5-pandas-reshape.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandasでデータの整形\n",
    "\n",
    "データの整形、条件抽出、並べ替えを行います。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 内容\n",
    "\n",
    "- データ抽出\n",
    "- データ型変換\n",
    "- 並べ替え\n",
    "- 不要なカラム削除\n",
    "- 組合せデータの挿入"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## データ抽出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"data/201704health.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日付</th>\n",
       "      <th>歩数</th>\n",
       "      <th>摂取カロリー</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>5439</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-04-02</td>\n",
       "      <td>2510</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-04-03</td>\n",
       "      <td>10238</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-04</td>\n",
       "      <td>8209</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>9434</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>7593</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>9320</td>\n",
       "      <td>1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>4873</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>12045</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-04-10</td>\n",
       "      <td>7493</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-04-11</td>\n",
       "      <td>7289</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-04-12</td>\n",
       "      <td>6481</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017-04-13</td>\n",
       "      <td>10287</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2017-04-14</td>\n",
       "      <td>8043</td>\n",
       "      <td>1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>7435</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017-04-16</td>\n",
       "      <td>7529</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017-04-17</td>\n",
       "      <td>8031</td>\n",
       "      <td>1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017-04-18</td>\n",
       "      <td>8475</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>8132</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2017-04-20</td>\n",
       "      <td>15328</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2017-04-21</td>\n",
       "      <td>12849</td>\n",
       "      <td>1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2017-04-22</td>\n",
       "      <td>4029</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2017-04-23</td>\n",
       "      <td>3890</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2017-04-24</td>\n",
       "      <td>8093</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2017-04-25</td>\n",
       "      <td>7823</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2017-04-26</td>\n",
       "      <td>7492</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>7203</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>7302</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2017-04-29</td>\n",
       "      <td>6033</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>4093</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            日付     歩数  摂取カロリー\n",
       "0   2017-04-01   5439    2500\n",
       "1   2017-04-02   2510    2300\n",
       "2   2017-04-03  10238    1950\n",
       "3   2017-04-04   8209    1850\n",
       "4   2017-04-05   9434    1930\n",
       "5   2017-04-06   7593    1800\n",
       "6   2017-04-07   9320    1940\n",
       "7   2017-04-08   4873    2300\n",
       "8   2017-04-09  12045    1950\n",
       "9   2017-04-10   7493    1850\n",
       "10  2017-04-11   7289    1930\n",
       "11  2017-04-12   6481    2300\n",
       "12  2017-04-13  10287    1800\n",
       "13  2017-04-14   8043    1940\n",
       "14  2017-04-15   7435    2300\n",
       "15  2017-04-16   7529    2300\n",
       "16  2017-04-17   8031    1940\n",
       "17  2017-04-18   8475    2300\n",
       "18  2017-04-19   8132    1950\n",
       "19  2017-04-20  15328    1800\n",
       "20  2017-04-21  12849    1940\n",
       "21  2017-04-22   4029    2300\n",
       "22  2017-04-23   3890    1950\n",
       "23  2017-04-24   8093    1850\n",
       "24  2017-04-25   7823    1950\n",
       "25  2017-04-26   7492    1850\n",
       "26  2017-04-27   7203    1930\n",
       "27  2017-04-28   7302    1850\n",
       "28  2017-04-29   6033    2300\n",
       "29  2017-04-30   4093    1950"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 歩数が10000歩以上の日のみを抽出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2      True\n",
       "3     False\n",
       "4     False\n",
       "5     False\n",
       "6     False\n",
       "7     False\n",
       "8      True\n",
       "9     False\n",
       "10    False\n",
       "11    False\n",
       "12     True\n",
       "13    False\n",
       "14    False\n",
       "15    False\n",
       "16    False\n",
       "17    False\n",
       "18    False\n",
       "19     True\n",
       "20     True\n",
       "21    False\n",
       "22    False\n",
       "23    False\n",
       "24    False\n",
       "25    False\n",
       "26    False\n",
       "27    False\n",
       "28    False\n",
       "29    False\n",
       "Name: 歩数, dtype: bool"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, \"歩数\"] >= 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fill = df[df.loc[:, \"歩数\"] >= 10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日付</th>\n",
       "      <th>歩数</th>\n",
       "      <th>摂取カロリー</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-04-03</td>\n",
       "      <td>10238</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>12045</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017-04-13</td>\n",
       "      <td>10287</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2017-04-20</td>\n",
       "      <td>15328</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2017-04-21</td>\n",
       "      <td>12849</td>\n",
       "      <td>1940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            日付     歩数  摂取カロリー\n",
       "2   2017-04-03  10238    1950\n",
       "8   2017-04-09  12045    1950\n",
       "12  2017-04-13  10287    1800\n",
       "19  2017-04-20  15328    1800\n",
       "20  2017-04-21  12849    1940"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fill.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日付</th>\n",
       "      <th>歩数</th>\n",
       "      <th>摂取カロリー</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017-04-13</td>\n",
       "      <td>10287</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2017-04-20</td>\n",
       "      <td>15328</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            日付     歩数  摂取カロリー\n",
       "12  2017-04-13  10287    1800\n",
       "19  2017-04-20  15328    1800"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('歩数 >= 10000 and 摂取カロリー <= 1800')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## データ型変換"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2017-04-01\n",
       "1     2017-04-02\n",
       "2     2017-04-03\n",
       "3     2017-04-04\n",
       "4     2017-04-05\n",
       "5     2017-04-06\n",
       "6     2017-04-07\n",
       "7     2017-04-08\n",
       "8     2017-04-09\n",
       "9     2017-04-10\n",
       "10    2017-04-11\n",
       "11    2017-04-12\n",
       "12    2017-04-13\n",
       "13    2017-04-14\n",
       "14    2017-04-15\n",
       "15    2017-04-16\n",
       "16    2017-04-17\n",
       "17    2017-04-18\n",
       "18    2017-04-19\n",
       "19    2017-04-20\n",
       "20    2017-04-21\n",
       "21    2017-04-22\n",
       "22    2017-04-23\n",
       "23    2017-04-24\n",
       "24    2017-04-25\n",
       "25    2017-04-26\n",
       "26    2017-04-27\n",
       "27    2017-04-28\n",
       "28    2017-04-29\n",
       "29    2017-04-30\n",
       "Name: 日付, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, \"日付\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:, \"date\"] = df.loc[:, \"日付\"].apply(lambda x: pd.to_datetime(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "カラム 日付 に対して、applyメソッドを使うことで、データ変換し \"date\"カラムに挿入\n",
    "apply は、データ一つづつに順次関数を適用するものです。\n",
    "lambda は Pythonの無名関数です。ここでは、引数を x とし、先程実行した日付型を返すpandasの関数 to_datetime を実行しています。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2017-04-01\n",
       "1    2017-04-02\n",
       "2    2017-04-03\n",
       "3    2017-04-04\n",
       "4    2017-04-05\n",
       "5    2017-04-06\n",
       "6    2017-04-07\n",
       "7    2017-04-08\n",
       "8    2017-04-09\n",
       "9    2017-04-10\n",
       "10   2017-04-11\n",
       "11   2017-04-12\n",
       "12   2017-04-13\n",
       "13   2017-04-14\n",
       "14   2017-04-15\n",
       "15   2017-04-16\n",
       "16   2017-04-17\n",
       "17   2017-04-18\n",
       "18   2017-04-19\n",
       "19   2017-04-20\n",
       "20   2017-04-21\n",
       "21   2017-04-22\n",
       "22   2017-04-23\n",
       "23   2017-04-24\n",
       "24   2017-04-25\n",
       "25   2017-04-26\n",
       "26   2017-04-27\n",
       "27   2017-04-28\n",
       "28   2017-04-29\n",
       "29   2017-04-30\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, \"date\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日付</th>\n",
       "      <th>歩数</th>\n",
       "      <th>摂取カロリー</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>5439</td>\n",
       "      <td>2500</td>\n",
       "      <td>2017-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-04-02</td>\n",
       "      <td>2510</td>\n",
       "      <td>2300</td>\n",
       "      <td>2017-04-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-04-03</td>\n",
       "      <td>10238</td>\n",
       "      <td>1950</td>\n",
       "      <td>2017-04-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-04</td>\n",
       "      <td>8209</td>\n",
       "      <td>1850</td>\n",
       "      <td>2017-04-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>9434</td>\n",
       "      <td>1930</td>\n",
       "      <td>2017-04-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>7593</td>\n",
       "      <td>1800</td>\n",
       "      <td>2017-04-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>9320</td>\n",
       "      <td>1940</td>\n",
       "      <td>2017-04-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>4873</td>\n",
       "      <td>2300</td>\n",
       "      <td>2017-04-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>12045</td>\n",
       "      <td>1950</td>\n",
       "      <td>2017-04-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-04-10</td>\n",
       "      <td>7493</td>\n",
       "      <td>1850</td>\n",
       "      <td>2017-04-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-04-11</td>\n",
       "      <td>7289</td>\n",
       "      <td>1930</td>\n",
       "      <td>2017-04-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-04-12</td>\n",
       "      <td>6481</td>\n",
       "      <td>2300</td>\n",
       "      <td>2017-04-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017-04-13</td>\n",
       "      <td>10287</td>\n",
       "      <td>1800</td>\n",
       "      <td>2017-04-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2017-04-14</td>\n",
       "      <td>8043</td>\n",
       "      <td>1940</td>\n",
       "      <td>2017-04-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>7435</td>\n",
       "      <td>2300</td>\n",
       "      <td>2017-04-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017-04-16</td>\n",
       "      <td>7529</td>\n",
       "      <td>2300</td>\n",
       "      <td>2017-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017-04-17</td>\n",
       "      <td>8031</td>\n",
       "      <td>1940</td>\n",
       "      <td>2017-04-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017-04-18</td>\n",
       "      <td>8475</td>\n",
       "      <td>2300</td>\n",
       "      <td>2017-04-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>8132</td>\n",
       "      <td>1950</td>\n",
       "      <td>2017-04-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2017-04-20</td>\n",
       "      <td>15328</td>\n",
       "      <td>1800</td>\n",
       "      <td>2017-04-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2017-04-21</td>\n",
       "      <td>12849</td>\n",
       "      <td>1940</td>\n",
       "      <td>2017-04-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2017-04-22</td>\n",
       "      <td>4029</td>\n",
       "      <td>2300</td>\n",
       "      <td>2017-04-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2017-04-23</td>\n",
       "      <td>3890</td>\n",
       "      <td>1950</td>\n",
       "      <td>2017-04-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2017-04-24</td>\n",
       "      <td>8093</td>\n",
       "      <td>1850</td>\n",
       "      <td>2017-04-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2017-04-25</td>\n",
       "      <td>7823</td>\n",
       "      <td>1950</td>\n",
       "      <td>2017-04-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2017-04-26</td>\n",
       "      <td>7492</td>\n",
       "      <td>1850</td>\n",
       "      <td>2017-04-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>7203</td>\n",
       "      <td>1930</td>\n",
       "      <td>2017-04-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>7302</td>\n",
       "      <td>1850</td>\n",
       "      <td>2017-04-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2017-04-29</td>\n",
       "      <td>6033</td>\n",
       "      <td>2300</td>\n",
       "      <td>2017-04-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>4093</td>\n",
       "      <td>1950</td>\n",
       "      <td>2017-04-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            日付     歩数  摂取カロリー       date\n",
       "0   2017-04-01   5439    2500 2017-04-01\n",
       "1   2017-04-02   2510    2300 2017-04-02\n",
       "2   2017-04-03  10238    1950 2017-04-03\n",
       "3   2017-04-04   8209    1850 2017-04-04\n",
       "4   2017-04-05   9434    1930 2017-04-05\n",
       "5   2017-04-06   7593    1800 2017-04-06\n",
       "6   2017-04-07   9320    1940 2017-04-07\n",
       "7   2017-04-08   4873    2300 2017-04-08\n",
       "8   2017-04-09  12045    1950 2017-04-09\n",
       "9   2017-04-10   7493    1850 2017-04-10\n",
       "10  2017-04-11   7289    1930 2017-04-11\n",
       "11  2017-04-12   6481    2300 2017-04-12\n",
       "12  2017-04-13  10287    1800 2017-04-13\n",
       "13  2017-04-14   8043    1940 2017-04-14\n",
       "14  2017-04-15   7435    2300 2017-04-15\n",
       "15  2017-04-16   7529    2300 2017-04-16\n",
       "16  2017-04-17   8031    1940 2017-04-17\n",
       "17  2017-04-18   8475    2300 2017-04-18\n",
       "18  2017-04-19   8132    1950 2017-04-19\n",
       "19  2017-04-20  15328    1800 2017-04-20\n",
       "20  2017-04-21  12849    1940 2017-04-21\n",
       "21  2017-04-22   4029    2300 2017-04-22\n",
       "22  2017-04-23   3890    1950 2017-04-23\n",
       "23  2017-04-24   8093    1850 2017-04-24\n",
       "24  2017-04-25   7823    1950 2017-04-25\n",
       "25  2017-04-26   7492    1850 2017-04-26\n",
       "26  2017-04-27   7203    1930 2017-04-27\n",
       "27  2017-04-28   7302    1850 2017-04-28\n",
       "28  2017-04-29   6033    2300 2017-04-29\n",
       "29  2017-04-30   4093    1950 2017-04-30"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:, \"摂取カロリー\"] = df.loc[:, \"摂取カロリー\"].astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日付</th>\n",
       "      <th>歩数</th>\n",
       "      <th>摂取カロリー</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>5439</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-02</th>\n",
       "      <td>2017-04-02</td>\n",
       "      <td>2510</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03</th>\n",
       "      <td>2017-04-03</td>\n",
       "      <td>10238</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-04</th>\n",
       "      <td>2017-04-04</td>\n",
       "      <td>8209</td>\n",
       "      <td>1850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-05</th>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>9434</td>\n",
       "      <td>1930.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    日付     歩数  摂取カロリー\n",
       "date                                 \n",
       "2017-04-01  2017-04-01   5439  2500.0\n",
       "2017-04-02  2017-04-02   2510  2300.0\n",
       "2017-04-03  2017-04-03  10238  1950.0\n",
       "2017-04-04  2017-04-04   8209  1850.0\n",
       "2017-04-05  2017-04-05   9434  1930.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "インデックをdateで置き換えました。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 並べ替え"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日付</th>\n",
       "      <th>歩数</th>\n",
       "      <th>摂取カロリー</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-02</th>\n",
       "      <td>2017-04-02</td>\n",
       "      <td>2510</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-23</th>\n",
       "      <td>2017-04-23</td>\n",
       "      <td>3890</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-22</th>\n",
       "      <td>2017-04-22</td>\n",
       "      <td>4029</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-30</th>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>4093</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-08</th>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>4873</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>5439</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-29</th>\n",
       "      <td>2017-04-29</td>\n",
       "      <td>6033</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-12</th>\n",
       "      <td>2017-04-12</td>\n",
       "      <td>6481</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-27</th>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>7203</td>\n",
       "      <td>1930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-11</th>\n",
       "      <td>2017-04-11</td>\n",
       "      <td>7289</td>\n",
       "      <td>1930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-28</th>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>7302</td>\n",
       "      <td>1850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-15</th>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>7435</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26</th>\n",
       "      <td>2017-04-26</td>\n",
       "      <td>7492</td>\n",
       "      <td>1850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-10</th>\n",
       "      <td>2017-04-10</td>\n",
       "      <td>7493</td>\n",
       "      <td>1850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-16</th>\n",
       "      <td>2017-04-16</td>\n",
       "      <td>7529</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-06</th>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>7593</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25</th>\n",
       "      <td>2017-04-25</td>\n",
       "      <td>7823</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-17</th>\n",
       "      <td>2017-04-17</td>\n",
       "      <td>8031</td>\n",
       "      <td>1940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-14</th>\n",
       "      <td>2017-04-14</td>\n",
       "      <td>8043</td>\n",
       "      <td>1940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24</th>\n",
       "      <td>2017-04-24</td>\n",
       "      <td>8093</td>\n",
       "      <td>1850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-19</th>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>8132</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-04</th>\n",
       "      <td>2017-04-04</td>\n",
       "      <td>8209</td>\n",
       "      <td>1850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-18</th>\n",
       "      <td>2017-04-18</td>\n",
       "      <td>8475</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-07</th>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>9320</td>\n",
       "      <td>1940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-05</th>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>9434</td>\n",
       "      <td>1930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03</th>\n",
       "      <td>2017-04-03</td>\n",
       "      <td>10238</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-13</th>\n",
       "      <td>2017-04-13</td>\n",
       "      <td>10287</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-09</th>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>12045</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-21</th>\n",
       "      <td>2017-04-21</td>\n",
       "      <td>12849</td>\n",
       "      <td>1940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-20</th>\n",
       "      <td>2017-04-20</td>\n",
       "      <td>15328</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    日付     歩数  摂取カロリー\n",
       "date                                 \n",
       "2017-04-02  2017-04-02   2510  2300.0\n",
       "2017-04-23  2017-04-23   3890  1950.0\n",
       "2017-04-22  2017-04-22   4029  2300.0\n",
       "2017-04-30  2017-04-30   4093  1950.0\n",
       "2017-04-08  2017-04-08   4873  2300.0\n",
       "2017-04-01  2017-04-01   5439  2500.0\n",
       "2017-04-29  2017-04-29   6033  2300.0\n",
       "2017-04-12  2017-04-12   6481  2300.0\n",
       "2017-04-27  2017-04-27   7203  1930.0\n",
       "2017-04-11  2017-04-11   7289  1930.0\n",
       "2017-04-28  2017-04-28   7302  1850.0\n",
       "2017-04-15  2017-04-15   7435  2300.0\n",
       "2017-04-26  2017-04-26   7492  1850.0\n",
       "2017-04-10  2017-04-10   7493  1850.0\n",
       "2017-04-16  2017-04-16   7529  2300.0\n",
       "2017-04-06  2017-04-06   7593  1800.0\n",
       "2017-04-25  2017-04-25   7823  1950.0\n",
       "2017-04-17  2017-04-17   8031  1940.0\n",
       "2017-04-14  2017-04-14   8043  1940.0\n",
       "2017-04-24  2017-04-24   8093  1850.0\n",
       "2017-04-19  2017-04-19   8132  1950.0\n",
       "2017-04-04  2017-04-04   8209  1850.0\n",
       "2017-04-18  2017-04-18   8475  2300.0\n",
       "2017-04-07  2017-04-07   9320  1940.0\n",
       "2017-04-05  2017-04-05   9434  1930.0\n",
       "2017-04-03  2017-04-03  10238  1950.0\n",
       "2017-04-13  2017-04-13  10287  1800.0\n",
       "2017-04-09  2017-04-09  12045  1950.0\n",
       "2017-04-21  2017-04-21  12849  1940.0\n",
       "2017-04-20  2017-04-20  15328  1800.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"歩数\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日付</th>\n",
       "      <th>歩数</th>\n",
       "      <th>摂取カロリー</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-20</th>\n",
       "      <td>2017-04-20</td>\n",
       "      <td>15328</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-21</th>\n",
       "      <td>2017-04-21</td>\n",
       "      <td>12849</td>\n",
       "      <td>1940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-09</th>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>12045</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-13</th>\n",
       "      <td>2017-04-13</td>\n",
       "      <td>10287</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03</th>\n",
       "      <td>2017-04-03</td>\n",
       "      <td>10238</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    日付     歩数  摂取カロリー\n",
       "date                                 \n",
       "2017-04-20  2017-04-20  15328  1800.0\n",
       "2017-04-21  2017-04-21  12849  1940.0\n",
       "2017-04-09  2017-04-09  12045  1950.0\n",
       "2017-04-13  2017-04-13  10287  1800.0\n",
       "2017-04-03  2017-04-03  10238  1950.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"歩数\", ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 不要なカラム削除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(\"日付\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>歩数</th>\n",
       "      <th>摂取カロリー</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-26</th>\n",
       "      <td>7492</td>\n",
       "      <td>1850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-27</th>\n",
       "      <td>7203</td>\n",
       "      <td>1930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-28</th>\n",
       "      <td>7302</td>\n",
       "      <td>1850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-29</th>\n",
       "      <td>6033</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-30</th>\n",
       "      <td>4093</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              歩数  摂取カロリー\n",
       "date                    \n",
       "2017-04-26  7492  1850.0\n",
       "2017-04-27  7203  1930.0\n",
       "2017-04-28  7302  1850.0\n",
       "2017-04-29  6033  2300.0\n",
       "2017-04-30  4093  1950.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 組合せデータの挿入"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "歩数 / 摂取カロリー という新たなカラムを追加します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:, \"歩数/カロリー\"] = df.loc[:, \"歩数\"] / df.loc[:, \"摂取カロリー\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>歩数</th>\n",
       "      <th>摂取カロリー</th>\n",
       "      <th>歩数/カロリー</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <td>5439</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2.175600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-02</th>\n",
       "      <td>2510</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>1.091304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03</th>\n",
       "      <td>10238</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>5.250256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-04</th>\n",
       "      <td>8209</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>4.437297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-05</th>\n",
       "      <td>9434</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>4.888083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-06</th>\n",
       "      <td>7593</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>4.218333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-07</th>\n",
       "      <td>9320</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>4.804124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-08</th>\n",
       "      <td>4873</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>2.118696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-09</th>\n",
       "      <td>12045</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>6.176923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-10</th>\n",
       "      <td>7493</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>4.050270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-11</th>\n",
       "      <td>7289</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>3.776684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-12</th>\n",
       "      <td>6481</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>2.817826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-13</th>\n",
       "      <td>10287</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>5.715000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-14</th>\n",
       "      <td>8043</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>4.145876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-15</th>\n",
       "      <td>7435</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>3.232609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-16</th>\n",
       "      <td>7529</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>3.273478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-17</th>\n",
       "      <td>8031</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>4.139691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-18</th>\n",
       "      <td>8475</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>3.684783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-19</th>\n",
       "      <td>8132</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>4.170256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-20</th>\n",
       "      <td>15328</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>8.515556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-21</th>\n",
       "      <td>12849</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>6.623196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-22</th>\n",
       "      <td>4029</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>1.751739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-23</th>\n",
       "      <td>3890</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1.994872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24</th>\n",
       "      <td>8093</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>4.374595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25</th>\n",
       "      <td>7823</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>4.011795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26</th>\n",
       "      <td>7492</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>4.049730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-27</th>\n",
       "      <td>7203</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>3.732124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-28</th>\n",
       "      <td>7302</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>3.947027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-29</th>\n",
       "      <td>6033</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>2.623043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-30</th>\n",
       "      <td>4093</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>2.098974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               歩数  摂取カロリー   歩数/カロリー\n",
       "date                               \n",
       "2017-04-01   5439  2500.0  2.175600\n",
       "2017-04-02   2510  2300.0  1.091304\n",
       "2017-04-03  10238  1950.0  5.250256\n",
       "2017-04-04   8209  1850.0  4.437297\n",
       "2017-04-05   9434  1930.0  4.888083\n",
       "2017-04-06   7593  1800.0  4.218333\n",
       "2017-04-07   9320  1940.0  4.804124\n",
       "2017-04-08   4873  2300.0  2.118696\n",
       "2017-04-09  12045  1950.0  6.176923\n",
       "2017-04-10   7493  1850.0  4.050270\n",
       "2017-04-11   7289  1930.0  3.776684\n",
       "2017-04-12   6481  2300.0  2.817826\n",
       "2017-04-13  10287  1800.0  5.715000\n",
       "2017-04-14   8043  1940.0  4.145876\n",
       "2017-04-15   7435  2300.0  3.232609\n",
       "2017-04-16   7529  2300.0  3.273478\n",
       "2017-04-17   8031  1940.0  4.139691\n",
       "2017-04-18   8475  2300.0  3.684783\n",
       "2017-04-19   8132  1950.0  4.170256\n",
       "2017-04-20  15328  1800.0  8.515556\n",
       "2017-04-21  12849  1940.0  6.623196\n",
       "2017-04-22   4029  2300.0  1.751739\n",
       "2017-04-23   3890  1950.0  1.994872\n",
       "2017-04-24   8093  1850.0  4.374595\n",
       "2017-04-25   7823  1950.0  4.011795\n",
       "2017-04-26   7492  1850.0  4.049730\n",
       "2017-04-27   7203  1930.0  3.732124\n",
       "2017-04-28   7302  1850.0  3.947027\n",
       "2017-04-29   6033  2300.0  2.623043\n",
       "2017-04-30   4093  1950.0  2.098974"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ここで計算した、歩数/カロリーを元に、\n",
    "新たに、運動指数カラムを作ります。\n",
    "3以下をLow, 3を超え6以下をMid、6を超えるのをHighとします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def exercise_judge(ex):\n",
    "    if ex <= 3.0:\n",
    "        return \"Low\"\n",
    "    elif 3 < ex <= 6.0:\n",
    "        return \"Mid\"\n",
    "    else:\n",
    "        return \"High\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:, \"運動指数\"] = df.loc[:, \"歩数/カロリー\"].apply(exercise_judge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>歩数</th>\n",
       "      <th>摂取カロリー</th>\n",
       "      <th>歩数/カロリー</th>\n",
       "      <th>運動指数</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <td>5439</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2.175600</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-02</th>\n",
       "      <td>2510</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>1.091304</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03</th>\n",
       "      <td>10238</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>5.250256</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-04</th>\n",
       "      <td>8209</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>4.437297</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-05</th>\n",
       "      <td>9434</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>4.888083</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-06</th>\n",
       "      <td>7593</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>4.218333</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-07</th>\n",
       "      <td>9320</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>4.804124</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-08</th>\n",
       "      <td>4873</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>2.118696</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-09</th>\n",
       "      <td>12045</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>6.176923</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-10</th>\n",
       "      <td>7493</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>4.050270</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-11</th>\n",
       "      <td>7289</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>3.776684</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-12</th>\n",
       "      <td>6481</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>2.817826</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-13</th>\n",
       "      <td>10287</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>5.715000</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-14</th>\n",
       "      <td>8043</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>4.145876</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-15</th>\n",
       "      <td>7435</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>3.232609</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-16</th>\n",
       "      <td>7529</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>3.273478</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-17</th>\n",
       "      <td>8031</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>4.139691</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-18</th>\n",
       "      <td>8475</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>3.684783</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-19</th>\n",
       "      <td>8132</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>4.170256</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-20</th>\n",
       "      <td>15328</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>8.515556</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-21</th>\n",
       "      <td>12849</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>6.623196</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-22</th>\n",
       "      <td>4029</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>1.751739</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-23</th>\n",
       "      <td>3890</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1.994872</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24</th>\n",
       "      <td>8093</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>4.374595</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25</th>\n",
       "      <td>7823</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>4.011795</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26</th>\n",
       "      <td>7492</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>4.049730</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-27</th>\n",
       "      <td>7203</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>3.732124</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-28</th>\n",
       "      <td>7302</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>3.947027</td>\n",
       "      <td>Mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-29</th>\n",
       "      <td>6033</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>2.623043</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-30</th>\n",
       "      <td>4093</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>2.098974</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               歩数  摂取カロリー   歩数/カロリー  運動指数\n",
       "date                                     \n",
       "2017-04-01   5439  2500.0  2.175600   Low\n",
       "2017-04-02   2510  2300.0  1.091304   Low\n",
       "2017-04-03  10238  1950.0  5.250256   Mid\n",
       "2017-04-04   8209  1850.0  4.437297   Mid\n",
       "2017-04-05   9434  1930.0  4.888083   Mid\n",
       "2017-04-06   7593  1800.0  4.218333   Mid\n",
       "2017-04-07   9320  1940.0  4.804124   Mid\n",
       "2017-04-08   4873  2300.0  2.118696   Low\n",
       "2017-04-09  12045  1950.0  6.176923  High\n",
       "2017-04-10   7493  1850.0  4.050270   Mid\n",
       "2017-04-11   7289  1930.0  3.776684   Mid\n",
       "2017-04-12   6481  2300.0  2.817826   Low\n",
       "2017-04-13  10287  1800.0  5.715000   Mid\n",
       "2017-04-14   8043  1940.0  4.145876   Mid\n",
       "2017-04-15   7435  2300.0  3.232609   Mid\n",
       "2017-04-16   7529  2300.0  3.273478   Mid\n",
       "2017-04-17   8031  1940.0  4.139691   Mid\n",
       "2017-04-18   8475  2300.0  3.684783   Mid\n",
       "2017-04-19   8132  1950.0  4.170256   Mid\n",
       "2017-04-20  15328  1800.0  8.515556  High\n",
       "2017-04-21  12849  1940.0  6.623196  High\n",
       "2017-04-22   4029  2300.0  1.751739   Low\n",
       "2017-04-23   3890  1950.0  1.994872   Low\n",
       "2017-04-24   8093  1850.0  4.374595   Mid\n",
       "2017-04-25   7823  1950.0  4.011795   Mid\n",
       "2017-04-26   7492  1850.0  4.049730   Mid\n",
       "2017-04-27   7203  1930.0  3.732124   Mid\n",
       "2017-04-28   7302  1850.0  3.947027   Mid\n",
       "2017-04-29   6033  2300.0  2.623043   Low\n",
       "2017-04-30   4093  1950.0  2.098974   Low"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 別の章で使うのでデータをPickle形式で保存しておきます"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_pickle(\"data/df_201704health.pickle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One-hot Encording\n",
    "(データフレームの結合は次の次の章で)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_moved = pd.get_dummies(df.loc[:, \"運動指数\"], prefix=\"運動\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>運動_High</th>\n",
       "      <th>運動_Low</th>\n",
       "      <th>運動_Mid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-02</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-04</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-05</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-06</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-07</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-08</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-09</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-12</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-20</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-21</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-22</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-23</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-29</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-30</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            運動_High  運動_Low  運動_Mid\n",
       "date                               \n",
       "2017-04-01        0       1       0\n",
       "2017-04-02        0       1       0\n",
       "2017-04-03        0       0       1\n",
       "2017-04-04        0       0       1\n",
       "2017-04-05        0       0       1\n",
       "2017-04-06        0       0       1\n",
       "2017-04-07        0       0       1\n",
       "2017-04-08        0       1       0\n",
       "2017-04-09        1       0       0\n",
       "2017-04-10        0       0       1\n",
       "2017-04-11        0       0       1\n",
       "2017-04-12        0       1       0\n",
       "2017-04-13        0       0       1\n",
       "2017-04-14        0       0       1\n",
       "2017-04-15        0       0       1\n",
       "2017-04-16        0       0       1\n",
       "2017-04-17        0       0       1\n",
       "2017-04-18        0       0       1\n",
       "2017-04-19        0       0       1\n",
       "2017-04-20        1       0       0\n",
       "2017-04-21        1       0       0\n",
       "2017-04-22        0       1       0\n",
       "2017-04-23        0       1       0\n",
       "2017-04-24        0       0       1\n",
       "2017-04-25        0       0       1\n",
       "2017-04-26        0       0       1\n",
       "2017-04-27        0       0       1\n",
       "2017-04-28        0       0       1\n",
       "2017-04-29        0       1       0\n",
       "2017-04-30        0       1       0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_moved"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### このデータもPickle形式で保存しておきます"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_moved.to_pickle(\"data/df_201704moved.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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