09 熊貓數據分析.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 08 熊貓數據分析\n",
    "\n",
    "今天介紹資料分析近來很紅的 pandas 套件, 作者是 Wes McKinney。Python 會成為一個數據分析的熱門語言, 和 pandas 的出現也有相當的關係。\n",
    "\n",
    "但是 pandas 雖然功能強, 但有些地方沒那麼直覺, 有時會讓大家以為是個深奧的套件。其實你大約可以把 pandas 想成「Python 的 Excel」, 但是功能更強、更有彈性、也有更多的可能性。\n",
    "\n",
    "下面介紹個基本上就是把 pandas 當 Excel 學的影片, 相信大家會覺得很親切。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import YouTubeVideo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/https://youtu.be/9d5-Ti6onew\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x104a453c8>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo(\"https://youtu.be/9d5-Ti6onew\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 準備套件\n",
    "\n",
    "我們來看幾乎以後都是標準的引入套件, 這次當然增加了引入 `pandas`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 08-1 開始使用 `pandas`\n",
    "\n",
    "首先我們來讀入一個 CSV 檔, 這裡有個「假的」學測成績, 叫 `grades.csv` 我們來練習一下。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"grades.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用 `df` 是標準的叫法 (雖然這名稱我們隨便取也可以), 意思是 Data Frame, 這是 `pandas` 兩大資料結構之一。我們可以把 Data Frame 想成一張表格 (雖然其實可以是很多張表格)。\n",
    "\n",
    "我們來看看我們 `df` 的前五筆資料。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>國文</th>\n",
       "      <th>英文</th>\n",
       "      <th>數學</th>\n",
       "      <th>自然</th>\n",
       "      <th>社會</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>劉俊安</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>胡玉華</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>黃淑婷</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>陳上紫</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>崔靜成</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名  國文  英文  數學  自然  社會\n",
       "0  劉俊安   9  10  15  10  13\n",
       "1  胡玉華  10  10  10   8   9\n",
       "2  黃淑婷  13  15   8  11  14\n",
       "3  陳上紫  10  10   8   9  14\n",
       "4  崔靜成  13  12  14  12  13"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果你曾經手動讀入 CSV 檔, 就知道這省了多少事 (雖然我個人還挺喜歡純手動帶進 CSV)。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Excel 檔也可以快速讀入\n",
    "\n",
    "不只 CSV 檔, 很多資料檔案, 像 Excel 檔都很容易在 `pandas` 完成。使用法是這樣:\n",
    "\n",
    "    df2 = pd.read_excel('filename.xls', 'sheetname')\n",
    "    \n",
    "其中 sheetname 那裡要放工作表的名稱, 如果是中文的最好改成英文。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 08-2 Pandas 基本資料結構\n",
    "\n",
    "Pandas 有兩個基本資料結構:\n",
    "\n",
    "* <b style=\"color:red;\">DataFrame</b>: 可以想成一個表格。\n",
    "* <b style=\"color:red;\">Series</b>: 表格的某一列、某一行, 基本上就是我們以前的 list 或 array\n",
    "\n",
    "一個 DataFrame, 我們有 `index` (列的名稱), `columns` (行的名稱)。\n",
    "\n",
    "#### DataFrame\n",
    "\n",
    "![DataFrame 的結構](images/indexcol.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Series\n",
    "\n",
    "剛剛說 series 大概就是一個 list, 一個 array。其實更精準的說, 其實是一個有 \"index\" 的 array。\n",
    "\n",
    "DataFrame 的每一行或每一列其實也都是一個 series。我們來看個例子, 例如所有同學的國文成績, 就是一個 series。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      9\n",
       "1     10\n",
       "2     13\n",
       "3     10\n",
       "4     13\n",
       "5     13\n",
       "6     11\n",
       "7      8\n",
       "8      9\n",
       "9     10\n",
       "10    14\n",
       "11    13\n",
       "12    11\n",
       "13    15\n",
       "14     8\n",
       "15    11\n",
       "16    14\n",
       "17    12\n",
       "18     9\n",
       "19    14\n",
       "20    13\n",
       "21     9\n",
       "22    12\n",
       "23    10\n",
       "24    10\n",
       "25    13\n",
       "26    13\n",
       "27     8\n",
       "28    15\n",
       "29    14\n",
       "      ..\n",
       "70     9\n",
       "71     9\n",
       "72    10\n",
       "73    13\n",
       "74    14\n",
       "75    13\n",
       "76     9\n",
       "77    13\n",
       "78    12\n",
       "79     9\n",
       "80    13\n",
       "81    11\n",
       "82    11\n",
       "83    10\n",
       "84    10\n",
       "85    10\n",
       "86    11\n",
       "87    10\n",
       "88    11\n",
       "89    13\n",
       "90     8\n",
       "91    14\n",
       "92     8\n",
       "93    11\n",
       "94    10\n",
       "95     9\n",
       "96     8\n",
       "97    14\n",
       "98    15\n",
       "99     9\n",
       "Name: 國文, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"國文\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在 Python 3 中, 我們終於可以和英文同步, 用這種很炫的方式叫出所有國文成績。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      9\n",
       "1     10\n",
       "2     13\n",
       "3     10\n",
       "4     13\n",
       "5     13\n",
       "6     11\n",
       "7      8\n",
       "8      9\n",
       "9     10\n",
       "10    14\n",
       "11    13\n",
       "12    11\n",
       "13    15\n",
       "14     8\n",
       "15    11\n",
       "16    14\n",
       "17    12\n",
       "18     9\n",
       "19    14\n",
       "20    13\n",
       "21     9\n",
       "22    12\n",
       "23    10\n",
       "24    10\n",
       "25    13\n",
       "26    13\n",
       "27     8\n",
       "28    15\n",
       "29    14\n",
       "      ..\n",
       "70     9\n",
       "71     9\n",
       "72    10\n",
       "73    13\n",
       "74    14\n",
       "75    13\n",
       "76     9\n",
       "77    13\n",
       "78    12\n",
       "79     9\n",
       "80    13\n",
       "81    11\n",
       "82    11\n",
       "83    10\n",
       "84    10\n",
       "85    10\n",
       "86    11\n",
       "87    10\n",
       "88    11\n",
       "89    13\n",
       "90     8\n",
       "91    14\n",
       "92     8\n",
       "93    11\n",
       "94    10\n",
       "95     9\n",
       "96     8\n",
       "97    14\n",
       "98    15\n",
       "99     9\n",
       "Name: 國文, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.國文"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 資料畫出來\n",
    "\n",
    "要畫個圖很容易。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114ff7128>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3jhBhD79O+NyAMhqYukn/xAkZ0NMFmgZ417tkv9Mk8vTTwAc+APzYj5kXbb6g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\ngBO+mZlZBzjhm5mZdYATvpmZWQe8Bk87srKqg7wiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f8a128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.國文.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "當然, 在這個例子中, 其實畫 histogram 圖更有意義一點。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11747e9b0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AlqirTYGZPUXSNZL2uvt3u7ktAADQma41BWZ2qqSPKLxLsLlb2+k/+3IXUAhyaNi3jywk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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11748c978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.國文.hist(bins=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 08-3 一些基本的資料分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "算平均。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.39"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.國文.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "算標準差。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1968526614594834"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.國文.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不如就該算的都幫我們算算..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>國文</th>\n",
       "      <th>英文</th>\n",
       "      <th>數學</th>\n",
       "      <th>自然</th>\n",
       "      <th>社會</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.390000</td>\n",
       "      <td>11.380000</td>\n",
       "      <td>11.570000</td>\n",
       "      <td>11.03000</td>\n",
       "      <td>11.83000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.196853</td>\n",
       "      <td>2.273164</td>\n",
       "      <td>2.310516</td>\n",
       "      <td>2.21772</td>\n",
       "      <td>2.48655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>8.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>9.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.00000</td>\n",
       "      <td>12.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>14.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.00000</td>\n",
       "      <td>15.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               國文          英文          數學         自然         社會\n",
       "count  100.000000  100.000000  100.000000  100.00000  100.00000\n",
       "mean    11.390000   11.380000   11.570000   11.03000   11.83000\n",
       "std      2.196853    2.273164    2.310516    2.21772    2.48655\n",
       "min      8.000000    8.000000    8.000000    8.00000    8.00000\n",
       "25%      9.000000    9.000000   10.000000    9.00000    9.00000\n",
       "50%     11.000000   11.000000   11.000000   11.00000   12.00000\n",
       "75%     13.000000   13.000000   14.000000   13.00000   14.00000\n",
       "max     15.000000   15.000000   15.000000   15.00000   15.00000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有時我們很愛看的相關係數矩陣。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>國文</th>\n",
       "      <th>英文</th>\n",
       "      <th>數學</th>\n",
       "      <th>自然</th>\n",
       "      <th>社會</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>國文</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.160158</td>\n",
       "      <td>-0.310899</td>\n",
       "      <td>-0.110236</td>\n",
       "      <td>-0.028421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英文</th>\n",
       "      <td>0.160158</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025656</td>\n",
       "      <td>0.113929</td>\n",
       "      <td>-0.063512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>數學</th>\n",
       "      <td>-0.310899</td>\n",
       "      <td>0.025656</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014371</td>\n",
       "      <td>0.041651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>自然</th>\n",
       "      <td>-0.110236</td>\n",
       "      <td>0.113929</td>\n",
       "      <td>0.014371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.156594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>社會</th>\n",
       "      <td>-0.028421</td>\n",
       "      <td>-0.063512</td>\n",
       "      <td>0.041651</td>\n",
       "      <td>-0.156594</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          國文        英文        數學        自然        社會\n",
       "國文  1.000000  0.160158 -0.310899 -0.110236 -0.028421\n",
       "英文  0.160158  1.000000  0.025656  0.113929 -0.063512\n",
       "數學 -0.310899  0.025656  1.000000  0.014371  0.041651\n",
       "自然 -0.110236  0.113929  0.014371  1.000000 -0.156594\n",
       "社會 -0.028421 -0.063512  0.041651 -0.156594  1.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "只算兩科間的相關係數當然也可以。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.31089898221793311"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.國文.corr(df.數學)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 08-4 手工打造一個 DataFrame\n",
    "\n",
    "有時我們用手工打造一個簡單的 DataFrame, 可以更理解整個結構。其實很容易, 一個 DataFrame 基本上就包含兩個主要部份:\n",
    "\n",
    "* 資料本身: 通常一個二維陣列 (矩陣)\n",
    "* 行、列的名稱\n",
    "\n",
    "我們來個簡單的小例子。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata = np.random.randn(4,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.15521252,  0.97934249,  0.34267215],\n",
       "       [ 0.59882935,  0.18311593,  1.76097643],\n",
       "       [ 0.64171534, -0.28273844,  1.62997553],\n",
       "       [-0.33948626,  1.64623964, -0.22692633]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "把行列的名字放進去, 就成一個 DataFrame。我們列的部份先讓 Python 自己產生。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame(mydata, columns=list(\"ABC\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.155213</td>\n",
       "      <td>0.979342</td>\n",
       "      <td>0.342672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.598829</td>\n",
       "      <td>0.183116</td>\n",
       "      <td>1.760976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.641715</td>\n",
       "      <td>-0.282738</td>\n",
       "      <td>1.629976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.339486</td>\n",
       "      <td>1.646240</td>\n",
       "      <td>-0.226926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0  0.155213  0.979342  0.342672\n",
       "1  0.598829  0.183116  1.760976\n",
       "2  0.641715 -0.282738  1.629976\n",
       "3 -0.339486  1.646240 -0.226926"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 兩個表格上下貼起來\n",
    "\n",
    "我們再來生一個 DataFrame, 再「貼」起來。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df3 = pd.DataFrame(np.random.randn(3,3), columns=list(\"ABC\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.571494</td>\n",
       "      <td>-1.153108</td>\n",
       "      <td>1.104366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.368999</td>\n",
       "      <td>1.338462</td>\n",
       "      <td>0.574410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.316672</td>\n",
       "      <td>-1.047993</td>\n",
       "      <td>0.403293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0  0.571494 -1.153108  1.104366\n",
       "1  1.368999  1.338462  0.574410\n",
       "2  1.316672 -1.047993  0.403293"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df4 = pd.concat([df2, df3], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.155213</td>\n",
       "      <td>0.979342</td>\n",
       "      <td>0.342672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.598829</td>\n",
       "      <td>0.183116</td>\n",
       "      <td>1.760976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.641715</td>\n",
       "      <td>-0.282738</td>\n",
       "      <td>1.629976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.339486</td>\n",
       "      <td>1.646240</td>\n",
       "      <td>-0.226926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.571494</td>\n",
       "      <td>-1.153108</td>\n",
       "      <td>1.104366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.368999</td>\n",
       "      <td>1.338462</td>\n",
       "      <td>0.574410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.316672</td>\n",
       "      <td>-1.047993</td>\n",
       "      <td>0.403293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0  0.155213  0.979342  0.342672\n",
       "1  0.598829  0.183116  1.760976\n",
       "2  0.641715 -0.282738  1.629976\n",
       "3 -0.339486  1.646240 -0.226926\n",
       "0  0.571494 -1.153108  1.104366\n",
       "1  1.368999  1.338462  0.574410\n",
       "2  1.316672 -1.047993  0.403293"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "前面我們弄得亂七八糟的 index 重設一下。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df4.index = range(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.155213</td>\n",
       "      <td>0.979342</td>\n",
       "      <td>0.342672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.598829</td>\n",
       "      <td>0.183116</td>\n",
       "      <td>1.760976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.641715</td>\n",
       "      <td>-0.282738</td>\n",
       "      <td>1.629976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.339486</td>\n",
       "      <td>1.646240</td>\n",
       "      <td>-0.226926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.571494</td>\n",
       "      <td>-1.153108</td>\n",
       "      <td>1.104366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.368999</td>\n",
       "      <td>1.338462</td>\n",
       "      <td>0.574410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.316672</td>\n",
       "      <td>-1.047993</td>\n",
       "      <td>0.403293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0  0.155213  0.979342  0.342672\n",
       "1  0.598829  0.183116  1.760976\n",
       "2  0.641715 -0.282738  1.629976\n",
       "3 -0.339486  1.646240 -0.226926\n",
       "4  0.571494 -1.153108  1.104366\n",
       "5  1.368999  1.338462  0.574410\n",
       "6  1.316672 -1.047993  0.403293"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 横向的貼"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df5 = pd.concat([df2, df3], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "等等, 這大小好像不太對也可以嗎? 答案是可以的!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.155213</td>\n",
       "      <td>0.979342</td>\n",
       "      <td>0.342672</td>\n",
       "      <td>0.571494</td>\n",
       "      <td>-1.153108</td>\n",
       "      <td>1.104366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.598829</td>\n",
       "      <td>0.183116</td>\n",
       "      <td>1.760976</td>\n",
       "      <td>1.368999</td>\n",
       "      <td>1.338462</td>\n",
       "      <td>0.574410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.641715</td>\n",
       "      <td>-0.282738</td>\n",
       "      <td>1.629976</td>\n",
       "      <td>1.316672</td>\n",
       "      <td>-1.047993</td>\n",
       "      <td>0.403293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.339486</td>\n",
       "      <td>1.646240</td>\n",
       "      <td>-0.226926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         A         B         C\n",
       "0  0.155213  0.979342  0.342672  0.571494 -1.153108  1.104366\n",
       "1  0.598829  0.183116  1.760976  1.368999  1.338462  0.574410\n",
       "2  0.641715 -0.282738  1.629976  1.316672 -1.047993  0.403293\n",
       "3 -0.339486  1.646240 -0.226926       NaN       NaN       NaN"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 大一點的例子\n",
    "\n",
    "我們來做前面「假的」學測資料。首先要有「假的」同學名單, 如果有興趣產生很多名字, 可以用這個服務。\n",
    "\n",
    "[中文姓名產生器](http://www.richyli.com/name/index.asp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_names = pd.read_csv('names.csv', names=[\"姓名\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>羅雅慧</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>王紹尹</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>黃季坤</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>許正書</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>張鈞蓮</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名\n",
       "0  羅雅慧\n",
       "1  王紹尹\n",
       "2  黃季坤\n",
       "3  許正書\n",
       "4  張鈞蓮"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_names.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_grades = pd.DataFrame(np.random.randint(6,16,(100,5)), \n",
    "                      columns=[\"國文\", \"英文\", \"數學\", \n",
    "                               \"社會\", \"自然\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df6 = pd.concat([df_names, df_grades], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>國文</th>\n",
       "      <th>英文</th>\n",
       "      <th>數學</th>\n",
       "      <th>社會</th>\n",
       "      <th>自然</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>羅雅慧</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>王紹尹</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>黃季坤</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>許正書</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>張鈞蓮</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名  國文  英文  數學  社會  自然\n",
       "0  羅雅慧   8  12  12  14  13\n",
       "1  王紹尹  12  14   7   7  10\n",
       "2  黃季坤  14  13  11  11   6\n",
       "3  許正書  14  14  10   9  13\n",
       "4  張鈞蓮   6  11  13  10   6"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 08-5 做些簡單的計算\n",
    "\n",
    "我們先來計算總級分。基本上就是挑出要算的部份 (我們的例子就是所有科目), 再來就是全部加起來。其實全部加起來有個模糊的地方: 要一列一列加起來 (0), 還是一行一行加起來 (1)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df6[\"總級分\"] = df6[[\"國文\", \"英文\", \"數學\", \"社會\", \"自然\"]].sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>國文</th>\n",
       "      <th>英文</th>\n",
       "      <th>數學</th>\n",
       "      <th>社會</th>\n",
       "      <th>自然</th>\n",
       "      <th>總級分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>羅雅慧</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>王紹尹</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>黃季坤</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>許正書</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>張鈞蓮</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名  國文  英文  數學  社會  自然  總級分\n",
       "0  羅雅慧   8  12  12  14  13   59\n",
       "1  王紹尹  12  14   7   7  10   50\n",
       "2  黃季坤  14  13  11  11   6   55\n",
       "3  許正書  14  14  10   9  13   60\n",
       "4  張鈞蓮   6  11  13  10   6   46"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 某種主科加權\n",
    "\n",
    "現在有個系可能想看\n",
    "\n",
    "    數學 x 1.5 + 英文\n",
    "    \n",
    "這樣的分數。怎麼算很容易。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df6[\"主科\"] = df6.數學 * 1.5 + df6.英文"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>國文</th>\n",
       "      <th>英文</th>\n",
       "      <th>數學</th>\n",
       "      <th>社會</th>\n",
       "      <th>自然</th>\n",
       "      <th>總級分</th>\n",
       "      <th>主科</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>羅雅慧</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>59</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>王紹尹</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>黃季坤</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>55</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>許正書</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>60</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>張鈞蓮</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>46</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名  國文  英文  數學  社會  自然  總級分    主科\n",
       "0  羅雅慧   8  12  12  14  13   59  30.0\n",
       "1  王紹尹  12  14   7   7  10   50  24.5\n",
       "2  黃季坤  14  13  11  11   6   55  29.5\n",
       "3  許正書  14  14  10   9  13   60  29.0\n",
       "4  張鈞蓮   6  11  13  10   6   46  30.5"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 排排序\n",
    "\n",
    "我們來看總級分最高的 20 位同學。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>國文</th>\n",
       "      <th>英文</th>\n",
       "      <th>數學</th>\n",
       "      <th>社會</th>\n",
       "      <th>自然</th>\n",
       "      <th>總級分</th>\n",
       "      <th>主科</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>呂建中</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>68</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>張靜如</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>63</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>李千惠</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>62</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>鄭科季</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>62</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>陳偉翔</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>62</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>丁佳伶</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>61</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>黃綺恆</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>61</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>吳佳慧</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>61</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>許正書</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>60</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>林君鑫</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>59</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>吳立偉</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>59</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>蔡姿群</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>59</td>\n",
       "      <td>26.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>司阿青</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>59</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>羅雅慧</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>59</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>葉旻芸</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>59</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>許家文</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>58</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>李傑亞</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>58</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>吳宜平</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>58</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>張秀靖</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>57</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>邱美玲</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>57</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     姓名  國文  英文  數學  社會  自然  總級分    主科\n",
       "93  呂建中  15  14  15  12  12   68  36.5\n",
       "57  張靜如   6  14  15  14  14   63  36.5\n",
       "29  李千惠  12  15  15   9  11   62  37.5\n",
       "47  鄭科季  14   9  14  10  15   62  30.0\n",
       "80  陳偉翔  11  12  13  15  11   62  31.5\n",
       "67  丁佳伶  13  12  14  10  12   61  33.0\n",
       "43  黃綺恆  13  13  14  11  10   61  34.0\n",
       "35  吳佳慧  15  12  10  14  10   61  27.0\n",
       "3   許正書  14  14  10   9  13   60  29.0\n",
       "55  林君鑫  12  10  14  15   8   59  31.0\n",
       "71  吳立偉  15  14   6   9  15   59  23.0\n",
       "75  蔡姿群  14  13   9  14   9   59  26.5\n",
       "91  司阿青  12   7  14  12  14   59  28.0\n",
       "0   羅雅慧   8  12  12  14  13   59  30.0\n",
       "8   葉旻芸  13  13  12   9  12   59  31.0\n",
       "72  許家文   6  12  14  15  11   58  33.0\n",
       "34  李傑亞  10  13   8  15  12   58  25.0\n",
       "56  吳宜平  10  13  12  15   8   58  31.0\n",
       "92  張秀靖  11  12  13   9  12   57  31.5\n",
       "86  邱美玲  11  15  14   9   8   57  36.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.sort_values(by=[\"總級分\"], ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "先看主科, 再比總級分!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>國文</th>\n",
       "      <th>英文</th>\n",
       "      <th>數學</th>\n",
       "      <th>社會</th>\n",
       "      <th>自然</th>\n",
       "      <th>總級分</th>\n",
       "      <th>主科</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>李千惠</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>62</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>呂建中</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>68</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>張靜如</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>63</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>邱美玲</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>57</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>黃綺恆</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>61</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>張靖雯</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>52</td>\n",
       "      <td>33.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>丁佳伶</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>61</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>許家文</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>58</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>吳建銘</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>53</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>刁穎春</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>54</td>\n",
       "      <td>32.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>王裕仁</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>53</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>陳偉翔</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>62</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>張秀靖</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>57</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>葉旻芸</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>59</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>林君鑫</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>59</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>吳宜平</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>58</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>葉俊彥</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>57</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>蔡卉以</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>56</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>姚筱玫</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>49</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>張鈞蓮</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>46</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     姓名  國文  英文  數學  社會  自然  總級分    主科\n",
       "29  李千惠  12  15  15   9  11   62  37.5\n",
       "93  呂建中  15  14  15  12  12   68  36.5\n",
       "57  張靜如   6  14  15  14  14   63  36.5\n",
       "86  邱美玲  11  15  14   9   8   57  36.0\n",
       "43  黃綺恆  13  13  14  11  10   61  34.0\n",
       "89  張靖雯  13  11  15   6   7   52  33.5\n",
       "67  丁佳伶  13  12  14  10  12   61  33.0\n",
       "72  許家文   6  12  14  15  11   58  33.0\n",
       "90  吳建銘  11  12  14   7   9   53  33.0\n",
       "19  刁穎春   7  13  13  11  10   54  32.5\n",
       "79  王裕仁  11  11  14   6  11   53  32.0\n",
       "80  陳偉翔  11  12  13  15  11   62  31.5\n",
       "92  張秀靖  11  12  13   9  12   57  31.5\n",
       "8   葉旻芸  13  13  12   9  12   59  31.0\n",
       "55  林君鑫  12  10  14  15   8   59  31.0\n",
       "56  吳宜平  10  13  12  15   8   58  31.0\n",
       "25  葉俊彥  12  10  14  12   9   57  31.0\n",
       "17  蔡卉以  15  11  13   6  11   56  30.5\n",
       "9   姚筱玫  12   8  15   7   7   49  30.5\n",
       "4   張鈞蓮   6  11  13  10   6   46  30.5"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.sort_values(by=[\"主科\", \"總級分\"], ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 08-6 用實際資料來玩 `pandas`!\n",
    "\n",
    "我們這裡借用 Julia Evans \"Pandas Cookbook\" 裡面的資料來玩玩!\n",
    "\n",
    "<a href=\"https://github.com/jvns/pandas-cookbook/tree/master/data\">Pandas Cookbook</a>\n",
    "\n",
    "去下載 Montreal 官方的單車各路線使用狀況, 要 2012 年的哦。\n",
    "\n",
    "<a href=\"http://donnees.ville.montreal.qc.ca/dataset/velos-comptage\">下載網頁</a>\n",
    "\n",
    "我們這存成了 `2012.csv`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_bike = pd.read_csv(\"2012.csv\",\n",
    "                      index_col=\"Date\", \n",
    "                      parse_dates=True, dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>Brébeuf (données non disponibles)</th>\n",
       "      <th>Côte-Sainte-Catherine</th>\n",
       "      <th>Maisonneuve 1</th>\n",
       "      <th>Maisonneuve 2</th>\n",
       "      <th>du Parc</th>\n",
       "      <th>Pierre-Dupuy</th>\n",
       "      <th>Rachel1</th>\n",
       "      <th>St-Urbain (données non disponibles)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <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>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>51</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>153</td>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>104</td>\n",
       "      <td>248</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>58</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>116</td>\n",
       "      <td>318</td>\n",
       "      <td>111</td>\n",
       "      <td>8</td>\n",
       "      <td>61</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>197</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>330</td>\n",
       "      <td>97</td>\n",
       "      <td>13</td>\n",
       "      <td>95</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1  Brébeuf (données non disponibles)  Côte-Sainte-Catherine  \\\n",
       "Date                                                                            \n",
       "2012-01-01       35                                NaN                      0   \n",
       "2012-01-02       83                                NaN                      1   \n",
       "2012-01-03      135                                NaN                      2   \n",
       "2012-01-04      144                                NaN                      1   \n",
       "2012-01-05      197                                NaN                      2   \n",
       "\n",
       "            Maisonneuve 1  Maisonneuve 2  du Parc  Pierre-Dupuy  Rachel1  \\\n",
       "Date                                                                       \n",
       "2012-01-01             38             51       26            10       16   \n",
       "2012-01-02             68            153       53             6       43   \n",
       "2012-01-03            104            248       89             3       58   \n",
       "2012-01-04            116            318      111             8       61   \n",
       "2012-01-05            124            330       97            13       95   \n",
       "\n",
       "            St-Urbain (données non disponibles)  \n",
       "Date                                             \n",
       "2012-01-01                                  NaN  \n",
       "2012-01-02                                  NaN  \n",
       "2012-01-03                                  NaN  \n",
       "2012-01-04                                  NaN  \n",
       "2012-01-05                                  NaN  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bike.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "這裡說的是每個一天, 每個腳踏車道有多少人使用。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 縮小一點的表格\n",
    "\n",
    "我們來縮小成三條路線的 DataFrame。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_sbike = df_bike[[\"Berri 1\", \"Côte-Sainte-Catherine\", \n",
    "                    \"Maisonneuve 1\"]].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "這裡的結尾多個神秘的 `.copy()` 是什麼意思呢? 這是說我們想要把 `df_sbike` 從原本所有的資料中 copy 出一個新的 DataFrame, 而不是只在原來的 DataFrame 取出一部份工作。這樣我們對 `df_sbkie` 的修改都不會影響到原來的東西。\n",
    "\n",
    "其實沒有這麼做也不會怎樣, 只是會得到一個叫 SettingWithCopyWarning 的警告。想瞭解到底發生什麼事可參考下面的影片。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/https://youtu.be/4R4WsDJ-KVc\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x117ac5048>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo(\"https://youtu.be/4R4WsDJ-KVc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "順道一提, 類似這樣的「小問題」常常讓我們用 `pandas` 時有許多挫折感, 上面的影片其實是一系列 `pandas` Q&A 其中之一。非常棒! 極力推薦大家看看, 尤其選擇不太明白的主題。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>Côte-Sainte-Catherine</th>\n",
       "      <th>Maisonneuve 1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>83</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>135</td>\n",
       "      <td>2</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>197</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1  Côte-Sainte-Catherine  Maisonneuve 1\n",
       "Date                                                     \n",
       "2012-01-01       35                      0             38\n",
       "2012-01-02       83                      1             68\n",
       "2012-01-03      135                      2            104\n",
       "2012-01-04      144                      1            116\n",
       "2012-01-05      197                      2            124"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbike.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 三條路線畫圖比較"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117ad5160>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9zj333FgvyzM+n49LLrmEuXPn8p1D8HaH5ZtVi4BPLUMBKioqXCf6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xexFwbcTv3wMfdsaWvvDW3mlEZIyIzBWR70VkG+amoMDQFk2XRHzfhHkLzOshM8G4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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117ac55c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sbike.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然後我們可以做各種分析。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [Q1] 哪條路線最受歡迎?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Berri 1                  2985.048387\n",
       "Côte-Sainte-Catherine    1233.351613\n",
       "Maisonneuve 1            1983.325806\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbike.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看中位數、畫個圖也不錯。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11742d588>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R8J+rarZtmZJ2QQaHJaA9bvobVXXfAx09XVVfW6SyNOGS3A78TFVd23ctmnxJ\nfgN4Fc2hdsdU1XeTvBa4rqq2d86D5sE1DkvDPwEHAze1nw8fivIfR04DHs2qUVkHPJXm/hTS2CR5\nNfBW4Czgv/Djf8duBV7L9g+I0jwYHJaGRwM3D3wuLYYLgT9N8tPA14G7Bzur6uO9VKVJdBrw21X1\nt0n+cKD9H4E/66mmieWliiUkyYOAc4Ezquq6vuvRZGtvQLQ93nhII5PkR8AT2ssTt/HjG6o9Dvha\nVT245xInikdOLyFVdTfw0r7r0NKwvRsPtQ9Dg0bpOuBnZ2l/Ls2dWTVCXqpYev4WeBGwtu9CJGlE\nzgTOSbI3zXqtI5NMAX9Ec1t3jZDBYen5V+CNSZ5Gc2e52wc7q+rsXqrSREjye8D/rKqt7efb5XtN\no1JV720vV/wxsA/wIeAG4PSq+utei5tArnFYYpLsaG1DVdVjFq0YTZz2/fXUqvqh7zX1Ick+wEOq\n6qa+a5lUBgdJ0i4tyRuA8130vThcHLmEJHlQku8kWdl3LZpsvte0yP4v4NtJLk3yu0kO7LugSWZw\nWELaXRV7912HJp/vNS2mqjoCeDLweeB1wA1JLkzysvbShUbISxVLTJL/DDwe+K2quqfvejS5fK+p\nL+3i75fRzETsXVX79VzSRHFXxdLzc8BxwHOSfJ3/c1fFS3qpSpPI95r6cjvwI+Au4Cd6rmXiGByW\nnluBj/RdhJYE32taNEkeTTPL8DLgcOALwJuAC/qsaxJ5qUKStEtL8hWaGa6vAecD01X1/X6rmlzO\nOCxBSfYAngE8FvhQVd2W5BBgS1X9e6/FaaL4XtMiuQQ4par+pe9ClgJnHJaYJD8JXAwcCuwFPL69\nGcw7gL2q6lW9FqiJ4XtNmkzOOCw976C51ewRwA8H2j8KvKeXijSpfK9pUSR53476q+qUxaplKTA4\nLD1PB36+qu5KMth+PfCIXirSpPK9psXy0KHnDwKeBCwHPrv45Uw2g8PSsxsw2y2NHwnctsi1aLL5\nXtOiqKoXD7cl2Q34C+A7i1/RZPPkyKXnU8BrB55XkocAbwEu6qckTSjfa+pNVd1Hc7vtNX3XMmlc\nHLnEJHkksI7mnvWPo7kG/ThgBjjWO8ppVHyvqW9Jng/8VVU9rO9aJonBYQlqt8j9Os2itYcA62nu\nLPejXgvTxGnfayfQ3EfA95rGIsmZw03Aw4EX0ASH1yx+VZPL4CBJ2qUl+dxQ033AzTQLI9/nvVJG\ny+CwxCTzfnHFAAAOEUlEQVQ5oKp+2H7+KOC3gQcDf1dVf99rcZo4SR4HPBM4iKE1VVX11l6KkrQg\nBoclIsnPAH8HPAr4V5rp44uBfYEC9gF+tar+trciNVGS/DbNqvYZ4Eaa99k2VVWreilME8lTSheP\nwWGJSPJJ4B7gvwO/AfwSzcK1326HvBNYXVVH91OhJk2S7wLvrqq39V2LJpunlC4ug8MSkWQGeFZV\nfa3dErcF+Lmq+mrb/wTgK1W1vM86NTmSbAF+tqqu7bsWTbYkf0tzNsj/TXNK6RFtcHgG8J6qelyf\n9U0az3FYOvanmS6mnba7Hdg00L8J71uv0frfwHP6LkJLwtOBP66qu4bar8dTSkfOkyOXluHpJaeb\nNFJJfm/g6beBM5IcDXwduHtwbFWdvZi1aaJ5Suki8lLFEpHkPuCTwJ1t0y/TbFW6vX2+F/Dcqprt\nPz6pkyTXdRxaVfWYsRajJSPJ3wCbq+qVSW6jOTfkZuBjwIaqOrnXAieMwWGJSPL+LuP8D0zSrsZT\nSheXwUHSWCR5I/BnVXXHUPuDgf/Hcxw0Sp5SungMDpLGIsm9wMOH/9pLcgBwk5fFpF2TiyMljUuY\nfQHuEcAti1yLJpynlC4eg4OkkUqyiSYwFPCtJIPhYXeaaeT/0UdtmkwPdEopYHAYIS9VSBqpJL9J\nM9vwPuC1wOaB7ruA66vqsj5q02TylNLFZXCQNBZJfhG4tKrufsDB0gJ4SuniMjhIGrskewN7DrZV\n1ZaeytGESXIe8A9V5SWwReAaB0ljkWQf4O3ArwEHzDLEXRUaFU8pXUTOOEgaiyTn0Kxy/6/A/wJO\npblvwO8Af1hV5/dYnibIA5xY6imlI2ZwkDQWSTYAJ1XV59tr0Kuq6ttJfgOYqqrn91yipHnw7piS\nxmV/YNtitS3tc4AvAcf2UpGkBXONg6RxuRZ4NLABuJpmrcMVNDdYu7XHujRhkuwOvAI4jtkPgHpW\nD2VNLIODpHF5P80pkV8A/jvwd0leAzwI+P0+C9PEeQdNcLgQ+Aazn1iqEXGNg6RFkeQngdXAt6vq\na33Xo8mRZIZmPc1FfdeyFDjjIGlRVNV3ge/2XYcm0l00WzK1CFwcKWmkkjwryb8k2W+WvmVJvpnk\n+D5q08T6c+D0JOm7kKXASxWSRirJx4HPVdXa7fT/HvCcqvqlxa1MkyrJR2nODLkF+Cb/5wFQL+mj\nrknlpQpJo3YE8Pod9H8KeN0i1aKl4Vbgo30XsVQYHCSN2gqG/uIbcg/wsEWqRUtAVZ3cdw1LicFB\n0qh9H3gS21+s9mTgB4tXjpaKJA8DDm+fXlNVN/dZz6RycaSkUbuI5oZDew93JHkw8BbgE4telSZW\nkn2TvI8mkP59+7ghyXntzdY0Qi6OlDRSSVYA64F7gXcB17RdT6C50dXuNPet2NhPhZo0Sc4Fng28\nBvhy2/wLwNnAp6vq1X3VNokMDpJGrj3s6S+A44FtW+QKWAecWlU7upuhNCftAVC/WlWfH2p/JvDh\nqnJNzQi5xkHSyLWHPT0/yUOBw2jCw79W1aZ+K9OE2geYbQbrprZPI+QaB0ljU1WbquofgMfSnO4n\njcNlwFsG19W062ne1PZphLxUIWnskmwBfraqrn3AwdIcJXkSzWWwvYB/bpuPALYCx1fVN/uqbRIZ\nHCSNXZLbgCMMDhqXdvfEy2kW4QJcBZxfVT/qr6rJZHCQNHYGB2lyuMZB0mJ4Hs3BUNLIJfnNJC8Y\neP72JLcmubTd4aMRMjhIGpskeyR5NvBEYM+27ZAkD+m3Mk2Y/wz8CCDJMTTnOfwBMAPMerM1zZ+X\nKiSNRfuX3sXAoTSL1h5fVdcmeQewV1W9qtcCNTGS3AE8oao2JHkb8PCqOinJE4HPe47DaDnjIGlc\n3gH8I/BQ2r8GWx8FjuulIk2qfwcOaD9/DvDp9vOtwIN7qWiCeQCUpHF5OvDzVXVXksH264FH9FKR\nJtWngfcmuRJ4PM39UqC5RHZ9X0VNKmccJI3LbjT3pRj2SOC2Ra5Fk+1UmoOeHga8tKp+2LavBqZ7\nq2pCucZB0lgk+Rtgc1W9st2O+WTgZuBjwIaqOrnXAiXNi8FB0lgkeSTNaX4BHkez3uFxNCvdj62q\nm3osT7u4JE8GvlFV97Wfb1dVfW2RyloSDA6SxibJHsCv0xz/+xCa2217mp8WLMl9wMFVdVP7efHj\nO7Ey8LyqarZLZpong4OksUhyLHBpVd0z1L4HzaLJv++nMk2CdrvvhqqqBzrkqb1bq0bE4CBpLJLc\nS7Of/qah9gOAm/wrUNo1uR1T0riEZrp42AHA7Ytci5aAJD9Nc+DYnoPtVfXxfiqaTAYHSSOV5P9r\nPy3gL5PcOdC9O83uiksXvTBNrCSPoTlY7Ge4/1qHbcHV2a0R8hwHSaO2uX2E5ryGzQOPG4H/CZzY\nW3WaRO8ArgMOAu6gOfjpWJqdPM/or6zJ5BoHSWOR5E3An1WVlyU0VklmgGdV1deSbAaOrKprkjwL\n+POqekrPJU4UZxwkjUVVvcXQoEWyOz8+jXQGOKT9/LvA4b1UNMEMDpLGIsmKJP8ryQ1J7kly7+Cj\n7/o0Ub5Bc1YIwOXAHyR5GvBG4NreqppQLo6UNC5/SbPC/QzgB8y+w0IahT8G9m0/fyPwCeCLwA9p\nDiDTCLnGQdJYtPeneHpV/VPftWjpSbI/sKn8JTdyzjhIGpd/4/5HAEuLpqpu6buGSeWMg6SxSPIc\n4D8Bv1NV1/dcjiZQkvd1GVdVp4y7lqXE4CBpLJJsAvahmdm8A7h7sL+q9u+jLk2O9uZW3wWuZAez\nW1X14kUragnwUoWkcXlt3wVo4v0FMAU8Gng/8EEvUYyfMw6SpF1Wkr2AlwCnAD8PXAicB3zKhZHj\nYXCQNDJJ9quqLds+39HYbeOkUWlvr/0K4CSaGfUnVtW/91rUBPJShaRR2pRk2620b2X2sxu23TXT\nGw9p1O7jxze58v01JgYHSaP0LGDbNeZn9lmIloahSxW/QHP402uAi6vqvj5rm1ReqpAk7ZKSvBs4\ngebMkPcB51fVTL9VTT6Dg6SxSrIPzdHTew62V9XX+qlIk6LdjrmBZjvmdn+ZVdVLFq2oJcBLFZLG\nIsnDaLbIPW87Q7wGrYX6AN4DZdEZHCSNy1nAcuAo4PPAi4EVwBtoTpSUFqSqXtF3DUuRwUHSuDwL\n+JWq+sdtJ/xV1aeTbAH+iGa/vaRdzG59FyBpYu0L3NR+vgl4WPv514FVvVQkacEMDpLG5Rrg8Pbz\nfwZ+J8kjgFcBP+itKkkL4qUKSePyDuDh7edvAS4GXg7cRXO6n6RdkNsxJS2KdlvmE4AN7rWXdl0G\nB0mLIskewN7eO0DatbnGQdJIJfnlJK8YavsvwL8Dtyb5VJKH9lKcpAUzOEgatd+n2VEBQJKfB94K\nnAH8GvAo4L/2U5qkhfJShaSRSnITcHxVXdk+PxP46ap6bvv8+cA7qupxPZYpaZ6ccZA0aj8B/HDg\n+S8Alww8/yZwyKJWJGlkDA6SRu37wEqAJA8BjgAuHeg/ALijh7okjYDBQdKo/W/grCS/AbwHuBH4\nykD/U2kOh5K0C/IAKEmj9lbgEcDZNKHhxKq6d6B/Cvi7PgqTtHAujpQkSZ15qULS2CX5wyTL+65D\n0sI54yBp7Npbaf9sVV3bdy2SFsYZB0mLIX0XIGk0DA6SJKkzL1VIGrskjwK+X1X39V2LpIVxxkHS\n2CRZnuS3gN8Flrdtq5I8ot/KJM2X5zhIGoskTwY+A2wGformMKhbgJcAhwIn9VacpHlzxkHSuJwJ\n/GV7M6utA+0XAcf2U5KkhTI4SBqXnwPOnaX9+8DBi1yLpBExOEgalzuB/WZpfzxw8yLXImlEDA6S\nxuXjwBuTPKh9XkkOBd4GfKS/siQthNsxJY1FkmXABTR3w/wJ4AaaSxSXAc+vqtt7LE/SPBkcJI1V\nkqcBRwAPAdZX1Wd6LknSAhgcJI1FkpOAv6mqO4fa9wROqKoP9FOZpIUwOEgaiyT3Ag+vqpuG2g8A\nbqqq3fupTNJCuDhS0rgEmO0vk0fSHAolaRfkyZGSRirJlTSBoYBLktwz0L078Gjg4j5qk7RwBgdJ\no/a37cefBdYB/z7QdxdwPW7HlHZZrnGQNBZJfpNmceTWBxwsaZdhcJA0VklWAyvbp9+sqiv7rEfS\nwhgcJI1UklRVJTkI+GvgGcCtbfdy4HM02zE9dlraBbmrQtLIJDkc+Gz79J00J0Y+sar2r6r9gSfR\n3L/i7J5KlLRAzjhIGokkLwL+X+A3q+ofk2wGnl1V/zA07kjgU1W1vI86JS2MMw6SRmVv4B5g2z0o\ndgPunmXc3fhvj7TLcsZB0si0CyHfWVU/n+RjNGsapqrqhrb/EcD5wKaqenGPpUqaJ4ODpJFKskdV\n3ZPkUTS31n4i8G9t96OAbwAvrKrv9VWjpPkzOEgamyQBng08oW26yrtjSrs2g4OkkUryLOBdwNFV\ntWWobxlwKfD7VbWuj/okLYwLlCSN2muB9wyHBoCq2gycC5y26FVJGgmDg6RRO4Id38TqU8CTF6kW\nSSNmcJA0aiuYfRvmNvcAD1ukWiSNmMFB0qh9n+aEyO15MvCDRapF0ogZHCSN2kXAGUn2Hu5I8mDg\nLcAnFr0qSSPhrgpJI5VkBbAeuJdmd8U1bdcTgFOB3YFVVbWxnwolLYTBQdLIJflJ4C+A44G0zQWs\nA06tquv6qk3SwhgcJI1NkocCh9GEh3+tqk09lyRpgQwOkiSpMxdHSpKkzgwOkiSpM4ODJEnqzOAg\nSZI6MzhIkqTODA6SJKkzg4MkSers/wdTGi0iDjp+OAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117c58eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sbike.median().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [Q2] 哪些是最可怕的日子 (沒什麼人在騎單車)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2012-01-01     True\n",
       "2012-01-02    False\n",
       "2012-01-03    False\n",
       "2012-01-04    False\n",
       "2012-01-05    False\n",
       "2012-01-06    False\n",
       "2012-01-07    False\n",
       "2012-01-08    False\n",
       "2012-01-09    False\n",
       "2012-01-10    False\n",
       "2012-01-11    False\n",
       "2012-01-12    False\n",
       "2012-01-13    False\n",
       "2012-01-14     True\n",
       "2012-01-15     True\n",
       "2012-01-16    False\n",
       "2012-01-17    False\n",
       "2012-01-18    False\n",
       "2012-01-19    False\n",
       "2012-01-20    False\n",
       "2012-01-21     True\n",
       "2012-01-22     True\n",
       "2012-01-23    False\n",
       "2012-01-24    False\n",
       "2012-01-25    False\n",
       "2012-01-26    False\n",
       "2012-01-27    False\n",
       "2012-01-28    False\n",
       "2012-01-29    False\n",
       "2012-01-30    False\n",
       "              ...  \n",
       "2012-10-07    False\n",
       "2012-10-08    False\n",
       "2012-10-09    False\n",
       "2012-10-10    False\n",
       "2012-10-11    False\n",
       "2012-10-12    False\n",
       "2012-10-13    False\n",
       "2012-10-14    False\n",
       "2012-10-15    False\n",
       "2012-10-16    False\n",
       "2012-10-17    False\n",
       "2012-10-18    False\n",
       "2012-10-19    False\n",
       "2012-10-20    False\n",
       "2012-10-21    False\n",
       "2012-10-22    False\n",
       "2012-10-23    False\n",
       "2012-10-24    False\n",
       "2012-10-25    False\n",
       "2012-10-26    False\n",
       "2012-10-27    False\n",
       "2012-10-28    False\n",
       "2012-10-29    False\n",
       "2012-10-30    False\n",
       "2012-10-31    False\n",
       "2012-11-01    False\n",
       "2012-11-02    False\n",
       "2012-11-03    False\n",
       "2012-11-04    False\n",
       "2012-11-05    False\n",
       "Name: Berri 1, dtype: bool"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbike['Berri 1']<75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>Côte-Sainte-Catherine</th>\n",
       "      <th>Maisonneuve 1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-14</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-15</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-21</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-22</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-05</th>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-11</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-25</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1  Côte-Sainte-Catherine  Maisonneuve 1\n",
       "Date                                                     \n",
       "2012-01-01       35                      0             38\n",
       "2012-01-14       32                      0             54\n",
       "2012-01-15       54                      0             33\n",
       "2012-01-21       53                      0             47\n",
       "2012-01-22       71                      0             41\n",
       "2012-02-05       72                      0             46\n",
       "2012-02-11       71                      0             63\n",
       "2012-02-25       62                      0             48"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbike[df_sbike['Berri 1']<75]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [Q3] 大家都是週末還是平日騎車呢?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sbike[\"weekday\"] = df_sbike.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>Côte-Sainte-Catherine</th>\n",
       "      <th>Maisonneuve 1</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-11-01</th>\n",
       "      <td>2405</td>\n",
       "      <td>1208</td>\n",
       "      <td>1701</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-02</th>\n",
       "      <td>1582</td>\n",
       "      <td>737</td>\n",
       "      <td>1109</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-03</th>\n",
       "      <td>844</td>\n",
       "      <td>380</td>\n",
       "      <td>612</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-04</th>\n",
       "      <td>966</td>\n",
       "      <td>446</td>\n",
       "      <td>710</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-05</th>\n",
       "      <td>2247</td>\n",
       "      <td>1170</td>\n",
       "      <td>1705</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1  Côte-Sainte-Catherine  Maisonneuve 1  weekday\n",
       "Date                                                              \n",
       "2012-11-01     2405                   1208           1701        3\n",
       "2012-11-02     1582                    737           1109        4\n",
       "2012-11-03      844                    380            612        5\n",
       "2012-11-04      966                    446            710        6\n",
       "2012-11-05     2247                   1170           1705        0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbike.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counts_by_day = df_sbike.groupby(\"weekday\").aggregate(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>Côte-Sainte-Catherine</th>\n",
       "      <th>Maisonneuve 1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>134298</td>\n",
       "      <td>60329</td>\n",
       "      <td>90051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>135305</td>\n",
       "      <td>58708</td>\n",
       "      <td>92035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>152972</td>\n",
       "      <td>67344</td>\n",
       "      <td>104891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>160131</td>\n",
       "      <td>69028</td>\n",
       "      <td>111895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>141771</td>\n",
       "      <td>56446</td>\n",
       "      <td>98568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>101578</td>\n",
       "      <td>34018</td>\n",
       "      <td>62067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>99310</td>\n",
       "      <td>36466</td>\n",
       "      <td>55324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Berri 1  Côte-Sainte-Catherine  Maisonneuve 1\n",
       "weekday                                               \n",
       "0         134298                  60329          90051\n",
       "1         135305                  58708          92035\n",
       "2         152972                  67344         104891\n",
       "3         160131                  69028         111895\n",
       "4         141771                  56446          98568\n",
       "5         101578                  34018          62067\n",
       "6          99310                  36466          55324"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_by_day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 修改 index\n",
    "\n",
    "我們準備畫出大家最愛在哪些天騎車, 我們當然不想用 0, 1, 2, ... 代表星期幾, 於是我們把這些都換成「人話」。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counts_by_day.index = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>Côte-Sainte-Catherine</th>\n",
       "      <th>Maisonneuve 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>134298</td>\n",
       "      <td>60329</td>\n",
       "      <td>90051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tue</th>\n",
       "      <td>135305</td>\n",
       "      <td>58708</td>\n",
       "      <td>92035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wed</th>\n",
       "      <td>152972</td>\n",
       "      <td>67344</td>\n",
       "      <td>104891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>160131</td>\n",
       "      <td>69028</td>\n",
       "      <td>111895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>141771</td>\n",
       "      <td>56446</td>\n",
       "      <td>98568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>101578</td>\n",
       "      <td>34018</td>\n",
       "      <td>62067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>99310</td>\n",
       "      <td>36466</td>\n",
       "      <td>55324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Berri 1  Côte-Sainte-Catherine  Maisonneuve 1\n",
       "Mon   134298                  60329          90051\n",
       "Tue   135305                  58708          92035\n",
       "Wed   152972                  67344         104891\n",
       "Thu   160131                  69028         111895\n",
       "Fri   141771                  56446          98568\n",
       "Sat   101578                  34018          62067\n",
       "Sun    99310                  36466          55324"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_by_day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然後我們來畫畫 Berri 1 這條線大家使用情況:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117c3cef0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UhI//BJb2tS0Ffrf9fjkQmtmN3lmJ2cCdPTXbJdm5b/Zjdts3WtO/+2VrYNe+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11747b2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts_by_day[\"Berri 1\"].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [Q4] 冷暖會不會影響騎車意願?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_weather = pd.read_csv(\"weather_2012.csv\", \n",
    "                         index_col=\"Date/Time\", \n",
    "                         parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp (C)</th>\n",
       "      <th>Dew Point Temp (C)</th>\n",
       "      <th>Rel Hum (%)</th>\n",
       "      <th>Wind Spd (km/h)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Stn Press (kPa)</th>\n",
       "      <th>Weather</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date/Time</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>2012-01-01 00:00:00</th>\n",
       "      <td>-1.8</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>86</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>101.24</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>-1.8</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>87</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>101.24</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>-1.8</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>89</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>101.26</td>\n",
       "      <td>Freezing Drizzle,Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>88</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>101.27</td>\n",
       "      <td>Freezing Drizzle,Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>88</td>\n",
       "      <td>7</td>\n",
       "      <td>4.8</td>\n",
       "      <td>101.23</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Temp (C)  Dew Point Temp (C)  Rel Hum (%)  \\\n",
       "Date/Time                                                        \n",
       "2012-01-01 00:00:00      -1.8                -3.9           86   \n",
       "2012-01-01 01:00:00      -1.8                -3.7           87   \n",
       "2012-01-01 02:00:00      -1.8                -3.4           89   \n",
       "2012-01-01 03:00:00      -1.5                -3.2           88   \n",
       "2012-01-01 04:00:00      -1.5                -3.3           88   \n",
       "\n",
       "                     Wind Spd (km/h)  Visibility (km)  Stn Press (kPa)  \\\n",
       "Date/Time                                                                \n",
       "2012-01-01 00:00:00                4              8.0           101.24   \n",
       "2012-01-01 01:00:00                4              8.0           101.24   \n",
       "2012-01-01 02:00:00                7              4.0           101.26   \n",
       "2012-01-01 03:00:00                6              4.0           101.27   \n",
       "2012-01-01 04:00:00                7              4.8           101.23   \n",
       "\n",
       "                                  Weather  \n",
       "Date/Time                                  \n",
       "2012-01-01 00:00:00                   Fog  \n",
       "2012-01-01 01:00:00                   Fog  \n",
       "2012-01-01 02:00:00  Freezing Drizzle,Fog  \n",
       "2012-01-01 03:00:00  Freezing Drizzle,Fog  \n",
       "2012-01-01 04:00:00                   Fog  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weather.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我要一天的平均溫度!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sbike[\"mean temp\"] = df_weather[\"Temp (C)\"].resample('D').apply(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>Côte-Sainte-Catherine</th>\n",
       "      <th>Maisonneuve 1</th>\n",
       "      <th>weekday</th>\n",
       "      <th>mean temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>6</td>\n",
       "      <td>0.629167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>83</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>135</td>\n",
       "      <td>2</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>-14.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>116</td>\n",
       "      <td>2</td>\n",
       "      <td>-13.645833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>197</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>3</td>\n",
       "      <td>-6.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1  Côte-Sainte-Catherine  Maisonneuve 1  weekday  mean temp\n",
       "Date                                                                         \n",
       "2012-01-01       35                      0             38        6   0.629167\n",
       "2012-01-02       83                      1             68        0   0.041667\n",
       "2012-01-03      135                      2            104        1 -14.416667\n",
       "2012-01-04      144                      1            116        2 -13.645833\n",
       "2012-01-05      197                      2            124        3  -6.750000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x117cbf7b8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x118a31390>], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M0XoLBx+sfXv33ez1OCyRKC3V47tiZZwiAQGRWLVKt7XXxkUmW32UaWPz5vTj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tSSVKPuf4PdBxuJqmP5x7A12JptyCWRSAY+EwMnciD7AHmVefx65tceFQ6tcbFzYYzfGH4580uX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118001668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sbike[[\"Berri 1\", \"mean temp\"]].plot(subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [Q5] 下雨天就不騎車了吧?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sbike[\"Rain\"] = df_weather[\"Weather\"].str.contains(\"Rain\").resample(\"D\").apply(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>Côte-Sainte-Catherine</th>\n",
       "      <th>Maisonneuve 1</th>\n",
       "      <th>weekday</th>\n",
       "      <th>mean temp</th>\n",
       "      <th>Rain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>6</td>\n",
       "      <td>0.629167</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>83</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>135</td>\n",
       "      <td>2</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>-14.416667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>116</td>\n",
       "      <td>2</td>\n",
       "      <td>-13.645833</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>197</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>3</td>\n",
       "      <td>-6.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1  Côte-Sainte-Catherine  Maisonneuve 1  weekday  mean temp  \\\n",
       "Date                                                                            \n",
       "2012-01-01       35                      0             38        6   0.629167   \n",
       "2012-01-02       83                      1             68        0   0.041667   \n",
       "2012-01-03      135                      2            104        1 -14.416667   \n",
       "2012-01-04      144                      1            116        2 -13.645833   \n",
       "2012-01-05      197                      2            124        3  -6.750000   \n",
       "\n",
       "                Rain  \n",
       "Date                  \n",
       "2012-01-01  0.125000  \n",
       "2012-01-02  0.041667  \n",
       "2012-01-03  0.000000  \n",
       "2012-01-04  0.000000  \n",
       "2012-01-05  0.000000  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x11871d160>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x118815518>], dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M0XoLBx+sfXv33ez1OCyRKC3V47tiZZwiAQGRWLVKt7XXxkUmW32UaWPz5vTj\nh2t4QLqTKWif//M/9f8VK9SXQyRw9gwrEkOG6P8lJZmjP4yBa67RrKGZiMRHHzVWpTp1Cq6Va97I\nlUjU1QWRM5BOJCzCOSQgut7Gf/4nfP3r6ef34IO515zxUOQrauMDNJ31gNTfl+wKEfkpcAVwCTAe\n2ALMFBH3UbsZOAXNGzER2BN4InSMR4BRaDbLU1Lt7spT/z08dio01bSxcqXOTL/0JZ19gpZ3/va3\n4Sc/CbZzTRsQXbzK7r+wUAfRrl3VZp6NSBQX62zRLl+3TgejvfYKojbCpg1bUtzmfrCDB6Sn7t62\nLT2SJAyrdlhfB9e8UVub7iPhlu8ZPlwdKaNySFj07ZtM4YHA78S9plaRcAfK3r21z/a3fu01jVaZ\nOFGJxLp1ei2sGcQlEp9+Gqgb2RwSV67Uc1uzprGPhIUxmudh2LDG29vB3yUrYSJhfSQyZcx095WU\nSERVAF22LN1vp6xMHZSTFg3zSEe+iES9MWa9MWZd6s+9Ja8EbjDGPGOM+QA4DyUKZ8AX6bKnANOM\nMa+myoRfCBwlIuNTbUYBJwAXGWPeMca8BUwFJomIr/zp4eGgoUFnbk0xbbzxhn4eeWQwGL/0kn7e\ncouaCOx2SZwDbVijjYQoKIiO2rBZMiEYUMrLNR/B736npKa4OMgjEaVIQJAMyR3kR43Swecb39Dv\nmcIlLUmxiafcENCamkCR6N49XXUYNkxn+GVl8TUrsvlIhK8npF/Tigo9rutIGC5YtnChnv8FFyiJ\nWL48OJcwWXB9BrIRCXtfrF2rA711onQH/Q0btB9RJdiTEAmrSMRlt8xGJKKyWkJwL4QJkJvyfNEi\n/UziLrd5cxAdEoXbb2/sX7OzI19EYriIfCYiy0TkIREZCCAiQ1GF4iXbMJUOezZwRGrRWNRXw22z\nGFjptJkAVKZIhsWLaHnyw/N0Dh4eOwWsmaIpisQbb6g9vX//QJH45z81b8HEiZrcqL5eyYo7kNpc\nEm6eArt/69A3cKCqElF5JFyFwRKJjz7Sv+XLdWAtLIxXJPr100F23jwdFF3v/MJCdcK74gr9nsm8\n4Zo2oLEi0b27FqO688707YYP12vyr3/FE4lcFInevbW9G5FRUREMom4795zWrAkyX4KqE3bQD5MF\n95glJenJvsKwRMIqElblcImETY0dRSTiTBsiARnq1k3vj6YqEpWVek5hIhFlMqmubjqR+O//Vmfe\nKNTUwA8tZdCXAAAgAElEQVR+AH/+c/b97EzIB5H4F3ABqhhcBgwFXkv5LwxAB/tw2Za1qXWgJpG6\nUNXPcJsBQDjL5Q6gwmnj4eFBQAqaqkh8KWWYtIrEokUacjdkSLrJJDyDrqlpnHTIkoRf/UqdB6OI\nhJuMCYJZsk0iNWuWRmB07x6vSIioKrFhQ7pZw8JK+xCvSDQ0BI57hYXaPqxIFBXpdTjvvPRtrZwf\nlUPCYrfddDCL+l3CRAI0G+VNN8FbqdKGFRWNwyLDisSaNepsaBWaBQviFQn3utv9xvlJvP66DuA1\nNUrKevVqnIkyE5GIUyR69QqUKMic3TIbkbAkIBz9HUVQqqvTSVM2InHHHRp5AmoyWrIkcB52YY/x\n9tvR+9lZ0eyoDWPMTOfrByIyB638eSZaF6PNMW3aNHq7WifRJVM9PHYGWFKQq7NlRYWGMP74x7qs\nd2+VyXfsUD+DZcv05Wu3CSsSoDZ9tx6FHaysJ32XLtFEIkqRsERiv/10gHEViajy2YMH64AQRSTs\n+UC8IhFOvT1gQLQiEYW99gr6l8lHAoIS7C6iiMR//RfMng1nnaWqTGVlYyIRPqfPP9d+77WXDtA7\ndqQrEu7g5x7TJRLhqIeKCvjwQ82Mef/9OoiWlKSnJQe9P/r1C8iNi+Ji/e3DRCKssGSqt5GNSNi+\nhF71QGOCEmfaiFOM/va3QB2y13DuXDjxxPR29hhz5kTvpz1jxowZzJgxI21ZdTbv5BTyHv5pjKkW\nkY+BYWjxLUFVB1eVKAWsaLcGKBCRXiFVojS1zrYJR3F0Bvo6bWIxffp0Ro8enfvJeHh0QFgbc1Ii\nYcnC3LlqmrCPig0LXL9eicTq1fryjVIkhg1TVWDRIg29dPfvtuvaNdpHIo5IFBUF37t317Y2DDMM\nOwtvLpGwfSktDRSJHTv0XFz/BBedOulM/MMPMysSEE0kwtcA9FpddZWmsN6wIVqRiDJtHHigbrvX\nXupQ6SoSbpIt16RkB+coPwnrwHrGGUokPv4Yvva1aEUiSo0AvTdKStJNG5WV6ZEvkFmRqKrS87K/\nQZhI2O2iiEx4v9XVev4NDfrbZVIkjNFnY8MGvQ9sm3feiScS1jF1QAfSy6Mm1/PmzWPMmDFZt817\nrQ0R6YGSiNXGmDJ0oD/WWd8L9WtICXbMBepDbUYAg4BZqUWzgD4icphzqGNRkjI73+fg4dGRkYtp\nw6aEhkABcG3MViY++OBgILeKhEsQiorUvBHOjhj2f0jiI+GaNgYMCPwdbJvq6nhFAuKJhB1gkhIJ\nV5GIKhQWhjVvZPKRgGDWu2qVOrBCtCIBgZpgS62HZ/Bxpg0IrofdR0lJvI+Eq5aEYaNFxo/Xz02b\n4k0bcUQClDSEFYkoIpHJ2bKkJLgf4hQJ6xMR3q/tqzFB261b9ZzXr9d9RykSq1fr+oaGoC0okQjD\nvR67knkjH3kkficiE0VksIgcCTwJbAdsGfGbgWtF5DQROQh4AFgFPAVfOF/eC9wkIkeLyBjgPuBN\nY8ycVJtFwEzgbhEZJyJHoWXJZxhjdjH/WA+PzMjVtGEHkWXLdKB3Z7277aZ/e+yhA2xtbbRpA6LT\nLIf9H+JMG24bq0B88km6zG4H8crKxrN3CAbOuPTK3brpX5yPRJgs2IJTv/tdYP+PUyQgyNUQZ9pw\nFQnQRFlXXqkKTRyRsGqCJRLhcyss1N+huloHSHcWbK9HlI+EMY2dLSHaR2L1au37gAFBBsmePXMn\nEuFcFXFEIpNpwyVStu9hIpFNkaipCarBbt0a5B456qhoRcItJ79unRKJHj1UpQjDHqNr1+zmjcce\n08yrOwPyYdrYG83xsBuwHngDmGCM2QBgjLlRRIrQnA99gNeBk4wx7mtuGrADeBzoBjwP/CB0nLOB\nP6DRGg2ptlfmof8eHjsV7IwuqbOlSyRcBQB0dm2zNVpnxyjTBiiReOyxxvvP1bTRvbser64uXRq2\nbSorm6ZIQOMETi6szdweZ9gwNQ1cfXVAJDIpEieeqMQjjmzYQdDOeu2gY+X+bIpElI+Ee06VlenX\nLKxI9O2b7uxpTHDMggJVgqIUCRtS2amTkpLVq3Ww7tUrmJ1v2aL+GVE5JNzzDxOJ/fZLb5PNR8Il\nElGmja5do6+jSyRcIllbq2YNEZgwAV5+ufG28+bpuTc0qK9KTU1QBC1svrDP3hFHZFckbNr2XFFb\nq+fYKe/2hKaj2V0xxkw2xuxtjOlujBlkjDk7ZdJw21xnjNnTGFNkjDnBGLM0tH6bMWaqMaafMaan\nMeY7xphwlEaVMeYcY0xvY0yJMeZiY0wNHh4eacikSDz3HLzwQvDdJRJLlzZ2tLv1VrWLQ+BMGGXa\nAM3XsHx5enbAJKaNMJEQCcwb7kvaDuJVVdGKhE0H3VQiETZtXH65euhPmBA42mUiEsceqypDHAoK\ndECzg7Ud0CyxiBoAi4v1mGvWRCsSEDg9WjNM2LThKhKgA3I4CZhdn4lIuPsKmzYyRWxY9OnTOPyz\nOYpElGmjZ8/owlzuft3f3xKJQYM0PHnLlsYhsPPmBWYdq7hZJSGsSthjTJyYuZiaPfaWLYE6kgTG\naHj2gw8m36Y10I44jYeHRz6QiUj8v/8HN94YfA8rEmEi0a9fMKO1PhL2RRtl2jAmvWJoEtNGmGxA\nYN5w+2PbNDRED+h77w1TpsTH+EPjSAMXYSLRpYsOMCNHavVQyGzaSAJ3sE5CJET0+i9bpgNO2EcC\nAnJkiYQlX8cfr2Gq1mfD9YOIIoN9+8abNiyRsPsOmzb++lf97mYRDSOpaSObj4RFFJGIMmvY/cYp\nEuXlel5RqbRBicRXvqLX6oMPdNmhh+pnOAR00yYlf6NGqYqUKfmZvd8ytQlj9WrNyGn9VtoLPJHw\n8NjJYF/EtjCRi02bdJZt4RKJ2trMXuZ28LYv5ChFAoJZW9gOD/GmjfC+LJGIUiTC/1t06gT33ttY\nLneRxLQR3vfIkcmcLZPALdxlr6MlFlFEApRI2KiCOEWiulpNCxBcsyFDVE2yRcaiiIRL4MLOmBZu\nISxXkbCkrLZWa3BMmRLt6OjuP58+ErkSCftcuL//1q1KvIuLA8dil0hs2qROsQcfrOdu7+099tDr\n6oaQ2vY9ewb+MkuWRPfHHtv2Oyk+/FA/M6V5bwt4IuHhsZPBNS2EZ/+bNumMxhKMbdvSX85RVSst\n3KgJaKxI9Oyp8rB92dpj52ragGjThtsmyrSRBLmYNixGjAj+b21FApRIWJUnzkfCmjZ69owvle2a\nNuIUiTCRaGhIL80dZdp4+GHdburU6ONauOGf4QJcFrkQCVu50/WRiCMSru9F2LSxZYuut0TCdbi0\nv1GfPvo72HvbZlINm0GaQiQSpmoAguN7IuHh4dGiyEQkNm/Wl5DNQJkLkbCzcfviixr49t8/sBtH\n2eGThH9CtGkjmyKRBE0hErYEd3OOaxGlSCQhErbEdzbTRiZFyc0VEUUkoiqAbtigv1ecaWPzZvj9\n77WSZib/CNDBuLpayYn9DcKKRC7Olp06qSoQ9pGIQibTxubNSr6iTBv2WSou1t/BZjctKgqimFxY\nItGnj5INN29HGHbbXBQJTyQ8PDxaBa6NOewnYV+mVpXYvl1finZASWLayEQkJk+Gf/xDbeZRg1WX\nLtmjNiDatJEPRSKJj0SYLOyzTxD22FaKhLt9GK5pI9PvV1io/c/kIxFWJGxxqjhFwhg1u/z7v8cf\n16KkRNtXVzcu2GXRs6f2LUw2t2/X+zpMpAoKcveRiFMkbCZXV5GwdWuKioLfoX9//bRRTC42bw7I\nzPDhLadIxNVEiUK4/k1LIK9EQkSuEZEGEbkptPx6EVktIjUi8oKIDAut7yYit4lIuYhsEpHHRSSc\nybJERB4WkWoRqRSRe1L1PDw8OgS2bs3NQ7upcBUJl0gYE5CMFSvSwzitHJ7EtGEHgbBpA9S5b9Ik\nuPjioHJlEtNGeBAtLlZHQ/vShpZXJOygED6vgoJgtt1cRcIt3JWLIgE6A48aKF3TRqbfzx6/oiJ5\n1Ib1u7BEYvhwHWwHDAj6cuKJQVRDJlgSUFUVDLBxBbbCDpf2nstEJDKZNnr21HZ1demEY+vWQJEQ\nURXBJRJhRQICE0gUkdi0KSDB++2XXx8JY5rmI3HooXDPPcnbNwV5IxIiMg64BHgvtPynwBWpdeOB\nLcBMEXEf15uBU4BvARPRMuNPhA7xCDAKzWh5SqrdXfnqv4dHS+P44+E3v2n548SZNmpqAt+IFSvS\nZ6VJiEQSRUJEEzht2gRvvtm4XVIfCWuztnZw9/jh/3NBNtNGYWF0+OCIEaqmdGlm5h3XtJGrIlFS\nEp07oFcvHfzefz8+q6aFdaiMM21UVaU76FpFwiodY8dqxMCee+qxOneGX/4y8zEt7L21ZImGIA8c\n2NgcElWp87PP4Ec/0v/DqcXDikQm04bdb3V1sB+rSNj73/19IJ1I2G0yKRLWtAH5VyTWrAkIVRyR\nqKsLMtSCmoPef1+Tu7Uk8kIkUmmxHwK+B1SFVl8J3GCMecYY8wFwHkoUzkht2wuYAkwzxryaKhV+\nIXCUiIxPtRmFVhe9yBjzjjHmLWAqMElEOlA2c49dFQ0NmlLXjZhoKWzZEtRgcBUJ9+UcRyR2T9MB\n0+E6W1r7dBTsoGPt+tlMG1E+Envs0Ti5UdeujdNl54pevfQ6hKNZIJrQWIwc2Xw1AnSgqqnRASip\nImEHsCj/CNDf2pLEf/u3zMfv0UPvj6iojb59dR/uDHn1ah04XUJn+3PIIeprM2FC5mNajBqlZqLH\nHlPz1/HHNyZtdjbv3qtTp8Lf/w7Tpzc+Vi6mDbvfjRv1XG3UhTVtQDJFwiUScc6WoESiokLLjl94\nYeM+5eojYc0aQ4fGE4m779bfxe7bKhjWRNNSyJcicRvwtDHmn+5CERmKlvl+yS5LpcSeDdjSPmPR\nDJtum8XASqfNBKAyRTIsXkRLlB+ep3Pw8Ggx2IJXrlrQUnBtyVFEorg4mkj06xdtrrBwE0Jlatel\niw6Ylkg0JWrjF7/QiosubHZNty+5whKsKIe+TNU9p0yB3/62acd0YQehdesC+d6aE+JIjJuZMgqW\nuD34oM7yM6G4OJ1IhBUJSHe4dHNIRCGuT1EQUbPXX/6ig+LXvta4jVu63mLOHLjkEiVJYeLRFCJR\nXa33gVUUrGnDHj+TsyUEpo1MzpYQRG5cc40mggsjV0ViwQL9vfbfP95H4p13lDTYZFiWSIT7mW/k\no9bGJOBQ4GcRqwegg/3a0PK1qXWgVT7rQpU/w20GAOFMlzuACqeNh0e7hZUbW4NIbNkSOLG5g7Yd\nuA44IJpIZKtU6Jo24mbPFm6kQRLTRpSPhH1hu7CDbXPCPyH65Z1Nkbj88qYd04UlEsuXB8uSmjbi\nBu1TTlEJPUndhqIiHWiiiIQdhF2SlY1I5IqzztL7UEQzgYYxYoQmFPuP/1Dlau1aNW3EFW+2RGLH\nDr3vkyoSvXrp/WyjmOIUiZoa7WthYTJnyzCR6NRJr3HUwJ+rj8Qrr6giU1wcr0jYxGn/+pd+2gRa\nLa1INMviJyJ7o/4NxxljEmT2bxtMmzaN3qEi9VElUz08WgrWVhqXtS+f2LIlsEdHKRIHHAD/+7/p\ng0mPHtkHZ9e0kUmRAH3ZWvKUybRhTOYBPIx8KRJRL+9c+tFUWHLk2qyzEQm7TRyR6NIlc40LF0VF\nqoZkIhIuyVq7Nj38tbk46CDdX1FRNFG0PjbjxsGf/xwUQMtGJOxzldRHYuBAvYfstbeKhDV9WWzZ\non0VCUw6rrNl+Hl2iUSPHvDSSzB7tipsYVgSkkSRqK/XOiBXXaXvkigiUV8fKBCWSORi2pgxYwYz\nZsxIW1adUC5pbtGuMUB/YJ7IF6JTZ2CiiFwBjERLfZeSrkqUAtZMsQYoEJFeIVWiNLXOtglHcXQG\n+jptYjF9+nRGx92JHh6tAEskWtu04c7+7QvywAPhT38Kckl06wY33JB9v7kqErYgUSbTRn29komk\nA7ht11QikamUeGsQCTubtUSiW7dgQIm7prYia5yPRC6wikRU1Ea4JDmoGSsfx7UQ0WybUQ6tFmPH\nwplnqinp/PNVXRs6NLqtJRKZKn9CejRIdbW2KywM1AdLJOz1sXAdMUtL4dJLAyWle/f0FNk2Ksol\nM0cfrb/19u2qmtgwYshNkZg7V9sdd5yGbkcRCUswxo1rmiIRNbmeN28eY8aMybptc00bLwIHoaaN\nQ1J/76COl4cYYz5BB/ovRKyUc+XhwFupRXOB+lCbEcAgYFZq0Sygj4gc5hz7WJSkzG7mOXh4tDha\n27SRyUfigAPS+9Stm874snFt6+yYlEjY+PVMpo24JFBxsASiJUwbmXwk8gVbhMsSCascde6cPsiE\nceaZcMwx+Tm+9ZHo0iU9CiSOSIRzPTQX48frYJcJU6dqfZE779T7Mo54WCJh7+1MmS0hMG1YHwlL\nBOz6sLnCJRKdOml/9tlHv9sidhY1NXrP231Z2HsqbN7IxUfixReVoIwbp89TFJGwZo3vfU/NivPn\n6/kVF7dz04YxZgvwkbtMRLYAG4wxC1OLbgauFZGlwHLgBmAV8FRqHxtF5F7gJhGpBDYBtwBvGmPm\npNosEpGZwN0i8n2gALgVmGGMyapIeHi0NVrCtBGe4VhkIxI25C7KhyETrLNjdXV2fwo3+iNT0a6o\nmXEmNFeRaKqPRD7Rr19QLXOPPdRfItv533FHfo7t+khEpSXv1KnliUQSHHWURnksXAhnnx3fLqxI\nxJk2bHnxsLNlLopEGGHSYZ+vcB/sdd66NdiXNel17qx9Ly/XNOhHHRV9rJdeUnWjS5d4n4sFC9Sf\n5ZRT9PuvfqWfY8Z0nKgNF2l5tIwxN6KD/l2oetAdOMkY4+bcmwY8AzwOvAKsRnNKuDgbWISqIM8A\nrwGX5r/7Hh75RUODzv533z1/ioT14D7sMHj66WD59u36Yo0zbfToEdh6cyUSoC/Phobs27iJpMKm\nDddHorUVieJinbVGxdW3FpHo37+xIpHLb9AcuEQifEyR9Myf27bpNQm5l7UKRNSMADoQxiGpaQN0\ngK+qCpwyXSJhVQR7fayalg8iYe9Zt21dnR5j992V2Nxyiyb2igpLrquDt94KFKnCwnhF4uCD1a/k\nsMPgiSeUBB58cAckEsaYrxpjfhRadp0xZk9jTJEx5gRjzNLQ+m3GmKnGmH7GmJ7GmO8YY8JRGlXG\nmHOMMb2NMSXGmIuNMS18eTw8mobFi1WOLi9Xz/etWzXDXHOIhDHBTPaTT1SRWLdObc4Wdv9RioS1\n31rHyqYQCTvQZnO2dBUJNwdB2LQRlc8g2/FFsh8/DiJwxBH6Yg6jNUwboETCSurtiUhAOpGIq4fR\nWrjgAs2/EBUmapHUtAF679sEW717R/tI2N/f3pe2tkYUmqJIWNj/d99dr/eiRfp8rlzZ+Djvv6/9\nOSKVDCHOtLFggTqzgvpUbNig74levTogkfDw8FBHw8ceg5NPVsdGUCJRW9v0NNlvvaVpdz//PHjZ\nH3lkusNXmEhEKRIi+gJbtUqX56pIJNnGEolu3dLt21266PnbGV9TFInu3TM762XDkUfqtQzP/lrT\ntAF6bezv1FpEwvWRiCMSlkDE1cNoLfTuDffdF+SWiEJS04Zd9+KL+v/w4ekJpVzTBgQDb0spEva4\npaV6vW11148+ohHmzNHn5tBD9XsckSgvD4ipSOCgGzbXtAQ8kfDwaAGsW6cvkEWLNIXw6aerxAhN\nf6hXr9bBb906fXEWFKiMaWdVy5YFXtpxPhL2Jde/f9NNG0m2sUQiPDBbdcKaN5riI9Hcwf6oo3SQ\nXLgwfXlrmjYgUIegdRWJ+nq9F5IqEm1h2kgKl0h07545hXnPnmpi3Hdfnbm76pNr2oBkRCLsbBkX\ngppJkSgt1f7bBFxxROKQQ4L9RBEJY+LVE08kPDw6KNau1ZC1BQs0oc7//V/wQm6qecO+2Kurg6Q6\nrkx+0UWqgEB2ImFLIkPLmDbsYBnetyUSVilpqiLRHBx+uDoV2logFq1NJHr1ahsiAZq9MupcXSLR\n1opEErhEIpNZA4J7/5vfTM+S2qVLcD+H1YMkioRV13JRJOz/1l+prk77FEckDnfyN0c5W9bV6SQj\nE5FoySqgnkh4eLQA1q3Tl8TgwYHcaF9I+SQS/fqpLbShQe2rbvIckcamDZdIQOMQwGxIqkiUlKhH\nejYi0RQfieYO9j166Awv7CfRWj4S1rTRVooEKJHIpkh0JCKRqfKnhb33v5Vy47f3kRuumatpw5j0\n6qPuPiyy+UhYHHlkYyJRXa1qhVtdtbBQzYOuidS+U+KIREND+qQi3/BEwsMjDzj5ZI0xt1i3rnEB\nLPtCamoIqCs5u4pEQ4MODGvWwHXX6cto8ODgJbtunZINN1mO68OQC1x5NRM6ddK+hQd9Kz2HTRtJ\nycHQofHJiXLBUUfBrFSWmtdf14yBtbWtr0jYe6I1fSQgGZGwxDWT30Fbw1UksvWzpERNgTaHhSWN\nLlEIE4lszpYQqAvWBylMzLP5SIAS7JNP1mfXVQ7mztXvLpGwv5tr3rD9jSI94XNqCeSj1sZlIvKe\niFSn/t4SkRNDba4XkdUiUiMiL4jIsND6biJym4iUi8gmEXlcRMKZLEtE5OHUMSpF5B4RieGKHh6t\nh4ULtTrhU08Fy6KIhJ355FuRAPXOrq3VF6VNaWwjJC6+WOtEhH0kIPcBzL4Uk0RN9O+f3LSRtB9X\nX62VI5uLffYJnE2fegp+/3v93lamjdY4LgSDSkVF9DXv3TtdkejVK3OirLaGJRKVldmVk5//HJ59\nNhjo7b3sKhK5mjbctu7z5SJKkQibNqzfxqZNagq1eO89Pc6IEcGyTEQiTpFw27QE8qFIfAr8FBiN\npsz+J/BUqvQ3IvJT4ArgEmA8sAWYKSLuq+hm4BQ0d8REtMz4E6HjPAKMQjNanpJqd1ce+u/h0Sz8\n9a/6OWdOMJvIpEi0BJGwWe2sGQXSFYl33ok2bTSVSCTZbvfd82/aEGlexIZFaam+WLdsCVKFG7Pr\nmDbiiETYtNGezRoQ3OPr16fnLonCoEFq0rJIokhkc7aE7KQjkyJhn8P99tOqnpBu3ti0Scmdq3LY\n3y2cVdPtv4sOQSSMMc8aY543xiwzxiw1xlwLbEZLfwNcCdxgjHnGGPMBcB5KFM6AL1JmTwGmGWNe\nTZUKvxA4SkTGp9qMAk4ALjLGvGOMeQuYCkwSEV/906PNYIyWRR40SF/ONr9DeXkw27DIF5HYuDHd\ntAHq1AnpRKJr16Cg0SefqAOoHbiaa9pIokjsuWfjGVqcaaO1BlILe/7r1umfzdS5sztbJjFtuIR1\nZyISYdjfOo5IGJNMkbD3cE1NdNsuXVTViYvaACUSQ4bo+Xz8cdAuym/H9ntnUyS+gIh0SpUVLwLe\nEpGhaJnvl2ybVGGu2UAqvQZj0VTdbpvFwEqnzQSgMkUyLF5Es2g6/qweHq2L99/XGHCbjvbttwPn\nx3z7SIQViZ49NVZcJJpIFBTozN8Sl6qq1lUkfvMbuCukGUaZNgoKcnP4zAdcIrF2LZxxBvzP/8Bp\np7X8sUtK9Hx79mx9Hwk7qNTXx0dt1NTo+qqq9h36Cc0jEtlMG1u3KplIatrIJeeEJRK9e2t+iKOP\nVrLRt29QkdTuO0wkokwb2ZwtoWWJRHOrfwIgIgeihbUK0VoZ3zDGLBaRI9DBfm1ok7UowQCt8lkX\nqvwZbjMACGe63CEiFU4bD49Wx5tv6ozjrLPU0XHOHK2uCY2JRGGhDiBNVSSinC3ty+eDD/SF4b4U\nwyWWoXWJxN57N14WRSRaW42AxopEaSn88Ietc+xOnbSM9YABbWfaiDumjXzYtKnjmDa2btXnoalE\nwh38u3bV59mavcLro7a3BCGTY2Y454TdprAQ3nWmx337qlrktktCJNra2TIvRAKtgXEI0Bv4NvCA\niEzM076bjWnTptE7RK2jSqZ6eOSKsjI1axQUqGf1nDlw6qm6LkwkRILMgnGYPFmjEn7zm8bronwk\nQG3uixerw5brP2CdLaOIRFOdLXMxbUQhbNqIKh7VGrB+CmvXRvuztDRee037YEP42huR2LhR77Mh\nQ1qlW01GQUEw8OZDkYAg70Imc4G7fRIiEaVIdO3a2JG1pERNpBa5EokkisT27fq+2H//dCVwxowZ\nzJgxI23b6iSlSckTkTDG1AO2DM67Kd+GK4Eb0VLfpaSrEqWA5WFrgAIR6RVSJUpT62ybcBRHZ6Cv\n0yYW06dPZ3S2GskeHk1AWVkQjjhunEYA2GiAqMGpuDizaWPOHPjb3+AnPwmSSllkIxKuWQP0JVtb\nm/4Cs0SisFD/b0lFIgpRikRbEIkuXTT18uLFSmrC/iwtjUGD9LO1iYTN7ZEpRTbo/dVRTBsW+fCR\ngGDQz6ZIhJ0ta2riCWlYkYi773NRJKKcLaOchcNE4tFH4Zxz1Ifp/vvhuON0edTket68eYzJVDUt\nhZayTHYCuhljytCB/li7IuVceThg08HMBepDbUYAg1BzCanPPiJymHOMY1GSMruFzsGjnePFFzWb\nY1vCJRITJ+qD/9RT+kCHZzqgyzIpEuvX6wN/992N11VX60BQXq4vEfvSty/QcGnvrl2DpEI2JNR1\nfoyKqsiGpHkk4tBeiATo+VvfktZWJCw6d9Z7pTXNO3ZgSUIkOoJpw8KqTEkRZdqAQJFIatpwnS1z\nUSSi7vskikScs6U1ncb10xKJzz/Xfu6xh5pj84F85JH4jYh8WUQGi8iBIvJfwFeAh1JNbgauFZHT\nROQg4AFgFfAUfOF8eS9wk4gcLSJjgPuAN40xc1JtFgEzgbtFZJyIHIWWJp9hjMmqSHjsnPjjH7Wo\nj/uAtjZcIjF6tL7Mnn5aB6aoMMVMpo1t29Q2XVICt96aXm572zb923vvQPFwFQmIViTs7GZCKoYq\nTHzZtWMAACAASURBVCRyHcRzySMRBWva2LYNrrlGs0u2hY8EqArR1kQClFy2FyJhFYjq6o4TtWGR\nb9NGU3wkktbliEt+1lQfiUyOnjYFuCUS1dWqxl11lfp4LV0avV0uyIcisTtwP+on8SKaS+J4Y8w/\nAYwxN6KD/l2oetAdOMkY4ybsnAY8AzwOvAKsRnNKuDjbOcYzwGvApXnov0cHxI4d8FIqzicfD0JT\nUF2tD70lEp06wQkn6Gw7bmDKZNqwxbcuvFDJwpIlwTrraDlwYPCCS0Ik7Ozm9NNVvbHKBMAxx6Rn\nzEuCfJk2li2D//5veOONtpPPd989KCvd2qYNFz/5CXz96613PEsk4qI2QO/rjRs7jmlDJHOV0Chk\nUiSSmDasj4MbtZGLIhFlhmiOj0Tcse05uUSid299J/TureaN5qLZPhLGmO8laHMdcF2G9dvQvBBT\nM7SpAs7JvYceOyPmzQseuMWLNStca6OsTD/dlM0nnggPP5yZSMQpErb4llUPVqyAUaP0f+sfYW3r\n0Ni0ESYSXbsGWfL23hvuuSd9/a9/Hd2PTGius6UlEva3e/BBrTHQFrC/UdeubTtg/uQnrXu8TCGn\nxcU6KFvVq6MoEn375p6BM6rWBujAncTZ0u6jKVEb2XwkjNHfoSWJRPfuGm12//1q4mhOBlNfa8Oj\nQ+KFF/QF0KdPegKX1kQUkTj+eP2Mm+Fm8pGwisShh+pDvWJFsC4TkbCKRNhHwjVtRPlrNAXNVSSs\nacPGyo8dq+mq2wKWSMSZoXZWZDJt2PwWtsR8RyESuZo1oPk+EnYfTY3aiPORqKsLBv1cnC2zEQl7\nfEskAKZM0d965sz4bZPAEwmPDol//AO++lWV6tuKSCxfrg+5qz7svjucfTZ8+cvR2yRRJAYMUAVh\n+fJgnWvasEiiSOSbSOTL2dIqEm1ZEMr+bm1p1mgLZCISoPfVypX6/85MJGzq6bCTpmvacEuMR6F7\n92TOlrn4SEDw3EYRCZspMxdFwqoskE4kxo9X367bb4/fNgk8kfDocKiv18qNxxyjqWXzRSRWrAge\n9rffDhSCOJSVaZx9eDb78MNwwQXR22TykVi/Xl/uPXrofpMqEhMnwg03BImwLAoKNMMm5F+RyJdp\noz0QibZ0tGwLJCESs1OxcDszkSgp0WRQX/ta+nI76GZyYHTb1taqX9T27fnxkYCASMS1syG8Ftn6\nGmXaAH13XX45PPdc+sQlV3gi4dHhsGKFyn/7769EYvHi5u/TGDj8cPjRj3RAnzgRbr458zZuxEZS\nZFIkysv1hSiiZcDdB9sSCatI2ORWdp/XXtvYxukO9tleiEmRL2dLSyTyRXCagl1VkciWlnvwYB2k\n/v3fNclZe0ZziATAwQc3Dpl0TRtJiUSmzJKQ3EfCEgn7fEQpEtCYSDTFR8Ji8mQl9A89FL1tEuQj\n/PNnIjJHRDaKyFoReVJE9oto50uJe+QFNkpj+HAlEhUV6fnpm4LlyzXL4X33wfXX64P+ySfx7Xfs\n0Cp9uRKJTD4S69cHMuuQIY2JRGFhsL5Xr+x2fTtoFxQE/zcX+cpsWVGh16K1a2y4sARiV1Uk4kJ/\nn3hCcw38+tft33ekuUQiCta0kSmc08I6WybJgukqEs0xbYASiVx9JOKIRFGRTsqaE/2Wj8f4y2h4\n5+HAcUBX4B8i8sXp+1LiHvnEkiX6Ahk0SIkENN+8MW+eforAH/6g/1s7cRT+8AdVJHLNsp7NtGFf\niEOG6Mvczjrsw29fAElMAvYlm89Z/4gRcPHF6eWYc4GrSLSlWQO8aSNOkejevXXKqecDLUEkrGlj\n/fpgYM/UNgmRSKpIWFNSRYWacOvr86tIGNOYSIC+S6Ped1GJ8aKQjzLiJxtjHjTGLDTGLAAuQLNS\nunk1fSlxj7xhyRL19O/cGYaldK3XXmvePufOVWfFyy7T79/+drqPgosVK1T2/cEP4KijcjtOEtMG\nqLwMwcNtY/qLi/W8rX9EJrQEkSgs1ERgTQ2XFNH+V1QkO4eWRM+e8J3vaOXFXQnZiERHQksqEitW\nBM9hHKyzZa6KRJzvQ9euel9WVgbto9oVFjaNSNTWKjkJP78DBwaROi6efDJ+ny5aQljsg1b8rADw\npcQ98o2lSwMCUVysCZyuuQamT2/6PufNgzFj1Gnx5ZfVAWv16iCVs4t//EMfyKjCWtnQo0fgmBVG\n2LQBgXnDziJEdABOMgjb2X9b+iFEoUsXJVNtrUiIaN2BsWPbth+tjdYuXd6SsM+E64TcXNhBd+XK\n7PvNtyIBQVKqTESiqc6W1tcqSpH49FNVLFwkrNmVXyIhIoKaKN4wxnyUWjyAFiwljhIWr0jsQliy\nRP0jLO65R7M2/sd/NG1/xqgiMXq0DrpHH60zkYaGIKGTi4oKfdibMhDahz3KvOGaNvbeW1+QVhVx\n5cjevXNTJPLlaJkvWILT1kRiV8XOpEjsuaf6KuUzqVn37jowf/ppMkXCzYLZXB8JCJJS5UIkkioS\ncURi4EDdnw1BByU7LvnJhHwrErcD+wOT8rxfDw9AZbmysnQi0amTRlls3pz+cCXFqlVqVnCL3NkX\nSJR5o6Iiu+00DvYB3hiizA0N6jBqiURBAey1V7oiYclDUiLRXhUJ26+2Nm3sqtiZiARoLpl8OoUW\nFel7pq4uuyIRdrbMFLVRVxeEY+eiSES1s86Wl10Gr77afCJhz9M1b+TiwJ6XMuIAIvIH4GTgy8aY\nz51Va2jjUuLTpk2jd+jKRZVM9Wjf+PnPNVyrvj4wbVi43s7hDI/ZMHeufrpEwoZZRjkgWUWiKbC3\nYVVV+mynslJfMm5ynJEjg7599hkckTLyffnL2WdK0DI+EvmAjdzwikTbIFvUxq4Od0BOqkgk8ZEA\nHfyLiuJ9JCC5IlFdrSGbPXrkR5GAGVx22YwvEtvpZCeZbSMvRCJFIk4HvmKMSXv1GmPKRMSWEn8/\n1d6WEr8t1cwtJf5kqk1sKXHHTyJRKfHp06czevToZp2jR9uirk59EmyuBFeRgIBIVFTkTiTKyvRB\n23PPYFlxsQ7qUYpEZWXTFQnrlV1drcThww+1ToiVFF2nsVNPhauv1qQ5H3wQmG5uvTXZsdorkfCm\njbbFzuQj0RJwB+5sioQdoC2RiCMHlrS5RCKTIrFiRXZny8WL1Sy7eLGGoychElVV+j1MJPr3h27d\nJnPuuZP54Q912csvw1e/Oo/0uIlo5COPxO3Ad9HqnFtEpDT1514mX0rco1mwMpsxOkC6qaKhcSKX\nXFBerqQhLI8OHpx/04YlElVVmt/+0EP13GwWTZdInH66EqhLL9WXyckn53Ysb9rwiMIhh8CXvtQ4\nNbSHwg7IPXtmz+y522767NbU6H0dl6/FLTm+dq1+xr1DkioSNu/Dhx/qZyZfqD59dH+22m2YxIvo\nO9VVYFvbtHEZ6kz5Smj5hShhwBhzo4gUoTkf+gCvE11KfAdaSrwb8Dzwg9A+zwb+gEZrNKTaXpmH\nc/Bo57A39W23qU9EOItjOJFLrvuOKkEcF1tdUaEJXJoC17RhbaZr1gSKhPtyHzJEicbbb2s4aq5O\nk+3V2dKbNtoWw4fD66+3dS/aLyyRGDQou+/FgAGqLm7YkL1KKKgS8fzz+gycdFJ02759dX/ZiMSO\nHfq/LR6Y6fj2fTVrlj53UZU+wyGgGzbo+YcjOaKQjzLiiVQNX0rcozmwROKYYzQpUhjNUSTiiMTg\nwfD3vzde3hxFols3falUVwcvig0blEiINN7vN74B8+crkcgVXpHw8MgdduBO4odkzajWPJptn7W1\n8Je/aJXgqHcO6GSiqgo2bUrf1kWUWSobkRCBN9+MzwEzaFB6uYENG/QZTRIC6mtteHQIWCIRJ8cW\nFOiAmW8isXJl4Glt0RwiAfogV1UF51Rern+77dZ4pnDBBTBpkvpL5ArvI+HhkTtcRSIbbJr1srLM\nyp9VJJYsgTfe0Gc6DvYdZ0PPMxEJNytrNh+JfffVfcYRiShFImniOU8kPDoErMyWyWbZt29+icQB\nB+gMwk2/vW2bxow3h0j06aMs3/a1vDw9GZWLQYNgxoymmSc8kfDwyB12QM5Fkfjkk2SKxP33Kwk4\n/fT4tvY98Omn+s6LqmtjicmxxwbLsr0jbHXgTIrE558HyfI8kfDY6VBeruaLKNuehY2/zhVxROLw\nw/VBnjUrWGZ9MJoa/glKJMKKhJuMKl9or6YN6yPhTRse7RE9e+pzv88+2dv266c+W59+msxH4tln\n4ZRTMt/7LpHo3j3aT8MqEocfHhCITMeH7ERiyBBVX60qUVGRvIy8JxIeHQJxg72LfCsSvXqpKhFF\nJJpr2nAVCRu1kW8i0V6dLb0i4dGe0bu3+hJ885vZ23burM+tMckUifr6zGYNaEwkomCJxL77BhWI\n80EkIHDe9IqEx06HpEQi16iNmhr1pI7b9xFHpBMJO/g317RRVZXMtNEctFdFwhMJj/aOI44IlLNs\nsOaNJIpEcbEqEplQUqIqx8qV2YnEPvsEBCAbkTjoIP3MZNoQCbLptiqREJEvi8jfROQzEWkQka9H\ntLleRFaLSI2IvCAiw0Lru4nIbSJSLiKbRORxEQlnsSwRkYdFpFpEKkXkHhFpZ3Mtj5bChg3ZB9qm\nKBLWvJCJSHz4YeC5nA8iEeVs2RKmjfbqI+FNGx47EyyRyKT8WUJw+unZB/xOnfR9tG5d9gRXQ4cG\nikS20u/DhyuJjyMH3bppWv62UiSKgfnA5Wg+iTSIyE+BK4BLgPHAFmCmiLguJDcDpwDfAiaiJcaf\nCO3qEWAUms3ylFS7u/LQf48OABvVkAlN8ZFIQiSMgTlz9Lvdf3N9JMLOli1p2mhvRMIrEh47E2zk\nRiaC0KULnH02/Nu/JdunnTTFkYNTToHf/lbXH3BAdMRXGF27wnXXwRlnxLcZOlSJRENDbj4S+cgj\n8TyaPMpW/wzjSuAGY8wzqTbnoTU3zgAeTaXLngJMMsa8mmpzIbBQRMYbY+aIyCjgBGCMTY8tIlOB\nZ0XkKp/ZcudHPn0kVq3SsKmCguxEYr/99KH+0Y/0wa2o0IE5ypM6Kfr0Ue/orVt1BrBypUaH5JtI\nHHCAzoDCdUnaGu3V5OLh0RQkMW2IwMMPJ99nNiIxcqT+AUyZkt1cYvHv/555/ZAhmjHTpvBPqhq2\nqI+EiAxFS3y/ZJel0mHPBlIliBiLEhq3zWJgpdNmAlDp1NgAzW5p0JodHjs5khKJqqrGeR9cbN2q\nhb9+9KNgvxC/706d4OmnVYH4+tdh4cLmmTVA5UJbRny//TSzJeTfR6JfP/i//2t/A3aXLvrSTWqD\n9vBoz0hCJHKFnVRkM1eAEvO9987PcYcOVR8J+15sL86WA9DBfm1o+drUOtAKn3Whqp/hNgOAde5K\nY8wOoMJp47GToqFBnSiT+EgYk56JbelSeOqp4Pvzz+u+7rpL123YoJJgpgdmwgR47DHtxxNPNJ9I\nuHLhfvsF/+dbkWiv6NrVmzU8dh4kMW3kimyKREth6FBVS20yLB/+6bHTwKoMSRQJCMwbr7wC48Zp\nGNe2bbrsr3/VdLGlpfCLXyiR6Ns3e0790lItM96cEuIWnkh4IuGx86AlFIm2JBIA8+bpZ1JFoqXF\nxTVome9S0lWJUuBdp02BiPQKqRKlqXW2TTiKozPQ12kTi2nTptE7dEUmT57M5MmTk5+JR5vBVsZM\n4mwJOtgPGaIEwpo7li7Vh+Tpp9VO2KcPXHEFfPe72fdrceKJMHdufkwbFi6R2FWqMXbt6iM2PHYe\nJInayBVtQSRmzJjBfffNANQpUwR+85sEhTZoYSJhjCkTkTVopMX7ACnnysOB21LN5gL1qTZPptqM\nAAYBNoJ/FtBHRA5z/CSORUnK7Gz9mD59OqNHj87LOXm0PrL5MVi4isTChYEJ48wztRjNokWa3vrM\nM3VG/IMfwJNPaoXNJDjxRPj1r/Nn2hDRhDKgfgw2pGtnx9SpATn08OjosEQin4N+WxCJyZMnc+aZ\nkykshI0b4aab4CtfmceYMWOybttsIpHK5TAMHdQB9hGRQ4AKY8ynaGjntSKyFFgO3ACsAp4Cdb4U\nkXuBm0SkEtgE3AK8aYyZk2qzSERmAneLyPeBAuBWYIaP2Ng5cOutqiDstVfjdU0hEp99po6SJ56o\nA/fixWr7GzYsiGIYP17DOpMqEhMm6L523z1720ywikRJSbCvXcWsAXDYYW3dAw+P/KGkBH73u/iy\n4E1BLs6W+UTnzhrtNXKkhqq++272bSA/isRY4GXUqdIAv08tvx+YYoy5UUSK0JwPfYDXgZOMMXXO\nPqYBO4DHgW5oOOkPQsc5G/gDGq3RkGp7ZR7679HG+PRT+OEPNSnT9dfrsqoqDYs8+ODkRKK4WGXz\n9evho4/UF6JnT30oFi3Sv8OdGJ/TTsuNSHTpAv/8Z/M9pK0i0bev/t+p065j1vDw2NkgAlddld99\ntpWPBGh68LgaH3FotrOlMeZVY0wnY0zn0N8Up811xpg9jTFFxpgTjDFLQ/vYZoyZaozpZ4zpaYz5\njjEmHKVRZYw5xxjT2xhTYoy52BhT09z+e7Q9bArql14Kll1zDXzpSxquWV6u0r9NCxsHEVUNnnxS\nCcL48bp8xAh4/32YPz+dSNjS3EmJBOhsurnqQY8eSh769tUZQN++u5Yi4eHhkRltSSSKi/X9lAt8\n1IZHm+Ott/Rz9my1zdXWwl/+Aps2Kbl45ZUgT3w2XHaZtn/vvXQiMX8+1NWlE4lDDlFicPDB+Tyb\n7BBR84YlMLvt5omEh4dHgLYkEk2BTwnj0eaYNUsH+Nmz4bXX1CGyuloH1z/+Ef7+d7jllmT7+ta3\n4MorVcWwRMJmgCsoUPJgIRKEObU2+vQJfDp+8QsYPLht+uHh4dH+UFys7z+bo6K9wxMJjzZFba0O\n5jffrM6Qf/87fPyx1rj40pfUialrVzjrrGT769YNLroIbr89KJs7YoR+HnpodvNIa2HkSBg1Sv//\n7nfbti8eHh7tCyKwYEHzI8RaC960EYGaXdTzYs0a+NrX4KGHNEPkunXZt0mKJ5+Eiy9ufG3nzoX6\nejjySDjuOCUAL74Il18O3/iGtjn11Nz8GK67Dt55J6jpsO++avM7vB0lU3/uOfj5z9u6Fx4eHu0V\npaXBO6y9wxOJEO69V8N5Xnkl//vesqVlSIoxGqVQV5e53aZNKv2PHavRB+F9XHQRvP46nHuuMuHS\nUvjzn5vfv40b4dJL4Z574PjjlSRce62ue/VVzQh30EHqYHnTTerPcM45OvB/85vw4x/ndrzCwvRE\nT926wfTp2gcPDw8PjzzDGNOh/tCw0DKgFvgXMC5D29GAmTt3rkmCzz83pndvY7p3N6Z/f2M+/TTR\nZon2+93vGlNYaEy3bsaccYYxGzdm3uaRRx7JuP7ZZ405+mhj/uu/jPnOd4wBY0pLjXnggfR2DQ3G\n/PSnxowaZcygQcb06mXMuHHa/o47gnZ33qnLnnnGmCefNOYXvzDm1FON2W03Y9avz36Omfp7zTV6\nTR991JgBA4wZOFCP9eqrxuy1lzHnnZd9/y2JbNe6PaIj9tmiI/a9I/bZmI7bb2M6Zt87Yp8tovo+\nd+5cm9ZhtMk0Lmda2d7+gLOArcB5wEg0N0UF0C+mfU5EYtIkY/r1M2bhQh3sDj/cmK1bE20ai+XL\njRk2TAfQ3/7WmJtuMqZrV/10sWOHMa+/bszmzfr9tNNOM6tW6fZh3Hij/nKHHmpMQYGSn7vu0oG/\nTx9jtmwJ2t5wg7Y991xjzj/fmA8+UHJxxRXGdO6shOTjj40pKjLm0kvTj7Nmje7voouiz+2dd4z5\n/veNWbpU+xuFOXOUPP3Hf+j3hgY91/33N6ZvX2NEjPnoo+zXsSUR1/f2jI7YZ4uO2PeO2GdjOm6/\njemYfe+IfbaI6vvOSiT+BfyP813QLJlXx7T//+x9e5wcVZn2c/o+PdPJDAxJZiAQIFxdLskIgqjo\n5ufGG7iusBhwRdhPBPlWjOt6+RRBFmVFIQqKimACArPEFSVyERYQQS4iE0JCSCCE3GAmk0wymemZ\nvned74/qt/pUdVV1VXfVdHfmPL9ffpmpqcvp6lOn3vM8z/seXSAxOcn5/fdz3tfH+Uc+or4ICQ8/\nrN6NX/9a/f2FF9SX9CWXqC8/Efv2cX7TTZz393O+ejXnf/0r52efzfnixZzffrv6ouSc81SK82OP\n5fyIIzh/883y8Z/+NOfz5nFeKJS3/eAH6vXjcfXFvXDhWbyri/NAgPN//mfOd+1S99u2TX0xX3GF\n2q5duzjfs0f92+bN6ov5V79Sf//DH9Rzfuc7vAL5POcf/aj694MOUoOdZLJyvx//WG3D+vXlbek0\n5xdfrB7LGOdnnql2QuN92rqV854ezk87TT1GxL33qsd/8pOV15xqtOLD34ptJrRi21uxzZy3brs5\nb822t2KbCfUEEi3jkWCMhQH0AdDKFnHOOdRKl6fbHftf//VHHH20mlLz8Y+rCwa9+abqFTjnHODO\nO4HLLgMWLQICAXXRklNOUY1/t96qavqFgnquQgE491zgiiv6sWQJsHChquW/9hqQz6s+AzLRffOb\nwJYtwGWX9WurqgHA0qXqmu8//7n69z/+UV1IatGifvy//6cuLLV6tdq+m25S/Rpnnqle49vfVmsQ\nnHhiPxhTU4TI2XvEEcDixcDPfgYMDgIXX6waFefP76+4J6EQ8Pvfq76Fww9XDZZ/+EPlfp//PDB3\nrnrdYlFt27vfDfz61/249Va17X/+s5rCGYmonoY//lH9jMcdB+Ry/fjd7yrXkTjnHOA73wGuv768\nrb+/8vpmcLqfPGfjztno68tzNv85G319vz6T1+ds9Gd3BLsoo5n+AeiBWhr7XYbt3wfwnMUxCwFw\n4H387LNVtuH559WZfD7P+fLlKjMAqF6ATZsqo7Lbb+c8FOL8hBNUSeGjH1V/P/30s/jevZw/9xzn\nDz3EeS6n7n/DDer5TjxRna3fcIN5pPfhD6v70b93vpPzj35U3W9igvNTTz2L5/Pqvq+9xvkhh5T3\n/clPrCPf3/++vN/s2ZwPDzuPkq32W75cPV8spv5/6qmcv+995X0vvpjzUOgs/uUvq9cEVInoW9/i\n/EMfch6h19vOWvc966yzPD+nm/1qOafTz+Xltb3a17i9Fe59K/YRJ8c0ax8x/q0V732rnbMeRmJ/\nryMRA4Cjj07iqqvKlYdoIZITTwT+539UloExNbtgbGwMq4UqRSefrM7af/lL4GtfU1MJr74aePTR\nMWzZshqRiJrdsG6duv+ZZ6qz8U2b1LUc3vte4P779ecEgKuuAi68UF1Hor0dOOoo4BvfKO8Xi41h\n7dryMXfeqVZrTCZVtmTlyspzAuqiV1dfraY7LlgAvPVW5WeygtV+xx+vMjbxuFrQ6R3vAJYuLe97\n2WXA66+P4YILVuPss9W00cMPV9vw1FPOrm13/Vr3c7rv2Ji6VG6zt1Pcz+nnalQ77fY1bvfzPnl1\nzlbsI06OadY+YvxbK/SRVj+n2bYNGzbQj7ZrEzOuztybHiVpIwXgk5zzVcL2FQBmcs4/YXLM+QDu\nnrJGSkhISEhI7H+4gHN+j9UfW4aR4JznGWMDABYBWAUAjDFW+t2qgPIjAC6Aunx5ZgqaKSEhISEh\nsb8gBmAe1HepJVqGkQAAxtg/A1gB4FIAL0BdfvwcAMdyznc3sGkSEhISEhLTEi3DSAAA53wlY6wb\nwDUAZgNYA2CxDCIkJCQkJCQag5ZiJCQkJCQkJCSaCy1TR0JCQkJCQkKi+SADCQkJCQkJCYmaIQMJ\nCQkJCQkJiZohAwkJCQkJCQmJmiEDCQkJCQkJCYmaIQMJCQkJCQkJiZohAwkJCQkJCQmJmiEDCQkJ\nCQkJCYmaIQMJCQkJCQkJiZohAwkJCQkJCQmJmiEDCQkJCQkJCYmaIQMJCQkJCQkJiZohAwkJCQkJ\nCQmJmiEDCQkJCQkJCYmaIQMJCQkJCQkJiZrR0ECCMXY5Y2wLYyzNGHueMXaKzb7LGWMKY6xY+p/+\nrZvKNktISEhISEiU0bBAgjF2HoAbAFwFYAGAlwE8whjrtjjkiwDmAOgp/X8IgL0AVvrfWgkJCQkJ\nCQkzMM55Yy7M2PMA/so5v6L0OwOwA8BNnPPrHRz/jwD+B8DhnPMdvjZWQkJCQkJCwhQNYSQYY2EA\nfQAep21cjWgeA3C6w9NcDOAxGURISEhISEg0DqEGXbcbQBDAsGH7MIBjqh3MGOsB8GEAn6qy34EA\nFgPYCiBTS0MlJCQkJCSmKWIA5gF4hHO+x2qnRgUS9eKzAEYB3F9lv8UA7va9NRISEhISEvsvLgBw\nj9UfGxVIjAAoApht2D4bwE4Hx18E4E7OeaHKflsBYMGCBbjtttscNWzp0qVYtmyZZ/vVes5qx/h9\nfbf7Wh3bbO202gdA07dT3M/p52pUO+32ddp36rm+7CPejiNT3UeMf2uFPtLq5zTbtmHDBnz6058G\nSu9SKzQkkOCc5xljAwAWAVgFaGbLRQBusjuWMfZ+AEcCuN3BpTIAkEgksHDhQkdtmzlzpqN9ne5X\n6zmrHeP39d3ua3Vss7XTah8ATd9OcT+nn6tR7bTb12nfqef6so94O45MdR8x/q0V+kirn7PKsbbW\ngEZKGzcCWFEKKF4AsBRAHMAKAGCMXQegl3N+oeG4f4Wa7bHB6YUWL17suFFLlizxdL9Gn7PR15fn\nnJ7nbPT15Tmb/5yNvr5fn8nrczb6szsC57xh/wB8ASplkgbwHIB3Cn9bDuAJw/4zAEwAuNjh+RcC\n4AMDA7zVcNZZZzW6Ca7Qau0V0Yptb8U2E1qx7a3YZs5bt92ct2bbW7HNBLO2DwwMcAAcwEJu865t\nqNmSc34LgFss/naRybZxAB1+t0tCwm9sH9uORCSBrrYu367BOce6Xetw4uwTfbuGhITE1COdAdmQ\ncgAAIABJREFUT2PH+A4cfeDRnpzvzdE3UVCqWQ6tIdfaaFL4QaX5iVZrr4hGtP28/zkP33v6ezUf\n76TNL+18CSf9/CRs3be15uv4gVbsK63YZqB12w20Ztunqs2/XvtrnPGrMzw738fu+RjaF7bXfLwM\nJJoUrfYQtVp7RTSi7ePZcYxmRms+3kmbk9mk7v9mQSv2lVZsM9C67QZas+1T1eZkNonx7Lhn5xvL\njmHuGXNrPr5V60h4hu3bt2NkZKTRzZjW6O7uxqGHHtroZkwp8sU8ssWsr9cgqrLIi75eR0JCYmpR\nUAp1SRFG5It5pPPpmo+f1oHE9u3bcdxxxyGVSjW6KdMa8XgcGzZsmFbBREEpIFPwt9gqBRBeDjgS\nEhKNR5EXoXAFClcQYPULCwWlgEyx9vFoWgcSIyMjSKVSuOuuu3Dcccc1ujnTElTwZGRkZNoFEtmC\nv4xEUZGBhITE/gh6totKEYFg/YFEXpGMRN047rjjHBf8kJDwAgWl4Lu0QYwEDToSEhL7B0S2MRwM\n132+ehlSabaUkGgA8kpeMhISEhI1QWMkPPI/5Yt5pAu1MxINDSQYY5czxrYwxtKMsecZY6dU2T/C\nGPsuY2wrYyzDGHuTMfbZKWquhIRnmAqPBAUQMpBwhq/979ewcWRjo5shIVEVXj7bnHMUebGu8ahh\n0gZj7DwANwC4BOUS2Y8wxo7mnFulUfwGwEFQF+3aDKAHklWRaEFMqbQhszaqQuEKrn/2eszrnIdj\nu49tdHMkJGzhpZGaztGqHomlAH7BOb8TABhjlwL4KICLAVxv3Jkx9iEA7wVwBOd8X2nz9ilqq4SE\np5Bmy+ZCrpgDIO+VRGvAy2c7r+QBoPU8EoyxMIA+AI/TNs45B/AYgNMtDjsLwIsAvsYYe4sx9hpj\n7AeMsZjvDZbQ8NnPfhaHH354o5vR8piKOhLSbOkcFNTJQEKiFeAlI5EvqoFEK3okugEEAQwbtg8D\nmGNxzBFQGYl3APhHAFcAOAfAT31qY8vjjjvuQCAQ0P6Fw2EccsghuOiiizA4OFjTORljCASkmlQP\nvNAknUAyEs5B34WUgSRaAV4+23SOlvRI1IAAAAXA+ZzzCQBgjH0ZwG8YY1/gnPs7vWtRMMbwn//5\nn5g3bx4ymQyef/55LF++HM888wxeeeUVRCIRV+e77bbboCiKT62dHqCXle/ShixI5RjEDsl7JdEK\n8JSRKEkbreiRGAFQBDDbsH02gJ0WxwwBeJuCiBI2AGAADoFqvjTF0qVLMXPmTN22JUuW4JhjjnHZ\n7NbEhz70Ia1OxsUXX4wDDzwQ119/PVatWoVzzjnH1bmCwSCCwaAfzZw2oIdflshuHkhpQ6KV4GXW\nBp1jYmACZ599tu5vY2Njjs7REI6ac54HMABgEW1jjLHS789aHPYMgF7GWFzYdgxUluItu+stW7YM\nq1at0v1rxQVhvMJ73/tecM6xeXM59lq1ahU+9rGP4eCDD0YsFsP8+fNx7bXXVrAPRo/Etm3bEAgE\ncOONN+KXv/wl5s+fj1gshlNPPRUvvvjilH2mVoIWSLSo2bLeRcDyxbzvso5bUHtkICHRCvDUbFny\nSOTfkcf999+ve08uW7bM0TkaKW3cCGAFY2wA5fTPOIAVAMAYuw5AL+f8wtL+9wD4FoDljLGroaaB\nXg/gdilruMOWLVsAAF1dXdq2FStWIJFI4N///d/R0dGBJ554At/+9reRTCbx/e9/X9uPMQY15tPj\n7rvvxsTEBC699FIwxvD9738fn/zkJ/Hmm29KBsMA7cFV8p7VyjeDH9LGYHIQ8340D+suW4djumtj\n9L71xLewfvd6PHD+A561q15IaUOileCHtKFwpeZKmQ0LJDjnKxlj3QCugSpprAGwmHO+u7TLHABz\nhf0nGWMfBHAzgL8B2APgXgBXTlWbU/mU7wVrju0+FvFwvPqOLjA2NoY9e/ZoHolrrrkGbW1t+NjH\nPqbt09/fj2g0qv1+ySWXoKurC7fccguuvfZahMP2nWvHjh144403MGPGDADA0UcfjX/8x3/EI488\ngo985COefp5Wh/jwZwtZtIXbfLmOWI/fK+xJ7UFeyWPX5K6aA4mdkzsxPGn0WTcWUtqQaCX4UUcC\nUJm5lgokAIBzfguAWyz+dpHJttcBLPa7XVbYOLIRfbf2+XqNgUsGsLDHu3U/OOdYtGiRbtvhhx+O\ne+65B729vdo2MYiYmJhANpvFe97zHtx6663YuHEjTjjhBNvrfOpTn9KCCKAsn7z55psefZL9B7pA\nouhjIOEDI+GFNuv1EsheQDISEq0EP6QNQE0BTUQTrs/RSlkbDcex3cdi4JIB36/hJRhjuOWWW3DU\nUUdhbGwMv/rVr/DUU09VZGu8+uqr+OY3v4k//elPGB8f1x3vxHAzd+5c3e+dnZ0AgNHRUQ8+xf4F\nIyPh93W8NFt6US2zoBSarraF9EhItBK0Z9uD58jISNQCGUi4QDwc95QtmCqccsopWtbGxz/+cbzn\nPe/B+eefj9deew3xeBxjY2N43/veh87OTlx77bU44ogjEIvFMDAwgK9//euO0j2tfBBqnTEJEaRJ\nAvXlbleDH2bL/ZaRkNKGRAvBD48EUHsKqAwkphkCgQCuu+46fOADH8BPfvITfPWrX8WTTz6J0dFR\n3H///TjjjDO0fcWsDgnvYJQ2/EKzShv5Yr7pXthS2pBoJfglbdQ6sZElCqchzjzzTJx66qn40Y9+\nhFwuh2AwCM65jnnI5XK45RZT+4pEnZgqacMPs6UX5ywohaarbSGlDYlWgl9my1rLZMtAYj+HlbTw\nH//xH9i5cydWrFiBd7/73ejq6sJnPvMZLFu2DMuWLcPpp59umuYpUT+mOyMhpQ2JZgDnHD9+/sd1\n10XxCyvWrMCOsR2mf/Nj0S5AMhISFrAKBv7pn/4JRx55JH74wx+iq6sLDz74IHp7e3HllVfixhtv\nxOLFi3H99RWLsJqe06q2hNX26Q4vqEQnkB4J56CArtmYEgn/sGtyF770yJfwxJYnGt0UU1z24GX4\n7Ybfmv7Nj0W7AOmRkDDBhRdeiAsvvND0b4wxbNq0Sfv9tNNOwzPPPFOxX7GoH1iXL1+u+/2www6r\n2MfqWAkV0z1rI6/kmy5rQzIS0w9elpn2A/li3nJ88KNENtCijARj7HLG2BbGWJox9jxj7BSbfc9k\njCmGf0XG2KypbLOERL2Q0kbzMRLSIzH90MyBBK0QnCvmTP/ul7TRch4Jxth5AG4AcBWABQBeBvBI\nqdqlFTiAo6BWvZwDoIdzvsvvtkpIeIlWNlvur4GEzNqYfmjmQIJe7paBhI+VLWtBIxmJpQB+wTm/\nk3O+EcClAFIALq5y3G7O+S7653srJSQ8xpTVkfCBkdhfszaktDH90MyBRLUVgn2rbFmjR6IhgQRj\nLAygD8DjtI2r6QWPATjd7lAAaxhjg4yxRxlj7/a3pRIS3mPKpI0mNVs2Yx0JKW1MPzRzIEEv96lg\nJGhi0x5ubzlGohtAEIBx5Z5hqJKFGYYAfB7AJwH8E4AdAJ5kjJ3sVyMlJPzAlEkbHhgj/ThnM5bI\nltLG9EMzBxLUpmoeCS+e7YJSAANDe6S9Zo9Ey2RtlBbsel3Y9Dxj7EioEol5aoKERBPCyEhccN8F\nOKX3FHzptC/5cp1mYySkR0KiGdDMgQSxBFORtZEv5hEOhhELxVpurY0RAEWoy4eLmA1gp4vzvADg\njGo7LV26FDNnztRtW7JkCY45prZlkCUk6gE9/AwMmUIGz7/1PGLBmOfXaeasDQ4OhSsIsOYoZSM9\nEtMPfjB2XkGTNpSpkTZCgRDya/Lo/+9+rJ21VvubkwUbgQYFEpzzPGNsAMAiAKsAgKmVixYBuMnF\nqU6GKnnYYtmyZdqiVSJWr17t4lISEt6ABomOSAeyhSxG06PIFL03XTZriWyabRWVIgLB5ggkpEdi\n+qGZGQnNbGnBSHjpfyooBYQDYcw6bRbOOPcM/PSjP9X+tnr1avT19VU9RyOljRsBrCgFFC9AlSji\nAFYAAGPsOgC9nPMLS79fAWALgPUAYgA+B+ADAD5Yb0M2bNhQ7ykkasR0vPf08LdHVHPTvsy+mt3S\ndtBmLbz5GAn6PxwMe9KueiGljemHZg4kpjL9k6SNtnBby0kb4JyvLNWMuAaqpLEGwGLO+e7SLnMA\nzBUOiUCtO9ELNU10LYBFnPOnam1Dd3c34vE4Pv3pT9d6CgkPEI/H0d1tVz5k/4IWSITbsWtyFzi4\nL2mgzVxHot5zeA0pbUw/NGM/JFTN2vCYkQgFQmgLtbWm2ZJzfgsA0yUmOecXGX7/AYAfeHn9Qw89\nFBs2bMDIyIiXp5Vwie7ubhx66KGNbsaUgR7+eDiOnZOqJajWB9jJdTytI+FR1ka95/AaFMg1WzaJ\nhH9o5kCiWh0JT82WSh7hQGuaLZsGhx566LR6iUk0HmRuioViGJ5QM6B9YSSa1GxJs61mGsCltDH9\n0MyBxFRLG6FACG3hNqTyqZrO0RxOJwkJC/xpy58wnh337fzP7ngWI6mpZaTI3BQNRbFzosRI+OGR\n8DDXnOCltNFMs38pbZSxdngttoxuaXQzfEczBxJO60h4ZrasM/1TBhISTQvOORbftRgr16/07Rrn\nrDwHt62+zbfzm4E0yWiwHEi0CiPhVYls8f9mgGQkyrjswcvwvae/1+hm+I5m7IcEYu0sszY8Tv8M\nB8KqR6KVSmRLSDhBkReRV/K+zNYJqXzK1/ObgQKJWCimUZh+eCSasUS2whVwcM/bVS9k+mcZ49lx\nTOYnG90M39HUgUQ1acMHs6VkJCT2S0yFlt6IKoukSUZDUW2bn4yEp3Uk6jRbive6mcyW2UIWkWCk\nKV8qU43J3KTlC2x/QjMHElUX7fLw2dbSP+vI2pCBhETTggYzcbVMr5FX8r6e3wykSUaD5UDCD1ak\nGUtkiysNNtMAni1m0RHpaKo2NQqpfEoGEg1GtfTPZsvakIGERNOCHqL9jZEQqUSCr3UkmshsKR7X\nTAN4tpBFe7i9qdrUKKTyKV9XpW0WNHMgMdVmS8raaEmPBGPscsbYFsZYmjH2PGPsFIfHncEYyzPG\nZI3r/RjEFIizWC+hcAUKV3w7vxVEsyUhr+Q9z2Lw1WzphbTRJFkbnHNki1m0R2QgwTmfNoyEHwXb\nvEK1Rbs8N1u2atYGY+w8qJUqrwKwAMDLAB4pVbu0O24mgDsAPOZ7IyUaCr+lDQogplraoDoSokcC\n8J6VaEazZTMyEtTPJCNRCmh5cVoEElpf9rCEvFdwXNnSg7ZrdSRa1COxFMAvOOd3cs43ArgUaunr\ni6sc93MAdwN43uf2STQYfksbtbwUr3j4Cjzw+gN1XzccKHskuuNq7Oz0Ib72qWux/KXlVffzw2xZ\nt0dCaT6PBNH40iMBrSCR1Ux4f4JX0obCFXzi3k9g7fBa2/3O+5/zMDA44KptU1GQisajWChWs9Tb\nkECCMRYG0AfgcdrGOedQWYbTbY67CMDhAL7jdxslGg+NMfBJetCkExeMxH0b78Oft/65rusaPRI9\nHT0AnDMSD256EI9vebzqfn4wEvUGJ82YtUH3XUobasYGYP0C25/gVSCRKWTw+42/xzPbn7Hcp6gU\nsXL9Srw4+KKjc2or5PJixbOmcEX72UtpIx6OA6jN+N0oRqIbQBDAsGH7MNTFuirAGDsKwPcAXMC5\ncCcl9ltMmbThIlDJF/N1SxCaR6IkbfQk1EDC6QOcL+YdDfTNmLXRjNIGzb7bw+3g4LqBerqBGAkZ\nSDgHjR+jmVHLfeh+Or2v4phkPMbrZ4jGIwokaimT3RJZG4yxAFQ54yrO+Wba3MAmSUwBmlHayBVz\n3gUSJWnDLSORK+Ycueq9WGDLCC8DiWYxudG9bI+0A2iedjUCmrQhszYcg8ap0bR3gYTYJuN3IfZP\nr9baCAfCdQUSjVq0awRAEery4SJmA9hpsn8CwDsBnMwY+2lpWwAAY4zlAPwD5/xJq4stXboUM2fO\n1G1bsmQJlixZUlvrJaYEfmdt1CJt5JV83VUoRZc0APQmegE490jkFWeMhK/SRo3BSTPWkSBGoiPc\nAaBc52M6QjIS7kHjhx0j4XasEfczfhfis+eVtBEKhPCXh/4C3ANc/OzFSEQTAICxsTFH52hIIME5\nzzPGBgAsArAKUCOC0u83mRwyDuDvDNsuB/ABAJ8EsNXuesuWLcPChQvrbLXEVENjJHxyVdcykPgi\nbbhkJJxKG81otmxGaUP0SADN065GQJot3cMPaUNsU0Ug4TEjQWbLT5z7CVy952pc/3+uxykHq5UY\nVq9ejb6+vqrnaOQy4jcCWFEKKF6AmsURB7ACABhj1wHo5ZxfWDJivioezBjbBSDDOd8wpa2WmDJo\nHgm/GIlaPBIeMBJGaUNjJJx6JJS8o4HeF0aiztz7ZjRbEnVM1K4MJCQj4QYaI+GhtCGOScZnnZ6b\ncCDsaYnsVpQ2wDlfWaoZcQ1USWMNgMWc892lXeYAmNuo9kn4gzf2voF5nfMQClTven7XeXBLN1IB\nK7/Mll4zEq1gthzLjCGVT2n3oBEQzZbUrukKWqxLBhLO4YSRcDtpsZM2qL3RUNQXs2UtC7Y11GzJ\nOb+Fcz6Pc97GOT+dc/6i8LeLOOd/b3PsdzjnUq9oIeSLeZzwsxOw6rVVjvZvNrMlDQL1rotB5qbD\nZh6GRCSBeZ3z1PO68Eg4NVuGA+GmMlsa60hc/eTVWPLbxnqVKIDriJQ9EtMVotlSJYL3X3jNSOzL\n7LPcxw9pg+o+1Ataa6MlGQmJ6YdMIYNMIWNLAYpoNmmDBgwvGIlYKIZ3HfIu7P3aXm3A9twjoRQ9\nm7Vo5/Ry9U+liNHMKPak93jStlphzNqQgYSK/d106jkj4Ze0YczaKD170aA3zzZVttzv0z8l9g+4\nfphqyKpwA9dOamIkPPJIAEAoEEI4GEaQBZ0HEk6zNnjR86WxvZY2csWcLyufuoGUNsoQXyL7u7zh\nlRmZxo+x7JjluTzN2ihdw0tpIxwMIxxQxyEZSEg0Ndw+TE0nbXjISBg9IrFQzFVBKqdmy2gw2rRZ\nG0VeRF6pPwumXkhGogzxJbK/15LwmpEA1GDCDLVIG8QQWKV/esZIlKQNxhjaI+0ykJBobrh9mJpO\n2vDKI1GqIyGiLdzm6IXKOXfFSHgubdSZtWGsI5Er5upmeOoF3XeZtTG9GAmvPRKAtbxRi7RB/bEi\na8NjRoKkDUB9BmQgIdHUcBsY0H77JSPBTBgJBy9Umo04zdrwatYinlP8v9bj6WcvKoXWi2whi2gw\ninAgrLVruoLW2gBkIOEU4nhmlbnhNgOtoBQ0869l1oZHz7bohYmH47o+4BQykJCYMlRbGtcI39fa\naAKPBKEt5IyRoHviKGujZLYs8qJnDnyvS2Tni3mk8+mGZghki1lEQ1HtO2mW+haNQKogSBv7eVEq\nr0tkAx4yEkpe8+xYmS29ztoAJCMh0QJoeWlDKQdC9SzsVI9Hwk0wRmZLAJ4tROUma+PhTQ/jzpfv\n1G0zYyQ4uONg7qltT+HnL/7cRYurI1PIIBosBxLTmZFI5VPaC2y6MhIKV/CVR7+CoeSQo/PopA0L\nRqKWQMLSI+GD2bKlpQ3G2OWMsS2MsTRj7HnG2Ck2+57BGPsLY2yEMZZijG1gjH1pKtsrUR/cMgy0\nX7NIG+IDXQ8dT5XkRDj1SNA9UbhS1adAZkvAu1m2m3t217q78LMXf2Z6fDgQRkEpaJ/Hqe/kN+t/\ngx//9cdumlwVE7kJdEQ6EAwEdW2cjkjlU+iMdQKYvoHESGoENzx3A57e/rSj8+ikDQtGwu26QQWl\noJl/fTdbCuNRPBzXsVJO0bBAgjF2HoAbAFwFYAGAlwE8Uqp2aYZJADcDeC+AYwH8J4BrGWP/Zwqa\nK+EBxBm9EzSrtAHUF0hYMhIOJBO7/HIRClfAwbXqmV69HN2YLXPFXAU9TveaJBf6jt2sfOr1C240\nPYquti7JSEANJLraugBM36wNt2XCqU+3h9u9YySKeY0lszNb1jtBIPN2KzMSSwH8gnN+J+d8I4BL\nAaQAXGy2M+d8Def8Xs75Bs75ds75PQAegRpYSLQA9hdpA6gvc6Mej4RdfrkIbbAJehtIuGEksoVs\nxcuooBQQYAGNkXAbSGSLWc+1+9HMKLpiMpAAJCMBuF+4jMaPWe2zrBkJl/4werlHghFLs6UXNWJI\n8iSPRHu4hdI/GWNhAH0AHqdtpYW5HgNwusNzLCjt+6QPTZTwAbWmWzaLtNFsjIRtIMHLsxbAuxVA\n3dwzs4wM+uyhQEgzWwLODay+MBIZyUgQJnOT6IqpjMR0DSQoa8HNS5+B4cD4gVUZCTdZG+Fg2DSQ\n8FLaoPa0atZGN4AggGHD9mGoi3VZgjG2gzGWgbpi6E8558v9aaKE19AYCaVFpQ2Rkagjc6OgFLQZ\nAMGtRwKwnzH5xUiQgdMJpWombdBnDwVCNTESvkkbkpEAYJA2pmnWhmtpo+Qx6Ix1eipt0ArBFVkb\nHq61QcfXK2204lob7wHQAeA0AN9njL3BOb+3wW2ScIBapY39jZEQNUmC26wNwBkjQVkbXkobTgew\nbLFS2qABMhgI6gIJp1KR2TnrhZQ2ykjlU+iMTg9pwyoDSVy4zAkofbIr1oW96b2W+wDuKluGA1PA\nSJTGk3rTPxsVSIwAKEJdPlzEbAA77Q7knG8r/bieMTYHwNUAbAOJpUuXYubMmbptS5YswZIljV11\nsFlRVIpQuOL5gj1unctu93fdnmI5A0LhCgLMnqAT2QCvpY1aPBJ2A52RkfAyayMajCKZS1bd14qR\n0KSNUolsoAkYiQZKG7liDqFAqGr/mwqIHglptnTHSHTFurB5dLPpPq6L8ZWq30aCEd8qW+aKOe07\nDgVC6O/vx6qbV2HH+A6c/ejZAICxMfOS30Y0JJDgnOcZYwMAFgFYBQCMMVb6/SYXpwoCiFbbadmy\nZVi4UK447hRXP3k1Xtr5Eh44/wFPz1uz2dJnaQMouaRD9l1JHAS8Nlv67ZHwMmsjGoraLplMyBay\nth4JHSPhUCrKFrJQuGJ6D2uBwhXsy+xrKCPxrtvehc8t/By+cMoXpvS6ZhCljf2dkRADCc451FdQ\nDWZLYiTauiyfi5qkDRZCNBStykiIbXeDd9/+biw6fBEA1SOxZMkSbJu7DT989odY9dVVAIDVq1ej\nr6+v6rkaKW3cCGBFKaB4AWoWRxzACgBgjF0HoJdzfmHp9y8A2A5gY+n4MwH8O4AfTW2z939sH9+O\n7WPbPT9vs0ob9HO0SkzqJSNRUUfC46wNsYwu4K3ZckZwhmOzZV7J69ge+uxBppc23DAS9L8XgUQy\nmwQHbygjsX1sO3aM7ZjSa5ohX8wjr+QxM6qyt9MhkCDpQOEKgkytI1IrI2H3DLvN2rAzW2rPdmmS\nILbdDXaM78Ca4TUA6s/aaFggwTlfWaoZcQ1USWMNgMWc892lXeYAmCscEgBwHYB5AAoANgP4D875\nrVPW6GkCs5mkF3Bbb36qpA2nbdIxEnWYLcVFcghOPRLioOLIbOk1I1FaCIyDV5WDxJd+LBQDUPaH\nVGRtOGR4xHNS5b96QOY4kZHwcrVUJ8gUMk0hI9ALpCPSYUqp728gv0+umENBKWgFySbz7rI2csUc\nwoEwoqGo5T2rpRgfeSTszJb0OajtbpAtZLFtn+oUEM2W6ULakdQroqFmS875LQBusfjbRYbffwLg\nJ1PRrumObDHry4qMtTIStFZELfSdHYyMRDX46pFwmrXRJGZLQB3QAkHrwYYGwGwhqxv0yGyZV/Ku\nPRLiOb0A5f03ipHgnCOdTzfFS5sCiXg4bjoT3t9AfXk8O65jJGsyWwbDphkWhHqyNuykDfoc1dhU\nM2SLWY15FtM/ATWwp8qaTtB4d49E0yFTyPjCSLg1HIkPkB+Du9EjUXX/Yh4BFgADq9kjQRUna/ZI\n1FiQymuzJf1sBzPZQvRIiNvd1JEQ/68XIiNB9PBUBhJ5JQ8O3vAVUIHpG0jQz4SapI0SI2EpbdSS\ntWFVR8IDtpFztc/RcycyEgBcyxsykJCoQLaQrctMaAW3DxM9oOKxnrbHrbRRohtjoVjNA7+41oSI\ntlCbo8XAnJbI9ttsKV7DCmYrlYp1JMTgoRaPhBcQGQnGGAIsMKWBBD1nzSRtxMNx29n1/gLPAgkl\nj0gwoqVFmz3Dot/LyUq3ttIGr3+SYBzvxPRPQAYSEh4gW/THI1GLtEEd24/B3bW0QaaqcFvN0g8F\nAmaMBFD9hdrIEtmccxR50XTwNQPR9SJtr9WRYEHdYOW4jgSd06OXHDESZDCkbJKpAn3fzfDSloyE\nCtfSRrEsbQDmspt4L51MWoi5M83a8ICRMLbRKG3IQGI/QEEp4Oa/3uyL6evFwRfx2JuP2e6TLWTV\nHH+PTY7VDEeb927G7zb8Trc/dWw3bXlr/C3c+0r1GmVupQ0yVXnBSJh5JADgK49+BV9+5Mv43tPf\nM525OPVIGJ3dXvQlM23WCpxzS0aCpA0xeGgkIzEzOlMzq011IEEBaTN5JNoj7U0XSChcwc1/vdnT\nCY6XjARJG4B5X642Adi6bytWrl9Z3r+Yr561UcckwRgk0XhEvggZSOwHWD20Gl/84xfx8vDLnp/7\nyj9dia899jXbfehB8JqVqJYCtWLNCnz+gc+X91fyWsd2I230r+vHRfdfVHW/mqSNUppXrdKPVSBx\nwqwTsGDOAjy59Unct+E+fPOJb5oWt3FcItvFS98pjDMhu+CkyIvgUAMhnUeCC4GEwOo00iNBdROA\nxjESzeCRmMhNAChJGzYZCI3AxpGN+OIfv4iHNj3k2Tmt/D5uszZoXKCgxIzJ0DESJpOW21ffjkv+\ncInunLRoV0VBKg9kS2N/M0obdA+cQgYSTQh6SXntU+CcY2BwAEPJIdv96EHwOnOj2ksuhDEEAAAg\nAElEQVQgXUhjNDOqzcRrlTYm85NIF9JVjynwgqushnyxrIXW7ZEw1JE46sCjsPrzq/Hq5a/i4Qse\nBgDT74kGoSALOpI26PN5YbZ0MxMSBz/xZ62OREAvbTQya4MWqQIawEg0kUeCAolEJNF0jATdp4HB\nAc/OWVXacLH6ZzhgL23ki+rCXoD5+DeYHEQyl9TGPvISmWZtKPVnZBnbKM2W+yHoBe71i/yt8bew\nO7Ubw5PDtrNJ6mRez5KqZW2k8+rLn6LhfDFfk7RBDwENjFbIF/NoC7U5Pj9RmHV5JBRzj4SI3kQv\nAHVwsWqDmXYqwg+zpZtAQlfvoljpkTBKG07upyiX7G+MRDPM/pO5JBhYU3okqH8MDHkXSFgZh2uS\nNoJlacOKkSB21TSQmBiEwhWtP4jShpnZMsiCdaUrG88pPRL7IfyiO1cPrQag6o27JndZ7qcxEh4z\nItWyNujzkpM+V8yhPaw+fG4eFnoIkln79SAKSsEV40EPtx8eCREzojPQFmrD0IQ5I1EtZx3wx2xJ\ng61WR8KG5bAqnKXVkWBB7eXQHm53dD/Fz+BpINFARqKZzJbJbBIdkQ4wxpoua4Pu08DQgKOsByfw\nPP0zaO2RyBVz6Ih0qPubyKjEPtIaNvScWKV/BgN1BhKlZ5JSnls6a4MxdjljbAtjLM0Ye54xdorN\nvp9gjD3KGNvFGBtjjD3LGPuHqWzvVMEvaUOM5s1eUgS/Ahkn0gZQdtLrzJYuPBJaIFFlYam8ktdM\njm7SP9tCtTMSTgIJxhh6Ej3m0oaQFjbVZkvjOW2lDeElZFVHgr6nGdEZjvq6eE7PsjbSo9oiVUDj\nzJbN4JFI5pJIRBMA0HyMRKl/jKRGsGPcm3LinmVtGD0SZtKGktcmRWb3lcbjZFaVNzTm0UTaKCiF\nuhkJ6m9zZ6rFo+lc0WAUDKx1AgnG2HkAbgBwFYAFAF4G8EipbLYZ3gfgUQAfBrAQwJ8A/IExdtIU\nNHdK4deLfGBoACfPORmAOW1OoAfBL4+E1UubPi8tfJNXmkzaENI/va4jYURvoheDEybSRtF6VUAR\nvpotHazfYSVt6OpIlF4OM2MzHd1P8ZxeveRowS7CdJY2JnITSETUQCIaak5GAvDOJ1FQCogFvS1I\nBbiXNvLFvMYQT+QmtGfXcvVPXtSCcWPbnYLaeHjn4dq1AHUS0x5xv95GIxmJpQB+wTm/k3O+EcCl\nAFIALjbbmXO+lHP+Q875AOd8M+f8mwA2AThr6po8NfDDI0FGyw/P/zACLGBpuOSca53M86yNUgBh\nVbRFYyTSlYyEW7Ml4EzaIEbCaYlsSv/0OmvDiJ4Oc0ZCrOvvqI6EDx4JJ3UkqkobgbK0MSM6w1Ff\ntzJw1gMzj4RXVUCdoJnMlslsEzMSpf7RFevyzCdhxUhM5tTxw2kfyxVzjupIaNKGYdIyPDms/ZzM\nJXVjhN/ShhZICBObeDiu3QOnaEggwRgLA+gD8Dht46rw9RiA0x2egwFIANjrRxu9whUPX4Grn7za\n1TF+MBLDk8MYnhzGKb2nYFb7LEtGQmQLvJZWqqVAaR6JqZQ2Qi6ljSqr/FU9h0VBKiN6E73WZkuL\n/HIRxrU2tuzbgqNvPlp3zq8/9nVc+cSVjtvuStoomMsQ4qJd1P4Z0RkNYSQ45y3DSJz7m3Nxx5o7\nfG1LMpfUXnaNCiR2T+7G0TcfjbfG39JtzxQyYGA47ZDTsGbnGlfnnMxN4vifHo9Xd7+qbaNS9VbS\nxszoTG/rSBStpQ3xmUxmk9oYoS0E5qPZcv4B83XnAkqBRIukf3YDCAIYNmwfhrrqpxP8B4B2ACur\n7dhIvDD4An638XfVdxTgh0diT2oPAGBOxxz0JnotPRLiQ+CXRwIwf3HT5x1Nj6KoFKFwRXv4apE2\nqjESbrNCiMJ0ui6GGVwxEhZmy0gw4tps+cquV7Bp7yY8ve1pbZ9ndjyDF4dedNx2N6V5xe/ayiNB\ncOqR8DqQ2JPeg4JSwJyO8pDTrB6JZ3c86/oF6hY6aSPYmDoSW/dtxaa9m/DG3jd029P5NGKhGOZ0\nzMHetLu5486JndgwsgHrhtdp26zYNYUrSBfS6Grrci1tVKsjYSVtiMzjRG5Cl9llVrPGC0aC+ttF\nCy7CY595TJeO3hHpcM1INHT1z1rBGDsfwJUAzuacj1Tbf+nSpZg5c6Zu25IlS7BkyRKfWlhGKp/C\n+l3rkSlktI5WDX4wEmL5256OHktGQhw8vPZI0EswV8yZPqQiI0EPUy3ShlOPRE3SRp2MhFUdCSN6\nE73Yl9mHdD6ttVFrgwOzpTH9c3dqNwA1c+e8vzsPgBqwuVkquFazpZm0IV7XKSPhtdmSnoGeRI+2\nLciCTZm1kcqnXOvWbpHMJbXU40YxEvQZjZ+Vxs94OO76PtD+xHQC1oEEfR+dsU7Nq1UNtNZGNWlD\nmxQZJlGDyUEEWRBFXtRJG+FgGPFwHNliVgsegEpGohYjNbWxK9aF9897PwCgv78f/f39eHv72/hd\n5HfY8rMtGBsbc3S+RgUSIwCKAGYbts8GsNPuQMbYpwDcCuAczvmfnFxs2bJlWLhwYS3trBupfApF\nXsTa4bU49eBTHR3jh0fCGEisGTaf3Vi57b0APUxWgYTokaC/N5W0UZxCj0Tp5TY0MYQjuo7Qt8GB\ntGGs+bB7Ug0kRH15NDOqC1Kctr2eOhJkthSXhJ8RceaR8JqRoJkgvTyBxhWkIt+QVWCXyqeQKvgc\nSGST6DigsdKGVSCRLqgBdV2BRLoykDAGxbRvV6wLrxdfd3R+LSW7SolsK2ljaGJIY1qM0gaxGOlC\nWpOdCkqhfo9E6Zkk6RMoT67/4df/gJmxmfjNub/B6tWr0dfXV/V8DZE2OOd5AAMAFtG2kudhEYBn\nrY5jjC0BcDuAT3HO/+h3O70AdUw3TmO/GQkr/R0wMBI+eCS0ktdVPBL0d9rfrzoSFEi4YiTqyNpw\nUpAKUKUNoDK7RtRjnUgbxHyMpFTibvXQai0PfzQ96mpQpnNqdSQcZG2EAiHTRbvEz9+orA2Sjhop\nbYif20pKKCgF5Io53xmJidyEZrZsVB2JRjMStG9nrNO1R4KYNitpgwIBM49ET6IHiWgCE7kJ3WTD\nrK5DUfEga6OQRSQY0QX0BGqHGzQya+NGAJ9jjH2GMXYsgJ8DiANYAQCMsesYY5q7qCRn3AHg3wH8\njTE2u/RvxtQ33TlIa3LjNPbDI6FjJBI9GJ4wr27pp0cir+QtHyZA8EhkTBgJFx4JuueO0j/DztM/\n/Vy0ywiaJRszN5wyEiRtEAVK0sZoZhRb9m1Rl4ovpF1pobWYLY2yhViQiuBY2vA4a2MwOYgD2w7U\nzcoa5ZEArJ83enbd6tZukcwlNY9E0zES+TTaQrUxEmQcNGMkjIEE3ePOWKdldpkR9EzS+SylDYtJ\n1NDEEHoTveiIdCCZS2qTDZI2AEMg4YHZ0k5q74h0VJ2EGdGwQIJzvhLAVwBcA+AlACcCWMw5313a\nZQ6AucIhn4Nq0PwpgEHh34+mqs21IJVPIciCrgKJTDGj+98L0MNE0kaRF7VZqggxmvajjoSVTgjo\nK1tOlbQRCUYQYAHn0kbQm4JU1epIdMY6EQ1GKwyXjj0SpSCRKNCJ3AQOm3kYAJUdo9mZm0G5Fmlj\nRnSG5eqfAMDA0B5ud2W29OolN5Qc0skaQIMZCQsGwOrl6jWosiXQuECCxilj0CQyEm4zCmphJCiT\nx8k9oGcSsGZy7LI2hpJD6OnoQSKSqJA2tEW0hPvhSfpnMas9x0YkIomqY6cRDa1syTm/hXM+j3Pe\nxjk/nXP+ovC3izjnfy/8/gHOedDkn2ndianAq7tfxY4x6ypr+WIeeSWPvt4+vLLrFd2g8fqe17Fl\ndIvpcX4xEkEWRCQYsV3LQYymffFI2NSbTxdUZ7ZotnRbIpvuOVA9kCC9PhwIu64jkSlkbEv1ZgtZ\nPLn1SdNrAtUZCcaYqQSl1fWv4qoXGQma/R/TfQwOThyMgaEBbXbmStqooUR2IpKwXLQLUF9YbeE2\nFHmx6ndA5+yIdHjykhucGNQZLYHGMhJW36fXgURBKeCJLU/otnHO9dJGg1b/tJQ2ihm0hds0j5X4\nHT325mO2zIFZIGFVXE2UNgCHgYTASERD0aolss2kjd5Eryop5B1IGyVGgp7rWqUNYhaNoIDGDeRa\nG3Xgc3/4HK758zWWf6dB4tTeU1FQCrrA4YsPf9FyOW+/PBLxcByMMczuUD2uZuttUDRtXFTJC+SL\n1tJGQSmgoBTQ09Fjzkg4lDbEgdlJ+mcoEEI4GHaV/kmfwU46WfXaKvz9HX+P8ey4bjsNztWyNgBg\ndsfsiu+I2uCUkRBn/12xLpw05ySs371ex0g4XbuglqyNRDRhWUcCgMbwANUDZ+2ckYQn+n2zMBJ0\nL6aKkXjkjUew6M5FGJ4oZ9+n8ilw8KaWNoiRoN8BYMvoFnzw1x/EU9ueqnpOJ9KGxkiUipQ5CaYq\nGAnDMQpXUORFtIXbwMB07CfnHLtTuzGrfVaZkagmbfjNSLSYR6LlsTe9F3sz1jnN9OXTrEecIe9N\n79V0ayP8ytqgTkmDhdmMXUx/8itrA6gMDOhavYlec4+EQ2lDpCareiRKs/tQIOS4IFUkGNFlVFhh\nb3ovODjGMvr0KbrnFIzYwYxidFqQigYXccDpinXh4MTBGEoOaaltHNzxS9ltiWwzP4lR2qBl2YHq\ngbPGckQT3jASyUHN1EpoRCBBs99qHgmvAgmqwyCmN1I/E82WzRRIZAoZzSMh/p0+i11tCd+lDaNH\nwvA8iVKF8blN5VNQuIIZ0Rllj4SYtVEaL8X7YXyGfPFItJK00eqYyE3Yvqzoy5/dPlvbXzxWjJBF\n+MlIAOWXmNmMnaLpzlinPx4JC2mDPmtPoge5Yk57AcdCMTAwxw8LaYmzO2Z7L22UBgx6+ViVGQfK\n37Wxf0zkJtAWaqsqbQDqoG78jrQS2dUKUonSRklG6Grr0uQSse85fUG5YSRyxZwWJJh5JIiWJWkD\nqB44eyltcM41k5uIRqR/zoyqNW6mStow65vUz0SPRDNlbZDsaQwkrJ4zs3O6YSTcSBv0TALm0obo\n7TEGEloAF0kgEXGYteFFZcsq0kamkHF1XhlI1IFkNmlLn4svNdpfOzaX1EXIIvzySFCnJI3d7OGj\nwWNm1FlKnhvklTw6wubSBn1WeklT/flIMKIyBg6lDTF481zaKFGYdh4TAg0QxmBGXM+gGjoiHRXf\nkeOsDaUIBgbGmI6R6OnowfDksM5o6zQbwM1aG5ReZqR6xUW7AOgqAlbrb3SejkhH3S+5vem9yBVz\npozEVK61ITISVp+Jvh+3JkMrmPVN6meitKFwxZNVY91ACyQKJoxEuJKR0D6Lg3F4LDumfR7qu5Sx\no2Vt5CfBwDAjqiYDVutn2kqdQWtpg9jOSDBSMdbQfe+IdGgTB6fSBtUc8UPaENvmBDKQqBGccyRz\nSdtZL335s9pnAdA/uMlsckoZicncpNYpgdJs16TtIiPhp9nSKCVojERpYCdvAD18Th8W8Z47qWxJ\n0oYbRiIRTaA93G4rbdDAZhzgxPUMqsFS2qA6ElXMlsRE0Oy/q60LPYkeKFzB63vKxXacznTdlsiO\nhqIVMzRjHYlIMOLYI0Eshxe0O313zWC21AIJB4yEUz+LHcz6ZoW0UZqtTrW8YZW1kc6nEQuWGQnj\nwnxOxmEAmmdJrB4pfuc04XL6+bWVOgVGwhh80DnMJgDU/kQ0oUkKxkW7AiygCyKJkaBJQq2BhJ20\nIbbNCWQgUSMyhQwUrtjebOrAB7YdiCALavuKQYhZJ/DFI1FIaS9xwNqZS4O+0xUZ3UD0SFQwEqVr\n0WyfAolwUJUe3HokZrdXlzbI+Of0/KKpqidhvjonwYqRENczqAaz78gNI0EBhMhI0P19daS8gFFD\npI1SkBMOumAkilmNHq43o4DYJKO0EQxMfYlspx4Ju33cwKxvmkkbwNSvSmrrkQi3aWNYLdIGUPZJ\naB6ikkRgDCS0z1+ln2l+BhuPhChtGMcao7RhTP9kjFXUzhAnCbUyaJlCxlbaENvmBDKQqBF0k510\n4PZIu84JS0EIANN67plCBuFA2DePBGBOmwNlyque6o1m4JyjoBQceSQAaI7yWqWNOR1z1PLkNtQs\nZUA4Pb9oqupN9GJwwlrasBrgkrk6pQ0lj0igUms1gl7YgBBItHVpjM/6Xeu1F5jrQMJBHQl66ZtJ\nG1ZmSyceiWgw6klGAQWBYlVLoMEeiSpZG8afa4VZ3zSTNoCpZyTceiScSBupQkrr68QCi7N+U0Yi\n6IyREGULQH02jOMmjS1mHgmjtCEu2kVjTXu4vcJsKU4SavZIVJE2WoaRYIxdzhjbwhhLM8aeZ4yd\nYrPvHMbY3Yyx1xhjRcbYjV604dt/+jbu23Cf7T7XPnUt7n3lXt02N5RaPBzX0dTiMWbyRjqvrj7n\nl0cCsJc2oqEoYsHa15Mwg7EuhPHFTdeiGeLOSXXJlVqlDfKl0IP6hQe/oEsR45yjyIsq4+Hw/DpG\nosMhI2GUNrIupA2T70ir6+/AbKlJG2S2jHVhdsdsMDDsTu3GwYmDAbiQNoSU0iALVs3aiAajFe3U\n6kiYmC2rtUOTNkLeSBsHtB1QQe+GmP3AfPH9F+PZHZZV/F1D55GoIm0Yf7bDlU9cid+++lvTv5n1\nTdpGgb7dAlReYF9mH87qP6ti/HObteF0HKa+bmQkrAIJp4GUyB4A5vU3NGnDJGtDlDYSkQTySl6T\ndSjYrmAkSiWyaR83VX8J2aK92RJoEY8EY+w8ADcAuArAAgAvA3iEMdZtcUgUwC4A/wnAs/V071p7\nFx7a9JDtPvesuwcPbHpAt41ustUCVEBZx2sLtenKjooPsJnhMlPIoCvW5SsjYVW9jNKCvGYkRMe9\n+Lt4XUA1eR6cOBhrh9cCgJZV4VTaoHtOvpSJ3AQ457j9pdvx+JuPa/uJA4ljacPISNiZLS0GOLEM\ncTWYuafdVLaskDbauhAKhLR7c8iMQwA4N/GJ96yaBKCZLQWPBAVvujoSgTBmt89GNBjFpj2bbK9P\n54wE6s8oMEv9BOxneEWliOVrluO/X/nvuq4tQvRIWD1v4vfj9Lu6e93duP+1+03/ZtY3k9kk2sPt\nmoFP08ldpgE6xSu7XsEDrz+AV3a9otvutI6EG2ljMjep9fVqjMRYZgwzojMcSztG9qCqtGEwW4rp\n4HTP6Z1AwYmdtNEZ63S9rDpQPf1TbJsTNJKRWArgF5zzOznnGwFcCiAFwLRSJed8G+d8Kef8LgDj\nZvvUgtHMqGX2hG4fQ+Rs5ng2IpVPqS/CkkHPrNMbz1tUisgreZWRKKQ9MVdRW+IhAyNhlv5ZkjZi\noZinHgl6eGKhGAIsYOmRiIVi6Ovtw9Z9WwHUJm2EA2EtDzyZSyJdSCNXzOm+Z20AcCNtGBkJG7Ol\nXfqn40DChGJ0s9aGqKMC5dx4ko/cMhLG2hSOPRKlGZpx8AbKg+tJc06qWkaeDJyeSBsmqZ/UNqvP\nNZZVU5LdlLuvhkwhg45Ih+ViT0BtjMRoZtSyf1pJG6LkRgWZrAzh9YLOaxx7q3kkwgGVzaqQNqow\nEnM65oCBVWUkRjOj6Grrcmy2rGAkqmRtmDESFMDR/afAgIITM0aCJgm9iV7bccgK+4W0wRgLA+gD\noE0RufrGfAzA6VPVDoUrGMuMVX1YRtOVwYaOFrS44cYiUKbShuG8NCtxUxDFCVJ5vdnS0iNRkjba\nQv4wEmaGI6D8udvCbejrKS9b60Z6AMr3XHwYzAYtHSMRDKPAq58/V8zpGInx7Lhl6qQX0oZZBU0x\na8NuUSFxsAmyIBiYdk/oBUr/u83a0KQNu6wNpZS1IUgbRkYDKGvLfT19jgIJr6QNWnHRCLtAgvrR\nmp1rPEuLpJm2XclzCo7p52qgcc2KMbOSNsQAl8afapOsWkHnNZM2woGw7nNyzjWPBGMM7ZH2ykCi\nium9I9KBzlhnVUZiNDOKrliXc2nDwEiYeSQqsjYUvUeCnnO6/9RGS2lDmCT0dPTYMqNWsEv/jAaj\nCAVCLcFIdENdgGvYsH0Y6mJdU4KxzBg4uO3Dks6nkS1mKzq8rpiLxQ03FoEy6/RGsyXNzGlG4BUr\nUJH+aSFt6BgJDz0SdkVZgLJHIhaKaYFEkKm50m6zNuLhuO4lbDZoiW7rcKB6HQkyi4pZG4B1dUtb\nacOh2dLMPS0yEoD1QGdkJDpjnRptTZT+AW0HoC3U5oqRYGAIsEBVRkKUNoyMhK6ORGkA7uvpw8aR\njbY1LahvepG1QQslGWEbSAhlxV/b81pd1wfU+0Glk80ocUIqn8JB7QdpP1cDjWtWHh4raaMZGInJ\n3CQOaj9IJ+FQH6c04Xg4XrHCbzXTezwcR1dbl3Y9MSgWMx9G02og4dQjYuqRqJa1YZA26L7T/9RG\nmgi0R9r16Z/CJKEaM2oFu4JUjDHLiaYVqpfX2w/wpS99CZ2dnbptS5Yswbs+9C4A9g+L9hIyMhIm\nqVNG6BiJaALb9m3THRsOhCuubWQkvGIFTD0SFumffngkxMg9EoyYlsimh7qvVw0k6GXpxpmsMRLC\nS5heWmaMBL3Uqp1fzDsHoKtuOf+A+RX7e5L+aSZtCB4JQB2kzLROY9YGvRiAMhPR1dblallmKoRD\n56xmtqSsDepHVtIGAPT19kHhCtbsXIMzDj3D9pz1Shucc22hJCOcMBKAuoLq8QcdX3MbAH3wbLXY\nE6D26e54NwaTg46+K3HMMtPCTdM/DfVNYqGYtoCeH7BjJA6ecTAGk4NQuIIAC2j3hT6H2Gedmi3j\n4bglIxFkwQppo1ZGwmwZcbusjWS2zASJHolQIATGmPZ5xTV7CkpBuxe9iV5b07cVzPpFf38/+vv7\n1b+/mcFt99+GB8MPOjpfowKJEQBFALMN22cD2On1xb753W9i8XsXV2wfGFSpVLuHRYucjR4JYXC3\nitwmc5OanEDlT8X9D5lxSMW1aXChQMIrVsBV+mfIe4+E0XBk5pGgjj2nYw56E71a+5xWngTKzIvZ\nS1jHSJQGAK2yZRXGQ/RUALCtbqlwpWLGRHBb2dJ4DpJXdDMmk4mFTtoIBLX+BJSDoK6Yu0BCDE6q\nmS0pwKGZNlUABMqDN1AOJI4/6HhEghGsHlpdNZCotyDVvsw+ZIvZmhmJWe2zMDA0gH856V9qbgMg\nyHmhNltpYzI/ie646kF3FEgI/XwoOYTDuw7X/d3SI2EIcLtiXVPKSChcQbqQ1j5rOp9Ge6RdG4co\nu0cXSDipbJlXx4SuWFd1j0SJkaDKkdX6mZiRAVSRNkxM0hN5c2mDzkefd+dE+bWokzYSPdiT3mPL\nMJjBTNpYsmQJlixZAgB4xy3vwAeP+CA+M+sz6OvrMzuFDg2RNjjneQADABbRNqaGX4sAeJdbVYJY\nDlgEdarx7Ljl7Ir2yRazupf6RG5C+7JrkTbaQm3ojndbMhLVnNxuQA+o4/TPoOqRoBU5AXWVvXqM\nn3YPE1BO7yL09fTpGAm7F/1b429p5yMvSFuoDQEW0K1pojNbupQ2jIVnZkRnoC3UZkor0kqK4UBY\nN8BlC1m1TLiLypaAibQhMBJ/3vZn08HeKG3YMRJuSmSLLIfZC3csM4Y9qT3lOhKlwS2v5E0ZCXqG\nIsEITpx9Ih7Z/Aie3fGs6fNIQW6960BYFaOitk3mJvH0tqcr+uhoehQBFsD7573fE8OlaDCuJm0c\n0HYAgHLFR4UrutWEde0U+rmxfxaVouZDqPBIGAJcUQoQ8ebom9rPVm2oBjOml8Y6Y9Bkx0jQOOxW\n2jALJApKAclcUntWqqVYA5XjglHa2Lpvq9Z+LWtDyWP72Hbki3ndxILGhbfG39KtDhwPWZstKRgW\nAw0nqBZ4iFmGTtDIrI0bAXyOMfYZxtixAH4OIA5gBQAwxq5jjN0hHsAYO4kxdjKADgAHlX4/rtqF\nrFbZFAdgs8JQxn3ETp/MJbViNpbSRsFgthRouEQ0Yfqg+uGRoI5slDbMUldFRoKOHUwO4qibj8L/\nvvm/NbfBSO8ZAwMynRE+MO8D2kNit6gW5xwn/fwkLH9pOYDyPWdMrZe/L7PPlEZ1K20YGQnGmGUK\nKH3PczrmVNDHAOqXNoJhrU7Gub85F//28L9VHGscbOZ3leWXY7uPRSQYwWEzD9MZ16qBSvMC1hX1\nvvLoV3D+fefr2ANAHbjoc8TD8QqzJQC8Z+578OCmB3HGr87AH17/Q8W5vTJbWpXHBtSX2J70Hrxv\nxftwxxrd8IPRzCg6Y51YMGeBlp5cD0SDsV3J81Q+hfZwu+4FunL9Shz30+NMX6BiPzf2T9rf2DdH\nUiPojOrlX3EGT3hux3OYf9N8bNu3DS+8/QKOvOnImoIJs2eSPlt3mz6QoAmc6JGgtTiS2SR6Ej1I\nF9Kmz3BBKWgVdQ+IHYA9qT3adkAfSNA7gNg7JxKacVwQmaVcMYd33PIO/HL1L7XzRYIRjGXGcPxP\nj8c96+7RmVzDwTBmtc/C5tHNWjClfV4Ls6WTdX/MYGe2BEoMer4F6khwzlcC+AqAawC8BOBEAIs5\n5/TWnwNgruGwl6AyGQsBnA9gNYCqIk41RsL4s+U+QqdPZpM4oO0ARIIR2/RP0SMhUoqJSML0QfXD\nI6FV2AwLJbItUnxEjwT9/sLbL6DIi9i8d3PNbTAajswYCTGQ+OK7vogXPvcCANhKD/sy+7A3vReb\nRzdrn5Xu+ZyOOdg5sVNLpxJZJdfShhAIEXoS5kYn+p5FeUbc7lTaMHNPEyOxsGch3lr6Fv51wb/i\nr2//teJYcbBZ/vHluOnDN2l/O6b7GOz96l4c1nmYblCuBp20wcyljTdG38DmvZu1glRiQLpmp1r+\n5YTZJ1R4JADgB//wA2z6t03oTfTir29VfiavPBKkKZtJG1845QvY/MXNODhxsK33Gl0AACAASURB\nVJaCTBhNq4HE3BlzMZ4dd2VGM4M40zajxAnUp8UXyrM7nkW2mDV9gYxmRsHAEA1GK/Rzs76ZKWTw\n6u5XceLsE3X7drVVShubRzeDg2Prvq3YvFf9ecu+GgIJE5aQPpvRWGpkJMRKj8lcUvsezb4Pet7j\n4Th6Ej3azN0slZnaRJM4R4GETYnsnRM7kcqn8Jftf9H2CQfCWLNzDSbzk9g8ulnnkQCAjZdvxKZ/\n24TVl6zWttmlf1YzfVvBro4EYF0ewAoNrWzJOb+Fcz6Pc97GOT+dc/6i8LeLOOd/b9g/wDkPGv4d\nUe06uyerMxJWWqAdI0HVyJxKG5P5SW19jo5Ih6kGqXkk2rzzSBAdavRIAJUPH0kbWtnifBqrh9RO\nXUuaEcFotjTzSFDwAqgPOLXBjjGgNtH/4j0nR7PZd2i1aI+T9hOsjE7UH3oSPaZpwk6lDaN7uqgU\nVcmk1IaDZxyMMw87E2/sfUNbdp0gltGlFFoR5N2p1SNhdc8Gk4MYmhjSZW0AahA3MDSAQ2ceiu54\nd4W0Qeecf8B8y1RQ6puRYMQ29bUaBpOD6Ix16vobgTGGI7qOwNyZcytKoFNqIM0CazG5iRBn2tWk\nDSMjQffHNJAoBTw9icrUQLO+uW54HQpKQTM5E8wmOuLzZnz23MCMkaBx6qC4GkhQpoKZR2IyNwnO\nOSZyE9r3YfbiE6sL08q3RaVoutYGtYkmcdUWxgNMGIkSW6ZwRbsv9DlIkqTfB5ODuvRPQB335x8w\nHzNjM7Vt7ZF2nfwoPtsHth2IcCDsqi/Ss2Mnbdi918wwLdbasGMkqAPYMRLaPkKnJ1bBLnIz1pGg\n4+ykDT8ZCaO0AVT6O0jaIBoxU8hog1YtaUYEY/qnWdaGVYRs52GgNtH/4j0n6cHsO6TzaZUtnXok\nhBefVQ439Yeejp66pA3al85nrOsPQBv8KdgjiGV07VBr1kYwYF4ieyg5hFQ+hd2p3RXSxsDQgC61\n1/hZtM9UCiSMnhyjXFIrKzE0YZ76KcKsBDo5+mudBRqhYyRM0gYJRkaiqBQ1dsfsBUISjFlqoNg3\nqerrwNAAQoFQJSNhMtGh6w1NDJWfvRoCKjIUmjESTj0S6UIaCldsGQlx7OtN9ELhCnZN7tJexuIK\nml4wEmLfFO9LkAURDAR1/X1oYshROriZtEHPNmPMNGC0AwVHdtKG2/TPaRFI2Hkk5nXO03622uew\nzsPUnw2MBJU1tYrcJnOTmpwgSgmki5k9qFog0eZzIGEhbVQwEoW0lt1SDyNRkbWh2JstRdhJD8ZZ\n0WRuUqvgqTESGeF7Ln2H4kzCSZ0KM0bCKoeb+kNvole3cJhYV98pRFOsWTBzzIHHoD3cXjGDF6UN\nO3hptqQgGVBfLrSMOKB+v6uHVmNhz0LteMAikOjtw970Xmwb26bbLtaRAGpfB8Iq9VOEmf+FHP21\n6tJGiGZL26yNUiZSe1itJ7BxZKP2TFsxEl1tXaafQeybHByT+UkMDA7gHQe9oyKQ74x1VjISE94x\nEvM655l7JOIOPBL5VDkoKgV2ZuMwzf5J2gDUF7hZX65gJJyYLY2LdoXKgbN4X2jcEJ/doeRQhbRh\nhng4rgVNgD6gB9zXkqDPVJWRaBVpY6pgx0gcOvNQBFjAlpGY0zEHbaG2Co8ELf3qyCMhMBJEZ3W1\ndWEsO6ab2Wlmy5h3ZkuzQMJK2jB6JDbv3YzhyeGaC58QxJeglbRhxUjYSQ/aDClZZiSItifpYTQ9\niiO6VAXMmEfuWNoweYn3JnqxL7OvQn6ie0ozJRrMjCssOoE4MzALZoKBIE6ec3JlICHoqHYwOsLt\nUC2QEGdgHFy3sueGkQ3Yl9mnMRLGglQiaB8KYAmiR4J+rwVDE0OmRksRZv2dpI1EJIF4OF63tGE0\nWzr1SNB3PadjjukzSe00+wzGvjmRm8DA0IAW4Ikw80iYMhI16POZQgZHdB2h8y25ZSTEoAioLm2I\nAaBpIJEeRZAFdUupuy2RTW3MFrMYmhjCQfGDEGRlJoL+JzbTKG2YgcZtug+i6Rmw9mpZwXg/zWCV\n1WeFaRFIWDISmVEc0HaArlCJ2T5dsa4KGYJq09vdcKNHAlCjZgpCKFigGv6A+iWLndlTs6VQIttW\n2hAYiWd2PAMA+NjRH/OMkbCSNsw0a8Be2qA2jWXHkMqn9B6JRA8m85PYPra9HEhkTKQNB3UqTBkJ\nC4pbzNoQfxcX6HEKUas0C2aAkhQwWBsjUWvWhlmJbON9EGWI53Y8p7a1JMWYZW0QehI96OnoqQiO\nxKwN+r0WDCYH0dthz0j0JHowkhrRXYNm+oyxugNrQF+QysojUVSKyBaz+kBicADzD5iPYw48xtJs\naclIZPUv35HUCF7Z9YquLD2hK6au9yMyJaKcQed2ex9orDU+k1ZmS6NHgsyWonEUsJc22iPtmNU+\nCwwMQ0lrRoK+X8Bl1oZB2sgUMloND6qRQucE1PF0eHIYRV50JG2In8XISPR22C8gaIRTaUMyEgbs\nTe011XOJqhRNRTsnduKr//tVfYGStq4KGYLkCTsKyJi1AQjSRjSh1YoQz0tpkIwxz8pUu5Y2BI/E\nXWvvQne8G6cefCp2T+6u+sL9y/a/4Gd/+1nF9mpZG8b0TxF20sbQxJD2cJI+L5otAeDt5NuY0zFH\nrdSXrlHasGAkgEpql2SvGdEZ2u+Aeq+jwajpLNwKogfHLJgBgIU9C7Fp7yad4dI4a7ECvZx+++pv\ncd+G+2z3tWIkHnz9Qdyz7h7tPmg0b7Asbdy97m4cMuMQbeVRM7OliL7eSsMl9U2nKzOagXO1dHQ1\nRkJbzl7Iz6dJBf19MDmIl3e+jHN/cy7O/c25mm/BCb771Hdxw3M3ALCXNugl2h5p1zESfT19lgs2\n0bjWk+jB3vRe3WQkmUsiwALa9/DcjueQV/IVRktAKJNdGhupImgkGFFNtckh7Wcz3L32bnxy5Sfx\n2d9/Vief0fmMLCGNU12xLgRYoIKRoBdfhbTRYS1tiGMfrXxrx0iIhduMvhWFK/jGY9/AjrEd2jaz\nEtlASdqYUCW0vt4+7e/EgC4+slwgsRpDSfK4FkiYMBKuAgmH0oZVSq0ZpkUgoXBFSw8UQRGoSOH9\n9tXf4gfP/gDrhteV9zFhJCjzws6UYidtJCIJzXvxxt43tGPEmXksFPOEkRB1QgIxAxVZGyVGoifR\ng3858V+woGcBvvGeb2ia6vCkcXkUPW5+4WZ8/fGvVzjqq2Vt2HkkQsw+a+OEWScAAF7d/SpGM6Pa\nwCLq4MaA0aqynRWsPBJApdmM6EoK1nRpvy78EYC+IqolI1F6CYgvMnGgtAMNytc+fS2++/R3bfet\nKJFdYiS+/8z3cfWTV2MoOYR4OI7DO9VKipFgBIfOPBTnn3A+FvQswFff/VXtXHYeCUBlWVYPrdYZ\nLr2QNnaM70C6kNZeYlYwfre0EBa9XIlOvuPlO/Do5kfx6OZHK+pOWCFTyODqP1+NglLA/z3l/yIU\nCFnq8eKLMB6OI5lLYs3ONejr6bM0+9KYdWTXkQCA1/e8rv3N2Df/vO3PCLIgTpp9UsV56KVK9RWS\nuSRS+RROmHUCtu7bimQuiRNmnYCh5JBpsbrr/nId/vb233DHy3fgqW1PldtnwUhM5ifBwLTlwin4\nECdXdC8m85Na4NAd70aQBatKG0B5tUzRjEx9md4HBOM49fqe1/Ffz/wX7l1/r7ZNTCMHysFOtpjV\n1nO5ZOEl+NJpXwKgMhHXvP8aXf9zykjQ/SgoBR0jMf+A+RhJjWg1MqrBCSNBy64PjjsLUKZFIAFU\n6q2A+oB0xjrRFevCvqz6sNAsiP43Yy0AQdqwSJMRKUmgUtroiHTgiK4jMCM6QzfzEr0CbaE2zzwS\nlFcuwswoSh6JUCCEOz9xJx6+4GF8+fQvW740jRgYHMB4dryi5kSumAMD0/TCioJUNh4JO+lhaGJI\no2Uf2vQQgPKLVZx1UsBIg6KusqUDacNYChdQzWjRYNRU2iC2in4HKtczcALxO7JiJI7tPhZtoTZd\nPzLSn1aIh+MYy47hlV2vYN3wOlsDo1mJbIUreGnnS9i0dxM2jmxEb6JXC+CIPbj7n+7Gwxc8jH97\nV7lwll3WBqAGEiOpEewYL8/+vMjaoHHAjMoXYWSbxrPj4OBlRqJEJw8MDWDxkYvxofkfclztcu3w\nWhSUAm4961bc/JGbAVhPGsTU7Xg4jrXDazGZn0Rfb58azJg8j/sy+9DV1oWT55wMBqYb+4x988mt\nT+L4g443lRWNC3fRvejr6dOCnr6ePt1LXWz3hpEN+Nb7voWuWJfu3tgxElRMTsxUMGZ0xcNxZAoZ\nbf0JkpjNJnTG1HcKAC2ljZh1IEH3Ufws+WJety6GsZBfT0cPTp97Or56hhpEn3rwqfjGe7+hG5uc\neiREaUOcJJC/xWn/c+KRoHNuHNno6JwNDSQYY5czxrYwxtKMsecZY6dU2f/9jLEBxliGMfY6Y+xC\nJ9fpSVTqrQpX1AcupmckaD9KpzNjLajUsV36pzESjofjCLCATtoIsAAW9izUtY1m5v39/Z4xEuID\nKsJMlrEqnVrNqd7f3499mX0a82Omb4eDYTDGTNfasGUkLBgDolqPO+g4xEIxPLDpAbSH23HMgccA\nUB9QGjCtGIlwIIyNf9ro3GwpvMStqlvS9ysGjwAcObSNEL8jMZihxXUA9f4YDZdupI1MIYOCUkBe\nyeOVXa9Y7ms2+L6+53VtAH/ojYfQ09GjDZJWQUJ/f7+t2RIoB4PiS9CLrI2BoQHMbp9dNWvjwPiB\nCAVCWpB4x10q2yAyEoPJQbw09BL6evrQ19OHl3a+5Ki2xcBgZbqlVc0CIyNBL8+FPQvRm+hFMpfU\nvUDFcS0RTWDOljm6fmHsm28n3zaVNYDKpcQpaBH3p5+NAc3a4bVQuIJ39r6zYoyjcZSYK9EjIY6X\nf3vkbwAqxwbaZ9fkLgDl59xK2giyYHmNnFIAaBYUk4xNMMpN2iRT6JO5Yk43uXhs1WMA1Inm7tRu\ny35GJkygurRREUgYnu35B8xHIpIwnSybwUraEMeUg9oPwtwZc7FhZIOjczYskGCMnQfgBgBXAVgA\n4GUAjzDGui32nwfgAQCPAzgJwI8B3MYY+2C1ax3XfVzFiy2ZTULhStn/kBlFOp/G+l3rEQ1GMTA0\noLmLjS8hrR6ADSNhrCZJxYVGUiMoKAWt8xiNckTjUSDhlUdClDUIZkZRq9KpB7WrHd/KWNXf368F\nX9FgtKJT54v5suEoUGm2tPVIWHgYxrJjyBQy6E30oqejB2+Nv4WT55ysT40qvdSMwaBISa5/Yr3r\nRbvE81cwEuSfiVYyEq6lDbP0z6A+kAAq+5EbRgIo5bmzoO2spsJsqRS1a0aCEbw1/pb2XdA2M/T3\n99uaLQE1cJ3ToX8JeiFtDAwNoK+3ryKoNiLAAmpWROkFed9K1T9CL9eejh6MZ8eRzCWxsGch+nr6\nMJGb0MkIdm0wpls6kTZoLDmy60itTgSgf4mL4xoABNYH9IFEKZgVjddW7IyRkaB+LmZ40M/GZ2Bg\naACRYAR/N+vvKvrmaGYUsVAMM2MzddlwxkBi3eOqvGxkK2mf4YlhtIfbEWAB2wmdOIkiFkeUBxwz\nEkMDiAajOj8Slawn/PH3fwQAbB/brl3PDMFAUCtzX6/Z0mxCagcraaNiTOnta/5AAsBSAL/gnN/J\nOd8I4FIAKfz/9s48vKrqWuC/lQkCCaMCCaKAVXFAFBS1ah1aWyvPWrXY6murdrKDc622tlprbR2e\n2qdWpdaKdQC1Di28OmCdqFBRuYABZagCAkmYCSSQEMh+f6x9bk4O9yYx5p57Y9fv+/jI2Wefc9fe\nZw9rr7323vCtNPF/AHzgnLvSObfIOXc38KR/T6vsv9v+JKoSLUYL4TXDgSPlO6vfYafbyfgDxzOv\nel5yR8yos2UwAmjNRyLdksugwgUjgjFlY1heszw5vxX2keiso7zDSyLDRGUPTNWpOvSgYW3NqWd2\n5Wx6FvZMaeYNOgFI7Q3d6qqNNFMP4cOXAs0/2igmD6iKKIPhTllEPvKhXeH3pzrPoKSopMXBYeHw\nj0LK5Z8pHBTHlI9h8frFyca0vRaJoHM6cMCBHLD7Aa2OalJZJGZXzWZ43+HJfG8xtdHKHGxbPhLA\nLjtcftxVG845ZlfObnNaIyD8bYO8DzrX8EhzdNnoZvNyO0aF4Y25AtqySAQ7W0KzFSCVlTC6F0Lv\n7r2ZVz0vaXELTpvMk7wWbVAqiguKKcovSr6zckslpUWl7NNvn6RMgfUvWgdmV81m5ICRFOUXMaZ8\nDCs2r0i2p2GnxrDvWbid6lnYkx1OZY62DUGcNXVrkmlorR0Ot33lpeVU11bTsLOhTWfLcDvV5JqY\nUzWH8QeOB2BO9Rygecv6gDzRLjU4f6Q1y1dwr602IZA/nUUCdq0rrdEeZ8vgnTmtSIhIITAGtS4A\n4NRb5x/AUWkeO9LfD/NCK/GT1L1Xt8u8fXgXs6Awz66aTd78PM4ddS4NOxuSSx+jzpZbGrZABclR\nZ2AaDhNWJAJNr7SoNKlIBFpo0oRbNZvJkye30L5bO8o7qj22RmJaIrVFImJNefjRh4H0BSw8J5vq\n92dXzeaQQYdwePnhSUe5IF7YBJjuGPH3XkldaKNTG8E7w2cmBJp/1EzbMFcrTVQZDDtbigjb5rRu\n+Ql34uG0p9oBMTjR77HHHmt56uv29FMb6b5naVFpclOrxp2NUJF+7wWHSzZwO5t2UvWvtpfl9Sjs\nARXNnWFrjdGOph1seFPPLQkrEuGOtKykLDlSfuP5N9K+K7xqI13aR5eNZnal7nCZ9Md4cU67Vm2k\neufKzStZu3XtLh1nut8PL/HcvnM7VIQsEr68De0zlP49+tO3uC/D+w5P5l+6d9bvqNfllr6cBvHS\nTWOG25GFr+h89ehBo1vIELYGbNy2UeX0Ck+fbn20bq3VuhU9bVLmC6MG7epoCWpFDerM5MmTk6td\ngpNvy0rLktMk4TowefLkFgpbcl+QwO8s5NSYNz8vrUUiWGm3rXEbWxPNS5STFom61cm0hNuycN5H\nrbFlJWXsdDuprq2mLqH+E4Ezd9TZMmwl+t0ff8eW7Vs456Bz1B/JK4xRi0TQwQfnj7S2g2rTOzqw\nbUuRmPrU1GRaILW1cUz5GJZtWsZ9D97X6rvA+0hUtO4jAfrdahvat7tltiwSuwH5QHQJwGr0sK5U\nDEoTv5eItKpaLZq1CIBXlr1C1ZYqqrZUJefyg5FqTX0NM1bMoGRxCUfucSSC8Pcleh5Y0AnV76hn\n2aZluuNeRfPUBujKi+DdVVuqkqatFopEt1KWb9Ld+oLngvmt6cunM/HhiWyq35ScDywuKGZj/cYW\n7w3+TXx4YsrwVP8qXqpIO7Wxbuu6ZLyHHn0ISD+SLC8tZ1nNspS/X7+jnrcr39b54vIx1DTU8Oaq\nN5PxNtZvbGGR2Nq4tcXz2xq38c5LqU9ULMwrTK7LDv92oC2XlZYl9wWIdhKr31jd/J2L+7J+23qq\ntlQlNynLl3zyyGP73O2t5mEwmopOK5SXlrNqy6oWcTfWb6S0qJTJkyer8ujDN2zbkNaMmVaR8PHf\n36gbg1GR2iIR+Im8tuy15DLYVTNXpXxnmECRCOb5K9ZUsKJmRco8qN1ey9pZmg/5efnUNda18BEI\n8iMYac14dkba3w07W6ZL+5iyMazdupa51XOT9emtF95KlqPq2uq03ytV/Xh56cv63vL2KRLlpeV8\nWPNhsnxSQfIMhFQWsNFlo5m1alar9XP68unsaNqRVLyC3w4O7YrGX7VlVfI7BfUjkL+0qJSehT1Z\ntG5Ry3atoqVFAtSpMlkGi5o735JFJSnbhoC+xX1ZuXklEx+eyNJNSykvLU/6BgV5UF5a3qL9e+Ch\nB3h37bvJNA7vO5ze3Xozffl0jVNblZRv65ytVNbqUtL129a3UCQadzaqzPUbqHm7eWlzEGfl5pXN\naQm1ZeG8X7t1bYv0BTK/v/F9Nr+t/iYFeQXUbq9lc8PmXSwSddvrqNpSlfSRGTt4LKMGjWLmypnJ\nze5SWSSWbFhCvuQn98RIxfo311NcUNzm6qqnnngKQZJLbhubGnd5JiiH9/35vjb7g9W12o60ZjGE\nXetJa7S9Pqxr0x2gfms9g2sHc8F9F3ABFyRviggrF62krqoOV+mYVDmJQTsHsXj+YobXD+eR5x8B\noGpxFVvWbYFKGHaVOghRDysXrWTDpg1QCfv/PPVp5isXraSmpoZEIkHh6sLkxjzVS6pJrFGfghGN\nI/jN47+BBcBrcNLeJ1FTU0NBZQFTlk5hyitTdn3xAii/onWHsSRrYHD1YBKJlucxUAmvvvcq5TP9\ne94D9oLVS1aT2JbY5TXFa4uZMn8K5a+X7/r7C4AhMGDgAApXFyJVwpG/PrJFvGF9h5FIJKhdXsv7\nC97fRf7GrY27ygjULKuhanFVc/zQO/t078PCioUUrCmgZF0JWz/cSmJl8zvyGvLotaEXC95ZwPaV\n26leXJ18triwmDlz5rB963aatjW1mZ8F+QXMnzc/+T0BmlY1semDTbs8e0jTIdTU1NBjXQ9uf/p2\nbn/6dgBGHDwidRpD7wyzYZWWr/2uVhMy9fDBux+kjL/f9v24dtK1XDvpWgAGbhuY8p1h1mxYA/XQ\nZ2MfmlwT21dsZ88r90wbv199PxKJBA0rGnh18asA9D2oL70aekElNKxooLakVv+ua0ib1vcXvI9U\nCWv+vSZt2rvXdYdKGH1d85x8Q10DS+YvQaqE8+89P33C0tSP3Xruxuolq1kja1rIk+r3C6oLWDBv\nAeXzymGFKpFz5+gSW+ccJetLGFzbXK8G1w7myVlPUj4rRf0Ivze/gKbKJhJrEsnf3rh0I40rG1M+\nk5+Xz+L5i2nc2khBdQH51fkkNulvDtg8gOsmX8d1k69rfqDBtzvdaqjbUsewbcO4+IGLuZiLAdjr\noL1IJBL0WNeDnk09Wy0jPdf15L537tP6/TKM23cciUSC3Wp2o1djLxKJBH039mVCxQQmTJ2gD70L\n7Am9NvRKvntE4whufOJGbnziRgBOGHYCiUSCvIY8nvzHkzz5jycBOG7ocSQSCVyVY836Ncn86NvQ\nN/muqi1VUAlzKucwdo+xGl4JLy18ifIZu+b9oWWHJp/dtHUTVMKblW9SWl9KIpGgbnkd8yvUyXjL\n8i0k0LjbVmyjYm4F5XPLYTnsUbcHyxcuZ9i2YUx+czJPv6R+M0G7BlC7uZaC6gJmVs6krLQsWV5S\nkdeQR+8NvdusozU1NZRuKOXKh67kSnT1x4ayDSSKm59rck2UrC9h9gez29UvSIOw4J0FScUn+J2o\nLH3r+rKRjeD70rTvS7X+N9P4qY2twJnOuSmh8AeB3s6501M88xow2zl3eSjsPOB3zrm+0fj+/jnA\no50rvWEYhmH8R/HfzrlJ6W5mxSLhnGsUkdnAZ4EpAKJutZ8F7kzz2L+AL0bCPu/D0/EC8N/AMuDj\ney0ahmEYxn8O3YGhaF+alqxYJABE5CzgQXS1xpvo6ouvACOcc2tF5Eag3Dl3ro8/FKgA7gEeQJWO\n/wVOcc5FnTANwzAMw4iBrPlIOOee8HtGXA8MBOYCX3DOBSdsDQKGhOIvE5FxwO+Ai4GVwLdNiTAM\nwzCM7JE1i4RhGIZhGF2f/5izNgzDMAzD6HxMkTAM4z8O79yNiJwvIu1cR20YRipyVpGQtjbDN2Kl\nq34PEcnZMv5Joqvls3POiciewJ+A00Wky+2p09Xy/JOA37rAiJBzBVFEt7xzEeeNrtqRdXWCfPcN\nb35X+g4iIs614zjGHKIr5S80d2bRfM71dIhIgXPuQ+Au4HLgU1kWqd2E6mRTKCzn2vJ05HrZSIWI\nfMmXmUZ/3SfbMn0UMl0+cqbwhRqknf76myJyk4iM9+Fd2itUlK7WEecF+S4i3wMeAfbOrlRtE1ZG\nRaRcRF4VkROzLVd7COX3F3K5sYoqEH6KYIKIXOgVuJytryKS75wLDm+5DN2y/3wRSX1qXI4QtCGh\nMjJMRO6CXRW5XCQqfxCWTZnag9964GngKhEZJSJvAd/LqlAfkVA9zUifnzOKRCihxSLyGPAb4HDg\nYRG5JavCdRARGSgi00RkN6fs9J1b/2zL1h6cc02+sToS+BEwDUh//GeOEFJG9wd+iO6i+n5XaLQA\nROQA4Dmgn7/OOblD9bWniEwAfgX0B24DHhGR9IcMZJlQ+ejv03E1Wr4PzapgbRC0ISKyh4hcgC6D\n/24w2Mplq0QwKPHyD/YK52FAX38/58p4aECyDN2m4OfAW8CLwB3Zk+yj4xWgvwIHZeL9OVPwvLY6\nAbgOWIMm+BR0Z8ofi0h0V8uuQAFwMHAzgIjsLiJ/AWaIyFQROS2ImKMVaQjwPjqP/KBzbiLQ+jGZ\nWSDagIpIdxG5Gz0h4DDgm8655bk8Sg7waalB91U5EnLHGhc0rKHru9EGNQ84wjk3HvgMcDpwpoik\nPyM8i3gr1TTgPADn3N3ACuAyEUm53X6uICIXAh+ipx4PAprQ9rHEK/45145AC8Xz22he/xD4G/BX\nEemdK2UcWigQgcLZHfgcusvjC865q51z6Y+ezTKB/JGysB34Ejqo6vT+JlvHiEcbpMAUugn4CVDi\nnKtxzjU4554CpgLXiW5g1WVwzq0CLkHNpocDN6DKxe1AHTpyu0BECrNZkaLfI8A5twL4LbA/2njl\nTKcW4E2lLcy6zrl64HlgCdDgnFuX46O1s0RkDCQb3G1AUeh+TnQOoYY1mHJZAnwL7dCqfZxZ6BTY\nD9BykzUkjWOcc64ScMBxvl6CynsmcGKu5HcKBXk34HzgMufcec65s4ErgF7gT3PKUUTkeBG5E1Xs\nPw+MBs5CNyOcmCvTeL4vCsr5eBG5B5X5ZPSIhnEicqy/n5Ntirf6FIem94AH1gAAFm5JREFUwPLQ\nYyJmAV/2cTq1HY81I7zVIS/0ocqhRaJ+CywGCkQkL1ShL0XNjl/JlUqejhTyPYd2as8Ag9HdOO9z\nzn0NmAycDRwTr5TNRL7H50TkDBEZGYpyJ7AOOCIrAqYhPGoQkd1E5AZRv5pRPsprwLNox1DsR2tZ\nrfiRMh2EHQX8DHhCRPb1SuUGdOfWU7IhZzpCI/lvAzjn/heYA5SgfgYBP0GVi/EiUuqfja3e+ny+\nBLjVX3cTkTMj0y3XAPsCXxCRHs65V4G/Az8F9ohL1tbwZTasjPUHytFOIWAy2saMF5F9/NRp1tpI\n38ZHB4pFwBeAr6PT1TNRBX8GcA46Uv503LJ62ST8d8iv6iX0OIZqtHzXo3L/FT2aIfg+We+PgnYt\nkMW3Ka+LyBWQHJwUoQP1nhkRwjmX8X9AYeT6YHSeaS56GMj40L1vAA3A6ECh8v//FlgP7BuHzB1M\nZ0Ga8KPR6Zp/APlAng/fCz28+2dBOmOSczB6ympwPQJ4HVgFzAM2AN8L3b8E2ALsl+08TpGWE1Hr\nzmz0AOMt6EgnDzgQHTXf5ePmZVHO/NDfA8Oy+OtngLeBH/mwa1AHr15ZkLWwlXsvoAftHeavj0fN\n62cAEqqvl/pydHKW8nsC8AYwFj3DZxtwRiTOHcAMdGt+0E663stelAWZ8yLXe/u8vdxfj0YHWueE\n2wvgv4DNwN3ZyOuQHOEyXgL0Abr760OAl4CFoThF/v9ngWf833G2g0WoH1J+JPx631YPTPHM4ej0\nwLmhsNjraFBeIu1IUPcGo07EW4EfA7v78AeBlzORz3Ek9BLgjlDYRWineidqZrnWV/JjaO5gZ6Cj\ng8LQc0XAB8DR2fho0XS1cq8E+KVP54l45cKnd3VQ6EJpfRh4PsaCdy1QC1zlw/ZDlbr70SklfOGr\nBj4Teq4CNVnHVtHTpCGoLKNRS881wPmhMnI/2iEHncMlqM/Bfv46P0ZZow1Uf2AS6rvxInB16F53\n4BfAWuA7wE3AdEKdc0zlI1lfgW6ouX/3UJyxwEIvaw8fNtXn+ZDI+94ETspS+RiFKj0P+OsXgSeA\nYaG4g32bcg8wwIfdhc4nD81G+Y6kpQdwC2oRDNqLV1FFbq9QvKPQNvWDIL+zWU/RKdylqFXwtUBW\n1CdlFfB1fx20jff6utwtRhl/ilrT3vDl+WLgU/7eu8BvIvHz/f+FwI2owjkOtU68BOwZcx6HFcmD\nfRn+DdqPBvXyJ2hfOtVfn+7bnk6XNY4EByOD4/31r4CzQve/hWrdfwUG+7BPo1aJ08OZRoydQFsf\nz19/E+gZuv462mm96hvXBcBt/t5QVJG4OfKOJ1FlIuOjZaA3qhCsA54CRgID0BNYg4pyhZdzh/8m\nQQP7RaCRmEeY6KjsGeAHkfCx6CisAXX0C8L7+8pzN81H4E4DXolZ7p+gI95u/vpUYLnP99OB7wI7\nfZkpjpSpN1EL0Q5CHUZMcnfaSD6OMt1GWi5HLVUnA2OAKp/vBaE4E4F/A98IhX0xZjkDJeEI4NHI\nvf3QqYwJ/vowVNn8H9Ti1g3tQO4BHgPujVHuo9HBUjAAKfZt2VzgNLSDex6dEhgN7A486vO7H+ov\nVoDO3V8Xk8z7+fK9yNe97wD3oaP311HTfyXwLR9/F8sUalV+CrXeJvDW8xhkLw7VuUJ0kPErX0cf\n9WV9IXB/KM4YVMm8F3X6fw44pNNly2CiwyOD54A/+4SP9B/iCF/gFqBmmCa0QwvMXX/Bz09FP2Ic\nHy1FesImpNPQOey1wB4+bHdU+w5PCdyDjm7G+etL0c74NtQk/H1U8Tg7BvkDReFe1Nz/DHBjqIAO\nQJWfCnRu/jj/Tb5G88hhOnBnzPn+fbQD2Oz/HurDe6KjinqaRxKFoXxe6hupfLRzXkVMo0x0eq7J\n5+VXfdiFwCWhOEH+JoCDI89/wX+fHcCJMcmckZE8WVAmQmkp9+3I331ZuB/4JzAmVO7/hs4dPwj0\nj0m+dMrx13yZ+EoorAC4wIfv48MuQjvfVegKiIWoX8dT/l9G20hfRqb7duT3NE9zDUOXR47y12Xo\nqH8hcKQP+xI6UFmFduDz/XtGxpT316NTKb0j4d9Dp0hv9f8WpHh2DH7Q4svOXjGW6TzUUtwE9PFh\n+6DKQ9DGlADnoorFqaFnj0Z9aZb75z/d6fLFlAmXo6OswKTVB3gFPRK8vw/7J6q5jvTXA/EdcK78\nQ0e3r/uG54rIvWN8JcpDnbieRX06fkzzPOFAH96EapLvAudkUNZrgKP8dQGqyF3iK/8E3xgEGu65\nvnE6yF+P9AXydWCED+uZCVnbSMelwK/RabBJvtwU+3tBhzYh8sw4dPpmL39dSgxmU5pHlsf6b/x3\nL3N/tPPoDQxBFesqn7b16Ai/NPKunqjydFb43THleXtG8g+Q5ZF8G2kIlIlzfL38AeoQOg+d/z7d\nl6u7gBOIdCwZli2qHA/z4WXAH4AlkfhD0EHXc/4637cl42lp3X0euCfD+flDdIT7e9S3ap9QnNOA\nuf7viai/0gSgLBSnFO2o/+3TfkqM+V7mZfq+v076GKB90i2o8n4OqqBdT/NU9H6oUtqiHsRcpoej\nitkkf30catUcHsnf+4koQr4M/Qntc4bQxXwkwiODJ4D/Q0e+X0StDYFD5Z6+sjehTpVZ+VBtpKUv\n8I4vYD1S3D8FtVLc4Qvrg/i5KF9gh/q/z0LN8RmbU/OyLvT5uRztDILO9yp01DIEtaDc7+V7BO2k\nA4vQN4A/olr6UVnI76DsHIVOxXRDO+Q3gcdptvJ8x1emrwYNFmr9msKuzmudPlJDp08OiMg8EFUi\n/uzzOGyJeMjn/3B/fZcvU58NxQm+wfPArVnI85wcyX+M7/MHL/9gdJ+LKajFah4wNhQ3FmWN1pXj\nI315uCZcblHls4lIx+vrxCC0w/4QOCaDcpd4WS9Oc3+Er6s70LY+nLeHAif4v49H/Qr+ELqf8TYf\nHRzV4S0+7DpVfTxqKbkJdR7egDq3PoMOTB4novBnUNa9UEvU/pFycI4vB4ei00ULiCjwqEL3Hn5Q\nGArPmN9MHBkSHhnMQneQG+Yz4zz/903oKOhEoF8cH6qDafm5r0gnhMJ+5BuqEnTebSVwaOh+HjoX\ndxnaGRbFURjR3fr+4QvaJLzzEDoyrvQN0I/QkeepNHvf34P6F6z1BTU2B6g06Sjzef7lUNgkdCR/\nPNrpPe5lfxq1alURg5MfqjxuRhv+Y0Ph+6AN/2dQxeEFdL54KGrWDRqy7miH3OTjl4Te0cuXp7My\nnY5ImnJ2JN+BtASjzRNRReL20L19Q3/H5czamnL8BN7/CN2UbxN+1YCP8xBqHZwUeedVaOf3LzIw\n9x35rZO83KNCYcOBA9BVGYf6tuPfkeeK0TYyaIMKUF+sBTQ7h8bhI7YfquRcREvrWvBdStD2+2Z/\nfQw65XEzMQ6m0IHgEt8uLEPb537+3u7oAOOfaN8yx8vXP/T8eb6dGRSbzDFmTjAymI5qW7eiDf5G\nX6AOCcVtV6GKqwEI/V4f/xH/gO50NhcdBYz3H/WnqNPOEb7xLUSdSRego5DYLC2+MD6NOmCd4eX8\nBWqunuQrfX+0I3vKV/aLUD+JfxLqGLP5Dx1tvUWzH8TdPo/noD4Iv0YVik2oYnREjLIdj45Y1qBK\n8oU0+5PMQTvhUWhHcT2qRG70Mn8KHXHcgypsB4be2wMd6S8GyrOU7zk3kv+Y6bkB7Yg/HwmP3eeK\n1pXjY9GO+Vm0077Fl/+HSDHIQn0jjotJ7mJ0ZP4galkNrJjvoh30dN/GzPFl5Tu+7Znr/x0VetdI\nX7b+FHPev4I6W+6V4l4h2gFfE6dMaeT8mW9T3vP5+k+88uvr4g50Y6/xPs4NqAPrbqgl9DH8lHos\n8saUKdGRwS3+ej/8ao4OvLM3KbTKGNJyFrptdKP/2P0i959BpxMSvgHYQGjNccyF8Wu+0oxD1z8/\nia4oqaHZzHi27xiu9dexFb52yB+MFF5ER+fr0WWGx/vwS1Fl9DnUW3xJ6Nm0eyF0pnyoYvMX//9T\neK97dKfBYP+KW9FVDoehU0brvdyVhFZE0NKhNysKRFgOcmQk30llaH/fiVyVbblpWzn+JToQuA0d\nuKRcipgl2c/ybchmdNBxIdqhnezz90Wf16+iVpL5wG/TvOugOGX3v3kq2gn/iub9FQJH9K+i09dD\nQvGzUlbQQevTqLI2yufnYnT5/hB0pU6lj3uJb1MqfLsyP+68zUYG3YCanz8XCW9X5UC1xrt9wzwN\ndSiMc2+AQtQM+TLQN5A91GD1QE2X5wLfzUYhjMj6ODrX3QudBrgfNZmdHYo3kSwpO+1Mx7WoReWH\ntNxbJB81Px6OmljX4EcTcTUA6DTGi77RH4KOzm73YXf6OKNo3qujAB3hfy4O+TohfTkzkv8YaQjq\n5kyal1Fmq4NoSzkOOoVHUQtWi/Ke7bz0cvTCO17TcjB3t09LQTRejsl/KzoQ/Bs6uh+FDgrXoJbD\ngmyVj4icX/X5GfiDfQOdRp2JLi/fgHf6R5cCnwp8KSuyxpgpH3tkgM7RLUG1sy+jXqgLgV+HfyOG\ntIz1H/PqOH6vE2R9I5AVHUXvHfkmse/i9xHTcDXwdmvfGFWabkK9wWNND81myIPRKYsbUGVtFc1L\nUq9BRwpHR57NOcfiSNnImZH8x0zPvuiI/6Jsy+LlSacc56HTG4dHwnI671H/gucIWR9CZSjn5Ect\nhotRq+B8/y8ru7C2ImMwEPw/mvdYOhh16K/0bUwToQ3jsvUv+NCxENrLfCbwjnPu+6EDu9p6thc6\n6mtAD61p9Hu4X416Op/hnNua0QQ0yyLo0tWDvSzz/OFRO+P4/Y9CRNbLnXNzfXieixx2lav4sz/e\nRldHvJ+uzPgTEGuzIF8ftMJXo2ep7BCRa4BZzrlpPk5vYDfn3Ptxy9dRPk59zTVE5Geob8Llzrkd\nOSDP1WibdVgr5Tmn89qfo9IdbVuuR30oznfOzcuqYO3E18lu6F5AiWzLkwoRGYvunvmsc+6GUPgp\nqHJfiE5db8pmWYn1ECPfKO2LFrgFQVg7HxfUxHq/VyLEObfdv6vYObc1rgNUvMy3oh/xxz4s55QI\nSMp6Gyrr5aHwLqFEeLajZscRkL7MZEOJ8L+7CbWOjUDNj6Dz2tNCcWoCJSgbMnaEj1lfc42bnXMX\n54IS4ZkKjBSRvX0+71IucjmvvfL8JDqH/wjwlnNudFdRIiBZJ9fkqhLheQt11j5RRA6GpIL5LLri\n5dPOuY3ZLivZOA3xTNSB697WInmNK3mymf/ofw5G1DTL3hvdlCjWiuecW4luIf12rncOTo8D7xKy\npmExanb8e7YFaYVn0GmVM0Wk3KU5bTTbFb4DtKu+5jo5qDi3SznOVbzyfBta7sc45y6F5lN5jc4h\nMhC8IhSGH0jnBLFObUDbJnURGYcuOytHN1eZmeqZkNl1FnCfc+5PcZsCc930GKYryZqOQAnK1XSI\nyNFo2b3GOfdMtuXpDLrSFFhXwpflA5xzC7ItS2fgFYimXK2bXR0R+THqIHpXLuZx7IpEa4jIMeim\nT/9GN07a3Tl3eCvxh6Kmn2Occ4t82HDn3AfWABpx4zuHYc65D7Iti9E1yHXluD18EgYpuU6u53E2\npjZ2IWQCXo0u6bwd9XI/QES+HYkT5mTgQ+fcIhE51Fsn3hCRAlMijLhxygfQ3EEYRmv4MpOzHUR7\n6OrydwVyPY+zapEQkVHodqp1obB859xOESlAl/N9E90gpCEUJ5jWuBPd3GUluv56IvCjcFzDMAzD\nMDJHViwSInKmiKxAdwKcKyI/9UtxAJq8orAD3eCkHt3Fq4UZ0M/JfQn4Crrd80jn3HdMiTAMwzCM\n+MiGs+VYdC/we9FNnY5Dt4T9PbpEqyZklRD0qNk70a15l/q9IwrQjTguA2aHl9kZhmEYhhEfsSkS\noemI76OHWx0UrPv3m8Wchp5sd2fkuX7omuu16KYnNwEPOeceiUVwwzAMwzDSEtvURshZZBi6L0B4\nA6ffoys1xonIPtBi/4gNwB/RaYy30J0tn45JbMMwDMMwWiFjioSInCQid4rIpX46I2AGupf8IB8v\n3zm3BT1BsR96CBN+Q58iEfkhumvgdOBg59ypLqatsA3DMAzDaJ1OVyREpExEpqLbpvYDvgVMCykT\n04Bl6D7hSZxzf0N37zogFNwXPWznfOfc8Z+UzVsMwzAM45NCp/pIiEgP4B70IJefOeeW+vBZwLvO\nufP9aotzgAeBY51zM0PPPwV0d86N6zShDMMwDMPIGJ1qkfBTDg3Ag36FRYG/9Sx6HHFwuNUT6Fnw\nfxSRY0EtGcBQYHJnymQYhmEYRubo9FUbIlLonGv0f+d5X4dHgTrn3PdCqze6o+fX7w/MQY+iXQac\n5Zxb1alCGYZhGIaREWJZ/ikirwN/dM792e8Nkef3iRiIKhBHAEudc49mXBjDMAzDMDqNjCsSIjIc\n3XhqnHNutg8ryqUjUA3DMAzD6BiZXP4ZHFp0DFAbUiJ+CdwhIgMy9duGYRiGYcRDQdtROkZoA6qx\nwFMichJwH9AD+IZzbk2mftswDMMwjHjI6NSGd6isAPYGtgO/dM7dnLEfNAzDMAwjVuLwkXgRWAJc\n7pyrz+iPGYZhGIYRK3EoEvl+7wjDMAzDMD5hxH6MuGEYhmEYnxxiO/3TMAzDMIxPHqZIGIZhGIbR\nYUyRMAzDMAyjw5giYRiGYRhGhzFFwjAMwzCMDmOKhGEYhmEYHcYUCcMwDMMwOowpEoZhGIZhdBhT\nJAzD+FiIyEQRaRKRnSKyXUSqRWSaiJwfOgW4Pe85V0Q2ZlJWwzA6H1MkDMPoDJ4DBgF7AScDLwN3\nAFNFpL3tjAC21a5hdDFMkTAMozNocM6tdc5VOefmOuduAk4DTgHOAxCRy0TkHRGpFZEPReRuEenh\n7x0HPAD0Dlk3rvX3ikTkVhFZ6Z/9l49vGEYOYIqEYRgZwTn3CjAPOMMH7QQuAg4AvgmcANzi780E\nLgU2AwOBMuBWf+9u4AjgLGAk8BfgORHZO/OpMAyjLezQLsMwPhYiMhHo7Zw7I8W9ycBI59xBKe6d\nCdzrnBvgr88Ffuec6xeKMwT4ABjinKsOhb8IzHLO/aLTE2QYxkeiINsCGIbxiSbp9yAinwN+CowA\neqHtTzcR6e6cq0/z/EggH1gccdwsAtZlTGrDMNqNKRKGYWSS/YGlIrIXMBWdprga2AAcC9yPKgXp\nFIkSYAcwGmiK3KvNhMCGYXw0TJEwDCMjiMiJqEXhNmAMOpV6Rej+1yKPbEetD2Hm+LCBzrkZGRTX\nMIwOYoqEYRidQTcRGYjv9IEvotMYU4CHUYWiUEQuRi0TxwAXRN6xDCjxCsg8YKtzbomITAIeEpEr\nUMViAHAiMM8591zGU2YYRqvYqg3DMDqDk4FKYCm6p8RxwIXOuS875R3gcuBKoAI4G1U0kjjn/gVM\nAB4H1gA/8bfOAx5CV3EsBJ4GDgM+zGySDMNoD7ZqwzAMwzCMDmMWCcMwDMMwOowpEoZhGIZhdBhT\nJAzDMAzD6DCmSBiGYRiG0WFMkTAMwzAMo8OYImEYhmEYRocxRcIwDMMwjA5jioRhGIZhGB3GFAnD\nMAzDMDqMKRKGYRiGYXQYUyQMwzAMw+gwpkgYhmEYhtFh/h9VblTrxCEmEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118755e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sbike[[\"Berri 1\", \"Rain\"]].plot(subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n",
       "       5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
       "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n",
       "       5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
       "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0], dtype=int32)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbike.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sbike[\"weekday\"] = df_sbike.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>Côte-Sainte-Catherine</th>\n",
       "      <th>Maisonneuve 1</th>\n",
       "      <th>weekday</th>\n",
       "      <th>mean temp</th>\n",
       "      <th>Rain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>6</td>\n",
       "      <td>0.629167</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>83</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>135</td>\n",
       "      <td>2</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>-14.416667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>116</td>\n",
       "      <td>2</td>\n",
       "      <td>-13.645833</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>197</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>3</td>\n",
       "      <td>-6.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1  Côte-Sainte-Catherine  Maisonneuve 1  weekday  mean temp  \\\n",
       "Date                                                                            \n",
       "2012-01-01       35                      0             38        6   0.629167   \n",
       "2012-01-02       83                      1             68        0   0.041667   \n",
       "2012-01-03      135                      2            104        1 -14.416667   \n",
       "2012-01-04      144                      1            116        2 -13.645833   \n",
       "2012-01-05      197                      2            124        3  -6.750000   \n",
       "\n",
       "                Rain  \n",
       "Date                  \n",
       "2012-01-01  0.125000  \n",
       "2012-01-02  0.041667  \n",
       "2012-01-03  0.000000  \n",
       "2012-01-04  0.000000  \n",
       "2012-01-05  0.000000  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "berri = df_sbike[[\"Berri 1\", \"weekday\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>144</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>197</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1  weekday\n",
       "Date                        \n",
       "2012-01-01       35        6\n",
       "2012-01-02       83        0\n",
       "2012-01-03      135        1\n",
       "2012-01-04      144        2\n",
       "2012-01-05      197        3"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekday_counts = berri.groupby(\"weekday\").aggregate(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>134298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>135305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>152972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>160131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>141771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>101578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>99310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Berri 1\n",
       "weekday         \n",
       "0         134298\n",
       "1         135305\n",
       "2         152972\n",
       "3         160131\n",
       "4         141771\n",
       "5         101578\n",
       "6          99310"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekday_counts.index = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \n",
    "                        \"Fri\", \"Sat\", \"Sun\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>134298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tue</th>\n",
       "      <td>135305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wed</th>\n",
       "      <td>152972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>160131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>141771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>101578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>99310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Berri 1\n",
       "Mon   134298\n",
       "Tue   135305\n",
       "Wed   152972\n",
       "Thu   160131\n",
       "Fri   141771\n",
       "Sat   101578\n",
       "Sun    99310"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118acde80>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i4Yh4CJgGnCxpVHqe40gC0KcjYnlENAEXAedKGliBazczM7MuVCJ8PARMkvQu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\nH2ZmZlYohw8zMzMr1P8PImBx34wN9qwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118e879b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekday_counts.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2012-01-01     True\n",
       "2012-01-02    False\n",
       "2012-01-03    False\n",
       "2012-01-04    False\n",
       "2012-01-05    False\n",
       "2012-01-06    False\n",
       "2012-01-07    False\n",
       "2012-01-08    False\n",
       "2012-01-09    False\n",
       "2012-01-10    False\n",
       "2012-01-11    False\n",
       "2012-01-12    False\n",
       "2012-01-13     True\n",
       "2012-01-14     True\n",
       "2012-01-15     True\n",
       "2012-01-16    False\n",
       "2012-01-17    False\n",
       "2012-01-18    False\n",
       "2012-01-19    False\n",
       "2012-01-20    False\n",
       "2012-01-21     True\n",
       "2012-01-22     True\n",
       "2012-01-23    False\n",
       "2012-01-24    False\n",
       "2012-01-25    False\n",
       "2012-01-26    False\n",
       "2012-01-27    False\n",
       "2012-01-28    False\n",
       "2012-01-29    False\n",
       "2012-01-30    False\n",
       "              ...  \n",
       "2012-10-07    False\n",
       "2012-10-08    False\n",
       "2012-10-09    False\n",
       "2012-10-10    False\n",
       "2012-10-11    False\n",
       "2012-10-12    False\n",
       "2012-10-13    False\n",
       "2012-10-14    False\n",
       "2012-10-15    False\n",
       "2012-10-16    False\n",
       "2012-10-17    False\n",
       "2012-10-18    False\n",
       "2012-10-19    False\n",
       "2012-10-20    False\n",
       "2012-10-21    False\n",
       "2012-10-22    False\n",
       "2012-10-23    False\n",
       "2012-10-24    False\n",
       "2012-10-25    False\n",
       "2012-10-26    False\n",
       "2012-10-27    False\n",
       "2012-10-28    False\n",
       "2012-10-29    False\n",
       "2012-10-30    False\n",
       "2012-10-31    False\n",
       "2012-11-01    False\n",
       "2012-11-02    False\n",
       "2012-11-03    False\n",
       "2012-11-04    False\n",
       "2012-11-05    False\n",
       "Name: Berri 1, dtype: bool"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbike[\"Berri 1\"] < 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>Côte-Sainte-Catherine</th>\n",
       "      <th>Maisonneuve 1</th>\n",
       "      <th>weekday</th>\n",
       "      <th>mean temp</th>\n",
       "      <th>Rain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>6</td>\n",
       "      <td>0.629167</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-13</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>4</td>\n",
       "      <td>-4.600000</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-14</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>5</td>\n",
       "      <td>-16.295833</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-15</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>-18.983333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-21</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>5</td>\n",
       "      <td>-14.833333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-22</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>6</td>\n",
       "      <td>-14.166667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-05</th>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>6</td>\n",
       "      <td>-8.850000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-11</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>5</td>\n",
       "      <td>-11.637500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-12</th>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>-14.220833</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-25</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>5</td>\n",
       "      <td>-2.350000</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-26</th>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>6</td>\n",
       "      <td>-9.337500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1  Côte-Sainte-Catherine  Maisonneuve 1  weekday  mean temp  \\\n",
       "Date                                                                            \n",
       "2012-01-01       35                      0             38        6   0.629167   \n",
       "2012-01-13       75                      0             41        4  -4.600000   \n",
       "2012-01-14       32                      0             54        5 -16.295833   \n",
       "2012-01-15       54                      0             33        6 -18.983333   \n",
       "2012-01-21       53                      0             47        5 -14.833333   \n",
       "2012-01-22       71                      0             41        6 -14.166667   \n",
       "2012-02-05       72                      0             46        6  -8.850000   \n",
       "2012-02-11       71                      0             63        5 -11.637500   \n",
       "2012-02-12       78                      0             36        6 -14.220833   \n",
       "2012-02-25       62                      0             48        5  -2.350000   \n",
       "2012-02-26       78                      0             76        6  -9.337500   \n",
       "\n",
       "                Rain  \n",
       "Date                  \n",
       "2012-01-01  0.125000  \n",
       "2012-01-13  0.125000  \n",
       "2012-01-14  0.000000  \n",
       "2012-01-15  0.000000  \n",
       "2012-01-21  0.000000  \n",
       "2012-01-22  0.000000  \n",
       "2012-02-05  0.000000  \n",
       "2012-02-11  0.000000  \n",
       "2012-02-12  0.000000  \n",
       "2012-02-25  0.083333  \n",
       "2012-02-26  0.000000  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbike[df_sbike[\"Berri 1\"] < 80]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Julia Evans 很棒的 pandas 介紹影片!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x11903e668>"
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     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo(\"rEalbu8UGeo\")"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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