{
 "metadata": {
  "name": "",
  "signature": "sha256:617afe906295ba54d4e544ac10ea08e6374f5971628b128ca9b89708f34ea1af"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trajets = pd.read_hdf('../datachallenge/divvy-trips.h5', 'fixed')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Afficher les statistiques sur la dur\u00e9e"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trajets.tripduration.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "count    1548935.000000\n",
        "mean         996.651052\n",
        "std         1746.803926\n",
        "min           60.000000\n",
        "25%          422.000000\n",
        "50%          724.000000\n",
        "75%         1200.000000\n",
        "max        86392.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Les m\u00eames, mais en minutes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(trajets.tripduration / 60).describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "count    1548935.000000\n",
        "mean          16.610851\n",
        "std           29.113399\n",
        "min            1.000000\n",
        "25%            7.033333\n",
        "50%           12.066667\n",
        "75%           20.000000\n",
        "max         1439.866667\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Abonn\u00e9s vs occasionnels\n",
      "\n",
      "Attention! `sum()` n'est pas la m\u00eame chose que `count()`. Ici on veut compter le nombre de lignes (peu importe quelle colonne)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trajets.groupby('usertype').count().trip_id.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7faaba1c8350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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eQHm/qOsyhWMG5bUUQXkR3g8ZXSb9emXbJohrFrYXSXOAGez9RmI3ZZH7XyLi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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7faabc2ccb10>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Une solution alternative\n",
      "trajets.usertype.value_counts().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7faaba152310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0hpe3ZQpnYDhZ2BEk/VHafATYIemzqX0h8I08UZkd1Y+AF+UOYr5zsrDpFlJO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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7faab9855f10>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Age maximum et minimum"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(2014 - trajets.birthyear).describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "count    1071696.000000\n",
        "mean          35.155730\n",
        "std           10.476417\n",
        "min           16.000000\n",
        "25%           27.000000\n",
        "50%           32.000000\n",
        "75%           41.000000\n",
        "max          116.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Distribution des sexes.\n",
      "\n",
      "M\u00eames remarques qu'avant."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trajets.groupby('gender').count().trip_id.plot(kind='pie')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7faaba12f690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0wZDvxHknwIYtbWAyQxnG+NAxpKaM8lf9ksBBJIzDgBEbAx8OnWRdNWRpm5nRxPc4gHYd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\n0hYRiRGVtohIjKi0RURiRKUtIhIj/wsrcJeSCjWSggAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7faab988d7d0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Trajets en fonction de l'age.\n",
      "\n",
      "Encore une fois, on veut compter, pas sommer\u202f!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trajets.groupby(2014 - trajets.birthyear).count().trip_id.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7faab97d0350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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StCD6OQOYApwqaQkwIprHzBYBdwGLgAeBiYlD9onArcBSYJmZzY7abwN2k7QU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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7faab975b290>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trajets.set_index('starttime', inplace=True, drop=False)\n",
      "trajets['date'] = trajets.index.date\n",
      "trajets['joursemaine'] = trajets.index.weekday\n",
      "trajets['heure'] = trajets.index.hour"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Dur\u00e9e moyenne par jour (calendrier).\n",
      "\n",
      "On ne veut plus compter\u202f: on veut une moyenne (_mean_)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trajets.groupby('date').mean().tripduration.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7faab3f6b2d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VPsmHB58vi8YsWLqiB9/amjm9Y7HEFnsFb/6B2mHMGHuC//OfRb1bbwxuiteu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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7faab975b710>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Par jour de la semaine"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trajets.groupby('joursemaine').mean().tripduration.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7faab9626550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7faab962d390>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    }
   ],
   "metadata": {}
  }
 ]
}