08 熊貓數據分析.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "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 0x7f3004449240>"
      ]
     },
     "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": [
    "## 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": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 6,
     "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": [
    "## 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": 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, Length: 100, dtype: int64"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "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, Length: 100, dtype: int64"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2fc9116320>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eFNCVUr+a/fP3AvjskOcB6qk/y3UzoNvTW/Z7toDb4Nk3UBe3HrJAxLtJoyCo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2fc90e8630>"
      ]
     },
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2fc9055128>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAE+pJREFUeJzt3X+MHPd93vH3p1JdU7yUtsN47dBqT0kcoSnPMcpNmyZw\nehc5AWsZVZImiAXZkBq3V7SwrQYsDDpG6xaFUbYO6wRq0YCJWRopqytqK5Er5YdUV2e1gO32qMo+\nylJiIyFsMTJpgwld2kRsxp/+cUt2dLrl7s7M3tx+834Bh5uZnR+PdmcfDed2diIzkSTNvj/TdQBJ\nUjssdEkqhIUuSYWw0CWpEBa6JBXCQpekQljoklQIC12SCmGhS1IhbtzOje3duzfn5+drLfvVr36V\n3bt3txtoimYp7yxlhdnKO0tZYbbyzlJWaJb31KlTX87Mbxs5Y2Zu28+BAweyrscee6z2sl2Ypbyz\nlDVztvLOUtbM2co7S1kzm+UF1nKMjvWUiyQVwkKXpEJY6JJUCAtdkgphoUtSISx0SSqEhS5JhbDQ\nJakQFrokFWJbL/2XJID5ww83XsehhSvcM1jPmSO3N15fCTxCl6RCWOiSVAgLXZIKYaFLUiEsdEkq\nhIUuSYWw0CWpECMLPSKOR8T5iDi9afrbI+KZiHgqIv719CJKksYxzhH6CeBgdUJELAF3AN+bmX8Z\n+Pn2o0mSJjGy0DPzceDCpsn/ADiSmX88mOf8FLJJkiZQ9xz6dwOvi4hPRsTHIuL72gwlSZpcbNxQ\nesRMEfPAQ5m5fzB+GngMeAfwfcB/Br4jt1hZRCwDywC9Xu/AyspKraCXLl1ibm6u1rJdmKW8s5QV\nZivvLGWF7cu7fvZi43X0dsG5yxvDC/v2NF7ftDV5bpeWlk5lZn/UfHW/nOtZ4IFBgf+viPgmsBf4\n0uYZM/MYcAyg3+/n4uJirQ2urq5Sd9kuzFLeWcoKs5V3lrLC9uW9p6Uv5zq6vlFhZ+5abLy+aduO\n57buKZdfB5YAIuK7gRcBX24rlCRpciOP0CPifmAR2BsRzwLvAY4DxwenXr4O3L3V6RZJ0vYZWeiZ\neeeQh97cchZJUgNeKSpJhbDQJakQFrokFcJCl6RCWOiSVAgLXZIKYaFLUiHqXvqvLcxXLmc+tHCl\n8eXNZ47c3jSS/hSab7DfbbXfuh/ODo/QJakQFrokFcJCl6RCWOiSVAgLXZIKYaFLUiEsdEkqhIUu\nSYUYWegRcTwizg/uTrT5sUMRkRGxdzrxJEnjGucI/QRwcPPEiLgZ+FHg8y1nkiTVMLLQM/Nx4MIW\nD70feCfgvUQlaQeIce7tHBHzwEOZuX8wfgfww5l5b0ScAfqZ+eUhyy4DywC9Xu/AyspKraCXLl1i\nbm6u1rLbZf3sxWvDvV1w7nKz9S3s29Mw0fNV81U1ydp2xnHMwr5wVRdZh73O49hqX5jGa9wk41XV\nrF3sh5Nqsi8sLS2dysz+qPkm/nKuiLgJ+Dk2TreMlJnHgGMA/X4/FxcXJ90kAKurq9Rddrvcs+nL\nuY6uN/vuszN3LTZM9HzDviysSda2M45jFvaFq7rI2uRL4bbaF6bxGjf94jp4ftYu9sNJbce+UOdT\nLt8J3AJ8anB0/irgiYh4RZvBJEmTmfiwLDPXgZdfHR91ykWStD3G+dji/cDHgVsj4tmIeOv0Y0mS\nJjXyCD0z7xzx+HxraSRJtXmlqCQVwkKXpEJY6JJUCAtdkgphoUtSISx0SSqEhS5JhbDQJakQFrok\nFcJCl6RCWOiSVAgLXZIKYaFLUiEsdEkqhIUuSYUY5wYXxyPifEScrkx7X0Q8ExGfjohfi4iXTDem\nJGmUcY7QTwAHN017FNifma8Bfhd4V8u5JEkTGlnomfk4cGHTtEcy88pg9BNs3ChaktShNs6h/wzw\nmy2sR5LUQGTm6Jki5oGHMnP/punvBvrAT+SQFUXEMrAM0Ov1DqysrNQKeunSJebm5motu13Wz168\nNtzbBecuN1vfwr49DRM9XzVfVZOsbWccxyzsC1d1kXXY6zyOrfaFabzGTTJeVc3axX44qSb7wtLS\n0qnM7I+ar3ahR8Q9wN8HbsvMr40Tqt/v59ra2jizvsDq6iqLi4u1lt0u84cfvjZ8aOEKR9dH3oP7\nus4cub1ppOep5qtqkrXtjOOYhX3hqi6yDnudx7HVvjCN17hJxquqWbvYDyfVZF+IiLEKvda7OCIO\nAu8E/sa4ZS5Jmq5xPrZ4P/Bx4NaIeDYi3gr8W+BbgEcj4smI+KUp55QkjTDyCD0z79xi8gemkEWS\n1IBXikpSISx0SSqEhS5JhbDQJakQFrokFcJCl6RCWOiSVIhm16Zvo/WzF7mnhcuFr5qFS4X/NBrn\nkvBDC1fG3he26+sThhknq/ui2uIRuiQVwkKXpEJY6JJUCAtdkgphoUtSISx0SSqEhS5JhRjnBhfH\nI+J8RJyuTHtZRDwaEZ8d/H7pdGNKkkYZ5wj9BHBw07TDwEcz89XARwfjkqQOjSz0zHwcuLBp8h3A\nBwfDHwR+rOVckqQJ1T2H3svM5wbDXwR6LeWRJNUUmTl6poh54KHM3D8Y/6PMfEnl8T/MzC3Po0fE\nMrAM0Ov1DqysrNQKev7CRc5drrXolhb27WlvZQPrZy9eG+7tonHetjNW81U1ybpdGasmydtFvqpx\nsnadsWqrvNN+r9RVzTqNjG27dOkSc3NztZZdWlo6lZn9UfPV/XKucxHxysx8LiJeCZwfNmNmHgOO\nAfT7/VxcXKy1wftOPsjR9fa+S+zMXfVyXE/1S5gOLVxpnLftjMO+JKpJ1u3KWDVJ3i7yVY2TteuM\nVVvlnfZ7pa5q1mlkbNvq6ip1+29cdU+5fAS4ezB8N/BgO3EkSXWN87HF+4GPA7dGxLMR8VbgCPAj\nEfFZ4PWDcUlSh0b+uzUz7xzy0G0tZ5EkNeCVopJUCAtdkgphoUtSISx0SSqEhS5JhbDQJakQFrok\nFcJCl6RCWOiSVAgLXZIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQjQq9Ij42Yh4KiJOR8T9\nEfHitoJJkiZTu9AjYh/wDqCfmfuBG4A3tRVMkjSZpqdcbgR2RcSNwE3AHzSPJEmqIzKz/sIR9wLv\nBS4Dj2TmXVvMswwsA/R6vQMrKyu1tnX+wkXOXa4d9QUW9u1pb2UD62cvXhvu7aJx3rYzVvNVNcm6\nXRmrJsnbRb6qcbJ2nbFqq7zTfq/UVc06jYxtu3TpEnNzc7WWXVpaOpWZ/VHz1S70iHgp8GHgp4E/\nAv4L8KHM/I/Dlun3+7m2tlZre/edfJCj6yPvaT22M0dub21dV80ffvja8KGFK43ztp2xmq+qSdbt\nylg1Sd4u8lWNk7XrjFVb5Z32e6WuatZpZGzb6uoqi4uLtZaNiLEKvckpl9cDv5+ZX8rMbwAPAD/Q\nYH2SpAaaFPrnge+PiJsiIoDbgKfbiSVJmlTtQs/MTwIfAp4A1gfrOtZSLknShBqd5M3M9wDvaSmL\nJKkBrxSVpEJY6JJUCAtdkgphoUtSISx0SSqEhS5JhbDQJakQFrokFcJCl6RCWOiSVAgLXZIKYaFL\nUiEsdEkqhIUuSYWw0CWpEI0KPSJeEhEfiohnIuLpiPjrbQWTJE2m6V2XfxH4rcz8yYh4EXBTC5kk\nSTXULvSI2AP8EHAPQGZ+Hfh6O7EkSZNqcsrlFuBLwH+IiP8TEb8SEbtbyiVJmlBkZr0FI/rAJ4Af\nzMxPRsQvAl/JzH+yab5lYBmg1+sdWFlZqbW98xcucu5yrUW3tLBvT3srG1g/e/HacG8XjfO2nbGa\nr6pJ1u3KWDVJ3i7yVY2TteuMVVvlnfZ7pa5q1p30HA5zy54bmJubq7Xs0tLSqczsj5qvSaG/AvhE\nZs4Pxl8HHM7M24ct0+/3c21trdb27jv5IEfXm57y///OHBkas7b5ww9fGz60cKVx3rYzVvNVNcm6\nXRmrJsnbRb6qcbJ2nbFqq7zTfq/UVc26k57DYU4c3M3i4mKtZSNirEKvfcolM78IfCEibh1Mug34\nTN31SZKaaXrI+3bg5OATLr8H/J3mkSRJdTQq9Mx8Ehj5zwBJ0vR5pagkFcJCl6RCWOiSVAgLXZIK\nYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQljoklQIC12SCmGhS1IhLHRJKoSFLkmFaFzoEXHD\n4CbRD7URSJJUTxtH6PcCT7ewHklSA40KPSJeBdwO/Eo7cSRJdTU9Qv8F4J3AN1vIIklqIDKz3oIR\nbwTekJn/MCIWgX+cmW/cYr5lYBmg1+sdWFlZqbW98xcucu5yrUW3tLBvT3srG1g/e/HacG8XjfO2\nnbGar6pJ1u3KWDVJ3i7yVY2TteuMVVvlnfZ7pa5q1p30HA5zy54bmJubq7Xs0tLSqcwcef/mJoX+\nL4G3AFeAFwN/HnggM988bJl+v59ra2u1tnffyQc5ut7ontbPc+bI7a2t66r5ww9fGz60cKVx3rYz\nVvNVNcm6XRmrJsnbRb6qcbJ2nbFqq7zTfq/UVc26k57DYU4c3M3i4mKtZSNirEKvfcolM9+Vma/K\nzHngTcB/v16ZS5Kmy8+hS1IhWjmHkZmrwGob65Ik1eMRuiQVwkKXpEJY6JJUCAtdkgphoUtSISx0\nSSqEhS5JhbDQJakQFrokFcJCl6RCWOiSVAgLXZIKYaFLUiEsdEkqhIUuSYWw0CWpELULPSJujojH\nIuIzEfFURNzbZjBJ0mSa3LHoCnAoM5+IiG8BTkXEo5n5mZaySZIm0OQm0c9l5hOD4f8LPA3sayuY\nJGkykZnNVxIxDzwO7M/Mr2x6bBlYBuj1egdWVlZqbeP8hYucu9wsZ9XCvj3trWxg/ezFa8O9XTTO\n23bGar6qJlm3K2PVJHm7yFc1TtauM1ZtlXfa75W6qll30nM4zC17bmBubq7WsktLS6cysz9qvsaF\nHhFzwMeA92bmA9ebt9/v59raWq3t3HfyQY6ut3JPawDOHLm9tXVdNX/44WvDhxauNM7bdsZqvqom\nWbcrY9UkebvIVzVO1q4zVm2Vd9rvlbqqWXfSczjMiYO7WVxcrLVsRIxV6I0+5RIRfxb4MHByVJlL\nkqaryadcAvgA8HRm/pv2IkmS6mhyhP6DwFuAH46IJwc/b2gplyRpQrVP8mbm/wSixSySpAa8UlSS\nCmGhS1IhLHRJKoSFLkmFsNAlqRAWuiQVwkKXpEJY6JJUCAtdkgphoUtSISx0SSqEhS5JhbDQJakQ\nFrokFcJCl6RCNL0F3cGI+J2I+FxEHG4rlCRpck1uQXcD8O+Avwl8D3BnRHxPW8EkSZNpcoT+V4HP\nZebvZebXgRXgjnZiSZIm1aTQ9wFfqIw/O5gmSepAZGa9BSN+EjiYmX93MP4W4K9l5ts2zbcMLA9G\nbwV+p2bWvcCXay7bhVnKO0tZYbbyzlJWmK28s5QVmuX9i5n5baNmqn2TaOAscHNl/FWDac+TmceA\nYw22A0BErGVmv+l6tsss5Z2lrDBbeWcpK8xW3lnKCtuTt8kpl/8NvDoibomIFwFvAj7STixJ0qRq\nH6Fn5pWIeBvw28ANwPHMfKq1ZJKkiTQ55UJm/gbwGy1lGaXxaZttNkt5ZykrzFbeWcoKs5V3lrLC\nNuSt/UdRSdLO4qX/klSImSj0iPjZiHgqIk5HxP0R8eKuMw0TEfcOcj4VEf+o6zybRcTxiDgfEacr\n014WEY9GxGcHv1/aZcarhmT9qcFz+82I2FGfcBiS930R8UxEfDoifi0iXtJlxquGZP0Xg5xPRsQj\nEfHtXWas2ipv5bFDEZERsbeLbJsNeW7/WUScHTy3T0bEG6ax7R1f6BGxD3gH0M/M/Wz8AfZN3aba\nWkTsB/4eG1fRfi/wxoj4rm5TvcAJ4OCmaYeBj2bmq4GPDsZ3ghO8MOtp4CeAx7c9zWgneGHeR4H9\nmfka4HeBd213qCFO8MKs78vM12Tma4GHgH+67amGO8EL8xIRNwM/Cnx+uwNdxwm2yAq8PzNfO/iZ\nyt8ed3yhD9wI7IqIG4GbgD/oOM8wfwn4ZGZ+LTOvAB9jo3x2jMx8HLiwafIdwAcHwx8EfmxbQw2x\nVdbMfDoz616cNlVD8j4y2BcAPsHG9RqdG5L1K5XR3cCO+QPbkP0W4P3AO5mNrFO34ws9M88CP8/G\n/4GfAy5m5iPdphrqNPC6iPjWiLgJeAPPv/hqp+pl5nOD4S8CvS7DFOxngN/sOsT1RMR7I+ILwF3s\nrCP0F4iIO4CzmfmprrOM6e2DU1rHp3Vac8cX+uA//A7gFuDbgd0R8eZuU20tM58G/hXwCPBbwJPA\nn3QaakK58bGnHXO0U4qIeDdwBTjZdZbrycx3Z+bNbOR826j5uzI4YPo5dvj/dCr+PfAdwGvZODA9\nOo2N7PhCB14P/H5mfikzvwE8APxAx5mGyswPZOaBzPwh4A/ZOG+6052LiFcCDH6f7zhPUSLiHuCN\nwF05O58TPgn87a5DXMd3snGQ96mIOMPGqawnIuIVnaYaIjPPZeafZOY3gV9m4+9srZuFQv888P0R\ncVNEBHAb8HTHmYaKiJcPfv8FNs6f/6duE43lI8Ddg+G7gQc7zFKUiDjIxjnev5WZX+s6z/VExKsr\no3cAz3SVZZTMXM/Ml2fmfGbOs/Ftr38lM7/YcbQtXT1gGvhxNk7Pti8zd/wP8M/Z2LlOA78K/Lmu\nM10n6/8APgN8Crit6zxb5LufjX/yfYONN8FbgW9l49MtnwX+G/CyrnNeJ+uPD4b/GDgH/HbXOUfk\n/RwbXzP95ODnl7rOeZ2sHx68xz4N/FdgX9c5r5d30+NngL1d57zOc/urwPrguf0I8MppbNsrRSWp\nELNwykWSNAYLXZIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQvw/KXKsg9uINKsAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2fc9017438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.國文.hist(bins=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 一些基本的資料分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "算平均。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.39"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.國文.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "算標準差。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1968526614594834"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 13,
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 14,
     "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.31089898221793327"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.國文.corr(df.數學)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mydata = np.random.randn(4,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.64992902, -0.64672481, -0.0898755 ],\n",
       "       [ 0.12574951,  0.10084494,  0.82797814],\n",
       "       [ 1.46816039,  0.12456301,  0.8985012 ],\n",
       "       [-0.37883872,  1.6644449 , -0.20941679]])"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame(mydata, columns=list(\"ABC\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.649929</td>\n",
       "      <td>-0.646725</td>\n",
       "      <td>-0.089875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.125750</td>\n",
       "      <td>0.100845</td>\n",
       "      <td>0.827978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.468160</td>\n",
       "      <td>0.124563</td>\n",
       "      <td>0.898501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.378839</td>\n",
       "      <td>1.664445</td>\n",
       "      <td>-0.209417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0  1.649929 -0.646725 -0.089875\n",
       "1  0.125750  0.100845  0.827978\n",
       "2  1.468160  0.124563  0.898501\n",
       "3 -0.378839  1.664445 -0.209417"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df3 = pd.DataFrame(np.random.randn(3,3), columns=list(\"ABC\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.473924</td>\n",
       "      <td>-0.511648</td>\n",
       "      <td>-0.320315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.176162</td>\n",
       "      <td>-0.570373</td>\n",
       "      <td>0.416175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.189362</td>\n",
       "      <td>0.252501</td>\n",
       "      <td>-1.190146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0 -0.473924 -0.511648 -0.320315\n",
       "1 -1.176162 -0.570373  0.416175\n",
       "2 -1.189362  0.252501 -1.190146"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df4 = pd.concat([df2, df3], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.649929</td>\n",
       "      <td>-0.646725</td>\n",
       "      <td>-0.089875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.125750</td>\n",
       "      <td>0.100845</td>\n",
       "      <td>0.827978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.468160</td>\n",
       "      <td>0.124563</td>\n",
       "      <td>0.898501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.378839</td>\n",
       "      <td>1.664445</td>\n",
       "      <td>-0.209417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.473924</td>\n",
       "      <td>-0.511648</td>\n",
       "      <td>-0.320315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.176162</td>\n",
       "      <td>-0.570373</td>\n",
       "      <td>0.416175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.189362</td>\n",
       "      <td>0.252501</td>\n",
       "      <td>-1.190146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0  1.649929 -0.646725 -0.089875\n",
       "1  0.125750  0.100845  0.827978\n",
       "2  1.468160  0.124563  0.898501\n",
       "3 -0.378839  1.664445 -0.209417\n",
       "0 -0.473924 -0.511648 -0.320315\n",
       "1 -1.176162 -0.570373  0.416175\n",
       "2 -1.189362  0.252501 -1.190146"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df4.index = range(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.649929</td>\n",
       "      <td>-0.646725</td>\n",
       "      <td>-0.089875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.125750</td>\n",
       "      <td>0.100845</td>\n",
       "      <td>0.827978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.468160</td>\n",
       "      <td>0.124563</td>\n",
       "      <td>0.898501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.378839</td>\n",
       "      <td>1.664445</td>\n",
       "      <td>-0.209417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.473924</td>\n",
       "      <td>-0.511648</td>\n",
       "      <td>-0.320315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.176162</td>\n",
       "      <td>-0.570373</td>\n",
       "      <td>0.416175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.189362</td>\n",
       "      <td>0.252501</td>\n",
       "      <td>-1.190146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0  1.649929 -0.646725 -0.089875\n",
       "1  0.125750  0.100845  0.827978\n",
       "2  1.468160  0.124563  0.898501\n",
       "3 -0.378839  1.664445 -0.209417\n",
       "4 -0.473924 -0.511648 -0.320315\n",
       "5 -1.176162 -0.570373  0.416175\n",
       "6 -1.189362  0.252501 -1.190146"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 横向的貼"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>1.649929</td>\n",
       "      <td>-0.646725</td>\n",
       "      <td>-0.089875</td>\n",
       "      <td>-0.473924</td>\n",
       "      <td>-0.511648</td>\n",
       "      <td>-0.320315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.125750</td>\n",
       "      <td>0.100845</td>\n",
       "      <td>0.827978</td>\n",
       "      <td>-1.176162</td>\n",
       "      <td>-0.570373</td>\n",
       "      <td>0.416175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.468160</td>\n",
       "      <td>0.124563</td>\n",
       "      <td>0.898501</td>\n",
       "      <td>-1.189362</td>\n",
       "      <td>0.252501</td>\n",
       "      <td>-1.190146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.378839</td>\n",
       "      <td>1.664445</td>\n",
       "      <td>-0.209417</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  1.649929 -0.646725 -0.089875 -0.473924 -0.511648 -0.320315\n",
       "1  0.125750  0.100845  0.827978 -1.176162 -0.570373  0.416175\n",
       "2  1.468160  0.124563  0.898501 -1.189362  0.252501 -1.190146\n",
       "3 -0.378839  1.664445 -0.209417       NaN       NaN       NaN"
      ]
     },
     "execution_count": 27,
     "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": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_names = pd.read_csv('names.csv', names=[\"姓名\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_names.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "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": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df6 = pd.concat([df_names, df_grades], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>姓名</th>\n",
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       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>7</td>\n",
       "      <td>7</td>\n",
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      "text/plain": [
       "    姓名  國文  英文  數學  社會  自然\n",
       "0  羅雅慧  11   8  13  13   8\n",
       "1  王紹尹  12   6   7  15   8\n",
       "2  黃季坤  12   7  10  12   8\n",
       "3  許正書  14   9   7  13   6\n",
       "4  張鈞蓮   7   7  13   8   8"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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": 34,
   "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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>國文</th>\n",
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       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>49</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>張鈞蓮</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>43</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  11   8  13  13   8   53\n",
       "1  王紹尹  12   6   7  15   8   48\n",
       "2  黃季坤  12   7  10  12   8   49\n",
       "3  許正書  14   9   7  13   6   49\n",
       "4  張鈞蓮   7   7  13   8   8   43"
      ]
     },
     "execution_count": 35,
     "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": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df6[\"主科\"] = df6.數學 * 1.5 + df6.英文"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>53</td>\n",
       "      <td>27.5</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>黃季坤</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>49</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>許正書</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>49</td>\n",
       "      <td>19.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>張鈞蓮</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>43</td>\n",
       "      <td>26.5</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  11   8  13  13   8   53  27.5\n",
       "1  王紹尹  12   6   7  15   8   48  16.5\n",
       "2  黃季坤  12   7  10  12   8   49  22.0\n",
       "3  許正書  14   9   7  13   6   49  19.5\n",
       "4  張鈞蓮   7   7  13   8   8   43  26.5"
      ]
     },
     "execution_count": 37,
     "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": 38,
   "metadata": {},
   "outputs": [
    {
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       "      <th>14</th>\n",
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       "      <th>70</th>\n",
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       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
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       "      <td>33.0</td>\n",
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       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>陳秀彥</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>60</td>\n",
       "      <td>29.0</td>\n",
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       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>丁佳伶</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>60</td>\n",
       "      <td>31.5</td>\n",
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       "      <th>55</th>\n",
       "      <td>林君鑫</td>\n",
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       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>60</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>張秀靖</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
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       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>趙仁珊</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>59</td>\n",
       "      <td>25.5</td>\n",
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       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>宋辛芬</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>59</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>劉家琇</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>59</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>張靖雯</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>59</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>趙雅茹</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>58</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>吳小鑫</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>58</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>林政盈</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>57</td>\n",
       "      <td>35.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>張靜如</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>57</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>袁杰綸</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>57</td>\n",
       "      <td>33.5</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",
       "46  李家銘  13  13   9  15  15   65  26.5\n",
       "99  胡筱芸  15  14  12  15   8   64  32.0\n",
       "32  鐘常宸  14  15   9  11  15   64  28.5\n",
       "14  劉怡茹  12  12  15   8  15   62  34.5\n",
       "61  呂偉傑  10   9  14  14  14   61  30.0\n",
       "97  林欣怡  11   7  15  13  15   61  29.5\n",
       "70  馮仕書  14  12  14   7  14   61  33.0\n",
       "68  陳秀彥   8  11  12  15  14   60  29.0\n",
       "67  丁佳伶  14  12  13   6  15   60  31.5\n",
       "55  林君鑫  12   8  15  11  14   60  30.5\n",
       "92  張秀靖  14  12  10  15   8   59  27.0\n",
       "40  趙仁珊  10  15   7  15  12   59  25.5\n",
       "54  宋辛芬  15   9  12  10  13   59  27.0\n",
       "85  劉家琇  13  15   8  15   8   59  27.0\n",
       "89  張靖雯   8  14  11  12  14   59  30.5\n",
       "52  趙雅茹  11  11  14   7  15   58  32.0\n",
       "26  吳小鑫  12  11  12   9  14   58  29.0\n",
       "13  林政盈  11  13  15   7  11   57  35.5\n",
       "57  張靜如  14  14  11  12   6   57  30.5\n",
       "73  袁杰綸   6  11  15  15  10   57  33.5"
      ]
     },
     "execution_count": 38,
     "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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>13</th>\n",
       "      <td>林政盈</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>57</td>\n",
       "      <td>35.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>賴財仰</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>53</td>\n",
       "      <td>35.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>涂與斌</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>57</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>張政峰</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>56</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>劉怡茹</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>62</td>\n",
       "      <td>34.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>鄭耿幸</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>55</td>\n",
       "      <td>34.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>吳立偉</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>52</td>\n",
       "      <td>34.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>賴佳宏</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>51</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>袁杰綸</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>57</td>\n",
       "      <td>33.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>李千惠</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>55</td>\n",
       "      <td>33.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>馮仕書</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>61</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>王惠文</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>57</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>姜蕙珮</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>56</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>潘淑鈺</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>54</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>吳建銘</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>32.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>胡筱芸</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>趙雅茹</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>58</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>丁佳伶</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>60</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>李傑亞</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>林政枝</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>50</td>\n",
       "      <td>31.5</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",
       "13  林政盈  11  13  15   7  11   57  35.5\n",
       "60  賴財仰  11  13  15   7   7   53  35.5\n",
       "76  涂與斌  13  14  14  10   6   57  35.0\n",
       "12  張政峰  10  14  14   6  12   56  35.0\n",
       "14  劉怡茹  12  12  15   8  15   62  34.5\n",
       "50  鄭耿幸  14  15  13   6   7   55  34.5\n",
       "71  吳立偉   6  12  15   7  12   52  34.5\n",
       "41  賴佳宏   8  13  14  10   6   51  34.0\n",
       "73  袁杰綸   6  11  15  15  10   57  33.5\n",
       "29  李千惠   8  14  13   7  13   55  33.5\n",
       "70  馮仕書  14  12  14   7  14   61  33.0\n",
       "24  王惠文   7  15  12  14   9   57  33.0\n",
       "49  姜蕙珮  11  12  14   7  12   56  33.0\n",
       "16  潘淑鈺  10  12  14   6  12   54  33.0\n",
       "90  吳建銘   7  10  15   7  14   53  32.5\n",
       "99  胡筱芸  15  14  12  15   8   64  32.0\n",
       "52  趙雅茹  11  11  14   7  15   58  32.0\n",
       "67  丁佳伶  14  12  13   6  15   60  31.5\n",
       "34  李傑亞   8   9  15  12   6   50  31.5\n",
       "39  林政枝   7  15  11  10   7   50  31.5"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.sort_values(by=[\"主科\", \"總級分\"], ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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": 41,
   "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <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",
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       "    <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",
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       "    <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",
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      ],
      "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": 42,
     "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": 43,
   "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": 44,
   "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 0x7f2fc36c9d30>"
      ]
     },
     "execution_count": 44,
     "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": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 45,
     "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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2fc36ca550>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZiFIoCkFFSLCQZaKrH30PJ9z2Wui1+c1l+bmF1S5wiGwi33209xZQWx5DQlMC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Or3WzCIw261kG95NAKIddWtugyxuibRFB2Eev8NMwjiihloWYW0P3nnDwpEs+E6zKBdB4NGFpXL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\nroR2AFeXGbVHjOnICewBcfMF9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a03UBuqZXvvdm584efc/epdZJRw+VHvds/N1ce9j+8u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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2fc36c9cc0>"
      ]
     },
     "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": 47,
   "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": 47,
     "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": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2fc36f0eb8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2fc3744240>"
      ]
     },
     "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": 49,
   "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, Length: 310, dtype: bool"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbike['Berri 1']<75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 50,
     "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": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_sbike[\"weekday\"] = df_sbike.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbike.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counts_by_day = df_sbike.groupby(\"weekday\").aggregate(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\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": 54,
     "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": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counts_by_day.index = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 56,
     "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": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2fc1c977b8>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2fc1c6bdd8>"
      ]
     },
     "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": 59,
   "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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 60,
     "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": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_sbike[\"mean temp\"] = df_weather[\"Temp (C)\"].resample('D').apply(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f2fc1c49f60>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f2fc1b2b320>], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1TSDTHBBLps1BYM0ViGUMZinTvY7Gc2sCWY5hXRMo8rnpjSSyhNDuAYVAyrDtt/Vo7VrM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2fc1baf940>"
      ]
     },
     "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": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_sbike[\"Rain\"] = df_weather[\"Weather\"].str.contains(\"Rain\").resample(\"D\").apply(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f2fc1adc2e8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f2fc1945b70>], dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pAzmOXoXpGE5qSCk5YmgC2eaAhJYxB4E9VyCRNZmlLfc6nsyvCeQ4hg1NoCrgZSCWyhFC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\nx8iZQI4cOXJsx/j/arLFlBUnmBwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2fc1c72908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sbike[[\"Berri 1\", \"Rain\"]].plot(subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "            ...\n",
       "            5, 6, 0, 1, 2, 3, 4, 5, 6, 0],\n",
       "           dtype='int64', name='Date', length=310)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbike.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_sbike[\"weekday\"] = df_sbike.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "berri = df_sbike[[\"Berri 1\", \"weekday\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekday_counts = berri.groupby(\"weekday\").aggregate(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekday_counts.index = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \n",
    "                        \"Fri\", \"Sat\", \"Sun\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2fc16dada0>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aBrwF+CUwC1iWnl8GzE7Ls4DlEfFqRGwGOoFzJZ0EnBgRayIigLurxvRu635geu9eiJmZ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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2fc35b45f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekday_counts.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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, Length: 310, dtype: bool"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbike[\"Berri 1\"] < 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 78,
     "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": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/rEalbu8UGeo\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7f2fc16a4828>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo(\"rEalbu8UGeo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}