Pandas Tour

PandasTour.ipynb

{
 "metadata": {
  "name": "PandasTour"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "from datetime import time\n",
      "pd.set_option('html', False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import Image\n",
      "Image('http://akamaicovers.oreilly.com/images/0636920023784/lrg.jpg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "jpeg": 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mdZ6T1ag39Ny6sqsRvNbgS3d9FtrPp0v/AJFrU32T+9+DaPx3GdDykTv+nH9P5/8AI/pf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\nb3I8L2tleT4KCQkJPHN0cml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6q9+y+sn09jHbTVm21W7t2xn07qLv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\nbi9ra2xLL2EzU3Y4Q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dtgqqNyTixn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n+R8X/NWKu/xH5e/6ulp/yPi/5qxV3+I/L3/V0tP+R8X/ADVirv8AEfl7/q6Wn/I+L/mrFXf4j8vf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\ny48q8eXHfjir/9k=\n",
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<IPython.core.display.Image at 0x1087cdb50>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Important Features in pandas\n",
      "==="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Fast tabular data IO\n",
      "---"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temp = '/Users/wesm/Downloads/minutebars/%s.csv'\n",
      "path = temp % 'AAPL'\n",
      "!wc -l $path"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  489598 /Users/wesm/Downloads/minutebars/AAPL.csv\r\n"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aapl_bars = pd.read_csv(temp % 'AAPL')\n",
      "aapl_bars"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 489597 entries, 0 to 489596\n",
        "Data columns:\n",
        "volume         489597  non-null values\n",
        "high           489597  non-null values\n",
        "low            489597  non-null values\n",
        "close_price    489597  non-null values\n",
        "dt             489597  non-null values\n",
        "open_price     489597  non-null values\n",
        "dtypes: float64(4), int64(1), object(1)"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%time _ = pd.read_csv(path)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 0.72 s, sys: 0.18 s, total: 0.90 s\n",
        "Wall time: 0.90 s\n"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Time series operations\n",
      "---"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aapl_bars.dt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "0     2008-01-07 14:31:00+00:00\n",
        "1     2008-01-07 14:32:00+00:00\n",
        "2     2008-01-07 14:33:00+00:00\n",
        "3     2008-01-07 14:34:00+00:00\n",
        "4     2008-01-07 14:35:00+00:00\n",
        "5     2008-01-07 14:36:00+00:00\n",
        "6     2008-01-07 14:37:00+00:00\n",
        "7     2008-01-07 14:38:00+00:00\n",
        "8     2008-01-07 14:39:00+00:00\n",
        "9     2008-01-07 14:40:00+00:00\n",
        "10    2008-01-07 14:41:00+00:00\n",
        "11    2008-01-07 14:42:00+00:00\n",
        "12    2008-01-07 14:43:00+00:00\n",
        "13    2008-01-07 14:44:00+00:00\n",
        "14    2008-01-07 14:45:00+00:00\n",
        "...\n",
        "489582    2013-01-07 20:46:00+00:00\n",
        "489583    2013-01-07 20:47:00+00:00\n",
        "489584    2013-01-07 20:48:00+00:00\n",
        "489585    2013-01-07 20:49:00+00:00\n",
        "489586    2013-01-07 20:50:00+00:00\n",
        "489587    2013-01-07 20:51:00+00:00\n",
        "489588    2013-01-07 20:52:00+00:00\n",
        "489589    2013-01-07 20:53:00+00:00\n",
        "489590    2013-01-07 20:54:00+00:00\n",
        "489591    2013-01-07 20:55:00+00:00\n",
        "489592    2013-01-07 20:56:00+00:00\n",
        "489593    2013-01-07 20:57:00+00:00\n",
        "489594    2013-01-07 20:58:00+00:00\n",
        "489595    2013-01-07 20:59:00+00:00\n",
        "489596    2013-01-07 21:00:00+00:00\n",
        "Name: dt, Length: 489597"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aapl_bars.index = pd.to_datetime(aapl_bars.pop('dt'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aapl_bars.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "                     volume    high     low  close_price  open_price\n",
        "dt                                                                  \n",
        "2008-01-07 14:31:00  593143  182.07  181.00       182.03      181.25\n",
        "2008-01-07 14:32:00  344139  182.56  181.92       182.54      182.04\n",
        "2008-01-07 14:33:00  257436  182.75  182.23       182.50      182.53\n",
        "2008-01-07 14:34:00  235358  182.62  182.15       182.30      182.51\n",
        "2008-01-07 14:35:00  281763  182.50  181.71       181.97      182.33"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def load_bars(ticker):\n",
      "    bars = pd.read_csv(temp % ticker)\n",
      "    bars.index = pd.to_datetime(bars.pop('dt'))\n",
      "    return bars"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aapl_bars.at_time(time(15, 0)).head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "                     volume    high     low  close_price  open_price\n",
        "dt                                                                  \n",
        "2008-01-07 15:00:00  148512  183.10  182.60       182.64     182.860\n",
        "2008-01-08 15:00:00  136202  179.44  178.87       179.40     179.020\n",
        "2008-01-09 15:00:00  179490  173.49  172.71       172.80     173.431\n",
        "2008-01-10 15:00:00  253765  177.08  176.08       176.19     176.540\n",
        "2008-01-11 15:00:00   96055  176.70  176.17       176.43     176.520\n",
        "2008-01-14 15:00:00  181223  176.70  176.00       176.53     176.600\n",
        "2008-01-15 15:00:00  166167  177.78  177.19       177.72     177.200\n",
        "2008-01-16 15:00:00  292435  165.91  164.70       164.76     165.402\n",
        "2008-01-17 15:00:00  152760  164.24  163.29       163.29     163.600\n",
        "2008-01-18 15:00:00  217712  163.97  163.50       163.75     163.500"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aapl_bars.close_price['2009-10-15']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "dt\n",
        "2009-10-15 14:31:00    189.870\n",
        "2009-10-15 14:32:00    189.800\n",
        "2009-10-15 14:33:00    189.890\n",
        "2009-10-15 14:34:00    189.930\n",
        "2009-10-15 14:35:00    189.920\n",
        "2009-10-15 14:36:00    189.976\n",
        "2009-10-15 14:37:00    190.030\n",
        "2009-10-15 14:38:00    189.940\n",
        "2009-10-15 14:39:00    189.910\n",
        "2009-10-15 14:40:00    190.069\n",
        "2009-10-15 14:41:00    190.020\n",
        "2009-10-15 14:42:00    189.990\n",
        "2009-10-15 14:43:00    189.920\n",
        "2009-10-15 14:44:00    189.990\n",
        "2009-10-15 14:45:00    189.830\n",
        "...\n",
        "2009-10-15 20:46:00    189.990\n",
        "2009-10-15 20:47:00    189.940\n",
        "2009-10-15 20:48:00    189.910\n",
        "2009-10-15 20:49:00    189.821\n",
        "2009-10-15 20:50:00    189.860\n",
        "2009-10-15 20:51:00    189.940\n",
        "2009-10-15 20:52:00    189.940\n",
        "2009-10-15 20:53:00    189.980\n",
        "2009-10-15 20:54:00    190.010\n",
        "2009-10-15 20:55:00    190.090\n",
        "2009-10-15 20:56:00    190.139\n",
        "2009-10-15 20:57:00    190.170\n",
        "2009-10-15 20:58:00    190.210\n",
        "2009-10-15 20:59:00    190.200\n",
        "2009-10-15 21:00:00    190.560\n",
        "Name: close_price, Length: 390"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aapl_bars.close_price"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "dt\n",
        "2008-01-07 14:31:00    182.03\n",
        "2008-01-07 14:32:00    182.54\n",
        "2008-01-07 14:33:00    182.50\n",
        "2008-01-07 14:34:00    182.30\n",
        "2008-01-07 14:35:00    181.97\n",
        "2008-01-07 14:36:00    181.48\n",
        "2008-01-07 14:37:00    181.04\n",
        "2008-01-07 14:38:00    180.68\n",
        "2008-01-07 14:39:00    180.80\n",
        "2008-01-07 14:40:00    180.86\n",
        "2008-01-07 14:41:00    181.03\n",
        "2008-01-07 14:42:00    180.88\n",
        "2008-01-07 14:43:00    181.11\n",
        "2008-01-07 14:44:00    181.24\n",
        "2008-01-07 14:45:00    181.84\n",
        "...\n",
        "2013-01-07 20:46:00    523.480\n",
        "2013-01-07 20:47:00    522.970\n",
        "2013-01-07 20:48:00    522.954\n",
        "2013-01-07 20:49:00    523.850\n",
        "2013-01-07 20:50:00    523.750\n",
        "2013-01-07 20:51:00    524.270\n",
        "2013-01-07 20:52:00    524.230\n",
        "2013-01-07 20:53:00    524.940\n",
        "2013-01-07 20:54:00    524.580\n",
        "2013-01-07 20:55:00    524.490\n",
        "2013-01-07 20:56:00    524.143\n",
        "2013-01-07 20:57:00    523.750\n",
        "2013-01-07 20:58:00    524.040\n",
        "2013-01-07 20:59:00    523.780\n",
        "2013-01-07 21:00:00    523.970\n",
        "Name: close_price, Length: 489597"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mth_mean = aapl_bars.close_price.resample('M', how=['mean', 'median', 'std'])\n",
      "mth_mean"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "                  mean    median        std\n",
        "dt                                         \n",
        "2008-01-31  155.579126  161.1000  19.396180\n",
        "2008-02-29  125.582646  125.7500   4.720866\n",
        "2008-03-31  130.494650  127.3600   8.403366\n",
        "2008-04-30  158.145335  154.7350   9.087710\n",
        "2008-05-31  184.774497  185.0800   3.618693\n",
        "2008-06-30  178.654374  178.9300   6.429312\n",
        "2008-07-31  167.784954  169.9300   7.347889\n",
        "2008-08-31  171.233317  174.0500   7.244400\n",
        "2008-09-30  141.942077  140.9100  16.775448\n",
        "2008-10-31   99.343456   97.6300   6.335940\n",
        "2008-11-30   93.985805   92.2400   7.549505\n",
        "2008-12-31   91.581060   90.2600   4.812012\n",
        "2009-01-31   88.675004   89.8200   4.586041\n",
        "2009-02-28   94.207834   94.3400   4.092791\n",
        "2009-03-31   97.352191   96.9470   7.816247\n",
        "2009-04-30  119.858558  120.3500   4.900853\n",
        "2009-05-31  127.983364  128.2600   4.061250\n",
        "2009-06-30  139.360858  139.3000   2.799540\n",
        "2009-07-31  149.103911  148.3800   9.518534\n",
        "2009-08-31  166.170257  165.9500   2.354870\n",
        "2009-09-30  177.893266  181.7000   7.385285\n",
        "2009-10-31  193.195646  190.5500   6.595516\n",
        "2009-11-30  200.248734  202.0900   5.619620\n",
        "2009-12-31  199.102938  197.2200   6.731990\n",
        "2010-01-31  208.156303  209.4800   5.058381\n",
        "2010-02-28  198.559427  198.3900   3.454915\n",
        "2010-03-31  223.336387  224.3300   7.781226\n",
        "2010-04-30  250.766901  245.9600  11.538351\n",
        "2010-05-31  251.805447  252.0230   7.846905\n",
        "2010-06-30  261.842737  262.5325   8.737081\n",
        "2010-07-31  254.440632  256.2700   5.695913\n",
        "2010-08-31  251.561496  250.5900   7.495469\n",
        "2010-09-30  273.745057  272.1700  13.610222\n",
        "2010-10-31  300.674357  302.7455  10.336923\n",
        "2010-11-30  311.558581  311.1500   5.419399\n",
        "2010-12-31  321.606753  321.5000   2.633648\n",
        "2011-01-31  338.037888  338.5950   5.570475\n",
        "2011-02-28  351.196638  352.7500   7.126330\n",
        "2011-03-31  347.578442  349.0200   7.478078\n",
        "2011-04-30  340.500490  339.4450   8.392912\n",
        "2011-05-31  341.849013  342.9230   5.946428\n",
        "2011-06-30  331.625547  331.5605   8.191484\n",
        "2011-07-31  371.942253  365.4800  19.378892\n",
        "2011-08-31  377.103717  377.3100  10.823605\n",
        "2011-09-30  393.123445  391.6450  13.226161\n",
        "2011-10-31  397.190649  400.4600  15.248496\n",
        "2011-11-30  385.337475  383.8800  11.555804\n",
        "2011-12-31  393.056772  392.8600   8.102446\n",
        "2012-01-31  428.571704  424.3000  12.856495\n",
        "2012-02-29  496.902626  502.0500  25.815138\n",
        "2012-03-31  576.550595  587.1600  30.231559\n",
        "2012-04-30  607.127307  609.0175  21.316798\n",
        "2012-05-31  565.350624  567.3405  13.847976\n",
        "2012-06-30  574.423152  573.5770   7.848743\n",
        "2012-07-31  600.786165  604.4400  11.827663\n",
        "2012-08-31  642.572344  635.9400  23.148544\n",
        "2012-09-30  682.100657  679.4900  13.630957\n",
        "2012-10-31  635.947102  634.1975  20.621637\n",
        "2012-11-30  564.845334  563.0600  21.851129\n",
        "2012-12-31  532.420270  528.8600  21.166298\n",
        "2013-01-31  536.401272  536.9800  10.776982"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mth_mean.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10db20e50>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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0P7MtGBc2loyGnzBqzAWu2MjWzSrNIiIiIlzdOSPjgNYz24J6lerxaL2HMTWZ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\nIiIi0uL/ATY6kn90xRVLAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10db20bd0>"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "close = aapl_bars.close_price\n",
      "close / close.shift(1) - 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "dt\n",
        "2008-01-07 14:31:00         NaN\n",
        "2008-01-07 14:32:00    0.002802\n",
        "2008-01-07 14:33:00   -0.000219\n",
        "2008-01-07 14:34:00   -0.001096\n",
        "2008-01-07 14:35:00   -0.001810\n",
        "2008-01-07 14:36:00   -0.002693\n",
        "2008-01-07 14:37:00   -0.002425\n",
        "2008-01-07 14:38:00   -0.001989\n",
        "2008-01-07 14:39:00    0.000664\n",
        "2008-01-07 14:40:00    0.000332\n",
        "2008-01-07 14:41:00    0.000940\n",
        "2008-01-07 14:42:00   -0.000829\n",
        "2008-01-07 14:43:00    0.001272\n",
        "2008-01-07 14:44:00    0.000718\n",
        "2008-01-07 14:45:00    0.003311\n",
        "...\n",
        "2013-01-07 20:46:00   -0.000363\n",
        "2013-01-07 20:47:00   -0.000974\n",
        "2013-01-07 20:48:00   -0.000031\n",
        "2013-01-07 20:49:00    0.001713\n",
        "2013-01-07 20:50:00   -0.000191\n",
        "2013-01-07 20:51:00    0.000993\n",
        "2013-01-07 20:52:00   -0.000076\n",
        "2013-01-07 20:53:00    0.001354\n",
        "2013-01-07 20:54:00   -0.000686\n",
        "2013-01-07 20:55:00   -0.000172\n",
        "2013-01-07 20:56:00   -0.000662\n",
        "2013-01-07 20:57:00   -0.000750\n",
        "2013-01-07 20:58:00    0.000554\n",
        "2013-01-07 20:59:00   -0.000496\n",
        "2013-01-07 21:00:00    0.000363\n",
        "Name: close_price, Length: 489597"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "minute_returns = aapl_bars.close_price.pct_change()\n",
      "std_10day = pd.rolling_std(minute_returns, 390 * 10)\n",
      "std_10day.resample('B').plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10d94ca90>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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pJtf5oakmIiIi8is21WSJH5pq5uFIhXVBKqwLMmJNkAoz1eQ6PzTVRERERH7Fppos8UNT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       "text": [
        "<matplotlib.figure.Figure at 0x10d97b350>"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Data alignment\n",
      "---"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ts1 = pd.Series(np.random.randn(10), \n",
      "                index=pd.date_range('1/1/2000', periods=10))\n",
      "ts1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "2000-01-01    0.583378\n",
        "2000-01-02   -1.542445\n",
        "2000-01-03   -0.215126\n",
        "2000-01-04   -1.231664\n",
        "2000-01-05    0.334308\n",
        "2000-01-06    1.234491\n",
        "2000-01-07   -0.678003\n",
        "2000-01-08   -0.496512\n",
        "2000-01-09   -2.023730\n",
        "2000-01-10   -0.464823\n",
        "Freq: D"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ts2 = ts1[[0, 2, 4, 5, 6, 7, 8]]\n",
      "ts2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "2000-01-01    0.583378\n",
        "2000-01-03   -0.215126\n",
        "2000-01-05    0.334308\n",
        "2000-01-06    1.234491\n",
        "2000-01-07   -0.678003\n",
        "2000-01-08   -0.496512\n",
        "2000-01-09   -2.023730"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ts1 + ts2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "2000-01-01    1.166756\n",
        "2000-01-02         NaN\n",
        "2000-01-03   -0.430252\n",
        "2000-01-04         NaN\n",
        "2000-01-05    0.668617\n",
        "2000-01-06    2.468981\n",
        "2000-01-07   -1.356005\n",
        "2000-01-08   -0.993023\n",
        "2000-01-09   -4.047460\n",
        "2000-01-10         NaN"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame({'A': ts1, 'B': ts2})\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 89,
       "text": [
        "                   A         B\n",
        "2000-01-01  0.583378  0.583378\n",
        "2000-01-02 -1.542445       NaN\n",
        "2000-01-03 -0.215126 -0.215126\n",
        "2000-01-04 -1.231664       NaN\n",
        "2000-01-05  0.334308  0.334308\n",
        "2000-01-06  1.234491  1.234491\n",
        "2000-01-07 -0.678003 -0.678003\n",
        "2000-01-08 -0.496512 -0.496512\n",
        "2000-01-09 -2.023730 -2.023730\n",
        "2000-01-10 -0.464823       NaN"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ibm_bars = load_bars('IBM')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def subsample(frame, pct=0.9):\n",
      "    N = len(frame)\n",
      "    indexer = np.sort(np.random.permutation(N)[:pct*N])\n",
      "    return frame.take(indexer)\n",
      "\n",
      "f1 = subsample(ibm_bars)\n",
      "f2 = subsample(aapl_bars)\n",
      "f1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 91,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 440607 entries, 2008-01-07 14:31:00 to 2013-01-07 21:00:00\n",
        "Data columns:\n",
        "volume         440607  non-null values\n",
        "high           440607  non-null values\n",
        "low            440607  non-null values\n",
        "close_price    440607  non-null values\n",
        "open_price     440607  non-null values\n",
        "dtypes: float64(4), int64(1)"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "both = pd.concat([f1, f2], axis=1, keys=['IBM', 'AAPL'])\n",
      "both.head(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 92,
       "text": [
        "                        IBM                                             AAPL           \\\n",
        "                     volume    high     low  close_price  open_price  volume     high   \n",
        "dt                                                                                      \n",
        "2008-01-07 14:31:00  173600  100.47  100.02      100.100     100.250  593143  182.070   \n",
        "2008-01-07 14:32:00   93937  100.13   99.77       99.900     100.110  344139  182.560   \n",
        "2008-01-07 14:33:00   75100   99.93   99.75       99.890      99.900  257436  182.750   \n",
        "2008-01-07 14:34:00   60300   99.93   99.55       99.560      99.900  235358  182.620   \n",
        "2008-01-07 14:35:00   66300   99.84   99.51       99.659      99.550  281763  182.500   \n",
        "2008-01-07 14:36:00  115600   99.70   99.56       99.610      99.694  184191  182.000   \n",
        "2008-01-07 14:37:00   68400   99.65   99.50       99.600      99.620     NaN      NaN   \n",
        "2008-01-07 14:38:00   94800   99.68   99.55       99.590      99.620  324597  181.100   \n",
        "2008-01-07 14:39:00   71200   99.60   99.25       99.320      99.590  274712  181.160   \n",
        "2008-01-07 14:40:00   61900   99.56   99.27       99.550      99.300  403421  181.040   \n",
        "2008-01-07 14:41:00   50400   99.75   99.54       99.630      99.550  342292  181.172   \n",
        "2008-01-07 14:42:00   64300   99.87   99.56       99.775      99.660  229040  181.430   \n",
        "2008-01-07 14:43:00   41500   99.79   99.69       99.760      99.770  203759  181.120   \n",
        "2008-01-07 14:44:00   60900   99.85   99.71       99.720      99.780  163075  181.240   \n",
        "2008-01-07 14:45:00   46400  100.00   99.72      100.000      99.720  263733  181.870   \n",
        "2008-01-07 14:46:00   64800  100.42   99.98      100.330     100.000  239652  182.500   \n",
        "2008-01-07 14:47:00   43200  100.39  100.17      100.180     100.340  217670  182.680   \n",
        "2008-01-07 14:48:00   66400  100.49  100.14      100.300     100.230  235761  182.420   \n",
        "2008-01-07 14:49:00   80954  100.59  100.26      100.550     100.300  159339  182.380   \n",
        "2008-01-07 14:50:00   81100  100.59  100.41      100.490     100.550     NaN      NaN   \n",
        "\n",
        "                                                      \n",
        "                        low  close_price  open_price  \n",
        "dt                                                    \n",
        "2008-01-07 14:31:00  181.00       182.03     181.250  \n",
        "2008-01-07 14:32:00  181.92       182.54     182.040  \n",
        "2008-01-07 14:33:00  182.23       182.50     182.530  \n",
        "2008-01-07 14:34:00  182.15       182.30     182.510  \n",
        "2008-01-07 14:35:00  181.71       181.97     182.330  \n",
        "2008-01-07 14:36:00  181.41       181.48     181.904  \n",
        "2008-01-07 14:37:00     NaN          NaN         NaN  \n",
        "2008-01-07 14:38:00  180.65       180.68     181.050  \n",
        "2008-01-07 14:39:00  180.62       180.80     180.680  \n",
        "2008-01-07 14:40:00  180.21       180.86     180.800  \n",
        "2008-01-07 14:41:00  180.67       181.03     180.860  \n",
        "2008-01-07 14:42:00  180.83       180.88     181.025  \n",
        "2008-01-07 14:43:00  180.80       181.11     180.890  \n",
        "2008-01-07 14:44:00  180.99       181.24     181.120  \n",
        "2008-01-07 14:45:00  181.19       181.84     181.240  \n",
        "2008-01-07 14:46:00  181.84       182.50     181.880  \n",
        "2008-01-07 14:47:00  182.20       182.35     182.500  \n",
        "2008-01-07 14:48:00  181.62       181.63     182.240  \n",
        "2008-01-07 14:49:00  181.67       181.99     181.720  \n",
        "2008-01-07 14:50:00     NaN          NaN         NaN  "
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Missing data handling\n",
      "---"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 93,
       "text": [
        "                   A         B\n",
        "2000-01-01  0.583378  0.583378\n",
        "2000-01-02 -1.542445       NaN\n",
        "2000-01-03 -0.215126 -0.215126\n",
        "2000-01-04 -1.231664       NaN\n",
        "2000-01-05  0.334308  0.334308\n",
        "2000-01-06  1.234491  1.234491\n",
        "2000-01-07 -0.678003 -0.678003\n",
        "2000-01-08 -0.496512 -0.496512\n",
        "2000-01-09 -2.023730 -2.023730\n",
        "2000-01-10 -0.464823       NaN"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 94,
       "text": [
        "A    10\n",
        "B     7"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "both.count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 95,
       "text": [
        "IBM   volume         440607\n",
        "      high           440607\n",
        "      low            440607\n",
        "      close_price    440607\n",
        "      open_price     440607\n",
        "AAPL  volume         440637\n",
        "      high           440637\n",
        "      low            440637\n",
        "      close_price    440637\n",
        "      open_price     440637"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 96,
       "text": [
        "A   -4.500125\n",
        "B   -1.261193"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.mean(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "2000-01-01    0.583378\n",
        "2000-01-02   -1.542445\n",
        "2000-01-03   -0.215126\n",
        "2000-01-04   -1.231664\n",
        "2000-01-05    0.334308\n",
        "2000-01-06    1.234491\n",
        "2000-01-07   -0.678003\n",
        "2000-01-08   -0.496512\n",
        "2000-01-09   -2.023730\n",
        "2000-01-10   -0.464823\n",
        "Freq: D"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 98,
       "text": [
        "                   A         B\n",
        "2000-01-01  0.583378  0.583378\n",
        "2000-01-03 -0.215126 -0.215126\n",
        "2000-01-05  0.334308  0.334308\n",
        "2000-01-06  1.234491  1.234491\n",
        "2000-01-07 -0.678003 -0.678003\n",
        "2000-01-08 -0.496512 -0.496512\n",
        "2000-01-09 -2.023730 -2.023730"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.fillna(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 99,
       "text": [
        "                   A         B\n",
        "2000-01-01  0.583378  0.583378\n",
        "2000-01-02 -1.542445  0.000000\n",
        "2000-01-03 -0.215126 -0.215126\n",
        "2000-01-04 -1.231664  0.000000\n",
        "2000-01-05  0.334308  0.334308\n",
        "2000-01-06  1.234491  1.234491\n",
        "2000-01-07 -0.678003 -0.678003\n",
        "2000-01-08 -0.496512 -0.496512\n",
        "2000-01-09 -2.023730 -2.023730\n",
        "2000-01-10 -0.464823  0.000000"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.fillna(method='ffill')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 100,
       "text": [
        "                   A         B\n",
        "2000-01-01  0.583378  0.583378\n",
        "2000-01-02 -1.542445  0.583378\n",
        "2000-01-03 -0.215126 -0.215126\n",
        "2000-01-04 -1.231664 -0.215126\n",
        "2000-01-05  0.334308  0.334308\n",
        "2000-01-06  1.234491  1.234491\n",
        "2000-01-07 -0.678003 -0.678003\n",
        "2000-01-08 -0.496512 -0.496512\n",
        "2000-01-09 -2.023730 -2.023730\n",
        "2000-01-10 -0.464823 -2.023730"
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.asfreq('4h')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 101,
       "text": [
        "                            A         B\n",
        "2000-01-01 00:00:00  0.583378  0.583378\n",
        "2000-01-01 04:00:00       NaN       NaN\n",
        "2000-01-01 08:00:00       NaN       NaN\n",
        "2000-01-01 12:00:00       NaN       NaN\n",
        "2000-01-01 16:00:00       NaN       NaN\n",
        "2000-01-01 20:00:00       NaN       NaN\n",
        "2000-01-02 00:00:00 -1.542445       NaN\n",
        "2000-01-02 04:00:00       NaN       NaN\n",
        "2000-01-02 08:00:00       NaN       NaN\n",
        "2000-01-02 12:00:00       NaN       NaN\n",
        "2000-01-02 16:00:00       NaN       NaN\n",
        "2000-01-02 20:00:00       NaN       NaN\n",
        "2000-01-03 00:00:00 -0.215126 -0.215126\n",
        "2000-01-03 04:00:00       NaN       NaN\n",
        "2000-01-03 08:00:00       NaN       NaN\n",
        "2000-01-03 12:00:00       NaN       NaN\n",
        "2000-01-03 16:00:00       NaN       NaN\n",
        "2000-01-03 20:00:00       NaN       NaN\n",
        "2000-01-04 00:00:00 -1.231664       NaN\n",
        "2000-01-04 04:00:00       NaN       NaN\n",
        "2000-01-04 08:00:00       NaN       NaN\n",
        "2000-01-04 12:00:00       NaN       NaN\n",
        "2000-01-04 16:00:00       NaN       NaN\n",
        "2000-01-04 20:00:00       NaN       NaN\n",
        "2000-01-05 00:00:00  0.334308  0.334308\n",
        "2000-01-05 04:00:00       NaN       NaN\n",
        "2000-01-05 08:00:00       NaN       NaN\n",
        "2000-01-05 12:00:00       NaN       NaN\n",
        "2000-01-05 16:00:00       NaN       NaN\n",
        "2000-01-05 20:00:00       NaN       NaN\n",
        "2000-01-06 00:00:00  1.234491  1.234491\n",
        "2000-01-06 04:00:00       NaN       NaN\n",
        "2000-01-06 08:00:00       NaN       NaN\n",
        "2000-01-06 12:00:00       NaN       NaN\n",
        "2000-01-06 16:00:00       NaN       NaN\n",
        "2000-01-06 20:00:00       NaN       NaN\n",
        "2000-01-07 00:00:00 -0.678003 -0.678003\n",
        "2000-01-07 04:00:00       NaN       NaN\n",
        "2000-01-07 08:00:00       NaN       NaN\n",
        "2000-01-07 12:00:00       NaN       NaN\n",
        "2000-01-07 16:00:00       NaN       NaN\n",
        "2000-01-07 20:00:00       NaN       NaN\n",
        "2000-01-08 00:00:00 -0.496512 -0.496512\n",
        "2000-01-08 04:00:00       NaN       NaN\n",
        "2000-01-08 08:00:00       NaN       NaN\n",
        "2000-01-08 12:00:00       NaN       NaN\n",
        "2000-01-08 16:00:00       NaN       NaN\n",
        "2000-01-08 20:00:00       NaN       NaN\n",
        "2000-01-09 00:00:00 -2.023730 -2.023730\n",
        "2000-01-09 04:00:00       NaN       NaN\n",
        "2000-01-09 08:00:00       NaN       NaN\n",
        "2000-01-09 12:00:00       NaN       NaN\n",
        "2000-01-09 16:00:00       NaN       NaN\n",
        "2000-01-09 20:00:00       NaN       NaN\n",
        "2000-01-10 00:00:00 -0.464823       NaN"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.asfreq('4h').ffill(limit=3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 102,
       "text": [
        "                            A         B\n",
        "2000-01-01 00:00:00  0.583378  0.583378\n",
        "2000-01-01 04:00:00  0.583378  0.583378\n",
        "2000-01-01 08:00:00  0.583378  0.583378\n",
        "2000-01-01 12:00:00  0.583378  0.583378\n",
        "2000-01-01 16:00:00       NaN       NaN\n",
        "2000-01-01 20:00:00       NaN       NaN\n",
        "2000-01-02 00:00:00 -1.542445       NaN\n",
        "2000-01-02 04:00:00 -1.542445       NaN\n",
        "2000-01-02 08:00:00 -1.542445       NaN\n",
        "2000-01-02 12:00:00 -1.542445       NaN\n",
        "2000-01-02 16:00:00       NaN       NaN\n",
        "2000-01-02 20:00:00       NaN       NaN\n",
        "2000-01-03 00:00:00 -0.215126 -0.215126\n",
        "2000-01-03 04:00:00 -0.215126 -0.215126\n",
        "2000-01-03 08:00:00 -0.215126 -0.215126\n",
        "2000-01-03 12:00:00 -0.215126 -0.215126\n",
        "2000-01-03 16:00:00       NaN       NaN\n",
        "2000-01-03 20:00:00       NaN       NaN\n",
        "2000-01-04 00:00:00 -1.231664       NaN\n",
        "2000-01-04 04:00:00 -1.231664       NaN\n",
        "2000-01-04 08:00:00 -1.231664       NaN\n",
        "2000-01-04 12:00:00 -1.231664       NaN\n",
        "2000-01-04 16:00:00       NaN       NaN\n",
        "2000-01-04 20:00:00       NaN       NaN\n",
        "2000-01-05 00:00:00  0.334308  0.334308\n",
        "2000-01-05 04:00:00  0.334308  0.334308\n",
        "2000-01-05 08:00:00  0.334308  0.334308\n",
        "2000-01-05 12:00:00  0.334308  0.334308\n",
        "2000-01-05 16:00:00       NaN       NaN\n",
        "2000-01-05 20:00:00       NaN       NaN\n",
        "2000-01-06 00:00:00  1.234491  1.234491\n",
        "2000-01-06 04:00:00  1.234491  1.234491\n",
        "2000-01-06 08:00:00  1.234491  1.234491\n",
        "2000-01-06 12:00:00  1.234491  1.234491\n",
        "2000-01-06 16:00:00       NaN       NaN\n",
        "2000-01-06 20:00:00       NaN       NaN\n",
        "2000-01-07 00:00:00 -0.678003 -0.678003\n",
        "2000-01-07 04:00:00 -0.678003 -0.678003\n",
        "2000-01-07 08:00:00 -0.678003 -0.678003\n",
        "2000-01-07 12:00:00 -0.678003 -0.678003\n",
        "2000-01-07 16:00:00       NaN       NaN\n",
        "2000-01-07 20:00:00       NaN       NaN\n",
        "2000-01-08 00:00:00 -0.496512 -0.496512\n",
        "2000-01-08 04:00:00 -0.496512 -0.496512\n",
        "2000-01-08 08:00:00 -0.496512 -0.496512\n",
        "2000-01-08 12:00:00 -0.496512 -0.496512\n",
        "2000-01-08 16:00:00       NaN       NaN\n",
        "2000-01-08 20:00:00       NaN       NaN\n",
        "2000-01-09 00:00:00 -2.023730 -2.023730\n",
        "2000-01-09 04:00:00 -2.023730 -2.023730\n",
        "2000-01-09 08:00:00 -2.023730 -2.023730\n",
        "2000-01-09 12:00:00 -2.023730 -2.023730\n",
        "2000-01-09 16:00:00       NaN       NaN\n",
        "2000-01-09 20:00:00       NaN       NaN\n",
        "2000-01-10 00:00:00 -0.464823       NaN"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Groupby operations\n",
      "---"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import random, string\n",
      "import matplotlib as mpl\n",
      "def rands(n):\n",
      "    choices = string.ascii_letters\n",
      "    return ''.join([random.choice(choices) for _ in xrange(n)])\n",
      "mpl.rc('figure', figsize=(12, 8))\n",
      "\n",
      "ind_names = np.array(['ENERGY', 'FINANCIAL', 'TECH', \n",
      "                      'CONSDUR', 'SERVICES', 'UTILITIES'], dtype='O')\n",
      "ccys = np.array(['USD', 'EUR'], dtype='O')\n",
      "\n",
      "Nfull = 2000\n",
      "tickers = np.array(sorted(rands(5).upper() for _ in xrange(Nfull)), dtype='O')\n",
      "tickers = np.unique(tickers)\n",
      "\n",
      "industries = pd.Series(ind_names.take(np.random.randint(0, 6, Nfull)), \n",
      "                       index=tickers, name='industry')\n",
      "ccy = pd.Series(ccys.take(np.random.randint(0, len(ccys), Nfull)), \n",
      "                index=tickers, name='ccy')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ccy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 104,
       "text": [
        "AAEUC    EUR\n",
        "AAKGK    EUR\n",
        "AATPS    EUR\n",
        "AAUGH    EUR\n",
        "AAXBK    USD\n",
        "ABQLB    USD\n",
        "ACPJP    EUR\n",
        "ACSGL    EUR\n",
        "ADCYV    EUR\n",
        "ADFEZ    EUR\n",
        "ADNMK    USD\n",
        "ADUCW    EUR\n",
        "AENYT    EUR\n",
        "AEQKA    EUR\n",
        "AETPI    USD\n",
        "...\n",
        "ZVHFY    EUR\n",
        "ZVHKA    USD\n",
        "ZVVJW    EUR\n",
        "ZWCVS    EUR\n",
        "ZWMOZ    EUR\n",
        "ZXBDD    USD\n",
        "ZXDQC    EUR\n",
        "ZXNPV    EUR\n",
        "ZYLEW    EUR\n",
        "ZYTMF    EUR\n",
        "ZYYHV    USD\n",
        "ZZBYE    USD\n",
        "ZZENJ    EUR\n",
        "ZZJOX    EUR\n",
        "ZZZCZ    USD\n",
        "Name: ccy, Length: 2000"
       ]
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame({'Momentum' : np.random.randn(1000) / 200 + 0.03,\n",
      "                'Value' : np.random.randn(1000) / 200 + 0.08,\n",
      "                'ShortInterest' : np.random.randn(1000) / 200 - 0.02},\n",
      "                index=tickers.take(np.random.permutation(Nfull)[:1000]))\n",
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 105,
       "text": [
        "       Momentum  ShortInterest     Value\n",
        "WPZGV  0.030320      -0.022210  0.067899\n",
        "KTCLL  0.031851      -0.016965  0.079431\n",
        "RMXIV  0.031954      -0.018996  0.077051\n",
        "SUYKU  0.025723      -0.022544  0.076665\n",
        "EEYEA  0.022493      -0.026726  0.079324"
       ]
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "means = df.groupby(industries).mean()\n",
      "means"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 106,
       "text": [
        "           Momentum  ShortInterest     Value\n",
        "industry                                    \n",
        "CONSDUR    0.029303      -0.020244  0.080010\n",
        "ENERGY     0.029526      -0.019828  0.079985\n",
        "FINANCIAL  0.029805      -0.019937  0.079605\n",
        "SERVICES   0.030393      -0.020437  0.079563\n",
        "TECH       0.029396      -0.019980  0.080333\n",
        "UTILITIES  0.029690      -0.020159  0.080312"
       ]
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "means.plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 107,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x13025aa50>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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z2a77+OOPa+7cuZo/f74yMzOVmZmpSZMm3bHGdevW6cyZM5Kk8+fPa+vWrWrW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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x124407250>"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "means = df.groupby([industries, ccy]).mean()\n",
      "means"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 108,
       "text": [
        "               Momentum  ShortInterest     Value\n",
        "industry  ccy                                   \n",
        "CONSDUR   EUR  0.029077      -0.020063  0.080763\n",
        "          USD  0.029529      -0.020424  0.079257\n",
        "ENERGY    EUR  0.029349      -0.019963  0.079538\n",
        "          USD  0.029728      -0.019673  0.080498\n",
        "FINANCIAL EUR  0.029460      -0.020284  0.079718\n",
        "          USD  0.030215      -0.019524  0.079471\n",
        "SERVICES  EUR  0.030222      -0.020150  0.079125\n",
        "          USD  0.030539      -0.020683  0.079939\n",
        "TECH      EUR  0.029018      -0.019371  0.080187\n",
        "          USD  0.029731      -0.020521  0.080461\n",
        "UTILITIES EUR  0.029562      -0.020285  0.080697\n",
        "          USD  0.029847      -0.020006  0.079843"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "keys = [industries, ccy]\n",
      "zscore = lambda x: (x - x.mean()) / x.std()\n",
      "normed = df.groupby(keys).apply(zscore)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 109
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "normed.groupby(keys).agg(['mean', 'std'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 110,
       "text": [
        "                   Momentum       ShortInterest              Value     \n",
        "                       mean  std           mean  std          mean  std\n",
        "industry  ccy                                                          \n",
        "CONSDUR   EUR -1.717145e-16    1   1.281938e-15    1  2.158274e-15    1\n",
        "          USD -1.576517e-15    1  -1.245300e-15    1 -3.043491e-15    1\n",
        "ENERGY    EUR -4.680352e-16    1  -2.024524e-15    1  1.094985e-15    1\n",
        "          USD  6.536594e-16    1   9.056426e-16    1 -1.187564e-15    1\n",
        "FINANCIAL EUR -1.144085e-15    1   1.731948e-16    1 -7.722989e-16    1\n",
        "          USD -4.130294e-17    1   4.378112e-16    1  2.775558e-15    1\n",
        "SERVICES  EUR -3.221210e-16    1  -1.845159e-16    1 -5.222739e-15    1\n",
        "          USD -2.193694e-16    1  -1.553643e-15    1  1.317888e-15    1\n",
        "TECH      EUR -1.467181e-15    1   8.108844e-16    1 -2.259093e-15    1\n",
        "          USD  1.066563e-15    1  -1.777604e-15    1  2.604658e-15    1\n",
        "UTILITIES EUR -2.628850e-15    1   1.698377e-15    1 -3.649858e-15    1\n",
        "          USD -6.454177e-16    1  -6.082091e-16    1 -1.024141e-15    1"
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Hierarchical indexing\n",
      "---"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "means"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 111,
       "text": [
        "               Momentum  ShortInterest     Value\n",
        "industry  ccy                                   \n",
        "CONSDUR   EUR  0.029077      -0.020063  0.080763\n",
        "          USD  0.029529      -0.020424  0.079257\n",
        "ENERGY    EUR  0.029349      -0.019963  0.079538\n",
        "          USD  0.029728      -0.019673  0.080498\n",
        "FINANCIAL EUR  0.029460      -0.020284  0.079718\n",
        "          USD  0.030215      -0.019524  0.079471\n",
        "SERVICES  EUR  0.030222      -0.020150  0.079125\n",
        "          USD  0.030539      -0.020683  0.079939\n",
        "TECH      EUR  0.029018      -0.019371  0.080187\n",
        "          USD  0.029731      -0.020521  0.080461\n",
        "UTILITIES EUR  0.029562      -0.020285  0.080697\n",
        "          USD  0.029847      -0.020006  0.079843"
       ]
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "means['Momentum']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 112,
       "text": [
        "industry   ccy\n",
        "CONSDUR    EUR    0.029077\n",
        "           USD    0.029529\n",
        "ENERGY     EUR    0.029349\n",
        "           USD    0.029728\n",
        "FINANCIAL  EUR    0.029460\n",
        "           USD    0.030215\n",
        "SERVICES   EUR    0.030222\n",
        "           USD    0.030539\n",
        "TECH       EUR    0.029018\n",
        "           USD    0.029731\n",
        "UTILITIES  EUR    0.029562\n",
        "           USD    0.029847\n",
        "Name: Momentum"
       ]
      }
     ],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "means.ix['TECH']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 113,
       "text": [
        "     Momentum  ShortInterest     Value\n",
        "ccy                                   \n",
        "EUR  0.029018      -0.019371  0.080187\n",
        "USD  0.029731      -0.020521  0.080461"
       ]
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "means.stack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 114,
       "text": [
        "industry   ccy               \n",
        "CONSDUR    EUR  Momentum         0.029077\n",
        "                ShortInterest   -0.020063\n",
        "                Value            0.080763\n",
        "           USD  Momentum         0.029529\n",
        "                ShortInterest   -0.020424\n",
        "                Value            0.079257\n",
        "ENERGY     EUR  Momentum         0.029349\n",
        "                ShortInterest   -0.019963\n",
        "                Value            0.079538\n",
        "           USD  Momentum         0.029728\n",
        "                ShortInterest   -0.019673\n",
        "                Value            0.080498\n",
        "FINANCIAL  EUR  Momentum         0.029460\n",
        "                ShortInterest   -0.020284\n",
        "                Value            0.079718\n",
        "           USD  Momentum         0.030215\n",
        "                ShortInterest   -0.019524\n",
        "                Value            0.079471\n",
        "SERVICES   EUR  Momentum         0.030222\n",
        "                ShortInterest   -0.020150\n",
        "                Value            0.079125\n",
        "           USD  Momentum         0.030539\n",
        "                ShortInterest   -0.020683\n",
        "                Value            0.079939\n",
        "TECH       EUR  Momentum         0.029018\n",
        "                ShortInterest   -0.019371\n",
        "                Value            0.080187\n",
        "           USD  Momentum         0.029731\n",
        "                ShortInterest   -0.020521\n",
        "                Value            0.080461\n",
        "UTILITIES  EUR  Momentum         0.029562\n",
        "                ShortInterest   -0.020285\n",
        "                Value            0.080697\n",
        "           USD  Momentum         0.029847\n",
        "                ShortInterest   -0.020006\n",
        "                Value            0.079843"
       ]
      }
     ],
     "prompt_number": 114
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "means.stack().unstack('industry')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 115,
       "text": [
        "industry            CONSDUR    ENERGY  FINANCIAL  SERVICES      TECH  UTILITIES\n",
        "ccy                                                                            \n",
        "EUR Momentum       0.029077  0.029349   0.029460  0.030222  0.029018   0.029562\n",
        "    ShortInterest -0.020063 -0.019963  -0.020284 -0.020150 -0.019371  -0.020285\n",
        "    Value          0.080763  0.079538   0.079718  0.079125  0.080187   0.080697\n",
        "USD Momentum       0.029529  0.029728   0.030215  0.030539  0.029731   0.029847\n",
        "    ShortInterest -0.020424 -0.019673  -0.019524 -0.020683 -0.020521  -0.020006\n",
        "    Value          0.079257  0.080498   0.079471  0.079939  0.080461   0.079843"
       ]
      }
     ],
     "prompt_number": 115
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Merging and joining\n",
      "---"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "base = '/Users/wesm/Dropbox/book/svn/book_scripts/movielens/ml-1m'\n",
      "get_path = lambda x: '%s/%s.dat' % (base, x)\n",
      "\n",
      "unames = ['user_id', 'gender', 'age', 'occupation', 'zip']\n",
      "users = pd.read_table(get_path('users'), sep='::', header=None, names=unames)\n",
      "\n",
      "rnames = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
      "ratings = pd.read_table(get_path('ratings'), sep='::', header=None, names=rnames)\n",
      "mnames = ['movie_id', 'title', 'genres']\n",
      "movies = pd.read_table(get_path('movies'), sep='::', header=None, names=mnames)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "movies.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 119,
       "text": [
        "   movie_id                               title                        genres\n",
        "0         1                    Toy Story (1995)   Animation|Children's|Comedy\n",
        "1         2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
        "2         3             Grumpier Old Men (1995)                Comedy|Romance\n",
        "3         4            Waiting to Exhale (1995)                  Comedy|Drama\n",
        "4         5  Father of the Bride Part II (1995)                        Comedy"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ratings.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 120,
       "text": [
        "   user_id  movie_id  rating  timestamp\n",
        "0        1      1193       5  978300760\n",
        "1        1       661       3  978302109\n",
        "2        1       914       3  978301968\n",
        "3        1      3408       4  978300275\n",
        "4        1      2355       5  978824291"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "users.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 121,
       "text": [
        "   user_id gender  age  occupation    zip\n",
        "0        1      F    1          10  48067\n",
        "1        2      M   56          16  70072\n",
        "2        3      M   25          15  55117\n",
        "3        4      M   45           7  02460\n",
        "4        5      M   25          20  55455"
       ]
      }
     ],
     "prompt_number": 121
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.merge(pd.merge(ratings, users), movies)\n",
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 122,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 1000209 entries, 0 to 1000208\n",
        "Data columns:\n",
        "user_id       1000209  non-null values\n",
        "movie_id      1000209  non-null values\n",
        "rating        1000209  non-null values\n",
        "timestamp     1000209  non-null values\n",
        "gender        1000209  non-null values\n",
        "age           1000209  non-null values\n",
        "occupation    1000209  non-null values\n",
        "zip           1000209  non-null values\n",
        "title         1000209  non-null values\n",
        "genres        1000209  non-null values\n",
        "dtypes: int64(6), object(4)"
       ]
      }
     ],
     "prompt_number": 122
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rating_counts = data.groupby('title').size()\n",
      "freq_titles = rating_counts.index[rating_counts > 1000]\n",
      "freq_titles"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 123,
       "text": [
        "Index([2001: A Space Odyssey (1968), Abyss, The (1989), African Queen, The (1951), Air Force One (1997), Airplane! (1980), Aladdin (1992), Alien (1979), Aliens (1986), Amadeus (1984), American Beauty (1999), American Pie (1999), American President, The (1995), Animal House (1978), Annie Hall (1977), Apocalypse Now (1979), Apollo 13 (1995), Arachnophobia (1990), Armageddon (1998), As Good As It Gets (1997), Austin Powers: International Man of Mystery (1997), Austin Powers: The Spy Who Shagged Me (1999), Babe (1995), Back to the Future (1985), Back to the Future Part II (1989), Back to the Future Part III (1990), Batman (1989), Batman Returns (1992), Beauty and the Beast (1991), Beetlejuice (1988), Being John Malkovich (1999), Big (1988), Big Lebowski, The (1998), Blade Runner (1982), Blair Witch Project, The (1999), Blazing Saddles (1974), Blues Brothers, The (1980), Boat, The (Das Boot) (1981), Boogie Nights (1997), Braveheart (1995), Breakfast Club, The (1985), Bug's Life, A (1998), Bull Durham (1988), Butch Cassidy and the Sundance Kid (1969), Casablanca (1942), Chicken Run (2000), Chinatown (1974), Christmas Story, A (1983), Citizen Kane (1941), Clear and Present Danger (1994), Clerks (1994), Clockwork Orange, A (1971), Close Encounters of the Third Kind (1977), Clueless (1995), Cocoon (1985), Contact (1997), Crying Game, The (1992), Dances with Wolves (1990), Die Hard (1988), Die Hard 2 (1990), Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1963), E.T. the Extra-Terrestrial (1982), Edward Scissorhands (1990), Election (1999), Erin Brockovich (2000), Face/Off (1997), Fargo (1996), Ferris Bueller's Day Off (1986), Few Good Men, A (1992), Fifth Element, The (1997), Fight Club (1999), Fish Called Wanda, A (1988), Fly, The (1986), Forrest Gump (1994), Four Weddings and a Funeral (1994), Fugitive, The (1993), Full Metal Jacket (1987), Full Monty, The (1997), Galaxy Quest (1999), Gattaca (1997), Get Shorty (1995), Ghost (1990), Ghostbusters (1984), Gladiator (2000), Glory (1989), Godfather, The (1972), Godfather: Part II, The (1974), Gone with the Wind (1939), Good Morning, Vietnam (1987), Good Will Hunting (1997), GoodFellas (1990), Graduate, The (1967), Green Mile, The (1999), Grosse Pointe Blank (1997), Groundhog Day (1993), High Fidelity (2000), Honey, I Shrunk the Kids (1989), Hunt for Red October, The (1990), Independence Day (ID4) (1996), Indiana Jones and the Last Crusade (1989), Indiana Jones and the Temple of Doom (1984), Jaws (1975), Jerry Maguire (1996), Jurassic Park (1993), L.A. Confidential (1997), League of Their Own, A (1992), Lethal Weapon (1987), Lethal Weapon 2 (1989), Life Is Beautiful (La Vita \ufffd bella) (1997), Lion King, The (1994), Little Mermaid, The (1989), Lost World: Jurassic Park, The (1997), M*A*S*H (1970), Mad Max (1979), Mad Max 2 (a.k.a. The Road Warrior) (1981), Magnolia (1999), Maltese Falcon, The (1941), Mars Attacks! (1996), Mary Poppins (1964), Mask, The (1994), Matrix, The (1999), Men in Black (1997), Mission: Impossible (1996), Mission: Impossible 2 (2000), Monty Python and the Holy Grail (1974), Mummy, The (1999), My Cousin Vinny (1992), North by Northwest (1959), One Flew Over the Cuckoo's Nest (1975), Patriot Games (1992), Patriot, The (2000), Perfect Storm, The (2000), Planet of the Apes (1968), Platoon (1986), Pleasantville (1998), Predator (1987), Pretty Woman (1990), Princess Bride, The (1987), Psycho (1960), Pulp Fiction (1994), Raiders of the Lost Ark (1981), Rain Man (1988), Raising Arizona (1987), Rear Window (1954), Reservoir Dogs (1992), Robocop (1987), Rock, The (1996), Rocky (1976), Rocky Horror Picture Show, The (1975), Romancing the Stone (1984), Run Lola Run (Lola rennt) (1998), Rushmore (1998), Saving Private Ryan (1998), Schindler's List (1993), Seven (Se7en) (1995), Shakespeare in Love (1998), Shawshank Redemption, The (1994), Shining, The (1980), Silence of the Lambs, The (1991), Sixth Sense, The (1999), Sleepy Hollow (1999), Sling Blade (1996), Sneakers (1992), South Park: Bigger, Longer and Uncut (1999), Speed (1994), Splash (1984), Stand by Me (1986), Star Trek IV: The Voyage Home (1986), Star Trek VI: The Undiscovered Country (1991), Star Trek: First Contact (1996), Star Trek: The Wrath of Khan (1982), Star Wars: Episode I - The Phantom Menace (1999), Star Wars: Episode IV - A New Hope (1977), Star Wars: Episode V - The Empire Strikes Back (1980), Star Wars: Episode VI - Return of the Jedi (1983), Stargate (1994), Starship Troopers (1997), Sting, The (1973), Superman (1978), Talented Mr. Ripley, The (1999), Taxi Driver (1976), Terminator 2: Judgment Day (1991), Terminator, The (1984), Thelma & Louise (1991), There's Something About Mary (1998), This Is Spinal Tap (1984), Thomas Crown Affair, The (1999), Three Kings (1999), Time Bandits (1981), Titanic (1997), Top Gun (1986), Total Recall (1990), Toy Story (1995), Toy Story 2 (1999), True Lies (1994), Truman Show, The (1998), Twelve Monkeys (1995), Twister (1996), Untouchables, The (1987), Usual Suspects, The (1995), Wayne's World (1992), When Harry Met Sally... (1989), Who Framed Roger Rabbit? (1988), Willy Wonka and the Chocolate Factory (1971), Witness (1985), Wizard of Oz, The (1939), X-Men (2000), Young Frankenstein (1974)], dtype=object)"
       ]
      }
     ],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "highest_rated = data.groupby('title').rating.mean()[freq_titles].order()[-20:]\n",
      "highest_rated"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 124,
       "text": [
        "title\n",
        "Life Is Beautiful (La Vita \ufffd bella) (1997)                      4.329861\n",
        "Monty Python and the Holy Grail (1974)                          4.335210\n",
        "Saving Private Ryan (1998)                                      4.337354\n",
        "Chinatown (1974)                                                4.339241\n",
        "Silence of the Lambs, The (1991)                                4.351823\n",
        "Godfather: Part II, The (1974)                                  4.357565\n",
        "North by Northwest (1959)                                       4.384030\n",
        "Citizen Kane (1941)                                             4.388889\n",
        "One Flew Over the Cuckoo's Nest (1975)                          4.390725\n",
        "Maltese Falcon, The (1941)                                      4.395973\n",
        "Sixth Sense, The (1999)                                         4.406263\n",
        "Casablanca (1942)                                               4.412822\n",
        "Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1963)    4.449890\n",
        "Star Wars: Episode IV - A New Hope (1977)                       4.453694\n",
        "Rear Window (1954)                                              4.476190\n",
        "Raiders of the Lost Ark (1981)                                  4.477725\n",
        "Schindler's List (1993)                                         4.510417\n",
        "Usual Suspects, The (1995)                                      4.517106\n",
        "Godfather, The (1972)                                           4.524966\n",
        "Shawshank Redemption, The (1994)                                4.554558\n",
        "Name: rating"
       ]
      }
     ],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filtered = data[data.title.isin(highest_rated.index)]\n",
      "filtered.title = filtered.title.str[:25]\n",
      "filtered.groupby(['title', 'gender']).rating.count().unstack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 125,
       "text": [
        "gender                       F     M\n",
        "title                               \n",
        "Casablanca (1942)          505  1164\n",
        "Chinatown (1974)           255   930\n",
        "Citizen Kane (1941)        280   836\n",
        "Dr. Strangelove or: How I  231  1136\n",
        "Godfather, The (1972)      483  1740\n",
        "Godfather: Part II, The (  342  1350\n",
        "Life Is Beautiful (La Vit  367   785\n",
        "Maltese Falcon, The (1941  235   808\n",
        "Monty Python and the Holy  352  1247\n",
        "North by Northwest (1959)  332   983\n",
        "One Flew Over the Cuckoo'  444  1281\n",
        "Raiders of the Lost Ark (  572  1942\n",
        "Rear Window (1954)         291   759\n",
        "Saving Private Ryan (1998  575  2078\n",
        "Schindler's List (1993)    615  1689\n",
        "Shawshank Redemption, The  627  1600\n",
        "Silence of the Lambs, The  706  1872\n",
        "Sixth Sense, The (1999)    664  1795\n",
        "Star Wars: Episode IV - A  647  2344\n",
        "Usual Suspects, The (1995  413  1370"
       ]
      }
     ],
     "prompt_number": 125
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pivot tables\n",
      "---"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_ratings = data.pivot_table('rating', rows='title',\n",
      "                                cols='gender', aggfunc='mean')\n",
      "mean_ratings.tail(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 126,
       "text": [
        "gender                                             F         M\n",
        "title                                                         \n",
        "Year of the Horse (1997)                         NaN  3.250000\n",
        "Yellow Submarine (1968)                     3.714286  3.689286\n",
        "Yojimbo (1961)                              4.423077  4.402116\n",
        "You Can't Take It With You (1938)           4.192308  3.921569\n",
        "You So Crazy (1994)                         3.666667  2.300000\n",
        "You've Got Mail (1998)                      3.542424  3.275591\n",
        "Young Doctors in Love (1982)                1.923077  2.742424\n",
        "Young Frankenstein (1974)                   4.289963  4.239177\n",
        "Young Guns (1988)                           3.371795  3.425620\n",
        "Young Guns II (1990)                        2.934783  2.904025\n",
        "Young Poisoner's Handbook, The (1995)       4.000000  3.532258\n",
        "Young Sherlock Holmes (1985)                3.514706  3.363344\n",
        "Young and Innocent (1937)                   2.500000  3.500000\n",
        "Your Friends and Neighbors (1998)           2.888889  3.536585\n",
        "Zachariah (1971)                                 NaN  3.500000\n",
        "Zed & Two Noughts, A (1985)                 3.500000  3.380952\n",
        "Zero Effect (1998)                          3.864407  3.723140\n",
        "Zero Kelvin (Kj\ufffdrlighetens kj\ufffdtere) (1995)       NaN  3.500000\n",
        "Zeus and Roxanne (1997)                     2.777778  2.357143\n",
        "eXistenZ (1999)                             3.098592  3.289086"
       ]
      }
     ],
     "prompt_number": 126
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Data summary, statistics\n",
      "---\n",
      "summary, value_counts, etc."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.title.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 127,
       "text": [
        "American Beauty (1999)                                   3428\n",
        "Star Wars: Episode IV - A New Hope (1977)                2991\n",
        "Star Wars: Episode V - The Empire Strikes Back (1980)    2990\n",
        "Star Wars: Episode VI - Return of the Jedi (1983)        2883\n",
        "Jurassic Park (1993)                                     2672\n",
        "Saving Private Ryan (1998)                               2653\n",
        "Terminator 2: Judgment Day (1991)                        2649\n",
        "Matrix, The (1999)                                       2590\n",
        "Back to the Future (1985)                                2583\n",
        "Silence of the Lambs, The (1991)                         2578\n",
        "Men in Black (1997)                                      2538\n",
        "Raiders of the Lost Ark (1981)                           2514\n",
        "Fargo (1996)                                             2513\n",
        "Sixth Sense, The (1999)                                  2459\n",
        "Braveheart (1995)                                        2443\n",
        "...\n",
        "Beauty (1998)                                                   1\n",
        "Legal Deceit (1997)                                             1\n",
        "Silence of the Palace, The (Saimt el Qusur) (1994)              1\n",
        "Relative Fear (1994)                                            1\n",
        "For Ever Mozart (1996)                                          1\n",
        "White Boys (1999)                                               1\n",
        "Terror in a Texas Town (1958)                                   1\n",
        "Schlafes Bruder (Brother of Sleep) (1995)                       1\n",
        "Follow the Bitch (1998)                                         1\n",
        "Even Dwarfs Started Small (Auch Zwerge haben klein angefangen) (1971)    1\n",
        "Low Life, The (1994)                                            1\n",
        "Wooden Man's Bride, The (Wu Kui) (1994)                         1\n",
        "Back Stage (2000)                                               1\n",
        "One Man's Hero (1999)                                           1\n",
        "Beloved/Friend (Amigo/Amado) (1999)                             1\n",
        "Length: 3706"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.rating.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 128,
       "text": [
        "count    1000209.000000\n",
        "mean           3.581564\n",
        "std            1.117102\n",
        "min            1.000000\n",
        "25%            3.000000\n",
        "50%            4.000000\n",
        "75%            4.000000\n",
        "max            5.000000"
       ]
      }
     ],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "by_gender = data.groupby('gender').rating.describe()\n",
      "by_gender"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 129,
       "text": [
        "gender       \n",
        "F       count    246440.000000\n",
        "        mean          3.620366\n",
        "        std           1.111228\n",
        "        min           1.000000\n",
        "        25%           3.000000\n",
        "        50%           4.000000\n",
        "        75%           4.000000\n",
        "        max           5.000000\n",
        "M       count    753769.000000\n",
        "        mean          3.568879\n",
        "        std           1.118724\n",
        "        min           1.000000\n",
        "        25%           3.000000\n",
        "        50%           4.000000\n",
        "        75%           4.000000\n",
        "        max           5.000000"
       ]
      }
     ],
     "prompt_number": 129
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "by_gender.unstack(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 130,
       "text": [
        "gender              F              M\n",
        "count   246440.000000  753769.000000\n",
        "mean         3.620366       3.568879\n",
        "std          1.111228       1.118724\n",
        "min          1.000000       1.000000\n",
        "25%          3.000000       3.000000\n",
        "50%          4.000000       4.000000\n",
        "75%          4.000000       4.000000\n",
        "max          5.000000       5.000000"
       ]
      }
     ],
     "prompt_number": 130
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}