analysis.ipynb

{
 "metadata": {
  "name": "analysis"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "index_cols = ['date', 'id_route', 'id_trip', 'trip_direction']\n",
      "time_cols = [\n",
      "    'time_arrival', 'time_departure',\n",
      "    'time_scheduled_arrival', 'time_scheduled_departure'\n",
      "    ]   \n",
      "\n",
      "def load_data(city):\n",
      "    df = pd.read_csv('data/common_format/{}.csv'.format(city), \n",
      "    parse_dates=[0]).set_index(index_cols)\n",
      "    for tcol in filter(lambda c: c in time_cols, df.columns):\n",
      "        df[tcol] = pd.DatetimeIndex(df[tcol]).values\n",
      "    return df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = dict(\n",
      "    SF = load_data('san-francisco'),\n",
      "    GN = load_data('geneva'),\n",
      "    ZR = load_data('zurich')\n",
      "    )\n",
      "\n",
      "\n",
      "#GN = pd.read_csv('data/common_format/geneva.csv',\n",
      "#    parse_dates=[0]).set_index(index_cols)\n",
      "#ZR = pd.read_csv('data/common_format/zurich.csv'.format(city), \n",
      "#    parse_dates=[0]).set_index(index_cols)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['SF'].groupby(level=[0,1,2,3])['count'].max()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "date        id_route  id_trip  trip_direction\n",
        "2012-10-01  1         744      1                 28\n",
        "                      758      1                 46\n",
        "                      814      0                 22\n",
        "                      828      0                 22\n",
        "                      847      1                 41\n",
        "                      902      1                 20\n",
        "                      917      0                 15\n",
        "                      924      0                  0\n",
        "                      932      0                 12\n",
        "                      1020     1                 57\n",
        "                      1110     0                 37\n",
        "                      1205     1                 24\n",
        "                      1255     0                 32\n",
        "                      1350     1                 25\n",
        "                      1440     0                 43\n",
        "...\n",
        "2012-10-07  708       1851     1                 40\n",
        "                      1915     0                 69\n",
        "                      1940     1                 34\n",
        "                      2006     0                 63\n",
        "                      2040     1                 23\n",
        "                      2100     0                 48\n",
        "                      2125     1                 36\n",
        "                      2145     0                 58\n",
        "                      2210     1                 30\n",
        "                      2230     0                 48\n",
        "                      2255     1                 10\n",
        "                      2315     0                 52\n",
        "                      2340     1                 17\n",
        "                      2400     0                 15\n",
        "                      2445     0                 12\n",
        "Name: count, Length: 12391"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['GN'].groupby(data['GN'].index)['count'].max()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "(2012-10-01 00:00:00, 1, 27080332, 1)     2\n",
        "(2012-10-01 00:00:00, 1, 27080675, 0)    10\n",
        "(2012-10-01 00:00:00, 1, 27087869, 1)    35\n",
        "(2012-10-01 00:00:00, 1, 27087871, 0)    36\n",
        "(2012-10-01 00:00:00, 1, 27087872, 1)    14\n",
        "(2012-10-01 00:00:00, 1, 27087874, 0)    40\n",
        "(2012-10-01 00:00:00, 1, 27087875, 1)    27\n",
        "(2012-10-01 00:00:00, 1, 27087877, 0)    39\n",
        "(2012-10-01 00:00:00, 1, 27087878, 1)    28\n",
        "(2012-10-01 00:00:00, 1, 27087880, 0)    46\n",
        "(2012-10-01 00:00:00, 1, 27087881, 1)    38\n",
        "(2012-10-01 00:00:00, 1, 27087883, 0)    44\n",
        "(2012-10-01 00:00:00, 1, 27087884, 1)    34\n",
        "(2012-10-01 00:00:00, 1, 27087886, 0)    40\n",
        "(2012-10-01 00:00:00, 1, 27087888, 1)    68\n",
        "...\n",
        "(2012-10-07 00:00:00, Z, 27170820, 1)    21\n",
        "(2012-10-07 00:00:00, Z, 27170821, 0)    25\n",
        "(2012-10-07 00:00:00, Z, 27170822, 1)    18\n",
        "(2012-10-07 00:00:00, Z, 27170823, 0)     8\n",
        "(2012-10-07 00:00:00, Z, 27170824, 1)    26\n",
        "(2012-10-07 00:00:00, Z, 27170825, 0)    23\n",
        "(2012-10-07 00:00:00, Z, 27170826, 1)    28\n",
        "(2012-10-07 00:00:00, Z, 27170827, 0)    13\n",
        "(2012-10-07 00:00:00, Z, 27170828, 1)    20\n",
        "(2012-10-07 00:00:00, Z, 27170829, 0)    14\n",
        "(2012-10-07 00:00:00, Z, 27170830, 1)    22\n",
        "(2012-10-07 00:00:00, Z, 27170831, 0)    12\n",
        "(2012-10-07 00:00:00, Z, 27170832, 1)    12\n",
        "(2012-10-07 00:00:00, Z, 27170833, 0)    10\n",
        "(2012-10-07 00:00:00, Z, 27170836, 1)     3\n",
        "Name: count, Length: 49926"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['SF'].groupby(pd.DatetimeIndex(data['SF'].time_arrival).hour).count.sum().plot();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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CnC/7v7/+uI7VudMYox7a/Qpz4iMJU6s5NULtrVDrhTdaO/jVkVq++4D+pjNCf83Fiuyp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\n"
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['GN'].groupby(pd.DatetimeIndex(data['GN'].time_arrival).hour).count.sum().plot();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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C1WoFAGg0GqhUqsGVSqWIj4+HVqtFU1MTrFYrwsIGf2WpVCqUlZUBAMrKypCW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\nUCgUsFi4uwZaLBaEhYUhLCx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      }
     ],
     "prompt_number": 24
    }
   ],
   "metadata": {}
  }
 ]
}