{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 4 - Find out on which weekday people bike the most with groupby and aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "line_width has been deprecated, use display.width instead (currently both are\n",
      "identical)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "\n",
    "# Make the graphs a bit prettier, and bigger\n",
    "pd.set_option('display.mpl_style', 'default')\n",
    "figsize(15, 5)\n",
    "\n",
    "# Always display all the columns\n",
    "pd.set_option('display.line_width', 5000) \n",
    "pd.set_option('display.max_columns', 60) \n",
    "\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Adding a 'weekday' column to our dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bike = pd.read_csv('./data/bikes.csv', sep=';', encoding='latin1', parse_dates='Date', dayfirst=True, index_col='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>Brébeuf (données non disponibles)</th>\n",
       "      <th>Côte-Sainte-Catherine</th>\n",
       "      <th>Maisonneuve 1</th>\n",
       "      <th>Maisonneuve 2</th>\n",
       "      <th>du Parc</th>\n",
       "      <th>Pierre-Dupuy</th>\n",
       "      <th>Rachel1</th>\n",
       "      <th>St-Urbain (données non disponibles)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>51</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>153</td>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>104</td>\n",
       "      <td>248</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>58</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>116</td>\n",
       "      <td>318</td>\n",
       "      <td>111</td>\n",
       "      <td>8</td>\n",
       "      <td>61</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>197</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>330</td>\n",
       "      <td>97</td>\n",
       "      <td>13</td>\n",
       "      <td>95</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1  Brébeuf (données non disponibles)  Côte-Sainte-Catherine  Maisonneuve 1  Maisonneuve 2  du Parc  Pierre-Dupuy  Rachel1  St-Urbain (données non disponibles)\n",
       "Date                                                                                                                                                                            \n",
       "2012-01-01       35                                NaN                      0             38             51       26            10       16                                  NaN\n",
       "2012-01-02       83                                NaN                      1             68            153       53             6       43                                  NaN\n",
       "2012-01-03      135                                NaN                      2            104            248       89             3       58                                  NaN\n",
       "2012-01-04      144                                NaN                      1            116            318      111             8       61                                  NaN\n",
       "2012-01-05      197                                NaN                      2            124            330       97            13       95                                  NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike[: 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10572b190>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P2hohobJnP/Zi6QiIRdzOUzJ64nIFoF4ZbV1mJW5uERACuLI6nm208syZQSVfAXr+KeCW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\n7XldcN/8B3b8n3FuG4ZJaHPs3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YbrrruOxMREAObNm8eaNWvYu3cvbdq04e677y62DRER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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1057d45d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bike['Berri 1'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "berri_bike = bike[['Berri 1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1\n",
       "Date               \n",
       "2012-01-01       35\n",
       "2012-01-02       83\n",
       "2012-01-03      135\n",
       "2012-01-04      144\n",
       "2012-01-05      197"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bike[: 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-01-01', '2012-01-02', '2012-01-03', '2012-01-04', '2012-01-05', '2012-01-06', '2012-01-07', '2012-01-08', '2012-01-09', '2012-01-10',\n",
       "               ...\n",
       "               '2012-10-27', '2012-10-28', '2012-10-29', '2012-10-30', '2012-10-31', '2012-11-01', '2012-11-02', '2012-11-03', '2012-11-04', '2012-11-05'], dtype='datetime64[ns]', name=u'Date', length=310, freq=None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bike.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
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       "        6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,\n",
       "       23, 24, 25, 26, 27, 28, 29, 30, 31,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
       "       26, 27, 28, 29, 30, 31,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
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       "       29, 30,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,\n",
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       "        2,  3,  4,  5], dtype=int32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bike.index.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
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       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n",
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       "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n",
       "       5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
       "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0], dtype=int32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bike.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wangshuailong/Library/Python/2.7/lib/python/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "berri_bike['weekday'] = berri_bike.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bike[: 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Adding up the cyclists by weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekday_counts = berri_bike.groupby('weekday').aggregate(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>134298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>135305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>152972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>160131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>141771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>101578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>99310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Berri 1\n",
       "Monday      134298\n",
       "Tuesday     135305\n",
       "Wednesday   152972\n",
       "Thursday    160131\n",
       "Friday      141771\n",
       "Saturday    101578\n",
       "Sunday       99310"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x107b6edd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x107b23d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekday_counts.plot(kind='bar')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}