん</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>快晴</td>\n",
              "      <td>--</td>\n",
              "      <td>15.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-11-21</th>\n",
              "      <td>2013-11-21</td>\n",
              "      <td>120</td>\n",
              "      <td>木</td>\n",
              "      <td>1</td>\n",
              "      <td>若鶏ピリ辛焼</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>快晴</td>\n",
              "      <td>--</td>\n",
              "      <td>15.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-11-22</th>\n",
              "      <td>2013-11-22</td>\n",
              "      <td>130</td>\n",
              "      <td>金</td>\n",
              "      <td>1</td>\n",
              "      <td>ビッグメンチカツ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>快晴</td>\n",
              "      <td>--</td>\n",
              "      <td>16.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f6a75317-4133-420d-8194-d03915dc093f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-f6a75317-4133-420d-8194-d03915dc093f');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "train.index = pd.to_datetime(train[\"datetime\"])\n",
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "-7-03QZhGCSn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "0138eeee-7b33-49bf-a3aa-4ce719ab16d0"
      },
      "outputs": [
        {
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              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# pandas.to_datetime\n",
        "\n",
        "# Convert argument to datetime.\n",
        "\n",
        "# This function converts a scalar, array-like, Series or DataFrame/dict-like \n",
        "# to a pandas datetime object."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
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          "data": {
            "text/plain": [
              "                y     soldout        kcal  payday  temperature\n",
              "count  207.000000  207.000000  166.000000    10.0   207.000000\n",
              "mean    86.623188    0.449275  404.409639     1.0    19.252174\n",
              "std     32.882448    0.498626   29.884641     0.0     8.611365\n",
              "min     29.000000    0.000000  315.000000     1.0     1.200000\n",
              "25%     57.000000    0.000000  386.000000     1.0    11.550000\n",
              "50%     78.000000    0.000000  408.500000     1.0    19.800000\n",
              "75%    113.000000    1.000000  426.000000     1.0    26.100000\n",
              "max    171.000000    1.000000  462.000000     1.0    34.600000"
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              "      <th>temperature</th>\n",
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              "  </thead>\n",
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              "      <td>10.0</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
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              "      <td>0.449275</td>\n",
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              "      <td>1.0</td>\n",
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              "      <th>std</th>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.200000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>386.000000</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>50%</th>\n",
              "      <td>78.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>408.500000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>19.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>113.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>426.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>26.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>171.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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      "cell_type": "code",
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        {
          "output_type": "display_data",
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            "text/plain": [
              "pandas.core.frame.DataFrame"
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      "cell_type": "code",
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        "id": "09LA581rF7bL",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime\n",
              "2013-11-18         厚切りイカフライ\n",
              "2013-11-19          手作りヒレカツ\n",
              "2013-11-20       白身魚唐揚げ野菜あん\n",
              "2013-11-21           若鶏ピリ辛焼\n",
              "2013-11-22         ビッグメンチカツ\n",
              "2013-11-25             鶏の唐揚\n",
              "2013-11-26         豚のスタミナ炒め\n",
              "2013-11-27         ボローニャ風カツ\n",
              "2013-11-28            ハンバーグ\n",
              "2013-11-29    タルタルinソーセージカツ\n",
              "                  ...      \n",
              "2014-09-16         チーズメンチカツ\n",
              "2014-09-17         チキンフリカッセ\n",
              "2014-09-18      カレイ唐揚げ 甘酢あん\n",
              "2014-09-19          厚切イカフライ\n",
              "2014-09-22              筑前煮\n",
              "2014-09-24      白身魚のマスタード焼き\n",
              "2014-09-25          牛カルビ焼き肉\n",
              "2014-09-26         ランチビュッフェ\n",
              "2014-09-29         豚肉と玉子の炒め\n",
              "2014-09-30     鶏肉とカシューナッツ炒め\n",
              "Name: name, Length: 207, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "train_name = train[\"name\"]\n",
        "train_name"
      ]
    },
    {
      "cell_type": "code",
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            "text/plain": [
              "pandas.core.series.Series"
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      ],
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        "type(train_name)"
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    },
    {
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              "207"
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    {
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              "<IPython.core.display.HTML object>"
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            "text/plain": [
              "{'いか天ぷら',\n",
              " 'かじきの甘辛煮',\n",
              " 'きのこソースハンバーグ',\n",
              " 'さっくりメンチカツ',\n",
              " 'さわら焼味噌掛け',\n",
              " 'さんま辛味焼',\n",
              " 'たっぷりベーコンフライ',\n",
              " 'ひやしたぬきうどん・炊き込みご飯',\n",
              " 'ぶりレモンペッパー焼き',\n",
              " 'ますのマスタードソース',\n",
              " 'やわらかロースのサムジョン',\n",
              " 'アジ唐揚げ南蛮ソース',\n",
              " 'エビフライ',\n",
              " 'カキフライタルタル',\n",
              " 'カレイの唐揚げ',\n",
              " 'カレイ唐揚げ 甘酢あん',\n",
              " 'カレイ唐揚げ夏野菜あん',\n",
              " 'カレイ唐揚げ野菜あんかけ',\n",
              " 'カレイ唐揚げ野菜餡かけ',\n",
              " 'カレイ野菜あんかけ',\n",
              " 'カレー入りソーセージカツ',\n",
              " 'キスと野菜の天ぷら',\n",
              " 'キーマカレー',\n",
              " 'クリームチーズ入りメンチ',\n",
              " 'ゴーヤチャンプルー',\n",
              " 'サバ焼味噌掛け',\n",
              " 'サーモンのムニエル2色ソース',\n",
              " 'サーモンフライ・タルタル',\n",
              " 'ジャンボチキンカツ',\n",
              " 'ジューシーメンチカツ',\n",
              " 'タルタルinソーセージカツ',\n",
              " 'タンドリーチキン',\n",
              " 'チキンのコーンクリーム焼き',\n",
              " 'チキンの辛味噌焼き',\n",
              " 'チキンカレー',\n",
              " 'チキンクリームシチュー',\n",
              " 'チキンステーキ・きのこソース',\n",
              " 'チキンフリカッセ',\n",
              " 'チキン南蛮',\n",
              " 'チキン香草焼きマスタードソース',\n",
              " 'チャプチェ',\n",
              " 'チンジャオロース',\n",
              " 'チーズハンバーグ',\n",
              " 'チーズメンチカツ',\n",
              " 'チーズ入りハンバーグ',\n",
              " 'チーズ入りメンチカツ',\n",
              " 'ハンバーグ',\n",
              " 'ハンバーグカレーソース',\n",
              " 'ハンバーグケッチャップソース',\n",
              " 'ハンバーグデミソース',\n",
              " 'ハンバーグ和風きのこソース',\n",
              " 'ハンバーグ（デミきのこバター）',\n",
              " 'バーベキューチキン',\n",
              " 'ビッグメンチカツ',\n",
              " 'ビーフカレー',\n",
              " 'ビーフシチュー',\n",
              " 'ビーフストロガノフ',\n",
              " 'ビーフトマト煮',\n",
              " 'プルコギ',\n",
              " 'ホタテクリ―ムシチュー',\n",
              " 'ボローニャ風カツ',\n",
              " 'ポークカレー',\n",
              " 'ポークソテー韓国ソース',\n",
              " 'ポークハヤシ',\n",
              " 'ポーク味噌焼き',\n",
              " 'ポーク生姜焼き',\n",
              " 'マーボ茄子',\n",
              " 'マーボ豆腐',\n",
              " 'メダイ照り焼',\n",
              " 'メンチカツ',\n",
              " 'ランチビュッフェ',\n",
              " 'ロコモコ丼',\n",
              " 'ロース甘味噌焼き',\n",
              " '中華丼',\n",
              " '五目御飯',\n",
              " '八宝菜',\n",
              " '厚切りイカフライ',\n",
              " '厚切イカフライ',\n",
              " '厚切ハムカツ',\n",
              " '厚揚げ豚生姜炒め',\n",
              " '名古屋味噌カツ',\n",
              " '和風ソースハンバーグ',\n",
              " '和風ハンバーグ',\n",
              " '回鍋肉',\n",
              " '手ごね風ハンバーグ',\n",
              " '手作りひれかつ',\n",
              " '手作りひれかつとカレー',\n",
              " '手作りチキンカツ',\n",
              " '手作りトンカツ',\n",
              " '手作りヒレカツ',\n",
              " '手作りロースカツ',\n",
              " '洋食屋さんのメンチカツ',\n",
              " '海老クリーミ―クノーデル',\n",
              " '海老フライとホタテ串カツ',\n",
              " '海老フライタルタル',\n",
              " '炊き込みご飯',\n",
              " '牛すき焼き風',\n",
              " '牛カルビ焼き肉',\n",
              " '牛スキヤキ',\n",
              " '牛丼',\n",
              " '牛丼風煮',\n",
              " '牛肉すき焼き風',\n",
              " '牛肉コロッケ',\n",
              " '牛肉ニンニクの芽炒め',\n",
              " '牛肉筍煮',\n",
              " '白身魚のマスタード焼き',\n",
              " '白身魚の南部焼き',\n",
              " '白身魚フライ',\n",
              " '白身魚ムニエル',\n",
              " '白身魚唐揚げ野菜あん',\n",
              " '白身魚唐揚げ野菜あんかけ',\n",
              " '白身魚唐揚げ野菜餡かけ',\n",
              " '筑前煮',\n",
              " '肉じゃが',\n",
              " '肉団子のシチュー',\n",
              " '肉団子クリームシチュー',\n",
              " '若鶏ピリ辛焼',\n",
              " '親子煮',\n",
              " '豚すき焼き',\n",
              " '豚のスタミナ炒め',\n",
              " '豚の冷しゃぶ',\n",
              " '豚キムチ炒め',\n",
              " '豚ロースのピザ風チーズ焼き',\n",
              " '豚冷シャブ野菜添え',\n",
              " '豚味噌メンチカツ',\n",
              " '豚肉と玉子の炒め',\n",
              " '豚肉と茄子のピリ辛炒め',\n",
              " '豚肉のマスタード焼き',\n",
              " '豚肉の生姜焼',\n",
              " '豚肉の生姜焼き',\n",
              " '豚肉の胡麻シャブ',\n",
              " '越冬キャベツのメンチカツ',\n",
              " '酢豚',\n",
              " '酢豚orカレー',\n",
              " '青梗菜牛肉炒め',\n",
              " '青椒肉絲',\n",
              " '鶏のから揚げねぎ塩炒めソース',\n",
              " '鶏のから揚げスイートチリソース',\n",
              " '鶏のカッシュナッツ炒め',\n",
              " '鶏のトマトシチュー',\n",
              " '鶏のピリ辛焼き',\n",
              " '鶏のレモンペッパー焼orカレー',\n",
              " '鶏の味噌漬け焼き',\n",
              " '鶏の唐揚',\n",
              " '鶏の唐揚げ',\n",
              " '鶏の唐揚げおろしソース',\n",
              " '鶏の唐揚げ甘酢あん',\n",
              " '鶏の塩から揚げ',\n",
              " '鶏の天ぷら',\n",
              " '鶏の照り焼きマスタード',\n",
              " '鶏の照り焼きマヨ',\n",
              " '鶏の親子煮',\n",
              " '鶏チリソース',\n",
              " '鶏肉とカシューナッツ炒め',\n",
              " '鶏肉のカレー唐揚',\n",
              " '鶏肉の山賊焼き'}"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "train_name_set = set(train_name)\n",
        "train_name_set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "ciPT5wldF7lY",
        "colab": {
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          "height": 34
        },
        "outputId": "e26bc8a5-4b66-41e3-c660-047deb53e617"
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      "outputs": [
        {
          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
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              "\n",
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              "  </style>\n",
              "  "
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            "text/plain": [
              "156"
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          "metadata": {},
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        }
      ],
      "source": [
        "len(train_name_set)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "kc4pO6evF7n-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "outputId": "3a72907d-aed7-456a-a2cb-f404690e9eec"
      },
      "outputs": [
        {
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              "<IPython.core.display.HTML object>"
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              "\n",
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              "  "
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        {
          "output_type": "stream",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "              datetime    y week  soldout           name   kcal   remarks  \\\n",
            "datetime                                                                    \n",
            "2013-12-10  2013-12-10  155    火        0  やわらかロースのサムジョン    NaN       NaN   \n",
            "2014-01-10   2014-1-10   87    金        0       手作りロースカツ  440.0       NaN   \n",
            "2014-02-10   2014-2-10  137    月        0    鶏の唐揚げおろしソース  423.0       NaN   \n",
            "2014-03-10   2014-3-10  123    月        0   海老クリーミ―クノーデル  385.0       NaN   \n",
            "2014-04-10   2014-4-10   87    木        0    鶏の唐揚げおろしソース  362.0       NaN   \n",
            "2014-05-09    2014-5-9   58    金        1       手作りチキンカツ  434.0       NaN   \n",
            "2014-06-10   2014-6-10   56    火        0    カレイ唐揚げ野菜餡かけ  426.0       NaN   \n",
            "2014-07-10   2014-7-10   48    木        0           プルコギ  386.0       NaN   \n",
            "2014-08-08    2014-8-8  129    金        0         チキンカレー    NaN  お楽しみメニュー   \n",
            "2014-09-10   2014-9-10   54    水        0    ぶりレモンペッパー焼き  450.0       NaN   \n",
            "\n",
            "           event  payday weather precipitation  temperature  \n",
            "datetime                                                     \n",
            "2013-12-10   NaN     1.0       曇           0.5          7.1  \n",
            "2014-01-10   NaN     1.0      快晴            --          5.1  \n",
            "2014-02-10   NaN     1.0       曇            --          7.8  \n",
            "2014-03-10   NaN     1.0       曇            --          7.6  \n",
            "2014-04-10   NaN     1.0      晴れ            --         21.3  \n",
            "2014-05-09   NaN     1.0      晴れ            --         23.4  \n",
            "2014-06-10   NaN     1.0       曇            --         25.5  \n",
            "2014-07-10   NaN     1.0       曇            --         28.8  \n",
            "2014-08-08   NaN     1.0       曇            --         31.1  \n",
            "2014-09-10   NaN     1.0       曇             0         25.0  \n"
          ]
        }
      ],
      "source": [
        "print(type(train))\n",
        "print(train.loc[train['payday'] == 1.0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "_4VqD43bEPyo",
        "colab": {
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          "height": 219
        },
        "outputId": "6fcd025d-02f3-4d6d-d1a5-a92563ada379"
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      "outputs": [
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              "\n",
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              "  </style>\n",
              "  "
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          datetime week   name   remarks event weather precipitation\n",
              "count          207  207    207        21    14     207           207\n",
              "unique         207    5    156         6     2       7             8\n",
              "top     2013-11-18    水  メンチカツ  お楽しみメニュー  ママの会      快晴            --\n",
              "freq             1   43      6        12     9      53           169"
            ],
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th></th>\n",
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              "      <td>207</td>\n",
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      ],
      "source": [
        "train.describe(include=\"O\")"
      ]
    },
    {
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      "execution_count": 35,
      "metadata": {
        "id": "01-PhdTrRmvT",
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        "outputId": "1933b433-ca56-43be-b921-f195c2c1a9f9"
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              "                y     soldout        kcal  payday  temperature\n",
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              "min     29.000000    0.000000  315.000000     1.0     1.200000\n",
              "25%     57.000000    0.000000  386.000000     1.0    11.550000\n",
              "50%     78.000000    0.000000  408.500000     1.0    19.800000\n",
              "75%    113.000000    1.000000  426.000000     1.0    26.100000\n",
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          },
          "metadata": {},
          "execution_count": 35
        }
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      "source": [
        "train.describe()"
      ]
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    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
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            "text/plain": [
              "          datetime           y week     soldout   name        kcal   remarks  \\\n",
              "count          207  207.000000  207  207.000000    207  166.000000        21   \n",
              "unique         207         NaN    5         NaN    156         NaN         6   \n",
              "top     2013-11-18         NaN    水         NaN  メンチカツ         NaN  お楽しみメニュー   \n",
              "freq             1         NaN   43         NaN      6         NaN        12   \n",
              "mean           NaN   86.623188  NaN    0.449275    NaN  404.409639       NaN   \n",
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              "min            NaN   29.000000  NaN    0.000000    NaN  315.000000       NaN   \n",
              "25%            NaN   57.000000  NaN    0.000000    NaN  386.000000       NaN   \n",
              "50%            NaN   78.000000  NaN    0.000000    NaN  408.500000       NaN   \n",
              "75%            NaN  113.000000  NaN    1.000000    NaN  426.000000       NaN   \n",
              "max            NaN  171.000000  NaN    1.000000    NaN  462.000000       NaN   \n",
              "\n",
              "       event  payday weather precipitation  temperature  \n",
              "count     14    10.0     207           207   207.000000  \n",
              "unique     2     NaN       7             8          NaN  \n",
              "top     ママの会     NaN      快晴            --          NaN  \n",
              "freq       9     NaN      53           169          NaN  \n",
              "mean     NaN     1.0     NaN           NaN    19.252174  \n",
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              "min      NaN     1.0     NaN           NaN     1.200000  \n",
              "25%      NaN     1.0     NaN           NaN    11.550000  \n",
              "50%      NaN     1.0     NaN           NaN    19.800000  \n",
              "75%      NaN     1.0     NaN           NaN    26.100000  \n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3d2b5250-fc11-4bef-9e63-5360911fad3f 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-3d2b5250-fc11-4bef-9e63-5360911fad3f');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "train.describe(include=\"all\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "hksPOuGxuu0p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "28dd1033-abf5-4003-ac71-e8b52c4d04e8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime\n",
              "2013-11-18   NaN\n",
              "2013-11-19   NaN\n",
              "2013-11-20   NaN\n",
              "2013-11-21   NaN\n",
              "2013-11-22   NaN\n",
              "2013-11-25   NaN\n",
              "2013-11-26   NaN\n",
              "2013-11-27   NaN\n",
              "2013-11-28   NaN\n",
              "2013-11-29   NaN\n",
              "              ..\n",
              "2014-09-16   NaN\n",
              "2014-09-17   NaN\n",
              "2014-09-18   NaN\n",
              "2014-09-19   NaN\n",
              "2014-09-22   NaN\n",
              "2014-09-24   NaN\n",
              "2014-09-25   NaN\n",
              "2014-09-26   NaN\n",
              "2014-09-29   NaN\n",
              "2014-09-30   NaN\n",
              "Name: payday, Length: 207, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "train[\"payday\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "38m3lk2gRm0w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "42665cb7-0a8c-4bd7-e81f-fb7c135c8060"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "train[\"payday\"] = train[\"payday\"].fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "v55q9Z6WRm87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "ff4a0a40-add9-47b7-9d50-b410adf5cefa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime\n",
              "2013-11-18    0.0\n",
              "2013-11-19    0.0\n",
              "2013-11-20    0.0\n",
              "2013-11-21    0.0\n",
              "2013-11-22    0.0\n",
              "2013-11-25    0.0\n",
              "2013-11-26    0.0\n",
              "2013-11-27    0.0\n",
              "2013-11-28    0.0\n",
              "2013-11-29    0.0\n",
              "             ... \n",
              "2014-09-16    0.0\n",
              "2014-09-17    0.0\n",
              "2014-09-18    0.0\n",
              "2014-09-19    0.0\n",
              "2014-09-22    0.0\n",
              "2014-09-24    0.0\n",
              "2014-09-25    0.0\n",
              "2014-09-26    0.0\n",
              "2014-09-29    0.0\n",
              "2014-09-30    0.0\n",
              "Name: payday, Length: 207, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "train[\"payday\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "bDekW8QauS0N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "58c7e26b-af38-4d29-f03f-8907706ef004"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0.0, 1.0}"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "set(train[\"payday\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "UWcqE_6zuS2z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "85bd6f8f-5d7d-45ec-e866-bb4b73a8f37b"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime\n",
              "2013-11-18    --\n",
              "2013-11-19    --\n",
              "2013-11-20    --\n",
              "2013-11-21    --\n",
              "2013-11-22    --\n",
              "2013-11-25    --\n",
              "2013-11-26    --\n",
              "2013-11-27    --\n",
              "2013-11-28    --\n",
              "2013-11-29    --\n",
              "              ..\n",
              "2014-09-16    --\n",
              "2014-09-17    --\n",
              "2014-09-18    --\n",
              "2014-09-19    --\n",
              "2014-09-22    --\n",
              "2014-09-24     0\n",
              "2014-09-25     0\n",
              "2014-09-26    --\n",
              "2014-09-29    --\n",
              "2014-09-30    --\n",
              "Name: precipitation, Length: 207, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "train[\"precipitation\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "lOljThPYuS5q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "4169c751-6145-4c37-fe7a-c365ff184f43"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
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          "metadata": {}
        }
      ],
      "source": [
        "train[\"precipitation\"] = train[\"precipitation\"].apply(lambda x: -1 if x == \"--\" else float(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "7-T0MyuuuS8B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
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        "outputId": "e6cbbbad-dd13-4518-be8e-f4a71aaa3507"
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      "outputs": [
        {
          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "  <style>\n",
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              "        white-space: pre-wrap;\n",
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              "  </style>\n",
              "  "
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          "metadata": {}
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime\n",
              "2013-11-18   -1.0\n",
              "2013-11-19   -1.0\n",
              "2013-11-20   -1.0\n",
              "2013-11-21   -1.0\n",
              "2013-11-22   -1.0\n",
              "2013-11-25   -1.0\n",
              "2013-11-26   -1.0\n",
              "2013-11-27   -1.0\n",
              "2013-11-28   -1.0\n",
              "2013-11-29   -1.0\n",
              "             ... \n",
              "2014-09-16   -1.0\n",
              "2014-09-17   -1.0\n",
              "2014-09-18   -1.0\n",
              "2014-09-19   -1.0\n",
              "2014-09-22   -1.0\n",
              "2014-09-24    0.0\n",
              "2014-09-25    0.0\n",
              "2014-09-26   -1.0\n",
              "2014-09-29   -1.0\n",
              "2014-09-30   -1.0\n",
              "Name: precipitation, Length: 207, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "train[\"precipitation\"]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set(train[\"precipitation\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "n8OGZq-9m505",
        "outputId": "cb4cf17c-0e43-4c05-810e-998e6597289e"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
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              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{-1.0, 0.0, 0.5, 1.0, 1.5, 2.5, 6.0, 6.5}"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
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    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "i-pdcxWTuS-y",
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          "height": 416
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        "outputId": "d4b98848-8fe7-4586-9128-96396719e75a"
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          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
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            "text/html": [
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              "  <style>\n",
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              "  </style>\n",
              "  "
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          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime\n",
              "2013-11-18              NaN\n",
              "2013-11-19              NaN\n",
              "2013-11-20              NaN\n",
              "2013-11-21              NaN\n",
              "2013-11-22              NaN\n",
              "2013-11-25              NaN\n",
              "2013-11-26              NaN\n",
              "2013-11-27              NaN\n",
              "2013-11-28              NaN\n",
              "2013-11-29              NaN\n",
              "                  ...      \n",
              "2014-09-16              NaN\n",
              "2014-09-17             ママの会\n",
              "2014-09-18              NaN\n",
              "2014-09-19              NaN\n",
              "2014-09-22              NaN\n",
              "2014-09-24              NaN\n",
              "2014-09-25              NaN\n",
              "2014-09-26    キャリアアップ支援セミナー\n",
              "2014-09-29              NaN\n",
              "2014-09-30              NaN\n",
              "Name: event, Length: 207, dtype: object"
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          },
          "metadata": {},
          "execution_count": 45
        }
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      "source": [
        "train[\"event\"]"
      ]
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    {
      "cell_type": "code",
      "source": [
        "set(train[\"event\"])"
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        "colab": {
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          "height": 34
        },
        "id": "msqafqnfnOWg",
        "outputId": "46a0349d-7305-4433-8c6a-3f0928bf4791"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "  <style>\n",
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              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{nan, 'キャリアアップ支援セミナー', 'ママの会'}"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "34dTY2fruTBq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
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        "outputId": "0542a410-161a-45ab-84d5-76cedbadc24d"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "train[\"event\"] = train[\"event\"].fillna(\"なし\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "17UfAJH-uTD5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
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        "outputId": "22fdee3f-d485-4553-abd5-9ab4acf0c429"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime\n",
              "2013-11-18               なし\n",
              "2013-11-19               なし\n",
              "2013-11-20               なし\n",
              "2013-11-21               なし\n",
              "2013-11-22               なし\n",
              "2013-11-25               なし\n",
              "2013-11-26               なし\n",
              "2013-11-27               なし\n",
              "2013-11-28               なし\n",
              "2013-11-29               なし\n",
              "                  ...      \n",
              "2014-09-16               なし\n",
              "2014-09-17             ママの会\n",
              "2014-09-18               なし\n",
              "2014-09-19               なし\n",
              "2014-09-22               なし\n",
              "2014-09-24               なし\n",
              "2014-09-25               なし\n",
              "2014-09-26    キャリアアップ支援セミナー\n",
              "2014-09-29               なし\n",
              "2014-09-30               なし\n",
              "Name: event, Length: 207, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "train[\"event\"]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set(train[\"event\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "_fYIvFPJnTuu",
        "outputId": "18ea433b-44d0-44fc-a5f4-17e030d1b1e8"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
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              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'なし', 'キャリアアップ支援セミナー', 'ママの会'}"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "_uGg69zxuTGh",
        "colab": {
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          "height": 416
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        "outputId": "d93c27b2-26ad-46a3-f443-de8b66d4343a"
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      "outputs": [
        {
          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
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              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime\n",
              "2013-11-18                NaN\n",
              "2013-11-19                NaN\n",
              "2013-11-20                NaN\n",
              "2013-11-21                NaN\n",
              "2013-11-22                NaN\n",
              "2013-11-25                NaN\n",
              "2013-11-26                NaN\n",
              "2013-11-27                NaN\n",
              "2013-11-28                NaN\n",
              "2013-11-29                NaN\n",
              "                   ...       \n",
              "2014-09-16                NaN\n",
              "2014-09-17       料理長のこだわりメニュー\n",
              "2014-09-18                NaN\n",
              "2014-09-19                NaN\n",
              "2014-09-22                NaN\n",
              "2014-09-24                NaN\n",
              "2014-09-25                NaN\n",
              "2014-09-26    スペシャルメニュー（800円）\n",
              "2014-09-29                NaN\n",
              "2014-09-30                NaN\n",
              "Name: remarks, Length: 207, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "train[\"remarks\"]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set(train[\"remarks\"])"
      ],
      "metadata": {
        "colab": {
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          "height": 139
        },
        "id": "75FxKVCDncmU",
        "outputId": "c3bc369d-f6ee-4117-c99a-415d7353cf3d"
      },
      "execution_count": 51,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{nan,\n",
              " 'お楽しみメニュー',\n",
              " 'スペシャルメニュー（800円）',\n",
              " '手作りの味',\n",
              " '料理長のこだわりメニュー',\n",
              " '酢豚（28食）、カレー（85食）',\n",
              " '鶏のレモンペッパー焼（50食）、カレー（42食）'}"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "MQ0EanwtuTJW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "3159ba0e-918a-4cda-e225-b0a1bcf9c59e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "train[\"remarks\"] = train[\"remarks\"].fillna(\"なし\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set(train[\"remarks\"])"
      ],
      "metadata": {
        "id": "VIuXxX-Ihjx4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "494e2ea6-c91f-4825-a839-cb2edc5bcee5"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'お楽しみメニュー',\n",
              " 'なし',\n",
              " 'スペシャルメニュー（800円）',\n",
              " '手作りの味',\n",
              " '料理長のこだわりメニュー',\n",
              " '酢豚（28食）、カレー（85食）',\n",
              " '鶏のレモンペッパー焼（50食）、カレー（42食）'}"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "HPLRnUi9RnAZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "4d7567b6-76a8-4376-b878-b11d502de323"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# train[\"remarks\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "UV2ew9f9EP1G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "72a617be-5035-4a54-93d0-97d7579793db"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# train[\"datetime\"] "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "FyDleQzqEP32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "18ecc979-395f-4c71-ad23-d3b2d9a3650e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "train[\"month\"] = train[\"datetime\"].apply(lambda x : int(x.split(\"-\")[1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "SoOqqMTqz6gC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "e984bf1c-1c9b-448c-c36e-41f812169c72"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime\n",
              "2013-11-18    11\n",
              "2013-11-19    11\n",
              "2013-11-20    11\n",
              "2013-11-21    11\n",
              "2013-11-22    11\n",
              "2013-11-25    11\n",
              "2013-11-26    11\n",
              "2013-11-27    11\n",
              "2013-11-28    11\n",
              "2013-11-29    11\n",
              "              ..\n",
              "2014-09-16     9\n",
              "2014-09-17     9\n",
              "2014-09-18     9\n",
              "2014-09-19     9\n",
              "2014-09-22     9\n",
              "2014-09-24     9\n",
              "2014-09-25     9\n",
              "2014-09-26     9\n",
              "2014-09-29     9\n",
              "2014-09-30     9\n",
              "Name: month, Length: 207, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "train[\"month\"]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"day\"] = train[\"datetime\"].apply(lambda x: int(x.split('-')[2]))\n",
        "train[\"day\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "B_peX-CNrJw7",
        "outputId": "de714588-4885-4d27-8eba-535e5111508b"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime\n",
              "2013-11-18    18\n",
              "2013-11-19    19\n",
              "2013-11-20    20\n",
              "2013-11-21    21\n",
              "2013-11-22    22\n",
              "2013-11-25    25\n",
              "2013-11-26    26\n",
              "2013-11-27    27\n",
              "2013-11-28    28\n",
              "2013-11-29    29\n",
              "              ..\n",
              "2014-09-16    16\n",
              "2014-09-17    17\n",
              "2014-09-18    18\n",
              "2014-09-19    19\n",
              "2014-09-22    22\n",
              "2014-09-24    24\n",
              "2014-09-25    25\n",
              "2014-09-26    26\n",
              "2014-09-29    29\n",
              "2014-09-30    30\n",
              "Name: day, Length: 207, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   Check the distribution of the sales of bentos\n",
        "*   The sales is decreasing as days go by\n",
        "*   Nevertheless, there are some spikes. Is there any factor contributing to the sales amount?\n",
        "\n"
      ],
      "metadata": {
        "id": "GqYljLxCMn9o"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "EAFNcpqZz6dm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "6e4a851e-62e8-4dfd-e0ee-ac54e7965cad"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5ebaefd10>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.font_manager:findfont: Font family ['IPAexGothic'] not found. Falling back to DejaVu Sans.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family ['IPAexGothic'] not found. Falling back to DejaVu Sans.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "train[\"y\"].plot(figsize=(15, 4))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Check the scatter plot of the objective variable and explanatory variables (numerical variables)\n",
        "* Though there seems to be high correlation between month/temperature and sales amout, it is decreasing as days go by, so they are likely "
      ],
      "metadata": {
        "id": "ipcBaBYeQUS-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "Vrw4bZXaq5rE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        },
        "outputId": "22df1363-6574-4471-c32d-292e9982d9fc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x432 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots(2,3,figsize=(9,6))\n",
        "train.plot.scatter(x=\"soldout\", y=\"y\", ax=ax[0][0])\n",
        "train.plot.scatter(x=\"kcal\", y=\"y\", ax=ax[0][1])\n",
        "train.plot.scatter(x=\"precipitation\", y=\"y\", ax=ax[0][2])\n",
        "train.plot.scatter(x=\"payday\", y=\"y\", ax=ax[1][0])\n",
        "train.plot.scatter(x=\"temperature\", y=\"y\", ax=ax[1][1])\n",
        "train.plot.scatter(x=\"month\", y=\"y\", ax=ax[1][2])\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "0wumcqM2t-mI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "ea43de40-6308-41ae-fc5f-bcb806756300"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Jupyter Notebook 上で Matplotlib はどう動いているのか？\n",
        "# https://qiita.com/U25CE/items/c2714bf5903de84bf47b"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "4s7ETyvOt-v7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "6d563f47-5622-40cd-9203-2e6eb5c7658b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.gcf()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "GlmTRxTMgoXf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "4d85c77c-f924-44d8-90cb-6632c1dc0b76"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Matplotlib plt.subplots()の使い方｜FigureとAxesを同時生成！\n",
        "# https://www.yutaka-note.com/entry/matplotlib_subplots"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "5kx95NCagoap",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "122830fe-5dbe-479c-8f1b-766b612dab50"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# matplotlibのめっちゃまとめ\n",
        "# https://qiita.com/nkay/items/d1eb91e33b9d6469ef51"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "id": "LFoyD1kHql-b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "380177e6-05a9-49ce-8010-3ca4c13c504d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'apt autoremove' to remove it.\n",
            "The following additional packages will be installed:\n",
            "  fonts-ipafont-mincho\n",
            "The following NEW packages will be installed:\n",
            "  fonts-ipafont-gothic fonts-ipafont-mincho\n",
            "0 upgraded, 2 newly installed, 0 to remove and 29 not upgraded.\n",
            "Need to get 8,251 kB of archives.\n",
            "After this operation, 28.7 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 fonts-ipafont-gothic all 00303-18ubuntu1 [3,526 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic/universe amd64 fonts-ipafont-mincho all 00303-18ubuntu1 [4,725 kB]\n",
            "Fetched 8,251 kB in 1s (9,061 kB/s)\n",
            "Selecting previously unselected package fonts-ipafont-gothic.\n",
            "(Reading database ... 123942 files and directories currently installed.)\n",
            "Preparing to unpack .../fonts-ipafont-gothic_00303-18ubuntu1_all.deb ...\n",
            "Unpacking fonts-ipafont-gothic (00303-18ubuntu1) ...\n",
            "Selecting previously unselected package fonts-ipafont-mincho.\n",
            "Preparing to unpack .../fonts-ipafont-mincho_00303-18ubuntu1_all.deb ...\n",
            "Unpacking fonts-ipafont-mincho (00303-18ubuntu1) ...\n",
            "Setting up fonts-ipafont-gothic (00303-18ubuntu1) ...\n",
            "update-alternatives: using /usr/share/fonts/opentype/ipafont-gothic/ipag.ttf to provide /usr/share/fonts/truetype/fonts-japanese-gothic.ttf (fonts-japanese-gothic.ttf) in auto mode\n",
            "Setting up fonts-ipafont-mincho (00303-18ubuntu1) ...\n",
            "update-alternatives: using /usr/share/fonts/opentype/ipafont-mincho/ipam.ttf to provide /usr/share/fonts/truetype/fonts-japanese-mincho.ttf (fonts-japanese-mincho.ttf) in auto mode\n",
            "Processing triggers for fontconfig (2.12.6-0ubuntu2) ...\n"
          ]
        }
      ],
      "source": [
        "# Google Colabでまたまた日本語表示が豆腐不可避な方に（2019/10/27時点）\n",
        "# https://qiita.com/siraasagi/items/d72f60c25b30269d89e4\n",
        "\n",
        "# 日本語フォントをダウンロードする。\n",
        "!apt-get -y install fonts-ipafont-gothic"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "id": "Uc6z8o7ErFcC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "a58a99eb-45dd-46db-a409-8b34f9059715"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# キャッシュを削除する。\n",
        "# !rm /root/.cache/matplotlib/fontList.json　# 旧cache\n",
        "!rm /root/.cache/matplotlib/fontlist-v310.json # 消すべきcache\n",
        "\n",
        "# ランタイムを再起動する。(2020/10/14 この手順は不要ですので、スキップしてください)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "id": "k1VK990Ysy5H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8e1fe09d-52dd-4e70-9127-561d049dadd0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/root/.cache/matplotlib'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 110
        }
      ],
      "source": [
        "import matplotlib\n",
        "matplotlib.get_cachedir()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "id": "Y_UqutABs0AD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9eb1881f-bf70-42ba-8e65-bf42480541bd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[0m\u001b[01;34mtex.cache\u001b[0m/\n"
          ]
        }
      ],
      "source": [
        "ls /root/.cache/matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "id": "PhcDeQ95tCTE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "bb3bc604-c1bc-4938-c848-720831776540"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "ls /root/.cache/matplotlib/tex.cache"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "id": "QSNToowYrmk_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "310a2f1a-41cd-4c40-93d3-baac721326af"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set(font=\"IPAGothic\") # , style=\"white\""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Check the objective variable and explanatory variables (Category variables) and box plot\n",
        "* Whether the value of \"remarks\" is a special menue or none seems to have a notable impact on the sales volume"
      ],
      "metadata": {
        "id": "W9bI8xxHnIv3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "id": "c0kW0gh7j4Ni",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "84f0ac5d-beb7-42a8-d00c-e6114676321d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5e73cbdd0>"
            ]
          },
          "metadata": {},
          "execution_count": 114
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.font_manager:findfont: Font family ['IPAGothic'] not found. Falling back to DejaVu Sans.\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 26376 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 28779 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 27700 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 26408 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 37329 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "WARNING:matplotlib.font_manager:findfont: Font family ['IPAGothic'] not found. Falling back to DejaVu Sans.\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 24555 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 26228 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 26311 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12428 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 34180 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 38632 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 38634 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 38647 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 38651 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12394 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12375 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 40335 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12398 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12524 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12514 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12531 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12506 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12483 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12497 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12540 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 28988 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 65288 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 39135 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 65289 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12289 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12459 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 37218 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 35930 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12362 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 27005 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12415 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12513 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12491 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12517 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 26009 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 29702 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 38263 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12371 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12384 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12431 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12426 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 25163 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 20316 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 21619 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12473 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12471 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12515 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12523 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 20870 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12510 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 20250 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12461 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12522 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12450 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12503 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 25903 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 25588 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12475 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12511 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 12490 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 26376 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 28779 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 27700 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 26408 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 37329 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 24555 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 26228 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 26311 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12428 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 34180 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 38632 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 38634 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 38647 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 38651 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12394 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12375 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 40335 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12398 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12524 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12514 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12531 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12506 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12483 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12497 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12540 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 28988 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 65288 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 39135 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 65289 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12289 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12459 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 37218 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 35930 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12362 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 27005 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12415 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12513 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12491 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12517 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 26009 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 29702 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 38263 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12371 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12384 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12431 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12426 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 25163 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 20316 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 21619 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12473 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12471 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12515 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12523 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 20870 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12510 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 20250 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12461 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12522 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12450 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12503 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 25903 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 25588 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12475 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12511 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 12490 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots(2, 2, figsize=(12, 7), tight_layout=True)\n",
        "sns.boxplot(x=\"week\", y=\"y\", data=train, ax=ax[0][0])\n",
        "sns.boxplot(x=\"weather\", y=\"y\", data=train, ax=ax[0][1])\n",
        "sns.boxplot(x=\"remarks\", y=\"y\", data=train, ax=ax[1][0])\n",
        "ax[1][0].set_xticklabels(ax[1][0].get_xticklabels(), rotation=30)\n",
        "sns.boxplot(x=\"event\", y=\"y\", data=train, ax=ax[1][1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "id": "jA6QDjfSt_Fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "6c7cd3e0-4b26-47dc-a3c4-1ad1666adc4e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Matplotlibで簡単に日本語を表示する方法（Windows）\n",
        "# https://gammasoft.jp/blog/matplotlib-japanese-fonts/\n",
        "# -> As a result, I didn't use this knowledge above but the one below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "id": "xx19RYzAt_PY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "b4bac88c-bcef-4ee6-d5a2-5c0bd5d0f8b3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Google Colabでまたまた日本語表示が豆腐不可避な方に（2019/10/27時点）\n",
        "# https://qiita.com/siraasagi/items/d72f60c25b30269d89e4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "id": "oOu65kPAt_h7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "65a91c63-0791-45b8-f59e-96fbad90bcad"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from IPython.display import HTML, display\n",
        "\n",
        "def set_css():\n",
        "  display(HTML('''\n",
        "  <style>\n",
        "    pre {\n",
        "        white-space: pre-wrap;\n",
        "    }\n",
        "  </style>\n",
        "  '''))\n",
        "get_ipython().events.register('pre_run_cell', set_css)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "id": "GsJKMQqZkgfr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "outputId": "bddde34c-1ef8-4dc2-c708-72a45e8da5f1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5e70b0e10>"
            ]
          },
          "metadata": {},
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "train[train[\"remarks\"]!=\"お楽しみメニュー\"][\"y\"].plot(figsize=(15,4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "id": "emMFTF4Ekgiw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "cba2863c-03e1-4002-dab0-a2ff5acc6bd5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5e7091710>"
            ]
          },
          "metadata": {},
          "execution_count": 119
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "train[\"fun\"] = train[\"remarks\"].apply(lambda x: 1 if x==\"お楽しみメニュー\" else 0)\n",
        "sns.boxplot(x=\"fun\", y=\"y\", data=train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "id": "cNkrNLeGkglb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "0dd9233f-9c6e-4b9d-cb85-514abc8eda56"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from scipy.stats import median_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "id": "-MPEkHnMkgoL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "89675ddc-54af-44c5-dfa1-8d7c517fe186"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# median_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "id": "J5qubzVEkgq7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "ff8ed7ae-9520-49f0-cbb3-2389f411ffe6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "stat,p,med,tbl = median_test(train[train[\"fun\"]==1][\"y\"], train[train[\"fun\"]==0][\"y\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "id": "fQucPjI7kgtp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ab9f5d8f-e117-493f-f240-7a68380f22e7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "p 0.007057960766247775 stat 7.2581589841730345\n"
          ]
        }
      ],
      "source": [
        "print(\"p\", p, \"stat\", stat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {
        "id": "DrApo8SA8-ih",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "59d5e2e6-89e3-4823-c13b-aa06ab84cffd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# type(train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "id": "I_yQwTCckgv5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "outputId": "357caa52-78df-4940-e98f-d3afe476b50e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5e6fad7d0>"
            ]
          },
          "metadata": {},
          "execution_count": 125
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "train[train[\"remarks\"]==\"お楽しみメニュー\"][\"y\"].plot(figsize=(15,4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {
        "id": "t2-fQ-WM9BJ9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "5e8ce9ac-ddbb-4d9b-bbbf-cba4da7922a9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# train[train[\"remarks\"]==\"お楽しみメニュー\"][\"y\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "id": "xP0YzMX39BMk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "36998def-9aaa-4d83-9ae0-d11cbf7c8a53"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# type(train[train[\"remarks\"]==\"お楽しみメニュー\"][\"y\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "id": "k685GS629BPJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "07326f1a-cc65-4e43-b0f3-3702644b962d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# train[train[\"remarks\"]==\"お楽しみメニュー\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "metadata": {
        "id": "JIlmyPk89BRo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "bd55970e-e3c6-4ab8-efc8-db7c5cae0f21"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# type(train[train[\"remarks\"]==\"お楽しみメニュー\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "metadata": {
        "id": "AfhpTA2-9yz-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "5fadc8ab-63aa-41d0-fbcb-040aa5719fbd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# type(train[train[\"remarks\"]==\"お楽しみメニュー\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "metadata": {
        "id": "cYV7yhao9y34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "5dda51c9-4b8b-4b8c-8897-59cd0fec4bfb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# type(train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "metadata": {
        "id": "gZ4exyBI9y7Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "1e588f28-0452-4172-9ca7-a89dd35abe1a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "train[\"curry\"] = train[\"name\"].apply(lambda x: 1 if x.find(\"カレー\")>0 else 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {
        "id": "8Iexu4AGljNX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "4bc4ba9c-b2b8-4a3b-b5c7-4259467e1d95"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5e6f25fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 133
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.boxplot(x=\"curry\", y=\"y\", data=train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "metadata": {
        "id": "JVIWeIvklqoJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "9ae16953-5347-482e-9b34-4e85b2614d6a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "metadata": {
        "id": "fsHwFeYRcKpV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "4a896cc0-cd58-450f-98b2-62a4ef5dea9f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# type(train[\"name\"].apply)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "metadata": {
        "id": "lLY4Fh8thtJV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "681cd981-e89d-4931-c68e-959dabc269d1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# type(train[\"name\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 137,
      "metadata": {
        "id": "NT9roMOciPKG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "ef886634-a5a2-46be-e692-b68286fe3c91"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# type(train[\"name\"][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 138,
      "metadata": {
        "id": "kEZubo2BiXdj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "3507a9c5-da2e-4361-f9db-86d92e6fff55"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# type(train[\"name\"][0].find)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "metadata": {
        "id": "7Sck16ophwFd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "c0d4f3ae-22e3-45a6-d2ff-16eeaa909d34"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# help(train[\"name\"][0].find)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 140,
      "metadata": {
        "id": "Pa0irpW-cKr0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "46b90b84-c664-41bf-b66f-1e22f2eee7f6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# help(train[\"name\"].apply)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 141,
      "metadata": {
        "id": "oWz_okQWcKul",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "f8e614f4-6867-4c89-8702-03e8dee767cc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "stat, p, md, tbl = median_test(train[train[\"curry\"]==1][\"y\"], train[train[\"curry\"]==0][\"y\"])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# md"
      ],
      "metadata": {
        "id": "5MVoPCY6ZkzY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "3edfbda1-478b-4bca-e1e1-82f1e8b17420"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# tbl"
      ],
      "metadata": {
        "id": "hPr7Zn9gZmMS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "26dc68ca-337c-448e-9d71-06b6ff36ca8b"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 144,
      "metadata": {
        "id": "a8vzEYLYJsy7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "76604a8d-6ec0-45d1-c11b-d5430ae5ae9e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "p:  0.018152625492681023 stat 5.581359548021204\n"
          ]
        }
      ],
      "source": [
        "print(\"p: \", p, \"stat\", stat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 145,
      "metadata": {
        "id": "-KBpZckvJvwp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "f4e92879-888c-4eac-db4e-ecc8f93a4f86"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 値が合わない"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "metadata": {
        "id": "jPFF_VLUJs8G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "b9419f19-8254-4289-ab4e-07a95f59e38d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "train = pd.read_csv(\"/drive/My Drive/Colab Notebooks/signate/bento/train.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "metadata": {
        "id": "0_2-uNWxJtG6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "30e8c706-0aa2-4c57-cc4d-1b3a4eae5323"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "test = pd.read_csv(\"/drive/My Drive/Colab Notebooks/signate/bento/test.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 148,
      "metadata": {
        "id": "Xwr5kjU3JtSP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "1a043476-aa5f-49eb-e136-f6538c21fd64"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "sample = pd.read_csv(\"/drive/My Drive/Colab Notebooks/signate/bento/sample.csv\", header=None)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# train.info()"
      ],
      "metadata": {
        "id": "BndEvOIOXRuG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "fb816064-bd1e-4e33-f8fd-71258605e382"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# type(train.info)"
      ],
      "metadata": {
        "id": "W6DT2gBdXTX8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "ef62022e-a9fd-4f3a-ce28-174477186a50"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# help(train.info)"
      ],
      "metadata": {
        "id": "2PVNZRZSXXLH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "d0a7e0d0-25f6-4598-f805-fa4c7064a55c"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 152,
      "metadata": {
        "id": "_ouAYuRjJtdt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "fd6999d7-b103-43d7-8597-b5fe75e0d3f7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# test.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 153,
      "metadata": {
        "id": "769t6iUAJtpX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "56f42c60-db1d-4e77-caf8-7a34804ec9ba"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# sample.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sample"
      ],
      "metadata": {
        "id": "-ueiQqyHXQZh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "9f09083c-17fb-4373-91ea-30d3a1f99a60"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_with_header = pd.read_csv(\"/drive/My Drive/Colab Notebooks/signate/bento/sample.csv\")"
      ],
      "metadata": {
        "id": "wt_SafZ7XQcL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "1fc0bcff-bb1f-44a7-d4b7-1876034c8b2e"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sample_with_header"
      ],
      "metadata": {
        "id": "m4JL7bUxXQer",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "d1d62b39-3641-4ebd-e8c8-64d20f715bae"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"t\"] = 1"
      ],
      "metadata": {
        "id": "ls0gnkcGXQho",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "c5e52e82-b78a-4744-9b31-8e143afd1973"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {
        "id": "BJr0r6OPcKxb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "21cb57f0-d743-413b-c02b-aa6d144ed914"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "test[\"t\"] = 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {
        "id": "d2lgeqPl9y_z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "94a6968b-933a-4e05-d8dc-7d20a35705ea"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ],
      "source": [
        "type(test[\"t\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 160,
      "metadata": {
        "id": "sKnxNOHQ9zER",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "1153c1ab-0b50-4e6f-cd2b-a951dae70a01"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "dat = pd.concat([train, test], sort=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pd.set_option('display.max_rows', 10)"
      ],
      "metadata": {
        "id": "zmSWxSel4o0y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "5f646774-d9f4-4e8e-c529-bc7f2a0e9492"
      },
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dat"
      ],
      "metadata": {
        "id": "ohxhLHmRYrto",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "d88f326c-d99a-4459-e2f7-9aca63fbb6ad"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dat = dat.reset_index(drop=True)\n",
        "# dat"
      ],
      "metadata": {
        "id": "aqA44XSzYrwj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "299a25aa-7ea6-44b2-b5e5-997eb89dda3a"
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dat[\"days\"] = dat.index"
      ],
      "metadata": {
        "id": "x3jrOXvNYrzK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "e152222c-fc29-4825-c2c0-ef30a7fd9486"
      },
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pd.set_option('display.max_rows', 10)"
      ],
      "metadata": {
        "id": "MFUIhZ2sRIH7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "499e1f69-e9a6-4f53-87b9-63cf8e0e43fe"
      },
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dat[\"days\"]"
      ],
      "metadata": {
        "id": "8lnmoYY0Yr2L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "09e4aafe-0674-4a4f-85bf-483b9cb73411"
      },
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        0\n",
              "1        1\n",
              "2        2\n",
              "3        3\n",
              "4        4\n",
              "5        5\n",
              "6        6\n",
              "7        7\n",
              "8        8\n",
              "9        9\n",
              "      ... \n",
              "237    237\n",
              "238    238\n",
              "239    239\n",
              "240    240\n",
              "241    241\n",
              "242    242\n",
              "243    243\n",
              "244    244\n",
              "245    245\n",
              "246    246\n",
              "Name: days, Length: 247, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dat"
      ],
      "metadata": {
        "id": "wvaipCliYr4m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "80367dbd-7b7d-4f0f-b529-2d6ec662c603"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dat[\"precipitation\"] = dat[\"precipitation\"].apply(lambda x: -1 if x==\"--\" else x).astype(np.float)"
      ],
      "metadata": {
        "id": "aEud_cPVYr7I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "6fbbf14b-acda-4cbf-fde1-4ece7518bb70"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dat[\"fun\"] = dat[\"remarks\"].apply(lambda x: 1 if x==\"お楽しみメニュー\" else 0)\n",
        "dat[\"curry\"] = dat[\"name\"].apply(lambda x: 1 if x.find(\"カレー\")>=0 else 0)"
      ],
      "metadata": {
        "id": "rRjSGcE8ZnnV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "91d0d64a-718d-4db2-d13b-9d0722c42b07"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dat"
      ],
      "metadata": {
        "id": "SCwcapBBR_yO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c045399d-1881-4f12-aa47-85ff87cfb17c"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       datetime event   kcal            name  payday  precipitation  \\\n",
              "0    2013-11-18   NaN    NaN        厚切りイカフライ     NaN           -1.0   \n",
              "1    2013-11-19   NaN    NaN         手作りヒレカツ     NaN           -1.0   \n",
              "2    2013-11-20   NaN    NaN      白身魚唐揚げ野菜あん     NaN           -1.0   \n",
              "3    2013-11-21   NaN    NaN          若鶏ピリ辛焼     NaN           -1.0   \n",
              "4    2013-11-22   NaN    NaN        ビッグメンチカツ     NaN           -1.0   \n",
              "5    2013-11-25   NaN    NaN            鶏の唐揚     NaN           -1.0   \n",
              "6    2013-11-26   NaN    NaN        豚のスタミナ炒め     NaN           -1.0   \n",
              "7    2013-11-27   NaN    NaN        ボローニャ風カツ     NaN           -1.0   \n",
              "8    2013-11-28   NaN    NaN           ハンバーグ     NaN           -1.0   \n",
              "9    2013-11-29   NaN    NaN   タルタルinソーセージカツ     NaN           -1.0   \n",
              "..          ...   ...    ...             ...     ...            ...   \n",
              "237  2014-11-14   NaN  427.0      エビフライ・エビカツ     NaN           -1.0   \n",
              "238  2014-11-17   NaN  433.0      野菜ごろごろシチュー     NaN           -1.0   \n",
              "239  2014-11-18   NaN  409.0            麻婆豆腐     NaN           -1.0   \n",
              "240  2014-11-19   NaN  430.0  ベルギー風チキンのクリーム煮     NaN           -1.0   \n",
              "241  2014-11-20   NaN  421.0          スタミナ炒め     NaN           -1.0   \n",
              "242  2014-11-21   NaN    NaN           ロコモコ丼     NaN           -1.0   \n",
              "243  2014-11-25   NaN  460.0     なすと挽肉のはさみ揚げ     NaN            1.5   \n",
              "244  2014-11-26   NaN  415.0         鶏肉の治部煮風     NaN            1.0   \n",
              "245  2014-11-27   NaN  420.0             牛丼風     NaN           -1.0   \n",
              "246  2014-11-28   NaN  416.0    鶏肉のスイートチリソース     NaN           -1.0   \n",
              "\n",
              "          remarks  soldout  t  temperature weather week      y  days  fun  \\\n",
              "0             NaN        0  1         19.8      快晴    月   90.0     0    0   \n",
              "1             NaN        1  1         17.0      快晴    火  101.0     1    0   \n",
              "2             NaN        0  1         15.5      快晴    水  118.0     2    0   \n",
              "3             NaN        1  1         15.2      快晴    木  120.0     3    0   \n",
              "4             NaN        1  1         16.1      快晴    金  130.0     4    0   \n",
              "5             NaN        1  1         14.6       曇    月  135.0     5    0   \n",
              "6             NaN        0  1         17.9      快晴    火  145.0     6    0   \n",
              "7             NaN        1  1         14.7      晴れ    水  140.0     7    0   \n",
              "8             NaN        0  1         17.7      薄曇    木  151.0     8    0   \n",
              "9             NaN        0  1         12.1      快晴    金  116.0     9    0   \n",
              "..            ...      ... ..          ...     ...  ...    ...   ...  ...   \n",
              "237           NaN        1  0         15.8      快晴    金    NaN   237    0   \n",
              "238           NaN        1  0         14.3      薄曇    月    NaN   238    0   \n",
              "239           NaN        1  0         16.1      快晴    火    NaN   239    0   \n",
              "240  料理長のこだわりメニュー        0  0         14.9      快晴    水    NaN   240    0   \n",
              "241           NaN        1  0         10.5       曇    木    NaN   241    0   \n",
              "242      お楽しみメニュー        0  0         14.9      快晴    金    NaN   242    1   \n",
              "243           NaN        1  0         11.4       雨    火    NaN   243    0   \n",
              "244           NaN        1  0          9.2       雨    水    NaN   244    0   \n",
              "245           NaN        0  0         15.1      快晴    木    NaN   245    0   \n",
              "246           NaN        0  0         15.3       曇    金    NaN   246    0   \n",
              "\n",
              "     curry  \n",
              "0        0  \n",
              "1        0  \n",
              "2        0  \n",
              "3        0  \n",
              "4        0  \n",
              "5        0  \n",
              "6        0  \n",
              "7        0  \n",
              "8        0  \n",
              "9        0  \n",
              "..     ...  \n",
              "237      0  \n",
              "238      0  \n",
              "239      0  \n",
              "240      0  \n",
              "241      0  \n",
              "242      0  \n",
              "243      0  \n",
              "244      0  \n",
              "245      0  \n",
              "246      0  \n",
              "\n",
              "[247 rows x 16 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5aed6ded-15db-4e73-ab8f-25fd8a9490e8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "    }\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>datetime</th>\n",
              "      <th>event</th>\n",
              "      <th>kcal</th>\n",
              "      <th>name</th>\n",
              "      <th>payday</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>remarks</th>\n",
              "      <th>soldout</th>\n",
              "      <th>t</th>\n",
              "      <th>temperature</th>\n",
              "      <th>weather</th>\n",
              "      <th>week</th>\n",
              "      <th>y</th>\n",
              "      <th>days</th>\n",
              "      <th>fun</th>\n",
              "      <th>curry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2013-11-18</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>厚切りイカフライ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>19.8</td>\n",
              "      <td>快晴</td>\n",
              "      <td>月</td>\n",
              "      <td>90.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2013-11-19</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>手作りヒレカツ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>17.0</td>\n",
              "      <td>快晴</td>\n",
              "      <td>火</td>\n",
              "      <td>101.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013-11-20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>白身魚唐揚げ野菜あん</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>15.5</td>\n",
              "      <td>快晴</td>\n",
              "      <td>水</td>\n",
              "      <td>118.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2013-11-21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>若鶏ピリ辛焼</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>15.2</td>\n",
              "      <td>快晴</td>\n",
              "      <td>木</td>\n",
              "      <td>120.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2013-11-22</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ビッグメンチカツ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>16.1</td>\n",
              "      <td>快晴</td>\n",
              "      <td>金</td>\n",
              "      <td>130.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2013-11-25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>鶏の唐揚</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14.6</td>\n",
              "      <td>曇</td>\n",
              "      <td>月</td>\n",
              "      <td>135.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2013-11-26</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>豚のスタミナ炒め</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>17.9</td>\n",
              "      <td>快晴</td>\n",
              "      <td>火</td>\n",
              "      <td>145.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2013-11-27</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ボローニャ風カツ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14.7</td>\n",
              "      <td>晴れ</td>\n",
              "      <td>水</td>\n",
              "      <td>140.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2013-11-28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ハンバーグ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>17.7</td>\n",
              "      <td>薄曇</td>\n",
              "      <td>木</td>\n",
              "      <td>151.0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2013-11-29</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>タルタルinソーセージカツ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>12.1</td>\n",
              "      <td>快晴</td>\n",
              "      <td>金</td>\n",
              "      <td>116.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>2014-11-14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>427.0</td>\n",
              "      <td>エビフライ・エビカツ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>15.8</td>\n",
              "      <td>快晴</td>\n",
              "      <td>金</td>\n",
              "      <td>NaN</td>\n",
              "      <td>237</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238</th>\n",
              "      <td>2014-11-17</td>\n",
              "      <td>NaN</td>\n",
              "      <td>433.0</td>\n",
              "      <td>野菜ごろごろシチュー</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>14.3</td>\n",
              "      <td>薄曇</td>\n",
              "      <td>月</td>\n",
              "      <td>NaN</td>\n",
              "      <td>238</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>2014-11-18</td>\n",
              "      <td>NaN</td>\n",
              "      <td>409.0</td>\n",
              "      <td>麻婆豆腐</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>16.1</td>\n",
              "      <td>快晴</td>\n",
              "      <td>火</td>\n",
              "      <td>NaN</td>\n",
              "      <td>239</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>2014-11-19</td>\n",
              "      <td>NaN</td>\n",
              "      <td>430.0</td>\n",
              "      <td>ベルギー風チキンのクリーム煮</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>料理長のこだわりメニュー</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14.9</td>\n",
              "      <td>快晴</td>\n",
              "      <td>水</td>\n",
              "      <td>NaN</td>\n",
              "      <td>240</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>2014-11-20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>421.0</td>\n",
              "      <td>スタミナ炒め</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>10.5</td>\n",
              "      <td>曇</td>\n",
              "      <td>木</td>\n",
              "      <td>NaN</td>\n",
              "      <td>241</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>2014-11-21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ロコモコ丼</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>お楽しみメニュー</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14.9</td>\n",
              "      <td>快晴</td>\n",
              "      <td>金</td>\n",
              "      <td>NaN</td>\n",
              "      <td>242</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>2014-11-25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>460.0</td>\n",
              "      <td>なすと挽肉のはさみ揚げ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>11.4</td>\n",
              "      <td>雨</td>\n",
              "      <td>火</td>\n",
              "      <td>NaN</td>\n",
              "      <td>243</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>244</th>\n",
              "      <td>2014-11-26</td>\n",
              "      <td>NaN</td>\n",
              "      <td>415.0</td>\n",
              "      <td>鶏肉の治部煮風</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9.2</td>\n",
              "      <td>雨</td>\n",
              "      <td>水</td>\n",
              "      <td>NaN</td>\n",
              "      <td>244</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>245</th>\n",
              "      <td>2014-11-27</td>\n",
              "      <td>NaN</td>\n",
              "      <td>420.0</td>\n",
              "      <td>牛丼風</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15.1</td>\n",
              "      <td>快晴</td>\n",
              "      <td>木</td>\n",
              "      <td>NaN</td>\n",
              "      <td>245</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>2014-11-28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>416.0</td>\n",
              "      <td>鶏肉のスイートチリソース</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>曇</td>\n",
              "      <td>金</td>\n",
              "      <td>NaN</td>\n",
              "      <td>246</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>247 rows × 16 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5aed6ded-15db-4e73-ab8f-25fd8a9490e8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-5aed6ded-15db-4e73-ab8f-25fd8a9490e8 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-5aed6ded-15db-4e73-ab8f-25fd8a9490e8');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols = [\"precipitation\", \"weather\", \"days\", \"fun\", \"curry\", \"y\"]"
      ],
      "metadata": {
        "id": "xgBagJw2R_7Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "755df8f7-3041-4c3f-dfb9-f47439ab0f60"
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cols"
      ],
      "metadata": {
        "id": "ZalM3limSABk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "cc4a7fd8-81f7-40fb-9378-c529f3ef7119"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold"
      ],
      "metadata": {
        "id": "Shg6vL_oSAFb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "b8c7c8e5-0df2-4cf2-c012-864198cfe4a3"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# KFold"
      ],
      "metadata": {
        "id": "Mn6NvR33SAKk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "803150b5-7b7e-43dd-807f-b658ee5e5925"
      },
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error as MSE"
      ],
      "metadata": {
        "id": "k8gQ6v1uYr9y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "95ec3339-e7dc-4386-e9c7-29573d63a494"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MSE"
      ],
      "metadata": {
        "id": "yUD7oh1UcHGv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "406249da-d6a3-4608-c8e7-cb57dc609755"
      },
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression as LR"
      ],
      "metadata": {
        "id": "kWO5dBNucHJO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "71806b16-1f30-4ae8-df18-5b2453aa56d7"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LR"
      ],
      "metadata": {
        "id": "iXWVp5lDcHLx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "98b98e4e-b8a8-4c90-80ab-378a78a7ca54"
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor as RF"
      ],
      "metadata": {
        "id": "gegmM1WAci0x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "2ef6de4f-44bf-4ad7-e9ad-44fbd2d805e3"
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# RF"
      ],
      "metadata": {
        "id": "hR1eLpGhci4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "f59d08bc-70ea-4453-ab74-47919656d1c2"
      },
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def learning(trainX, y_train):\n",
        "    model1 = LR()\n",
        "    model2 = RF(n_estimators=100, max_depth=4, random_state=777)\n",
        "    model1.fit(trainX[\"days\"].values.reshape(-1, 1), y_train)\n",
        "    pred = model1.predict(trainX[\"days\"].values.reshape(-1,1))\n",
        "\n",
        "    pred_sub = y_train - pred\n",
        "    model2.fit(trainX.iloc[:, ~trainX.columns.str.match(\"y\")], pred_sub)\n",
        "    return model1, model2"
      ],
      "metadata": {
        "id": "xrbonbCuci7k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "8d288b06-1d4f-47dd-fceb-c0fb610c13de"
      },
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# help(learning)"
      ],
      "metadata": {
        "id": "QXlE0UfPci-K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "4d996a92-d92e-4ca6-cf15-63d3e351db1f"
      },
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DLxIPmTMcjA2"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PahY8X4YcjDt"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Zm1TtDPycjIh"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nNQ7qLZxcjL4"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nsdAAf22cHOf"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Yqy_7DptcHQ6"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4qoPb_D4cHVF"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FzdyiUaxcHnl"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7EY3sGwfcHrD"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 182,
      "metadata": {
        "id": "ufkQvaEg9zK1"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 182,
      "metadata": {
        "id": "p3e6Cqm39zP8"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 182,
      "metadata": {
        "id": "yEsWIrd39zUB"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": [],
      "authorship_tag": "ABX9TyPbBFAU+NC2IFCBaI9j0j0Z",
      "include_colab_link": true
    },
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