Basics.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "from pandas import *\n",
      "import pandas\n",
      "import numpy as np\n",
      "\n",
      "def side_by_side(*objs, **kwds):\n",
      "    from pandas.core.common import adjoin\n",
      "    space = kwds.get('space', 4)\n",
      "    reprs = [repr(obj).split('\\n') for obj in objs]\n",
      "    print adjoin(space, *reprs)\n",
      "\n",
      "plt.rc('figure', figsize=(10, 6))\n",
      "#pandas.set_printoptions(notebook_repr_html=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Series\n",
      "======"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "labels = ['a', 'b', 'c', 'd', 'e']\n",
      "s = Series(randn(5), index=labels)\n",
      "s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "a   -1.298518\n",
        "b   -0.616228\n",
        "c   -0.930714\n",
        "d   -0.898232\n",
        "e   -1.059762\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'b' in s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s['b']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "-0.61622827623767917"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "a   -1.298518\n",
        "b   -0.616228\n",
        "c   -0.930714\n",
        "d   -0.898232\n",
        "e   -1.059762\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mapping = s.to_dict()\n",
      "mapping"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "{'a': -1.2985179400295874,\n",
        " 'b': -0.61622827623767917,\n",
        " 'c': -0.9307141320014477,\n",
        " 'd': -0.89823183953916175,\n",
        " 'e': -1.0597624564395374}"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = Series(mapping)\n",
      "s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "a   -1.298518\n",
        "b   -0.616228\n",
        "c   -0.930714\n",
        "d   -0.898232\n",
        "e   -1.059762\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s[:3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "a   -1.298518\n",
        "b   -0.616228\n",
        "c   -0.930714\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "Index([u'a', u'b', u'c', u'd', u'e'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "DataFrame: 2D collection of Series\n",
      "=================================="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = DataFrame({'a': np.random.randn(6),\n",
      "                'b': ['foo', 'bar'] * 3,\n",
      "                'c': np.random.randn(6)})\n",
      "df"
     ],
     "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>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.539650</td>\n",
        "      <td> foo</td>\n",
        "      <td>-0.194138</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1.186066</td>\n",
        "      <td> bar</td>\n",
        "      <td> 1.971302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>-2.087308</td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.207287</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.452987</td>\n",
        "      <td> bar</td>\n",
        "      <td>-0.174403</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1.275745</td>\n",
        "      <td> foo</td>\n",
        "      <td>-0.538236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>-1.239446</td>\n",
        "      <td> bar</td>\n",
        "      <td> 0.608301</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "          a    b         c\n",
        "0  0.539650  foo -0.194138\n",
        "1  1.186066  bar  1.971302\n",
        "2 -2.087308  foo  0.207287\n",
        "3  0.452987  bar -0.174403\n",
        "4  1.275745  foo -0.538236\n",
        "5 -1.239446  bar  0.608301"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[2:4,'b':'c']"
     ],
     "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>b</th>\n",
        "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> foo</td>\n",
        "      <td> 0.207287</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> bar</td>\n",
        "      <td>-0.174403</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> foo</td>\n",
        "      <td>-0.538236</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "     b         c\n",
        "2  foo  0.207287\n",
        "3  bar -0.174403\n",
        "4  foo -0.538236"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "Int64Index([0, 1, 2, 3, 4, 5], dtype='int64')"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "Index([u'a', u'b', u'c'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = DataFrame({'a': np.random.randn(6),\n",
      "                'b': ['foo', 'bar'] * 3,\n",
      "                'c': np.random.randn(6)},\n",
      "               index=date_range('1/1/2000', periods=6))\n",
      "df"
     ],
     "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>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2000-01-01</th>\n",
        "      <td>-1.398518</td>\n",
        "      <td> foo</td>\n",
        "      <td>-2.467587</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2000-01-02</th>\n",
        "      <td>-2.352558</td>\n",
        "      <td> bar</td>\n",
        "      <td> 1.305317</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2000-01-03</th>\n",
        "      <td>-0.587046</td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.266353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2000-01-04</th>\n",
        "      <td>-1.182446</td>\n",
        "      <td> bar</td>\n",
        "      <td>-0.179490</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2000-01-05</th>\n",
        "      <td> 1.559324</td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.417516</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2000-01-06</th>\n",
        "      <td> 0.399447</td>\n",
        "      <td> bar</td>\n",
        "      <td>-0.945818</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "                   a    b         c\n",
        "2000-01-01 -1.398518  foo -2.467587\n",
        "2000-01-02 -2.352558  bar  1.305317\n",
        "2000-01-03 -0.587046  foo  0.266353\n",
        "2000-01-04 -1.182446  bar -0.179490\n",
        "2000-01-05  1.559324  foo  0.417516\n",
        "2000-01-06  0.399447  bar -0.945818"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = DataFrame({'a': np.random.randn(6),\n",
      "                'b': ['foo', 'bar'] * 3,\n",
      "                'c': np.random.randn(6)},\n",
      "               columns=['a', 'b', 'c', 'd'])\n",
      "df"
     ],
     "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>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1.408472</td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.144131</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1.485471</td>\n",
        "      <td> bar</td>\n",
        "      <td> 0.511041</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.403451</td>\n",
        "      <td> foo</td>\n",
        "      <td>-0.280004</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.555599</td>\n",
        "      <td> bar</td>\n",
        "      <td>-0.253252</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>-0.056142</td>\n",
        "      <td> foo</td>\n",
        "      <td>-0.669728</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 0.431971</td>\n",
        "      <td> bar</td>\n",
        "      <td>-1.021842</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "          a    b         c    d\n",
        "0  1.408472  foo  0.144131  NaN\n",
        "1  1.485471  bar  0.511041  NaN\n",
        "2  0.403451  foo -0.280004  NaN\n",
        "3  0.555599  bar -0.253252  NaN\n",
        "4 -0.056142  foo -0.669728  NaN\n",
        "5  0.431971  bar -1.021842  NaN"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "isnull(df)"
     ],
     "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>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "       a      b      c     d\n",
        "0  False  False  False  True\n",
        "1  False  False  False  True\n",
        "2  False  False  False  True\n",
        "3  False  False  False  True\n",
        "4  False  False  False  True\n",
        "5  False  False  False  True"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Creation from nested dicts\n",
      "--------------------------\n",
      "\n",
      "These arise naturally in Python code"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = {}\n",
      "for col in ['foo', 'bar', 'baz']:\n",
      "    for row in ['a', 'b', 'c', 'd']:\n",
      "        data.setdefault(col, {})[row] = randn()\n",
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "DataFrame(data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Data alignment\n",
      "=============="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "close_px = read_csv('stock_data.csv', index_col=0, parse_dates=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "close_px"
     ],
     "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>AA</th>\n",
        "      <th>AAPL</th>\n",
        "      <th>GE</th>\n",
        "      <th>IBM</th>\n",
        "      <th>JNJ</th>\n",
        "      <th>MSFT</th>\n",
        "      <th>PEP</th>\n",
        "      <th>SPX</th>\n",
        "      <th>XOM</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2007-10-29</th>\n",
        "      <td> 37.41</td>\n",
        "      <td> 185.09</td>\n",
        "      <td> 34.46</td>\n",
        "      <td> 106.78</td>\n",
        "      <td> 57.13</td>\n",
        "      <td> 31.78</td>\n",
        "      <td> 65.67</td>\n",
        "      <td> 1540.98</td>\n",
        "      <td> 85.51</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-10-30</th>\n",
        "      <td> 36.43</td>\n",
        "      <td> 187.00</td>\n",
        "      <td> 34.39</td>\n",
        "      <td> 106.15</td>\n",
        "      <td> 56.99</td>\n",
        "      <td> 32.70</td>\n",
        "      <td> 65.80</td>\n",
        "      <td> 1531.02</td>\n",
        "      <td> 83.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-10-31</th>\n",
        "      <td> 36.79</td>\n",
        "      <td> 189.95</td>\n",
        "      <td> 34.97</td>\n",
        "      <td> 108.01</td>\n",
        "      <td> 57.30</td>\n",
        "      <td> 33.84</td>\n",
        "      <td> 65.69</td>\n",
        "      <td> 1549.38</td>\n",
        "      <td> 84.03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-01</th>\n",
        "      <td> 35.22</td>\n",
        "      <td> 187.44</td>\n",
        "      <td> 34.27</td>\n",
        "      <td> 105.72</td>\n",
        "      <td> 56.85</td>\n",
        "      <td> 34.07</td>\n",
        "      <td> 64.51</td>\n",
        "      <td> 1508.44</td>\n",
        "      <td> 80.84</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-02</th>\n",
        "      <td> 35.83</td>\n",
        "      <td> 187.87</td>\n",
        "      <td> 34.27</td>\n",
        "      <td> 106.59</td>\n",
        "      <td> 56.95</td>\n",
        "      <td> 34.07</td>\n",
        "      <td> 65.03</td>\n",
        "      <td> 1509.65</td>\n",
        "      <td> 80.32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-05</th>\n",
        "      <td> 35.22</td>\n",
        "      <td> 186.18</td>\n",
        "      <td> 34.15</td>\n",
        "      <td> 105.48</td>\n",
        "      <td> 56.70</td>\n",
        "      <td> 33.77</td>\n",
        "      <td> 64.84</td>\n",
        "      <td> 1502.17</td>\n",
        "      <td> 80.07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-06</th>\n",
        "      <td> 35.84</td>\n",
        "      <td> 191.79</td>\n",
        "      <td> 34.14</td>\n",
        "      <td> 105.27</td>\n",
        "      <td> 56.80</td>\n",
        "      <td> 33.47</td>\n",
        "      <td> 65.49</td>\n",
        "      <td> 1520.27</td>\n",
        "      <td> 82.56</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-07</th>\n",
        "      <td> 34.76</td>\n",
        "      <td> 186.30</td>\n",
        "      <td> 33.20</td>\n",
        "      <td> 103.69</td>\n",
        "      <td> 56.19</td>\n",
        "      <td> 32.65</td>\n",
        "      <td> 64.46</td>\n",
        "      <td> 1475.62</td>\n",
        "      <td> 79.96</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-08</th>\n",
        "      <td> 35.00</td>\n",
        "      <td> 175.47</td>\n",
        "      <td> 33.15</td>\n",
        "      <td>  99.05</td>\n",
        "      <td> 56.79</td>\n",
        "      <td> 31.94</td>\n",
        "      <td> 65.50</td>\n",
        "      <td> 1474.77</td>\n",
        "      <td> 82.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-09</th>\n",
        "      <td> 34.47</td>\n",
        "      <td> 165.37</td>\n",
        "      <td> 32.61</td>\n",
        "      <td>  93.58</td>\n",
        "      <td> 57.29</td>\n",
        "      <td> 31.01</td>\n",
        "      <td> 65.48</td>\n",
        "      <td> 1453.70</td>\n",
        "      <td> 79.64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-12</th>\n",
        "      <td> 33.33</td>\n",
        "      <td> 153.76</td>\n",
        "      <td> 32.50</td>\n",
        "      <td>  94.70</td>\n",
        "      <td> 58.30</td>\n",
        "      <td> 30.69</td>\n",
        "      <td> 64.67</td>\n",
        "      <td> 1439.18</td>\n",
        "      <td> 77.52</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-13</th>\n",
        "      <td> 34.04</td>\n",
        "      <td> 169.96</td>\n",
        "      <td> 33.31</td>\n",
        "      <td>  98.27</td>\n",
        "      <td> 59.18</td>\n",
        "      <td> 31.79</td>\n",
        "      <td> 65.35</td>\n",
        "      <td> 1481.05</td>\n",
        "      <td> 79.67</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-14</th>\n",
        "      <td> 34.25</td>\n",
        "      <td> 166.11</td>\n",
        "      <td> 33.14</td>\n",
        "      <td>  96.56</td>\n",
        "      <td> 58.81</td>\n",
        "      <td> 31.30</td>\n",
        "      <td> 65.99</td>\n",
        "      <td> 1470.58</td>\n",
        "      <td> 79.15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-15</th>\n",
        "      <td> 33.76</td>\n",
        "      <td> 164.30</td>\n",
        "      <td> 32.55</td>\n",
        "      <td>  96.71</td>\n",
        "      <td> 58.80</td>\n",
        "      <td> 31.14</td>\n",
        "      <td> 65.57</td>\n",
        "      <td> 1451.15</td>\n",
        "      <td> 77.48</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-16</th>\n",
        "      <td> 33.74</td>\n",
        "      <td> 166.39</td>\n",
        "      <td> 32.84</td>\n",
        "      <td>  97.82</td>\n",
        "      <td> 59.56</td>\n",
        "      <td> 31.44</td>\n",
        "      <td> 66.08</td>\n",
        "      <td> 1458.74</td>\n",
        "      <td> 78.04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-19</th>\n",
        "      <td> 32.59</td>\n",
        "      <td> 163.95</td>\n",
        "      <td> 32.42</td>\n",
        "      <td>  95.42</td>\n",
        "      <td> 59.42</td>\n",
        "      <td> 31.32</td>\n",
        "      <td> 66.90</td>\n",
        "      <td> 1433.27</td>\n",
        "      <td> 77.13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-20</th>\n",
        "      <td> 32.88</td>\n",
        "      <td> 168.85</td>\n",
        "      <td> 32.32</td>\n",
        "      <td>  96.54</td>\n",
        "      <td> 59.47</td>\n",
        "      <td> 31.90</td>\n",
        "      <td> 66.69</td>\n",
        "      <td> 1439.70</td>\n",
        "      <td> 80.53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-21</th>\n",
        "      <td> 32.68</td>\n",
        "      <td> 168.46</td>\n",
        "      <td> 31.58</td>\n",
        "      <td>  95.42</td>\n",
        "      <td> 59.03</td>\n",
        "      <td> 31.57</td>\n",
        "      <td> 66.87</td>\n",
        "      <td> 1416.77</td>\n",
        "      <td> 79.82</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-23</th>\n",
        "      <td> 32.66</td>\n",
        "      <td> 171.54</td>\n",
        "      <td> 32.01</td>\n",
        "      <td>  97.13</td>\n",
        "      <td> 59.16</td>\n",
        "      <td> 31.46</td>\n",
        "      <td> 67.29</td>\n",
        "      <td> 1440.70</td>\n",
        "      <td> 80.96</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-26</th>\n",
        "      <td> 32.41</td>\n",
        "      <td> 172.54</td>\n",
        "      <td> 31.21</td>\n",
        "      <td>  95.19</td>\n",
        "      <td> 59.60</td>\n",
        "      <td> 30.41</td>\n",
        "      <td> 66.56</td>\n",
        "      <td> 1407.22</td>\n",
        "      <td> 78.57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-27</th>\n",
        "      <td> 32.56</td>\n",
        "      <td> 174.81</td>\n",
        "      <td> 31.82</td>\n",
        "      <td>  96.92</td>\n",
        "      <td> 59.92</td>\n",
        "      <td> 30.49</td>\n",
        "      <td> 67.65</td>\n",
        "      <td> 1428.23</td>\n",
        "      <td> 79.21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-28</th>\n",
        "      <td> 33.65</td>\n",
        "      <td> 180.22</td>\n",
        "      <td> 32.68</td>\n",
        "      <td> 100.23</td>\n",
        "      <td> 60.46</td>\n",
        "      <td> 31.08</td>\n",
        "      <td> 68.76</td>\n",
        "      <td> 1469.02</td>\n",
        "      <td> 80.62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-29</th>\n",
        "      <td> 33.97</td>\n",
        "      <td> 184.29</td>\n",
        "      <td> 32.40</td>\n",
        "      <td> 100.35</td>\n",
        "      <td> 60.51</td>\n",
        "      <td> 30.98</td>\n",
        "      <td> 68.20</td>\n",
        "      <td> 1469.72</td>\n",
        "      <td> 81.24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-11-30</th>\n",
        "      <td> 33.80</td>\n",
        "      <td> 182.22</td>\n",
        "      <td> 32.53</td>\n",
        "      <td>  98.18</td>\n",
        "      <td> 59.93</td>\n",
        "      <td> 30.99</td>\n",
        "      <td> 68.78</td>\n",
        "      <td> 1481.14</td>\n",
        "      <td> 81.76</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-12-03</th>\n",
        "      <td> 32.98</td>\n",
        "      <td> 178.86</td>\n",
        "      <td> 31.38</td>\n",
        "      <td>  98.79</td>\n",
        "      <td> 59.90</td>\n",
        "      <td> 30.36</td>\n",
        "      <td> 68.24</td>\n",
        "      <td> 1472.42</td>\n",
        "      <td> 81.48</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-12-04</th>\n",
        "      <td> 32.76</td>\n",
        "      <td> 179.81</td>\n",
        "      <td> 30.80</td>\n",
        "      <td>  99.54</td>\n",
        "      <td> 60.10</td>\n",
        "      <td> 30.23</td>\n",
        "      <td> 68.30</td>\n",
        "      <td> 1462.79</td>\n",
        "      <td> 80.81</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-12-05</th>\n",
        "      <td> 33.09</td>\n",
        "      <td> 185.50</td>\n",
        "      <td> 31.19</td>\n",
        "      <td> 100.97</td>\n",
        "      <td> 60.34</td>\n",
        "      <td> 31.50</td>\n",
        "      <td> 68.30</td>\n",
        "      <td> 1485.01</td>\n",
        "      <td> 82.46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-12-06</th>\n",
        "      <td> 33.32</td>\n",
        "      <td> 189.95</td>\n",
        "      <td> 31.66</td>\n",
        "      <td> 102.40</td>\n",
        "      <td> 60.42</td>\n",
        "      <td> 31.87</td>\n",
        "      <td> 68.98</td>\n",
        "      <td> 1507.34</td>\n",
        "      <td> 83.85</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-12-07</th>\n",
        "      <td> 34.30</td>\n",
        "      <td> 194.30</td>\n",
        "      <td> 31.63</td>\n",
        "      <td> 101.62</td>\n",
        "      <td> 59.87</td>\n",
        "      <td> 31.85</td>\n",
        "      <td> 68.95</td>\n",
        "      <td> 1504.66</td>\n",
        "      <td> 83.91</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007-12-10</th>\n",
        "      <td> 35.17</td>\n",
        "      <td> 194.21</td>\n",
        "      <td> 31.78</td>\n",
        "      <td> 102.11</td>\n",
        "      <td> 59.97</td>\n",
        "      <td> 32.06</td>\n",
        "      <td> 69.20</td>\n",
        "      <td> 1515.96</td>\n",
        "      <td> 84.39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-02</th>\n",
        "      <td> 12.04</td>\n",
        "      <td> 374.05</td>\n",
        "      <td> 15.61</td>\n",
        "      <td> 166.98</td>\n",
        "      <td> 64.07</td>\n",
        "      <td> 25.80</td>\n",
        "      <td> 63.30</td>\n",
        "      <td> 1173.97</td>\n",
        "      <td> 72.14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-06</th>\n",
        "      <td> 11.77</td>\n",
        "      <td> 379.74</td>\n",
        "      <td> 15.11</td>\n",
        "      <td> 165.11</td>\n",
        "      <td> 64.64</td>\n",
        "      <td> 25.51</td>\n",
        "      <td> 62.45</td>\n",
        "      <td> 1165.24</td>\n",
        "      <td> 71.15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-07</th>\n",
        "      <td> 12.25</td>\n",
        "      <td> 383.93</td>\n",
        "      <td> 15.65</td>\n",
        "      <td> 167.31</td>\n",
        "      <td> 65.43</td>\n",
        "      <td> 26.00</td>\n",
        "      <td> 61.62</td>\n",
        "      <td> 1198.62</td>\n",
        "      <td> 73.65</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-08</th>\n",
        "      <td> 12.03</td>\n",
        "      <td> 384.14</td>\n",
        "      <td> 15.44</td>\n",
        "      <td> 165.25</td>\n",
        "      <td> 64.95</td>\n",
        "      <td> 26.22</td>\n",
        "      <td> 61.34</td>\n",
        "      <td> 1185.90</td>\n",
        "      <td> 72.82</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-09</th>\n",
        "      <td> 11.58</td>\n",
        "      <td> 377.48</td>\n",
        "      <td> 14.95</td>\n",
        "      <td> 161.37</td>\n",
        "      <td> 63.64</td>\n",
        "      <td> 25.74</td>\n",
        "      <td> 59.99</td>\n",
        "      <td> 1154.23</td>\n",
        "      <td> 71.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-12</th>\n",
        "      <td> 11.55</td>\n",
        "      <td> 379.94</td>\n",
        "      <td> 14.87</td>\n",
        "      <td> 162.42</td>\n",
        "      <td> 63.59</td>\n",
        "      <td> 25.89</td>\n",
        "      <td> 60.14</td>\n",
        "      <td> 1162.27</td>\n",
        "      <td> 71.84</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-13</th>\n",
        "      <td> 11.63</td>\n",
        "      <td> 384.62</td>\n",
        "      <td> 15.26</td>\n",
        "      <td> 163.43</td>\n",
        "      <td> 63.61</td>\n",
        "      <td> 26.04</td>\n",
        "      <td> 60.54</td>\n",
        "      <td> 1172.87</td>\n",
        "      <td> 71.65</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-14</th>\n",
        "      <td> 11.73</td>\n",
        "      <td> 389.30</td>\n",
        "      <td> 15.64</td>\n",
        "      <td> 167.24</td>\n",
        "      <td> 63.73</td>\n",
        "      <td> 26.50</td>\n",
        "      <td> 61.58</td>\n",
        "      <td> 1188.68</td>\n",
        "      <td> 72.64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-15</th>\n",
        "      <td> 11.98</td>\n",
        "      <td> 392.96</td>\n",
        "      <td> 16.08</td>\n",
        "      <td> 170.09</td>\n",
        "      <td> 64.40</td>\n",
        "      <td> 26.99</td>\n",
        "      <td> 63.22</td>\n",
        "      <td> 1209.11</td>\n",
        "      <td> 74.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-16</th>\n",
        "      <td> 11.97</td>\n",
        "      <td> 400.50</td>\n",
        "      <td> 16.33</td>\n",
        "      <td> 172.99</td>\n",
        "      <td> 64.59</td>\n",
        "      <td> 27.12</td>\n",
        "      <td> 62.05</td>\n",
        "      <td> 1216.01</td>\n",
        "      <td> 74.55</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-19</th>\n",
        "      <td> 11.58</td>\n",
        "      <td> 411.63</td>\n",
        "      <td> 16.18</td>\n",
        "      <td> 173.13</td>\n",
        "      <td> 64.14</td>\n",
        "      <td> 27.21</td>\n",
        "      <td> 60.56</td>\n",
        "      <td> 1204.09</td>\n",
        "      <td> 73.70</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-20</th>\n",
        "      <td> 11.25</td>\n",
        "      <td> 413.45</td>\n",
        "      <td> 16.04</td>\n",
        "      <td> 174.72</td>\n",
        "      <td> 64.22</td>\n",
        "      <td> 26.98</td>\n",
        "      <td> 60.39</td>\n",
        "      <td> 1202.09</td>\n",
        "      <td> 74.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-21</th>\n",
        "      <td> 10.84</td>\n",
        "      <td> 412.14</td>\n",
        "      <td> 15.38</td>\n",
        "      <td> 173.02</td>\n",
        "      <td> 63.13</td>\n",
        "      <td> 25.99</td>\n",
        "      <td> 60.79</td>\n",
        "      <td> 1166.76</td>\n",
        "      <td> 71.97</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-22</th>\n",
        "      <td> 10.11</td>\n",
        "      <td> 401.82</td>\n",
        "      <td> 15.04</td>\n",
        "      <td> 168.62</td>\n",
        "      <td> 61.92</td>\n",
        "      <td> 25.06</td>\n",
        "      <td> 60.92</td>\n",
        "      <td> 1129.56</td>\n",
        "      <td> 69.24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-23</th>\n",
        "      <td> 10.07</td>\n",
        "      <td> 404.30</td>\n",
        "      <td> 15.21</td>\n",
        "      <td> 169.34</td>\n",
        "      <td> 61.59</td>\n",
        "      <td> 25.06</td>\n",
        "      <td> 60.34</td>\n",
        "      <td> 1136.43</td>\n",
        "      <td> 69.31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-26</th>\n",
        "      <td> 10.45</td>\n",
        "      <td> 403.17</td>\n",
        "      <td> 15.57</td>\n",
        "      <td> 174.51</td>\n",
        "      <td> 62.69</td>\n",
        "      <td> 25.44</td>\n",
        "      <td> 61.89</td>\n",
        "      <td> 1162.95</td>\n",
        "      <td> 71.72</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-27</th>\n",
        "      <td> 10.48</td>\n",
        "      <td> 399.26</td>\n",
        "      <td> 15.76</td>\n",
        "      <td> 177.71</td>\n",
        "      <td> 63.82</td>\n",
        "      <td> 25.67</td>\n",
        "      <td> 62.43</td>\n",
        "      <td> 1175.38</td>\n",
        "      <td> 72.91</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-28</th>\n",
        "      <td>  9.97</td>\n",
        "      <td> 397.01</td>\n",
        "      <td> 15.45</td>\n",
        "      <td> 177.55</td>\n",
        "      <td> 63.25</td>\n",
        "      <td> 25.58</td>\n",
        "      <td> 61.97</td>\n",
        "      <td> 1151.06</td>\n",
        "      <td> 72.07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-29</th>\n",
        "      <td> 10.06</td>\n",
        "      <td> 390.57</td>\n",
        "      <td> 15.86</td>\n",
        "      <td> 179.17</td>\n",
        "      <td> 63.90</td>\n",
        "      <td> 25.45</td>\n",
        "      <td> 62.58</td>\n",
        "      <td> 1160.40</td>\n",
        "      <td> 73.88</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-09-30</th>\n",
        "      <td>  9.57</td>\n",
        "      <td> 381.32</td>\n",
        "      <td> 15.22</td>\n",
        "      <td> 174.87</td>\n",
        "      <td> 63.69</td>\n",
        "      <td> 24.89</td>\n",
        "      <td> 61.90</td>\n",
        "      <td> 1131.42</td>\n",
        "      <td> 72.63</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-10-03</th>\n",
        "      <td>  8.90</td>\n",
        "      <td> 374.60</td>\n",
        "      <td> 14.69</td>\n",
        "      <td> 173.29</td>\n",
        "      <td> 62.08</td>\n",
        "      <td> 24.53</td>\n",
        "      <td> 60.29</td>\n",
        "      <td> 1099.23</td>\n",
        "      <td> 71.15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-10-04</th>\n",
        "      <td>  9.12</td>\n",
        "      <td> 372.50</td>\n",
        "      <td> 14.86</td>\n",
        "      <td> 174.74</td>\n",
        "      <td> 62.17</td>\n",
        "      <td> 25.34</td>\n",
        "      <td> 60.45</td>\n",
        "      <td> 1123.95</td>\n",
        "      <td> 72.83</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-10-05</th>\n",
        "      <td>  9.37</td>\n",
        "      <td> 378.25</td>\n",
        "      <td> 15.27</td>\n",
        "      <td> 176.85</td>\n",
        "      <td> 62.35</td>\n",
        "      <td> 25.89</td>\n",
        "      <td> 60.29</td>\n",
        "      <td> 1144.03</td>\n",
        "      <td> 73.95</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-10-06</th>\n",
        "      <td>  9.88</td>\n",
        "      <td> 377.37</td>\n",
        "      <td> 15.53</td>\n",
        "      <td> 181.69</td>\n",
        "      <td> 62.81</td>\n",
        "      <td> 26.34</td>\n",
        "      <td> 60.57</td>\n",
        "      <td> 1164.97</td>\n",
        "      <td> 73.89</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-10-07</th>\n",
        "      <td>  9.71</td>\n",
        "      <td> 369.80</td>\n",
        "      <td> 15.50</td>\n",
        "      <td> 182.39</td>\n",
        "      <td> 63.13</td>\n",
        "      <td> 26.25</td>\n",
        "      <td> 61.02</td>\n",
        "      <td> 1155.46</td>\n",
        "      <td> 73.56</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-10-10</th>\n",
        "      <td> 10.09</td>\n",
        "      <td> 388.81</td>\n",
        "      <td> 16.14</td>\n",
        "      <td> 186.62</td>\n",
        "      <td> 64.43</td>\n",
        "      <td> 26.94</td>\n",
        "      <td> 61.87</td>\n",
        "      <td> 1194.89</td>\n",
        "      <td> 76.28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-10-11</th>\n",
        "      <td> 10.30</td>\n",
        "      <td> 400.29</td>\n",
        "      <td> 16.14</td>\n",
        "      <td> 185.00</td>\n",
        "      <td> 63.96</td>\n",
        "      <td> 27.00</td>\n",
        "      <td> 60.95</td>\n",
        "      <td> 1195.54</td>\n",
        "      <td> 76.27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-10-12</th>\n",
        "      <td> 10.05</td>\n",
        "      <td> 402.19</td>\n",
        "      <td> 16.40</td>\n",
        "      <td> 186.12</td>\n",
        "      <td> 64.33</td>\n",
        "      <td> 26.96</td>\n",
        "      <td> 62.70</td>\n",
        "      <td> 1207.25</td>\n",
        "      <td> 77.16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-10-13</th>\n",
        "      <td> 10.10</td>\n",
        "      <td> 408.43</td>\n",
        "      <td> 16.22</td>\n",
        "      <td> 186.82</td>\n",
        "      <td> 64.23</td>\n",
        "      <td> 27.18</td>\n",
        "      <td> 62.36</td>\n",
        "      <td> 1203.66</td>\n",
        "      <td> 76.37</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-10-14</th>\n",
        "      <td> 10.26</td>\n",
        "      <td> 422.00</td>\n",
        "      <td> 16.60</td>\n",
        "      <td> 190.53</td>\n",
        "      <td> 64.72</td>\n",
        "      <td> 27.27</td>\n",
        "      <td> 62.24</td>\n",
        "      <td> 1224.58</td>\n",
        "      <td> 78.11</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>1000 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "               AA    AAPL     GE     IBM    JNJ   MSFT    PEP      SPX    XOM\n",
        "2007-10-29  37.41  185.09  34.46  106.78  57.13  31.78  65.67  1540.98  85.51\n",
        "2007-10-30  36.43  187.00  34.39  106.15  56.99  32.70  65.80  1531.02  83.25\n",
        "2007-10-31  36.79  189.95  34.97  108.01  57.30  33.84  65.69  1549.38  84.03\n",
        "2007-11-01  35.22  187.44  34.27  105.72  56.85  34.07  64.51  1508.44  80.84\n",
        "2007-11-02  35.83  187.87  34.27  106.59  56.95  34.07  65.03  1509.65  80.32\n",
        "2007-11-05  35.22  186.18  34.15  105.48  56.70  33.77  64.84  1502.17  80.07\n",
        "2007-11-06  35.84  191.79  34.14  105.27  56.80  33.47  65.49  1520.27  82.56\n",
        "2007-11-07  34.76  186.30  33.20  103.69  56.19  32.65  64.46  1475.62  79.96\n",
        "2007-11-08  35.00  175.47  33.15   99.05  56.79  31.94  65.50  1474.77  82.00\n",
        "2007-11-09  34.47  165.37  32.61   93.58  57.29  31.01  65.48  1453.70  79.64\n",
        "2007-11-12  33.33  153.76  32.50   94.70  58.30  30.69  64.67  1439.18  77.52\n",
        "2007-11-13  34.04  169.96  33.31   98.27  59.18  31.79  65.35  1481.05  79.67\n",
        "2007-11-14  34.25  166.11  33.14   96.56  58.81  31.30  65.99  1470.58  79.15\n",
        "2007-11-15  33.76  164.30  32.55   96.71  58.80  31.14  65.57  1451.15  77.48\n",
        "2007-11-16  33.74  166.39  32.84   97.82  59.56  31.44  66.08  1458.74  78.04\n",
        "2007-11-19  32.59  163.95  32.42   95.42  59.42  31.32  66.90  1433.27  77.13\n",
        "2007-11-20  32.88  168.85  32.32   96.54  59.47  31.90  66.69  1439.70  80.53\n",
        "2007-11-21  32.68  168.46  31.58   95.42  59.03  31.57  66.87  1416.77  79.82\n",
        "2007-11-23  32.66  171.54  32.01   97.13  59.16  31.46  67.29  1440.70  80.96\n",
        "2007-11-26  32.41  172.54  31.21   95.19  59.60  30.41  66.56  1407.22  78.57\n",
        "2007-11-27  32.56  174.81  31.82   96.92  59.92  30.49  67.65  1428.23  79.21\n",
        "2007-11-28  33.65  180.22  32.68  100.23  60.46  31.08  68.76  1469.02  80.62\n",
        "2007-11-29  33.97  184.29  32.40  100.35  60.51  30.98  68.20  1469.72  81.24\n",
        "2007-11-30  33.80  182.22  32.53   98.18  59.93  30.99  68.78  1481.14  81.76\n",
        "2007-12-03  32.98  178.86  31.38   98.79  59.90  30.36  68.24  1472.42  81.48\n",
        "2007-12-04  32.76  179.81  30.80   99.54  60.10  30.23  68.30  1462.79  80.81\n",
        "2007-12-05  33.09  185.50  31.19  100.97  60.34  31.50  68.30  1485.01  82.46\n",
        "2007-12-06  33.32  189.95  31.66  102.40  60.42  31.87  68.98  1507.34  83.85\n",
        "2007-12-07  34.30  194.30  31.63  101.62  59.87  31.85  68.95  1504.66  83.91\n",
        "2007-12-10  35.17  194.21  31.78  102.11  59.97  32.06  69.20  1515.96  84.39\n",
        "...           ...     ...    ...     ...    ...    ...    ...      ...    ...\n",
        "2011-09-02  12.04  374.05  15.61  166.98  64.07  25.80  63.30  1173.97  72.14\n",
        "2011-09-06  11.77  379.74  15.11  165.11  64.64  25.51  62.45  1165.24  71.15\n",
        "2011-09-07  12.25  383.93  15.65  167.31  65.43  26.00  61.62  1198.62  73.65\n",
        "2011-09-08  12.03  384.14  15.44  165.25  64.95  26.22  61.34  1185.90  72.82\n",
        "2011-09-09  11.58  377.48  14.95  161.37  63.64  25.74  59.99  1154.23  71.01\n",
        "2011-09-12  11.55  379.94  14.87  162.42  63.59  25.89  60.14  1162.27  71.84\n",
        "2011-09-13  11.63  384.62  15.26  163.43  63.61  26.04  60.54  1172.87  71.65\n",
        "2011-09-14  11.73  389.30  15.64  167.24  63.73  26.50  61.58  1188.68  72.64\n",
        "2011-09-15  11.98  392.96  16.08  170.09  64.40  26.99  63.22  1209.11  74.01\n",
        "2011-09-16  11.97  400.50  16.33  172.99  64.59  27.12  62.05  1216.01  74.55\n",
        "2011-09-19  11.58  411.63  16.18  173.13  64.14  27.21  60.56  1204.09  73.70\n",
        "2011-09-20  11.25  413.45  16.04  174.72  64.22  26.98  60.39  1202.09  74.01\n",
        "2011-09-21  10.84  412.14  15.38  173.02  63.13  25.99  60.79  1166.76  71.97\n",
        "2011-09-22  10.11  401.82  15.04  168.62  61.92  25.06  60.92  1129.56  69.24\n",
        "2011-09-23  10.07  404.30  15.21  169.34  61.59  25.06  60.34  1136.43  69.31\n",
        "2011-09-26  10.45  403.17  15.57  174.51  62.69  25.44  61.89  1162.95  71.72\n",
        "2011-09-27  10.48  399.26  15.76  177.71  63.82  25.67  62.43  1175.38  72.91\n",
        "2011-09-28   9.97  397.01  15.45  177.55  63.25  25.58  61.97  1151.06  72.07\n",
        "2011-09-29  10.06  390.57  15.86  179.17  63.90  25.45  62.58  1160.40  73.88\n",
        "2011-09-30   9.57  381.32  15.22  174.87  63.69  24.89  61.90  1131.42  72.63\n",
        "2011-10-03   8.90  374.60  14.69  173.29  62.08  24.53  60.29  1099.23  71.15\n",
        "2011-10-04   9.12  372.50  14.86  174.74  62.17  25.34  60.45  1123.95  72.83\n",
        "2011-10-05   9.37  378.25  15.27  176.85  62.35  25.89  60.29  1144.03  73.95\n",
        "2011-10-06   9.88  377.37  15.53  181.69  62.81  26.34  60.57  1164.97  73.89\n",
        "2011-10-07   9.71  369.80  15.50  182.39  63.13  26.25  61.02  1155.46  73.56\n",
        "2011-10-10  10.09  388.81  16.14  186.62  64.43  26.94  61.87  1194.89  76.28\n",
        "2011-10-11  10.30  400.29  16.14  185.00  63.96  27.00  60.95  1195.54  76.27\n",
        "2011-10-12  10.05  402.19  16.40  186.12  64.33  26.96  62.70  1207.25  77.16\n",
        "2011-10-13  10.10  408.43  16.22  186.82  64.23  27.18  62.36  1203.66  76.37\n",
        "2011-10-14  10.26  422.00  16.60  190.53  64.72  27.27  62.24  1224.58  78.11\n",
        "\n",
        "[1000 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s1 = close_px['AAPL'][-20:]\n",
      "s2 = close_px['AAPL'][-25:-10]\n",
      "side_by_side(s1, s2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2011-09-19    411.63          2011-09-12    379.94      \n",
        "2011-09-20    413.45          2011-09-13    384.62      \n",
        "2011-09-21    412.14          2011-09-14    389.30      \n",
        "2011-09-22    401.82          2011-09-15    392.96      \n",
        "2011-09-23    404.30          2011-09-16    400.50      \n",
        "2011-09-26    403.17          2011-09-19    411.63      \n",
        "2011-09-27    399.26          2011-09-20    413.45      \n",
        "2011-09-28    397.01          2011-09-21    412.14      \n",
        "2011-09-29    390.57          2011-09-22    401.82      \n",
        "2011-09-30    381.32          2011-09-23    404.30      \n",
        "2011-10-03    374.60          2011-09-26    403.17      \n",
        "2011-10-04    372.50          2011-09-27    399.26      \n",
        "2011-10-05    378.25          2011-09-28    397.01      \n",
        "2011-10-06    377.37          2011-09-29    390.57      \n",
        "2011-10-07    369.80          2011-09-30    381.32      \n",
        "2011-10-10    388.81          Name: AAPL, dtype: float64\n",
        "2011-10-11    400.29                                    \n",
        "2011-10-12    402.19                                    \n",
        "2011-10-13    408.43                                    \n",
        "2011-10-14    422.00                                    \n",
        "Name: AAPL, dtype: float64                              \n"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s1 + s2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "2011-09-12       NaN\n",
        "2011-09-13       NaN\n",
        "2011-09-14       NaN\n",
        "2011-09-15       NaN\n",
        "2011-09-16       NaN\n",
        "2011-09-19    823.26\n",
        "2011-09-20    826.90\n",
        "2011-09-21    824.28\n",
        "2011-09-22    803.64\n",
        "2011-09-23    808.60\n",
        "2011-09-26    806.34\n",
        "2011-09-27    798.52\n",
        "2011-09-28    794.02\n",
        "2011-09-29    781.14\n",
        "2011-09-30    762.64\n",
        "2011-10-03       NaN\n",
        "2011-10-04       NaN\n",
        "2011-10-05       NaN\n",
        "2011-10-06       NaN\n",
        "2011-10-07       NaN\n",
        "2011-10-10       NaN\n",
        "2011-10-11       NaN\n",
        "2011-10-12       NaN\n",
        "2011-10-13       NaN\n",
        "2011-10-14       NaN\n",
        "Name: AAPL, dtype: float64"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = close_px.ix[-10:, :3]\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "side_by_side(s1.reindex(s2.index), s2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b, c  = s1.align(s2, join='inner')\n",
      "side_by_side(b, c)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b, c  = s1.align(s2, join='outer')\n",
      "side_by_side(b, c)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b, c  = s1.align(s2, join='right')\n",
      "side_by_side(b, c)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = close_px.ix[-10:, ['AAPL', 'IBM', 'MSFT']]\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2 = df.ix[::2, ['IBM', 'MSFT']]\n",
      "side_by_side(df, df2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df + df2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b, c = df.align(df2, join='inner')\n",
      "side_by_side(b, c) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Transposing: no copy if all columns are same type\n",
      "-------------------------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[:5].T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Columns can be any type\n",
      "-----------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n = 10\n",
      "foo = DataFrame(index=range(n))\n",
      "foo['floats'] = np.random.randn(n)\n",
      "foo['ints'] = np.arange(n)\n",
      "foo['strings'] = ['foo', 'bar'] * (n / 2)\n",
      "foo['bools'] = foo['floats'] > 0\n",
      "foo['objects'] = DateRange('1/1/2000', periods=n)\n",
      "foo"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "foo.dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "N.B. transposing is not roundtrippable in this case (column-oriented data structure)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "foo.T.T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "foo.T.T.dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Function application\n",
      "====================\n",
      "\n",
      "You can apply arbitrary functions to the rows or columns of a DataFrame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.apply(np.mean)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.apply(np.mean, axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can get as fancy as you want"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "close_px"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def peak_date(series):\n",
      "    return series.index[series.argmax()]\n",
      "close_px.apply(peak_date)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.apply(lambda x: x.max() - x.min()) # np.ptp"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.log(close_px)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plotting\n",
      "========\n",
      "\n",
      "Some basic plotting integration with matplotlib in Series / DataFrame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "close_px[['AAPL', 'IBM', 'MSFT', 'XOM']].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x113d7d690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAmkAAAFmCAYAAAA2zw2aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4VSspAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x113d7d9d0>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rets.ix[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "close_px.ix[-1].plot(kind='bar')\n",
      "title('Prices on %s' % close_px.index[-1])\n",
      "axhline(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "<matplotlib.lines.Line2D at 0x113f32750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAAGJCAYAAABW7E3LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x113d14290>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Hierarchical indexing\n",
      "---------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],\n",
      "                                   ['one', 'two', 'three']],\n",
      "                           labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],\n",
      "                                   [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]])\n",
      "hdf = DataFrame(np.random.randn(10, 3), index=index,\n",
      "                columns=['A', 'B', 'C'])\n",
      "hdf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hdf.ix['foo']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hdf.ix['foo'] = 0\n",
      "hdf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hdf.ix['foo', 'three']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Stacking and unstacking\n",
      "-----------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tuples = zip(*[['bar', 'bar', 'baz', 'baz',\n",
      "                'foo', 'foo', 'qux', 'qux'],\n",
      "               ['one', 'two', 'one', 'two',\n",
      "                'one', 'two', 'one', 'two']])\n",
      "index = MultiIndex.from_tuples(tuples)\n",
      "columns = MultiIndex.from_tuples([('A', 'cat'), ('B', 'dog'),\n",
      "                                  ('B', 'cat'), ('A', 'dog')])\n",
      "df = DataFrame(randn(8, 4), index=index, columns=columns)\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2 = df.ix[[0, 1, 2, 4, 5, 7]]\n",
      "df2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.unstack()['B']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "GroupBy\n",
      "======="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',\n",
      "                       'foo', 'bar', 'foo', 'foo'],\n",
      "                'B' : ['one', 'one', 'two', 'three',\n",
      "                       'two', 'two', 'one', 'three'],\n",
      "                'C' : np.random.randn(8),\n",
      "                'D' : np.random.randn(8)})\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for key, group in df.groupby('A'):\n",
      "    print key\n",
      "    print group"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.groupby('A')['C'].describe().T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.groupby('A').mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for key, group in df.groupby('A'):\n",
      "    print key\n",
      "    print group"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.groupby(['A', 'B']).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.groupby(['A', 'B'], as_index=False).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "GroupBy example: linear regression by group\n",
      "-------------------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scikits.statsmodels.api as sm\n",
      "rets = close_px / close_px.shift(1) - 1\n",
      "\n",
      "def get_beta(rets):\n",
      "    rets = rets.dropna()\n",
      "    rets['intercept'] = 1.\n",
      "    model = sm.OLS(rets['MSFT'], rets.ix[:, ['AAPL', 'intercept']]).fit()\n",
      "    return model.params\n",
      "\n",
      "get_beta(rets)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grouped = rets.groupby([lambda x: x.year, lambda x: x.month])\n",
      "beta_by_ym = grouped.apply(get_beta)\n",
      "beta_by_ym"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "beta_by_ym.unstack(0)['AAPL']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "GroupBy with hierarchical indexing\n",
      "----------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tuples = zip(*[['bar', 'bar', 'baz', 'baz',\n",
      "                'foo', 'foo', 'qux', 'qux'],\n",
      "               ['one', 'two', 'one', 'two',\n",
      "                'one', 'two', 'one', 'two']])\n",
      "index = MultiIndex.from_tuples(tuples)\n",
      "columns = MultiIndex.from_tuples([('A', 'cat'), ('B', 'dog'),\n",
      "                                  ('B', 'cat'), ('A', 'dog')])\n",
      "df = DataFrame(randn(8, 4), index=index, columns=columns)\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.groupby(level=0, axis=0).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.stack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.stack().mean(1).unstack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# could also have done\n",
      "df.groupby(level=1, axis=1).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": "&nbsp;"
    }
   ],
   "metadata": {}
  }
 ]
}