{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier\n",
      "figsize(15, 5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "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",
     "collapsed": false,
     "input": [
      "bikes = pd.read_csv('../data/bikes.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date')\n",
      "bikes['Berri 1'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x30d8610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAA3AAAAFNCAYAAABBkY2NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAACAiQA2DArd9AFhnwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x30cce90>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "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",
     "collapsed": false,
     "input": [
      "berri_bikes = bikes[['Berri 1']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "berri_bikes[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "            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"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "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",
     "collapsed": false,
     "input": [
      "berri_bikes.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2012-01-01 00:00:00, ..., 2012-11-05 00:00:00]\n",
        "Length: 310, Freq: None, Timezone: None"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "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",
     "collapsed": false,
     "input": [
      "berri_bikes.index.day"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
        "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,  1,  2,  3,\n",
        "        4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,\n",
        "       21, 22, 23, 24, 25, 26, 27, 28, 29,  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",
        "       12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,\n",
        "       29, 30,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,\n",
        "       16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,  1,\n",
        "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
        "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,  1,  2,  3,  4,  5,\n",
        "        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",
        "       12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,\n",
        "       29, 30,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,\n",
        "       16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,  1,\n",
        "        2,  3,  4,  5], dtype=int32)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We actually want the weekday, though:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "berri_bikes.index.weekday"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "array([6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
        "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
        "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n",
        "       5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
        "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
        "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
        "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
        "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
        "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
        "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n",
        "       5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
        "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
        "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
        "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0], dtype=int32)"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "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",
     "collapsed": false,
     "input": [
      "berri_bikes['weekday'] = berri_bikes.index.weekday\n",
      "berri_bikes[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "            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"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "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",
      "Dataframs 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",
     "collapsed": false,
     "input": [
      "weekday_counts = berri_bikes.groupby('weekday').aggregate(sum)\n",
      "weekday_counts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "         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"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "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",
     "collapsed": false,
     "input": [
      "weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
      "weekday_counts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "           Berri 1\n",
        "Monday      134298\n",
        "Tuesday     135305\n",
        "Wednesday   152972\n",
        "Thursday    160131\n",
        "Friday      141771\n",
        "Saturday    101578\n",
        "Sunday       99310"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weekday_counts.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x3216a90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAA3sAAAFjCAYAAACE1xI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x347a9d0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "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`, `np.median`, or any other function you like."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x37d3850>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAA3sAAAFjCAYAAACE1xI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x37d32d0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "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": {}
  }
 ]
}