pandas_tutorial.ipynb

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   "source": [
    "# データ分析で頻出のPandas基本操作\n",
    "\n",
    "機械学習や深層学習が人気の昨今ですが、それらのモデルの精度に最もクリティカルに影響するのはインプットする**データの質**です。データの質は、データを適切に把握し、不要なデータを取り除いたり、必要なデータを精査する**前処理**を行うことで担保することが出来ます。 \n",
    "\n",
    "本頁では、データ処理の基本ツールとしてPandasの使い方を紹介します。Pandasには便利な機能がたくさんありますが、特に分析業務で**頻出の**Pandas関数・メソッドを重点的に取り上げました。\n",
    "\n",
    "また、単に機能を説明するだけでは実際の処理動作がわかりにくいため、ここではSIGNATE(旧DeepAnalytics)の[お弁当の需要予想を行うコンペのデータ](https://signate.jp/competitions/24)を拝借し、このデータに対して一貫してPandasの処理を適応していくことで、一連のPandas操作（前処理のプロセス）を体験してもらいます。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![pandas](./figs/pandas.jpg)"
   ]
  },
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   "source": [
    "## Pandasについて\n",
    "- 公式ドキュメント：http://pandas.pydata.org/pandas-docs/stable/\n",
    "- pandasは、Pythonにおいて、データ解析を支援する機能を提供するライブラリである。特に、数表および時系列データを操作するためのデータ構造と演算を提供する。\n",
    "- pandasの特長\n",
    "    - データ操作のための高速で効率的なデータフレーム (DataFrame) オブジェクト\n",
    "    - メモリ内のデータ構造とその他のフォーマットのデータ間で相互に読み書きするためのツール群。フォーマット例: CSV、テキストファイル、Excel、SQLデータベース、HDF5フォーマットなど\n",
    "    - かしこいデータのアライメントおよび統合された欠損値処理\n",
    "    - データセットの柔軟な変形およびピボット\n",
    "    - ラベルに基づいたスライス、fancyインデクシング、巨大なデータセットのサブセット取得\n",
    "    - データセットに対するsplit-apply-combine操作を可能にするエンジンが提供するpowerful groupを使ったデータの集計および変換\n",
    "    - 高性能なデータセットのマージと結合\n",
    "    - 時系列データ: 日付範囲生成、周波数変換、移動窓を用いた統計値や線形回帰、シフトと遅延、など\n",
    "    - パフォーマンスのための高度な最適化。重要なコードはCythonまたはC言語で実装されている。"
   ]
  },
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   "metadata": {},
   "source": [
    "## 本頁で紹介するPandasメソッド一覧\n",
    "\n",
    "**① Pythonのバージョン確認、モジュールのimport、データの読み込み**\n",
    "- pd.read_csv()\n",
    "- df.head()\n",
    "- df.tail()\n",
    "\n",
    "**② 簡単にデータの状態を確認する（行数列数カウント・データの選択的表示・重複の有無など）**\n",
    "- df.shape()\n",
    "- df.index\n",
    "- df.columns\n",
    "- df.dtypes\n",
    "- df.loc[]\n",
    "- df.iloc[]\n",
    "- df.query()\n",
    "- df.unique()\n",
    "- df.drop_duplicates()\n",
    "- df.describe()\n",
    "\n",
    "**③ データの整形（データ型変更、列名変更、並び替えなど）**\n",
    "- df.set_index()\n",
    "- df.rename()\n",
    "- df.sort_values()\n",
    "- df.to_datetime()\n",
    "- df.sort_index()\n",
    "- df.resample()\n",
    "- df.apply()\n",
    "- pd.cut()\n",
    "\n",
    "**④ データの欠損状態の確認** \n",
    "- df.isnull()\n",
    "- df.any()\n",
    "\n",
    "**⑤ 値（欠損）の置き換えや削除**\n",
    "- df.fillna()\n",
    "- df.dropna()\n",
    "- df.replace()\n",
    "- df.mask()\n",
    "- df.drop()\n",
    "\n",
    "**⑥ 集計**\n",
    "- df.value_counts()\n",
    "- df.groupby()\n",
    "- df.diff()\n",
    "- df.rolling()\n",
    "- df.pct_change()\n",
    "\n",
    "**⑦ 可視化**\n",
    "- df.plot()\n",
    "- df.corr()\n",
    "- df.pivot()\n",
    "\n",
    "**⑧ 変数の前処理**\n",
    "- pd.get_dummies()\n",
    "\n",
    "**⑨ 最後に、出来たデータをもう一度眺める**\n",
    "- df.to_csv()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ① Pythonのバージョン確認、モジュールのimport、データの読み込み\n",
    "\n",
    "まずはpandasと今回使うデータのimportを行い表示してみます。"
   ]
  },
  {
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    {
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     "text": [
      "Python 3.5.2 :: Anaconda custom (x86_64)\r\n"
     ]
    }
   ],
   "source": [
    "# pythonのバージョン確認\n",
    "!python -V"
   ]
  },
  {
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   "source": [
    "# 一般的にpandasはpdと名前を付けてimportされる\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "# read_csvメソッドでお弁当需要予想に関するデータをcsvファイルとして読み込む\n",
    "df = pd.read_csv('./data/lunch_box.csv', sep=',')"
   ]
  },
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>y</th>\n",
       "      <th>week</th>\n",
       "      <th>soldout</th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "      <th>remarks</th>\n",
       "      <th>event</th>\n",
       "      <th>payday</th>\n",
       "      <th>weather</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-11-18</td>\n",
       "      <td>90</td>\n",
       "      <td>月</td>\n",
       "      <td>0</td>\n",
       "      <td>厚切りイカフライ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>19.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-11-19</td>\n",
       "      <td>101</td>\n",
       "      <td>火</td>\n",
       "      <td>1</td>\n",
       "      <td>手作りヒレカツ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-11-20</td>\n",
       "      <td>118</td>\n",
       "      <td>水</td>\n",
       "      <td>0</td>\n",
       "      <td>白身魚唐揚げ野菜あん</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     datetime    y week  soldout        name  kcal remarks event  payday  \\\n",
       "0  2013-11-18   90    月        0    厚切りイカフライ   NaN     NaN   NaN     NaN   \n",
       "1  2013-11-19  101    火        1     手作りヒレカツ   NaN     NaN   NaN     NaN   \n",
       "2  2013-11-20  118    水        0  白身魚唐揚げ野菜あん   NaN     NaN   NaN     NaN   \n",
       "\n",
       "  weather precipitation  temperature  \n",
       "0      快晴            --         19.8  \n",
       "1      快晴            --         17.0  \n",
       "2      快晴            --         15.5  "
      ]
     },
     "execution_count": 4,
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   ],
   "source": [
    "# データの確認をする（最初の3行を表示）\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 各列の説明\n",
    "データ詳細ページ: https://signate.jp/competitions/24/data\n",
    "\n",
    "|No.|列名|内容|説明|\n",
    "|:---|:---|:---|:---|\n",
    "|1|datetime|日付|インデックスとして使用|\n",
    "|2|y|販売数|目的変数|\n",
    "|3|week|曜日|月～金|\n",
    "|4|soldout|完売フラグ|0:完売せず、1:完売|\n",
    "|5|name|メインメニュー|マルチバイト文字列（日本語）|\n",
    "|6|kcal|カロリー（kcal）|整数。存在しないレコードあり|\n",
    "|7|remarks|特記事項|自由記述|\n",
    "|8|event|13時開始お弁当持ち込み可イベント|社内イベントの有無。存在しないレコードあり|\n",
    "|9|payday|給料日フラグ|1:給料日|\n",
    "|10|weather|天気|マルチバイト文字列（日本語）|\n",
    "|11|precipitation|降水量|実数。降水量 0 は \"--\" と表記|\n",
    "|12|temperature|気温|実数|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![dataframe](./figs/dataframe.png)"
   ]
  },
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>y</th>\n",
       "      <th>week</th>\n",
       "      <th>soldout</th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "      <th>remarks</th>\n",
       "      <th>event</th>\n",
       "      <th>payday</th>\n",
       "      <th>weather</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-11-18</td>\n",
       "      <td>90</td>\n",
       "      <td>月</td>\n",
       "      <td>0</td>\n",
       "      <td>厚切りイカフライ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>19.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-11-19</td>\n",
       "      <td>101</td>\n",
       "      <td>火</td>\n",
       "      <td>1</td>\n",
       "      <td>手作りヒレカツ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-11-20</td>\n",
       "      <td>118</td>\n",
       "      <td>水</td>\n",
       "      <td>0</td>\n",
       "      <td>白身魚唐揚げ野菜あん</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-11-21</td>\n",
       "      <td>120</td>\n",
       "      <td>木</td>\n",
       "      <td>1</td>\n",
       "      <td>若鶏ピリ辛焼</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-11-22</td>\n",
       "      <td>130</td>\n",
       "      <td>金</td>\n",
       "      <td>1</td>\n",
       "      <td>ビッグメンチカツ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     datetime    y week  soldout        name  kcal remarks event  payday  \\\n",
       "0  2013-11-18   90    月        0    厚切りイカフライ   NaN     NaN   NaN     NaN   \n",
       "1  2013-11-19  101    火        1     手作りヒレカツ   NaN     NaN   NaN     NaN   \n",
       "2  2013-11-20  118    水        0  白身魚唐揚げ野菜あん   NaN     NaN   NaN     NaN   \n",
       "3  2013-11-21  120    木        1      若鶏ピリ辛焼   NaN     NaN   NaN     NaN   \n",
       "4  2013-11-22  130    金        1    ビッグメンチカツ   NaN     NaN   NaN     NaN   \n",
       "\n",
       "  weather precipitation  temperature  \n",
       "0      快晴            --         19.8  \n",
       "1      快晴            --         17.0  \n",
       "2      快晴            --         15.5  \n",
       "3      快晴            --         15.2  \n",
       "4      快晴            --         16.1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "# 先頭の5行を表示\n",
    "df.head()"
   ]
  },
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>y</th>\n",
       "      <th>week</th>\n",
       "      <th>soldout</th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "      <th>remarks</th>\n",
       "      <th>event</th>\n",
       "      <th>payday</th>\n",
       "      <th>weather</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>2014-9-24</td>\n",
       "      <td>59</td>\n",
       "      <td>水</td>\n",
       "      <td>1</td>\n",
       "      <td>白身魚のマスタード焼き</td>\n",
       "      <td>408.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>曇</td>\n",
       "      <td>0</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>2014-9-25</td>\n",
       "      <td>50</td>\n",
       "      <td>木</td>\n",
       "      <td>0</td>\n",
       "      <td>牛カルビ焼き肉</td>\n",
       "      <td>394.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>曇</td>\n",
       "      <td>0</td>\n",
       "      <td>25.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>2014-9-26</td>\n",
       "      <td>45</td>\n",
       "      <td>金</td>\n",
       "      <td>0</td>\n",
       "      <td>ランチビュッフェ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>スペシャルメニュー（800円）</td>\n",
       "      <td>キャリアアップ支援セミナー</td>\n",
       "      <td>NaN</td>\n",
       "      <td>晴れ</td>\n",
       "      <td>--</td>\n",
       "      <td>27.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>2014-9-29</td>\n",
       "      <td>56</td>\n",
       "      <td>月</td>\n",
       "      <td>1</td>\n",
       "      <td>豚肉と玉子の炒め</td>\n",
       "      <td>404.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>26.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>2014-9-30</td>\n",
       "      <td>40</td>\n",
       "      <td>火</td>\n",
       "      <td>0</td>\n",
       "      <td>鶏肉とカシューナッツ炒め</td>\n",
       "      <td>398.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>28.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      datetime   y week  soldout          name   kcal          remarks  \\\n",
       "202  2014-9-24  59    水        1   白身魚のマスタード焼き  408.0              NaN   \n",
       "203  2014-9-25  50    木        0       牛カルビ焼き肉  394.0              NaN   \n",
       "204  2014-9-26  45    金        0      ランチビュッフェ    NaN  スペシャルメニュー（800円）   \n",
       "205  2014-9-29  56    月        1      豚肉と玉子の炒め  404.0              NaN   \n",
       "206  2014-9-30  40    火        0  鶏肉とカシューナッツ炒め  398.0              NaN   \n",
       "\n",
       "             event  payday weather precipitation  temperature  \n",
       "202            NaN     NaN       曇             0         24.8  \n",
       "203            NaN     NaN       曇             0         25.4  \n",
       "204  キャリアアップ支援セミナー     NaN      晴れ            --         27.1  \n",
       "205            NaN     NaN      快晴            --         26.6  \n",
       "206            NaN     NaN      快晴            --         28.1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 最後尾の5行を表示\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ② 簡単にデータの状態を確認する（行数列数カウント・データの選択的表示・重複の有無など）\n",
    "読み込んだデータがどんなものなのか、簡単に概要を掴むための操作を行います。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:16.071666Z",
     "start_time": "2018-04-15T20:56:16.060921Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataframeの行数・列数の確認==>\n",
      " (207, 12)\n",
      "indexの確認==>\n",
      " RangeIndex(start=0, stop=207, step=1)\n",
      "columnの確認==>\n",
      " Index(['datetime', 'y', 'week', 'soldout', 'name', 'kcal', 'remarks', 'event',\n",
      "       'payday', 'weather', 'precipitation', 'temperature'],\n",
      "      dtype='object')\n",
      "dataframeの各列のデータ型を確認==>\n",
      " datetime          object\n",
      "y                  int64\n",
      "week              object\n",
      "soldout            int64\n",
      "name              object\n",
      "kcal             float64\n",
      "remarks           object\n",
      "event             object\n",
      "payday           float64\n",
      "weather           object\n",
      "precipitation     object\n",
      "temperature      float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print('dataframeの行数・列数の確認==>\\n', df.shape)\n",
    "print('indexの確認==>\\n', df.index)\n",
    "print('columnの確認==>\\n', df.columns)\n",
    "print('dataframeの各列のデータ型を確認==>\\n', df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:58:00.010756Z",
     "start_time": "2018-04-15T20:57:59.993280Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-09</th>\n",
       "      <td>鶏チリソース</td>\n",
       "      <td>435.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>手作りロースカツ</td>\n",
       "      <td>440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-14</th>\n",
       "      <td>鶏の照り焼きマスタード</td>\n",
       "      <td>376.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-15</th>\n",
       "      <td>さんま辛味焼</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-16</th>\n",
       "      <td>カレイ唐揚げ野菜あんかけ</td>\n",
       "      <td>415.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name   kcal\n",
       "datetime                       \n",
       "2014-01-09        鶏チリソース  435.0\n",
       "2014-01-10      手作りロースカツ  440.0\n",
       "2014-01-14   鶏の照り焼きマスタード  376.0\n",
       "2014-01-15        さんま辛味焼  450.0\n",
       "2014-01-16  カレイ唐揚げ野菜あんかけ  415.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 任意の列だけ取り出したい場合\n",
    "df[['name', 'kcal']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:16.222177Z",
     "start_time": "2018-04-15T20:56:16.137478Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>y</th>\n",
       "      <th>week</th>\n",
       "      <th>soldout</th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "      <th>remarks</th>\n",
       "      <th>event</th>\n",
       "      <th>payday</th>\n",
       "      <th>weather</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2014-4-22</td>\n",
       "      <td>78</td>\n",
       "      <td>火</td>\n",
       "      <td>1</td>\n",
       "      <td>マーボ豆腐</td>\n",
       "      <td>382.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>曇</td>\n",
       "      <td>--</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2014-4-23</td>\n",
       "      <td>63</td>\n",
       "      <td>水</td>\n",
       "      <td>0</td>\n",
       "      <td>手作りひれかつ</td>\n",
       "      <td>340.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2014-4-24</td>\n",
       "      <td>56</td>\n",
       "      <td>木</td>\n",
       "      <td>1</td>\n",
       "      <td>鶏の照り焼きマヨ</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>晴れ</td>\n",
       "      <td>--</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2014-4-25</td>\n",
       "      <td>80</td>\n",
       "      <td>金</td>\n",
       "      <td>0</td>\n",
       "      <td>中華丼</td>\n",
       "      <td>NaN</td>\n",
       "      <td>お楽しみメニュー</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>晴れ</td>\n",
       "      <td>--</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>2014-4-28</td>\n",
       "      <td>54</td>\n",
       "      <td>月</td>\n",
       "      <td>0</td>\n",
       "      <td>タンドリーチキン</td>\n",
       "      <td>342.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>晴れ</td>\n",
       "      <td>--</td>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>2014-4-30</td>\n",
       "      <td>97</td>\n",
       "      <td>水</td>\n",
       "      <td>1</td>\n",
       "      <td>豚味噌メンチカツ</td>\n",
       "      <td>368.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>雨</td>\n",
       "      <td>0.5</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      datetime   y week  soldout      name   kcal   remarks event  payday  \\\n",
       "100  2014-4-22  78    火        1     マーボ豆腐  382.0       NaN   NaN     NaN   \n",
       "101  2014-4-23  63    水        0   手作りひれかつ  340.0       NaN   NaN     NaN   \n",
       "102  2014-4-24  56    木        1  鶏の照り焼きマヨ  360.0       NaN   NaN     NaN   \n",
       "103  2014-4-25  80    金        0       中華丼    NaN  お楽しみメニュー   NaN     NaN   \n",
       "104  2014-4-28  54    月        0  タンドリーチキン  342.0       NaN   NaN     NaN   \n",
       "105  2014-4-30  97    水        1  豚味噌メンチカツ  368.0       NaN   NaN     NaN   \n",
       "\n",
       "    weather precipitation  temperature  \n",
       "100       曇            --         18.8  \n",
       "101      快晴            --         19.4  \n",
       "102      晴れ            --         19.9  \n",
       "103      晴れ            --         20.8  \n",
       "104      晴れ            --         22.7  \n",
       "105       雨           0.5         17.5  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 100行目から105行目まで表示したい場合\n",
    "df[100:106]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:16.243454Z",
     "start_time": "2018-04-15T20:56:16.225081Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime         2014-4-22\n",
       "y                       78\n",
       "week                     火\n",
       "soldout                  1\n",
       "name                 マーボ豆腐\n",
       "kcal                   382\n",
       "remarks                NaN\n",
       "event                  NaN\n",
       "payday                 NaN\n",
       "weather                  曇\n",
       "precipitation           --\n",
       "temperature           18.8\n",
       "Name: 100, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# indexが100の行だけ取得したい場合\n",
    "df.loc[100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:16.264100Z",
     "start_time": "2018-04-15T20:56:16.246039Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-11-19</td>\n",
       "      <td>火</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-11-20</td>\n",
       "      <td>水</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-11-22</td>\n",
       "      <td>金</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     datetime week\n",
       "1  2013-11-19    火\n",
       "2  2013-11-20    水\n",
       "4  2013-11-22    金"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# もっとピンポイントに抽出したい場合\n",
    "# 例: 1,2,4 行目と 0-2 列目を取得\n",
    "df.iloc[[1,2,4],[0,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:16.310046Z",
     "start_time": "2018-04-15T20:56:16.266188Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>y</th>\n",
       "      <th>week</th>\n",
       "      <th>soldout</th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "      <th>remarks</th>\n",
       "      <th>event</th>\n",
       "      <th>payday</th>\n",
       "      <th>weather</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-1-8</td>\n",
       "      <td>128</td>\n",
       "      <td>水</td>\n",
       "      <td>0</td>\n",
       "      <td>豚肉の生姜焼</td>\n",
       "      <td>462.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>曇</td>\n",
       "      <td>--</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2014-1-31</td>\n",
       "      <td>92</td>\n",
       "      <td>金</td>\n",
       "      <td>1</td>\n",
       "      <td>メダイ照り焼</td>\n",
       "      <td>460.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>2014-9-9</td>\n",
       "      <td>63</td>\n",
       "      <td>火</td>\n",
       "      <td>0</td>\n",
       "      <td>ハンバーグデミソース</td>\n",
       "      <td>460.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>晴れ</td>\n",
       "      <td>--</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      datetime    y week  soldout        name   kcal remarks event  payday  \\\n",
       "29    2014-1-8  128    水        0      豚肉の生姜焼  462.0     NaN   NaN     NaN   \n",
       "45   2014-1-31   92    金        1      メダイ照り焼  460.0     NaN   NaN     NaN   \n",
       "193   2014-9-9   63    火        0  ハンバーグデミソース  460.0     NaN   NaN     NaN   \n",
       "\n",
       "    weather precipitation  temperature  \n",
       "29        曇            --          9.8  \n",
       "45       快晴            --         14.1  \n",
       "193      晴れ            --         26.8  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 条件を指定して抽出\n",
    "df[df['kcal'] > 450]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:16.348734Z",
     "start_time": "2018-04-15T20:56:16.312849Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>豚肉の生姜焼</td>\n",
       "      <td>462.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name   kcal\n",
       "29  豚肉の生姜焼  462.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# queryメソッドを使うと、複数条件の指定で、特定カラムだけ出力もできる\n",
    "df[['name', 'kcal']].query('kcal > 450 and name == \"豚肉の生姜焼\"') #query内のシングル/ダブルクオーテーションの使い方に注意"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:16.364559Z",
     "start_time": "2018-04-15T20:56:16.351298Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, '鶏のレモンペッパー焼（50食）、カレー（42食）', '酢豚（28食）、カレー（85食）', 'お楽しみメニュー',\n",
       "       '料理長のこだわりメニュー', '手作りの味', 'スペシャルメニュー（800円）'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'remarks(備考)'には例えばどんなデータが入っているか確認\n",
    "df['remarks'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:16.372212Z",
     "start_time": "2018-04-15T20:56:16.367574Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# datatime単位で重複したデータが存在しないか確認\n",
    "print(len(df) == len(df['datetime'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:16.396808Z",
     "start_time": "2018-04-15T20:56:16.375107Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(207, 12)\n"
     ]
    }
   ],
   "source": [
    "#行方向で重複行を削除\n",
    "df.drop_duplicates() \n",
    "print(df.shape) # 重複が存在しないので数は変わらないはず"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:16.457755Z",
     "start_time": "2018-04-15T20:56:16.401221Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>soldout</th>\n",
       "      <th>kcal</th>\n",
       "      <th>payday</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>207.000000</td>\n",
       "      <td>207.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>207.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>86.623188</td>\n",
       "      <td>0.449275</td>\n",
       "      <td>404.409639</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.252174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>32.882448</td>\n",
       "      <td>0.498626</td>\n",
       "      <td>29.884641</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.611365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>386.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>408.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>113.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>426.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>171.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>462.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                y     soldout        kcal  payday  temperature\n",
       "count  207.000000  207.000000  166.000000    10.0   207.000000\n",
       "mean    86.623188    0.449275  404.409639     1.0    19.252174\n",
       "std     32.882448    0.498626   29.884641     0.0     8.611365\n",
       "min     29.000000    0.000000  315.000000     1.0     1.200000\n",
       "25%     57.000000    0.000000  386.000000     1.0    11.550000\n",
       "50%     78.000000    0.000000  408.500000     1.0    19.800000\n",
       "75%    113.000000    1.000000  426.000000     1.0    26.100000\n",
       "max    171.000000    1.000000  462.000000     1.0    34.600000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 要約統計量の表示\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ③ データの整形（データ型変更、列名変更、並び替えなど）\n",
    "なんとなくのデータの雰囲気はわかったので、次は自分が操作しやすいように列名を変更したり、処理しやすいようにデータ型を変更したりしてみます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:16.527410Z",
     "start_time": "2018-04-15T20:56:16.470007Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>week</th>\n",
       "      <th>soldout</th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "      <th>remarks</th>\n",
       "      <th>event</th>\n",
       "      <th>payday</th>\n",
       "      <th>weather</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-11-18</th>\n",
       "      <td>90</td>\n",
       "      <td>月</td>\n",
       "      <td>0</td>\n",
       "      <td>厚切りイカフライ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>19.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-19</th>\n",
       "      <td>101</td>\n",
       "      <td>火</td>\n",
       "      <td>1</td>\n",
       "      <td>手作りヒレカツ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-20</th>\n",
       "      <td>118</td>\n",
       "      <td>水</td>\n",
       "      <td>0</td>\n",
       "      <td>白身魚唐揚げ野菜あん</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-21</th>\n",
       "      <td>120</td>\n",
       "      <td>木</td>\n",
       "      <td>1</td>\n",
       "      <td>若鶏ピリ辛焼</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-22</th>\n",
       "      <td>130</td>\n",
       "      <td>金</td>\n",
       "      <td>1</td>\n",
       "      <td>ビッグメンチカツ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              y week  soldout        name  kcal remarks event  payday weather  \\\n",
       "datetime                                                                        \n",
       "2013-11-18   90    月        0    厚切りイカフライ   NaN     NaN   NaN     NaN      快晴   \n",
       "2013-11-19  101    火        1     手作りヒレカツ   NaN     NaN   NaN     NaN      快晴   \n",
       "2013-11-20  118    水        0  白身魚唐揚げ野菜あん   NaN     NaN   NaN     NaN      快晴   \n",
       "2013-11-21  120    木        1      若鶏ピリ辛焼   NaN     NaN   NaN     NaN      快晴   \n",
       "2013-11-22  130    金        1    ビッグメンチカツ   NaN     NaN   NaN     NaN      快晴   \n",
       "\n",
       "           precipitation  temperature  \n",
       "datetime                               \n",
       "2013-11-18            --         19.8  \n",
       "2013-11-19            --         17.0  \n",
       "2013-11-20            --         15.5  \n",
       "2013-11-21            --         15.2  \n",
       "2013-11-22            --         16.1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# datetime列をindexにする\n",
    "df.set_index('datetime', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:16.563853Z",
     "start_time": "2018-04-15T20:56:16.535903Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2013-11-18', '2013-11-19', '2013-11-20', '2013-11-21', '2013-11-22',\n",
       "       '2013-11-25', '2013-11-26', '2013-11-27', '2013-11-28', '2013-11-29',\n",
       "       ...\n",
       "       '2014-9-16', '2014-9-17', '2014-9-18', '2014-9-19', '2014-9-22',\n",
       "       '2014-9-24', '2014-9-25', '2014-9-26', '2014-9-29', '2014-9-30'],\n",
       "      dtype='object', name='datetime', length=207)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:16.649651Z",
     "start_time": "2018-04-15T20:56:16.572904Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>week</th>\n",
       "      <th>soldout</th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "      <th>remarks</th>\n",
       "      <th>event</th>\n",
       "      <th>payday</th>\n",
       "      <th>weather</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-11-18</th>\n",
       "      <td>90</td>\n",
       "      <td>月</td>\n",
       "      <td>0</td>\n",
       "      <td>厚切りイカフライ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>19.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-19</th>\n",
       "      <td>101</td>\n",
       "      <td>火</td>\n",
       "      <td>1</td>\n",
       "      <td>手作りヒレカツ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-20</th>\n",
       "      <td>118</td>\n",
       "      <td>水</td>\n",
       "      <td>0</td>\n",
       "      <td>白身魚唐揚げ野菜あん</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-21</th>\n",
       "      <td>120</td>\n",
       "      <td>木</td>\n",
       "      <td>1</td>\n",
       "      <td>若鶏ピリ辛焼</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-22</th>\n",
       "      <td>130</td>\n",
       "      <td>金</td>\n",
       "      <td>1</td>\n",
       "      <td>ビッグメンチカツ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales week  soldout        name  kcal remarks event  payday  \\\n",
       "datetime                                                                  \n",
       "2013-11-18     90    月        0    厚切りイカフライ   NaN     NaN   NaN     NaN   \n",
       "2013-11-19    101    火        1     手作りヒレカツ   NaN     NaN   NaN     NaN   \n",
       "2013-11-20    118    水        0  白身魚唐揚げ野菜あん   NaN     NaN   NaN     NaN   \n",
       "2013-11-21    120    木        1      若鶏ピリ辛焼   NaN     NaN   NaN     NaN   \n",
       "2013-11-22    130    金        1    ビッグメンチカツ   NaN     NaN   NaN     NaN   \n",
       "\n",
       "           weather precipitation  temperature  \n",
       "datetime                                       \n",
       "2013-11-18      快晴            --         19.8  \n",
       "2013-11-19      快晴            --         17.0  \n",
       "2013-11-20      快晴            --         15.5  \n",
       "2013-11-21      快晴            --         15.2  \n",
       "2013-11-22      快晴            --         16.1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# カラム名を変更する（y を sales に変換）\n",
    "df.rename(columns={'y': 'sales'}, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:58:23.952518Z",
     "start_time": "2018-04-15T20:58:23.901759Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>week</th>\n",
       "      <th>soldout</th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "      <th>event</th>\n",
       "      <th>payday</th>\n",
       "      <th>weather</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>period</th>\n",
       "      <th>temperature_diff</th>\n",
       "      <th>temperature_rolling_mean</th>\n",
       "      <th>temperature_pct_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-09-22</th>\n",
       "      <td>29</td>\n",
       "      <td>月</td>\n",
       "      <td>0</td>\n",
       "      <td>筑前煮</td>\n",
       "      <td>395.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>晴れ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.2</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>下旬</td>\n",
       "      <td>1.6</td>\n",
       "      <td>24.300000</td>\n",
       "      <td>0.067797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-01</th>\n",
       "      <td>38</td>\n",
       "      <td>金</td>\n",
       "      <td>0</td>\n",
       "      <td>豚肉の生姜焼き</td>\n",
       "      <td>380.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>薄曇</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>上旬</td>\n",
       "      <td>0.2</td>\n",
       "      <td>31.933333</td>\n",
       "      <td>0.006154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-29</th>\n",
       "      <td>39</td>\n",
       "      <td>金</td>\n",
       "      <td>0</td>\n",
       "      <td>チキンの辛味噌焼き</td>\n",
       "      <td>398.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>曇</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>下旬</td>\n",
       "      <td>3.5</td>\n",
       "      <td>22.566667</td>\n",
       "      <td>0.162037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-12</th>\n",
       "      <td>40</td>\n",
       "      <td>火</td>\n",
       "      <td>1</td>\n",
       "      <td>厚切ハムカツ</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>雨</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.4</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>中旬</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>31.300000</td>\n",
       "      <td>-0.092593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-30</th>\n",
       "      <td>40</td>\n",
       "      <td>火</td>\n",
       "      <td>0</td>\n",
       "      <td>鶏肉とカシューナッツ炒め</td>\n",
       "      <td>398.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>快晴</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>下旬</td>\n",
       "      <td>1.5</td>\n",
       "      <td>26.700000</td>\n",
       "      <td>0.056391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-05</th>\n",
       "      <td>43</td>\n",
       "      <td>金</td>\n",
       "      <td>0</td>\n",
       "      <td>メンチカツ</td>\n",
       "      <td>380.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>晴れ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.2</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>上旬</td>\n",
       "      <td>3.7</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>0.139623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-11</th>\n",
       "      <td>45</td>\n",
       "      <td>月</td>\n",
       "      <td>0</td>\n",
       "      <td>サーモンフライ・タルタル</td>\n",
       "      <td>411.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>晴れ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.4</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>中旬</td>\n",
       "      <td>0.3</td>\n",
       "      <td>33.033333</td>\n",
       "      <td>0.009346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-19</th>\n",
       "      <td>45</td>\n",
       "      <td>金</td>\n",
       "      <td>0</td>\n",
       "      <td>厚切イカフライ</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>薄曇</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.6</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>中旬</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>24.233333</td>\n",
       "      <td>-0.020747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-18</th>\n",
       "      <td>46</td>\n",
       "      <td>木</td>\n",
       "      <td>0</td>\n",
       "      <td>カレイ唐揚げ 甘酢あん</td>\n",
       "      <td>395.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>曇</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.1</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>中旬</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>25.966667</td>\n",
       "      <td>-0.036000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-02</th>\n",
       "      <td>47</td>\n",
       "      <td>金</td>\n",
       "      <td>0</td>\n",
       "      <td>鶏の天ぷら</td>\n",
       "      <td>420.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>晴れ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>上旬</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>-0.029167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales week  soldout          name   kcal event  payday weather  \\\n",
       "datetime                                                                     \n",
       "2014-09-22     29    月        0           筑前煮  395.0   NaN     0.0      晴れ   \n",
       "2014-08-01     38    金        0       豚肉の生姜焼き  380.0   NaN     0.0      薄曇   \n",
       "2014-08-29     39    金        0     チキンの辛味噌焼き  398.0   NaN     0.0       曇   \n",
       "2014-08-12     40    火        1        厚切ハムカツ  400.0   NaN     0.0       雨   \n",
       "2014-09-30     40    火        0  鶏肉とカシューナッツ炒め  398.0   NaN     0.0      快晴   \n",
       "2014-09-05     43    金        0         メンチカツ  380.0   NaN     0.0      晴れ   \n",
       "2014-08-11     45    月        0  サーモンフライ・タルタル  411.0   NaN     0.0      晴れ   \n",
       "2014-09-19     45    金        0       厚切イカフライ  400.0   NaN     0.0      薄曇   \n",
       "2014-09-18     46    木        0   カレイ唐揚げ 甘酢あん  395.0   NaN     0.0       曇   \n",
       "2014-05-02     47    金        0         鶏の天ぷら  420.0   NaN     0.0      晴れ   \n",
       "\n",
       "            precipitation  temperature  month  day period  temperature_diff  \\\n",
       "datetime                                                                      \n",
       "2014-09-22            0.0         25.2      9   22     下旬               1.6   \n",
       "2014-08-01            0.0         32.7      8    1     上旬               0.2   \n",
       "2014-08-29            0.0         25.1      8   29     下旬               3.5   \n",
       "2014-08-12            0.0         29.4      8   12     中旬              -3.0   \n",
       "2014-09-30            0.0         28.1      9   30     下旬               1.5   \n",
       "2014-09-05            0.0         30.2      9    5     上旬               3.7   \n",
       "2014-08-11            0.0         32.4      8   11     中旬               0.3   \n",
       "2014-09-19            0.0         23.6      9   19     中旬              -0.5   \n",
       "2014-09-18            0.0         24.1      9   18     中旬              -0.9   \n",
       "2014-05-02            0.0         23.3      5    2     上旬              -0.7   \n",
       "\n",
       "            temperature_rolling_mean  temperature_pct_change  \n",
       "datetime                                                      \n",
       "2014-09-22                 24.300000                0.067797  \n",
       "2014-08-01                 31.933333                0.006154  \n",
       "2014-08-29                 22.566667                0.162037  \n",
       "2014-08-12                 31.300000               -0.092593  \n",
       "2014-09-30                 26.700000                0.056391  \n",
       "2014-09-05                 27.800000                0.139623  \n",
       "2014-08-11                 33.033333                0.009346  \n",
       "2014-09-19                 24.233333               -0.020747  \n",
       "2014-09-18                 25.966667               -0.036000  \n",
       "2014-05-02                 21.600000               -0.029167  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'sales'列を降順で並び替えもできる\n",
    "df.sort_values(by=\"sales\", ascending=True).head() # ascending=Trueで昇順"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:58:36.401041Z",
     "start_time": "2018-04-15T20:58:36.364226Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>week</th>\n",
       "      <th>soldout</th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "      <th>event</th>\n",
       "      <th>payday</th>\n",
       "      <th>weather</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>period</th>\n",
       "      <th>temperature_diff</th>\n",
       "      <th>temperature_rolling_mean</th>\n",
       "      <th>temperature_pct_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-02-03</th>\n",
       "      <td>139</td>\n",
       "      <td>月</td>\n",
       "      <td>1</td>\n",
       "      <td>ハンバーグカレーソース</td>\n",
       "      <td>445.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>晴れ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>上旬</td>\n",
       "      <td>1.8</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>0.127660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-17</th>\n",
       "      <td>137</td>\n",
       "      <td>月</td>\n",
       "      <td>1</td>\n",
       "      <td>チンジャオロース</td>\n",
       "      <td>416.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>晴れ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>中旬</td>\n",
       "      <td>10.4</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>8.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-10</th>\n",
       "      <td>137</td>\n",
       "      <td>月</td>\n",
       "      <td>0</td>\n",
       "      <td>鶏の唐揚げおろしソース</td>\n",
       "      <td>423.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>曇</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>上旬</td>\n",
       "      <td>2.3</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>0.418182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-15</th>\n",
       "      <td>134</td>\n",
       "      <td>水</td>\n",
       "      <td>0</td>\n",
       "      <td>さんま辛味焼</td>\n",
       "      <td>450.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>曇</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>中旬</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>-0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-03</th>\n",
       "      <td>131</td>\n",
       "      <td>月</td>\n",
       "      <td>0</td>\n",
       "      <td>ハンバーグカレーソース</td>\n",
       "      <td>407.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>曇</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>上旬</td>\n",
       "      <td>-9.9</td>\n",
       "      <td>11.333333</td>\n",
       "      <td>-0.559322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-17</th>\n",
       "      <td>129</td>\n",
       "      <td>月</td>\n",
       "      <td>0</td>\n",
       "      <td>鶏の唐揚げおろしソース</td>\n",
       "      <td>382.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>快晴</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>中旬</td>\n",
       "      <td>2.5</td>\n",
       "      <td>13.566667</td>\n",
       "      <td>0.217391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-09</th>\n",
       "      <td>129</td>\n",
       "      <td>木</td>\n",
       "      <td>1</td>\n",
       "      <td>鶏チリソース</td>\n",
       "      <td>435.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>曇</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>上旬</td>\n",
       "      <td>1.1</td>\n",
       "      <td>9.333333</td>\n",
       "      <td>0.112245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-21</th>\n",
       "      <td>129</td>\n",
       "      <td>火</td>\n",
       "      <td>1</td>\n",
       "      <td>サバ焼味噌掛け</td>\n",
       "      <td>447.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>快晴</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>下旬</td>\n",
       "      <td>3.9</td>\n",
       "      <td>8.233333</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-24</th>\n",
       "      <td>129</td>\n",
       "      <td>月</td>\n",
       "      <td>1</td>\n",
       "      <td>ポークハヤシ</td>\n",
       "      <td>408.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>曇</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>下旬</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>6.833333</td>\n",
       "      <td>-0.223529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-14</th>\n",
       "      <td>129</td>\n",
       "      <td>火</td>\n",
       "      <td>1</td>\n",
       "      <td>鶏の照り焼きマスタード</td>\n",
       "      <td>376.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>晴れ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>中旬</td>\n",
       "      <td>0.7</td>\n",
       "      <td>7.266667</td>\n",
       "      <td>0.137255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales week  soldout         name   kcal event  payday weather  \\\n",
       "datetime                                                                    \n",
       "2014-02-03    139    月        1  ハンバーグカレーソース  445.0   NaN     0.0      晴れ   \n",
       "2014-02-17    137    月        1     チンジャオロース  416.0   NaN     0.0      晴れ   \n",
       "2014-02-10    137    月        0  鶏の唐揚げおろしソース  423.0   NaN     1.0       曇   \n",
       "2014-01-15    134    水        0       さんま辛味焼  450.0   NaN     0.0       曇   \n",
       "2014-03-03    131    月        0  ハンバーグカレーソース  407.0   NaN     0.0       曇   \n",
       "2014-03-17    129    月        0  鶏の唐揚げおろしソース  382.0   NaN     0.0      快晴   \n",
       "2014-01-09    129    木        1       鶏チリソース  435.0   NaN     0.0       曇   \n",
       "2014-01-21    129    火        1      サバ焼味噌掛け  447.0   NaN     0.0      快晴   \n",
       "2014-02-24    129    月        1       ポークハヤシ  408.0   NaN     0.0       曇   \n",
       "2014-01-14    129    火        1  鶏の照り焼きマスタード  376.0   NaN     0.0      晴れ   \n",
       "\n",
       "            precipitation  temperature  month  day period  temperature_diff  \\\n",
       "datetime                                                                      \n",
       "2014-02-03            0.0         15.9      2    3     上旬               1.8   \n",
       "2014-02-17            0.0         11.6      2   17     中旬              10.4   \n",
       "2014-02-10            0.0          7.8      2   10     上旬               2.3   \n",
       "2014-01-15            0.0          2.9      1   15     中旬              -2.9   \n",
       "2014-03-03            0.0          7.8      3    3     上旬              -9.9   \n",
       "2014-03-17            0.0         14.0      3   17     中旬               2.5   \n",
       "2014-01-09            0.0         10.9      1    9     上旬               1.1   \n",
       "2014-01-21            0.0         10.4      1   21     下旬               3.9   \n",
       "2014-02-24            0.0          6.6      2   24     下旬              -1.9   \n",
       "2014-01-14            0.0          5.8      1   14     中旬               0.7   \n",
       "\n",
       "            temperature_rolling_mean  temperature_pct_change  \n",
       "datetime                                                      \n",
       "2014-02-03                 15.100000                0.127660  \n",
       "2014-02-17                  6.400000                8.666667  \n",
       "2014-02-10                  5.833333                0.418182  \n",
       "2014-01-15                  4.600000               -0.500000  \n",
       "2014-03-03                 11.333333               -0.559322  \n",
       "2014-03-17                 13.566667                0.217391  \n",
       "2014-01-09                  9.333333                0.112245  \n",
       "2014-01-21                  8.233333                0.600000  \n",
       "2014-02-24                  6.833333               -0.223529  \n",
       "2014-01-14                  7.266667                0.137255  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort_valuesは複数の列に対しても実行できる\n",
    "df.sort_values(['sales', 'temperature'], ascending=False).head() # ascending=Falseで昇順"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:16.946018Z",
     "start_time": "2018-04-15T20:56:16.937202Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2013-11-18', '2013-11-19', '2013-11-20', '2013-11-21', '2013-11-22',\n",
       "       '2013-11-25', '2013-11-26', '2013-11-27', '2013-11-28', '2013-11-29',\n",
       "       ...\n",
       "       '2014-9-16', '2014-9-17', '2014-9-18', '2014-9-19', '2014-9-22',\n",
       "       '2014-9-24', '2014-9-25', '2014-9-26', '2014-9-29', '2014-9-30'],\n",
       "      dtype='object', name='datetime', length=207)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# indexのデータ型を確認してみる\n",
    "df.index #  dtype='object'であることがわかる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:16.959218Z",
     "start_time": "2018-04-15T20:56:16.949955Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# indexであるdatetimeのdtype='object' を dtype='datetime64[ns]' に変更\n",
    "df.index = pd.to_datetime(df.index, format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:16.970418Z",
     "start_time": "2018-04-15T20:56:16.964700Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-11-18', '2013-11-19', '2013-11-20', '2013-11-21',\n",
       "               '2013-11-22', '2013-11-25', '2013-11-26', '2013-11-27',\n",
       "               '2013-11-28', '2013-11-29',\n",
       "               ...\n",
       "               '2014-09-16', '2014-09-17', '2014-09-18', '2014-09-19',\n",
       "               '2014-09-22', '2014-09-24', '2014-09-25', '2014-09-26',\n",
       "               '2014-09-29', '2014-09-30'],\n",
       "              dtype='datetime64[ns]', name='datetime', length=207, freq=None)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index # dtype='datetime64[ns]'になった"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### indexがdatetime型のときにできること"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:58:47.397514Z",
     "start_time": "2018-04-15T20:58:47.365389Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>week</th>\n",
       "      <th>soldout</th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "      <th>event</th>\n",
       "      <th>payday</th>\n",
       "      <th>weather</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>period</th>\n",
       "      <th>temperature_diff</th>\n",
       "      <th>temperature_rolling_mean</th>\n",
       "      <th>temperature_pct_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-09</th>\n",
       "      <td>129</td>\n",
       "      <td>木</td>\n",
       "      <td>1</td>\n",
       "      <td>鶏チリソース</td>\n",
       "      <td>435.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>曇</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>上旬</td>\n",
       "      <td>1.1</td>\n",
       "      <td>9.333333</td>\n",
       "      <td>0.112245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>87</td>\n",
       "      <td>金</td>\n",
       "      <td>0</td>\n",
       "      <td>手作りロースカツ</td>\n",
       "      <td>440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>快晴</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>上旬</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>-0.532110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-14</th>\n",
       "      <td>129</td>\n",
       "      <td>火</td>\n",
       "      <td>1</td>\n",
       "      <td>鶏の照り焼きマスタード</td>\n",
       "      <td>376.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>晴れ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>中旬</td>\n",
       "      <td>0.7</td>\n",
       "      <td>7.266667</td>\n",
       "      <td>0.137255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-15</th>\n",
       "      <td>134</td>\n",
       "      <td>水</td>\n",
       "      <td>0</td>\n",
       "      <td>さんま辛味焼</td>\n",
       "      <td>450.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>曇</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>中旬</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>-0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-16</th>\n",
       "      <td>107</td>\n",
       "      <td>木</td>\n",
       "      <td>0</td>\n",
       "      <td>カレイ唐揚げ野菜あんかけ</td>\n",
       "      <td>415.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>快晴</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>中旬</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.233333</td>\n",
       "      <td>1.413793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales week  soldout          name   kcal event  payday weather  \\\n",
       "datetime                                                                     \n",
       "2014-01-09    129    木        1        鶏チリソース  435.0   NaN     0.0       曇   \n",
       "2014-01-10     87    金        0      手作りロースカツ  440.0   NaN     1.0      快晴   \n",
       "2014-01-14    129    火        1   鶏の照り焼きマスタード  376.0   NaN     0.0      晴れ   \n",
       "2014-01-15    134    水        0        さんま辛味焼  450.0   NaN     0.0       曇   \n",
       "2014-01-16    107    木        0  カレイ唐揚げ野菜あんかけ  415.0   NaN     0.0      快晴   \n",
       "\n",
       "            precipitation  temperature  month  day period  temperature_diff  \\\n",
       "datetime                                                                      \n",
       "2014-01-09            0.0         10.9      1    9     上旬               1.1   \n",
       "2014-01-10            0.0          5.1      1   10     上旬              -5.8   \n",
       "2014-01-14            0.0          5.8      1   14     中旬               0.7   \n",
       "2014-01-15            0.0          2.9      1   15     中旬              -2.9   \n",
       "2014-01-16            0.0          7.0      1   16     中旬               4.1   \n",
       "\n",
       "            temperature_rolling_mean  temperature_pct_change  \n",
       "datetime                                                      \n",
       "2014-01-09                  9.333333                0.112245  \n",
       "2014-01-10                  8.600000               -0.532110  \n",
       "2014-01-14                  7.266667                0.137255  \n",
       "2014-01-15                  4.600000               -0.500000  \n",
       "2014-01-16                  5.233333                1.413793  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# indexに対してsortを行う\n",
    "df.sort_index().head() # object型のままだと正しくsortされない"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.090038Z",
     "start_time": "2018-04-15T20:56:17.062810Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>soldout</th>\n",
       "      <th>kcal</th>\n",
       "      <th>payday</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-11-30</th>\n",
       "      <td>124.600000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>139.722222</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.844444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-31</th>\n",
       "      <td>115.222222</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>427.555556</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.716667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-28</th>\n",
       "      <td>107.842105</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>420.947368</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.815789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-31</th>\n",
       "      <td>89.350000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>382.157895</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.505000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-30</th>\n",
       "      <td>85.761905</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.176190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-31</th>\n",
       "      <td>74.200000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>419.111111</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.505000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-30</th>\n",
       "      <td>67.285714</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>423.315789</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.409524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-31</th>\n",
       "      <td>61.363636</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>397.200000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31</th>\n",
       "      <td>61.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>403.187500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.227778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-30</th>\n",
       "      <td>55.550000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>408.611111</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.935000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sales   soldout        kcal  payday  temperature\n",
       "datetime                                                         \n",
       "2013-11-30  124.600000  0.500000         NaN     NaN    16.060000\n",
       "2013-12-31  139.722222  0.444444         NaN     1.0    10.844444\n",
       "2014-01-31  115.222222  0.611111  427.555556     1.0     8.716667\n",
       "2014-02-28  107.842105  0.526316  420.947368     1.0     7.815789\n",
       "2014-03-31   89.350000  0.450000  382.157895     1.0    12.505000\n",
       "2014-04-30   85.761905  0.476190  360.000000     1.0    18.176190\n",
       "2014-05-31   74.200000  0.550000  419.111111     1.0    22.505000\n",
       "2014-06-30   67.285714  0.333333  423.315789     1.0    25.409524\n",
       "2014-07-31   61.363636  0.454545  397.200000     1.0    29.350000\n",
       "2014-08-31   61.166667  0.333333  403.187500     1.0    30.227778\n",
       "2014-09-30   55.550000  0.300000  408.611111     1.0    25.935000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# resampleメソッドで、日単位や月単位で簡単に集計できる\n",
    "df.resample('M').mean() #月単位で平均値を出力"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.139651Z",
     "start_time": "2018-04-15T20:56:17.092953Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>week</th>\n",
       "      <th>soldout</th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "      <th>remarks</th>\n",
       "      <th>event</th>\n",
       "      <th>payday</th>\n",
       "      <th>weather</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-11-18</th>\n",
       "      <td>90</td>\n",
       "      <td>月</td>\n",
       "      <td>0</td>\n",
       "      <td>厚切りイカフライ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>19.8</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-19</th>\n",
       "      <td>101</td>\n",
       "      <td>火</td>\n",
       "      <td>1</td>\n",
       "      <td>手作りヒレカツ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-20</th>\n",
       "      <td>118</td>\n",
       "      <td>水</td>\n",
       "      <td>0</td>\n",
       "      <td>白身魚唐揚げ野菜あん</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>15.5</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-21</th>\n",
       "      <td>120</td>\n",
       "      <td>木</td>\n",
       "      <td>1</td>\n",
       "      <td>若鶏ピリ辛焼</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>15.2</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-22</th>\n",
       "      <td>130</td>\n",
       "      <td>金</td>\n",
       "      <td>1</td>\n",
       "      <td>ビッグメンチカツ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>16.1</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales week  soldout        name  kcal remarks event  payday  \\\n",
       "datetime                                                                  \n",
       "2013-11-18     90    月        0    厚切りイカフライ   NaN     NaN   NaN     NaN   \n",
       "2013-11-19    101    火        1     手作りヒレカツ   NaN     NaN   NaN     NaN   \n",
       "2013-11-20    118    水        0  白身魚唐揚げ野菜あん   NaN     NaN   NaN     NaN   \n",
       "2013-11-21    120    木        1      若鶏ピリ辛焼   NaN     NaN   NaN     NaN   \n",
       "2013-11-22    130    金        1    ビッグメンチカツ   NaN     NaN   NaN     NaN   \n",
       "\n",
       "           weather precipitation  temperature  month  day  \n",
       "datetime                                                   \n",
       "2013-11-18      快晴            --         19.8     11   18  \n",
       "2013-11-19      快晴            --         17.0     11   19  \n",
       "2013-11-20      快晴            --         15.5     11   20  \n",
       "2013-11-21      快晴            --         15.2     11   21  \n",
       "2013-11-22      快晴            --         16.1     11   22  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 簡単にmonth列やday列を作れるようになる\n",
    "df['month'] = list(pd.Series(df.index).apply(lambda x: x.month))\n",
    "df['day'] = list(pd.Series(df.index).apply(lambda x: x.day))\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.220309Z",
     "start_time": "2018-04-15T20:56:17.144230Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>week</th>\n",
       "      <th>soldout</th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "      <th>remarks</th>\n",
       "      <th>event</th>\n",
       "      <th>payday</th>\n",
       "      <th>weather</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>period</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-11-18</th>\n",
       "      <td>90</td>\n",
       "      <td>月</td>\n",
       "      <td>0</td>\n",
       "      <td>厚切りイカフライ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>19.8</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>中旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-19</th>\n",
       "      <td>101</td>\n",
       "      <td>火</td>\n",
       "      <td>1</td>\n",
       "      <td>手作りヒレカツ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>中旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-20</th>\n",
       "      <td>118</td>\n",
       "      <td>水</td>\n",
       "      <td>0</td>\n",
       "      <td>白身魚唐揚げ野菜あん</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>15.5</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>中旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-21</th>\n",
       "      <td>120</td>\n",
       "      <td>木</td>\n",
       "      <td>1</td>\n",
       "      <td>若鶏ピリ辛焼</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>15.2</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>下旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-22</th>\n",
       "      <td>130</td>\n",
       "      <td>金</td>\n",
       "      <td>1</td>\n",
       "      <td>ビッグメンチカツ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>16.1</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>下旬</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales week  soldout        name  kcal remarks event  payday  \\\n",
       "datetime                                                                  \n",
       "2013-11-18     90    月        0    厚切りイカフライ   NaN     NaN   NaN     NaN   \n",
       "2013-11-19    101    火        1     手作りヒレカツ   NaN     NaN   NaN     NaN   \n",
       "2013-11-20    118    水        0  白身魚唐揚げ野菜あん   NaN     NaN   NaN     NaN   \n",
       "2013-11-21    120    木        1      若鶏ピリ辛焼   NaN     NaN   NaN     NaN   \n",
       "2013-11-22    130    金        1    ビッグメンチカツ   NaN     NaN   NaN     NaN   \n",
       "\n",
       "           weather precipitation  temperature  month  day period  \n",
       "datetime                                                          \n",
       "2013-11-18      快晴            --         19.8     11   18     中旬  \n",
       "2013-11-19      快晴            --         17.0     11   19     中旬  \n",
       "2013-11-20      快晴            --         15.5     11   20     中旬  \n",
       "2013-11-21      快晴            --         15.2     11   21     下旬  \n",
       "2013-11-22      快晴            --         16.1     11   22     下旬  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cutメソッドでヒストグラムのビン指定的な処理ができる\n",
    "labels = ['上旬', '中旬', '下旬']\n",
    "df['period'] = pd.cut(list(df['day']),  bins=[0,10,20,31], labels=labels, right=True) # 0<day≦10, 10<day≦20, 20<day≦31\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ④ データの欠損状態の確認\n",
    "データに欠損値NaN(not a number)があるともろもろ問題がでてきます。今のデータにどれくらい欠損があるかをまずはざっと確認します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.237080Z",
     "start_time": "2018-04-15T20:56:17.224069Z"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sales              0\n",
       "week               0\n",
       "soldout            0\n",
       "name               0\n",
       "kcal              41\n",
       "remarks          186\n",
       "event            193\n",
       "payday           197\n",
       "weather            0\n",
       "precipitation      0\n",
       "temperature        0\n",
       "month              0\n",
       "day                0\n",
       "period             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 列単位で 欠損値NaN(not a number)が入っている個数をカウントする （正確には、isnull()でtureが返ってくる個数をカウントしている）\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.302659Z",
     "start_time": "2018-04-15T20:56:17.239419Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(207, 14)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>week</th>\n",
       "      <th>soldout</th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "      <th>remarks</th>\n",
       "      <th>event</th>\n",
       "      <th>payday</th>\n",
       "      <th>weather</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>period</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-11-18</th>\n",
       "      <td>90</td>\n",
       "      <td>月</td>\n",
       "      <td>0</td>\n",
       "      <td>厚切りイカフライ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>19.8</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>中旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-19</th>\n",
       "      <td>101</td>\n",
       "      <td>火</td>\n",
       "      <td>1</td>\n",
       "      <td>手作りヒレカツ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>中旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-20</th>\n",
       "      <td>118</td>\n",
       "      <td>水</td>\n",
       "      <td>0</td>\n",
       "      <td>白身魚唐揚げ野菜あん</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>15.5</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>中旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-21</th>\n",
       "      <td>120</td>\n",
       "      <td>木</td>\n",
       "      <td>1</td>\n",
       "      <td>若鶏ピリ辛焼</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>15.2</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>下旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-22</th>\n",
       "      <td>130</td>\n",
       "      <td>金</td>\n",
       "      <td>1</td>\n",
       "      <td>ビッグメンチカツ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>16.1</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>下旬</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales week  soldout        name  kcal remarks event  payday  \\\n",
       "datetime                                                                  \n",
       "2013-11-18     90    月        0    厚切りイカフライ   NaN     NaN   NaN     NaN   \n",
       "2013-11-19    101    火        1     手作りヒレカツ   NaN     NaN   NaN     NaN   \n",
       "2013-11-20    118    水        0  白身魚唐揚げ野菜あん   NaN     NaN   NaN     NaN   \n",
       "2013-11-21    120    木        1      若鶏ピリ辛焼   NaN     NaN   NaN     NaN   \n",
       "2013-11-22    130    金        1    ビッグメンチカツ   NaN     NaN   NaN     NaN   \n",
       "\n",
       "           weather precipitation  temperature  month  day period  \n",
       "datetime                                                          \n",
       "2013-11-18      快晴            --         19.8     11   18     中旬  \n",
       "2013-11-19      快晴            --         17.0     11   19     中旬  \n",
       "2013-11-20      快晴            --         15.5     11   20     中旬  \n",
       "2013-11-21      快晴            --         15.2     11   21     下旬  \n",
       "2013-11-22      快晴            --         16.1     11   22     下旬  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1つでもNaNが含まれる行だけを抽出（最初の5行のみ表示）\n",
    "print(df[df.isnull().any(axis=1)].shape)\n",
    "df[df.isnull().any(axis=1)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ⑤ 値（欠損）の置き換えや削除\n",
    "確認できた欠損値をなくすために欠損の削除や別の値への置き換えを行います。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.406970Z",
     "start_time": "2018-04-15T20:56:17.307747Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>sales</th>\n",
       "      <th>week</th>\n",
       "      <th>soldout</th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "      <th>remarks</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-11-18</th>\n",
       "      <td>90</td>\n",
       "      <td>月</td>\n",
       "      <td>0</td>\n",
       "      <td>厚切りイカフライ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>19.8</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>中旬</td>\n",
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       "    <tr>\n",
       "      <th>2013-11-19</th>\n",
       "      <td>101</td>\n",
       "      <td>火</td>\n",
       "      <td>1</td>\n",
       "      <td>手作りヒレカツ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>中旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-20</th>\n",
       "      <td>118</td>\n",
       "      <td>水</td>\n",
       "      <td>0</td>\n",
       "      <td>白身魚唐揚げ野菜あん</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>15.5</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>中旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-21</th>\n",
       "      <td>120</td>\n",
       "      <td>木</td>\n",
       "      <td>1</td>\n",
       "      <td>若鶏ピリ辛焼</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>15.2</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>下旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-22</th>\n",
       "      <td>130</td>\n",
       "      <td>金</td>\n",
       "      <td>1</td>\n",
       "      <td>ビッグメンチカツ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>快晴</td>\n",
       "      <td>--</td>\n",
       "      <td>16.1</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>下旬</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales week  soldout        name  kcal remarks event  payday  \\\n",
       "datetime                                                                  \n",
       "2013-11-18     90    月        0    厚切りイカフライ   NaN     NaN   NaN     0.0   \n",
       "2013-11-19    101    火        1     手作りヒレカツ   NaN     NaN   NaN     0.0   \n",
       "2013-11-20    118    水        0  白身魚唐揚げ野菜あん   NaN     NaN   NaN     0.0   \n",
       "2013-11-21    120    木        1      若鶏ピリ辛焼   NaN     NaN   NaN     0.0   \n",
       "2013-11-22    130    金        1    ビッグメンチカツ   NaN     NaN   NaN     0.0   \n",
       "\n",
       "           weather precipitation  temperature  month  day period  \n",
       "datetime                                                          \n",
       "2013-11-18      快晴            --         19.8     11   18     中旬  \n",
       "2013-11-19      快晴            --         17.0     11   19     中旬  \n",
       "2013-11-20      快晴            --         15.5     11   20     中旬  \n",
       "2013-11-21      快晴            --         15.2     11   21     下旬  \n",
       "2013-11-22      快晴            --         16.1     11   22     下旬  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'payday'列にあるNaNを'0.0'に置き換える\n",
    "df.fillna(value={'payday': 0.0}, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.422430Z",
     "start_time": "2018-04-15T20:56:17.413020Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sales              0\n",
       "week               0\n",
       "soldout            0\n",
       "name               0\n",
       "kcal              41\n",
       "remarks          186\n",
       "event            193\n",
       "payday             0\n",
       "weather            0\n",
       "precipitation      0\n",
       "temperature        0\n",
       "month              0\n",
       "day                0\n",
       "period             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() # 'payday'が0になった"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.435357Z",
     "start_time": "2018-04-15T20:56:17.424983Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(166, 14)\n"
     ]
    }
   ],
   "source": [
    "# 'kcal'列にNaNがある行を削除する\n",
    "df.dropna(subset=['kcal'], axis=0, inplace=True)\n",
    "print(df.shape) # 207-166=41行のデータを削除した"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.450081Z",
     "start_time": "2018-04-15T20:56:17.438912Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sales              0\n",
       "week               0\n",
       "soldout            0\n",
       "name               0\n",
       "kcal               0\n",
       "remarks          158\n",
       "event            155\n",
       "payday             0\n",
       "weather            0\n",
       "precipitation      0\n",
       "temperature        0\n",
       "month              0\n",
       "day                0\n",
       "period             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() # kcal'がNaNがなくなって0になっていることがわかる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.523973Z",
     "start_time": "2018-04-15T20:56:17.454228Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>week</th>\n",
       "      <th>soldout</th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "      <th>remarks</th>\n",
       "      <th>event</th>\n",
       "      <th>payday</th>\n",
       "      <th>weather</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>period</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>131</td>\n",
       "      <td>火</td>\n",
       "      <td>0</td>\n",
       "      <td>カレー入りソーセージカツ</td>\n",
       "      <td>404.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>快晴</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>上旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>128</td>\n",
       "      <td>水</td>\n",
       "      <td>0</td>\n",
       "      <td>豚肉の生姜焼</td>\n",
       "      <td>462.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>曇</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>上旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-09</th>\n",
       "      <td>129</td>\n",
       "      <td>木</td>\n",
       "      <td>1</td>\n",
       "      <td>鶏チリソース</td>\n",
       "      <td>435.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>曇</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>上旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>87</td>\n",
       "      <td>金</td>\n",
       "      <td>0</td>\n",
       "      <td>手作りロースカツ</td>\n",
       "      <td>440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>快晴</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>上旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-14</th>\n",
       "      <td>129</td>\n",
       "      <td>火</td>\n",
       "      <td>1</td>\n",
       "      <td>鶏の照り焼きマスタード</td>\n",
       "      <td>376.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>晴れ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>中旬</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales week  soldout          name   kcal remarks event  payday  \\\n",
       "datetime                                                                     \n",
       "2014-01-07    131    火        0  カレー入りソーセージカツ  404.0     NaN   NaN     0.0   \n",
       "2014-01-08    128    水        0        豚肉の生姜焼  462.0     NaN   NaN     0.0   \n",
       "2014-01-09    129    木        1        鶏チリソース  435.0     NaN   NaN     0.0   \n",
       "2014-01-10     87    金        0      手作りロースカツ  440.0     NaN   NaN     1.0   \n",
       "2014-01-14    129    火        1   鶏の照り焼きマスタード  376.0     NaN   NaN     0.0   \n",
       "\n",
       "           weather  precipitation  temperature  month  day period  \n",
       "datetime                                                           \n",
       "2014-01-07      快晴            0.0          7.3      1    7     上旬  \n",
       "2014-01-08       曇            0.0          9.8      1    8     上旬  \n",
       "2014-01-09       曇            0.0         10.9      1    9     上旬  \n",
       "2014-01-10      快晴            0.0          5.1      1   10     上旬  \n",
       "2014-01-14      晴れ            0.0          5.8      1   14     中旬  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'precipitation' 列の '--' を 0に置き換える\n",
    "df['precipitation'] = df['precipitation'].replace('--', 0).astype(float)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.546191Z",
     "start_time": "2018-04-15T20:56:17.527868Z"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-09</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-14</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales\n",
       "datetime         \n",
       "2014-01-07    100\n",
       "2014-01-08    100\n",
       "2014-01-09    100\n",
       "2014-01-10    100\n",
       "2014-01-14    100"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# maskメソッドを使う例。'sales'列が80よりも大きければ、その値を100に置換する\n",
    "pd.DataFrame(df['sales'].mask(df['sales'] > 80, 100)).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.627565Z",
     "start_time": "2018-04-15T20:56:17.549873Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>week</th>\n",
       "      <th>soldout</th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "      <th>event</th>\n",
       "      <th>payday</th>\n",
       "      <th>weather</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>period</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>131</td>\n",
       "      <td>火</td>\n",
       "      <td>0</td>\n",
       "      <td>カレー入りソーセージカツ</td>\n",
       "      <td>404.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>快晴</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>上旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>128</td>\n",
       "      <td>水</td>\n",
       "      <td>0</td>\n",
       "      <td>豚肉の生姜焼</td>\n",
       "      <td>462.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>曇</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>上旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-09</th>\n",
       "      <td>129</td>\n",
       "      <td>木</td>\n",
       "      <td>1</td>\n",
       "      <td>鶏チリソース</td>\n",
       "      <td>435.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>曇</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>上旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>87</td>\n",
       "      <td>金</td>\n",
       "      <td>0</td>\n",
       "      <td>手作りロースカツ</td>\n",
       "      <td>440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>快晴</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>上旬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-14</th>\n",
       "      <td>129</td>\n",
       "      <td>火</td>\n",
       "      <td>1</td>\n",
       "      <td>鶏の照り焼きマスタード</td>\n",
       "      <td>376.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>晴れ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>中旬</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales week  soldout          name   kcal event  payday weather  \\\n",
       "datetime                                                                     \n",
       "2014-01-07    131    火        0  カレー入りソーセージカツ  404.0   NaN     0.0      快晴   \n",
       "2014-01-08    128    水        0        豚肉の生姜焼  462.0   NaN     0.0       曇   \n",
       "2014-01-09    129    木        1        鶏チリソース  435.0   NaN     0.0       曇   \n",
       "2014-01-10     87    金        0      手作りロースカツ  440.0   NaN     1.0      快晴   \n",
       "2014-01-14    129    火        1   鶏の照り焼きマスタード  376.0   NaN     0.0      晴れ   \n",
       "\n",
       "            precipitation  temperature  month  day period  \n",
       "datetime                                                   \n",
       "2014-01-07            0.0          7.3      1    7     上旬  \n",
       "2014-01-08            0.0          9.8      1    8     上旬  \n",
       "2014-01-09            0.0         10.9      1    9     上旬  \n",
       "2014-01-10            0.0          5.1      1   10     上旬  \n",
       "2014-01-14            0.0          5.8      1   14     中旬  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'remarks(備考)'はデータとして不要な気がするので、データから列ごと削除\n",
    "df.drop(['remarks'], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ⑥ 集計\n",
    "ここまででデータはある程度きれいになった気がするので簡単に集計をしてみます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.646831Z",
     "start_time": "2018-04-15T20:56:17.631005Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "曇     44\n",
       "晴れ    41\n",
       "快晴    34\n",
       "薄曇    23\n",
       "雨     22\n",
       "雷電     1\n",
       "雪      1\n",
       "Name: weather, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# weather列の集計\n",
    "df['weather'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.670318Z",
     "start_time": "2018-04-15T20:56:17.651475Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "week\n",
       "月    34\n",
       "木    37\n",
       "水    37\n",
       "火    35\n",
       "金    23\n",
       "Name: soldout, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# groupbyメソッドで、'week'列ごとに'soldout'の数をカウントする\n",
    "df.groupby(['week'])['soldout'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.692114Z",
     "start_time": "2018-04-15T20:56:17.673992Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month  period\n",
       "1      上旬         475\n",
       "       中旬         581\n",
       "       下旬        1018\n",
       "2      上旬         725\n",
       "       中旬         715\n",
       "       下旬         609\n",
       "3      上旬         614\n",
       "       中旬         698\n",
       "       下旬         369\n",
       "4      上旬         792\n",
       "       中旬         344\n",
       "       下旬         457\n",
       "5      上旬         316\n",
       "       中旬         406\n",
       "       下旬         517\n",
       "6      上旬         467\n",
       "       中旬         440\n",
       "       下旬         311\n",
       "7      上旬         460\n",
       "       中旬         277\n",
       "       下旬         406\n",
       "8      上旬         288\n",
       "       中旬         250\n",
       "       下旬         334\n",
       "9      上旬         468\n",
       "       中旬         249\n",
       "       下旬         234\n",
       "Name: sales, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# groupbyメソッドは複数列に対しても行える\n",
    "# groupbyメソッドで、'month', 'period'列ごとに'sales'の数を合計する\n",
    "df.groupby(['month', 'period'])['sales'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.705315Z",
     "start_time": "2018-04-15T20:56:17.695029Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weather\n",
       "快晴    15.294118\n",
       "晴れ    22.558537\n",
       "曇     19.377273\n",
       "薄曇    23.926087\n",
       "雨     18.813636\n",
       "雪      1.200000\n",
       "雷電    19.000000\n",
       "Name: temperature, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# wetherごとにtemperatureの平均値を出す\n",
    "df.groupby(['weather'])['temperature'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.740054Z",
     "start_time": "2018-04-15T20:56:17.711753Z"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>temperature_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>7.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>9.8</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-09</th>\n",
       "      <td>10.9</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>5.1</td>\n",
       "      <td>-5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  temperature_diff\n",
       "datetime                                 \n",
       "2014-01-07          7.3               NaN\n",
       "2014-01-08          9.8               2.5\n",
       "2014-01-09         10.9               1.1\n",
       "2014-01-10          5.1              -5.8\n",
       "2014-01-14          5.8               0.7"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 前行との差分が欲しい時は .diff() を使う\n",
    "df['temperature_diff'] = df['temperature'].diff(periods=1)\n",
    "df[['temperature','temperature_diff']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.772704Z",
     "start_time": "2018-04-15T20:56:17.745295Z"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>temperature_diff</th>\n",
       "      <th>temperature_rolling_mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>7.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>9.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-09</th>\n",
       "      <td>10.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>9.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>5.1</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>8.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>7.266667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  temperature_diff  temperature_rolling_mean\n",
       "datetime                                                           \n",
       "2014-01-07          7.3               NaN                       NaN\n",
       "2014-01-08          9.8               2.5                       NaN\n",
       "2014-01-09         10.9               1.1                  9.333333\n",
       "2014-01-10          5.1              -5.8                  8.600000\n",
       "2014-01-14          5.8               0.7                  7.266667"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'temperature'列について、頭からwindowサイズ3で移動平均を計算する\n",
    "df['temperature_rolling_mean'] = df['temperature'].rolling(window=3).mean()\n",
    "df[['temperature', 'temperature_diff', 'temperature_rolling_mean']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.813818Z",
     "start_time": "2018-04-15T20:56:17.776217Z"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>temperature_diff</th>\n",
       "      <th>temperature_rolling_mean</th>\n",
       "      <th>temperature_pct_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>7.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>9.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.342466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-09</th>\n",
       "      <td>10.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>9.333333</td>\n",
       "      <td>0.112245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>5.1</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>-0.532110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>7.266667</td>\n",
       "      <td>0.137255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  temperature_diff  temperature_rolling_mean  \\\n",
       "datetime                                                              \n",
       "2014-01-07          7.3               NaN                       NaN   \n",
       "2014-01-08          9.8               2.5                       NaN   \n",
       "2014-01-09         10.9               1.1                  9.333333   \n",
       "2014-01-10          5.1              -5.8                  8.600000   \n",
       "2014-01-14          5.8               0.7                  7.266667   \n",
       "\n",
       "            temperature_pct_change  \n",
       "datetime                            \n",
       "2014-01-07                     NaN  \n",
       "2014-01-08                0.342466  \n",
       "2014-01-09                0.112245  \n",
       "2014-01-10               -0.532110  \n",
       "2014-01-14                0.137255  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['temperature_pct_change'] = df['temperature'].pct_change()\n",
    "df[['temperature', 'temperature_diff', 'temperature_rolling_mean', 'temperature_pct_change']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.827188Z",
     "start_time": "2018-04-15T20:56:17.816589Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 'temperature'列に一つでもNaNがある行を削除\n",
    "df.dropna(subset=['temperature_diff', 'temperature_rolling_mean', 'temperature_pct_change'], axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.860992Z",
     "start_time": "2018-04-15T20:56:17.837198Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sales                         0\n",
       "week                          0\n",
       "soldout                       0\n",
       "name                          0\n",
       "kcal                          0\n",
       "event                       153\n",
       "payday                        0\n",
       "weather                       0\n",
       "precipitation                 0\n",
       "temperature                   0\n",
       "month                         0\n",
       "day                           0\n",
       "period                        0\n",
       "temperature_diff              0\n",
       "temperature_rolling_mean      0\n",
       "temperature_pct_change        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 改めて、各列にNaNが入っていないか確認\n",
    "df.isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:17.956000Z",
     "start_time": "2018-04-15T20:56:17.870999Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>week</th>\n",
       "      <th>soldout</th>\n",
       "      <th>name</th>\n",
       "      <th>kcal</th>\n",
       "      <th>event</th>\n",
       "      <th>payday</th>\n",
       "      <th>weather</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>period</th>\n",
       "      <th>temperature_diff</th>\n",
       "      <th>temperature_rolling_mean</th>\n",
       "      <th>temperature_pct_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-09</th>\n",
       "      <td>129</td>\n",
       "      <td>木</td>\n",
       "      <td>1</td>\n",
       "      <td>鶏チリソース</td>\n",
       "      <td>435.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>曇</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>上旬</td>\n",
       "      <td>1.1</td>\n",
       "      <td>9.333333</td>\n",
       "      <td>0.112245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>87</td>\n",
       "      <td>金</td>\n",
       "      <td>0</td>\n",
       "      <td>手作りロースカツ</td>\n",
       "      <td>440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>快晴</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>上旬</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>-0.532110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-14</th>\n",
       "      <td>129</td>\n",
       "      <td>火</td>\n",
       "      <td>1</td>\n",
       "      <td>鶏の照り焼きマスタード</td>\n",
       "      <td>376.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>晴れ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>中旬</td>\n",
       "      <td>0.7</td>\n",
       "      <td>7.266667</td>\n",
       "      <td>0.137255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-15</th>\n",
       "      <td>134</td>\n",
       "      <td>水</td>\n",
       "      <td>0</td>\n",
       "      <td>さんま辛味焼</td>\n",
       "      <td>450.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>曇</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>中旬</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>-0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-16</th>\n",
       "      <td>107</td>\n",
       "      <td>木</td>\n",
       "      <td>0</td>\n",
       "      <td>カレイ唐揚げ野菜あんかけ</td>\n",
       "      <td>415.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>快晴</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>中旬</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.233333</td>\n",
       "      <td>1.413793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales week  soldout          name   kcal event  payday weather  \\\n",
       "datetime                                                                     \n",
       "2014-01-09    129    木        1        鶏チリソース  435.0   NaN     0.0       曇   \n",
       "2014-01-10     87    金        0      手作りロースカツ  440.0   NaN     1.0      快晴   \n",
       "2014-01-14    129    火        1   鶏の照り焼きマスタード  376.0   NaN     0.0      晴れ   \n",
       "2014-01-15    134    水        0        さんま辛味焼  450.0   NaN     0.0       曇   \n",
       "2014-01-16    107    木        0  カレイ唐揚げ野菜あんかけ  415.0   NaN     0.0      快晴   \n",
       "\n",
       "            precipitation  temperature  month  day period  temperature_diff  \\\n",
       "datetime                                                                      \n",
       "2014-01-09            0.0         10.9      1    9     上旬               1.1   \n",
       "2014-01-10            0.0          5.1      1   10     上旬              -5.8   \n",
       "2014-01-14            0.0          5.8      1   14     中旬               0.7   \n",
       "2014-01-15            0.0          2.9      1   15     中旬              -2.9   \n",
       "2014-01-16            0.0          7.0      1   16     中旬               4.1   \n",
       "\n",
       "            temperature_rolling_mean  temperature_pct_change  \n",
       "datetime                                                      \n",
       "2014-01-09                  9.333333                0.112245  \n",
       "2014-01-10                  8.600000               -0.532110  \n",
       "2014-01-14                  7.266667                0.137255  \n",
       "2014-01-15                  4.600000               -0.500000  \n",
       "2014-01-16                  5.233333                1.413793  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ⑦ 可視化\n",
    "\n",
    "Pythonでデータから図を描写（可視化）する際はmatplotlibがよく使われますが、matplotlibは図を描写するために細かいパラメータがたくさん必要であり、初学者にとっては本質的ではないところ時間を取られすぎることが良くあります。\n",
    "\n",
    "そこで、ここではpandasがメソッドとして用意しているplot機能を使います。このplot機能では、作成したデータフレームを対象にして少ない引数で簡単に図を描写できるため、「とにかく図を確認したいだけ」という場合はお手軽で便利です。\n",
    "\n",
    "（とは言いつつ、matplotlibの方が細かい調整がきくため操作法は知っておいた方が良いです。）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:18.601717Z",
     "start_time": "2018-04-15T20:56:17.958765Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#jupyternotebook内でmatplotlibで図を描写するときの必須のおまじない\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# おまじない: 以下を実行するとプロットスタイルが変更されていい感じになる\n",
    "plt.style.use('ggplot') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※注意点  \n",
    "以下の作図では、グラフタイトルや凡例などで図中に日本語を表示する必要があります。  \n",
    "しかし、デフォルト環境では日本語を表示するためのフォントが存在しないため文字化けします（豆腐みたいな記号が表示されます）  \n",
    "日本語フォントを指定する方法はいくつかありますが、ここでは[こちらの方法](https://qiita.com/ysdyt/items/3eb9b438980409c8f3e2)にて表示しています。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:18.614101Z",
     "start_time": "2018-04-15T20:56:18.604491Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 日本語フォントの設定\n",
    "from matplotlib.font_manager import FontProperties\n",
    "font_path = '/Users/ysdyt/Downloads/TakaoPGothic.ttf'  #DLしたフォントのパスを指定\n",
    "font_prop = FontProperties(fname=font_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:19.145287Z",
     "start_time": "2018-04-15T20:56:18.617038Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112f14a58>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SWijs378fd999N2699dau98rlMn7nd34Hb3rTm3DJJZdgz549OHz4ME6ePImFhQUAQL1e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\nb18AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11216c358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#超単純な折れ線グラフ\n",
    "df['sales'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "売上個数は右肩下がり"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:19.741093Z",
     "start_time": "2018-04-15T20:56:19.151684Z"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1159fa7b8>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eeeQRDAYDgwYNIjo6GovFwvXXX39Oz3Ft47Barfztb39jwoQJvPjii+72m266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\nvjQ0NIyYd7SLm+HVYsPbYI9OyB2LsrIypJRkZ2e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NfQgEF871kG0duHXJ8nJGw4e8FElQ\n0xOh+gSJYLK7Y8CvUE9rIEZTX4yVhfYxxUthdyK72gAocZvZ1xk6tiCc0TzJ7Hak30c0oXCkL8qS\nZC861/gVQycCYXcimxuGJjQchoAP8bGLNW9pjZMGUVqJ3PEWMhjI6LE5mIjcF0kMpaTPNOFQxjCv\njv4473YpFJXPobJ8IcSi0N6iimcTTcvNyVcr1tpbYcACAcBs0LGq+Cj/0Z4uono/tTioq2tmyQyI\nhTIaRdYdRFRUjziXHI2wWJFmCwR80z6OE4kMBaHuoBpKlkwi5pTCWesRAw+0anqidIfinFs+MUFY\nEws1xuWb3/wm3/zmN0/0MGYdixcv5umnnx5znvnzR291mm0sWbKE//mf/xlznvHWt6CgYDqHpKGh\noaGhcezU7odIGHHOxhEvSSn5qC3Evs4QVV4rK4vsx+6BZ7VCNEK2WV1Odyg+pu/hbEVGI8i2JsTC\n5eMLNoNCVSiUUSwkHFKrUIYhjCY448xRF9nQF+WdxgAeq4F1ZU5sxmEVoR4vHhGj3BRnT0eIiizL\ncU/4lVJCTyeivIqEolZr5HuclLjGub2y2SHYj5SSEreJXe1qK3Kxa4r7SDgIBqP6k/Y5DmhvoaYn\nAskElck+cIzu1XlCcTghHFQ9QJNJ5P5diNJKhMc7/ns1NGYLBSVgfB+O1MLCZeoxIuBTRTidDkfF\nfIQQ+KNJ8jMEf88IAxYQg0gpOdQdYUdrkFybgXNKnOrx3WyZdEK9MJqQ2TnIjpaUX+qoBPowCSgt\nyae2oYlF4RC6SQRcTYi6A+q/cxdMbH6n+5QJOZG93VC7H9nSCAYDorxKDRQbto0jCYWP2oJUZFnI\nsk5MBtTEQg2NKeJyuVi3bt2JHsZx5XRbXw0NDY2TBRkOgdk8boLh6YYM9SMP7UVUzEcc1WqUUCRv\nNwZo8sVGTzyeCgM3ZnYlis2opyuUOCnFQhrr1H+LM7fUpTF4Q5IhEVkmk2rFYYZWuExIKdnVHmJP\nR4hyj4Wzix0jq+6cLtAbWKIPcCRm5mBXmMX503zjOR79frUax5vHrvYQ4bjCuopskuHA2O+zO1Wv\nrEgYt9WGy2ygyXcsYuEwP8jh2OxEI1EOdgQpMyUxCzl72+0GxxUKQN1hkAosXHZix6ShMUmEwQBF\nZcjGWkjEke3NEBqwCdDp0FXOP66JyDIeh3g0dXxOKJL3mvup740wP8fKsoKxq6AngsgrQh7cjUwm\nx/Yh9PeB00XlvGLqGtroqG2gYPH0BZ3IeEytKiybhzBPzAJMuDzI9pbMyxvNUmMWIRUF2luQtfuh\nuwNsDsTiFVBamdHn9aNWdV9cXjixczHASdoToaGhoaGhoaGhAQMhFK//35C4o4EM9iN3vY987UUw\nGlUPvGGE4wp/r/XREoixrszJglzr9N0YWIaEM6/NQFcoPj3LPY6kBZuYJyBiGU2gN6jVYVLiiyTo\nCsVRpBzy1JtAFUk8KdnaEGBvR5hlBXbOKckgFKIGeODOwtHfQ5XXwr7OMNGEMtnVPDa6O0Do6Da7\nOdAZZkm+Dbd1AkEc9oGSooFAjxK3iUZflPb+qflbqp5kI7etYrXzViILJRZlsTkMCFWonI3YB8TC\nlkZkw2HE/DMmfMOvoTGrKJsLkQiyrRmRPwdxzgbEWetUm4ZIGI9Fjy+SOD5jSR177YTiSf5W00ej\nL8qaEicrizIfWydNXqHqOdsztq+99PchXB7yPHacbgc1De0Zba2mTN0hSCZh3gSrCkENOQn2IxPp\n5+idbUH+eqiPpDKN45tGZCKOrDuIfO0F5HubQUrEqvVqanzl/IxCYWcwTm1vhKUFNiyT8MvUKgs1\nNDQ0NDQ0NE5m4jG1eiA4TkXTaYDs6ULW7FcTKU0mROV8tRVnWCri8MTjCyrdeG3TnLRqHfDPi4Tw\n2pzsaj9GT7oTQU+nWjl3xlkTml2R0G5y09IepTXRS39MrZwx6XUUyiCFSQtFZhtjyY7BWJLN9X76\nY0nWlTvHrbQTnmxkewuLltmo6YmwrzM8qYqJY6ank6Qri/faInisehbkTtA30eYABAT7ISefBblW\nukIJXqvzs7zAPvkK13AIkTXSk+yjfgPtiomNHgVHOIC02TOnd88GTCYwmpEH96gtyeO1NGpozFKE\nJxsuvRKMptTfsfQPBGKGgrgsNtq7j9MDpJSfqY0PWoNEEkq69+t04PKA2QodrZCb2Y5KSqlWFuYW\nIoSgsjSP3Tv9xHp6MHuP3WpAJuLI2v2qdUEmG4zRcHoACQE/ZKnj2NepVrUDtAZis6orQEZCUHcI\nWX8Y4jFEUQmsWJPRk3I4yYGKUq/NyLzsyT2EmfKe8uc//5l3332XlpYWTCYT1dXVXHfddRQVDfmR\n/PznP+eNN95Ie9/y5cv59re/PdWP1dDQ0NDQ0NDQGE40qv4biZzYcZxgZG83cusraqLu0rOguGKE\nOHJ04rHdNP1t28JgRBrNEAqRk2UkqUj6IonpFyVnkoYaVdTKyR931r5IgldrfETjXmz9cYrLTRQ5\nTZgMghZ/jOaGbuoTWejqIpRmwdJ8Ow5z+nbvDMbZ0uDHoBNcNNeDZyI3sx4v1B7AnIyzINfKno4w\nOgGL82zHRZiV3Z3sd5Xjiya5ZJ5nwl6XQq9HWm2ppGKTXseGChc720J80NpPTzjO2cVODBNdh3AI\nitJvkOt7I+zvS7LCFCJfhpAB31D65yxECIF0OKG3C7HkTM1OQeOkZkS4hm3Q07Uft8tFNKEQSSiT\nqvCaEuEgCB3SbKE90Mc8r2V6hULUv13yCpEdrWoL7GjjiMdUYRGoKC9i594GGg4foXoaxELqD0M8\nDvMm2dbsdAMCAn2Q5aWuN8KHrUEW5dloC8Sp7Y3OCrFQ+nqhZj+ypQF0ekTpXKisRtjGNr6MJxV8\n0SQNvVH8kSQXV3km3UEx5b1l//79XHbZZVRWVqIoCk899RQ//vGPeeCBBzCZhtJ9li9fzh133JEq\nMzUaT6ILJQ0NDQ0NDQ2N2U40DICMhE8Jo+4p01QPFgti48cRupE3YdOaeDweVitEQmRbDeiEoDs0\nM2Kh9PfC4f2weMW0tW3KWBTZcgRRvWRCNxaHuiIYdIKNeeDu70Y/Z+gGJsdm5Iy+CP1OPy1znOzr\nDPHCwV6qvBYW59kwG3TUDnwvXpuBdWWuid9AD1SC4OtmUW4hAHs7wjT6Ypxd7CDXfuzbW0qp+hKG\ngxAOQyQ08P8QfcEoe+xOFuVaJ2wWn8LmSKsE1gnB8kI7WVYD7zQF8B3uY32Za4SoOmJ8g55ktiGx\nsCec4N2mfiqyLVR361RRst+PKCyZ3BiPMyK/CJwuRF7hiR6Khsa0IgxGNXk3FMSdq/5N+yIJLDOd\niBwOgcVKX0wSTSrkO2ZIh8krgsZaNfXelqG6e7Cy0q0m8NrMBopy3dS0dlGlJI/p4YBMJJA1+xEl\nFeOKZ0cjDAak3QH+Ppr9Md5p6qcy28LSfBs2Y4T3m4OE4sn0cK3jhJQSOlrVTomuNtWXdsEyKJur\nhoMNI6lIAtEkfZEEvmgSX0T9f3Cgwl8gWJRnnZJQPGWx8OjqwC996Uvcdttt1NbWsmDBUK+40WjE\n5ZqlZroaGhoaGhoaGic7g5WFA6Lh6YiUEtl6BFFYOkIonJHE4/Gw2JDhEHqdIMtqoDs0vR5VP95T\nLgAAIABJREFUUko4UoPc9T4oSURO/qSTLEeluQGkhNLxg00SiqTBF6XKayFLb0V2BUfOFA5ht1uZ\nn2OlMsvCwa4weztD1PZGKXAYafRFqcy2cNZkPbRsDjCaobcHfV4RZ+TbKXGbebepn1drfMzzWlhW\nYBtVFB4SAkMDImAo9X857P8ow7wQdTqk2UbA7OA91zwcWS4W500+WEXYHUh/7whxv8xjxm3Ws6XB\nz0uH+zi31EmhcwxBITLYZqjeoEcSClvq/bgtBlbNcSBa7GpVYSiohsLMYsRRvqIaGqcUNgeE+nGY\n9OiEwBc5DonIYdXPtC0Qw6AT5MxUdXtuPiCgsxXK5o183d+nHqstQ1YNlRVFbGntpvdIE9nlZVP/\n7MZaiEagaopJ7y4PHT0B3pR+5jhN6nFTCMo8Zj5oDVLXG53SMf5YkIk4cvub0NECnmzEmWuhsASE\nIBhT6PNH8YWT9EUT+CJJ/NFkqjDPZtTjtugpcZvxWPS4LQZcZv3EK9WPYtrqUEMh9WTlcKTv9Xv2\n7OG2227DbrezZMkS/umf/mnEPBoaGhoaGhoaGlNkUCSMnL5iIT1d6voXlaZNnrHE43EQVjuytwuA\nXLuRhr7ItKUryngcdr6LbG5AlFUhu9qhr3taxEIpJbLhMOQXTcj7qckXJZ5UqMiyQMIGsSgyEU8z\nWJfhoWoTo16wON/GXK+F3e0hansjrCxyUO2d/PcihEBmZSP7ulOim8di4MK5bg51R/joYAute/v4\nmDuGSz9oVC+RkciQOKgMSyXV6dSbWatdXfcsL1hsxM02eoSZLsVIV1xHTyhBNKmgF4KNpe6ptTzb\nHKqvZgY8VgMXz/PwVmOAN+r8LC2wsXC0AJ5h4TGKlGw7EiApJevK1DZmaXcg6w8BszgJWUPjNEDY\n7MhQEL1O4DTr8UdnPhFZhkMIq522/ji5duOM2TMIkxmZ5VVbkUcTC13pLbBFhV4sVjM1da1TFgtl\nMok8tBdRXIYYJbwpnlRtQHrC6k9vOEE8OSy4JJRNpKebnDkGzi11ph4kmvQ6StxmansiLJpiAJqU\nUg2e6/erQTDZuRm7HtLeE40g33mdZCBA1xnr8Tm8arVgrR9fJEFiIHTFqNfhsejJsxup8lpwmw24\nLXrM09zaPi1ioZSS3/zmNyxYsIDi4uLU9OXLl7N69Wry8vJob2/nqaee4ic/+Qk/+tGPZn0UtYbG\n6crXv/51XnzxRQ4ePHiih6KhoaGhMRGiA16F8RgykZi9IQYzScsRNYV4mNF3QpFsrvfTFYqzrsx5\nfL2HrFZoVR+kFzqM7O8M4YskJ+bFNwbS14vcvhWiEcSZaxFzyuCDt9MEs2Oirwf8fYiFyyc0e21v\nlFy7EZdZjxyobiMcBuewCpZQEDzZae+zGHScNcfBmUX2Y7onEB4vsqEmTYjVCUG1x0h+7y62Kl5e\niZtYZw+TZ0wCAmGxqOOx2tQfy8C/A23c/TGF7lCcrlCCzmAcX38SicSoU/DadFTlWMixGfHaDJim\n2spud6p/r7HoSH8zwGzQ8bFyF7vbQ3zUFqQnnGB1sWNklWQ4BAiwWNnbEaajP87GSteQF6fVPlQZ\nqYmFGhonDptDfagFuC16+iaQiCy7O5C73kcsPxvhmYK3XzhIwpNDVzDO0oKZDX8S+UXIw/uQGdqK\npa9vhL2AXieoKMqmpraFFeEwBusEA6KG01SnPiSsWgyoHn294WRKFOwJJwhE1eO3Xgg8VgO5NiNm\nw7BzjmJD132Y6gLTCDG1MstMfW+EjmCc/Em2jMtEAna+h2yqA5MZDu8Fo0lt2c4vgrzCEcd+GQwg\n336dcExhc8m59AVM6PtDuAYqBEvcJjwWVRS0GnTHRU+blqvJRx55hKamJu6666606eeee27q/yUl\nJZSWlvKVr3yFPXv2sGRJ5lLzrVu38uabb6ZNy8/P5+abb8blco0asa0bR6U9mdm+fTubN2/mtttu\nw+nMrJqfjjz77LN0dXXx+c9//kQP5bjy97//nQ8//JBvfvObM7J8IYQm5mdAp9ORnZ09/owaM4LR\naNS2v8akOJ32majJSGLgQttqs6A7zUQBqSiEfV0Y5i/CNGCWnlQkrx7qIiSNfGp5IQWuyfv5Hcs+\nlMgvJNpwCJvLhcujZ3tHgn5hoTJ76t+NTCYJb30J4XJjPvfT6AbaSuOl5cQ+fBeb24XQH9ulfbRm\nL0lvLtb5i8atgOiPJggkg6ytzCI724FiNhK2WrGYjegHtpuUkpAAU34hxhn4e0yUVRJtrMFqMaOz\nD3UuxQ/vw2KGqy8+jzdaY7wbiHJuRRbVuendTYmkQlcwRkd/jI7uGB39USJxteLHbTFSmucgz2Ei\nz2HCbTWO274+0X0miUJkrxWL0ZDaVpnY4PVSURBic20Pb7bGuaAqB7d1SIiNNetJeHPwmZzU+COs\nmZfPwuKhIJNEuJho7T6ExYqtQPMCPFk4nc5fpwvxgkJizXXY3G6Kc/XsbesnKytrzHuu2JHDxOMR\n2LEN8znnYSgun/DnGfR6rECnKw9TxMLCklyybDPnkZisXkSk4RAWJYk+Jzc1XSYShGQCU0nZiHPA\nmauWcuhIFx/Vd7N21WJskwgci8biNNc34itahC/uorsxij+SUIVBnSDbZmGu24jXbiLHbsJjzVxZ\nqQQ9hFs+xNTfjbGkOO21rCzJnl5JZ9zIwkn8PSrBANHtm1H8PswbLkVfNhelp4tkSyPJ1iMo+z6A\n/R+hz8lDX1SKvqgEmUgQff9N/BYn20qXozdZuGqel1yHaUZsUwb3u9/85je0t7envbZ27VrWrVsH\nTINY+Oijj/LBBx/wwx/+kKysrDHnzcvLw+l00tbWNqpYuG7dutTgjsbv9xOPZ44aP5UPqNu3b+eB\nBx7g2muv1cTCYfz5z3/m4MGDp6VYuGnTphkTCzUyoygKPT09J3oYpy3Z2dna9teYFKfTPqN0dYLO\nAMEAkdZWhHd6/fFmO7K7A9nTjVi0EtHTk2rHbPbH+Fi5C1MiRE9PaNLLPZZ9SMbiyHCYSGszwu7E\nqYtzoLmLYsvUvxvZ2qSu53mXEY4nYGBsUmdEBvuJ1NUihlVWTnr58Thy/27EvIVE+vrGnX93e4hY\nNIwbGz09MaSSRIYj6jqbVPFaRkLq2OJJxAz8PUqhV7dz3WHEQAu6lBL5wXvgzkGnKJyZq2dHXPLK\nnmYac21kWfR0hxJ0hRL0RhJIKTHoBF6bkSKbgZxsE16bYVg7VwwZidE3gbDxie4zMp5Qx93ShBBj\n3yA7gXVFJrY2BPj99nrWlDiZ41Jv+mV7G+Gk4OVdjbjMBkqsibTPH/wcbC4ip8nx8FTgdDp/nS7I\nhIIMhYi0NKFLmOgL9NPS0Y3VOPpDGaWpAZxZCIOB8N/+nxpwUbVoQkUdHouZcChIjT+OFFGUcICe\nyMwVg0gpkIokcnAvwjAkSsrebnW9ERnPAWfZI3zQ3EldspYl+TaqvJYRol4sqaQqBQerBv0d3dAB\nhopsPD0+sq0GyrwGsq0GnGb9sGXEIBrDFx1j7HPKCb/7JsJoQWTnpr2Wb06yp7mbapecUCW57GxD\nvv8mGAyIVR8j6sqC3l4QephTDnPKVU/cjhZkWzO89yYkE4Cgy5nHFvdCLIpkQ6ERYzxIX28GH+Bp\nwGg0kpuby8033zzmfMckFj766KNs376d73//++TkjH9x0t3dTSAQGFdU1Dg9CYfDWKdSgnyKjmM0\nRquu1dDQ0NA4TYmGwZWlpquejr6FLUfUdsusHKSUvNPYT5M/xrqyccIhZhLrgN9fKAh2dRw7WoPE\nk8qUU5hlw2HweBHuo66jXR7Vb8/Xk9aGPWlaGiCZhJLxvQ+llNT1RihxmzHq1ZsyodMjLVZ1nQcZ\n/L9tZgzihcWqtj/3dQ/5VbY3Q9CPWHEOoLa7nTXHjsui54OWIBKJw6Qnx26kMttMjs2I26Kf+dCb\n4eM2mpBGc1oi8li4LQYunufmrcZ+Ntf7OCPfzuI8K0ooyNsJD4qEc0ocI9dhwCtSzPJwEw2NU57B\nlOBQELdTvc/0RxNYjZnPUVJK8PUi5i6AqsUIhwu5/yNEMIBcugqhHycpPdQPQHvSQIHHNONdY0II\nyC1QfQsXDbOx8PcBAhzujO+ryHNR2N3GnqwqPmwNUtMTYXGelXBcoSeSoCeUoH8g1VevE2RZDBTY\njSwM1pFVZMFzZvGxH7sXLIXuTuT72+C8S9PagyuyzOxqC9HQF6XKO7o+IKWEmv3IvR9CTr5qE2LO\nbH0irDYom4com6e2K3e109wVYJuSTbbdxPoy17R7D06VKY/ikUceYevWrXz1q1/FbDbT19dHX18f\nsVgMgEgkwpNPPsmhQ4fo7Oxk165d/PSnP6WoqIhly5ZN2wqc6tx///386Ec/AmD16tUUFxdTUlJC\nc3MzAH/605+47LLLmDt3LosXL+ZLX/oSLS0tacu46qqruPDCC9m3bx9XXXUV8+bNY+3atbzwwgsA\nvPXWW1x++eXMnTuXj33sY2zZsiXt/f/xH/9BcXExhw8f5otf/CILFixgyZIlfO973yMaHSnTT2ZM\nu3bt4sorr2TevHnce++9ALz00kvceOONnHnmmVRWVrJ27VoefPBBlGFpdFdddRWvvvoqTU1NFBcX\nU1xczJo1awD4/e9/T3FxcWobDfLWW29RXFzM22+/PaFxgFrFd+WVV1JVVcX8+fO58cYbJ+3lN/i5\nzz//PD/5yU9YsWIFVVVV3HLLLSO2C8COHTu44YYbWLx4MVVVVVx44YU89thjAHzjG99g06ZNAKn1\nLikpmdR4Mi3/0UcfHTFfW1sbt956K9XV1SxdupS77rprhFD5y1/+kiuuuIIlS5Ywd+5cLrvsstR+\nNZzi4mL+7d/+jZdeeokLLriAyspKzj//fF5//fUR827bti21/6xdu5Ynn3wytQ8ezUT2NQ0NDY1T\nnmgU4XCC3jCUjnqaIBUF2dKIKFTPhe8199PQF+XcEifFruPoUXg0g+EgA+JtkdOElJL2/swdMuMh\nQ0HoaEWUzR3xmtDrVbG4t3vKwwWQDTWqj5JtfG+rjmCc/liSyqyj2ruttgEfvQHC6Wm9M4LHi+wd\nVk1Xsx+yctKqLIUQzM+xcvmCLD69yMsnFmSzpsRJlddKltVwXIXCFHY1HXWiGPU61pc5OSPfzu72\nEFsaAuzySdqlmTUlTmzGkeKBMBhVj6w8rQVZQ+OEYrUBQk1ENqsPJ/oiY4SchIMQj4FLbVUWC5Yi\nVq5BNtcj334NmeEefDgyFCQidfQkDRQ4ZygF+WjyisDfixx+HeLvA4dzdC/lLC/m/j7OzDNzSZUH\ns0HwVmOA3R0hwnGFOS4T55Q4uaw6i6sWe7lonoczRQ8VsS6yFi2elmO30OnVxOFEAvnB22n3uzaj\nnkKnkdqe0be3TMRhxzbk3g8Q8xYiztkwqlAopSQQTdLki7K7PcS25hAv+mxsVXIodFnYWOGeNUIh\nHENl4SuvvALA97///bTpX/rSlzjvvPPQ6XQ0NDTwxhtvEAqFyMrKYtmyZVx77bUYTkfj7Sny8Y9/\nnNraWp577rm0Vu/s7Gz+8z//k/vuu48rrriCz372s3R3d/PYY49x1VVX8dJLL6W1LPf29nLzzTdz\nxRVXcPnll/Pb3/6WO+64g2Qyyfe//31uvPFGrrzySn7+859z++23895772EbeAo8+CTi9ttvp7S0\nlG9/+9vs2LGDxx57DL/fz4MPPpj6nMmMqaenhxtuuIFPfvKTXHXVVanq1D/84Q84HA6+8IUvYLfb\nefPNN7nvvvsIBoN85zvfAeBrX/sagUCAtrY2fvCDHyClxG63p8Y72tOTTNNHG8cf//hHvvGNb7Bh\nwwa+853vEIlEeOKJJ/j0pz/Nyy+/zJw5cyb1Xf7sZz9Dp9Nxxx130NXVxa9//Ws+85nP8PLLL2Me\nOKBs3ryZm2++mfz8fD7/+c+Tm5vLoUOH+Nvf/satt97K9ddfT3t7O1u2bOG//uu/Jl1lONryX331\nVT73uc+l5kskElx33XWsXLmS733ve2zZsoVf/epXlJeXc8MNN6Tme/TRR7nkkku48soricfjPPfc\nc9x+++1s2rSJ888/P+2z33nnHV588UVuuukmHA4Hjz76KF/4whd499138Xg8AOzevZsbbriB/Px8\n/uVf/oVEIsGDDz5Idnb2iO9uMvuahoaGxqmKlFKtLDRb1J/oaVZZ2NOprnNRKW39cWp6IqwudlLq\nOYFCISAMBqTJnBLLHGY9TrOelkBsakErjbWg10NR5tRIkeVFdralQk6SipxU8qX09UJfN2LVxyY0\nf21vFIdJT649/Zpe2OxqAufghHAQjCbEKNUz04HIykYe2I1UFPD3QncH4qzMlkaOSXhizTTC7kAG\n+ycVTCOEYEm+jWyrgW1H/DSFjCyeYx6zglZ3zoZjHquGhsaxIXR6pNUGoSA6IXCZ9fSExrCl8PWq\n/3qGKslFcQVYHcjtW5BbX4azzxu1aliG+mnX2RF6HfmO4yQW5hYCAjpaoVR9sCX9vQiXZ/T3ZOUA\nEvq6ycot4IJKN6G4gtWoyygESimRB/eoD7ayphD6MgrCZocVq5HvbobaAzB3Qeq1udkWtjT46Qsn\nUiFlUkqSEhL9AZLb3yQRDKGcsRYlvwglnCShJEhKiaJAKK7giyToiyTxRRMkBxKNTQOJxvkOIwtz\nrZRnmU/Mg6sxmLJq9/vf/37M100mU0rY0Zg6g1V8zz33HJdccklKoGpubub+++/nX//1X7njjjtS\n83/84x/n4osvZtOmTXz5y19OTe/o6ODhhx/mk5/8JADr16/nvPPO4ytf+QrPP/98qtpz3rx5fPaz\nn+WFF17g6quvThtLeXk5jzzyCEBK8HniiSe4/fbbWbBgwaTH1NnZyb333stnP/vZtM95+OGHU+IZ\nwPXXX4/b7WbTpk3ceeedGI1G1q9fT0FBAX6/n0996lPHtI0zjSMUCvHv//7vXHfdddxzzz2p6Vdf\nfTXr16/nZz/7WVoF4kTw+Xxs3rw51eK8ZMkSbr/9dp566iluueUWFEXhW9/6FgUFBbz88ss4HI4R\nyxisttyyZcuk13siyx8kFotxxRVX8NWvfhVQv4NLL72Up59+Ok0s3Lp1a9p3dcstt3DJJZfwq1/9\naoRYWFNTw+uvv56qhFyzZg0XXXQRzz77bMov4b777kOv1/Pcc8+Rm6t6RnziE5/gvPPOS1vWZPc1\nDQ0NjVOWRFxNOzVbwWI9/dqQUy3IXo409eM066nIOrFCYQpLepVdocNEkz+Wltw7EaSiII/UIuaU\nIYyj3PR5vFB3ECUWZUdnnEZflI9XZ028QuFIjboP5Y9fgRZPKjT6oizOtY1cD6stvcIxFBxqyZ4p\nPF7V86nfDzUH1NTRgpHdCLMOmwO6O6f01iKXiYtLzDQd8jE/f940D0xDQ2NGsA1VE5d5zOxqD7Es\nbstYFYyvd+AhYHrrq/DmwrqLke++oQqGZ61D5BaMeLsSDNJucOK2GDIvfwYQZjPSk50SC6WUamXh\n3IWjv8nhAoNRPW/kFiCEGEpzz0RbMwT6EEtXTf/4C4qhcgFy34eQnZsSIwudJiwGHX+r8YEARZEk\npUQGA9DSBHoXzFmCCFggMNLvV68TuM0GPBY9ZR4zbov+uCYaHwuzp8bxOKIokkhYmdEfRZlZX7kX\nXngBKSWXX345PT09qZ+cnBwqKirYtm1b2vx2uz0lFALMnTsXt9tNVVVVWlv4ihUrADhy5Eja+4UQ\n3HTTTWnTbrnlFqSU/P3vf5/SmEwmE9dcc82IdRsuPgWDQXp6ejj77LMJh8McPnx4MptpQmQax+bN\nm/H7/VxxxRVp6yKEYMWKFSPWZSJcffXVaV6Il19+Ofn5+antt2vXLhobG/n85z8/ppA3VXbv3j2p\n5V9//fVpv5999tk0NDSkTRv+Xfl8Pnw+H2effTa7du0asbz169entUwvXLgQp9OZ2tcURWHr1q1c\ncsklKaEQoKysjI0bN6Yta7L7moaGxumFDAbUKqPTgchA6oLZrPq3RSaQwnCKIBUF2dqIKCpFkdDo\nj1HqNs+ei2+rLa0dq8hlIhRP4o+O0XqWic42tUKvbAxRyKMG/R1s6ORQd5hYUrKvc2LCsUwkkE31\niNJKhG78m8ojvhiKAuWZRFmrHSKhVOeDDIdmtgUZwJ0NCGhtQrYcQVTOHzfJeVZgd6jbKjG10Btn\nMsICfRCdfYa3r4aGxrQgbHbVUgKY57WgF4JD3ZnP2dLXC+7MacnC7kCsuwiyvMi3X0fWHxoxjxIM\n0KazU3C8qgoHx5ZfpIZ8KIr6sCweU311R5tfiAEria5xl61WFe4Gb74qms4Ei5aBKwv53hbkwIMv\nvU6wpsTJwlwri/OsLCuwsSrZwZq2j1ibKzhv/TIuWJjPhXM9XFqVxT9UZ/GJBdl8aqGXqxZ7uXqx\nl0uqPKwucbIg10qh04TNqJ891ypjcFr2A8eikoaasfv8j5WyuWYs1pnbAerr61EUhbVr1454TQiB\n8agnz4WFI58UO53OEdMHWzd9Pt+I+SsqKtJ+Ly8vR6fT0djYOKUxFRQUZGxJP3jwIPfeey/btm0j\nEBgyfhZCpP0+XWQaR11dHVLKEdWVg+NwuSZvFF1eXp5x2uD2a2hoQAhBdXX1pJc9Eerr6ye8fLPZ\nPCJh3OPxjNgvXnnlFX72s5+xd+/eNP9KXYaL9KKiohHT3G53apldXV1EIpER+xmM3HaT3dc0NDRO\nH2QigXztBaJz5yPnLzs5RINjYbDteLCyMDDy/D0TyLYmsFgRnulrAxrz83q71ZbjvEJwuNSL7O4O\niEagqJTW/hjxpHLC24+HIyy2tBugXLsRvRC0BGK4LRO/BJdHalRPQk/26DM5XLQIOx80+VmwoAyD\nDvZ1hKnyWsau0gBobVRv6ErHDjaRUtIbSXKwK0yB05h5uVabWukaDQ9UVgYR3rwJrOXUEUYj0ulC\nHtoDBsOEAlpmBfYBu5RwEJyZzf9HQyaTyMP7QKcH6/Q/YNbQ0JgBbA7oUL3VTXodc70WDnVHWJRr\nHRl85etFFJePuihhNMHZ58GeHcid76mV1YtWpK55/IEQIZ2bfMdxDvnKLYQDu9TQqYEsi7HEQhiw\n0ThSM37VfUcr+HoQa84ffZ5jROj0sGo9cvtW5JuvwMLlUDmfAqeJAqcJGY8jP3wbWhsRC5fA/DNO\nCtFvqpyWYqHJLCibO7MXkybzzO40iqKg0+l48sknMwoz9qOeMupHSUwabfpEvPCO/sOY7JgyJQ77\n/X6uvPJK3G43d955J6WlpZjNZnbt2sXdd9+dFnIy0XENkkxmfpKfaRyKoiCE4KGHHsqY9H2q+26O\ntl8M55133uHWW29lzZo13H333eTn52MwGPj973/Ps88+O+FlTiXdebL7moaGxmlENAyKQrKpHsJh\n5Io1p/SFHIMPaiyW49aGLPv9yO1bEYWlcOa5M/95ioLc8RYE/bAH9YYrf46a9mizgyebxsZ+3BYD\nnkmIcDOO1QYtQ5WFBp0gz2GkNRBn4QSLImQkBG3NiCUrx9yPfdEkb+kLKFL6WVZgI6lIDnVH2Nke\nYk3J2B6+suEw5BQg7CPnk1LSFUrQ5I/R5IvSH0ti0utYWTTKeTaV+BlSxcJQEIpnuA0ZEB4vMuBD\nVFaP3qo927ANiHzBwKTEQpmII9/bmvJmPGnWV0PjdMdmh2gEmYgjDEaqvRYOdIWp6YmyIHfoflRG\nI2pYmXuMB0SgCoNnnAUOF3LX+xDsh5XnIoxGWgIxdCYjefbjfHzwZIPRDO0tqs+u0TS+FUVWDhza\noz44sWV++JGqKszKgZz8GRj4EMJqg7UXwL6dyD07oKsdVpwD0Shy+xYIhxCrPoYoPAnsLo6RWXRF\ndfzQ6cSMVv1NN5kuDsvLy5FSUlJSkrESayaora1NS6Stq6tDURRKS0unbUxvvfUWPp+Pxx9/nFWr\nhrwIjm5/hdFFQbdbveDy+XxpISRNTU0THkdZWRlSSrKzs1m3LrNJ9mSpq6sbMa2+vp5FixYBQ9vv\nwIEDY37mVG96J7r8ifLiiy9isVh46qmn0sTTp59+ekrLy8nJwWw2Z9xOR087Efu/hobGScKAWGZc\nspLwe9sQegNy6apTVzCMhkGnUz1/LFaIx5CJxOjJg8eIlBK5czsoCjISnlQ4w5RpbYSgH7H2Qkgk\noL1ZrWwMBxHzFpGU0OSPsjB35kWpSWG1QTya9n0UOk182BoknpQY9RPYekfq1O93jAqTSEJhc70f\nu93GOZEadEKg0wvOyLfxfnOQBTlq4m8mZMAHPZ1qEuQAipR0BuM0+mI0+2OE4knMBh0lLjPFbhN5\nduPo4SmDLcfhEDIeUz01Z7oNGSA7F5rroWL+zH/WdGGxqpWBwYknIst4DPnOG2rV0erzMnqVaWho\nzFIGhbBQEFwe7CbVw+5Al1oFnjquDoabuMeuyBtEVFSD3YHc/qZaDbfyXFqigpxs08TOM9OI0Okg\nrwDZ2YqwOVJpzmMyGFTS2z2qWEhXO/R2IVZvOC7Xc0Knh8UrwJunJiS//n/q+cxiRay/ZNRgmVON\nU7w359RgMJV4eAvoZZddhk6n4/7778/4nt7e3mkdg5SSTZs2pU177LHHEEKwYcOGaRuTTqdDSplW\nQRiLxUZ8Nqjbxe/3j5g+KCS98847qWmKovDkk0+O+/mDbNiwAafTyUMPPUQig5dMT0/PhJc1yB//\n+EeCwWDq97/85S+0t7engkDOOOMMSktLeeSRRzKu1yCD+8NkW7InuvyJoterXgvDt09jYyMvvfTS\nlJan0+lYv349L730Eh0dHanpdXV1vP7662nzHu/9X0ND4yRiUCysXoxYvlqtmtqzY0pVzNPNjIwh\nGgGzVb14HjRCn8lE5JZG6GpT22KH+fHNFKlqgrwihDcPkV+EWLoKceEnERsvh/ln0BK7mW+RAAAg\nAElEQVSIkVDkrGpBBlQxCNK2U6HThCIl7cHYuG+XUiKP1CAKS0ZNE04qkq0NfpKK5GOVbozRUMon\nsTLLgsOs46O2YMb3AtBQAyYzybw5tPhjvNMU4Nm9Pfy91kezP0aJ28QFlR4+tTCbVcUOCp2msVOW\nDUb1JxxUf+D4iIUlFYjzP6FWhJwkCCFU38LQxMRCGY0g33wVAn7EuRdoQqGGxslGqvJ66Ji8MMdK\nKJ6k0TfMIs3Xqx5HM1R7j4bIK1J9DBNxkptfpi1pJN99go6HeUXQ14Ps7hw7CXkAYbaoIuEYvoXy\n4B610jJv/BCu6UQUzEGcd6lqf5JXeFoJhfD/2bvT4Eiv+tD/39P73q3WvkujmdEsnn28jMfb2Ngx\nTsCucIGiSCVAQlKBpEhBhYSEVLhQEKrYXjmpgKkiIS+A/w0kcG9sMNh4nbE99iz2bNpG+y71vnc/\n5//iSBpptGtay4zPp2pKmu6nn+d09yOp+9e/5V2aWXiz2b9/P1JKvv71r/P4449jsVh45JFH+Pzn\nP8/Xv/51+vr6ePTRR3G73fT29vLMM8/wB3/wB/zZn/1ZUdfR29vLxz/+cR544AFOnz7Nz372M37/\n93+f3bvVhKPGxsYbXtPRo0fx+/185jOf4ROf+AQAP/3pTxf8BGH//v384he/4H//7//NwYMHcblc\nPPzww+zcuZPDhw/zta99jVAoRCAQ4L//+79X9SbN4/HwT//0T3zmM5/h0Ucf5f3vfz+lpaUMDAzw\nm9/8hjvuuIOvfOUrq3j0VM+/J554gg9/+MOMjY3x1FNPsW3bNj7ykY8A6kXjP/3TP/Hxj3+cRx55\nhA9/+MNUVFTQ0dFBe3v7TLBz3759SCn54he/yAMPPIDJZOLxxx9f9vgr3f9KPfTQQ3z3u9/lox/9\nKE888QTj4+P827/9G83NzVy6dGlV+5r2uc99jhdffJHHH3+cP/zDPySfz/ODH/yA1tZWLl68OLNd\nMc41TdNuUakkmC0Iqw3RsA0KBeTbbyDMFth9YPnbrxPZ26nKhCproLYJKqoRK2j5sKxMGqaHTc0E\np9KrepOxUjKXQ154E6rrEcFy5OXzq57su2pDfRCLIA7cMediIQRMvWDvCycocVrw2Tdm4uOKua5l\n2eFRa/XaTHhsZoZjOep8ywQ3x0dUIOnQsUU36Q5nGE/keajFj9vkRAKEJqHahdkk2F/l5pWeKCPx\n7LzeVblcnsHuAQYC2xlsi5ArGHhsZrYFHdT7bQSdllU/t0IIpNOt7vP0G2LXBpQhm0zXHu+bicuD\nTMSXzdCVqSTy5HOQyyGOP4jwlWzI8jRNK6LpbOJZHxAEnBaqvTYujaVoDEwN6IqGwBdY/e9fX4D4\nne+h7eSb5PImqko3qZ/p9AcZmdSy/QqniZIyZGhiwd+FcmIMJkYQt9+7KVUiwuVG3L1+fRK3Mh0s\nvAkcOHCAz3/+8/zwhz/khRdewDAMTp06xac//WlaWlr43ve+x3e+8x1ADZE4ceIEjzzyyLL7FUIs\nPGFpgcuFEPzLv/wL3/jGN/j617+O2WzmE5/4BF/84hfnbHejayopKeHf//3f+fKXv8w3vvEN/H4/\nH/jABzh+/Dgf/ehH52z7R3/0R1y4cIGf/OQnPPXUU9TV1fHwww8D8OSTT/I3f/M3PPnkk/j9fj7y\nkY9w7NixmcDcSjzxxBNUVVXx5JNP8q//+q9kMhmqqqq48847+fCHP7zi/YB6/P7yL/+SS5cu8eST\nTxKPx7nvvvv46le/isPhmNnu/vvv5yc/+Qnf+c53+O53v4thGDQ2Ns6574899hif+MQn+PnPf87P\nfvYzpJQrChaudP/T613sfkw7fvw43/rWt3jyySf50pe+RENDA3//939PX1/fvGDhSs+1ffv28R//\n8R985Stf4Zvf/CZVVVV87nOfo7Ozk87Ozjm3vdFzTdO0W1Q6Nac/jmjeAYU88uIZ8PoQdZvTukAO\n9oLDiYxH4Y0XVR+f6joVOCytWPMgFjmVWQjMChauU2bhlfMqWLH3sMoAKORVWc4iWW83aiarsKwK\nEVy4yV+uIBmIZbmtYgtmlDmm1pS6llkohKDaa2Moll0+0NrbqXrZBef3TgaVVXhhNEm930a524qU\nFnUuhCfUuQXU+2yUuqycHUryyHYreUMyEM3SH80yODBKPuUh0FBOa5mDep8dv6MIExqdLmQqgUgl\nVQm1fX5vaE0Rbi9yZHDJbWQipgKFEsTxhxCed09Wi6bdSmY+TLkum3h3uZPnuiIMx3NUe23IcAix\nigy6giEZjGXpnEwzHMthrt7NoTInZSWb8wGKcDiR/iBEJlccLCQQhKE+pFFQJcCzyPZ3wBuAqlu/\nR+BWI+RWqMtZobGxMXK53ILXBYPBNZWGasv79re/zXe+8x3Onz9PSYn+JHO1Tp48yQc/+EG++93v\n8thjj232cm5Kf/zHf0xbWxsvvfTSpq1B/47ZXPrx11ZKvvkKMpOm7Pc+OHPOSCnhzCnkUB/i3kdW\nVBZT1DXl88hn/hOxaz9i+27VJ66/WwUQEzFVRlzTALUNUFK2qmCN8eIvEb6AKrmWEvk//586Tsuu\n4t6HSAj5wjOIPQcQ2/cgJ8aQrzyLOPG7iFVOcl3xMYf6kW+8iDj+nkUn6naH0pzsi/G+XUE8y039\nXaVi/N4xnvlPxLZWxM7bZi4biGZ5sTvC7+4swbfIQBaZySCf/Rli98FFn8uOiTSnB+I8ujMwM9jF\neO0FKBQwzcqCGEvk+HVnmFKXlVAqjyElpS4rNb3vUGfOELj/oRu6j/PWfu4NZHgCUV6JHOrH9ND7\nirr/rWy154y82oa88BbisQ8t+IGBjIaRp54HsxVx7ATiZsye1FZNv+a5dRmnngeTGdMd981cJqXk\nlx1hbGYTJ+pdyKf/D+LgHYiGliX3FcsU6JxMczWUJp03KHVZ2R50UO+3U1leuqnnkLx0DtlxCfHe\n/7WiHspychz58q9Ume90D0NAhiaQL/0SceQ4orZxPZf8rmK1WikvX37Sms4s1DRtS0mn03OyLbu6\nunjuuef40Ic+tImr0jTtZiHTqXl9y4QQyP1HIRJCnn4Z7vsdhGUDJwROjoFRmOm1I7x+VRK9az+E\nJ2GgBznYA1evqFLKmkaoawTvCsqQMmmwq9+ZQgikw1n0noVqqMkbqux329QAieksxlRyVZNcV3XM\ntnegtHLRQCFAbyRLqcta9EBh0UyX5M5S4bZiFoJX+2LsKnNS77fP7wPYPzXYa5FM2JmswoBtzgRo\nUVKK7Lw8J2ux3G2lJeggmilwsNpNnc+GK5dEXuxFLFHivGZOFwz2qjLkm6iH4KZwe8AwVF/L6xr7\ny/AE8uRvwelE3HUC4dAZmpp2sxNODzI0NvcyIdhd7uLV3ijtfUm2SYl5kUnIhans8I7JNCPxLFaz\nieaAnZagg8Aig6w2Rcsu1Wd4pcPW/CUqEz08cW3gCajXAW4fVNev00K1pWyhM0rTbi65XI5wOLzk\nNl5v8XtGLSQcDi+adQtqGEkwuPAfna3m7rvv5oMf/CCNjY309fXxwx/+ELvdzp//+Z9v9tI0Tdtg\na5rqm07NeaE5TViscPQe5IvPwLk3kIePbVzvm9EhVZJ6XVBNCKHWWlIKew6qoOJAD7KnEzouqu1r\nG6GuSU0VvI6Uck6wEFDfF7kM2bjaTm5yAvs9D10rD1rvkufRIYhMIo4t3icoWzAYimU5WL2Fs60c\nTmQqOacPk9UsuK/Jx8WxFCf7Yrw1mGBb0MH2Ugcem1kFSns61GAT+8J9DbtCaVI5Y375daAUclmV\nsTqrXPWOurmvR2Rn11Qp/Dq8AXNNTYGORRGBm+O1x6aZ/rlOxOcEC+XEqJp67PWrqce2LTa8R9O0\ntXG7YaB7XhuKer+N5hIHp7sGaMtXcFA6qJ21TXRWFmEmb1DutnJXvZd6vx3LUkOnNomw2Vc1jESY\nzap0OTQOzTsBVdHAyADi4F1rbtWi3RgdLNS0NTp9+jQf/OAHF71eCMG3v/1t6urq1v0N6Z/8yZ9w\n6tSpRa+vr6/n5MmT67qGYjlx4gQ///nPGR0dxW63c+TIEf72b/+WpqamzV6apmkbSPZ3I8+cgmMn\nEGWVK7uNlCqLa5EeacLrg4N3It98RfXAa95RzCUvvq6xITVFb4m/BcJkgrJK9W/fERgdVoHD9ovQ\neRkefnx+NmQ+pzIWZwULhcOpsiuLsW4p6e4b450zfWT8ezjhLGE6DCvMZqTVvi7BwpmswmC5ejwW\n0R/JIqV6k7VVCacLOTk27/Iqr40qr41oOk/HZJqOyTSXx1JUe61sN6eoikUR+44uuM+CIbk4mqIh\nYMN/fRnzdHAuPDEnWDibNArI3i5EXdPqg/ErMT39OBaGGp0NsiSXGxBzepjJ0UHk6y9BsAxxx30b\nmwWtadr6cnrU3+5s9tpwMsAkBHfVe9kxHOGM085LvXHK3RkaA3Z6wxlGEzlsZhPNJSqLcN7v/luA\nCJQiRwevvX5pv6A+RKnT5ceb5dY7y7Si++xnP8tnP/vZzV7GlrN3715+9KMfLblNa2sr5eXl9PX1\nretavvSlLy2Z5Ti7rHer+9a3vrXZS9A0bZPJoT4VKJQGjAwuGTCaI5dVwbMlSh9FbSNMjqmpviVB\nRGB+FmIxyWQCYhHYuW/FtxEmM1TVqn9TfXyIx64Fgqal0+qr3UEsU8BlNWFyONXxbmTNUg0NOT8Q\nJXylnRqXDVtDPb+9GuWhFv+1sldn8UueARgbhtC4Kr1cIsDaG8lQ5rbgsm7REmSYKkPuWfRqn8PC\n4RoP+yrd9EYytI2neKFtCLeoY4f00pI3sFvmZlQsmlWIyuaQbp+aiLzYMJ+RIfW8LdMPa81m//zp\nMuQlCZMZ6XKpzELUICT51qtQXo04ek9xJqZrmrZ1uKc+TEnF5wQLp5UkJnmwOshIs5+zQwlOD8Sp\ncFs5NpVFOK9lxa2kpBSuXkFmMpBNIwf7EAdunzfwRNs4OlioaWvk8/m45557NnsZANx2223Lb6Rp\nmnYTkCODKvOvph4QyPGRlWfJTWe5Ldfba88hCE1M9S98dH1L/MaGAAHlKwx4zmJISXvWzlCuhLsi\nYRzXBwuzKlg4WrDyfFuIlhIHRx3OG8r2G4lnOTecZDyRo2K0i/dYxim/7yGydhfPdUV4vivCQy0B\nfHazKrEtUhbjtJmswkAplFctul0iW2A4luNI7RYuQQYVUM1lkfnckhliVrOgJeig2SMYv9JNZ+VO\n3h5J8c5IivqAnR2lDkqdFgwJF0dTNAbsiw5HEYGgGjCyyLFkTwcEShH+dRpa53ACApDXsgy1xbm8\nyEQceruQZ19TH2gculO/Qda0W5FzuvVAQv2dm0UaBYhHEI0tVHttVHqsZAsSh+VdUoJbUqa+hidg\noFv9LVnsQy9tQ7xLzjxN0zRN07Y6OTaMfOMlqKiBQ3epAFskhMxmVraDFQYLhdmMOHoccnnkmVOq\nfHmdyNFhKClddUAynMrz684Ib42mGTV5eLkvQcG4bp3pNClp4tXRPDazoHMyzaRwTAWn8qs63ngy\nx3NdEZ7rUlmJJ8xjnEh2Un70KMLtwW4xcWKbH5vZxPNdEeLZghq4cN3wjhs2PgKTY4idty2ZVdg+\nkcZiFjQFtnjmvGMqsy61sgCuGOihjAx3HWjmid1B9lW6GE/keLYjzC87wrw+ECeVM9hbscQ5Xlah\nguHhiXlXyWQCRocQjeuUVchUZuz0z6Ce3rss4fbA6CDy7Cn1vBy6SwcKNe1WZbOBxaoyC68XjaiB\nR1Mf5JiEePcECkH9vbDZVU/H/h7E9t06u3qTvYvOPk3TNE3Ttio5OYZ8/UUorUAcOa7eLJdVAhIm\nRle2k/RU4GqRnoWzCZcHceguGBmAzksrXqchJfFsgeFYlo6JNGeHErzSE2Uolp1/nwwDxocRFTUr\n3n/ekJwdSvBMR5hcQfKelgD3lUrGYxlOD8bnBDaNTIqThSCYzTy6I4DPYebNqBkpWXF5cDiV58Xu\nKM92hEnnDe5t9PEef5rKrnOYdu5FVNXObOuwmDixzYdJwPNdEZKWG8tiXIhsuwD+IFQu/pjlDUnn\nZJptJXas5i1ekjWdWZdKLLupGmzSCZU1CIcLu8XE7goXv9dawv1NfpwWEz2hDI0li2cVAlDfDP4S\nFQgvFOZe19cFZrOauL2epsuPHboMeVluLxTyiJbdsP923chf025hQgjVhy+xwN+EaAgQ4Ats+Lq2\nAjX0rQzZ3616Ma9XqwxtxXQZsqZpmqZpm0qGJ5CnfguBIOL2e2c+SRYuD9LtVdlmK5namk6Bzb7i\nT6JFVS1s34O8dB5KyhClFYAaIJHIGcQzBWLZAolsgVimQDxrEM8WMKYCdgKB26be2A/1xnhsZ2Bu\n/7zQhOqjuMKJgEOxLKenMsf2VbjYVe7EbBLIoJvbkxO8PpnGb7ewq1wFQ8+PZRgzuXiowYfLauZI\njZvfXErQbTjZlk6pIMQiopkCb48k6A1n8NjMHKv30hCwIzIp5KmTKjutdX6fRZfVzIltfn7TGeG5\nqIMH0jk8hlGUAIecGIWJEcTt9y2ZVdgTzpArSHaWLh8U3nQzU6NXkIEZnoRoCLH7wJyLhRDU+GzU\n+Gykcga2ZQKkwmSGQ3epyd9X3laTtlHBa9nbhahtRFjXd2iGcHmQyYTOClmJxu0In1/1KdyoCe2a\npm0elxuZis9vFREJgcf7rh5qJEpKkSMDiJZd6zOAS1sV/QxomqZpmrZpZDSMPPlb8PoRd9w/78Wh\nKKtced/CdGrFmUx5Q2UIxmp2ERuKEXv1HImdB0lIM4msgUQFBE1C4LGZ8dhMVHuteGwO9X+7GbfV\nhNkkyOQNnm4P8Xp/nPubfNfe8I8OgtU+U1K06LLzBm8NxukJZ6j02Li/2a96Ak7z+mnOthEttXN2\nKIHXbsaQkksRg4PuHBUe9cai0mOjsdTFuR4v9ckktgVmtySyBd4ZTXI1lMFpMXF7rZfmEtU0XRoG\n8s2TIEAcvnvRAKDHZuahFj/Pnwvz62wJD8aS+P2eFT3uS5FtF1RGxaxsxnnbSEnbeIoanw2PfesH\nooTZjLQ7VlaG3NupMhGX6NXotK4sKCt8Adi5D3n5PFTVIYJlanBMKgGN21e6/LWrqkPo4SYrIqxW\n1XpB07R3BeHyIEcG510uI6H16yV7s6iqg8lxaNqAv1Pasm6ZYKFhGASDweU31DaEyWTCMIzNXoa2\nBdwq58KtcB80bauR8Sjy5PPgdCHuvH/hbKeySujpUIM0lulFKNOpef0KDSmZSOYZTeRmMgXj2QKp\n3LWfaXPpdjyxy3h62qnbuw+vw4rXbsJjM+O0mjAtk+1jnwq6vdgd4Woow7ag6qMnx4YRFVWLBt2k\nlHSFMpwdSiCAO+tU4G5edpHHD0gOePLEcjZe7Y0iENSb0rR65u77YJ2P/3vGwjujaQ7PSsZM5Qwu\njibpmExjNQsOVbvZHnTMnax4+bzqF3j3g8s+1h6bmYeavTzfY/CbjhAP7HEQdK79ZaWcHIOxITUB\ndonHezSRI5zOc6j6JuqF53Qtm1ko8zlkf7fq0VSsMtTtu2G4H3n2FNz3qBps4iuZP1V7HYjaBqht\nWPfjaJqm3XRcbkiptiLTf++kYajMwiU+LHs3EL4A4q4HNnsZ2pRbJlgYDoc3ewnaLMFgkMnJyc1e\nhrYF6HNB09aHHB+B4QHYe+imLF2TyTjy1efAZkMcO7H4AJCp0mDGR6CuaemdplMIX4BcweDqZJKL\nfTGGolkyBQOryYTPoTIEK9xWPDYzXrsZj82MwyKgViBPPo+IeRC1+1d9f2p9NppLHLw1mKDSY8Ul\n86qstGnHgttH03leH4gzlsjRVOLgcLUb+2KNzL0+AEQ8xrH6Bn7dGcaQcIc1gsnhn7Op22ZhryvH\nO+EcLek8DouJy+MproynMAnBbRUudpY55/X6k8P9yI6LiD0HZ8qxl+P0uHnQOsGL5HmuK8L9TT7K\n3Wsrn5JtF8DrX7bcvH0ijc9uodJzE5VpOVyqJHepbQZ6oVCA+m1FO6wwmVQ58gtPw/k3YGQQcdvh\nm/L3haZp2i3D5VGDTNIpcLqQ+TzyrVfVZWWVm706TZtxywQLNU3TNG0zyWQCpIFYok9cUY/XdkEN\nz6isWbJscSuSqaQKFJrNKlBoX3yirXA4kd7ACoOFSdKl1Tx9JYTJmsQu87SUOqjx2ih1WZbOECyv\ngl37kZfPQbBsVUNJph2ucTMcz6pyZOskIKFi7nNTMCQXx5JcHE3hspo40eynymtbcr/CZkfanRCL\nYK0VPLI9gARMfWmwz3/uW72Cq+ksL/fESOUNDClpLXOyq8y5YEBSJuLIM6dU+U/L7pXfYZsDu0lw\noiTHi9LM81cjPNwSoGSVGYYyPAGjg6r0eYnnKJEt0B/JcqTWfVMFvESwHHn5HDKTXvRclz0dUFGN\nKPL0YOH1q/P64lkwW5b/GdI0TdPW1/Tv+WQCaTIjX38BomHVszmwQP8QTdsketyWpmmaphWBPPsa\n8tXn5k8fXY9jJRMqeGYyI9svrvvxikmmU8iTz4GUKlC4gh6D030Ll9yvYUA6zZW8g4Ih+cD+at67\ns4QDVW7K3dZlS4kB2LEHKmqQb51Uj/Eq2cwm7qj1MhzP0tk7Br6SOfdvJJ7lmfYwF0ZT7C538t6d\nJcsGCmd4fchYFACzSWAWQDqtJgZex+JwcNgaI5kr0Byw877WIAeqFs5clIUC8vTLYLUhDt21qiCc\nMJnA7sCaTfNAsx+31czZodU/bvLKO+D2Qc3SZavtE2ksZkFTYPHg8pZUvw0Q0NO54NUyGoLwBKJx\nnSY/btulhmc0tiCsKzzfNE3TtPUxHSycGEG+8iwkYoi7H1JD1zRtC9HBQk3TNE27QTKXg8lRNTzg\n6pX1P2D/VZWVt/8ojA8jQxPrf8wikNkM8tTzkM+pvniuFQ7FKKuEZByZiC++TTZNRkJbysKOUid+\n5+rLVIUQiEPHwGxBvvkK0lh94LfGZ6O5xM6Z0TS/NtfydFuIX1ye5KcXJniuK4LNLHh0R4D9VW4s\nplUE5rx+iEeuXZDPgVFYMFiIw0l1Ic7/2lvKkVrP0kMx3nkTYhHVK3AtgSSHE9IpLCbB/ioXw/Es\nI/Hsim8uw5MwMoDYuXfJXn15Q9I5mWZbiX1eCfVWJ+x2RG0jsqdDBbWv19MJdidUrs+QC2EyqR5Q\new+vy/41TdO0lRMWK9gdagCVYSDueRhRojMKta1HBws1TdM07UaND6teM5W1yPaLyExm3Q4lpUT2\ndSOq66GuGTw+ZNs763a8YpG5rAoUplOIYw+urly7tBwQMLFEdmE6RVvBDWYru8qXHs6xFGG3I44c\nh8gkXDy7pn0ccmVpklG8wQDlbiv1fjut5U6ON/h4T4ufgGMNXWA8fojHrgUwM2n1dZFgIZn0slmC\nsu8qsqcDse8oYq1DLxxO5NTwjjqfjVKXlXPDSaSUK7q5bL+g+jfVNi653dVQmlxBsqN07c/tpmre\noT5MuG4Cpszn1WCT+maEaf2mOwshbqrSbU3TtFuaPwj+oAoUenybvRpNW5AOFmqapmnajRoZAK8f\ncfBOFTRsv7B+xwqNQyIK9c0qY2j7HhgZUKWMW5TM55CvvQCJOOKuEypLbhWEzQ6BElV6vYhsPElb\nwc32cjeOxQaFrPR4wTLEnkPIrivIwd5V3942OsBRZ4a7dtdxtNbDwWo3eytcNAQWmHS8Ul4fSAOm\nsyuXCxbmssh8btHdyWgIef4NRP02aFj7UA0xlVkIKiC1v9LFRDJHf3T57EIZDcNQ37JZhWOJHGeG\nEjQG7Hjt6xdQW08iUAolZcju9rlXDPVBLgvrVYKsaZqmbTni9nsR9/2O+huqaVvUmgec/OxnP+P1\n119ncHAQm83Gzp07+ehHP0pNzdwSih//+Mc899xzJBIJWltb+eQnP0lV1c3ViF3TNE3TFiOlRI4M\nIeqa1PCCHXtUpl/zToR7hWW2q9F3FZzuaxPz6hqh7W1ovwRH7i7+8W6QLBSQr78EkZDKKFxBBluu\nYNA2kaYnnKHKozIFnWWVyP5u1etwgYBb23gKQ5jYVbO6QOSimnciJseQZ18DX2BVn/zL4X5EVe2S\nAbBVmw6wxiLq+6WChfapNx+ZNFjml2PLXA75xivg9sC+ozeWcTYrWAhQ5bVR6bFxfjhJrc+2dK/I\n9guqd9MSQzdCqTwvdEcJOi3cXrcOP08bSDTtQJ45iYxFEVMTrmVPB5RVbthgJE3TNG3zCfPN+cGX\n9u6y5lexly9f5r3vfS9f/epX+Yd/+AcKhQJf/epXyWavfZL8X//1XzzzzDP86Z/+KV/72tew2+18\n9atfJZ/PF2XxmqZpmrbpIiHIpKCiWv1/WyvY7HD5fNEPJfN55GCvCkxOBWGEyYxo2YMc6CERjpDJ\nL9ATbZNIY2p4Rmgccef9iGDZkttn8gbnhxP89+UQ74wk8dnNdIUy/OJyiNeMINFkFuKxebfLFQza\nQlm2OQq4bMV5AS6EgAN3gt2JPP0ycoWvXWQsogJ61XVFWcfMeuwOdV5NDTkhkwaTCRbqMzg9VGVW\nEG9mfVLCudcgk1J9Ci1r/tz42rGymTmDfQ5UuYhm8nSHFi/Hl7EIcqAXsX3PouW3sUyB316N4LGZ\nuK/Jt6oej1tSTYMK7k5lF8pYBCbHEI3bN3lhmqZpmqZpc605WPiFL3yB++67j7q6OhoaGvjUpz7F\n+Pg4XV1dM9s8/fTTfOADH+DIkSM0NDTwF3/xF0xOTvL6668XZfGapmmatulGBlX2Vmk5oBpXi9b9\nyIFuZLjIg0dGBlTJYn3z3Mvrm8nbHDx7uou3Blc/jXY9SMNAvnkSxoYQt9+DmM6EXEAqZ3B2KMHP\nL09yeTzFthI779tVwj2NPh7fVcL+KhdDuHg6X8HLl4eYSM4tr22fSJPL5djtXe+FZM8AACAASURB\nVFmfvJUSVivi9uMqQPnOmyu70VA/mC1QXl3UtQAqozA+FSxMp8DmWDgr0OG4ts31pkqrxcE7i9Mn\nabqEKnPtWKUu1afx7ZEkBWOR56T9grpt/cIl0MlcgeevRrCZTTzQ7Mdmvvk75wizGdHQguzrUiXi\nPZ0qAFxV3MCypmmapmnajSraK69kUjW39nhUicjo6CjhcJh9+/bNbONyudixYwdtbW3FOqymaZqm\nbSo5Ogjl1RQwkS1MZfXVN4M3gLxwdsWDHlZ0rL4uKCmbF+QRFgttFa0kwxFGQomiHnMtpJRw9jUY\n7kccuQdRsfCU12SuwJsDcX5xeZKOiTQ7y5y8f1eQwzUeXFaVbWY1m9hd7uL9e8o4WiIIhWL8qiPM\n810RRuJZcgXJ5bEU28wpXK4FSnJvkPCVIPYfRfZ2Inu7lt1eDvchKmvWpcRIePwqGw0gm7kWFLye\nxaoCltcFC+XkGPLiWcS2XYiahuIsajpYmJp7rH2VLlI5g47J9LybyHgU2d+D2LFnwccpkzd4viuK\nlPBAs++Ge1BuKY3bIZ+H3i5k/1VEXbMuR9M0TdM0bcspyqsvKSU/+MEP2LVrF3V16tPRcDgMgN8/\nt3eQ3++fuU7TNE3TtjpZKGD89mkVFLz+ukwaQhOIympe64/xXxcnOT0QJ56TiN0H1PTekfm3W9M6\n0kkYHUYsMIwikze4ZCnDbzZIjo+TyG5eKbKUEs6fVhNeDx9DLFCOG88UeL0/xi8uh+gOZ9hT4eJ9\nu0o4ULX4cBKzSbC9Nshj+R6O1XtJ5w2e64rwP20hsoZkN9F1axQuGloQDduQb59ecpCMTCYgPAnV\n9euyDrw+iEdV1uZUZuGC6xViaiLytQCezKSRp1+BQBD2HCjemqYf8+sCk36HheYSOxdGk+QK1wWv\n2y+qQGfD/KEeuYLkhe4ombzBiWYf7iKVlW8VwuWGqlrkxbMq4KsHm2iapmmatgXdYKMa5amnnqK/\nv5+vfOUrN7yvl19+mVdeeWXOZZWVlXzsYx/D5/NteraEtjJWq5VgcPkm9tqtT58LWjFs5nlkxKKk\ncmlEx0WcLa0I67WBEfnudjJOB46dewhfDFFf7mQ8nWegN01jsJqd1S2U9rTh2LX0tNeVyF3uJ+tx\n49p7QE0HnuW1nhAuj4ffafTy32FJxuKiIei+oeOthTQMsmdfIz/aj/3+R7A075hzfTiV4/xglM6J\nNHaLheM7SthV6VlxiWlheyvpvk4OBu0c2lbNQCTNO0Mxyj12yicNLOWV2KbOk2KfM/K+R0j/+hdw\n8QyOh9+PWKBXYG58kKzbg2vX3gWvv1GFbAPpzos47TYyFjMmfwn2Re5jurQMYbFgDwaRhkHmxV9h\nOBw4Hn4fJlfxzg0pJUmPF5vdgvW6tdzj9vF/zg0xmreyr1xlwxrxKKmJEWyH78RaXj5n+7wh+fWV\nMXImG08crqDMXfzHcDXW6/dO4eDtpF94BlNZJc7G5uVvoN009GserRj0eaTdKH0OaUuZbmHzgx/8\ngJGRkTnXHT9+nHvuuQcoQrDw+9//PmfOnOHLX/4yJSUlM5cHAgEAIpHIzPfT/29qalp0f/fcc8/M\n4q4XjUbJ5XILXqdtLcFgkMnJyc1ehrYF6HNBK4bNPI/k+AgylYJUitTrL6uMwenr2i8j7U76J+JE\n4wnurAoQqHBwNZTm8liYi7Kaiv6L7D51mprWljVPnZVSIi+cQ/hLycQTwLW+hPFMgbe6Q9xW4cIc\nyuLM5mkfHKfUvPhwifUg00nkm6/CxBhi/1Ey/lKYes7CqTwXRpP0RbI4rSZ2lTtpCTqwmPLEIyuv\nNpDCgsxkSXdcQbTswgXcUWlB5jMkI2FELo+YOuZ6nDNyzyHki78k+fwzcOT4vOfTuHIRPH4ysXhR\njztzfEMiUynSfd3I0ATC5SWxyH2UBQM5PkpichJ55W1kdyfi2AnS6Qyki3tuGBLSoyOIsvnl5lUO\ng9c7R6i05jAL4K1XkYUC6UDFzHOl9iF5pSfGUCzL/c0+TJk4kxt7Cs+zXr93pNUBwQqobSKl/z7e\nUvRrHq0Y9Hmk3Sh9DmlLsVqtlJeX87GPfWzJ7W4ozeH73/8+p0+f5h//8R8pK5s74bCiooJAIMDb\nb789c1kymaS9vZ3W1tYbOaymaZqmbZy06skrWnYjOy/N9IyThoEcHUJU1DASz2E1mwg6LVhMgh2l\nTn63tYR7WispBMp54dIQT1+Z4GoovfjAh6VMT9itm5+FdH4kgd0i2FnmBJudctKMJTb2gzU5Ooj8\n7dOQiCGOP4RoUhmFE8kcL3ZHebo9xEQqz9FaD7/XWkJrmXNNk22F2QzBcuT43E9ByUz1xVunMuSZ\n43t8iIN3Igd7ZybaTpPplAqUFnkK8hx2p5p+HI1AOq2GYyy1bTqlnpsr7yBa9yHKq9ZnXQ7nwsNU\ngD3lLjIFSedkGjovIwd6EHsPzZnCLKXkjf44A7Esdzd6qfRsbkbhehNCII4cX7SXp6ZpmqZp2mZb\nc2bhU089xSuvvMLnP/957Hb7TB9Cl8uFzaZe5D322GP89Kc/paqqioqKCn70ox9RWlrK7bffXpzV\na5qmadp6S6dUgGbXfhjuR759Go49CKEJNZm4spbhySwVbiumWZlmJiFo8Nupu3Mno889y+VQkFNZ\nyfnhJK1lTlqCdqwrnfAangDEzMTlaRPJHD3hDLfXerGaBdJmp8IYoSNbIJkrzAwJWS/SMODyeWTH\nRaioRhw6hrA7GI3nuDCaZDiexWe3cFe9lwa/HfMaAoTXE2WVyPYLSKOAME3dv+lA1ToHCwE1GKS5\nFXnhLQiUIkpK1RUjA+prZe36HVsIpMenpmwbhaXvr8MBqSTyrZNQUQ07967fuhxOZCrFQs+u126m\nKWDnYucQ24bPYtmxBzEr6C2l5MxQgq5QmmP1Xup8SwRANU3TNE3TtA2x5mDhs88+C8CXvvSlOZd/\n6lOf4v777wfg8ccfJ5PJ8L3vfY9EIsHu3bv5u7/7OyyWorRK1DRN07T1l0qBw6my2vYdRZ56Hga6\nVXaX3UHOG2C8P8SRmoX7wJk8Xiq3NVHRc4HIniauRA3ODSd4ZzTJ9qCDnWWO5YN6oUnw+hCWa/0S\npZScG07is1vYFpwKsNjslBcSSAmj8RxNJesXLJS5HPKNl2BiFLH7IGzfDcCL3VEGohkCDgvHG3zU\n+W1zgqg3rKwSLp+DcAiCU1UNU9mfGxEsBGDvQQhPIE+/DPc9irDbkUP9UFqBsBd/IvNswutHDvWp\n/yyVWehwQiEPNrsK4hbzObie3amej0Xsdma52t9LV0UTO3fPHa5ycSzFlfEUh2s8NJWs72OnaZqm\naZqmrcyao3Y//vGPV7Tdhz70IT70oQ+t9TCapmmatqlkOgkOFwCiohpqGpAXzoLFiiivZixZQEq5\ndOnkjr3Q10Wg9xJ37TvK/ioXV8bTdEykuTKeoqnEzu4yJz7Hwn+WjfAEUXcpqWiWVN4gnTOIZQuM\nxLPc2+S7Foyz2XHIPD6rYCyRp6lkwd3dMJlOIk+9AKkE4tgJRFklAF2TaQaiGY7Ve2kM2NcnQBUI\ngsUK4yOzgoUpMFvU5RtAmMxw5DjyhWeQZ07C4WMwPozYc2j9D+71Q2+n+n6p4Kg3AFa7Kne1r3O2\nntMF6SRSynnPuUyn8J55mQZXCZeC29kuUb0LgfaJFOeHE9xW6aK1bIMCvZqmaZqmadqybmw0o6Zp\nmqbd6tIphHNWIGPvYcjnIBGFylpG4llcVjNe2+J/UoXdjtixB9ndgYxHcVnNHKp28/7dJeyvdDEU\ny/L/2kK82B2d028wnMpzdiDG/x238HQqwAvdEV7vj3FlIkUolae1zEmtd1aQcirTrNwmGV2nvoUy\nFkW+/CxkM4jj75kJFOYKkvMjCRr8dppKHOuWySZMJiitmNu3MJ0Ch2t9s+euX4fLjTh8DEYHka+/\nCIYBVevYr3Ca13ft+yWyGIW/BPHo7yOCZYtuUzTTWYz5/JyLZaGgsk+lwd5jh0gXBFdDampJdyjN\nmwMJdpY6ua3Ctf5r1DRN0zRN01ZM1wNrmqZtUXJyDCSI6/rUbegahvogm4HKGoTjXfqGPpVUpa9T\nhNMFuw8gL52DiiqGuhJUea3LB6qaW+FqO/LSecTt9wBgM5vYXeFiZ5mTnnCGS2Mpft0ZptRlJW9I\nIuk8tlyaOpGhflsAf2UQh8W0eO+/qWBhhdWgK5knnTdwWIr3uaCcHEe+/gLYHYi7H0C4rpVeXxxL\nks1LDlQvXI5dTKKsEnnpHLJQUOXh6dTGlSDPXkdlDezYi2y/oPoXutb/vuPxq68mk+qludT6Nip4\nOv3Yp5Ng9V+7vOsKRCYRxx8mEPBSH5FcHEvisApO9cdpLLFzuMa9oUFeTdM0TdM0bXk6WKhpmrZF\nyUvnQYC4+6HNOX4mg3zzVTVIAZD+oAqOVNUiAqWbsqaNJg1DTdp1zg2Uim2tUL+NFCaimTy3VS4f\nSBVmM+zajzxzEjk5hgheCwKbTYJtQQfNJXYGYlk6JtJ4bCYOVLmpnAhhGowhqssQlmV6EE5nFlry\ngI2xRI56f3FKUGU0jDz5HASCiNvvRczqlxfPFLgylmJXuROPbX2HqgAqeGsUIDQOZZXIdHLzgtmt\n+xCZNFRu0GRbp0uVXFttWyfINhMsTKkyaaamhXe3I+qaZobA7K1w8XR7iJe6o9T67NxR69k690HT\nNE3TNE2bocuQNU3TtqpETA3X2Cw97SAE4sHfQxy+G+HxIa+2IV/8JXJibPPWtZGyaZDGgllrwmpl\nJK5Kfas8K+yVV9cEvhLkhTNIKefvUwjqfHYeaPZzd4OPWp8NcyQEXj9iJcPBpjLN3DKL22aeU9J8\nwwZ7wWRG3HViTqAQ4OxwAptFsLt8gwJ2voAKjE6XIm9SZiGosmhx8E5Edf3GHE8IFZBb50Eqq2Kf\nFSycNjIIqQQ07Zi5KOC0sK3EQbXXxt0N3qJMx9Y0TdM0TdOKTwcLNU3TtiBZKKg33lNDAzbj+PJq\nO6KuGeHxqeygI3cjHn5CbZCIbviaNsV0sHaRrLXhWI4SpwX7Ckt9hRCIvYdURtxw/4puI8OTiEBw\nZfu3WFTWWSZDudta1L6FcnQIUVGlMiRnGYln6YtkOFjlxmremOCPEAJKK5Fjw+rnYxODhZtBVNUh\nyqs2exkzhMUCVvucYKG82gYlZfOykO+s9/JAsx+LDhRqmqZpmqZtWTpYqGmathUl44CcGhqwPoMq\nljTQDZkUbGudc7E0m7loKiGb3MSMx400Hfxwzg8WSikZjmepWmoK8gJEeRVUVCMvnkNOlXgvRubz\nEIuoCcArZbNDNkOl20o4VSBbMFa1vgXXkclAeBLKq+dcbkjJW4MJSl1WGgPrPHH3OqKsUq0pnVI/\nJ++inppi517EnoObvYy5HM6ZnxcZi6rp0M07lrmRpmmapmmathXpYKGmadpWlIhf+z6V3NBDSymR\nnVegshYxe/IqEE4XOG8EaJvMbuiaZpO5LPLiWZV9ud7SSTVIwjY/EBbJFEjnjZWXIM8idh9UZeY9\nXUtvGA2rMuhVBwuzlLutSCTjifzyt1nO2BAgoWJuNtvVUIZwOr85QyrKKtVjM9Cj/v8uyizckhxO\nZHrqd1V3uyqTrm7Y3DVpmqZpmqZpa6KDhZqmaVtRInbt+/QGZ/GNDUMsjGjZNe+qcCoPFiudsQLG\nJpRHA9Dfjey4COGJ9T9WKgkO54KBsOFYDrMQlLnXECz0lyDqm5FtbyNzS2SORiZVsNIbWPnObXZk\nNoPHZsJpNRWnFHlsGHyBeUNEesIZqr02ylyrfwxumMerAlT93er/Oli4qcRUZqHM55B9XYiGlnkl\n65qmaZqmadrNQQcLNU3TtqJkAtxe9X16gzMLuy6DPwilFfOuC6fzWKxWkpkCQ7FNKI8G5FCf+iYa\nWf+DpVOL9yuMZylzW9fee611H+Ry0Hlp8W3CkypIt4qgi5gqQxZCUO623vCQEynlVL/CuSXIBUMy\nnsitugy7WIQQqhQ5GlIX6GDh5pouQ+7vhnweGrdv9oo0TdM0TdO0NdLBQk3TimbJDCltVWQiBh6f\nKuXbwMxCGQ3D6BCipXXBbLpwukC1x0LASNE5md6wdc2sL5OB6UnMsfDS2759Gtm7TJnvcsdLJ1XG\n1HUKhmQskaN6DSXI04TLjdjWiuy8fK188/rjhycR/tIFr1vUVLAQoMJtZSKVJ2/cQBZoNKz6V17X\nr3AimacgJRU38BjcsLKpsmibXWexbTaHS2UWXm2H6jqEy73ZK9I0TdM0TdPWSAcLNU0rChkNIX/5\nn8jZvfa0tUvGEW4P2J3XJvJuhK7L6k1/zfxeY1JKwqk8freN7TLCQCRNIrsBfQNnG+kHKdUk3Nji\nmYXSKCB7OpEXzyBvZEBMauHMwp5whrwhqfTeYFbdjj1gNsOVd+ZdJfO51Q83ARUszKlgYbnbipTy\nxgK7o0NqwnKwfO7FiRxWs4mAYxODdGVT2a86q3DzOZyqh2QsjGjSg000TdM0TdNuZjpYqGlacSQS\nYBgQGtvsldz0pGGoacguDzhdi2adFf246RSyvxuxbSfCND8AlM5LMgWDgMdJoymF2cjTFSpudqEc\n6EF2XVn8+qF+CJYhyishGkEu1jcxHgWjoDLsrravbS1SqhLwWYGoWKbAC1cjvNYfo9Znp+QGA2XC\nakPs2KsCm9HrMiUjYUCuIVhog2wGKSV+u5kdpU7eGoxzdiix+OO1BDk6BGWV8zL3RuJZKtxWTBs9\n2GQW4fKocn0dLNx808+Bx6eGz2iapmmapmk3LR0s1DStOKYymQhPbu46bgXplAq8ur1qoMRGlSH3\ndoIwLdprLJxWU3UDPhdWIWl0SLomM0UbdCIzGeS515EXziCT8zNUZS4HY8OI6nrybr865zKLPDYR\nFXgTtU3Izktryy7M56CQB6eLvCE5P5zgf9pChNMF7mn0cW+jtzgTgJt3gMuNvHRu7uXhCTCZ4bqJ\n1Muy2dX5k88jhOBIjZvDNR4uj6V4qSdGrrDy50vmchAaW7Bf4UQyT+VmliBPEXsOIba1bvYyNKfK\nwBXNOzd+MramaZqmaZpWVDpYqGlacUz1SJOhDZhQe6ubnoTs8kwNDdigzMKRQaioRlgXLq0Np/NY\nTAKPT/Ui227PkcwVGIxli7OAjouqxNhqhY4Fhn6MDoFRYMxXxX8Omxg1bIsPOYlMqoyz3QdU0G8t\n2YVTQdq8zckz7SEujaXYXe7kd1tLqPfbixYQESYzYvdBGBlAjo/MvQ/+kgWzPJdks6uvUz+TQgha\ny5zc2+RjJJ7lN11hkrkVlo9PjKjA43XBwvFkTvUrXMMk6GIT1XWIiprNXsa7nnA4Efc8ogebaJqm\naZqm3QJ0sFDTtOLITgWMomFVRqutXTIBCHC5wemETBpZWN/egDKXhfAkorxq0W3C6QIBh0UFE80W\ngjJN0GmlY+LGS5FlKom82oZo2aWGfvR2Ia/PqBzuA3+Q7owZw2LjtFFCYZFgoYyEwVeihog0tCA7\nLq1+AE9KBWkH81ZimQIPbw+wv8q99unHS6mph0Cp6rE4lakpw5OI1ZYgw7xg4bRan42HWwJk8pJf\ndYSZSK7g8RgdUhmu05O5py9O5LBtdr9CbcsRwTKESb+01DRN0zRNu9npV3SaphVHLqtKWAt5iC8+\neEJbgUQMXC7VI256uMZi5bbFMj6qhhNcN/F2tnAqr4KFQqh1pZK0BB0Mx3LEb3TQSdsFNUSjZTc0\n7wSTSQ1bmSILeeTwAEZlHX2RLHV+OzGri0sjsXm7klJCNITwl6gLduyFQg6utq1uTVMZnT0pQanL\nStBpWfPdW44QArHnoCrjH+xVgc14FPzFCxYCBJwWHtkewGU185uuCH2R+dtMk1IiR4cWDCCPxnOU\nu6263FTTNE3TNE3TbkE6WKhpWlHIbGZqEIPQfQtvkExMDTeBa0MD1nsi8th0BplnwasLhiSaKRBw\nTmWSOZ2QSdEYsGMxCbpuYNquTMSQvZ2IHXsQVqsa+tG0A3m1XZ1XQGFkEAp5hn1VZAsG+ytd7A5Y\nuThZIJq5LlCZTCCzWS5LL/3RDMLpQjRuR3ZeXl12YTpF1uZkKJGnwW9f8/1bKVFWCZW1qnfh9KCg\ntWQWTpeRLxAsBHBaTTy4zU+tz8bLPVEujiYXHnySiKlBO9eVIOcNyfgW6VeoaZqmaZqmaVrx6WCh\npmnFkc2qyaRenw4W3qhk/FrZ59TQgPUeciLHRhBli5cgx7IFDCkJOFR2nXA4kakkVrOgMWDn8niK\n316NcGYoQddkmslUnryxwkEal98GuwOadly7rGWX+jqVDVjo7wG3j968DZ/dgt9hZm+VB0cuwRv9\nsTnBrlxogpfzJZxJWHmpO8rpgTj5bbtV1uvVxSctz5NK0W/yISU0BBbu41hsYs9BFex85y2VaelZ\n5XATQFgs6raLBAsBLCbB3fVe9la4ODec4LX+OIXrn6+xYZXhed1k24lkDkNKKrdAv0JN0zRN0zRN\n04pv/WqqNE17d8llwOdH+IOq19pmr+cmJaVUGV01DeoCi1UFftZxyIlMxiERhT0HFt0mnFKTkP3T\nPeocLphU2W/7q1zYLYJwukB/JMPlqZJkgcBjNxFwqOCe32EhYDfjsZsxTZWvykgIOdCNOHCHCnJN\nEXYHNLQgu9pgWyuFwV4KVXUMRLPsKXchhMDi93PU9A4vhBJcDTnYFnSQzBV4sStK1OTmvu1BUjmD\nM4MJRhNmjtVux995BZp3LjrEZc7jkk7SKzyUuy24rBvTm094/dDYguzpgGD52vu/2e1LBgtBlT7v\nr3Ljs5t5rT9OPFvg3kYfdos6phwdUmuwzA0KjsRVv0K/7leoaZqmaZqmabckHSzUNK04shlV/uj2\nqp5rRmH1U1w1NSgmn4OpcmAhBHKqP+C6GRsGxLwMstnC6QJumxmbeSp45XRBOoWUErvFxP4q98y2\nuYJBJFMgki4QSecJpwu0T6TJ5NXgG7MQ+KaCh77OS/jtQUqqGnBJObcHXssu6GlHvnUSmUkz6K0m\nH5Y0BKZKgn1+qk1Zmmw5zgwlcFpNvNYfg2SK95RD0Ke2K3dbeaUnxrOihkPZAVo62xC7blv2YUkn\n04zi50hg/UuQ52i9Dfq7EYHSte/Dar82dGgZTSUO3DYzL/VE+VVHmPubfHgtEsZHEDvnP06jiRwV\nul+hpmmapmmapt2ydLBQ07TiyGbVYIVAEIwCRCNr67f2bpeYGtjhmtU7cKo/4LoZG4aS0iWz7cLp\n/EwJMqB6KRqGChLbHXO2tZpNlLlMlLnmZqSl88ZM8DCczhMZD9M3liJf3YK4EsE6NV23KeBge6kD\n4XJDXbPqZxgso9dwEHRKvPapILTdCVY7h6xxhgwvv70aodRl5bgYxFXaPHPcgMPC7+wIcGYwwRvj\nTQxfHuT2xu04nHPXfb3eRAHc1g3pVzibcLjg3keu9atcC5sdmc2sOMO33G3lkZYAL3RH+VVnhOOW\nSSoLBahtmLNd3pBMJPMcrHYvsidN0zRN0zRN0252umehpmk3TObzKkBos4GvBBAQ0X0L1yQZV1+n\nexZyrT/gepCGofoVLjDxdjYVLJyVKTo9pXkV63JYTFR6bLSWObmj1sN7Elf4QFmW993ezL1NPnaX\nO7GYBG8MxOiPTpXQ7tgDCArVDQzFcjTOyvITQoDPjyMZ4a56L61lTk7U2HBmkjA9CXmKxSS4vc7D\nPfsbGc5b+eXrnYwlFh92Io0CvRkzVV7bTFnuRhK+AMK29iClsC1fhnw9j93Mw9v9lLks/LZtnA5f\nvepDOst4Qvcr1DRN0zRN07RbnQ4WaptCjg6pfljarWE6KGG1q75zPr8ecrJWiTjY7AjrrGCMw7V+\nPQsjIdVvsrx60U0yeYNUzpifWQhrH7wyNgQTo5h278drt1Dns7O3wsX9TT5qfXZO9cWJZwoItxdx\n1wMM1bRiLDBoRHj9yGiEWp+NwzUeLPGwuuK6YOG0hgo/j+7w4QwN8Zv2SS6MJDEWmASciCUYN2w0\nlCydfbhlrSFYCGAzm7g3kGN7boLTtmpO9sZmysdBlSDbdb9CTdM0TdM0Tbul6WChtuHk1Tbkqd8i\n2y9u9lK0YslN9UabyoQSATXkRFuDZHxOViEwpz9g0Y0NqSEqJYuXjIfTarjJnMxCuwOEaU1BTCkl\n8tJ5KCmDypo51wkhuKveg90seKknSt6QiIpqumKFhQeNeAMQjyKNwtRiQ+r+XJcRN5undQ8PWibZ\nkxvl7ZEkz3dFSOYKc7bpHYtjRlIXXHw/W9oag4UApt4ujnjy3NVaw2Asy/9rC9ETziClVP0KPbpf\noaZpmqZpmqbdytbcs/DSpUv8/Oc/p6uri3A4zF//9V9z9OjRmev/+Z//mRdeeGHObQ4ePMgXvvCF\nta9Wu6lJKeHyeWT7BfVGNr94CaB2k5kOStimsr78pdDfjSwUFr+NtiCZiM0r/VyqP+ANH29sBEor\nlhxGE04XMAuBx35tG2EyIe2OtWUWDvZBZBJx/D0LBp1sZhP3Nvr4VWeY0wNxDla7GYpm2FOyQFmu\nzw/SgHgMfAGIhsBfsmQwSzicmJu2c1vvZSqONXNqOMPTbWHurPdQNzUUpTeUosaUxuq5SXvzTQUL\n5fVDY5Yhczlkfzdi+262lTqp9tl5czDOq71Rur02JpJ5Dul+hZqmaZqmaZp2S1tzsDCTydDU1MSD\nDz7IN7/5zQW3OXjwIJ/+9KdnsmGsVt3j6N1KGgU49wayrwux55B6EzvYu9nL0opleurq9ICMQFAF\nt6JhKC/fvHXdjJLx+VOJZ/cHLGKwUOZzEBpD7D285HbhVB6/w4Lp+qCTw7nqzEJpGMgr56GiGlFa\nseh2AaeF22s9nOqLkcgWgEUGjXj86mssAr4AMhJCXJetuKDtu6Gng8qRF6oiawAAIABJREFULt67\n4zZe64/zUneUnaVOtgUdTCZy7LbmVJbizchmV0HUfO7az+VKDHRDoQANLQA4rSbuafTRF8nw5mAc\nQ0oqPDfpY6JpmqZpmqZp2oqsOVh48OBBDh48uOQ2VqsVn8+31kNoW4zM5SARQ6xywq3M55CnX4bx\nEcThuxF1Tci2Czqz8FaSywDiWlDCF1AlqroUeVVkPq8y9RbKLIS19wdczPioCuouO9ykMLcEeZrT\nhUylVjxxF4D+qxCPIg7fveymzSUOxhN5OiZT7Kj2LjhoRNjtSIcTomFkRY2aJu1f/neUcDihaQey\n6wq2ba3c2+ilbcLK2aEEnaE01kKWGpf55i23nc7yzWZXHCyUUiK7O6CyBuF0zbmu3m+n0mNlInnd\nVGxN0zRN0zRN024569qz8MKFC3zyk5/kr/7qr3jqqaeIx+PreThtvfW0I1/6FTIWXfFNZCaNfPU3\nMDmOuPMBRF2TusJq1cHCW0k2CzbbTGBFmM0qYBiZ2OSFrS8ZDSEvnlEZesWwwCRkYKY/YCGVoD+S\nIVcw5t92LcaHwemef7xZDCmJpPMEnPMDRMLhWlUAUxYKyCvvIGr+f/bu7DeOO7/3/vtX1dX7zn0R\nqV2WLFvyKo8tj2fLJCfnPBicJw9mbn0xyUWQXOYmAQIMkOUiF/kDcjMYBEEGyRMEOUgyT5IzZzxj\ne7zJm6zFokRJlCiyufXe1dXdVb/nopoUKe4UV/n7AgY0e63uLnLAj77L0Ib/0eH5/hhHMmGe7Vv9\nGEmk0eWi34IMkEpv7ICOnwat4dZ1lFKc6ozw3eNpEkGTI4E6ZiSyscfZj+Y3KW9mbmF+Fkp51OET\nKz+kadCX2ESVohBCCCGEEOJA2rHygPPnz3PhwgW6u7vJ5XL83d/9HX/5l3/Jn/3Znx3cSo2vukoJ\ntIe++gnqwhvr3lxXy+j3fgGtlj+brL2dtOVpPigYhBsxnnNdP1gSB1vDAWtpi6hKZ9H5mT06oJ2l\nmw348jL69ghoD5Xpgr7Bx3/g6nxY+MhMOKW4byb5/F6DcrnE2Z4oz/Q8/tw4PT2J6upd83dyxXFx\ntV65snCzbch3Rvxw8dSzG76LaSheOZQgmwwzN7fyc6lECp0b9ytZDRPiG6toV6EwHPGrCzn6FCoU\nIhMJ8N9OZnBzRVTkAM/m20pYOHbTD4/XqTQVQgghhBBCPNl2LCx89dWHLWaHDh1iaGiIP/zDP+TK\nlSucPXt21fu9/fbbvPPOO0su6+np4c033ySZTO7MNlCxIXXAS2fQpTnCLQezu2/V2xqlApFL76Bi\ncUJvfBejXbnktDz+68Y0k16EsJkgm4j77YDiQHNCQbxMlkj2YbVYc+gwjekJAkqRzW6udX2/0lrj\n3rlJ47MP0W6L4IWLNK99TgCX4Da8xub0AxrxBNG+gYUAL1d2+HCswLjRy4Bl0NmTId/yHvs99WpV\n7FaD0PFTBNZ4rOJsjUikzuG+LiKPbCJu9fTi3PmSaCKBWmcmrW42sMfvYJ55ltDw4U0fr2VZq77m\n5uAQjckxAs06Xm8/kc6Nz8nUL75KLTeONT1O8NmHS7pqpiLQ1bMtn+te0G6SWiRCKBJa8/NduH3D\noTY3jXX2OYKdnbtwhDtvrXNGiI2Qc0hslpwzYjvIeSQel5xDYi3zf2f++Mc/JpfLLbnutdde4+LF\ni8AOhoWP6u7uJpFIMDk5uWZYePHixYWDe1SpVKLZlNbVveJN51ADw+jZKex3/g/q67+5YkWSzj0g\nfPVj7EAQ9dyr2E4TnDnqLY9f3C5SaXgMRzxGGprcZI6gzLU88Lw5v93Ynns4o1CrALpWJTw9SdF4\nMhYi6EvvosfvoAaG4cxzOJEo3s0v4f49jL7hx3/8iXG0MnHyfjvtZ5NVrk7VSIcDvJqw6aPIvdAx\n3s2VGJs0iQe3XpWrx0bRdp26FUHNrT5b8m6uCi0Hu1zk0YZj3WiibZv6xDhqnWo+/eUX6FIB9eLr\nVNd4vtVks1nmVrmf1gpdq8HNL1GHjiw5DzdC9wxif/YRqmvAn4GoNXpuBtV7aM33Zr/zGk3q09Oo\nZMe6t9W3b6CrVeqZzgP9mhdb65wRYiPkHBKbJeeM2A5yHonHJeeQWItlWXR1dfHmm2+uebsdnVm4\n2OzsLOVymUwms1tPKbaRdl1/E2ssgXr6OSjOwf07y283Nor+4JeY3X2or30LFfJb4aoNl/+6VcBu\nenznaIqjWf/yQnUTLXJi/2o4qEeXKCRTYJh4c09GK7LWGj15H3XyLOqF1xYWQKhk2t/6vB3PUa1A\nzF9uUqy3uDZl83R3lN86kaY/GYa6TX/CwlCK8VLj8Z5sehLSmYWf0dX4y01W+Xel+apge+1WZO04\n6FvXUIdPoKI70NqbaAeVngvJLfx/zLHT/tdb1/yvDcdf/PLIko8DJxTaUBvywmKT3gF/DqUQQggh\nhBDiK23LYWG9XufOnTvcuXMHgFwux507d5iZmaFer/O3f/u3jIyMMD09zeXLl/mrv/or+vv7OXfu\n3HYdu9hNdhXQEI2hsl3QN4S+9pm/vZX2H5s3rqA/fQ81dJTQa99GBfyAoVRv8V+3imgN3zmWJh0J\nkIqGUGiKtccMPMT+0Gw8nJHWpgx/yYn3pMwtbDXBbUEitfTyZBqq5e1ZclKroNqbkD+brBENGjzd\nHfUreNvzAS3ToDtmPVZYqLVGz0yiulYfJQD+cpOZapPMCstNAJgPltZbcnLrqr9I5MTTWzja9amA\n9XCDdGrzYaEKhVBHTvrVdU794es56MFZcGNhIfkZKBdQw8d2/piEEEIIIYQQ+96W25BHR0f50Y9+\ntPD9T37yEwDeeOMNfvjDH3L37l3eeustarUamUyGc+fO8YMf/IBAYNc6n8V2WtjS6v9Brs6cQ/+f\nf4XR6+jjZ+CLS+g7I6hTz8LJp1GGn0PP1pq8dbtE2DL4xpEk0fbMMzMYJKFcira0lT8Rmv425Eep\ndPaJqSxcqJ57NEBKpgENpSJktz7rTXse1KoQSzBdbTJecvjaoQSm0W71j0Sg2UC3mgymglx6UMVp\neYQCW/g3n2IenDp09qx5s+lqE8f1GEyuvAFXBQJoK7hmWKjtGnr0Bur4aX+hyE5JpPz3L7nBTciP\nOnYabt/wqwuz3f5lB32eajCEbjisu1Lszk0/bF0nPBZCCCGEEEJ8NWw5uTtz5gw//elPV73+T/7k\nT7b60GI/qlZAGQtBiYol4PBJ9MhVyM9C7gHq3IUllSm5SoNf3imRCgd443ByaahhBUmpJoV6a7df\nidhmWmtoNJZtQwYgncWbGkc3m+suwNj35rf+PtqaGk8BCsqFxwoLsWv+tvFojE8mqmQjFsPpRe/p\noiq+gWSMj8YrTJQbHM5sIYCbmQQzANm1F4HcLzaIWibZ1SoL549rrY3IN674zzXf6rtDVGcP2m0t\nVDRv+v6hEBw9hR790q9URMFOhpu7QAVDaHvtqk/dcNAPxvz2+jW2YgshhBBCCCG+OnZtZqE44KoV\nvwXZWHTKnHwaDBNmcqiXv74kKLw7V+Ot2yU6oxbfPJJaXv1kGKRMl2LdlQ3XB12rCdpb1oYMQDrr\nt5+W8rt/XNvNtgEF4aUBkgoE/Jl5jzu3sFoG4L4XZrbW5FxvdGl4s2g+oB/gbb0VWU9NQkc3ylx9\nQYrWmvulBgPJ4NohUiSCXmVmoa6W0WO3UCfO7HhYrI49hfHqtx/vQY49BUqhb16DcHjp77uDyNpA\nG/K92/7P6NDR3TkmIYQQQgghxL53wP8SErtF1x4uXpingiHU176Bev27qN6BhctH5+r8/OYs/ckg\nXz+cxDKXBw1KKVIBcJoudsvb8eMXO6jRDqxWaEMmnvKrygpPwDaueq0dIC0P2FQyjV4UFrre5gJw\nrTXcv41rBPisqOlLBOlNPPJ+zlcWOn6l2GAyyINyY/PP1WrB3DSqq3fN283ZLWpNl0OplVuQ56lw\nZPU25OuX/eq8wyc2dYx7RQX92YW0mgd/XiGsO7NQa+2HuX2D/ucohBBCCCGEEDxGG7L4iqlV/MUm\nj1DpjiXffzlj8/GDCueGungqpTHWqEhKBxV4HsW6uzDLUBxA82HEo9uQAWUYGOmOJyMstGurB0iJ\nNExNoLXms8ka16ZrhEyDeMgkFjSIB832/wxiQZOoZSz92Rj9En3/DrePv0K1obk4vHxj8MJ8wHZb\n6UAyyOe5KrlKk/5VZgquaG7a3xq8Tlh4r9ggZBp0xdapCAxHYWpi2cW6lEeP30Wde2nLrcF74thT\n/uzCg74JGRbCQq31ytWhc9NQLsLZF3b/2IQQQgghhBD71gH6C07sFa2134Y8eGTN21zO1bgyVeN0\nV5TXjmTI59duPY1bJgHXb0XuS2z3UYtd02yHhSu1IQNGtgN9a2T9JQv7nK7XVg+QkmloNngwXeDa\ndIuTHRHCAYNKw6XScJmptrCbHhq/ClApRczyg8OYXSJ2Y4T40BmukGI4E1x7+3B7PmAq7AeQ46XG\n5sLC6Um/pfnRrc6LX6vW3C85DCSDawb+gP+e1Otoz1vStquvfe5XIx9a/ffGfqSCIXjx4sqVsgdN\nMOSPCGg1VwzzuXsTYol1F90IIYQQQgghvlokLBTrc+rgtpa1Ic/TWnPpQZWRWZvzvTFOd0c3NChf\nBS2S9ZYsOTno1mpDBsxsJ3x+Cd1soFYKLA4K20atFqqkMtja4P3befq6O3i+P7bsZ8D1NNWmR7Ud\nIFYaHpVylbmREcZCPTQD/QQ8zbM9a1S0RSLouo3CDxwHkkHGig4v6uXPtxo9PYHq6lvz9kXHpey4\nPNe38s/8EqEIoP3fE+0wVc9NQ24c9fyrK7Zt73eq+wnZCjz/M9lwloWF2nHQD+6hnnpGFpsIIYQQ\nQgghlpCwUKyvVvG/RpcHB66nef9+mbFCg5cHExzLbmJ7aMAiRZNi3d2mAxV7ouGAYfizCVdgZNob\nggtz67a+7mt2FSIrz3XToTDv606o21wYTKwYvpiGIhkySYb88Ey3mugvfwnxJrz+Kk0ziKchYq0+\nSlaFI+hSceH7gWSQL2ds5uwWHdH1F4jouu0vYjm+9mbi+8UGlmHQG9/AUpL5akvbr7zUWvtVhckM\nDAyvf3+xc0Ltal/H8SsIF7t/G9BwSBabCCGEEEIIIZaSBSdifauEhfWWx1t3StwrNnh1eJNBIaAW\nwsIWnmxEPriaDQiGVq1OUon2kpPiwZ1bqJvNNZdejMw5TAaSvGIV1wz7Fh5Pa/jsA6iWUS+/jhEK\nEwoY6993URsyQFfMImQa3N/oVuSZSf9r59qh7f2SQ1/SwjQ2UHH2yOIVpidgNod66lmpWNtr86MB\nHllyorVG37mJ6juECm3u97YQQgghhBDiySdhoVhftQqhMMp6WGWUqzT42UieQr3FN44kGUqtPK9u\nTQGLNA6u1lQashH5wGo0wFr981eGAenswV5yMh/QrTCzMG+3+HSiyqlskF57g6/x1jV/+cdzr6CS\nmY0fx6L5gACGUvQng9wvNmi4G/gZmpqEZGbNzbcVxyVvtziU3ODPdDAIhgl27WFVYaYTevo3dn+x\nc+Z/LpuPhMkzOaiWYPj47h+TEEIIIYQQYt+TNmSxvlploarQ05qrUzZf5Gp0xy1eORTf+iZjK0DK\nqwNQrLcW2jPFAdNw1l0GoVJZ9OT9g7vkxG6HhY9UFrqe5t2xMsmQyblEHD4vo1utNbf/6qkH6Kuf\noU48jeof2txxrDAfcDgd4na+zv97ZZZY0CQdDpCJmGTCAdLhALGggVLKD/JmJlEDh9d8ivulBqZS\n9CU20IKMPztRhyN+oDpxD4pzqNe+I1WF+4AyTbQZWFZZyNgtiCeho3tvDkwIIYQQQgixr0lYKNal\naxVUNI7T8nhnrMxUpcnTPRGe7o6uvyl1LYEgYbdByDQo1Fsc2kp1othzuumsugl5QToLo9fRjoMK\nHcDPeb6y8JGKvMlKk5LT4jdPZDCd9rbjchEyHSs+jK6U0Jfe9avuTj2z+eN4ZD4gQF8iyH8/mWHO\nbpGv+1WBIzN1nHaloWUapMMmac8hXYVMspu0p1dtMb5fcuhNBLHMTRSehyN+ZeHkOHT3oSSE2j9C\noSVhoXbq6Il7qNPnJdAVQgghhBBCrEjCQgGAnp2GSBQVjS2/slqGbBcjs3Vma02+eTRJT3wbttpa\nFqrVJB0JyJKTg6zRQCX94KracIlaxvIQIp31vxbn4CBumrVtvxXfXFr9mqs0iFommbAJVgpQ/gKR\nFcJC3WyiP/wVBMOo57/mt2dv1qPzAduS4QDJcIDD88+lNXbLo2C75OstCnaLift5RtwMejaAUZgl\nGWpXH85XIUYCaA0z1RYvD25gC/IiKhJFT9wDz0M9/+rmX5fYOcGlYSH3RgEFg0f27JCEEEIIIYQQ\n+5uEhQIA/cmvUZlOeGHpH/q61YK6DbEEs7UmnVFre4JCgIAFnkvSUkzWWtvzmGL3tduQc5UGPx8t\n0p8I8kJ/nPjitvJYAqygP7fwIIaF9dqK8wonK01645YfjgYC6HjCDwsfobVGf/JrsGuo17+Lsrb4\nMzQ/H7BWXfNmSimilknUMulP+s/lTVyi2Q/lk1nydmshSLxXcnA9f8GQZRig/C3LmxKO+kFh/xBq\nPhgW+0MwhG44KNrn4d1bqP5DB7PCVwghhBBCCLErJCwU/mZWp46emvD/4F9c8WS3Q4lYnPxUiyOZ\nbdycGfBnoqUtzU3Ho+VpAhvZvir2l/Y25PHSfEu5y7+N5Hm6O8pTnX7brlIKncqii3MHcm6htmvL\n5hXaTY9ivcWZroetySqZRpfyy1/jjSsweR/18tf97dBbpJRCx+LrhoWP0q4Ls9MET52lM2rRGX04\nj9DTmorjUqj74WE4YBAKbLLqMRoDZcCpZzd3P7HjVDDkn7/QXmxShvOv7O1BCSGEEEIIIfY1CQsF\ntFrgtv+Xn4WOrofXVSsA2MEodtMmE9nGU6a9BCIV0Gg0Jcclu52PL3ac9jw/LLRCTJSbDCSDPN8f\n54upGpdzNe7kHb5txQgDKp1Fj9/d60Pemnpt2Ry+XMXfMLuk0jaZ8Tcda73Qiq0n76O//Bx16llU\n7+DjH0s07s8R3cx95qb9n++u5VWdhlILbcxDbLHa7NARVLYLlUhu7f5i5wRDUMwDoO/chEQKsp17\nfFBCCCGEEEKI/WwLQ7PEE6dRf/jfUw+WXlergGGS9/xKpGx4G8M8y3/MlPLnFRZsaUU+cJp+YFY1\nLEpOi75EEMtUPNcX47eOpwkFFD+7PsWvx8rY8QzYVXTdXudB9yHbXlZZOFlpkg4HiFiLfo0m0/57\n0q7k0uUS+uNfQ98hOPn0thyKisY2XVnI9CSEwv7x7QAVsFCpzI48tnhM7ZmFum771a3Dx2WxiRBC\nCCGEEGJNEhYKqLfDwlQWnRtfel21AtEY+bpL0DSIBbfxlGm3IVu6RTxoUnRkycmB0/DDwslmAIWi\nJ/6wvTUdCfDtoykuHs0yUW7wb7MWI24UrzC3V0e7JbrVhKYDkYftxlprcpXmktcLPAzjSgV0s4H+\n4Jf+4qDzr2xfQBONQ63ijw/YID0zierslZDoq8gK+T+nY6OgFAwe3usjEkIIIYQQQuxzEhaKhcpC\nNXTUDzkWVS3pWgWicebsFplIYHvDhnZYSKtJKhyQysKDqOlvWZ1oKDqigWWz7pRSnOyK899PZTjU\nmeCSzvJfNwvM1pp7cbRbM18JuaiysNzwqDVdeh9d9hOJ+otcinn0pXehUffnFFqPhIqPIxr3W4qd\n+vq3BbTjQCEPXb3bdwzi4AiGQHvo2zdQA8OooCw2EUIIIYQQQqxNwkLhVxYqAwaG/a/TEw+vq1VQ\nsQR5u7X98wQXhYXpsEnR+eqFhXomdzDbcuc1HDwNOQf6Eqtv0A0FDC4cSvCdbo1bq/KfN4t8NF6h\n4Xq7eLBbNL8cYtE25FylgVKKrtjSEFApBck0+uZVmJpAvfAqKpbY3uOJxfyvtcrGbj8zCWgJC7+q\ngu2fS8eG4WN7eyxCCCGEEEKIA0HCQuFXFgZDfsVJthM96bcia62hWqEeilFrutu73ATANP1wstki\nFQ5gNz2c1gEIj7aJ9jz0+2/B7Rt7fSjr0mO30LeuL7+i0WBWWzQw6U2sXz3X1ZHmu3qc830xbufr\n/OuXee7k65tqqd1182HhosrCyXKTzmgAy1xeaauSGXBbqNPnUN392388kbj/daNh4fQEJFKoSHT9\n24onT6hdSZhMQ0YWmwghhBBCCCHWJ6tnhd/OGAoDoLr70Te+QLuu32LqueQDfsiw3ZWFSim0ZbXb\nkE0ACvXW0u2yT7JqGdwWulLe3GbbXaZLefTnH/qzz46eWtqK3mwwSZRQwNzY+ZHuwBi5wqmEZiid\n4eMHVX59r8ytuTrdcYtIwCRiGYQDikTIJGjug3/PqNf8MN30z1FPa3LVJqc6wyvffugoKhyB46d3\n5HCUZaFDYaiuv+REa42ezqH6tmELsziYQv6sTTV8QmZWCiGEEEIIITZEwkKBXhQW0tMP1z6F2Skw\n/dNjToWxDIP4di43mRfww8JE0MRQimLdpSe+/U+zLxXbiz6q5b09jjVoz0V/8r5fAerYYFf9mXnz\nGg4TRozehIWxkSAinfW/5ueI9g1ycTjJg1KDK1M1bs7WcVoajV9lGLEMvvdUdu8Djkc2IeftFk3X\nWz6vsE2lMrDTm4EjMbA3UFlYKfufWVffzh6P2LdUKAwXv/vwZ08IIYQQQggh1iFhoQCnjpoPgBIp\nP4jIPVgIPPLaIhNROxPaBCxoNjANRTJkUqh/heYWFvL+12oZrfXeh2IrGbkGpQLq5a+j3/8FzM0s\nCQudeoO8CnFyo9Wg4Yhf6VSYhXa1W38ySH/Sv7+nNfWWx/1ig0sPKtSaHrGgud2valN0vbZkXuFk\npUnAUNs/w3MTVCyOrlbXr0idngDDgI6u3TgssU+prLQfCyGEEEIIITZuH/T4iT23uA1ZKb8VeeqB\nPxMtHKHQ0Ns/r3Beuw0ZIB0JUKi7O/M8+5Au5f3qTbcFDWevD2cZXcyjR75AHT+N6umHWBLys0tu\nM1FroY3AhuYVQnsBSDqLnq+qfIShFFHLpL+9LKW4H84Hu4YKL15u0qQ7ZmEaexjuRuMbmlmopych\n04UKbOM2ZiGEEEIIIYQQTzQJC0U7LAw9/L6n3692m5rACSeoNNydq6IKWOhmOywM+5WF3n5edrFN\ntNZQzPshHOy7VmS//fg9iCfh5FkAVKYDnZ9ecrvJmkc66Ad8G6XSWSjMrbnUJBY0CBiK4n6oNF1U\nWdjyNDPVJr1rbH7eFdE42DW0t3qYqj0XZnIo2YIshBBCCCGEEGITtpwAXbt2jX/5l39hdHSUQqHA\nH/3RH/Hiiy8uuc1Pf/pTfv7zn1OtVjl16hS/+7u/S2+v/OG6n+hWy6/saw/BB6CzGwwTCrMUuo8B\n27/cZJ4KWOi6DUAmHMD1NJWGRzK0t62nO65WhWYD+obgwRhUK5DdR62iI1ehXES9/t2FxR5kO2H8\nLrrVQgUCaK2ZqMOR7Cb/zSGV9SspH51/uIhSilQ4QMHZ28pC3WpXfbbDwplqE1dreuJ7XKkXjQHa\nP4/iyZVvk5/1q1a75XeuEEIIIYQQQoiN23JloeM4HD58mB/+8IcrXv/P//zP/OxnP+P3fu/3+Iu/\n+AtCoRB//ud/Tqu1DyqFxEONuv91UWWhClh+YAjkAzEChr+ZdkcElrYhA1+NuYXzbbgdXf4cv31U\nWajLRfSNK6iTT/tVgPMynaC9hWMv1F3qLU1fbJPnRrq9/KOwcivyvFTY3PvKQscPsgn7YfpkpUk4\nYJDa6zA71g5Z12pFnp70N1gnd3jZihBCCCGEEEKIJ8qWw8Lz58/zgx/8gJdeemnF6//93/+d3/md\n3+GFF15gaGiIP/iDP2Bubo4PPvhgywf7pNKfvIf+/MO9efL6fFgYWXKx6hkAIG+EyUQCO7d8wwos\nhIXhgEE4YFCwvwphYR5CEVQ4ArGEX1m4X0xNgFJw4szSyxMpf8bi3AwAE+UGAa9JZzy0woOsToWj\n/nbhdcLCdDhAqe7ubVu6XfO/tisLc5UmvfHg3i+jCUf9DdW16qo30dOTqK4elCHTJoQQQgghhBBC\nbNyO/BU5NTVFoVDgmWeeWbgsGo1y4sQJbty4sRNPeaDpmRw692BvnnyFykIAegYgGGLOjO7s1tdF\nlYXgB0RfhSUnuphf2Dbtb7bdP5WFlIuQSKGMpdVzyjAg04me8+cWPig6dFPHfPTc2Yh0Fr1eZWHI\nxNWayl62ItfbYWE4gtPyyNutvW9Bpv1ZRKKrVhbqhuO3IXf17fKRCSGEEEIIIYQ46HYkLCwUCgCk\nUqkll6dSqYXrhE83m/7sNruKng8mNnrf65/7Sygeh+MACoLhJReraIzWb/xPKsrauU3I4IeFzYdh\nYSYS+Iq0IedR7bCQ6P6qLNTlIiqRWvE6f8nJLJcnq0yVbQ4ZdbA2v+xDpbNQXHvJyUJb+l6GhbYN\nVggVsJiuNtFouvdBWAhANI6urlJZOJMDNHT17OohCSGEEEIIIYQ4+HYwBdqat99+m3feeWfJZT09\nPbz55pskolEwn6zFF+5MjnrEbwEOuU0C2ew69/DphkNtYgzcFpFgAGO1JQfraEyM0UqniXZ2Lrtu\nolQnEqlxtK+TTHRzgZBlWWQ38FpapU6cUJBoKokyAwx5Ie5UZokl04QCT2b7pGfXsA0IDR8hkM3S\n6h/AuXuDaCyG2kqV3jbSWlNrNQgOHsJa4fNrHj7Gr2/luDlT57WhDCdmINLdjbnGZ73SueAOH6V+\nd4RI0MJIrH7uZu47aCtKNrtyeLnTnICJ19FJJJvlRilPd1oz1Ls/FtE4Pb14+RkiK7z3zug1vO5e\nIoNDe3BkO2Ojv1OEmCfnjHhccg6JzZJzRmwHOY/E45JzSKxlfqRJ9w3dAAAgAElEQVTWj3/8Y3K5\n3JLrXnvtNS5evAjsUFiYTqcBKBaLC/89//3hw4fXvO/FixcXDu5RpekpWsG9DVO2m753F113IBSi\nfmcUFdtYKKJHv0RXKmCa1D+7hDp9bmvPPzONdjX1ueUtoaPTNRpOHdcuM1ff3Iy2bDbL3AqPuez5\nqzbatqlPTaNCIQynhW3b3JmYpiu2Tyq4tpnOPfBfszZQc3Polud/f38MlenY22OrVdDl0sKxLeZp\nzYe5JrdsixdUgSFa1Os2Ts1edtvFVjoXNKb/mu/cQg0Mr3rfoNfg3nSe4ejeVBd60znwNPbcHCMT\neTpjgQ2d17tBe6Cnp7BXOB5vdATV07/idQfVRn+nCDFPzhnxuOQcEpsl54zYDnIeiccl55BYi2VZ\ndHV18eabb655ux0p3eru7iadTnP58uWFy2q1GiMjI5w6dWrrDzy/mfRJUipCLI7q6F6YBbcerTX6\nzgiqbxB16Ah6bBTtbTFMcezl8wrb8naLTDiAsZPLHALtvLrVACARMjGUIv8kLzkpzvmtu9GY//1G\nNtvullLR//pIG7Lrad67V+Z2yeVCsslJdw6a/meGtfkAX4VC/uvfwNzCPW1Lt2uoSBS76VFyWvTs\npwA7Godmw59PuIiulv1zqat3jw5MCCGEEEIIIcRBtuXKwnq9zuTk5ML3uVyOO3fuEI/H6ezs5Ld/\n+7f5p3/6J3p7e+nu7ubv//7v6ejoWHV78oY4dUhs/e77kS4XIJGGdAdM3EO7Lmq9VuuZHFRKcO5l\nsCy4fQMmx6F/8y2H2nH8jbwryNvuzi9zsNqP355baBqK5F4HRDtsfrnJfPmvsoLoYAj2w5KTctGf\nI9ne/jvvo/EK94oNXh1OcMhLoudmUKksoB5+hpuV6kAX5lgrik5HAozM1ml5moCxBxuI6zUID5Kr\n+MFoT3zz8xl3TKwdNtcqsLjienrS35TcIfMKhRBCCCGEEEJs3pbDwtHRUX70ox8tfP+Tn/wEgDfe\neIPf//3f53vf+x6O4/A3f/M3VKtVTp8+zR//8R8TCDxG53O9vvX77lflIurwCch2gedBMQ/Z5fMD\nF9O3b0AyDdkulFLoTCf6zk3UFsJCHBtSaVqepuy42E0Pu+VhNz3KjsvprpWDxG0TaAdNizciR57w\njcjFOVTfI59VbJ8sOSkX/E3Ii6pJ75ccRvN1LgwmGEqF0JlOGLvth1RW0N/MuwUqnUWPXEFrveT5\nFkuFTTSakuPu7FbuFWjX9f+BIhIhV22SDAWIWPtojmZkviK16v9jQ5uenoRMB2qrIa4QQgghhBBC\niK+0Lf/1febMGX7605+ueZvvf//7fP/739/qUyzTqNd3pm96j2in3q6WTEEqDYYJ+Zk1w0Jdq8Lk\nOOrZFx9Wpg0fR3/6HrpaRsU2WXrpOLhWmP9vpEDJeVjNFwoYZCIBehM7HDisFBaGTe4XnTVDpINK\nNxw/3JnfhNymYnH/89uj45qnS8UlcxOdlseH9yv0J4IcybSr1zKdgEZPT0DwMSrt0ln/c6+UYZUl\nJ6mQX2VbrLd2PSyk3h57EI4yNdekP7GPqgrBf+8D1pL2de15MJNDHX1qDw9MCCGEEEIIIcRBtu+2\nIa/l44LmnKuxzL2OVLZJqeB/TaRRholOZ/2wcC13b/pz/gYPP7ysfwiufOxfd+a5DT+99lxoOtx1\nQ5RaLV4/nCQTDhAOGJi71fK5YlgYoOVpKg2PROjJ2n5NMe9/fSQsJJaA6dzy2+8i7Xl+e/vQ0YXL\nLj2o4Gl4aTD+MLhNpPyZi4W5JRVtmzb/HhRnVw0LLdMgFjQp7kWlab0GQCUQodJo7nxL/iYppdDR\nOFSrDy8stGdJdkkLshBCCCGEEEKIrTlQhXplx+WdsRKup/f6ULZHuehXE7YXXKhsJ3pu9bBQuy56\n7Bbq0FFU4GFwoQIB1OAR9L3bm1t04jh4Gq5WTQaTIQaTIWJBc/eCQgDT9OerNR9WNabDfoadfxLn\nFhbzYAb8cHCxWBwcG70oNN111Qp47sJyk3tFh7sFhxf6Y0Sth6GtUuphSPgY28lVMOS/D+ssOUmH\nA3szw9L2w8KpVgCF2p/buWNx9OLFONOTfpD7OCGuEEIIIYQQQoivtAMVFj6n8kyWG7x/v4zWT0Bg\nWC5CPPlw5lumC+o1v9X40Zs6Lp9cucO1qsn9jsMU662loenh435L88T9jT+/U2fMi1DWJk/3RNe/\n/Q5QSvnVhYtCsohlEA4YFJ7EjcjFPCTTy+f8Rdvh4V7OLSy3K12Tab/9eLzCQDLEcHp5IKjarfLq\ncdqQac8tXG8jcngPKwutIFN1TSYSIBTYf78uVTS+tA15egI6e7Y8R1IIIYQQQgghhDhQbcgdOLza\nF+LdiQaWUeW5/tjebEjdJrpUQCVTDy+YnxWXn4VobOHiUr3Fz28Xce9N4Yb7cGc8mMmjUGQiAY5k\nQgynE1jZLvTdm6iB4Q09v1e3uerG6U+Fd38e3GJWYElYCPPVZE/ekhNdnEN19S58/2CsQSJtEp+v\nNKyWl7Uou56m6WnCOx1WlYsQCqNCYT4aK6E1vDQQX3luZKY9V9PaemUhAKksTI6jPW/VgCsVMqk1\nXZyWt6uBnZ6bQYdj5KoNDqfDu/a8mxKNQ63qt5C7LuRnUc+8sNdHJYQQQgghhBDiADtw5SeHrCYv\nDsS5la/zv67PcX3apnUA25K11n44k0gvXKbCEf+P//z0wmXzQaFVt/mt1l3+n/N9fO90lm8dTfHi\nQJyIZfDxRJV/vjbHu7EjTEwVcMvFDR3DvbxNSQc4259a/8Y76ZHKQvA3IhefsDZkPb/MI+mHga2W\nplxysWvew2UVK1QWXs7V+NlIHm+NalrtuujPPkAXZrd+fKUiJFJMlBuMtduPV93+m+kA1GO1IQP+\nkhO35c9KXO0m7bb0krN74bGemoDJ+1QOn8JuevtuXuGCaAy051dBzub8/14URgshhBBCCCGEEJt1\noCoLAbBrHO/roCducXWqxqeTVa5O1zjdFeF4NnJwlp/YNT8gSywN6lTGn1uo8IPC/z1aJGgqvlG6\nRiSZRPUOEDUMopZJTxyOd4Sptzzu5B1uzSrecruIfjTG0dNHOZIJr7ogRGvNldkGvVaLzsRjBj6P\nywpC89HKQpPr0y5N18MyD1ymvbJSAdALlYN12wP8MYFKKXQs4VcWLqK15m7BwW56TFeb9MRXafud\nm0bfvQnjd+HCG6iO7s0fX7mA6upjZLZOJhJYsf14nrKCcPb5xw+mUllA+XMLkw+Dc91woF5DJTMk\nQiZKKQr11q7MDdSui778EXT0kEv0oio1OqP7NSz0551Sq/rzCqPxzW9EF0IIIYQQQgghFjlYYaEy\n/T+KgUTI5MKhBE93R7kyXeOzyRrXpm1Od0Y43nEAQsP56r9kio8fVJiutshETDLBDjLjDzAqDm+N\nVQgFDL4ZmCFUnEa99p0VWzXDAYOnuiKc6gwzaye4de8BN6Z7uTJVoytmcTQT5lAqtOQ9uV9qUKy7\nvBTbB62+AQvdbLD4E5uvJivUXbpiT0hYWMyDYUC79Xw+LHRdv2JQtZdVLH4fZu0WtaaLoRT3io3V\nw8LCnF+ZmM6i3/sFvHQR1d2/4UPTrguVMvbwKR6UG7zQH1u5/XgRdfTUhh9/1cewLHQ8AcU5wN/C\nrGen0JfehYYDv/l/Y1oWydAuzi28eQ1qVdTLX2dqrkVnNLB/f59EY4CCWgU9NYnq6tvrIxJCCCGE\nEEIIccAdrBQmHF7YUDovHjK5MJjgf5zKMJgM8lmuxv+6PsfVqRpNdx+3J5cLYAbIuUG+nLEJBRQz\ntRYf2VH+w+ngZ19MEg4YfHMgROjLz/0NyOtUiyml6Dh+jJeY5XupCl87lMBQivfvl/nna7O8f6/M\ndLWJ1povcjV6DIfO6N7nxSpgQWtpy3FyUTXZE6M4B4k0yvCrPes1//x0W+3zNJZY1oZ8r9ggFDA4\nlg1zv+SsuthH52cgnUVd+AZ09qA/+CX6wdjGj61SAjSjOoahWLOqcLvNLznRWqNvfIF+539DOOKX\nXE5PAP6Sk904F3S1jB65gjr2FMST5CpNuvfjFuQ2ZZr+ezUzBdUSdPXs9SEJIYQQQgghhDjg9j4p\n2oxQZMnmz8XiQZOXBxOc6Y5ybcrmcq7G9WmbU10RTnaE918ra6mIl0jzyUSVjqjFG4eTKKVotpLk\ncx9RidQYOHqI4BcfoAHOnN/Qw6pEEt3Rg3nvFodfO8LhTJhKw+VO3mE0X2c0Xydq+QsjvmlWUaF9\nsLghYEFr6cw601CkQiYFex9UPm4TXSyg2i3IWut2ZaHCnX+JsTjYVXSrhQoE0Fpzr+hwKBliKBVi\nZNZmpra8FVdr7S+2GDrqh0cvXYRP3kd/9A6cb6GGjq5/cOUCWsOoYzGUChHczZ+XVBYe3PMrIqcn\nUSfOwKln0G/9DCbHoX+IdCjAZNlGa71uxeNWaa399uNwGE6epVB3abj7eF7hvFi8HQwr6JSwUAgh\nhBBCCCHE49lnCdraVCiMfqSy8FHxoMlLg3H+x6kMQ+kQX+Rq/Mv1PFemajRdb9uORd+9iffeL9Ab\nXCay7P7lIrcCWYp1d0nLpxUw6cwmOdyYIVicRY+Nok4/u6lQTx0+DrNTC8cWD5qc7Ynyf53K8K2j\nKbpjFkezYbq9KuyHsHCFbcgwvxF5Z6rJtOehP30ffeOLHXn85c/n+tWk7bCw2dC4riYSVQttyMzP\nmrP9Vvs5u0W14TKUDtIZCxAKGNwvNZY/uF0Dx17Ypq0ME557BTV8DP3pe+jRL9c/wFKRyWCaqgvH\nsrt8TqQ7/CrCUh71tW+iTp9DGQaqdwA99QDteaTCJg3Xw25t38/wMpP3YWoCdfYFVCDAg3IDU6n9\nO6+wTUXj/vuX6UA97sIZIYQQQgghhBBfeQcqLCQUXghS1hMLmrw44IeGw4tDw9zjh4Y69wD92YeQ\nn0G/9e/oL7/ww6CN3t/zqJfKXG7FOZoN0fFIGKGynTA3g778oR+kDB3b3AH2Dvpbau/eXPq4StET\nD/K1oQQXBhPQqO+PsHCFbcjgLzkp1Furtt5uldYavriEHhtFX/8cfev6tj7+ikpF8LxFy0381xSN\nm3hu+5hi7WUV7SUn94oNQqZBV8zCUIpDyRD3iiu0IufbG5DTHQsXKcOAZ19CHTuN/uIS+saVNd9H\nXS4yamVJhgK735qe6UA9+xLqjd9CLV6Y0jPgzy3MzyzMsNyJuYVaa3RhFn35EvQMoHoHuVd0uJyr\nMZwJYRr7dF7hvPaSEyVVhUIIIYQQQgghtsHBakMOh6FeR3vuwty39cyHhme6I1ydsrkyVeP6jM2p\nzggnO8ObbrfUpQL60jvQ04964TUYueJXpz0Yg/MXUJmO9R+kVuHzZhQVDnOuN7b8+kwHjFwBp476\n+ndXXGqyFmWaMHQUfXcUnjqHCiz/mLXngePsn7CwuUJYGAnQ8jSVhrfqVuctuXUNfWcEde4CVEro\nK59AOIoaGNq+53hUMQ+ohY2/ddvDChqEQgqtNZ4HRigCZgCqlYUW5MFUEKNddTqYCnJzzmbObi0N\nmPMzEI2hwpElT6mUQp857y8Ruf4ZqtVAnz6/YhtvvVRk3DzCcx3hHWvzXY0yDDh8YvkVmQ5/9MDk\nOLEzXZiGP8OyL7HKkpdN0K0mTOcgN47OPfArM0MR1NkXuFd0eGeszKFkkBf744/9XDsu2v4d0i3L\nTYQQQgghhBBCPL6DFRaGwoAG235YhbVBUethaHhtyubqQmgY5lRnZEOhoXbq6A9+6Qczz7/qh3Cn\nz0H/kN/S+qv/gKOn4Kln/KUdq5idyTPqRnlxME0osMLzZroAhTpyApXeQPi4kuHj/lbXiXtw6Mjy\n6xsOoCG4T8JCz10WAqfC/n8X661tCwv1+F301U9RJ57223S1Rjk2+pN3IRhcWtm2nYpzkEgunBd1\n2yMcUcy/XNfVmKaBjsWhWiZfd6k0XF5MxdFOHZw63fEUQdNvRV4cFur8DCrTueLTKqXg5FkwA+gr\nH6OaLfSzLy4JBHWzye2yRnWHObyLi03Wo5SC3gH05DjG08+RCQe4OVsnETIZSAQ3HWrqWgVyE+jc\nfX8hiOdCPIkaPAw9/ZDt4l65ybtjZQ6lggsLgva97n7UybOwyjkghBBCCCGEEEJsxgELC9uVU/Xa\npsPCeVHL5IWBOKe7I1yftrk2ZTNVafLtY+k176ddF/3hr8BtoV79Fsp6GNaoVAZe/y6MXkdfv+zP\nPjv38orBk9aaS/crpC3Nsd7kis+lQiH/8ZJrH9NaVCyB7uz1K+hWDAvr/tf9UFk4/142W7AoFIwE\nDIKmQbHuMph6/KfRs1PoT36NGjwCTz0LtKvvzl/wK1Y//BVc/A4qmXn8J3v0uYv5hcfVnqZua7qS\nJqbph1FuCwgC0Ti6WuFe0fFbkGsz6HffA9fF+O7/ZCAZ5F7R4dmeqH/snutXLfavXRWpjj0FloX+\n9AOU20Sfv7AQzOpSgVEvymBHbOXwei/1DMDdm+hyiZcH41x6UOVXd0p0xyzO98WWtfAvpj0PCrOQ\ne4DOjUOpAMqAjm7U6XPQO4CanxMJjBWdgxcU0v590T6fhRBCCCGEEEKIx3XAwsJ2sLXBuYVriVom\nz/fHSYYCfDReoeF6q1YXaq3hsw+gMOcHhdHlQaUyDDh+BvoOoT/9AP3rn8PQUTjz3MLSAafl8clE\nldmKw7ezBuYa7cUbamdehxo+jr70NrpUQD0aPNb3UVg4X4XZakLoYWWbUopU2KTgPP6SE10u+lWh\n2S44//KSqjRlmPDS6+h3/svfyHvxu6joCu3hW31uz/ODqr5DADiORmtNOGoshIVee8mJiiXwHtxj\nLF9noPQA49Zl/5jnpmHqAYdSvdzO1yk6rj/Hr1QAz6Uaz3B5rIyhIBo0iFkmEcsgZhlEgyYBQ6GG\njoFp+VWUrRa88BrKNJmaKVLWAV7q3/6Q9LF19vit2bn7pI6f4ZtHkkyUm3w6WeU/bhYYSoU43xcj\nFmwHn64LuXGYHEdPPfAraIMhVHc/nDgL3b0oy29jdj3NTKXBVLXJdLXFVLXJUCrIKwcoKBRCCCGE\nEEIIIbbbgQoLVSAAVghqa29E3oyeuIVGM11tMZBcZRZaYQ59/zbq/CuobNfaxxhLwKvfgrFb/iy8\n3ATe2ecZDXfz+WQNDbykZunOZLftNayqb8APA+/ehGdeXHrdfqwsXGUj8lR1+eWboes2+v1f+HMJ\nX3p9xXmXyrLglW+g3/4P/7avfWfVzbLzi0I23AZbLfulg/PLTWoeSilC4Yf3d+f3dsTiFGoNyrdu\n8rw7jjpzHo6fRv/qP9D379D74iEsw+Be0fHDwvwsM4R4Z8bEMJpELIOJSgO7uXSJT8g0iAYNolaa\nyJGvER35nGjtHWLPv8TIdIVEKEBPch+cC49QgQC6qxc9OY46fgalFP3JIL0Jizt5h89zVf7tRp5n\nOyxOFMfg7gg4dUimUcPH/fbidAfKMGh5mtlak6m5KlOVJrO1Fq7WWKZBd8zi+b4YxzvCEhQKIYQQ\nQgghhPhKO1BhIQCRKNjbFxbGgwZRy2Sq2lw9LCwVALVuq+c8pZQ/M7C7n+mPP+bSr78kH5vh2Klh\nnh1IERqdheQKrcHbTBkmHDqKvnsTTp9fuuikXgczsOLyk10XWD0sTIVNbs7VcT29pa20utVEf/AW\neB7q1TcWqspWosIReOWb6Lf/E/3+W/C1by15f5yWx83ZOjdmbXriFq8OrdxGvkxxrv1i/LDQtj2C\nIYXRfj1KKdx2ZSGxBPfcIEHt0HPxIqqz2798YBh97VOMVoP+pMX9YoNnemKMTeZ5Tw2QDQV4/XCS\ncLuN2PU0dtOj2nSpNT3/fw3/+2krSbX/GZr37sJbnwOac+nAri822SjVO+BX6zp1VDvcNpTiaDbM\ngFHns8ujXLpe4q7R5MKRIZInTqLiyYVwMDdlM1X1w0FPa4LtDdPn+mJ0xyxSYVMCQiGEEEIIIYQQ\nom0fJEWbFImi7Srb9ae9UorumMVUZY3qtXIRYvFNBWt20+OzGZfR+Cmyw2W+M/0FnV/eRtWPo7UH\niW0YwrcRw8fh5lV4cBeGjj28vFGHR7bn7pn5sLDZWHZVKhRAa0250W673QTteehL70K5hHrtOxtq\nLVbxJLz8BvrXP0d//C68eJFyU3NjxmY0XwcNybDJ/WKDlqcJbCTALBYgGl+oVKzbmmjsYQu6GQC3\n5YeFOtvFvd5TDPZ1EehcVH06MARXPoGJ+xxKD3K3UOKj8Qo3ci6HOyNcOJpaEqaahiIeMomvuhgm\ng3M0TvX9t6k3W3R3nVz/deyV7n7/a+6B39rfpsdGsT77gBeDIYZOneRDo4ufeQaHC4pyrrAQDoba\n4eD5vhg97XBwvwajQgghhBBCCCHEXjtwYaGKxtCzU9v6mD1xi7sFZ9W5hbpShPjGqsg8rRmZrXN5\nsoZS8PJggqPZTlRzAK58gr551b/hLoWFKhZHd/Wh797yZ9bNq9dhlTbbXbdOZSFAsb65sFBrDV9c\ngqkJ1IWvo9Ibb/tW2U544TWK77/Llbc/Yyw9SChgcKYryvGOME7T5V9vFJisNBhMrv8e6uLcQlWh\n62oajibb2V5s4mnqrkep0OJWs07eblGJZ3mhY+lcTBWOojt70ON36Dt0FNNQ3MhVOOvNcHb47EKV\n4maEOjsJvv4N9KfvoXr6N33/3aLCEXSmA50bRw0d9T/bkavo65/5rcZnX6DXNPlvnuaLXI17RYdM\nJMBzfTG64xapkISDQgghhBBCCCHERh24sJDw9rYhA3TH/LmFU9XmyuFPuYQaGF73caYqTS49qFCs\nuxzLhnm2N/pwu2wwBM+9AgPDUC6u2Q673dTh4+gPf+Vv5G2HVno/VRaapr+ltrl8kUkoYBCxDIr1\nFrCJcPPWNX8T9LkL/nKLTag0XK40k9zOniOUG+PFoMuRhIlxtwxXywSrZZKRU4x3HF83LNRaQzHv\nbyMGHNsDNOGIwZzd4pd3ijRnNJiKuDLIRgKc7IjQl1i+5VcNHkZ/+j6mY/PSQBwrP01/oLLuHM21\nqGQa9fXf2vL9d4vqHUTf+ALdasLVT/3P9tQzcPLsQhAYMBTn+2Kc79u+5TRCCCGEEEIIIcRXzcEL\nCyNRaDbQzaa/lGIbxObnFlaWh4W62fS3L69RCVhrunw6UeVuwaEjavEbx1N0RFc+NtXdB91923Lc\nG9bT7weDd2/Csy/5lzkOKr18q/NeUEqhA9aKlYXgtyIX6u6K161Ej99FX/0UdeJp1PCx9e/QZjc9\nrk7VuDlXxzIVz50+xLHuJsbIZahEIZ5AdfairSD9lQJ3Sg201mtXrdWqfnt1yq9stG2NYShmnCbv\n3iuTCgcY6g8RNQ2GjqwThvYNwucfwvhdjhw/jZ4qoIMhWGE79xOndwCufYp++z+hVESde9mvKhRC\nCCGEEEIIIcS2OoBhYbtqyK6Cld6Wh1RK0R23Vt66Wyn5XxPL25BdT3Nj1uaLXA1TKS4MJjiSCe27\nlseFRSe3b8CZ86iABY4N4X20/dYKrB4Whk3GS8vnGa5Ez06hP/k1avAIPPXshu7jtDyuz9h8OWNj\nKMXZnignOyJYpoLOp9Enzyz9TG9eZeD6Da43PWbtFp2rBMMAlPLtF9HehGx7FF2XS2M2/Ykgrw4l\nmJlo4dS91R+jTVlBdM8Aevwu6vhpdH7G3/S7z863HRFPQiwB1Qrq5a+jegf2+oiEEEIIIYQQQogn\n0gEMC6P+V7sGye0JCwF6YhZ38w5Oy3vYOgz+chNYNrNwttbkvXsVyo7L8Y4wz/REl95vvxk+BiNX\nYXwMPXQUHAeC+ygsXKOyMB0OcGOmvu5CEV0uoj/4JWS74PzL64ZoTddfXHJtxsbTmlOdEZ7qjCz7\nHJc9TiROh1sjpDzGS421w8LCHIQi/tw9rbmdq/Og1eD4UJjn+2MYSi1ZcLIeNTjst5SXi5CfQx09\ntaH7HXRKKXjxIhgGareWAwkhhBBCCCGEEF9BBy8sDEcABfWtzy3UnudXfM3k0DM5VDJN9/Fn0Wim\na4+0IleKEI351XhtM7UmvxgtkQiZ/OaJNJnI/n8bVTSO7u5D3x1B9Q6C9iC0TxacgB8WNlevLNRo\nivXWqu3dum6j3/8FhKOol173qynXMGe3ePtuCbvpcaIjzOmuKBFrg2FvLIahoD/oMl5qcK539Rl5\nupiHVAbX07x3t0yu1OTUsQjP9kcXQkjTVLgb7bLu7gcrCDe+gKYDmY4N3vHgm5+3KYQQQgghhBBC\niJ2zoynXP/zDP/CP//iPSy7r7+/nr//6r7f8mMow0OGIPwtug7TWUCnDzCR6JgczU37QYgYgHEHP\nThN76hyx4PK5hbpcgvjDSqbpapO3bpdIR0zeOJzEWmF78n6lDh/3K++mH/gXhPbJghOAgIVuNVmp\nFjAZ8k/TYt1dMSzUrSb6g7fA81CvvrHu8pjb+Tof3q+QCgf41pEU8dDaweIyEX9G4IDR4E7douy4\nJFZ7jGKe5uBR3rlTYmauyXAqzNMD0SXViqYJnqfRnkats9VYmSb0HUKP3fIv+AqFhUIIIYQQQggh\nhNh5O14Sd+jQIf70T//UD+wA09xkMLOSyPobkXWtCjO5hwFh3fY37mY6UEdPQmePH7Tk59Dv/CeU\nCnTHLHKVR6rbykVU3yHADwp/cbtIJhLgjcMpf6bdQdLdD+Eo+sYV//t9VFmorCDasVe8zjIV8aBJ\n0Vm5/E5/8p6/sfq176Ciq1f5uZ7m04kqN2ZtjmbCvDgQx1wnnFtRMAgBi15qmCrOg3KDUysEr7pe\no1p3+GUtgU2L5zriGA0IWEuf02yfR64LG+pkHxiGsVsQT+7qVm0hhBBCCCGEEEI8+XY8LDRNk2Ry\n+XKQx6GiMbRdW1KFph2nHQ76rcVUS4CCVAY1cBi6eiDbte9mLzAAACAASURBVKSdGECns2AYkJ+h\nOzXEnUVzC3Wr5VcwJpIHPyjEr8pk+Bj6y8v+BfusstD/zFaWCgco1lvLLtczOZi4h3rhNVQ6u+r9\n7abHO2MlZmotXhyIczwb3vJiEKUUOhrDsqt0Jy3GSw1OdS5/L/NTc7zV7MQIRfiNYylKEy5GZPlz\nmoH5sFAvCxJX1NHtt1tnO7d0/EIIIYQQQgghhBCr2fGwcOL/Z+/Oo+O8yvuBf++7zL5Io92y5UV2\n4uwJcRKI7bglKRDgxAmhDQcOEGhTKJyyHMofPXCAHppTaE9baAltgbZZIQtNIIFmIxvYTvjFdgKh\nON5t2daumdHsy/u+9/fHnffVSJZsyZIs2f5+zvGZ8WikeeedO/e997nPvbevD5/4xCfg8/mwZs0a\nfPCDH0Rz8yyDHMEQkByGHOhVwcGh/rFdZ8MxiJY24IJLgaY2iJNkzwldh4wngOQQWju71bqF+SqW\nxv1APgtAYkCL4FcHR5EIGrjuDA0Uerq6gd2/UwFSYxGttWgaU65ZCKh1Cw+lyuMek1JCvvlbIJ4A\nlnRN+bsjhSq2HM7CkRLXr4qjJXyCDUmmKxSBLObQudSHHb354zbG6ctWsOXAKCK6hk1rWxE0NQwW\nLSRajs+sdZdXnO66hULTgGvfrtYuJCIiIiIiIiKaQ/MaLVqzZg0+9alPYcmSJUin03jkkUfw1a9+\nFf/4j/+IQGAWO/EGI0AxP7ahRXMbsOp8oKUdwt0teQZEogXy2GFEfDoiPh2DbrAwO4pjjh/bRoCW\nqImNy2NndqAQgAiGINuWqOnVp5hZNy9OsBsyAMT9OgpVe3xQbqgPSA5BXPMHU76X/ckSth/LIRE0\nsH55FCFzDqbBo5bdOtCHzpgP24/l0JetYEWjKtMHkiX8v2M5tNl5XNsq4ffpKJccOI5EcLLMQn0s\ns3Darx+Z22xdIiIiIiIiIiJgnoOFl19+uXe/q6sLq1evxqc+9Sm8/PLL+MM//MNJf2fLli3YunXr\nuMfa2tpw++23IxaLqWyyyOWwmxLQEi0Q0fisg17WytUo9x5C0O/DqvYEhvMVJBIJ7N69H782OrGm\nswV/sLoJxqmsb7cIOdf9EWQxDz0x9bTd2TJNE4kZ/P1qogmVowbCU/1OoILXhy1owSgSUT+klCht\n/xWwdAUCay86rgxYjsSvD6ewe8TCpV3NuGZ545x+ftX2JagMHENTazOWDdkYdQw0Njbi9WMZ/HbE\nwmVdzbgs8zp8y1bAl0hgZKiMYFBHR2eDFxx0SSnReziFSDiMRGLxrCM5V2ZaFogmw3JEM8UyQ7PF\nMkQzxTJDc4HliGaLZYhOxI2d3H333RgYGBj3s/Xr12PDhg0ATsM05HqhUAgdHR3o7++f8jkbNmzw\nDm6iTCaDqjtVNdYEWA6QSs36uKRuQhaLKO3fg1C4Hb0jWWx908LrB9PoCkhclhDIpGf/OouKZgLJ\n5Lz9+UQigeQM/r4sFCFzOZSGhyC047P/bEeiVCrhUP8wzGoQsu8oZO8RiLe9HcUJZaBQtbH1cBbJ\nolqfsDuOOf/8pGVD5nMo9R5Dow7s6suiWMjjYKqES9vDOD9QQmlkEOXlqyGSSQwOVOE4DkZHJz+O\ncqWEVNKC0BfR1PA5MtOyQDQZliOaKZYZmi2WIZoplhmaCyxHNFssQ3QipmmipaUFt99++wmfN529\nV+dMqVRCf38/GhsbT+fLnpTwB4BwTK1bWFvP7vX+PLqdUbytzXdqO+bSzJi1dQSrx29iAgC6JhD1\n6Rgt2WNrFTa3QbS0j3veUL6Kp/emUag6uL47ju7ELKa7n0goom4LeXTGfKg6Dg6ly3jrsiguag1B\nZNLq53FV1osFB4HQ1F83XRczmoZMRERERERERDQf5jWN6b777sOVV16JlpYWJJNJPPzwwzAMA+vX\nr5/Plz0loqkZMjmMsE9HZ8yPBp/ARYcGIWJTb5xBc8jdpdqqAlNsStMQ0NWOyL09QDYNcdkfeT+T\nUmJfsoQdvXk0hwys74ohaM5jLDwUVreFHOKJZlzYGkJ7xERbpLbpyGgK0A0gHIXjSFTKEg2JqYPO\nuj79DU6IiIiIiIiIiObLvAYLR0ZG8C//8i/IZrOIxWJYu3Yt7rzzTkSj0fl82VOTaAF6DkJWq7hu\nRQwyk4aEA0TiC31k54b6YOEU4gED/UMFOAffgGjrhEi0AFBTlLcfy+FAqoTzmoK4vCM879mgwjAh\n/QGgkIMQApe1h8c/YTQFxBogNA3FvAMpJQJBZhYSERERERER0eI2r8HCz33uc/P55+dWYwsACaSG\ngdYOIJtRj3PX2dNjWsFCHeVUEqV8DqF11wJQ6xNuOZxFumjhrcuiWNk4T9OOJxMMA4X8pD+SmZTa\npRtAqehACAG//wSZhQZQrTBYSEREREREREQL67SuWbioRaKAzw8kh9T/c6OAPwAxxZRYmmPemoVT\nBwtjBoDhQYw2d0E0JJAqWnh23yiKVQc3rG44vYFCACIcgSzkjntcWlUVbK6tV1gqOggEBcQJsh1V\nZuG8HSoRERERERER0bScfVuvniIhBGRjM2RyCAIAsqOcgnw6TSOzMHJ0L3S7ikzXpXAyZbzck0XM\nb2DjiihC5vE7KM+7UARIDh//eCYNQAKxWrCw4CASO/HxabqAw2nIRERERERERLTAmFlYRzS1AKkR\nSMeBzGYgopyCfNroOiA0YJJMPQCQpQLEvt8j3tKEvXmBLYeyaI/4cH13fGEChYDa5KRYgHQmpASO\npgBNA2JxWJZEtXri9QoBwKhtcCIlA4ZEREREREREtHAYLKyXaAFsC0gngXwGiDKz8HQRQkAsWwG5\n5/8g3ang9d58A9B0xLuWIlexcUFrEBuWR2HM80YmJxSKAJBAsTD+8dEkEG2A0HSUig4AIBA68XFq\nuoCUEo4zT8dKRERERERERDQNDBbWiycATQeOHgQch5ubnG6XXAU0JCBf/dW4tQBlJgXZcwDi/Itx\nSWccf7gyjsvawxBiAQOFgMosBID8+GxIOZqGiI9NQdZ1AdM88bHqtQUBuCMyERERERERES0kBgvr\nCF1Xwaqjh9QDzCw8rYSuQ1y1EdB1yP/3S8jaZify/14HwlFgxWpEfDrao74FPtKaYAiAAIpjOyJL\nxway6brNTSQCIe2kgU1dVz+fOKOZiIiIiIiIiOh0YrBwApFoVptsmH7Af3p31yVA+AMQV28CCnnI\nndsgB3qBoT6ICy+H0BZobcIpCE1XAcP6zMLMqMpKjTdCSlnbCfnkXzO99tZsi5mFRERERERERLRw\nGCycKNGibqOxhZ/meo4SsQaIK9cDA72Qr/4KaGoF2jsX+rAmF4qMyyzEaAqAAGINqFYkbFsiGDx5\nOXIzC21mFhIRERERERHRAmKwcKJGFSwUnIK8oETbEoiL36LuX3TFog3cilAYclxmYUoFmg0TpaLK\nEpxOZqHQ1CYvXLOQiIiIiIiIiBaSsdAHsNgIvx9YvhroWLrQh3LOE6vOB7q6IYxFXExDEWCw1/uv\nTCe9zU2KRQemT4NunDzQKYSArnODEyIiIiIiIiJaWIs4CrNwxGVXL/QhUM2iDhQCakfkcgnSqqqd\ntDNpoGMZALUTcmAaU5Bdui44DZmIiIiIiIiIFtQij8QQLXKhiLot5AEhANtSm5s4EuWSRKxh+puy\n6AY3OCEiIiIiIiKihcVgIdFshMLqtpADLEvdjzeiVJKQUk5rvUKXpgs4zCwkIiIiIiIiogXEDU6I\nZiMQVNOPC3m1E3IoAuHzo1R0IISAPzDTacjMLCQiIiIiIiKihcPMQqJZEEJABsNAIQeZSQO1zU1K\nRQf+gICmzSRYCK5ZSEREREREREQLipmFRLMVDkPmc8BoytsJuVSc2RRkANANwTULiYiIiIiIiGhB\nMVhINEsiGAGSw0C1AsQTsG2JStmZebBQBxxHQjoMGBIRERERERHRwmCwkGi2wmGgWlb3440oFR0A\nQCA0/SnIgNrgBOBUZCIiIiIiIiJaOAwWTtPZvvFENmOjXHIW+jDOTMGIug0EIQJBlAoSmi7g880s\nWKh7wcKzu6wRERERERER0eLFYOE0FAsO9r9ZRiF/dgbTHFui72gVg33WQh/KmSkcVrexsc1NAgEN\nQsw0WKhumVlIC6VUdJDLsgASERERERGdyxgsnIZM2oaUEsMDVUh59mV95bIOpCNRyNsol8/OgOi8\nqmUWingjpJQqWDjDKciA2uAEYGYhnX6OIzHUX0XPgQp6e6qsB4iIiIiIiM5hDBaehHQkshkbgaCG\nYsFBIXf2daKzGRv+gAZdFxhNMqtoxnw+iFXnA0tXwKoCljXznZCBscxC5wwNFlYqDo4eqqBYOPu+\nI2ezQs7GoX0VpEZsNLUaMAxgeIBZxkREREREROcqBgtPopB3YFsSbUtMBEMahgetsyq70LYl8lkH\nsQYd8UYdmbQNh7vxzogQAuLiKyGicW9zk+ApBAuFENB0AfsMjdOMDFrI52wcOVjBaIpB58XOtiT6\nj1Vx5FAFpgmsWO1DU4uB5jYTuYzNoC8REREREdE5isHCk8iM2vD5NfgDAs2tRm1Nr7OnE53LOJBS\nIhpXwULblsiOnj3v73QrFh0YpoBhznwaMqA2OTkTpyFXyg4yaQct7SZiDTr6j1Uw2H/2Tdu3bXnG\nvycpJbKjNg7tKyM7aqNtiYmlK3zw+dXlIBrX4A9oGDoLPz8iIiIiIiI6OeN0vMhTTz2FJ554Aul0\nGitWrMDHPvYxrF69+nS89Kw4jkQu46Cx2YAQAqGIjlBYx8ighUh05htYTKVUdCAE4A+c/thtdtRG\nMKTBNAUA9R5HUxbijfppP5azQangnFJWoUvXT32Dk3LJgekT0LTZlUvHkSjmHYQi0y/jI0MWDBNo\nSOiqLPsFhgYslEsS8fjZEXzOZW30HakiENTQudyc9XleCFZVYqCvilzGRiSmo63DPC6wLYRAS7uB\no4cqyGUdRGOsC4iIiIiIiM4l8x6d2rZtG+677z78yZ/8Cf7+7/8ey5cvx5133olMJjPfLz1r+awD\nx5GIxcdOU3ObgXLJmbPsu2LBQc/BCo4crKBaOb1ZPJYlUcg7iMbHggENCR3FgoNy6fj3Vyw4GB6o\nLvg05dlkO0kpYdsS1apEuezM6fqAUkqUSqe2XqHrVDMLR1MWDu0r48DuMgb7qpN+ftMhpdoZ++jh\nCo4drsKyTn4sblZhotmApgkIIdDYbKBzuYly0cHeXdlTPp7FQEqJ5LCFY4erCAQFikUHxw4v/Pdg\nJqSUSCctHNxbRqngYMkyH5YsOz5Q6ApHdIQiOoYHTv+yC44jIRfZuXWzMafzfThbSClRLDjn1Hs+\n0zETmIiIiIjmyrxnFv785z/HDTfcgE2bNgEA7rjjDuzcuRMvvPACNm/ePN8vPyuZUbWxiTs9DwCC\nIQ3hqMoujMZnl11YKTs41lNBICBQrUr0Ha1g2QofxGnKWMplbEiJcZlDkYgGwxBIJ220LRl734W8\ng2OHK3AciXzOQWeX75Sn2p4qx1aBrFzWgWkCpk+D6RMwfQICKsjgOFD/bAm7dqsek3Bs9TNgrEOl\n6QINCR2NCWPW76dSVkGOU9kJ2aUbmHHQuFhwMNBrIRrXYZoCo2kbqRELwZCGeKOOaFyfdhbcyKCF\nXMZBU6uBdNLG4X1ldCw1EYpMnV3mZhVOzEYNR3R0rRJIjwA9ByroWGYiEj2zstQcR2Kwz8JoykKi\n2UBzm4FiwcHRw1Uc66mis2vuMwwdR6JcUrtql0sSgaBAQ+LUq+pK2cFAbxWFvIN4o4GWNsPbeftE\nWtoMHN5fxmjKntXrn4yUEpWyqlfyOQfFvANdx0nL3ekipSoD6aQFwxToWOpDKHz2ruBh2xKZlI1U\n0ka14kAIgViDjsYmfUGy30+FKvMWqqUidJ88I7OAZ6qQd9B3pIJoXEdLuzFnMx9o8XGDwqf7M7Zt\niWql9q8qEQprsxocJSIiOh0cR123qhUJqyoRDGlnTJt2oc1rsNCyLBw4cAC33HKL95gQApdccgn2\n7Nkzny89bVZVQmgqo6uebamNP1rajj9Fza2qE51J24g3ntoptCyJY4er0DWBJV0+VCsSRw5WMDRo\nobXdPO75jiNrm63UB74kdEMgGtNg+mZe4LOjDkJhbVyQTGgC8UYdqREbLW0GNF2gkLdx9HAVwaCG\nplYDfUcqOHygjKXLfaf0RZNSwrbUlNvpBkbV+aqgUpZoaTPGsgNLDnIZCQlA0wBdE9B0QNMEdB0w\nTQ2apn6m6aJ2330OkMs6SI/YSA3bs+4Qqw0hBAKzqHw0Xcwo29GqSvQeqcAfEGjvVIGr5lZDva+U\njf5jVQz2W4jV1qQ8UcM+k7YxMmShpc1EosVAQ6OBvqNVHDlURVOLCiBO7Jy4WYWtHcakHXKfX8Oa\nC2L4/RtFHDtcRXObRKJZX9QdWceRsC0JywKG+qsoFSXaO31eMDQU1rF0OeYkYCilRKUiUSo4KBbU\nbbksAUgIoQLhoykHtg00tcysrpGORHLExsigCnItW+GbUfAtENQQa1ADI7G4Dk2fu8/MrmU1qwCh\nrephIRAMa2huM5DPOicsd24dcqIAf6mogqSOowZ5giENwbCAaYpplz8pJQb7VaCwpc1ELmvj6KEK\nmtsMNDYdX46lIyEl5vRcucolB+mkDV2Ht47uXCw74KqUHaSSNjIpNYgUiWloX+JDsaCyUkdTFkJh\nHQ1NOoJBDbox98EK6Q74yLH7UqplDaZ7TktFB0cPVyAA9PeWYFkVtHYYCC/AQIWUqjyo/6hhKiEw\n58HLTFrV9aZPIDViQUqgtWNuAoaWJVEpO6iU1ABcICDgD6pBxbmmMv9V/WDb6jtuWxJW3WOaJhCJ\naQiFtXkLArttLNuRyOcs5LM27Fqby7YBn1/M6VI0JzqOakUNHlXKDipliXJZPWaYArG4hmiDDr9/\nbjo8UqrrngoIOqhW1PXJDRDWz3oQQkBKiUhMR1OLMa2gYS5rY7C3Ck0XaGya2UDmmeR0Z/hKqTq/\nxYLKBi8WHOiGQGu7MWV71rIkcqNqzRuh1TbY09R9NUOkdl+I2u3Y8xar+vJrVVUfQdfVEk8+vziu\nn0e02Nm2qv+tqoRhwFsTf6b1ppRqQD41bMO2JRLNxowSnqyqqv99PjHrhKZyyYHjAIHg9Nvi0zHZ\n9csNDlYr8rhZMkIIJFp0NDUbpy1J60w1r8HCbDYLx3EQj8fHPR6Px9Hb2zufLz0lq6o6qcWCg0Le\nQaXsQNcFlq7wjWvsZN2su/jxHYxAUEM0pqP/mIWRQcvLcPP5BEx/7dacunPjOBK9PRXYjkTXSh8M\nQ8AwBJrbDAz1VxEMaeOy/YoFB/3HqqiU1VROtWuuuqBblsRQv5r6Go3rXnbZdM9D25Lji0C80cDI\nkF3b3EV4gUJ3nbaubj+OHa6obLGlJiK1Y5VSNbAtSyIzWsVoyoJVVf+3LFXZWZbqAEgp4Q9o6Frp\nO2knsFqROHq4AtuSWLbSN6cj2aGwauiOJm2kah3icERHokVHMHR8Raqm5qlK2zehgV4qSvgD0+/U\nTkatWThWoanpozZKRQeNTeMb5NJRgUJIoLPL5108hCa8slCpOBhNqQBAOmkhEFTZhhODP6WiKmOx\nBh2NzerzNEyBpStMjAzZtZ2OHbS0GwiFx8rmyJAFwzg+q3D8exJYsszEyKCF4QE1RdoNbE4ln7OR\nHXWmDN5KRyKdspEasQGpgua6LqAbYx1xKes669L9P2qddznu/+4/25LjphcbhsCylT4EQ+OPIRTW\nsbQLONpTRW9PFW1LTJi+k3/ujiNRKqoGfanWqFeft4A/IBAIaWhoErWMZtUgGK6dNwEgMc2Aoft5\nlksqONvUOnkw92SaWw0cHFXfdX9QwPRp8PkEfH51vDMJupWKqrFSyNkoFtSH4vOrui4c0RCs6/w3\nNkkkh1W5K+QddCz11bKabeQyDnJZFWAMR9R7q/98pCMxMmQhOWzDHxAIhTUU8w5GU2qbccMQdcFD\nbcr3IaXEUL+F9IiFtiUmGhIqQDg8aGGov4piwUH7EhPVqkQh5yBfu67I2iCOaQov83ni/Zk0kKQj\nMTJsITlkQzcASDUQp6jPQr0fdWv6Zva5FPIOUiM28lmV0dnQpKMhYXjXkVAESDTryGYcpEYs9PZU\nAKjvtSoHasBJqMOBezMx4Fef+e097mZ9S3V/qk62EOq9hSMaQpGpP7N81kbvkSp8foHO5T5EIzHs\n2VXC0cMVRGIq426m5/9UlIrqXGVHnUnfkxBqMEvVWyq4rOv198d+puvCe2zid1gtkWBjeEDV3W1L\nTGTSNgZ6VcCwbcn0AoZu8L1cC0hVymogrlIeCxAJoQIIbv1omKqeikQ1xOL6pI1ty5JIDlnIZR01\nmFero91Ou11rG4wFCIH6zP9x50oX0A2BUlV9lzVNIBxV9YcQGBfYsiwJXRcwTHWcpiG8gTh3xkF9\n8M+xx4KDjj2+HAaDGRSLFfdooOnq+aYp0NBkIN6ojwtCuJnhlqUy76YToHAcOXbe3c+gpDo67rEY\nhoDPr4KkvoRAuSSRSqoBPn9ADeyEwhr8/hN35uqzK9x/9QHB+vdumKq+UsFRfawO86nPRA0w2ji8\nv3zCoKFjSwwNqEGXUERXgfxjVQwNWGhoVPWNrtfqDO96reoF99rtOADk8ddtn19M+p5tW9VthZy6\nxrrtbN0QMAwVtD/umyknf6zu5rj/SIna+VOfW7Ui4fenYdll1ReoXS9VH0GDYZ5awM0NpNdndnqf\nX3lsuQifT0MgJFAqShzeX0FDk/pc3HJoWRKpYQuppD323mYQ3BTjgofCCyJqmhjrB7kzfjR4iQ3u\nrTtYr+o1wNCn32aWjkS11pdwz4FVRd398eVX08W464rpU98P3Rg7Bn1cIkHtPblJBXWPzZTX1yw6\nEMDY6+nqvNjW+IERKeG1Yw3DPbYKctmxRczHHcUkh+Q+5Diq/Nu2unVsTEiUUPWwWQs8TTeI6ran\npXtNl9K777W3a4N9qr6a+d+ubx9MvD9ZG2Lyx9R997ppmGKsj6DBOwducBxQ1zagNpjnls85Do67\n16YTBcmqFQe5jCo3apCoVglOoNfqM9MnvGucUWtnGobw6hnHkcikbSSHLZRLDgK1gb6+oxWMDGpI\ntOjjrt9uW6BarSUxFFVfpVodawe47f9AQIM/KOD3a9OarWRVJYYHrbG2uCkQjam+6nQDh+Oy2yfU\ng9UJ33/3/Jg+1Q8Ya4Orwe7kkGpX5zKqTzrZtUtOuOagrtzXlz851f26ti8Arx3ntvXcdpFb5lQb\nq27A0q7rw3rH5N5xH5Nj9+X450z2mPu+ACAYBFpaTnraIeQ8DoGlUil88pOfxN/+7d9izZo13uP3\n338/du3ahTvvvPO439myZQu2bt067rG2tjbcfvvt6D2aQWOTOaMvb7XqIJexkMtWkctaKJdUxesP\n6IhEDYSjBoYHyiiXbKxcE0Ukqjrj+97MAgBWr41O+ndtWyKdrKBctlEpOSiXHZRL9rhAg2lq8AXU\nqK/Pr8HvV4GPwb4SMqNVdJ8fRTgy1vmXUuLQvjxyWQvnXRSFYWjoP1bE0EC5ls0UQiA4fiTWtiUy\n6SrSyQoyo2r30nDEQEPCh4aED6Y5eWBtaKCE3iNFXHRZHMYkzzmwJ4dS0YZlOQhHDKxcHRl3Qbdt\niZ4DeYymqwiFdViWRLXqeGuNaZoOx7FhGCrrUV2U3PsaNB04eqiAhoQPXSvDU35+paKN/XuyEBBY\ndX4EgcD8ZYc4jvpMB/vLKBUthMIGWtoCiDWYyGWqGE1XkUlXYVkODEPDeRfGxgUM3/xdBqGwfsL3\nczL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2TZ51dzrYlpyziLJlSeQyag24Ql5NdxFCoHutf96CcNOtoBxHoudABVICy1f5IAS8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\nIYQQ4gv/D9TmtdZP0XOYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1153c9d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 少しだけ情報量の多い折れ線グラフ\n",
    "df.plot(y=['temperature', 'temperature_rolling_mean', 'temperature_pct_change'],  figsize=(16,4), alpha=0.5) # x軸がindexである場合は明記しなくても可\n",
    "plt.title('気温変化に関する図', fontproperties=font_prop) # fontpropertiesは日本語表示に必要"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:20.298891Z",
     "start_time": "2018-04-15T20:56:19.742829Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115a39f28>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115a51048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ヒストグラム\n",
    "df.plot(kind='hist', y='sales' , bins=10, figsize=(16,4), alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "頻度的には、\n",
    "- 日別で60個くらい売れている日が多いらしい\n",
    "- 最大で140個くらい売れている日もあることがわかる　など"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:20.659974Z",
     "start_time": "2018-04-15T20:56:20.300568Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1153c9320>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NcXH4U0ocmq46ENvPhJT+MYiJCk3RiL+xVFfVe7wd1OoAhqd5/76WsQD62tcMhs7Er6Sk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\nEUnn/wN5FTGQIOQjiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115ac9438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 散布図\n",
    "df.plot(kind='scatter', x='kcal', y='sales')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 「売上個数」と「カロリー」には相関がなさそう　など"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:20.679963Z",
     "start_time": "2018-04-15T20:56:20.662278Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kcal</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kcal</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>0.129521</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           kcal     sales\n",
       "kcal   1.000000  0.129521\n",
       "sales  0.129521  1.000000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ちなみに相関係数はcorrメソッドで簡単に表示できる\n",
    "df[['kcal', 'sales']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:20.726665Z",
     "start_time": "2018-04-15T20:56:20.685611Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>period</th>\n",
       "      <th>上旬</th>\n",
       "      <th>中旬</th>\n",
       "      <th>下旬</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>216</td>\n",
       "      <td>581</td>\n",
       "      <td>1018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>725</td>\n",
       "      <td>715</td>\n",
       "      <td>609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>614</td>\n",
       "      <td>698</td>\n",
       "      <td>369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>792</td>\n",
       "      <td>344</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>316</td>\n",
       "      <td>406</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>467</td>\n",
       "      <td>440</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>460</td>\n",
       "      <td>277</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>288</td>\n",
       "      <td>250</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>468</td>\n",
       "      <td>249</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "period   上旬   中旬    下旬\n",
       "month                 \n",
       "1       216  581  1018\n",
       "2       725  715   609\n",
       "3       614  698   369\n",
       "4       792  344   457\n",
       "5       316  406   517\n",
       "6       467  440   311\n",
       "7       460  277   406\n",
       "8       288  250   334\n",
       "9       468  249   234"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ちょっとだけ複雑な集計 例: 月別・期間別の売上個数の状況を可視化したい場合\n",
    "monthly_df = pd.DataFrame(df.groupby(['month', 'period'])['sales'].sum())\n",
    "pivot_monthly_df = monthly_df.reset_index().pivot(index='month', columns='period', values='sales')\n",
    "pivot_monthly_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:21.320449Z",
     "start_time": "2018-04-15T20:56:20.728991Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1164456a0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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PNzs0AAAAAAC84tcFuSQNHTpUQ4cONTsMAAAAAAB8KmDm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\nABRyAAAAAAAMQCEHAAAAAMAA/wMzsIwLInMIJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1163f9c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 棒グラフ\n",
    "pivot_monthly_df.plot(kind='bar', alpha=0.6, figsize=(12,3)).legend(prop=font_prop) # legend(prop=font_prop)は日本語表示に必要\n",
    "plt.title('月別・期間別の売上個数', fontproperties=font_prop) # fontpropertiesは日本語表示に必要"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 1月は下旬（20日から31日まで）に売上個数が増える傾向\n",
    "- 4月だと上旬（1日から10日まで）が売上多い\n",
    "- 月が進むほど、期間に関係なく売上個数が少なくなっていく傾向　など"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ⑧ 変数の前処理\n",
    "\n",
    "**dummy化について**\n",
    "![get_dummies](./figs/get_dummies.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:21.337514Z",
     "start_time": "2018-04-15T20:56:21.324212Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['sales', 'week', 'soldout', 'name', 'kcal', 'event', 'payday',\n",
      "       'weather', 'precipitation', 'temperature', 'month', 'day', 'period',\n",
      "       'temperature_diff', 'temperature_rolling_mean',\n",
      "       'temperature_pct_change'],\n",
      "      dtype='object')\n",
      "16\n"
     ]
    }
   ],
   "source": [
    "# 現在のcolumnを確認\n",
    "print(df.columns)\n",
    "print(len(df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:21.412866Z",
     "start_time": "2018-04-15T20:56:21.342579Z"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>soldout</th>\n",
       "      <th>kcal</th>\n",
       "      <th>payday</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>temperature_diff</th>\n",
       "      <th>temperature_rolling_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>weather_快晴</th>\n",
       "      <th>weather_晴れ</th>\n",
       "      <th>weather_曇</th>\n",
       "      <th>weather_薄曇</th>\n",
       "      <th>weather_雨</th>\n",
       "      <th>weather_雪</th>\n",
       "      <th>weather_雷電</th>\n",
       "      <th>period_上旬</th>\n",
       "      <th>period_中旬</th>\n",
       "      <th>period_下旬</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <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>2014-01-09</th>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>435.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>9.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-14</th>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>376.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>7.266667</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-15</th>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-16</th>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.233333</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 157 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales  soldout   kcal  payday  precipitation  temperature  month  \\\n",
       "datetime                                                                       \n",
       "2014-01-09    129        1  435.0     0.0            0.0         10.9      1   \n",
       "2014-01-10     87        0  440.0     1.0            0.0          5.1      1   \n",
       "2014-01-14    129        1  376.0     0.0            0.0          5.8      1   \n",
       "2014-01-15    134        0  450.0     0.0            0.0          2.9      1   \n",
       "2014-01-16    107        0  415.0     0.0            0.0          7.0      1   \n",
       "\n",
       "            day  temperature_diff  temperature_rolling_mean    ...      \\\n",
       "datetime                                                       ...       \n",
       "2014-01-09    9               1.1                  9.333333    ...       \n",
       "2014-01-10   10              -5.8                  8.600000    ...       \n",
       "2014-01-14   14               0.7                  7.266667    ...       \n",
       "2014-01-15   15              -2.9                  4.600000    ...       \n",
       "2014-01-16   16               4.1                  5.233333    ...       \n",
       "\n",
       "            weather_快晴  weather_晴れ  weather_曇  weather_薄曇  weather_雨  \\\n",
       "datetime                                                               \n",
       "2014-01-09           0           0          1           0          0   \n",
       "2014-01-10           1           0          0           0          0   \n",
       "2014-01-14           0           1          0           0          0   \n",
       "2014-01-15           0           0          1           0          0   \n",
       "2014-01-16           1           0          0           0          0   \n",
       "\n",
       "            weather_雪  weather_雷電  period_上旬  period_中旬  period_下旬  \n",
       "datetime                                                            \n",
       "2014-01-09          0           0          1          0          0  \n",
       "2014-01-10          0           0          1          0          0  \n",
       "2014-01-14          0           0          0          1          0  \n",
       "2014-01-15          0           0          0          1          0  \n",
       "2014-01-16          0           0          0          1          0  \n",
       "\n",
       "[5 rows x 157 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# テキストデータが入っている列全てをdummy化する\n",
    "dummy_df = pd.get_dummies(df, columns=['week', 'name', 'event', 'weather', 'period'])\n",
    "dummy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:21.418943Z",
     "start_time": "2018-04-15T20:56:21.415288Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# jupyter notebookにおいて、pandasで表示が省略されるのを防ぐ\n",
    "pd.set_option('display.max_columns', 160) # 160番目の列まで全て強制表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:21.694169Z",
     "start_time": "2018-04-15T20:56:21.421248Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(164, 157)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>soldout</th>\n",
       "      <th>kcal</th>\n",
       "      <th>payday</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>temperature_diff</th>\n",
       "      <th>temperature_rolling_mean</th>\n",
       "      <th>temperature_pct_change</th>\n",
       "      <th>week_月</th>\n",
       "      <th>week_木</th>\n",
       "      <th>week_水</th>\n",
       "      <th>week_火</th>\n",
       "      <th>week_金</th>\n",
       "      <th>name_いか天ぷら</th>\n",
       "      <th>name_かじきの甘辛煮</th>\n",
       "      <th>name_きのこソースハンバーグ</th>\n",
       "      <th>name_さわら焼味噌掛け</th>\n",
       "      <th>name_さんま辛味焼</th>\n",
       "      <th>name_たっぷりベーコンフライ</th>\n",
       "      <th>name_ぶりレモンペッパー焼き</th>\n",
       "      <th>name_ますのマスタードソース</th>\n",
       "      <th>name_アジ唐揚げ南蛮ソース</th>\n",
       "      <th>name_エビフライ</th>\n",
       "      <th>name_カレイの唐揚げ</th>\n",
       "      <th>name_カレイ唐揚げ 甘酢あん</th>\n",
       "      <th>name_カレイ唐揚げ夏野菜あん</th>\n",
       "      <th>name_カレイ唐揚げ野菜あんかけ</th>\n",
       "      <th>name_カレイ唐揚げ野菜餡かけ</th>\n",
       "      <th>name_カレイ野菜あんかけ</th>\n",
       "      <th>name_キスと野菜の天ぷら</th>\n",
       "      <th>name_キーマカレー</th>\n",
       "      <th>name_ゴーヤチャンプルー</th>\n",
       "      <th>name_サバ焼味噌掛け</th>\n",
       "      <th>name_サーモンのムニエル2色ソース</th>\n",
       "      <th>name_サーモンフライ・タルタル</th>\n",
       "      <th>name_ジャンボチキンカツ</th>\n",
       "      <th>name_ジューシーメンチカツ</th>\n",
       "      <th>name_タンドリーチキン</th>\n",
       "      <th>name_チキンのコーンクリーム焼き</th>\n",
       "      <th>name_チキンの辛味噌焼き</th>\n",
       "      <th>name_チキンクリームシチュー</th>\n",
       "      <th>name_チキンステーキ・きのこソース</th>\n",
       "      <th>name_チキンフリカッセ</th>\n",
       "      <th>name_チキン南蛮</th>\n",
       "      <th>name_チキン香草焼きマスタードソース</th>\n",
       "      <th>name_チャプチェ</th>\n",
       "      <th>name_チンジャオロース</th>\n",
       "      <th>name_チーズハンバーグ</th>\n",
       "      <th>name_チーズメンチカツ</th>\n",
       "      <th>name_チーズ入りハンバーグ</th>\n",
       "      <th>name_チーズ入りメンチカツ</th>\n",
       "      <th>name_ハンバーグカレーソース</th>\n",
       "      <th>name_ハンバーグケッチャップソース</th>\n",
       "      <th>name_ハンバーグデミソース</th>\n",
       "      <th>name_ハンバーグ和風きのこソース</th>\n",
       "      <th>name_ハンバーグ（デミきのこバター）</th>\n",
       "      <th>name_バーベキューチキン</th>\n",
       "      <th>name_ビーフカレー</th>\n",
       "      <th>name_ビーフシチュー</th>\n",
       "      <th>name_ビーフストロガノフ</th>\n",
       "      <th>name_ビーフトマト煮</th>\n",
       "      <th>name_プルコギ</th>\n",
       "      <th>name_ホタテクリ―ムシチュー</th>\n",
       "      <th>name_ボローニャ風カツ</th>\n",
       "      <th>name_ポークソテー韓国ソース</th>\n",
       "      <th>name_ポークハヤシ</th>\n",
       "      <th>name_ポーク生姜焼き</th>\n",
       "      <th>name_マーボ茄子</th>\n",
       "      <th>name_マーボ豆腐</th>\n",
       "      <th>name_メダイ照り焼</th>\n",
       "      <th>name_メンチカツ</th>\n",
       "      <th>name_ロース甘味噌焼き</th>\n",
       "      <th>name_八宝菜</th>\n",
       "      <th>name_厚切イカフライ</th>\n",
       "      <th>name_厚切ハムカツ</th>\n",
       "      <th>name_名古屋味噌カツ</th>\n",
       "      <th>name_和風ソースハンバーグ</th>\n",
       "      <th>name_和風ハンバーグ</th>\n",
       "      <th>name_回鍋肉</th>\n",
       "      <th>name_手作りひれかつ</th>\n",
       "      <th>name_手作りひれかつとカレー</th>\n",
       "      <th>name_手作りチキンカツ</th>\n",
       "      <th>name_手作りトンカツ</th>\n",
       "      <th>name_手作りロースカツ</th>\n",
       "      <th>name_洋食屋さんのメンチカツ</th>\n",
       "      <th>name_海老クリーミ―クノーデル</th>\n",
       "      <th>name_海老フライとホタテ串カツ</th>\n",
       "      <th>name_海老フライタルタル</th>\n",
       "      <th>name_炊き込みご飯</th>\n",
       "      <th>name_牛すき焼き風</th>\n",
       "      <th>name_牛カルビ焼き肉</th>\n",
       "      <th>name_牛スキヤキ</th>\n",
       "      <th>name_牛丼風煮</th>\n",
       "      <th>name_牛肉すき焼き風</th>\n",
       "      <th>name_牛肉コロッケ</th>\n",
       "      <th>name_牛肉ニンニクの芽炒め</th>\n",
       "      <th>name_牛肉筍煮</th>\n",
       "      <th>name_白身魚のマスタード焼き</th>\n",
       "      <th>name_白身魚の南部焼き</th>\n",
       "      <th>name_白身魚フライ</th>\n",
       "      <th>name_白身魚ムニエル</th>\n",
       "      <th>name_白身魚唐揚げ野菜あんかけ</th>\n",
       "      <th>name_白身魚唐揚げ野菜餡かけ</th>\n",
       "      <th>name_筑前煮</th>\n",
       "      <th>name_肉じゃが</th>\n",
       "      <th>name_肉団子のシチュー</th>\n",
       "      <th>name_肉団子クリームシチュー</th>\n",
       "      <th>name_親子煮</th>\n",
       "      <th>name_豚すき焼き</th>\n",
       "      <th>name_豚の冷しゃぶ</th>\n",
       "      <th>name_豚キムチ炒め</th>\n",
       "      <th>name_豚ロースのピザ風チーズ焼き</th>\n",
       "      <th>name_豚冷シャブ野菜添え</th>\n",
       "      <th>name_豚味噌メンチカツ</th>\n",
       "      <th>name_豚肉と玉子の炒め</th>\n",
       "      <th>name_豚肉と茄子のピリ辛炒め</th>\n",
       "      <th>name_豚肉のマスタード焼き</th>\n",
       "      <th>name_豚肉の生姜焼き</th>\n",
       "      <th>name_豚肉の胡麻シャブ</th>\n",
       "      <th>name_越冬キャベツのメンチカツ</th>\n",
       "      <th>name_酢豚</th>\n",
       "      <th>name_酢豚orカレー</th>\n",
       "      <th>name_青梗菜牛肉炒め</th>\n",
       "      <th>name_青椒肉絲</th>\n",
       "      <th>name_鶏のから揚げねぎ塩炒めソース</th>\n",
       "      <th>name_鶏のから揚げスイートチリソース</th>\n",
       "      <th>name_鶏のカッシュナッツ炒め</th>\n",
       "      <th>name_鶏のトマトシチュー</th>\n",
       "      <th>name_鶏のピリ辛焼き</th>\n",
       "      <th>name_鶏のレモンペッパー焼orカレー</th>\n",
       "      <th>name_鶏の味噌漬け焼き</th>\n",
       "      <th>name_鶏の唐揚げ</th>\n",
       "      <th>name_鶏の唐揚げおろしソース</th>\n",
       "      <th>name_鶏の塩から揚げ</th>\n",
       "      <th>name_鶏の天ぷら</th>\n",
       "      <th>name_鶏の照り焼きマスタード</th>\n",
       "      <th>name_鶏の照り焼きマヨ</th>\n",
       "      <th>name_鶏の親子煮</th>\n",
       "      <th>name_鶏チリソース</th>\n",
       "      <th>name_鶏肉とカシューナッツ炒め</th>\n",
       "      <th>name_鶏肉のカレー唐揚</th>\n",
       "      <th>name_鶏肉の山賊焼き</th>\n",
       "      <th>event_キャリアアップ支援セミナー</th>\n",
       "      <th>event_ママの会</th>\n",
       "      <th>weather_快晴</th>\n",
       "      <th>weather_晴れ</th>\n",
       "      <th>weather_曇</th>\n",
       "      <th>weather_薄曇</th>\n",
       "      <th>weather_雨</th>\n",
       "      <th>weather_雪</th>\n",
       "      <th>weather_雷電</th>\n",
       "      <th>period_上旬</th>\n",
       "      <th>period_中旬</th>\n",
       "      <th>period_下旬</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-09</th>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>435.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>9.333333</td>\n",
       "      <td>0.112245</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>-0.532110</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-14</th>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>376.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>7.266667</td>\n",
       "      <td>0.137255</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-15</th>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-16</th>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>415.0</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.233333</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales  soldout   kcal  payday  precipitation  temperature  month  \\\n",
       "datetime                                                                       \n",
       "2014-01-09    129        1  435.0     0.0            0.0         10.9      1   \n",
       "2014-01-10     87        0  440.0     1.0            0.0          5.1      1   \n",
       "2014-01-14    129        1  376.0     0.0            0.0          5.8      1   \n",
       "2014-01-15    134        0  450.0     0.0            0.0          2.9      1   \n",
       "2014-01-16    107        0  415.0     0.0            0.0          7.0      1   \n",
       "\n",
       "            day  temperature_diff  temperature_rolling_mean  \\\n",
       "datetime                                                      \n",
       "2014-01-09    9               1.1                  9.333333   \n",
       "2014-01-10   10              -5.8                  8.600000   \n",
       "2014-01-14   14               0.7                  7.266667   \n",
       "2014-01-15   15              -2.9                  4.600000   \n",
       "2014-01-16   16               4.1                  5.233333   \n",
       "\n",
       "            temperature_pct_change  week_月  week_木  week_水  week_火  week_金  \\\n",
       "datetime                                                                     \n",
       "2014-01-09                0.112245       0       1       0       0       0   \n",
       "2014-01-10               -0.532110       0       0       0       0       1   \n",
       "2014-01-14                0.137255       0       0       0       1       0   \n",
       "2014-01-15               -0.500000       0       0       1       0       0   \n",
       "2014-01-16                1.413793       0       1       0       0       0   \n",
       "\n",
       "            name_いか天ぷら  name_かじきの甘辛煮  name_きのこソースハンバーグ  name_さわら焼味噌掛け  \\\n",
       "datetime                                                                \n",
       "2014-01-09           0             0                 0              0   \n",
       "2014-01-10           0             0                 0              0   \n",
       "2014-01-14           0             0                 0              0   \n",
       "2014-01-15           0             0                 0              0   \n",
       "2014-01-16           0             0                 0              0   \n",
       "\n",
       "            name_さんま辛味焼  name_たっぷりベーコンフライ  name_ぶりレモンペッパー焼き  name_ますのマスタードソース  \\\n",
       "datetime                                                                        \n",
       "2014-01-09            0                 0                 0                 0   \n",
       "2014-01-10            0                 0                 0                 0   \n",
       "2014-01-14            0                 0                 0                 0   \n",
       "2014-01-15            1                 0                 0                 0   \n",
       "2014-01-16            0                 0                 0                 0   \n",
       "\n",
       "            name_アジ唐揚げ南蛮ソース  name_エビフライ  name_カレイの唐揚げ  name_カレイ唐揚げ 甘酢あん  \\\n",
       "datetime                                                                  \n",
       "2014-01-09                0           0             0                 0   \n",
       "2014-01-10                0           0             0                 0   \n",
       "2014-01-14                0           0             0                 0   \n",
       "2014-01-15                0           0             0                 0   \n",
       "2014-01-16                0           0             0                 0   \n",
       "\n",
       "            name_カレイ唐揚げ夏野菜あん  name_カレイ唐揚げ野菜あんかけ  name_カレイ唐揚げ野菜餡かけ  \\\n",
       "datetime                                                            \n",
       "2014-01-09                 0                  0                 0   \n",
       "2014-01-10                 0                  0                 0   \n",
       "2014-01-14                 0                  0                 0   \n",
       "2014-01-15                 0                  0                 0   \n",
       "2014-01-16                 0                  1                 0   \n",
       "\n",
       "            name_カレイ野菜あんかけ  name_キスと野菜の天ぷら  name_キーマカレー  name_ゴーヤチャンプルー  \\\n",
       "datetime                                                                  \n",
       "2014-01-09               0               0            0               0   \n",
       "2014-01-10               0               0            0               0   \n",
       "2014-01-14               0               0            0               0   \n",
       "2014-01-15               0               0            0               0   \n",
       "2014-01-16               0               0            0               0   \n",
       "\n",
       "            name_サバ焼味噌掛け  name_サーモンのムニエル2色ソース  name_サーモンフライ・タルタル  \\\n",
       "datetime                                                           \n",
       "2014-01-09             0                    0                  0   \n",
       "2014-01-10             0                    0                  0   \n",
       "2014-01-14             0                    0                  0   \n",
       "2014-01-15             0                    0                  0   \n",
       "2014-01-16             0                    0                  0   \n",
       "\n",
       "            name_ジャンボチキンカツ  name_ジューシーメンチカツ  name_タンドリーチキン  \\\n",
       "datetime                                                     \n",
       "2014-01-09               0                0              0   \n",
       "2014-01-10               0                0              0   \n",
       "2014-01-14               0                0              0   \n",
       "2014-01-15               0                0              0   \n",
       "2014-01-16               0                0              0   \n",
       "\n",
       "            name_チキンのコーンクリーム焼き  name_チキンの辛味噌焼き  name_チキンクリームシチュー  \\\n",
       "datetime                                                           \n",
       "2014-01-09                   0               0                 0   \n",
       "2014-01-10                   0               0                 0   \n",
       "2014-01-14                   0               0                 0   \n",
       "2014-01-15                   0               0                 0   \n",
       "2014-01-16                   0               0                 0   \n",
       "\n",
       "            name_チキンステーキ・きのこソース  name_チキンフリカッセ  name_チキン南蛮  \\\n",
       "datetime                                                     \n",
       "2014-01-09                    0              0           0   \n",
       "2014-01-10                    0              0           0   \n",
       "2014-01-14                    0              0           0   \n",
       "2014-01-15                    0              0           0   \n",
       "2014-01-16                    0              0           0   \n",
       "\n",
       "            name_チキン香草焼きマスタードソース  name_チャプチェ  name_チンジャオロース  name_チーズハンバーグ  \\\n",
       "datetime                                                                     \n",
       "2014-01-09                     0           0              0              0   \n",
       "2014-01-10                     0           0              0              0   \n",
       "2014-01-14                     0           0              0              0   \n",
       "2014-01-15                     0           0              0              0   \n",
       "2014-01-16                     0           0              0              0   \n",
       "\n",
       "            name_チーズメンチカツ  name_チーズ入りハンバーグ  name_チーズ入りメンチカツ  name_ハンバーグカレーソース  \\\n",
       "datetime                                                                        \n",
       "2014-01-09              0                0                0                 0   \n",
       "2014-01-10              0                0                0                 0   \n",
       "2014-01-14              0                0                0                 0   \n",
       "2014-01-15              0                0                0                 0   \n",
       "2014-01-16              0                0                0                 0   \n",
       "\n",
       "            name_ハンバーグケッチャップソース  name_ハンバーグデミソース  name_ハンバーグ和風きのこソース  \\\n",
       "datetime                                                               \n",
       "2014-01-09                    0                0                   0   \n",
       "2014-01-10                    0                0                   0   \n",
       "2014-01-14                    0                0                   0   \n",
       "2014-01-15                    0                0                   0   \n",
       "2014-01-16                    0                0                   0   \n",
       "\n",
       "            name_ハンバーグ（デミきのこバター）  name_バーベキューチキン  name_ビーフカレー  name_ビーフシチュー  \\\n",
       "datetime                                                                      \n",
       "2014-01-09                     0               0            0             0   \n",
       "2014-01-10                     0               0            0             0   \n",
       "2014-01-14                     0               0            0             0   \n",
       "2014-01-15                     0               0            0             0   \n",
       "2014-01-16                     0               0            0             0   \n",
       "\n",
       "            name_ビーフストロガノフ  name_ビーフトマト煮  name_プルコギ  name_ホタテクリ―ムシチュー  \\\n",
       "datetime                                                                \n",
       "2014-01-09               0             0          0                 0   \n",
       "2014-01-10               0             0          0                 0   \n",
       "2014-01-14               0             0          0                 0   \n",
       "2014-01-15               0             0          0                 0   \n",
       "2014-01-16               0             0          0                 0   \n",
       "\n",
       "            name_ボローニャ風カツ  name_ポークソテー韓国ソース  name_ポークハヤシ  name_ポーク生姜焼き  \\\n",
       "datetime                                                                 \n",
       "2014-01-09              0                 0            0             0   \n",
       "2014-01-10              0                 0            0             0   \n",
       "2014-01-14              0                 0            0             0   \n",
       "2014-01-15              0                 0            0             0   \n",
       "2014-01-16              0                 0            0             0   \n",
       "\n",
       "            name_マーボ茄子  name_マーボ豆腐  name_メダイ照り焼  name_メンチカツ  name_ロース甘味噌焼き  \\\n",
       "datetime                                                                     \n",
       "2014-01-09           0           0            0           0              0   \n",
       "2014-01-10           0           0            0           0              0   \n",
       "2014-01-14           0           0            0           0              0   \n",
       "2014-01-15           0           0            0           0              0   \n",
       "2014-01-16           0           0            0           0              0   \n",
       "\n",
       "            name_八宝菜  name_厚切イカフライ  name_厚切ハムカツ  name_名古屋味噌カツ  \\\n",
       "datetime                                                        \n",
       "2014-01-09         0             0            0             0   \n",
       "2014-01-10         0             0            0             0   \n",
       "2014-01-14         0             0            0             0   \n",
       "2014-01-15         0             0            0             0   \n",
       "2014-01-16         0             0            0             0   \n",
       "\n",
       "            name_和風ソースハンバーグ  name_和風ハンバーグ  name_回鍋肉  name_手作りひれかつ  \\\n",
       "datetime                                                            \n",
       "2014-01-09                0             0         0             0   \n",
       "2014-01-10                0             0         0             0   \n",
       "2014-01-14                0             0         0             0   \n",
       "2014-01-15                0             0         0             0   \n",
       "2014-01-16                0             0         0             0   \n",
       "\n",
       "            name_手作りひれかつとカレー  name_手作りチキンカツ  name_手作りトンカツ  name_手作りロースカツ  \\\n",
       "datetime                                                                   \n",
       "2014-01-09                 0              0             0              0   \n",
       "2014-01-10                 0              0             0              1   \n",
       "2014-01-14                 0              0             0              0   \n",
       "2014-01-15                 0              0             0              0   \n",
       "2014-01-16                 0              0             0              0   \n",
       "\n",
       "            name_洋食屋さんのメンチカツ  name_海老クリーミ―クノーデル  name_海老フライとホタテ串カツ  \\\n",
       "datetime                                                             \n",
       "2014-01-09                 0                  0                  0   \n",
       "2014-01-10                 0                  0                  0   \n",
       "2014-01-14                 0                  0                  0   \n",
       "2014-01-15                 0                  0                  0   \n",
       "2014-01-16                 0                  0                  0   \n",
       "\n",
       "            name_海老フライタルタル  name_炊き込みご飯  name_牛すき焼き風  name_牛カルビ焼き肉  \\\n",
       "datetime                                                             \n",
       "2014-01-09               0            0            0             0   \n",
       "2014-01-10               0            0            0             0   \n",
       "2014-01-14               0            0            0             0   \n",
       "2014-01-15               0            0            0             0   \n",
       "2014-01-16               0            0            0             0   \n",
       "\n",
       "            name_牛スキヤキ  name_牛丼風煮  name_牛肉すき焼き風  name_牛肉コロッケ  name_牛肉ニンニクの芽炒め  \\\n",
       "datetime                                                                        \n",
       "2014-01-09           0          0             0            0                0   \n",
       "2014-01-10           0          0             0            0                0   \n",
       "2014-01-14           0          0             0            0                0   \n",
       "2014-01-15           0          0             0            0                0   \n",
       "2014-01-16           0          0             0            0                0   \n",
       "\n",
       "            name_牛肉筍煮  name_白身魚のマスタード焼き  name_白身魚の南部焼き  name_白身魚フライ  \\\n",
       "datetime                                                              \n",
       "2014-01-09          0                 0              0            0   \n",
       "2014-01-10          0                 0              0            0   \n",
       "2014-01-14          0                 0              0            0   \n",
       "2014-01-15          0                 0              0            0   \n",
       "2014-01-16          0                 0              0            0   \n",
       "\n",
       "            name_白身魚ムニエル  name_白身魚唐揚げ野菜あんかけ  name_白身魚唐揚げ野菜餡かけ  name_筑前煮  \\\n",
       "datetime                                                                  \n",
       "2014-01-09             0                  0                 0         0   \n",
       "2014-01-10             0                  0                 0         0   \n",
       "2014-01-14             0                  0                 0         0   \n",
       "2014-01-15             0                  0                 0         0   \n",
       "2014-01-16             0                  0                 0         0   \n",
       "\n",
       "            name_肉じゃが  name_肉団子のシチュー  name_肉団子クリームシチュー  name_親子煮  name_豚すき焼き  \\\n",
       "datetime                                                                       \n",
       "2014-01-09          0              0                 0         0           0   \n",
       "2014-01-10          0              0                 0         0           0   \n",
       "2014-01-14          0              0                 0         0           0   \n",
       "2014-01-15          0              0                 0         0           0   \n",
       "2014-01-16          0              0                 0         0           0   \n",
       "\n",
       "            name_豚の冷しゃぶ  name_豚キムチ炒め  name_豚ロースのピザ風チーズ焼き  name_豚冷シャブ野菜添え  \\\n",
       "datetime                                                                   \n",
       "2014-01-09            0            0                   0               0   \n",
       "2014-01-10            0            0                   0               0   \n",
       "2014-01-14            0            0                   0               0   \n",
       "2014-01-15            0            0                   0               0   \n",
       "2014-01-16            0            0                   0               0   \n",
       "\n",
       "            name_豚味噌メンチカツ  name_豚肉と玉子の炒め  name_豚肉と茄子のピリ辛炒め  name_豚肉のマスタード焼き  \\\n",
       "datetime                                                                      \n",
       "2014-01-09              0              0                 0                0   \n",
       "2014-01-10              0              0                 0                0   \n",
       "2014-01-14              0              0                 0                0   \n",
       "2014-01-15              0              0                 0                0   \n",
       "2014-01-16              0              0                 0                0   \n",
       "\n",
       "            name_豚肉の生姜焼き  name_豚肉の胡麻シャブ  name_越冬キャベツのメンチカツ  name_酢豚  \\\n",
       "datetime                                                              \n",
       "2014-01-09             0              0                  0        0   \n",
       "2014-01-10             0              0                  0        0   \n",
       "2014-01-14             0              0                  0        0   \n",
       "2014-01-15             0              0                  0        0   \n",
       "2014-01-16             0              0                  0        0   \n",
       "\n",
       "            name_酢豚orカレー  name_青梗菜牛肉炒め  name_青椒肉絲  name_鶏のから揚げねぎ塩炒めソース  \\\n",
       "datetime                                                                 \n",
       "2014-01-09             0             0          0                    0   \n",
       "2014-01-10             0             0          0                    0   \n",
       "2014-01-14             0             0          0                    0   \n",
       "2014-01-15             0             0          0                    0   \n",
       "2014-01-16             0             0          0                    0   \n",
       "\n",
       "            name_鶏のから揚げスイートチリソース  name_鶏のカッシュナッツ炒め  name_鶏のトマトシチュー  \\\n",
       "datetime                                                             \n",
       "2014-01-09                     0                 0               0   \n",
       "2014-01-10                     0                 0               0   \n",
       "2014-01-14                     0                 0               0   \n",
       "2014-01-15                     0                 0               0   \n",
       "2014-01-16                     0                 0               0   \n",
       "\n",
       "            name_鶏のピリ辛焼き  name_鶏のレモンペッパー焼orカレー  name_鶏の味噌漬け焼き  name_鶏の唐揚げ  \\\n",
       "datetime                                                                    \n",
       "2014-01-09             0                     0              0           0   \n",
       "2014-01-10             0                     0              0           0   \n",
       "2014-01-14             0                     0              0           0   \n",
       "2014-01-15             0                     0              0           0   \n",
       "2014-01-16             0                     0              0           0   \n",
       "\n",
       "            name_鶏の唐揚げおろしソース  name_鶏の塩から揚げ  name_鶏の天ぷら  name_鶏の照り焼きマスタード  \\\n",
       "datetime                                                                   \n",
       "2014-01-09                 0             0           0                 0   \n",
       "2014-01-10                 0             0           0                 0   \n",
       "2014-01-14                 0             0           0                 1   \n",
       "2014-01-15                 0             0           0                 0   \n",
       "2014-01-16                 0             0           0                 0   \n",
       "\n",
       "            name_鶏の照り焼きマヨ  name_鶏の親子煮  name_鶏チリソース  name_鶏肉とカシューナッツ炒め  \\\n",
       "datetime                                                                \n",
       "2014-01-09              0           0            1                  0   \n",
       "2014-01-10              0           0            0                  0   \n",
       "2014-01-14              0           0            0                  0   \n",
       "2014-01-15              0           0            0                  0   \n",
       "2014-01-16              0           0            0                  0   \n",
       "\n",
       "            name_鶏肉のカレー唐揚  name_鶏肉の山賊焼き  event_キャリアアップ支援セミナー  event_ママの会  \\\n",
       "datetime                                                                   \n",
       "2014-01-09              0             0                    0           0   \n",
       "2014-01-10              0             0                    0           0   \n",
       "2014-01-14              0             0                    0           0   \n",
       "2014-01-15              0             0                    0           0   \n",
       "2014-01-16              0             0                    0           0   \n",
       "\n",
       "            weather_快晴  weather_晴れ  weather_曇  weather_薄曇  weather_雨  \\\n",
       "datetime                                                               \n",
       "2014-01-09           0           0          1           0          0   \n",
       "2014-01-10           1           0          0           0          0   \n",
       "2014-01-14           0           1          0           0          0   \n",
       "2014-01-15           0           0          1           0          0   \n",
       "2014-01-16           1           0          0           0          0   \n",
       "\n",
       "            weather_雪  weather_雷電  period_上旬  period_中旬  period_下旬  \n",
       "datetime                                                            \n",
       "2014-01-09          0           0          1          0          0  \n",
       "2014-01-10          0           0          1          0          0  \n",
       "2014-01-14          0           0          0          1          0  \n",
       "2014-01-15          0           0          0          1          0  \n",
       "2014-01-16          0           0          0          1          0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dummy_df.shape)\n",
    "dummy_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ⑨ 最後に、出来たデータをもう一度眺める\n",
    "欠損値が残っていないかなどを改めて確認し、問題なさそうであれば一旦CSVファイルとして保存しておきます。  \n",
    "（簡易式ではありますが、）これでひとまずの前処理は完了です！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:21.885189Z",
     "start_time": "2018-04-15T20:56:21.697054Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sales                       0\n",
      "soldout                     0\n",
      "kcal                        0\n",
      "payday                      0\n",
      "precipitation               0\n",
      "temperature                 0\n",
      "month                       0\n",
      "day                         0\n",
      "temperature_diff            0\n",
      "temperature_rolling_mean    0\n",
      "temperature_pct_change      0\n",
      "week_月                      0\n",
      "week_木                      0\n",
      "week_水                      0\n",
      "week_火                      0\n",
      "week_金                      0\n",
      "name_いか天ぷら                  0\n",
      "name_かじきの甘辛煮                0\n",
      "name_きのこソースハンバーグ            0\n",
      "name_さわら焼味噌掛け               0\n",
      "name_さんま辛味焼                 0\n",
      "name_たっぷりベーコンフライ            0\n",
      "name_ぶりレモンペッパー焼き            0\n",
      "name_ますのマスタードソース            0\n",
      "name_アジ唐揚げ南蛮ソース             0\n",
      "name_エビフライ                  0\n",
      "name_カレイの唐揚げ                0\n",
      "name_カレイ唐揚げ 甘酢あん            0\n",
      "name_カレイ唐揚げ夏野菜あん            0\n",
      "name_カレイ唐揚げ野菜あんかけ           0\n",
      "                           ..\n",
      "name_鶏のから揚げねぎ塩炒めソース         0\n",
      "name_鶏のから揚げスイートチリソース        0\n",
      "name_鶏のカッシュナッツ炒め            0\n",
      "name_鶏のトマトシチュー              0\n",
      "name_鶏のピリ辛焼き                0\n",
      "name_鶏のレモンペッパー焼orカレー        0\n",
      "name_鶏の味噌漬け焼き               0\n",
      "name_鶏の唐揚げ                  0\n",
      "name_鶏の唐揚げおろしソース            0\n",
      "name_鶏の塩から揚げ                0\n",
      "name_鶏の天ぷら                  0\n",
      "name_鶏の照り焼きマスタード            0\n",
      "name_鶏の照り焼きマヨ               0\n",
      "name_鶏の親子煮                  0\n",
      "name_鶏チリソース                 0\n",
      "name_鶏肉とカシューナッツ炒め           0\n",
      "name_鶏肉のカレー唐揚               0\n",
      "name_鶏肉の山賊焼き                0\n",
      "event_キャリアアップ支援セミナー         0\n",
      "event_ママの会                  0\n",
      "weather_快晴                  0\n",
      "weather_晴れ                  0\n",
      "weather_曇                   0\n",
      "weather_薄曇                  0\n",
      "weather_雨                   0\n",
      "weather_雪                   0\n",
      "weather_雷電                  0\n",
      "period_上旬                   0\n",
      "period_中旬                   0\n",
      "period_下旬                   0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>soldout</th>\n",
       "      <th>kcal</th>\n",
       "      <th>payday</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>temperature_diff</th>\n",
       "      <th>temperature_rolling_mean</th>\n",
       "      <th>temperature_pct_change</th>\n",
       "      <th>week_月</th>\n",
       "      <th>week_木</th>\n",
       "      <th>week_水</th>\n",
       "      <th>week_火</th>\n",
       "      <th>week_金</th>\n",
       "      <th>name_いか天ぷら</th>\n",
       "      <th>name_かじきの甘辛煮</th>\n",
       "      <th>name_きのこソースハンバーグ</th>\n",
       "      <th>name_さわら焼味噌掛け</th>\n",
       "      <th>name_さんま辛味焼</th>\n",
       "      <th>name_たっぷりベーコンフライ</th>\n",
       "      <th>name_ぶりレモンペッパー焼き</th>\n",
       "      <th>name_ますのマスタードソース</th>\n",
       "      <th>name_アジ唐揚げ南蛮ソース</th>\n",
       "      <th>name_エビフライ</th>\n",
       "      <th>name_カレイの唐揚げ</th>\n",
       "      <th>name_カレイ唐揚げ 甘酢あん</th>\n",
       "      <th>name_カレイ唐揚げ夏野菜あん</th>\n",
       "      <th>name_カレイ唐揚げ野菜あんかけ</th>\n",
       "      <th>name_カレイ唐揚げ野菜餡かけ</th>\n",
       "      <th>name_カレイ野菜あんかけ</th>\n",
       "      <th>name_キスと野菜の天ぷら</th>\n",
       "      <th>name_キーマカレー</th>\n",
       "      <th>name_ゴーヤチャンプルー</th>\n",
       "      <th>name_サバ焼味噌掛け</th>\n",
       "      <th>name_サーモンのムニエル2色ソース</th>\n",
       "      <th>name_サーモンフライ・タルタル</th>\n",
       "      <th>name_ジャンボチキンカツ</th>\n",
       "      <th>name_ジューシーメンチカツ</th>\n",
       "      <th>name_タンドリーチキン</th>\n",
       "      <th>name_チキンのコーンクリーム焼き</th>\n",
       "      <th>name_チキンの辛味噌焼き</th>\n",
       "      <th>name_チキンクリームシチュー</th>\n",
       "      <th>name_チキンステーキ・きのこソース</th>\n",
       "      <th>name_チキンフリカッセ</th>\n",
       "      <th>name_チキン南蛮</th>\n",
       "      <th>name_チキン香草焼きマスタードソース</th>\n",
       "      <th>name_チャプチェ</th>\n",
       "      <th>name_チンジャオロース</th>\n",
       "      <th>name_チーズハンバーグ</th>\n",
       "      <th>name_チーズメンチカツ</th>\n",
       "      <th>name_チーズ入りハンバーグ</th>\n",
       "      <th>name_チーズ入りメンチカツ</th>\n",
       "      <th>name_ハンバーグカレーソース</th>\n",
       "      <th>name_ハンバーグケッチャップソース</th>\n",
       "      <th>name_ハンバーグデミソース</th>\n",
       "      <th>name_ハンバーグ和風きのこソース</th>\n",
       "      <th>name_ハンバーグ（デミきのこバター）</th>\n",
       "      <th>name_バーベキューチキン</th>\n",
       "      <th>name_ビーフカレー</th>\n",
       "      <th>name_ビーフシチュー</th>\n",
       "      <th>name_ビーフストロガノフ</th>\n",
       "      <th>name_ビーフトマト煮</th>\n",
       "      <th>name_プルコギ</th>\n",
       "      <th>name_ホタテクリ―ムシチュー</th>\n",
       "      <th>name_ボローニャ風カツ</th>\n",
       "      <th>name_ポークソテー韓国ソース</th>\n",
       "      <th>name_ポークハヤシ</th>\n",
       "      <th>name_ポーク生姜焼き</th>\n",
       "      <th>name_マーボ茄子</th>\n",
       "      <th>name_マーボ豆腐</th>\n",
       "      <th>name_メダイ照り焼</th>\n",
       "      <th>name_メンチカツ</th>\n",
       "      <th>name_ロース甘味噌焼き</th>\n",
       "      <th>name_八宝菜</th>\n",
       "      <th>name_厚切イカフライ</th>\n",
       "      <th>name_厚切ハムカツ</th>\n",
       "      <th>name_名古屋味噌カツ</th>\n",
       "      <th>name_和風ソースハンバーグ</th>\n",
       "      <th>name_和風ハンバーグ</th>\n",
       "      <th>name_回鍋肉</th>\n",
       "      <th>name_手作りひれかつ</th>\n",
       "      <th>name_手作りひれかつとカレー</th>\n",
       "      <th>name_手作りチキンカツ</th>\n",
       "      <th>name_手作りトンカツ</th>\n",
       "      <th>name_手作りロースカツ</th>\n",
       "      <th>name_洋食屋さんのメンチカツ</th>\n",
       "      <th>name_海老クリーミ―クノーデル</th>\n",
       "      <th>name_海老フライとホタテ串カツ</th>\n",
       "      <th>name_海老フライタルタル</th>\n",
       "      <th>name_炊き込みご飯</th>\n",
       "      <th>name_牛すき焼き風</th>\n",
       "      <th>name_牛カルビ焼き肉</th>\n",
       "      <th>name_牛スキヤキ</th>\n",
       "      <th>name_牛丼風煮</th>\n",
       "      <th>name_牛肉すき焼き風</th>\n",
       "      <th>name_牛肉コロッケ</th>\n",
       "      <th>name_牛肉ニンニクの芽炒め</th>\n",
       "      <th>name_牛肉筍煮</th>\n",
       "      <th>name_白身魚のマスタード焼き</th>\n",
       "      <th>name_白身魚の南部焼き</th>\n",
       "      <th>name_白身魚フライ</th>\n",
       "      <th>name_白身魚ムニエル</th>\n",
       "      <th>name_白身魚唐揚げ野菜あんかけ</th>\n",
       "      <th>name_白身魚唐揚げ野菜餡かけ</th>\n",
       "      <th>name_筑前煮</th>\n",
       "      <th>name_肉じゃが</th>\n",
       "      <th>name_肉団子のシチュー</th>\n",
       "      <th>name_肉団子クリームシチュー</th>\n",
       "      <th>name_親子煮</th>\n",
       "      <th>name_豚すき焼き</th>\n",
       "      <th>name_豚の冷しゃぶ</th>\n",
       "      <th>name_豚キムチ炒め</th>\n",
       "      <th>name_豚ロースのピザ風チーズ焼き</th>\n",
       "      <th>name_豚冷シャブ野菜添え</th>\n",
       "      <th>name_豚味噌メンチカツ</th>\n",
       "      <th>name_豚肉と玉子の炒め</th>\n",
       "      <th>name_豚肉と茄子のピリ辛炒め</th>\n",
       "      <th>name_豚肉のマスタード焼き</th>\n",
       "      <th>name_豚肉の生姜焼き</th>\n",
       "      <th>name_豚肉の胡麻シャブ</th>\n",
       "      <th>name_越冬キャベツのメンチカツ</th>\n",
       "      <th>name_酢豚</th>\n",
       "      <th>name_酢豚orカレー</th>\n",
       "      <th>name_青梗菜牛肉炒め</th>\n",
       "      <th>name_青椒肉絲</th>\n",
       "      <th>name_鶏のから揚げねぎ塩炒めソース</th>\n",
       "      <th>name_鶏のから揚げスイートチリソース</th>\n",
       "      <th>name_鶏のカッシュナッツ炒め</th>\n",
       "      <th>name_鶏のトマトシチュー</th>\n",
       "      <th>name_鶏のピリ辛焼き</th>\n",
       "      <th>name_鶏のレモンペッパー焼orカレー</th>\n",
       "      <th>name_鶏の味噌漬け焼き</th>\n",
       "      <th>name_鶏の唐揚げ</th>\n",
       "      <th>name_鶏の唐揚げおろしソース</th>\n",
       "      <th>name_鶏の塩から揚げ</th>\n",
       "      <th>name_鶏の天ぷら</th>\n",
       "      <th>name_鶏の照り焼きマスタード</th>\n",
       "      <th>name_鶏の照り焼きマヨ</th>\n",
       "      <th>name_鶏の親子煮</th>\n",
       "      <th>name_鶏チリソース</th>\n",
       "      <th>name_鶏肉とカシューナッツ炒め</th>\n",
       "      <th>name_鶏肉のカレー唐揚</th>\n",
       "      <th>name_鶏肉の山賊焼き</th>\n",
       "      <th>event_キャリアアップ支援セミナー</th>\n",
       "      <th>event_ママの会</th>\n",
       "      <th>weather_快晴</th>\n",
       "      <th>weather_晴れ</th>\n",
       "      <th>weather_曇</th>\n",
       "      <th>weather_薄曇</th>\n",
       "      <th>weather_雨</th>\n",
       "      <th>weather_雪</th>\n",
       "      <th>weather_雷電</th>\n",
       "      <th>period_上旬</th>\n",
       "      <th>period_中旬</th>\n",
       "      <th>period_下旬</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-09</th>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>435.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>9.333333</td>\n",
       "      <td>0.112245</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>-0.532110</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-14</th>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>376.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>7.266667</td>\n",
       "      <td>0.137255</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-15</th>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>-0.500000</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-16</th>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.233333</td>\n",
       "      <td>1.413793</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales  soldout   kcal  payday  precipitation  temperature  month  \\\n",
       "datetime                                                                       \n",
       "2014-01-09    129        1  435.0     0.0            0.0         10.9      1   \n",
       "2014-01-10     87        0  440.0     1.0            0.0          5.1      1   \n",
       "2014-01-14    129        1  376.0     0.0            0.0          5.8      1   \n",
       "2014-01-15    134        0  450.0     0.0            0.0          2.9      1   \n",
       "2014-01-16    107        0  415.0     0.0            0.0          7.0      1   \n",
       "\n",
       "            day  temperature_diff  temperature_rolling_mean  \\\n",
       "datetime                                                      \n",
       "2014-01-09    9               1.1                  9.333333   \n",
       "2014-01-10   10              -5.8                  8.600000   \n",
       "2014-01-14   14               0.7                  7.266667   \n",
       "2014-01-15   15              -2.9                  4.600000   \n",
       "2014-01-16   16               4.1                  5.233333   \n",
       "\n",
       "            temperature_pct_change  week_月  week_木  week_水  week_火  week_金  \\\n",
       "datetime                                                                     \n",
       "2014-01-09                0.112245       0       1       0       0       0   \n",
       "2014-01-10               -0.532110       0       0       0       0       1   \n",
       "2014-01-14                0.137255       0       0       0       1       0   \n",
       "2014-01-15               -0.500000       0       0       1       0       0   \n",
       "2014-01-16                1.413793       0       1       0       0       0   \n",
       "\n",
       "            name_いか天ぷら  name_かじきの甘辛煮  name_きのこソースハンバーグ  name_さわら焼味噌掛け  \\\n",
       "datetime                                                                \n",
       "2014-01-09           0             0                 0              0   \n",
       "2014-01-10           0             0                 0              0   \n",
       "2014-01-14           0             0                 0              0   \n",
       "2014-01-15           0             0                 0              0   \n",
       "2014-01-16           0             0                 0              0   \n",
       "\n",
       "            name_さんま辛味焼  name_たっぷりベーコンフライ  name_ぶりレモンペッパー焼き  name_ますのマスタードソース  \\\n",
       "datetime                                                                        \n",
       "2014-01-09            0                 0                 0                 0   \n",
       "2014-01-10            0                 0                 0                 0   \n",
       "2014-01-14            0                 0                 0                 0   \n",
       "2014-01-15            1                 0                 0                 0   \n",
       "2014-01-16            0                 0                 0                 0   \n",
       "\n",
       "            name_アジ唐揚げ南蛮ソース  name_エビフライ  name_カレイの唐揚げ  name_カレイ唐揚げ 甘酢あん  \\\n",
       "datetime                                                                  \n",
       "2014-01-09                0           0             0                 0   \n",
       "2014-01-10                0           0             0                 0   \n",
       "2014-01-14                0           0             0                 0   \n",
       "2014-01-15                0           0             0                 0   \n",
       "2014-01-16                0           0             0                 0   \n",
       "\n",
       "            name_カレイ唐揚げ夏野菜あん  name_カレイ唐揚げ野菜あんかけ  name_カレイ唐揚げ野菜餡かけ  \\\n",
       "datetime                                                            \n",
       "2014-01-09                 0                  0                 0   \n",
       "2014-01-10                 0                  0                 0   \n",
       "2014-01-14                 0                  0                 0   \n",
       "2014-01-15                 0                  0                 0   \n",
       "2014-01-16                 0                  1                 0   \n",
       "\n",
       "            name_カレイ野菜あんかけ  name_キスと野菜の天ぷら  name_キーマカレー  name_ゴーヤチャンプルー  \\\n",
       "datetime                                                                  \n",
       "2014-01-09               0               0            0               0   \n",
       "2014-01-10               0               0            0               0   \n",
       "2014-01-14               0               0            0               0   \n",
       "2014-01-15               0               0            0               0   \n",
       "2014-01-16               0               0            0               0   \n",
       "\n",
       "            name_サバ焼味噌掛け  name_サーモンのムニエル2色ソース  name_サーモンフライ・タルタル  \\\n",
       "datetime                                                           \n",
       "2014-01-09             0                    0                  0   \n",
       "2014-01-10             0                    0                  0   \n",
       "2014-01-14             0                    0                  0   \n",
       "2014-01-15             0                    0                  0   \n",
       "2014-01-16             0                    0                  0   \n",
       "\n",
       "            name_ジャンボチキンカツ  name_ジューシーメンチカツ  name_タンドリーチキン  \\\n",
       "datetime                                                     \n",
       "2014-01-09               0                0              0   \n",
       "2014-01-10               0                0              0   \n",
       "2014-01-14               0                0              0   \n",
       "2014-01-15               0                0              0   \n",
       "2014-01-16               0                0              0   \n",
       "\n",
       "            name_チキンのコーンクリーム焼き  name_チキンの辛味噌焼き  name_チキンクリームシチュー  \\\n",
       "datetime                                                           \n",
       "2014-01-09                   0               0                 0   \n",
       "2014-01-10                   0               0                 0   \n",
       "2014-01-14                   0               0                 0   \n",
       "2014-01-15                   0               0                 0   \n",
       "2014-01-16                   0               0                 0   \n",
       "\n",
       "            name_チキンステーキ・きのこソース  name_チキンフリカッセ  name_チキン南蛮  \\\n",
       "datetime                                                     \n",
       "2014-01-09                    0              0           0   \n",
       "2014-01-10                    0              0           0   \n",
       "2014-01-14                    0              0           0   \n",
       "2014-01-15                    0              0           0   \n",
       "2014-01-16                    0              0           0   \n",
       "\n",
       "            name_チキン香草焼きマスタードソース  name_チャプチェ  name_チンジャオロース  name_チーズハンバーグ  \\\n",
       "datetime                                                                     \n",
       "2014-01-09                     0           0              0              0   \n",
       "2014-01-10                     0           0              0              0   \n",
       "2014-01-14                     0           0              0              0   \n",
       "2014-01-15                     0           0              0              0   \n",
       "2014-01-16                     0           0              0              0   \n",
       "\n",
       "            name_チーズメンチカツ  name_チーズ入りハンバーグ  name_チーズ入りメンチカツ  name_ハンバーグカレーソース  \\\n",
       "datetime                                                                        \n",
       "2014-01-09              0                0                0                 0   \n",
       "2014-01-10              0                0                0                 0   \n",
       "2014-01-14              0                0                0                 0   \n",
       "2014-01-15              0                0                0                 0   \n",
       "2014-01-16              0                0                0                 0   \n",
       "\n",
       "            name_ハンバーグケッチャップソース  name_ハンバーグデミソース  name_ハンバーグ和風きのこソース  \\\n",
       "datetime                                                               \n",
       "2014-01-09                    0                0                   0   \n",
       "2014-01-10                    0                0                   0   \n",
       "2014-01-14                    0                0                   0   \n",
       "2014-01-15                    0                0                   0   \n",
       "2014-01-16                    0                0                   0   \n",
       "\n",
       "            name_ハンバーグ（デミきのこバター）  name_バーベキューチキン  name_ビーフカレー  name_ビーフシチュー  \\\n",
       "datetime                                                                      \n",
       "2014-01-09                     0               0            0             0   \n",
       "2014-01-10                     0               0            0             0   \n",
       "2014-01-14                     0               0            0             0   \n",
       "2014-01-15                     0               0            0             0   \n",
       "2014-01-16                     0               0            0             0   \n",
       "\n",
       "            name_ビーフストロガノフ  name_ビーフトマト煮  name_プルコギ  name_ホタテクリ―ムシチュー  \\\n",
       "datetime                                                                \n",
       "2014-01-09               0             0          0                 0   \n",
       "2014-01-10               0             0          0                 0   \n",
       "2014-01-14               0             0          0                 0   \n",
       "2014-01-15               0             0          0                 0   \n",
       "2014-01-16               0             0          0                 0   \n",
       "\n",
       "            name_ボローニャ風カツ  name_ポークソテー韓国ソース  name_ポークハヤシ  name_ポーク生姜焼き  \\\n",
       "datetime                                                                 \n",
       "2014-01-09              0                 0            0             0   \n",
       "2014-01-10              0                 0            0             0   \n",
       "2014-01-14              0                 0            0             0   \n",
       "2014-01-15              0                 0            0             0   \n",
       "2014-01-16              0                 0            0             0   \n",
       "\n",
       "            name_マーボ茄子  name_マーボ豆腐  name_メダイ照り焼  name_メンチカツ  name_ロース甘味噌焼き  \\\n",
       "datetime                                                                     \n",
       "2014-01-09           0           0            0           0              0   \n",
       "2014-01-10           0           0            0           0              0   \n",
       "2014-01-14           0           0            0           0              0   \n",
       "2014-01-15           0           0            0           0              0   \n",
       "2014-01-16           0           0            0           0              0   \n",
       "\n",
       "            name_八宝菜  name_厚切イカフライ  name_厚切ハムカツ  name_名古屋味噌カツ  \\\n",
       "datetime                                                        \n",
       "2014-01-09         0             0            0             0   \n",
       "2014-01-10         0             0            0             0   \n",
       "2014-01-14         0             0            0             0   \n",
       "2014-01-15         0             0            0             0   \n",
       "2014-01-16         0             0            0             0   \n",
       "\n",
       "            name_和風ソースハンバーグ  name_和風ハンバーグ  name_回鍋肉  name_手作りひれかつ  \\\n",
       "datetime                                                            \n",
       "2014-01-09                0             0         0             0   \n",
       "2014-01-10                0             0         0             0   \n",
       "2014-01-14                0             0         0             0   \n",
       "2014-01-15                0             0         0             0   \n",
       "2014-01-16                0             0         0             0   \n",
       "\n",
       "            name_手作りひれかつとカレー  name_手作りチキンカツ  name_手作りトンカツ  name_手作りロースカツ  \\\n",
       "datetime                                                                   \n",
       "2014-01-09                 0              0             0              0   \n",
       "2014-01-10                 0              0             0              1   \n",
       "2014-01-14                 0              0             0              0   \n",
       "2014-01-15                 0              0             0              0   \n",
       "2014-01-16                 0              0             0              0   \n",
       "\n",
       "            name_洋食屋さんのメンチカツ  name_海老クリーミ―クノーデル  name_海老フライとホタテ串カツ  \\\n",
       "datetime                                                             \n",
       "2014-01-09                 0                  0                  0   \n",
       "2014-01-10                 0                  0                  0   \n",
       "2014-01-14                 0                  0                  0   \n",
       "2014-01-15                 0                  0                  0   \n",
       "2014-01-16                 0                  0                  0   \n",
       "\n",
       "            name_海老フライタルタル  name_炊き込みご飯  name_牛すき焼き風  name_牛カルビ焼き肉  \\\n",
       "datetime                                                             \n",
       "2014-01-09               0            0            0             0   \n",
       "2014-01-10               0            0            0             0   \n",
       "2014-01-14               0            0            0             0   \n",
       "2014-01-15               0            0            0             0   \n",
       "2014-01-16               0            0            0             0   \n",
       "\n",
       "            name_牛スキヤキ  name_牛丼風煮  name_牛肉すき焼き風  name_牛肉コロッケ  name_牛肉ニンニクの芽炒め  \\\n",
       "datetime                                                                        \n",
       "2014-01-09           0          0             0            0                0   \n",
       "2014-01-10           0          0             0            0                0   \n",
       "2014-01-14           0          0             0            0                0   \n",
       "2014-01-15           0          0             0            0                0   \n",
       "2014-01-16           0          0             0            0                0   \n",
       "\n",
       "            name_牛肉筍煮  name_白身魚のマスタード焼き  name_白身魚の南部焼き  name_白身魚フライ  \\\n",
       "datetime                                                              \n",
       "2014-01-09          0                 0              0            0   \n",
       "2014-01-10          0                 0              0            0   \n",
       "2014-01-14          0                 0              0            0   \n",
       "2014-01-15          0                 0              0            0   \n",
       "2014-01-16          0                 0              0            0   \n",
       "\n",
       "            name_白身魚ムニエル  name_白身魚唐揚げ野菜あんかけ  name_白身魚唐揚げ野菜餡かけ  name_筑前煮  \\\n",
       "datetime                                                                  \n",
       "2014-01-09             0                  0                 0         0   \n",
       "2014-01-10             0                  0                 0         0   \n",
       "2014-01-14             0                  0                 0         0   \n",
       "2014-01-15             0                  0                 0         0   \n",
       "2014-01-16             0                  0                 0         0   \n",
       "\n",
       "            name_肉じゃが  name_肉団子のシチュー  name_肉団子クリームシチュー  name_親子煮  name_豚すき焼き  \\\n",
       "datetime                                                                       \n",
       "2014-01-09          0              0                 0         0           0   \n",
       "2014-01-10          0              0                 0         0           0   \n",
       "2014-01-14          0              0                 0         0           0   \n",
       "2014-01-15          0              0                 0         0           0   \n",
       "2014-01-16          0              0                 0         0           0   \n",
       "\n",
       "            name_豚の冷しゃぶ  name_豚キムチ炒め  name_豚ロースのピザ風チーズ焼き  name_豚冷シャブ野菜添え  \\\n",
       "datetime                                                                   \n",
       "2014-01-09            0            0                   0               0   \n",
       "2014-01-10            0            0                   0               0   \n",
       "2014-01-14            0            0                   0               0   \n",
       "2014-01-15            0            0                   0               0   \n",
       "2014-01-16            0            0                   0               0   \n",
       "\n",
       "            name_豚味噌メンチカツ  name_豚肉と玉子の炒め  name_豚肉と茄子のピリ辛炒め  name_豚肉のマスタード焼き  \\\n",
       "datetime                                                                      \n",
       "2014-01-09              0              0                 0                0   \n",
       "2014-01-10              0              0                 0                0   \n",
       "2014-01-14              0              0                 0                0   \n",
       "2014-01-15              0              0                 0                0   \n",
       "2014-01-16              0              0                 0                0   \n",
       "\n",
       "            name_豚肉の生姜焼き  name_豚肉の胡麻シャブ  name_越冬キャベツのメンチカツ  name_酢豚  \\\n",
       "datetime                                                              \n",
       "2014-01-09             0              0                  0        0   \n",
       "2014-01-10             0              0                  0        0   \n",
       "2014-01-14             0              0                  0        0   \n",
       "2014-01-15             0              0                  0        0   \n",
       "2014-01-16             0              0                  0        0   \n",
       "\n",
       "            name_酢豚orカレー  name_青梗菜牛肉炒め  name_青椒肉絲  name_鶏のから揚げねぎ塩炒めソース  \\\n",
       "datetime                                                                 \n",
       "2014-01-09             0             0          0                    0   \n",
       "2014-01-10             0             0          0                    0   \n",
       "2014-01-14             0             0          0                    0   \n",
       "2014-01-15             0             0          0                    0   \n",
       "2014-01-16             0             0          0                    0   \n",
       "\n",
       "            name_鶏のから揚げスイートチリソース  name_鶏のカッシュナッツ炒め  name_鶏のトマトシチュー  \\\n",
       "datetime                                                             \n",
       "2014-01-09                     0                 0               0   \n",
       "2014-01-10                     0                 0               0   \n",
       "2014-01-14                     0                 0               0   \n",
       "2014-01-15                     0                 0               0   \n",
       "2014-01-16                     0                 0               0   \n",
       "\n",
       "            name_鶏のピリ辛焼き  name_鶏のレモンペッパー焼orカレー  name_鶏の味噌漬け焼き  name_鶏の唐揚げ  \\\n",
       "datetime                                                                    \n",
       "2014-01-09             0                     0              0           0   \n",
       "2014-01-10             0                     0              0           0   \n",
       "2014-01-14             0                     0              0           0   \n",
       "2014-01-15             0                     0              0           0   \n",
       "2014-01-16             0                     0              0           0   \n",
       "\n",
       "            name_鶏の唐揚げおろしソース  name_鶏の塩から揚げ  name_鶏の天ぷら  name_鶏の照り焼きマスタード  \\\n",
       "datetime                                                                   \n",
       "2014-01-09                 0             0           0                 0   \n",
       "2014-01-10                 0             0           0                 0   \n",
       "2014-01-14                 0             0           0                 1   \n",
       "2014-01-15                 0             0           0                 0   \n",
       "2014-01-16                 0             0           0                 0   \n",
       "\n",
       "            name_鶏の照り焼きマヨ  name_鶏の親子煮  name_鶏チリソース  name_鶏肉とカシューナッツ炒め  \\\n",
       "datetime                                                                \n",
       "2014-01-09              0           0            1                  0   \n",
       "2014-01-10              0           0            0                  0   \n",
       "2014-01-14              0           0            0                  0   \n",
       "2014-01-15              0           0            0                  0   \n",
       "2014-01-16              0           0            0                  0   \n",
       "\n",
       "            name_鶏肉のカレー唐揚  name_鶏肉の山賊焼き  event_キャリアアップ支援セミナー  event_ママの会  \\\n",
       "datetime                                                                   \n",
       "2014-01-09              0             0                    0           0   \n",
       "2014-01-10              0             0                    0           0   \n",
       "2014-01-14              0             0                    0           0   \n",
       "2014-01-15              0             0                    0           0   \n",
       "2014-01-16              0             0                    0           0   \n",
       "\n",
       "            weather_快晴  weather_晴れ  weather_曇  weather_薄曇  weather_雨  \\\n",
       "datetime                                                               \n",
       "2014-01-09           0           0          1           0          0   \n",
       "2014-01-10           1           0          0           0          0   \n",
       "2014-01-14           0           1          0           0          0   \n",
       "2014-01-15           0           0          1           0          0   \n",
       "2014-01-16           1           0          0           0          0   \n",
       "\n",
       "            weather_雪  weather_雷電  period_上旬  period_中旬  period_下旬  \n",
       "datetime                                                            \n",
       "2014-01-09          0           0          1          0          0  \n",
       "2014-01-10          0           0          1          0          0  \n",
       "2014-01-14          0           0          0          1          0  \n",
       "2014-01-15          0           0          0          1          0  \n",
       "2014-01-16          0           0          0          1          0  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 改めて、各列にNaNが入っていないか確認\n",
    "print(dummy_df.isnull().sum()) \n",
    "dummy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-15T20:56:21.918978Z",
     "start_time": "2018-04-15T20:56:21.887981Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 出来たデータをutf-8で保存する\n",
    "dummy_df.to_csv('./data/processed_datamart.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## きれいになったデータはこの後どう活用するのか？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "例えば、'sales'を「目的変数」、それ以外の列を「説明変数」として、目的変数を予測する\"モデル\"を作成する などがよく行われる。\n",
    "\n",
    "\n",
    "![modeling](./figs/modeling.PNG)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "より目的変数に\"効く\"説明変数を作ることを**特徴量エンジニアリング**という  \n",
    "pandasを使った前処理とは、NaNや無関係な説明変数を削除しつつ、\"効く\"特徴量を探す（作成する）作業のこと\n",
    "\n",
    "地味だが、モデルの精度を上げるうえでは非常に大切な作業！！！データ分析のほとんどの時間はここに使われる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ここでは取り上げられなかったけど、よく分析で使うその他のPandas関数\n",
    "\n",
    "- df.where()　←df.mask()と反対の条件を対象にする。内容的には同じ処理。\n",
    "- df.merge()\n",
    "- df.concat()\n",
    "- df.melt() ← df.pivot()の逆操作をするもの\n",
    "\n",
    "などなど、他にも便利でマニアックなメソッドがたくさんあります。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参考リンク（もうちょっといろいろ知りたい人用）\n",
    "**基本操作系**\n",
    "- [Pandas Cheet Sheet - GitHub](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf)\n",
    "    - 基本操作について1ページでまとめてくれているCheet Sheet\n",
    "    \n",
    "- [StatsFragments by @sinhrks](http://sinhrks.hatenablog.com/)\n",
    "    - 各種Pandasメソッドを図付きで詳細に解説してくれています。非常に参考になる。\n",
    "    \n",
    "**集計テクニック系**\n",
    "- [pandasで条件分岐(case when的な)によるデータ加工を網羅したい](https://qiita.com/Hyperion13fleet/items/98c31744e66ac1fc1e9f)\n",
    "    - SQLのcase whenのような条件分岐の処理をpandasでやりたいときが良くありますがなかなか難しい。そんなときのお助け記事。\n",
    "    \n",
    "**作図系**\n",
    "- [PythonでPandasのPlot機能を使えばデータ加工からグラフ作成までマジでシームレス - Qiita](https://qiita.com/hik0107/items/de5785f680096df93efa)\n",
    "    - Pandas Plotでできることの概要がわかります\n",
    "- [pythonで美しいグラフ描画 -seabornを使えばデータ分析と可視化が捗る その1 - Qiita](https://qiita.com/hik0107/items/3dc541158fceb3156ee0)\n",
    "    - Pandas Plot, matplotlibと比較して簡単に美しいプロットがかけるmatplotのラッパーseabornの概要がわかります "
   ]
  }
 ],
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