5-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 学習済みモデルで予測を行う\n",
    "\n",
    "この章は、おまけの章です。\n",
    "\n",
    "## この章の内容\n",
    "\n",
    "- 前章(4章)で行った学習済みモデル(ランダムフォレスト)を呼び出す\n",
    "- 2017年の気象情報を元に、予測を行う\n",
    "- 予測が正しいかどうかを、グラフ化して確認する"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2017年1月以降の気象データを取得"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_weather2017 = pd.read_csv(\"data/気象庁千葉2017.csv\", encoding='shift_jis', skiprows=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2017年1月以降の気象データを読み込みます。\n",
    "\n",
    "以下でデータフレームの加工を行い、学習時と同じ形式にします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(177, 30)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weather2017.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年月日</th>\n",
       "      <th>平均気温(℃)</th>\n",
       "      <th>平均気温(℃).1</th>\n",
       "      <th>平均気温(℃).2</th>\n",
       "      <th>最高気温(℃)</th>\n",
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       "      <th>最高気温(℃).2</th>\n",
       "      <th>最低気温(℃)</th>\n",
       "      <th>最低気温(℃).1</th>\n",
       "      <th>最低気温(℃).2</th>\n",
       "      <th>...</th>\n",
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       "      <th>降水量の合計(mm).3</th>\n",
       "      <th>日照時間(時間)</th>\n",
       "      <th>日照時間(時間).1</th>\n",
       "      <th>日照時間(時間).2</th>\n",
       "      <th>日照時間(時間).3</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>品質情報</td>\n",
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       "      <th>2</th>\n",
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       "      <td>8.3</td>\n",
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       "      <td>13.0</td>\n",
       "      <td>8</td>\n",
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       "      <td>3.9</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
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       "      <td>2.4</td>\n",
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       "      <td>8.1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>13.2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>8</td>\n",
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       "      <td>8</td>\n",
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       "      <td>5.8</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017/1/3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        年月日  平均気温(℃) 平均気温(℃).1 平均気温(℃).2  最高気温(℃) 最高気温(℃).1 最高気温(℃).2  \\\n",
       "0       NaN      NaN       NaN       NaN      NaN       NaN       NaN   \n",
       "1       NaN      NaN      品質情報      均質番号      NaN      品質情報      均質番号   \n",
       "2  2017/1/1      8.3         8         1     13.0         8         1   \n",
       "3  2017/1/2      8.1         8         1     13.2         8         1   \n",
       "4  2017/1/3      8.7         8         1     13.9         8         1   \n",
       "\n",
       "   最低気温(℃) 最低気温(℃).1 最低気温(℃).2     ...       降水量の合計(mm).1 降水量の合計(mm).2  \\\n",
       "0      NaN       NaN       NaN     ...                NaN          NaN   \n",
       "1      NaN      品質情報      均質番号     ...             現象なし情報         品質情報   \n",
       "2      3.9         8         1     ...                  1            8   \n",
       "3      4.4         8         1     ...                  1            8   \n",
       "4      3.9         8         1     ...                  1            8   \n",
       "\n",
       "  降水量の合計(mm).3  日照時間(時間) 日照時間(時間).1 日照時間(時間).2  日照時間(時間).3 平均風速(m/s)  \\\n",
       "0          NaN       NaN        NaN        NaN         NaN       NaN   \n",
       "1         均質番号       NaN     現象なし情報       品質情報        均質番号       NaN   \n",
       "2            1       9.5          0          8           1       2.4   \n",
       "3            1       5.8          0          8           1       1.6   \n",
       "4            1       8.3          0          8           1       2.5   \n",
       "\n",
       "  平均風速(m/s).1  平均風速(m/s).2  \n",
       "0         NaN          NaN  \n",
       "1        品質情報         均質番号  \n",
       "2           8            1  \n",
       "3           8            1  \n",
       "4           8            1  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weather2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_weather2017 = df_weather2017.iloc[:, [i for i, t in enumerate(df_weather2017.iloc[1, :].isnull()) if t]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "      <td>1022.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>2.4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017/1/2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>13.2</td>\n",
       "      <td>4.4</td>\n",
       "      <td>56.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1016.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017/1/3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.9</td>\n",
       "      <td>3.9</td>\n",
       "      <td>48.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1013.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>2.5</td>\n",
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      "text/plain": [
       "        年月日  平均気温(℃)  最高気温(℃)  最低気温(℃)  平均湿度(％)  最小相対湿度(％)  平均現地気圧(hPa)  \\\n",
       "0       NaN      NaN      NaN      NaN      NaN        NaN          NaN   \n",
       "1       NaN      NaN      NaN      NaN      NaN        NaN          NaN   \n",
       "2  2017/1/1      8.3     13.0      3.9     51.0       36.0       1022.1   \n",
       "3  2017/1/2      8.1     13.2      4.4     56.0       41.0       1016.5   \n",
       "4  2017/1/3      8.7     13.9      3.9     48.0       27.0       1013.8   \n",
       "\n",
       "   降水量の合計(mm)  日照時間(時間)  平均風速(m/s)  \n",
       "0         NaN       NaN        NaN  \n",
       "1         NaN       NaN        NaN  \n",
       "2         0.0       9.5        2.4  \n",
       "3         0.0       5.8        1.6  \n",
       "4         0.0       8.3        2.5  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weather2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_weather2017 = df_weather2017.drop([0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_weather2017[\"年月日\"] = pd.to_datetime(df_weather2017[\"年月日\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_weather2017 = df_weather2017.set_index(\"年月日\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2017-01-01</th>\n",
       "      <td>8.3</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>8.1</td>\n",
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       "      <td>4.4</td>\n",
       "      <td>56.0</td>\n",
       "      <td>41.0</td>\n",
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       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>8.7</td>\n",
       "      <td>13.9</td>\n",
       "      <td>3.9</td>\n",
       "      <td>48.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1013.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>9.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>5.4</td>\n",
       "      <td>44.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1013.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>7.2</td>\n",
       "      <td>10.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>31.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1017.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            平均気温(℃)  最高気温(℃)  最低気温(℃)  平均湿度(％)  最小相対湿度(％)  平均現地気圧(hPa)  \\\n",
       "年月日                                                                      \n",
       "2017-01-01      8.3     13.0      3.9     51.0       36.0       1022.1   \n",
       "2017-01-02      8.1     13.2      4.4     56.0       41.0       1016.5   \n",
       "2017-01-03      8.7     13.9      3.9     48.0       27.0       1013.8   \n",
       "2017-01-04      9.0     12.8      5.4     44.0       29.0       1013.4   \n",
       "2017-01-05      7.2     10.7      3.6     31.0       22.0       1017.5   \n",
       "\n",
       "            降水量の合計(mm)  日照時間(時間)  平均風速(m/s)  \n",
       "年月日                                          \n",
       "2017-01-01         0.0       9.5        2.4  \n",
       "2017-01-02         0.0       5.8        1.6  \n",
       "2017-01-03         0.0       8.3        2.5  \n",
       "2017-01-04         0.0       8.6        3.6  \n",
       "2017-01-05         0.0       9.4        6.3  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weather2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>平均気温(℃)</th>\n",
       "      <th>最高気温(℃)</th>\n",
       "      <th>最低気温(℃)</th>\n",
       "      <th>平均湿度(％)</th>\n",
       "      <th>最小相対湿度(％)</th>\n",
       "      <th>平均現地気圧(hPa)</th>\n",
       "      <th>降水量の合計(mm)</th>\n",
       "      <th>日照時間(時間)</th>\n",
       "      <th>平均風速(m/s)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年月日</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-20</th>\n",
       "      <td>22.7</td>\n",
       "      <td>27.9</td>\n",
       "      <td>16.7</td>\n",
       "      <td>71.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1012.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-21</th>\n",
       "      <td>23.3</td>\n",
       "      <td>25.5</td>\n",
       "      <td>21.3</td>\n",
       "      <td>85.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1006.1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-22</th>\n",
       "      <td>24.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>22.2</td>\n",
       "      <td>58.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1005.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-23</th>\n",
       "      <td>25.0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>20.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1007.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-24</th>\n",
       "      <td>24.7</td>\n",
       "      <td>29.3</td>\n",
       "      <td>21.6</td>\n",
       "      <td>73.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            平均気温(℃)  最高気温(℃)  最低気温(℃)  平均湿度(％)  最小相対湿度(％)  平均現地気圧(hPa)  \\\n",
       "年月日                                                                      \n",
       "2017-06-20     22.7     27.9     16.7     71.0       55.0       1012.5   \n",
       "2017-06-21     23.3     25.5     21.3     85.0       75.0       1006.1   \n",
       "2017-06-22     24.8     28.8     22.2     58.0       28.0       1005.9   \n",
       "2017-06-23     25.0     30.4     20.5     67.0       38.0       1007.3   \n",
       "2017-06-24     24.7     29.3     21.6     73.0       49.0       1006.0   \n",
       "\n",
       "            降水量の合計(mm)  日照時間(時間)  平均風速(m/s)  \n",
       "年月日                                          \n",
       "2017-06-20         0.0      11.7        3.0  \n",
       "2017-06-21        16.0       0.0        8.3  \n",
       "2017-06-22         0.0       4.5        3.7  \n",
       "2017-06-23         0.0      11.8        3.1  \n",
       "2017-06-24         0.0       7.0        3.5  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weather2017.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(175, 9)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weather2017.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 学習済みモデルの呼び出し"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.externals import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = joblib.load(\"clf_rf.db\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.ensemble.forest.RandomForestClassifier"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 予測"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = clf.predict(df_weather2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 予測結果の可視化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import rcParams\n",
    "rcParams['font.sans-serif'] = \"Source Han Code JP\"\n",
    "rcParams['font.weight'] = \"regular\"\n",
    "rcParams['axes.titlesize'] = 15\n",
    "rcParams['ytick.labelsize'] = 12\n",
    "rcParams['xtick.labelsize'] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(pred, columns=[\"予測\"], index=df_weather2017.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>予測</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年月日</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            予測\n",
       "年月日           \n",
       "2017-01-01   0\n",
       "2017-01-02   0\n",
       "2017-01-03   0\n",
       "2017-01-04   0\n",
       "2017-01-05   1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10dc68518>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZF6L2aVXPPyCNjMkYOZ97lxVLUsP5uyY3JHveOHnql1htn3lLOrNcZUTUPlGq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\nBoPBsArx/wPsNaqBfqK4lAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dc70588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 実際にどうだったか？\n",
    "\n",
    "学習時に使用しなかった2017年のデータを読み込み可視化します"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_influ_2017 = pd.read_csv(\"data/201712influenza.csv\", encoding=\"shift_jis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_2017 = pd.DataFrame(df_influ_2017.iloc[1, 1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_2017.columns = ['報告数']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 54)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_influ_2017.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "date_index = pd.date_range(start=\"2017-1-2\", periods=53, freq=\"W-MON\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_2017.index = date_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>報告数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>7.535714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>16.14285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-16</th>\n",
       "      <td>31.35714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23</th>\n",
       "      <td>43.32142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-30</th>\n",
       "      <td>37.57142857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    報告数\n",
       "2017-01-02  7.535714286\n",
       "2017-01-09  16.14285714\n",
       "2017-01-16  31.35714286\n",
       "2017-01-23  43.32142857\n",
       "2017-01-30  37.57142857"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>報告数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-04</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-11</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-18</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-25</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            報告数\n",
       "2017-12-04  NaN\n",
       "2017-12-11  NaN\n",
       "2017-12-18  NaN\n",
       "2017-12-25  NaN\n",
       "2018-01-01  NaN"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2017.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_2017 = df_2017.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "報告数    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2017.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_2017['報告数'] = df_2017['報告数'].astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "報告数    float32\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2017.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10dde3898>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3jee+2MX64komDerdZhljDEXlh1lWUMbyneUs21lGScXhI/OLDx5m3jVTeipk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\nSqkQpMldKaVCkCZ3pZQKQZrclVIqBP1/R50s4QToR9AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dd7beb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_2017.plot(title=\"2017年のインフルエンザ報告件数\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10df43438>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hmLMi7bcA+MXJpxZGK85fGCpmeGqfsYgFzFLt07R5eUjtEzJ8Ic6/H7VPDecv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10df034a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(title=\"予測結果\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(175, 1)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>予測</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年月日</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            予測\n",
       "年月日           \n",
       "2017-01-01   0\n",
       "2017-01-02   0\n",
       "2017-01-03   0\n",
       "2017-01-04   0\n",
       "2017-01-05   1\n",
       "2017-01-06   1\n",
       "2017-01-07   1\n",
       "2017-01-08   1\n",
       "2017-01-09   1\n",
       "2017-01-10   0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e04f0f0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2RLzd4MgEQNxG8pxJ2aJ83rV8CQ8+28F/vLk/qm0XF+TxrU+cTXVp6Jc+Y0y6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\nByGwRNA2i5WoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e090978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(pd.Grouper(freq=\"w\"))[\"予測\"].mean().plot(title=\"予測結果を1週ごとの平均\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e1f0128>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PmS3m0/luEYZlJmpHZn6gvUIqpfxiX1kNP3t5K+/sPNKj/YjAsL6J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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dd801d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_2017.plot(title=\"報告数データ\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_weather2018 = pd.read_csv(\"data/気象庁千葉2018.csv\", encoding='shift_jis', skiprows=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_weather2018 = df_weather2018.iloc[:, [i for i, t in enumerate(df_weather2018.iloc[1, :].isnull()) if t]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年月日</th>\n",
       "      <th>平均気温(℃)</th>\n",
       "      <th>最高気温(℃)</th>\n",
       "      <th>最低気温(℃)</th>\n",
       "      <th>平均湿度(％)</th>\n",
       "      <th>最小相対湿度(％)</th>\n",
       "      <th>平均現地気圧(hPa)</th>\n",
       "      <th>降水量の合計(mm)</th>\n",
       "      <th>日照時間(時間)</th>\n",
       "      <th>平均風速(m/s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017/1/1</td>\n",
       "      <td>8.3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>51.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1022.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017/1/2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>13.2</td>\n",
       "      <td>4.4</td>\n",
       "      <td>56.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1016.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017/1/3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.9</td>\n",
       "      <td>3.9</td>\n",
       "      <td>48.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1013.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        年月日  平均気温(℃)  最高気温(℃)  最低気温(℃)  平均湿度(％)  最小相対湿度(％)  平均現地気圧(hPa)  \\\n",
       "0       NaN      NaN      NaN      NaN      NaN        NaN          NaN   \n",
       "1       NaN      NaN      NaN      NaN      NaN        NaN          NaN   \n",
       "2  2017/1/1      8.3     13.0      3.9     51.0       36.0       1022.1   \n",
       "3  2017/1/2      8.1     13.2      4.4     56.0       41.0       1016.5   \n",
       "4  2017/1/3      8.7     13.9      3.9     48.0       27.0       1013.8   \n",
       "\n",
       "   降水量の合計(mm)  日照時間(時間)  平均風速(m/s)  \n",
       "0         NaN       NaN        NaN  \n",
       "1         NaN       NaN        NaN  \n",
       "2         0.0       9.5        2.4  \n",
       "3         0.0       5.8        1.6  \n",
       "4         0.0       8.3        2.5  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weather2018.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(638, 9)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weather2018 = df_weather2018.drop([0, 1])\n",
    "df_weather2018[\"年月日\"] = pd.to_datetime(df_weather2018[\"年月日\"])\n",
    "df_weather2018 = df_weather2018.set_index(\"年月日\")\n",
    "df_weather2018.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred2018 = clf.predict(df_weather2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e3de630>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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If6FURgQSMefiGffEf3I6u+x+k97NoXqi2Oh/Avn8VfWVwCubtLlqmc9uwq2n2nJmc574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\n3zAMI4KY+BuGYUQQE3/DMIwI8n8BlgnKK4xmGewAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e36f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pred2018 = pd.DataFrame(pred2018, columns=[\"予測\"], index=df_weather2018.index)\n",
    "df_pred2018_mean = df_pred2018.groupby(pd.Grouper(freq=\"w\"))[\"予測\"].mean()\n",
    "df_pred2018_mean.plot(title=\"予測結果を1週ごとの平均\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_influ_2018 = pd.read_csv(\"data/201811influenza.csv\", encoding=\"shift_jis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>報告数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>9.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-15</th>\n",
       "      <td>23.892857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22</th>\n",
       "      <td>51.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-29</th>\n",
       "      <td>57.821430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-05</th>\n",
       "      <td>64.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  報告数\n",
       "2018-01-08   9.230769\n",
       "2018-01-15  23.892857\n",
       "2018-01-22  51.142857\n",
       "2018-01-29  57.821430\n",
       "2018-02-05  64.500000"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018 = pd.DataFrame(df_influ_2018.iloc[1, 1:])\n",
    "df_2018.columns = ['報告数']\n",
    "date_index = pd.date_range(start=\"2018-1-4\", periods=53, freq=\"W-MON\")\n",
    "df_2018.index = date_index\n",
    "df_2018 = df_2018.dropna()\n",
    "df_2018['報告数'] = df_2018['報告数'].astype(np.float32)\n",
    "df_2018.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1074c0208>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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79Frmggsu4O233+bpp5/myiuv5MILL+Tss8/m8ssvZ4cddgBgwYIFjBgxgnPOOQeAdevW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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dfed0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_2017_2018 = pd.concat([df_2017, df_2018], axis=1)\n",
    "df_2017_2018.plot(title=\"2017年のインフルエンザ報告件数\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e1e39e8>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Bz4j/oZkcpbIuOzmWXkb860X7zOVLFtZmdI3JqTm2r8DlA8vH+bfO52/XRBTo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\n3zAMI4KY+BuGYUQQE3/DMIwI8n8BlgnKK4xmGewAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107511dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pred2018_mean.plot(title=\"予測結果を1週ごとの平均\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}