{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "line_width has been deprecated, use display.width instead (currently both are\n",
      "identical)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/anaconda/envs/python3.6/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2862: FutureWarning: \n",
      "mpl_style had been deprecated and will be removed in a future version.\n",
      "Use `matplotlib.pyplot.style.use` instead.\n",
      "\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier\n",
    "plt.rcParams['figure.figsize'] = (15, 5)\n",
    "\n",
    "# This is necessary to show lots of columns in pandas 0.12. \n",
    "# Not necessary in pandas 0.13.\n",
    "pd.set_option('display.line_width', 5000) \n",
    "pd.set_option('display.max_columns', 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay! We're going back to our bike path dataset here. I live in Montreal, and I was curious about whether we're more of a commuter city or a biking-for-fun city -- do people bike more on weekends, or on weekdays?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.1 Adding a 'weekday' column to our dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to load up the data. We've done this before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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n4q7v6Tgd04lrGTb+x7+8zPVaTJ47ej3xdMXj2GgYuF3r4j3Hg3aZPHLKuHk4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3EcXvro+djX+VEE+sDvito5E7cRUcSpToivK5XKtYV9B99E5BUB41l6OeUwdZ4kfpfklM7H2TQs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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110bdff98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes = pd.read_csv('../data/bikes.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date')\n",
    "bikes['Berri 1'].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "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>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",
       "  </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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next up, we're just going to look at the Berri bike path. Berri is a street in Montreal, with a pretty important bike path. I use it mostly on my way to the library now, but I used to take it to work sometimes when I worked in Old Montreal. \n",
    "\n",
    "So we're going to create a dataframe with just the Berri bikepath in it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "berri_bikes = bikes[['Berri 1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "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",
       "\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",
       "    </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",
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       "    <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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bikes[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to add a 'weekday' column. Firstly, we can get the weekday from the index. We haven't talked about indexes yet, but the index is what's on the left on the above dataframe, under 'Date'. It's basically all the days of the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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='Date', length=310, freq=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bikes.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that actually some of the days are missing -- only 310 days of the year are actually there. Who knows why.\n",
    "\n",
    "Pandas has a bunch of really great time series functionality, so if we wanted to get the day of the month for each row, we could do it like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "            ...\n",
       "            27, 28, 29, 30, 31,  1,  2,  3,  4,  5], dtype='int64', name='Date', length=310)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bikes.index.day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We actually want the weekday, though:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "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], dtype='int64', name='Date', length=310)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bikes.index.weekday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the days of the week, where 0 is Monday. I found out that 0 was Monday by checking on a calendar.\n",
    "\n",
    "Now that we know how to *get* the weekday, we can add it as a column in our dataframe like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/anaconda/envs/python3.6/lib/python3.6/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"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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",
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       "    <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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bikes['weekday'] = berri_bikes.index.weekday\n",
    "berri_bikes[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
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       "      <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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# better : no danger\n",
    "\n",
    "bikes['weekday']= bikes.index.weekday\n",
    "berri_bikes = bikes[['Berri 1','weekday']]\n",
    "berri_bikes[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.2 Adding up the cyclists by weekday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This turns out to be really easy!\n",
    "\n",
    "Dataframes have a `.groupby()` method that is similar to SQL groupby, if you're familiar with that. I'm not going to explain more about it right now -- if you want to to know more, [the documentation](http://pandas.pydata.org/pandas-docs/stable/groupby.html) is really good.\n",
    "\n",
    "In this case, `berri_bikes.groupby('weekday').aggregate(sum)` means \"Group the rows by weekday and then add up all the values with the same weekday\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
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       "  <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",
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       "      <td>135305</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "    </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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_counts = berri_bikes.groupby('weekday').sum()\n",
    "weekday_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>Berri 1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>152972</td>\n",
       "      <td>44</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>160131</td>\n",
       "      <td>44</td>\n",
       "      <td>3639.340909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>141771</td>\n",
       "      <td>44</td>\n",
       "      <td>3222.068182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>101578</td>\n",
       "      <td>44</td>\n",
       "      <td>2308.590909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>99310</td>\n",
       "      <td>45</td>\n",
       "      <td>2206.888889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sum     len         mean\n",
       "        Berri 1 Berri 1      Berri 1\n",
       "weekday                             \n",
       "0        134298      45  2984.400000\n",
       "1        135305      44  3075.113636\n",
       "2        152972      44  3476.636364\n",
       "3        160131      44  3639.340909\n",
       "4        141771      44  3222.068182\n",
       "5        101578      44  2308.590909\n",
       "6         99310      45  2206.888889"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alternative with pivot table\n",
    "berri_bikes.pivot_table(index='weekday', aggfunc=[np.sum,len,np.mean] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's hard to remember what 0, 1, 2, 3, 4, 5, 6 mean, so we can fix it up and graph it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
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       "  <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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "weekday_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5wtQ0OjqalpaWqseAI8Z3+xTx2GOP5aabbsrnP//5sT+U49///d+TJA8//HBW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\nf92Mn7ctajxFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110e7e2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekday_counts.plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it looks like Montrealers are commuter cyclists -- they bike much more during the week. Neat!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.3 Putting it together"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's put all that together, to prove how easy it is. 6 lines of magical pandas!\n",
    "\n",
    "If you want to play around, try changing `sum` to `max`, `numpy.median`, or any other function you like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x107a4ad10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fat68ebrmmmv8HgsAABjGDVoAIEtc11UgEFAgEOAucQAAIOM4sgcA\nGTQ8PJw8hfP8aZw/+clPdPbsWf3Lv/yL3+MBAADDKHsAkEFf//rXNWvWrOQNWs7/N336dI7uAQCA\njKLsAQAAAIBB/G9lAAAAADCIsgcAAAAABlH2AAAAAMAgyh4AAAAAGPT/ADj0guAjXpp9AAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107a20110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes = pd.read_csv('../data/bikes.csv', \n",
    "                    sep=';', encoding='latin1', \n",
    "                    parse_dates=['Date'], dayfirst=True, \n",
    "                    index_col='Date')\n",
    "# Add the weekday column\n",
    "berri_bikes = bikes[['Berri 1']]\n",
    "berri_bikes['weekday'] = berri_bikes.index.weekday\n",
    "\n",
    "# Add up the number of cyclists by weekday, and plot!\n",
    "weekday_counts = berri_bikes.groupby('weekday').aggregate(sum)\n",
    "weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "weekday_counts.plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Your turn\n",
    "1. Do the same but now for all bike paths\n",
    "1. Plot the number of bikerides per weekday  in one graph\n",
    "2. Do the same for months\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/anaconda/envs/python3.6/lib/python3.6/site-packages/ipykernel/__main__.py:10: 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"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1113782b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes = pd.read_csv('../data/bikes.csv', \n",
    "                    sep=';', encoding='latin1', \n",
    "                    parse_dates=['Date'], dayfirst=True, \n",
    "                    index_col='Date')\n",
    "\n",
    "### Here we do it for months\n",
    "\n",
    "# Add the weekday column\n",
    "berri_bikes = bikes[['Berri 1','Maisonneuve 1','Maisonneuve 2']]\n",
    "berri_bikes['month'] = berri_bikes.index.month\n",
    "\n",
    "# Add up the number of cyclists by month, and plot!\n",
    "weekday_counts = berri_bikes.groupby('month').aggregate(sum)\n",
    "#weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "weekday_counts.plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/anaconda/envs/python3.6/lib/python3.6/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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110e5d3c8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i66rValRUVCxprtvtRllZWUDWfe211+ad55eSkoLKykrMzc0FZI31hM0dEREREZFMNDc3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\n3ImIiIiIiNQC/x/AOVptDbJlTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110be65f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "berri_bikes['weekdays']= berri_bikes.index.weekday\n",
    "\n",
    "berri_bikes.pivot_table(index=['weekdays'], columns=['month'],aggfunc=np.sum).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<style>\n",
    "    @font-face {\n",
    "        font-family: \"Computer Modern\";\n",
    "        src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
    "    }\n",
    "    div.cell{\n",
    "        width:800px;\n",
    "        margin-left:16% !important;\n",
    "        margin-right:auto;\n",
    "    }\n",
    "    h1 {\n",
    "        font-family: Helvetica, serif;\n",
    "    }\n",
    "    h4{\n",
    "        margin-top:12px;\n",
    "        margin-bottom: 3px;\n",
    "       }\n",
    "    div.text_cell_render{\n",
    "        font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
    "        line-height: 145%;\n",
    "        font-size: 130%;\n",
    "        width:800px;\n",
    "        margin-left:auto;\n",
    "        margin-right:auto;\n",
    "    }\n",
    "    .CodeMirror{\n",
    "            font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
    "    }\n",
    "    .text_cell_render h5 {\n",
    "        font-weight: 300;\n",
    "        font-size: 22pt;\n",
    "        color: #4057A1;\n",
    "        font-style: italic;\n",
    "        margin-bottom: .5em;\n",
    "        margin-top: 0.5em;\n",
    "        display: block;\n",
    "    }\n",
    "    \n",
    "    .warning{\n",
    "        color: rgb( 240, 20, 20 )\n",
    "        }  "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:python3.6]",
   "language": "python",
   "name": "conda-env-python3.6-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}