8-pandas-statistic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandasで統計データの出力"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 内容\n",
    "\n",
    "- 最大値、最小値、最頻値、算術平均、中央値、分散\n",
    "- 件数\n",
    "- 要約\n",
    "- 相関係数\n",
    "- 散布図行列\n",
    "- groupbyでデータの集約\n",
    "- pandasのデータフレームをnumpyのarrayを変換"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5章で保存したデータを読み出し"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle(\"data/df_201704health.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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",
       "      <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 最大値、最小値、最頻値、算術平均、中央値、分散"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2500.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, \"摂取カロリー\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1800.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, \"摂取カロリー\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2300.0\n",
       "dtype: float32"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, \"摂取カロリー\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2026.6666"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, \"摂取カロリー\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1945.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, \"摂取カロリー\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "205.54944"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, \"摂取カロリー\"].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 件数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "歩数         8\n",
       "摂取カロリー     8\n",
       "歩数/カロリー    8\n",
       "運動指数       8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.loc[:, \"摂取カロリー\"]==2300].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 要約"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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>歩数</th>\n",
       "      <th>摂取カロリー</th>\n",
       "      <th>歩数/カロリー</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7766.366667</td>\n",
       "      <td>2026.666626</td>\n",
       "      <td>3.929658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2689.269308</td>\n",
       "      <td>205.549438</td>\n",
       "      <td>1.563674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2510.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>1.091304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6661.500000</td>\n",
       "      <td>1870.000000</td>\n",
       "      <td>2.921522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7561.000000</td>\n",
       "      <td>1945.000000</td>\n",
       "      <td>4.030762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8408.500000</td>\n",
       "      <td>2300.000000</td>\n",
       "      <td>4.421622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15328.000000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>8.515556</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",
       "count     30.000000    30.000000  30.000000\n",
       "mean    7766.366667  2026.666626   3.929658\n",
       "std     2689.269308   205.549438   1.563674\n",
       "min     2510.000000  1800.000000   1.091304\n",
       "25%     6661.500000  1870.000000   2.921522\n",
       "50%     7561.000000  1945.000000   4.030762\n",
       "75%     8408.500000  2300.000000   4.421622\n",
       "max    15328.000000  2500.000000   8.515556"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 相関係数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>歩数</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.498703</td>\n",
       "      <td>0.982828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>摂取カロリー</th>\n",
       "      <td>-0.498703</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.636438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>歩数/カロリー</th>\n",
       "      <td>0.982828</td>\n",
       "      <td>-0.636438</td>\n",
       "      <td>1.000000</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",
       "歩数       1.000000 -0.498703  0.982828\n",
       "摂取カロリー  -0.498703  1.000000 -0.636438\n",
       "歩数/カロリー  0.982828 -0.636438  1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 散布図行列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import scatter_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kcBXwQ1XtT6Usx3GbIhGLKT988SxfffQNnj854nQ4GdM1OMlPX+mhq4DGbeofC/KzV3s42D12ruyZ40N89dE3+PHL3UWX3ItJrquqfgb8bF7ZF4AvLKfMmHRNzIR55FAfo1MhQpEYO9fWOh1SRvzstR5mwjFODk/yxzdtdDqclDx8qI/+sRmO9I2zutrHwwf7+OXhflpryzjaP8FMJJbyqLcmt+yefbPiqCZ+nA4kg2rLSub8LgSzsVb4PHSPBukeDVJdWsLodIita6otaeQxu3PcrCgVPi+3XL6KU8NTRTXN6rt2rKE3EGR1AY2Oe9uW1WxpqaKhwkdMlUq/B6+7lDuvWkNbXZnT4ZmLKPrE4fQ9F4XC6eOUq/tIXC7h3Ttbica0qBpffR43a+szcwNerrhdMifmj12/DtX4/8jkt6JPHMYspJiSRrEQEcT+LQXB2jiMMcakxRKHMcaYtFjiMKYIDE+GeOb4EEMTM06HAsCxgQmePzlCOMeDLprcsDYOY4rAD144w3gwwqtnA/zBDesdjSUcVe5/KT7F63gwzE02WGHRyfWQI7tF5DeJeTW+nCgLJObneExE6hJlH0ys9xMRqVqszBgTJ4lWZcmD1mWBc43c+RCPybxcX3GcBG5R1WBiQqY3Aa+q6k2zKyQmafoEsA94N/BxEfnK/DLgSzmO3Zi89a6r13B8cIL1Dc4PDOhxC3detYbAdJgtLfYdrxjlesiR3qSHYSAKXJGYn+PXwOeATcSTSUREHgH+GfjpAmWL6urqorOzE4Bs3eKlwMhkiHA0RlWpl1K7y/WSdHb+1bm/k/9/pvB0dXXxnrfeuPSKi5gORRkLhvG6XdSWl2DXLDm1I5WVHGnjEJFtQKOqHhSRTcAI8E3gDmCQDMzHsX///qy+hv6xIN955hQA7XVlvDuHk+cUu87Ozqz//0z2XOr/73vPnebs6DQAv3vtWpuTI4dE5IVU1st5r6pEO8Y3gI8BqOqwxofB/CGwlXhimL2+TZ6PY37ZHKp6r6p2qmpnY2Njdl8EUF/hY+OqCir9Hq5qvyCPGWOWacfaGip8HjY3VVJXQGNvrSQ5veIQEQ/wbeAzqtorIuVAUFWjxOfneBU4AmwVETfn595YqMxRbpdwx/YWp8Mwy5Dq8Co2na4zNq6qZOOqwp5XpNjluqrqPcAu4IuJ3hafA/5BRCaAE8BfqWpURP4ZeIJ4FdYHVDU8vyzHcRtjjEnIdeP4fcB984ovaIxR1W8B31qqzBhjTO7ZnePGGGPSYonDGGNMWixxGGOMSYslDmOMMWmxxGGMMSYtljiMMcakxRKHMcaYtFjiMMYYkxZLHMYYY9JiicMYY0xaLHEYY4xJi805bvKajWSbmlSPE9ixMpfOrjiMMcakpagTR/foNL2BYMrrhyIxjg1MMBWKpLUs2fBkiJNDk8TnpoLTw1P0jy8cg6pyrH+c508OMzmz8PNOhSIcG5hgJhJN+XUY47T558FCItEYxwcmmJj33j89PMXA+MyC2wyMz3B6eOrcegudW6/3xM+pUCQ2p3wsGOb4wATR2OIxmdQsq6pKRP5yiVX6VfWby3nuTDnaP86PX+4B4J1Xr2FdQ/mS2/zklW5ODk1RXerlI3s7SMwZsuSyWaNTIb7z9EkiMWXP+noqfB4eOdSHCLy3s42WmtI56z97Ypj//esTjE2HuXZDA39y80bcrvPPq6p897nTjE6Faa0t5T2dbcs9HMbkTDSm586D6zbUs3t9/YLrPXiglzf6Jij3ufnI3nV43S5ePj3KLw734xLhfbvaWF3tP7d+byDId587TUyV9rpSTg1P4xLhvbtaaa6On1vPnBjiH35xlFA0xtu3NvPh6zoACIajfOfpUwTDUba0VHHrltVZPw7FbLltHHuA98Oi88j/K/E5xB0TmA6f+3ss6e+LmV1vYiZCNKZ43OdfXuAiy2ZNhqJEEt9mxoJhYolvW6owHrzwimIsGGEmHCOm8aQTjem8xAETie3GFtjemHwUjWnSebD4+3ZsOr5sKhQlElW87vh5AxBTZWImDJxPHBMz58+p/vHQ+fWCEaiOrxOYChOKxlCFocnQuW1nIrFzV+12Ll265SaOqKqOLbZQRBy/FtzWWsPkTBQR2NJStfQGwG1bV/Py6QCbmirwuOfW4r31IstmrakpZd/mBkYmw+zZUI/HJYQiMfxeN5tWVVyw/nUb6glMhegbD3LbltWUeOY+r8slvGNbM0f6JtjWWp3iKzfGWSUe17nz4NoNC19tALzlyiZeODVCR305pSVuAHZ11BGJKqUlbjY0zj1nNjRWcO2GeqZDUa5ur+HFU6P4vW42Jp1b129qoH88yOh0mHfvaD1XXl3q5S1XNnFmZJpdHXUZfsUrz3ITx1KJYcHlIrIb+DIQA54Dvgb8W2L9M8DvJqaOfR3oSWz2x6p6UERuAf4GCCbWO3OxALxuF/s2N6b6egBori49d8mbzrJkO9fOfVPefPmqRdct93m4e4nqp/WNFaxvvDDpGJPP5p8HC2ms9HHbvCojv9e96DkjIuxJqvZaaL2yEg8f2tOx4PZbWqrZ0mJfwDJhuYnDKyKLfY0XwL3IspPALaoaFJHvAK3A7aoaEJG/Ad4O/BgYUNWb5m37F8CtwJXE5yr/k2XGbowx5hIsN3E8DXz6IssfWKhQVXuTHoaBIVUNJD2e7TpUJyKPA4eATxHv/TWtquPAMyLyhWXGbYwx5hJdSndcucjPxTcU2QY0qurBxOMW4C3AQ4lVrlfVfcSvUO4BaoDkNpULrmhE5B4R2S8i+wcGBpb9oowxxlzccq84drPMXlUiUgd8A3hv4rEvsf4fqmoEQFWHE6v/J/Bfgf8FJFeNXXBTg6reC9wL0NnZ6XjjvDHGFKuc9qoSEQ/wbeAzSdVW9wL/kHT1UQKIqs4Ae4FjqjopIqUiUkG8jePgMuM2xhhziXLaqwp4D7AL+GLiBrrPAe8C1orIp4GvAr8BHhCRCWAE+FBi278BHibeq+r3lhm3McaYS5TTXlWqeh9w37ziygVW3bHAto8Aj6QTpDHGmMzLaa8qY4wxhe9ShlVfsveUMcaY4pPzXlXGGGMKW9GOVWWMMSY7lnsD4HJ7VRljjClwuR6ryhhjTIGzXlXGGGPSYr2qjDHGpMV6VRljjEmL9aoyxhiTFutVZYwxJi3Wq8oYY0xarFeVMcaYtFivKmOMMWmxXlXGGGPSYr2qjDHGpMV6VRljjEnLchOHV0SqFvmpZpFeVSKyW0R+IyJPisiXE2V/lnj8HRHxplNmjDEm97LRq0pYvFfVSeAWVQ0mEsCNwM2qer2I/D/AO0XkV6mUAd9fZuzGGGMuQU4bx1W1N+lhGNgCPJZ4/AjwQWAyxTJLHMYY4wBHGsdFZBvQCIwCsURxAKhJ/IylUDb/Oe8B7gFob29P9XWYItHx2Z86HYIxK0bOG8dFpA74BvAx4klg9g70KuKJJNWyuTtUvVdVO1W1s7GxMcWXYYwxJl25bhz3AN8GPpOotnoOuDGx+M3E205SLTPGGOOAS20cX6yN48FFyt8D7AK+KCIAnwMeF5EngVPAV1Q1JCJLli0z7kU9f3KEQz1jzISjVJeV8JYrmqguy13nrWA4ykMH+4jGYmxsrOS17gCbVlXQ2VG3rOebiUR56EAf4WiMt1zZRKU/9deSHMtbrlxNhe9SBhgwZmnhaIxHDvZxYmgSv8fNm1qr2bXM936qpkIRHj7YBwq+EheBqTD7NjfSXF2a1vOcHZ3miSMDNNeUcuPm1Go7pkIRHjrQhwjceuVqSksKa3i/ZX0iqOpfL3O7+4D75hU/BXxh3npfSKUsU8LRGI8fGWBwYoZTw1PsaK/lhdMj3HzZqmzsbkGHe8c51j/B4MQM3/jFUWrKvFy3vp6ta6rxexd+U71wKp7sdrTXckXz3DEnj/ROcLR/AoBXzgTYu7Eh5VheT8QS33aU6zac3/bMyBRPvDFIc7Wfm3J4fExxOz4wyeHecV46PUpNqZfAdJg3XeS9nwkHusc4PjBJ9+gUz3WNUFtWAgjv29W24PpPvjHIqeEp9m6sZ219+bnyp48N0RMI0hMIcmVzFY2VvpT2fWJwEoCDPWPsXFt7btlTx4Y4PjjBnvX1bGisuLQXmSX2VRLwuISWGj/T4SgNFT5cIrTVpvetIx2jUyG+9dRJTo9Mcce2Fm7Y3EhztR+vWzjcM0ZMld5AkOlIFJ9n4drEaEx5/MgAp4enePzIAJ+59TI2NVWeW95U7aPE4yISVdbUpPdaZmOJKbTWlM1Z9tSxIXoDQXoDQbauqaahYumTxJilrKr04fO6qC71UFXqoanKT4l7uTXpC5ucifCvT3VxrH+Cd2xrZnNTJR6XcHJoilAkRv94kOHJmQW3DUyHea5rmP7xIE+fGOL3r+tgR3v8w76troxTw1NUl3qpKk3tI7W52o/HFa+waanxnyufCkV4+vgQAL8+OmiJI5+JCHfvbGM8GMbncRFVslo98+LpUfafHCYYjvHAa73sWldHU5Wfj12/nlWVPv79+TP4vG4+unc9iSq9C7hdQlOVn6eODdFQ4ePJo4NzEseqSj8f3buOmCrlab6WVYlYFtp2bX05Z0amqS3zUpVG9ZcxF1NbXsJH964jFIkSS5x/Lldmx1E90D3GsyeGmQ5FeeDVXvasr+djN6yjo76Mbz1zCq9LuH1by4Lblpe4aagoYX/XMKuq/Dx+ZICr22oQEa5ZV8dlTZWU+dx4U0x2rbVlfOyGdQCUlZw/x/weN01VfvrGgrTVlS22ueMscSS4XUJNWUlO9tVeV0ZteQkDYzNsXFV+7ptVaYmb9+5q54bNjVT6PVT4Lv7B/N6drQTDUUYmQ3QkXTrPupR608W2vWZdHZc3V1LqTf0kMSYVfq87q1VTrbWl1Jf76A5Ps2FVBX6PG5dLuOOqNexeX4/f66aqdOFzzuN28TvXtFPuc3NyaJq19WVzvtQtpz00OWHMcrniVWUTMxGqF4klH1jicMCGxgr+9q43MR2KUltWcsE3q1Qb59xuF793bQeToUhajd+Xyq40TCFqqSnlb+7aykQwTG25b855t6rKf5Et4zxuF3dd3cr4TISKBT70M8XtkrxOGmCJwzFlJZ4Fv3Gky+WSnCYNYwrZpV7ViIh9cWL593EYY4xZoSxxGGOMSYslDmOMMWmxNg6HvNE3ziMH+3jq+BAi8IHda7lty2qiMeX+l87y6OF+Wmr8vH9XO+vztC+3MYVqYibCD188y5NvDBKYDrN3UwP/5eaNhCIxvrf/dPweilUVfPjatayqXLrhfKWxxOGQl06P0h2Y5tjABE1Vfh452MdtW1YzNDnDq2cDDIzPMBOJ8erZgCUO44h0Rhzu+rt3ZDGSzDsxMMnA+AyHesYoLXHz5BsD/NG+9XSPTnOgO8DwZAgZmOBwz7gljgVYVZVDrmiuoqashObqUkrcrnPDetSVlbChsYIKv4emKh+Xr65a4pmMMelqryuj0u+hvb6MshI3V7fX4na7WF3tZ11DOX6vm+bqUjausi9tC7ErDodsXVPNlpYq5FYhFovhcsVzuMft4oN71vKB3fE5RRa7c9wYs3zVZV7+4Ib1fOz6dajqufPP73Vzz74N/OEN6wE7/xZjVxwOmn1TulwuVPWCZZl4085/3nxVKHGa4iIi55JG8nswU+ff/OfNR8uJz644HDY5E+H7+08zGYry29tbMjo+zcMH+zjQHWBHey37UhzuOdci0Rj/8cIZ+sZmePMVTVzZYlVzJvf6xoL84IWzeN3Ce3a2ZWRKBVXl/pe7OTE4yfUbG5Y9RUI2Lfczwq44HHZ2dJqRqTChSIwjfeMZe15V5UB3AFV4rTuQsefNtOHJEN2jQaIx5VDPorMRG5NVx/onCIajjAcjdA1NZuQ5p0JRjg9MohofYDHfXMpnhCUOh7XXlbG62k+l35PRb9siwtXttfi8rnPDP+ej+gof6xrKKS1xs72t2ulwzAq1eXUl1aVeGipKWN944YChy1FW4uaK5ir8XjdXtdVk5Dkz6VI+IyTf69+Wo6GhQTs6OpwOw6RoJhwjHItRVuLGJUJXVxet7WsJhqKUeFyULDIniclPXV1d2PlXmJ4A24wCAAAX5ElEQVR//nlV1SVPuKJs4+jo6GD//v1Oh2FSMDIZ4l+f6kIVNjVVcPu2Fjo7O/nU1/+dwYkQXrfwiRs34LEh3AtGZ2ennX8FSkReSGU9OxuNozxuwZ3oveLznB+1dPbvEo8Ll3WJNCavFOUVhykclX4v79vVxsDEDJclzWB4x/YWjg1M0FZblvGZ4Iwxl8YSh3Hcqir/BRPplJa42brGGsvN8hXzkClOs6oqY4wxabHEYYwxJi2WOIwxxqQlbxKHiLxVRB5L/PSIyDuTln1eRF5OLPtvTsZpjDErXd40jqvqg8CDACLyDPDIvFX+u6rOLzPGGJNjeXPFMUtE1gN9qjoxb9EXROQREbnKibiMMcbE5V3iAN4F/Oe8sq+p6k7gj4CvL7SRiNwjIvtFZP/AwEC2YzTGmBUrHxPHHcD9yQWqOpz4/cZiG6nqvaraqaqdjY35OYS4McYUg7xKHCKyGgip6tC88qrE7wbyqF3GGGNWonz7EL4T+NHsAxH5uqp+EviSiGwlnug+61RwxpjilOpd5naHeVxeJQ5V/ad5jz+Z+P1xZyIyxhgzX15VVRljjMl/ljiMMcakJa+qqkz+O9w7xi8O99NSXcod21twF9CQ56eHp/jZqz1UlXq56+o1+L3upTcyxlzArjhMWl45E2AmHOPE4CTDkyGnw0nLwZ4xpkJRegNBzo5OOx2OMQUrK1ccIvIb4GlAgPmTmgvQpqp3Z2PfJru2tlTTFwjSXFNKXXmJ0+Gk5YrVVRztn6Cq1MuamlKnwzGmYGWrqmpIVRcdjFBE5t8ZbgrElS1VXNlS5XQYy9JeX8af3LzR6TCMKXjZShzzrzLSXW4cFAxH+fmBXiJR5batq6nwFVdT2G+ODdI1OMW1G+pZ11DudDjGFBxr4zAXeL13nOMDk5wanuKVM6NOh5NREzMRnjk+TN9YkCePDjodjjEFKVtfJdeLyJ+yeBtHTZb2azKgudpPicdFNKa01ZY5HU5GlXrdNFb6GBifYW1dcb02Y3IlW4njHVy8Ouq7WdqvyYBVVX4+dv06YqqUlRRXNZXbJbx/VxuToSjVpV6nwzGmIGXrU+E+luhVBVivqizrHwtydGCCy5oqqa/wpbWt0/c4jE6FONgzxrqGcpqrM9sDyuN2UV1qtbTGLJf1qipSqsoPXjzLdCjK673jfGTvOqdDSstPXulhYHyGF0+N8vF96/G47YPemHyRrbPRelU5TETwJO7q9hTQ3d2zZmN2uwSRwovfmGJWXBXYZo67d7bSNTTF+sbC63J6x/YW3uifoK22tKCGNTFmJbBeVUXM7RLW1ZdT5S+8RuByn4er2rLzNglFYgSmwzRUlNjVjDHLYL2qilTfWJDvPXeaqCp3bG9hQ2OF0yHlhUg0xn3PnmJ4MsT2tmpuubzJ6ZCMKTh506tKRDqAZ4BDxKePvTVpWQvwbcAP/KWqPpKluIvGwPgMkVj80PcFgpY4EmYisXODM3aPBh2OxpjClG+9qh5W1Q8tUP5Z4C+Al4GfAJY4lrC5qZIzI9OEojG2Z6nKpxCV+zzs29zAicEpdq+rczock5Dq1K0mP+TbWFU3i8gTwA9U9ctJ5W8CPqWqKiLjIlKlqmMZibTAPfHGAF1D8Q/BzU2V58pLPC7eunW1g5Hll18dGeD08BR71texo72WnWstaRizXPnUq6oH2AzMAD8SkUdV9ZXEMreqziabAPHG9TmJQ0TuAe4BaG9vz03EDvvhi2e579lTrK7yE4nG5iQOc97oVIgXTo4wMhniV6/3c+2GBt5/TVvR3RVvTK7kTa8qVZ0hnjQQkZ8AW4HZxBFLWrUKuGDkPVW9F7gXoLOzs+jvE4nGlBODE1T6PQxNhuioL7wut7lS4fPQUOnj2EB8Lo7AdJgea/cxZtnypleViFSq6nji4V7g60mLXxGRa4knEqumIt7V9pp19ZT7PFzZXMX1mxqdDilvedwuPnBNO3vW1fHk0UGq/N6iG7zRmFzKm15VwA0i8j+IX3U8oarPiMjXVfWTwBeBfwNKgb/KUsx5Q1V5/uQII1Nhdq+vW/Q+jL0bG9i7sSHH0eWHZ08MMToVZu/GBspTmC/E7RI2NVWyyarzjLlkedOrSlV/BvxsXtknE7/PALdkOsh8FI7G+F9PHOeXrw+wrqGcmUiU27e1OB1WXjnSO843fnGUUDRGbyDIh6/rcDokY1YUG6sqDzx/coTHjwwQDEcZmQoxOh0mpsrgxExB3vWdCf3jQR491EfX4OQFy6ZCEULRGKoQmA6n/dyRaIzRqVAmwjRmRbJuJQ47PjDBLw714XG7UKC8xMXPD/QyFYpy65Ymrt9Y73SIjnjg1V6GJ0Mc7B7jj27aMGd03C1rqnnblib6xmd41441KT/ny2dG+cdfvMHQZJhd62rZs76BGzdb25Ax6cp2r6qF2FhVCYHJGb7088O8enaMy1ZXUuFz8+yJEaJRxeMSJoMRQlHFvwJHFC/3eRieDFFa4sY1bzyp8ekITx0fZnQ6zDXr6lmTYkP3g6/2MjQZ5uTQJAhU+LyWOIxZhownjkTvpzuAKPEksZDvZXq/hebFUyP84IUzHOoZp6G8hKmZCBMzEfxeFyVeNz6Pi9u3tTg+oZJTbt/WzMmhKVpq/LjmjY77/KlhDvaME40pP32lm5suW5XSc16zro4XTo3gdbso9biJxazG1KQnnTvcu/7uHVmMxFnZuOL4MLAbOAI8CDyoqr1Z2E9Be+n0KBPBCBU+DxV+L7dva6Z3LMj6xgo+vm89V7ZUX/CBuZL4vW4uW71wD6iW6lIq/R6mQ1E6GlK7F+Pk0CRet4uvvf8q7n+5h4mZiPWwMmaZMp44VPWPAETkcuBtwL+ISDXwS+KJ5NeqGs30fgvJyaFJHjnUR89okMubK/nye6/C53UzODFDLKasqvKfW/f4wARH+ibY1lpNS01mp1AtVJtXV3LPvvUEpsPctmXpYVUCUyH+x08OMh6MsHtdHZ+4aQNDEyHWFNHxPDU0xcGeMa5srqK93u5RMdmVtcZxVT0MHAa+LCKlwM3Ae4D/CXRma7+F4PjgJKVeN42VPlZV+c5dWTTMmxc8FlN++koPkZhydnSaj11fWNO/ZovX7eLOq1JvFJ8MRZiciQDQPz5DWYmHsrri6hfy01d7CIajnBic5I9u2uB0OKbIZeXsEZG/XKgYGAR+LCKfUNVvZmPfhWDbmmqOb2hgdCrEe3e14l1kPm0RqPB7GJ0KU+Uvrg+6XGquLuWuHa283jvOu9PohVVIqko9BMNRqkrtfWKyL1vvsj3A+1m8cfxfgRWbOOorfCldPYgI79vVRk8gSGtt8VSr5JqI8N7ONqfDyKp372jl7Oh0UVW/mfyVrcQRvdh4UiJi3VmI34j28wN9HB+Y4Jp1dexef+E9G2UlHhuMzyzphZMj/OrIADdubuS6FToMjcmdfJuPY0X5zbEhvrf/NMFwlEAwzPa2mhXb/dZcmn9+4jhToShH+sYtcZisy1bi8IpI1SLLBLBPR+JDo1eXegmGozRW+PB5VuCdfiYjqku9TIWiVJeuzCFqTG5lK3E8DXx6kWUCPJCl/RaU6zbGh0X3uITtbTWIrNz7Nsyl+dzbr+CVM6Nsa7VBGUz2ZStx7MYaxwlFYuzvGsbndbOj/cLE4PO4ucbmvTYZEFPF43YR0/ypBV7p84gX813m1jieRftPDvPMiWEAqvweu1PZZM39L3XH2zh6x/n4jXYfh8muvBlWXUR2i8hvRORJEfnyvGWfF5GXReQxEVl0ng+nxWLKLw/38x/Pn2FoYobSpIZua/Q22RSYDnOgO7CsYeaNSVc+NY6fBG5R1aCIfEdE3qSqryYt/++q+kjGI82gMyPTvHQ6Ph36syeGedubmqkq9eLzuGi1qUpNFnlcwqpK/6I3kxqTSdluHF+sjePB+QXzBkIMEx9dN9kXRGQE+IyqvpSRKDOsttxLaYmb6VCU5sSNWHYPhsmFjoZyRIS1Nk6VyYGsJA5V/evlbisi24BGVT2YVPw1Vf28iGwC/jdwwwLb3QPcA9De3r7c3V+SSr+X37+ug+lQlNryEkdiMCvTnVetYWQqRG2Zve9M9uXVda2I1AHfAD6WXK6qw4nfbyy2rareq6qdqtrZ2JidyXmC4Sia1GvlQHeAXxzuYyx4vl7Z73Vb0jA553YJFT4P7hwPxR8MR/nVkQGePzmc0/0aZ+XNiGgi4gG+TbwqqnfesipVHRORBrIYczAc5ccvdzMdjvK2rc00Vp4frfaXr/fz0qlROhrKuOvqVoYnQzx0oA+A8WAkrdFajbMePdTHicFJ9m5s4IrmxZriCsuDr/VwqGecK5oreevW5pzt95kTw7xwcgSAunIf6xrKc7Zv45y8SRzEh1zfBXwxcb/D54APqOongS+JyFbiV0ifzcbOf/l6P08cGWBiJkJzdSmvdQe4OWlmuWP9EwB0DU4RjsYo8bgo8bgIRWJU+PLpMJqLGZsO8Z8vnmV0KsTwZKhoEscbfRPnfr91a+72W+GL93MRgbIS6zm4UuTNJ56q3gfcN6/4qcSyj2dz3+FojJdOjVLicTE8HKK9rowN82aW27O+nue6hrmsqRKv24XX7eJ3rmlneHKGdSnOQmecpwoz4RjhqDITiTkdTsZct7Gel08H2N5WndP97lxbR125j7ISN01JE5CZ4pY3icNJXreLTU0VvHByhCuaK3nzFU0XzKK2dU01W9fMPSnrykuos/aMglLp9/L2bas5PjDJTZdlpy3MCfXlPpqq/NSX+5ZeOcOseurSpXqXeTp3mGfjOWdZ4ki4fVsL/WNBAtMRfn6gjw2rKqxPfBFyuYS7rm4lFtOimtP9Z6/1MBOOcXJ4kj++aaPT4ZgiZ5+MSeoS39aqSuMDD5riVUxJAzjXDde645pcsCuOJO/Y1kz36DSrKv02Uq0pKO/asYbeQJDV1dbOYLLPEkcSr9vF2nqrrzWFx+dx23vX5IxVVRljjElL0SaOWEx54dQIL50enXO3tzHFqH88yK+PDtI/HnQ6FLMCFG1V1atnA/zq9QEgPnLo/K60xhSTH73YzcRMhIPdY/zhvvVOh2OKXNFecXjc5xu3rVutKXaz7/fk970x2VK0VxxbWqrxul24BDauspn3THF7145WugYn6bCb8UwOSDHW/4vIAPGJobKlARjM4vNnQiHECAvHuQN4wYFYkuXr8SuEuLL1/8u3116M8axV1SWHVCjKxJFtIrJfVTudjuNiCiFGyN84La705CKufHvtKzkeq/w3xhiTFkscxhhj0mKJY3nudTqAFBRCjJC/cVpc6clFXPn22ldsPNbGYYwxJi12xWGMMSYtljiMMcakxRKHMcaYtBTtneOZIiJu4J3AtUANMAo8DfxQVSNOxpZMRHYyL0ZV3e9sVHPl67HM17jylYhUAJ8gfryqOX+8/klVx52MLdsSr70GGFXVCafjcYo1ji9BRL4FvAI8CgSAKuDNwHZV/ZCTsc0SkS8DPuAR5sYYUdVPORlbsnw9lnkcVwvw58AW4rUDUeAg8HeqesbBuO4HvsWFx+vDqnpHhvbxaVX9iohsB74OKPEvup9V1ScysY8047kF+AtgLPFTBVQCf6uqjzgQj7PHR1Xt5yI/wBPplDsU4+PplNuxLJi4HgV2zSu7BnjU4bh+DbjmlbmAX2dwH79I/H4I2Jj4uyGT+0gznieBsnll5Q7G4+jxsaqqpf1IRH4CPEb8m0Y1sA/4sZNBzbNfRP4JeJjz34Z+C+fHe5ovX49lvsZVChyYV3YgUe6kfwAeE5FXOH+8tgD/mMF91CW+5dep6lEAVR0UEaeqSGaAbcSr5Ga9CXBqAhRHj49VVaVARBqBTuInSADYr6oDzkY1l4hcDezhfIxPq+qLzkZ1oXw9lvkYl4jcTLx6ZIrzH9B+4tUjjzocmwfYxPnj9YZmsD1IRP4q6eFXVXVURCqBL6nqJzK1nzTiaQY+SzxZuIAY8erNL6nqWQficfT4WOJYQlLD6XXEG8VGyMOG00Tj+JwYNX8bx/PqWOZrXLNEpJR4XAFVncqDeCqAj3Ph8Sr6xnETZ4ljCYmG01e5sOE53xrHS7iwsTIfG8fz7ljmcVyXq+phESkB7gG2AseAf1TVSQfjynrj+EX2/dU8e09/TVX/1Ok4ZuXq+FjiWIKIPKGqN6Ra7gQReVxV96Va7pR8PZZ5HNcvVPUWEfkm0AX8ENgL3K6qdzkY16+BG1Q1llTmIt6ZYG8G97MFiKrq4aSyPar69EU2yzoR2Uoiiavqcw7F8NvAI05dgVrj+NLyteE0mTWOX5p8jWvW5Un11odF5HcdjSYHjeMi8vdAExAWkQbgo4k2p78FbsnUftKI50FVfauIfJr4ufVT4E9F5LSq/nmu4wG+CZwUkT7gP4H7VXUkVzu3K44U5GPD6XzWOH5p8jEuETkMDAB1wN5EA2gJ8JSq7nQ4ttnG8Rrix+tIhhvHz10ti8g24GvAZ4AvqqoTiWP26u9XwM2zV1si8qSqXu9APL9U1ZtFZB3wLuAO4j2/fqSqmezdtiC74lhCouF0H3MbAstFJC8aTpO4iP8/vYA78ZNX8vVY5mtcqnr5QsXA23IdSzIRqVHVUeCQiNwO3AgcE5F/18x9E3WLSImqhlT1FRG5C/g28SsbJ1wpIv8GbCB+s+10otzvUDwAqOoJ4O+BvxeRJuDOXOzXrjiWkK8Np8mscbxo4xLg7cTvGH8o6Vvunar6Iwfjmv32/f8ST7Q/It720qqqH8nQPq4BulS1P6nMDbxHVf9vJvaRZjxrkx52q2o40bvsBlV9wIF4blPVn+d6v+f2b4nj4vK14TSZNY5fmjyO69vEG8XDxBPZH6jq67Mf3A7Gda7aRlVvTCp/TFVvcioukztWVbW0++c1nFYRvzTPl4ZTsMbxS7XY//h+J4Mi/g3+QwAi8s/Av4jINxyOCWCHiDxOvPqmJtH24iI+dpNZAeyKIwVJDac7ifejP+pUN7zFFGDj+Gyj6nNON0IDiMg+4EriI72OAc8B61X1GQdjehx4x+xNdYmG8XuBu1S12qm4ErFsJd5V9lDicRmwzemusiY37IpjCUnd8C4j/sE8SLwb3hlV/ZzD4SXL+8ZxgESSeCCpL3wH8Z5Djkl0/VwFRIgPFPdRVR0Qke/iQNfPJB8GtonIkKoeVtUQ8PuJ+zock3y8krvKiogjXWVN7lniWFpJ4vddnO+G900RedLBmOaY1zh+kHhVy0dE5HfzrHF8sb7wTifhXfO6fn5fRD7jYDyzPkke3cuQJF+PV8YkkvO3VPXXTseSjyxxLC0vu+HNs3OBRvD/TFR15JN8TcL51vVzVr5+QOfr8VqQiHyeeG3BbNdqD/GxtS4oU9XPJx7vAb4kIgeAE0lP1wC8n/iXnksqV9U9l/K6nGSJY2m7E7//gsSbLNEN7y8ci+hChdI4nq9J+L8Sb3PpB1DVkcSQDu9xNKr8/YDO1+N1Me9P3HuCiNQAn16kDBG5AjhCvBv0P6vqV2afRERm/85UeUGyxLEEVT25QNkEkPO+24tR1f+W1Di+iXij873k3/83OQlH4VwSdrT3kqo+u0BZFMj5/QLz5OUHdB4fr0x5G/Cg00Hks3z7YDHLkOgK+XLi51wx8Tf/WxwJamGnkx8k4p4G8uJ+mHyzAj6g89VtwEc4X7Vq5rHEURwmmDszGcQTxzYHYrmY2TiF+NAZkJ9xmhUq0a24RlW7RaTD4XDyliWO4nCIeN/+QHKhiDzsUDyLKZQ4zcp1M/BLp4PIdy6nAzAZcTvnG5qTOToY3gIKJU6zcln7RgrsiqMIqGrPIuX5NHpvwcS5mHS6dSb+zlp5UrdRk1nXEe+UYC7CEocx6Um5W2cOyk1q+oF/E5HZGQtdxK8qLihT1R1J24WAO0XkpqSyWAbLC5YlDmNMUUtMbLTQ5EYXnfBIVbuJt3ksJFPlBcnaOIwxxqTFEocxxpi0WFVVnlvmODvGGJM1ljgKQzoNssYYk1VWVWWMMSYtdsVhTOpS7taZ+Dvb5cY4whKHMSlaRrfObJcb4wirqjLGGJMWSxzGGGPSYonDGGNMWqyNI/+l2yBrjDFZJaq69FrGGGNMglVVGWOMSYslDmOMMWmxxGGMMSYtljiMMcakxRKHMcaYtPz/B9Ulu06l7EkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109b87e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = scatter_matrix(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## groupbyでデータの集約"
   ]
  },
  {
   "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>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>運動指数</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>13407.333333</td>\n",
       "      <td>1896.666626</td>\n",
       "      <td>7.105225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>4668.500000</td>\n",
       "      <td>2237.500000</td>\n",
       "      <td>2.084007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mid</th>\n",
       "      <td>8180.052632</td>\n",
       "      <td>1958.421021</td>\n",
       "      <td>4.205369</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",
       "運動指数                                     \n",
       "High  13407.333333  1896.666626  7.105225\n",
       "Low    4668.500000  2237.500000  2.084007\n",
       "Mid    8180.052632  1958.421021  4.205369"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(df.loc[:, \"運動指数\"]).mean()"
   ]
  },
  {
   "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>運動指数</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Mid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">摂取カロリー</th>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1896.666626</td>\n",
       "      <td>2237.500000</td>\n",
       "      <td>1958.421021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>83.864967</td>\n",
       "      <td>190.394333</td>\n",
       "      <td>160.321320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1800.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1870.000000</td>\n",
       "      <td>2212.500000</td>\n",
       "      <td>1850.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1940.000000</td>\n",
       "      <td>2300.000000</td>\n",
       "      <td>1930.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1945.000000</td>\n",
       "      <td>2300.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1950.000000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>2300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">歩数</th>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13407.333333</td>\n",
       "      <td>4668.500000</td>\n",
       "      <td>8180.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1711.234739</td>\n",
       "      <td>1297.682109</td>\n",
       "      <td>960.355517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12045.000000</td>\n",
       "      <td>2510.000000</td>\n",
       "      <td>7203.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12447.000000</td>\n",
       "      <td>3994.250000</td>\n",
       "      <td>7492.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12849.000000</td>\n",
       "      <td>4483.000000</td>\n",
       "      <td>8031.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14088.500000</td>\n",
       "      <td>5587.500000</td>\n",
       "      <td>8342.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15328.000000</td>\n",
       "      <td>6481.000000</td>\n",
       "      <td>10287.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">歩数/カロリー</th>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.105225</td>\n",
       "      <td>2.084007</td>\n",
       "      <td>4.205369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.241598</td>\n",
       "      <td>0.526806</td>\n",
       "      <td>0.620617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.176923</td>\n",
       "      <td>1.091304</td>\n",
       "      <td>3.232609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.400059</td>\n",
       "      <td>1.934089</td>\n",
       "      <td>3.861855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.623196</td>\n",
       "      <td>2.108835</td>\n",
       "      <td>4.139691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.569376</td>\n",
       "      <td>2.287461</td>\n",
       "      <td>4.405946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.515556</td>\n",
       "      <td>2.817826</td>\n",
       "      <td>5.715000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "運動指数                   High          Low           Mid\n",
       "摂取カロリー  count      3.000000     8.000000     19.000000\n",
       "        mean    1896.666626  2237.500000   1958.421021\n",
       "        std       83.864967   190.394333    160.321320\n",
       "        min     1800.000000  1950.000000   1800.000000\n",
       "        25%     1870.000000  2212.500000   1850.000000\n",
       "        50%     1940.000000  2300.000000   1930.000000\n",
       "        75%     1945.000000  2300.000000   1950.000000\n",
       "        max     1950.000000  2500.000000   2300.000000\n",
       "歩数      count      3.000000     8.000000     19.000000\n",
       "        mean   13407.333333  4668.500000   8180.052632\n",
       "        std     1711.234739  1297.682109    960.355517\n",
       "        min    12045.000000  2510.000000   7203.000000\n",
       "        25%    12447.000000  3994.250000   7492.500000\n",
       "        50%    12849.000000  4483.000000   8031.000000\n",
       "        75%    14088.500000  5587.500000   8342.000000\n",
       "        max    15328.000000  6481.000000  10287.000000\n",
       "歩数/カロリー count      3.000000     8.000000     19.000000\n",
       "        mean       7.105225     2.084007      4.205369\n",
       "        std        1.241598     0.526806      0.620617\n",
       "        min        6.176923     1.091304      3.232609\n",
       "        25%        6.400059     1.934089      3.861855\n",
       "        50%        6.623196     2.108835      4.139691\n",
       "        75%        7.569376     2.287461      4.405946\n",
       "        max        8.515556     2.817826      5.715000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(df.loc[:, \"運動指数\"]).describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandasのデータフレームをnumpyのarrayを変換"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>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>2017-04-01</th>\n",
       "      <td>5439</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-02</th>\n",
       "      <td>2510</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03</th>\n",
       "      <td>10238</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-04</th>\n",
       "      <td>8209</td>\n",
       "      <td>1850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-05</th>\n",
       "      <td>9434</td>\n",
       "      <td>1930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-06</th>\n",
       "      <td>7593</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-07</th>\n",
       "      <td>9320</td>\n",
       "      <td>1940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-08</th>\n",
       "      <td>4873</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-09</th>\n",
       "      <td>12045</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-10</th>\n",
       "      <td>7493</td>\n",
       "      <td>1850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-11</th>\n",
       "      <td>7289</td>\n",
       "      <td>1930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-12</th>\n",
       "      <td>6481</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-13</th>\n",
       "      <td>10287</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-14</th>\n",
       "      <td>8043</td>\n",
       "      <td>1940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-15</th>\n",
       "      <td>7435</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-16</th>\n",
       "      <td>7529</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-17</th>\n",
       "      <td>8031</td>\n",
       "      <td>1940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-18</th>\n",
       "      <td>8475</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-19</th>\n",
       "      <td>8132</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-20</th>\n",
       "      <td>15328</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-21</th>\n",
       "      <td>12849</td>\n",
       "      <td>1940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-22</th>\n",
       "      <td>4029</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-23</th>\n",
       "      <td>3890</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24</th>\n",
       "      <td>8093</td>\n",
       "      <td>1850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25</th>\n",
       "      <td>7823</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\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",
       "2017-04-01   5439  2500.0\n",
       "2017-04-02   2510  2300.0\n",
       "2017-04-03  10238  1950.0\n",
       "2017-04-04   8209  1850.0\n",
       "2017-04-05   9434  1930.0\n",
       "2017-04-06   7593  1800.0\n",
       "2017-04-07   9320  1940.0\n",
       "2017-04-08   4873  2300.0\n",
       "2017-04-09  12045  1950.0\n",
       "2017-04-10   7493  1850.0\n",
       "2017-04-11   7289  1930.0\n",
       "2017-04-12   6481  2300.0\n",
       "2017-04-13  10287  1800.0\n",
       "2017-04-14   8043  1940.0\n",
       "2017-04-15   7435  2300.0\n",
       "2017-04-16   7529  2300.0\n",
       "2017-04-17   8031  1940.0\n",
       "2017-04-18   8475  2300.0\n",
       "2017-04-19   8132  1950.0\n",
       "2017-04-20  15328  1800.0\n",
       "2017-04-21  12849  1940.0\n",
       "2017-04-22   4029  2300.0\n",
       "2017-04-23   3890  1950.0\n",
       "2017-04-24   8093  1850.0\n",
       "2017-04-25   7823  1950.0\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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, [\"歩数\", \"摂取カロリー\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  5439.,   2500.],\n",
       "       [  2510.,   2300.],\n",
       "       [ 10238.,   1950.],\n",
       "       [  8209.,   1850.],\n",
       "       [  9434.,   1930.],\n",
       "       [  7593.,   1800.],\n",
       "       [  9320.,   1940.],\n",
       "       [  4873.,   2300.],\n",
       "       [ 12045.,   1950.],\n",
       "       [  7493.,   1850.],\n",
       "       [  7289.,   1930.],\n",
       "       [  6481.,   2300.],\n",
       "       [ 10287.,   1800.],\n",
       "       [  8043.,   1940.],\n",
       "       [  7435.,   2300.],\n",
       "       [  7529.,   2300.],\n",
       "       [  8031.,   1940.],\n",
       "       [  8475.,   2300.],\n",
       "       [  8132.,   1950.],\n",
       "       [ 15328.,   1800.],\n",
       "       [ 12849.,   1940.],\n",
       "       [  4029.,   2300.],\n",
       "       [  3890.,   1950.],\n",
       "       [  8093.,   1850.],\n",
       "       [  7823.,   1950.],\n",
       "       [  7492.,   1850.],\n",
       "       [  7203.,   1930.],\n",
       "       [  7302.,   1850.],\n",
       "       [  6033.,   2300.],\n",
       "       [  4093.,   1950.]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, [\"歩数\", \"摂取カロリー\"]].as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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