Basics.ipynb

{
 "metadata": {
  "name": "Basics"
 },
 "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",
      "import matplotlib.pyplot as plt\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": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(arange(10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[<matplotlib.lines.Line2D at 0x106b74e50>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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GGbF06dKYN29edHR0xNy5c+Oyyy7LeqxU9PT0xHPPPRcTJkyIG2+8MSZOnFj3\n5+u+kzYAQDtqv1tUAABGYUECADiIBQkA4CAWJACAg1iQAAAOYkECADiIBQkA4CD/Dx4wd2Pca+ZZ\nAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Series\n",
      "======"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "randn(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "array([ 0.75504938, -3.15693604,  1.05450449, -0.69477629, -1.55880322])"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "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": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "a   -0.139871\n",
        "b   -0.580239\n",
        "c    1.280341\n",
        "d   -1.299564\n",
        "e   -0.360330"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'b' in s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s['b']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "-0.58023865919519868"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "Index([a, b, c, d, e], dtype=object)"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "a   -0.139871\n",
        "b   -0.580239\n",
        "c    1.280341\n",
        "d   -1.299564\n",
        "e   -0.360330"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mapping = s.to_dict()\n",
      "mapping"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "{'a': -0.13987069666249202,\n",
        " 'b': -0.58023865919519868,\n",
        " 'c': 1.2803413944255728,\n",
        " 'd': -1.2995637798105613,\n",
        " 'e': -0.36033048832567999}"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = Series(mapping, index=['b', 'e', 'a', 'd', 'f'])\n",
      "s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "b   -0.580239\n",
        "e   -0.360330\n",
        "a   -0.139871\n",
        "d   -1.299564\n",
        "f         NaN"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s[notnull(s)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "b   -0.580239\n",
        "e   -0.360330\n",
        "a   -0.139871\n",
        "d   -1.299564"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "b   -0.580239\n",
        "e   -0.360330\n",
        "a   -0.139871\n",
        "d   -1.299564"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s * 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "b   -1.160477\n",
        "e   -0.720661\n",
        "a   -0.279741\n",
        "d   -2.599128\n",
        "f         NaN"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s[:3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "b   -0.580239\n",
        "e   -0.360330\n",
        "a   -0.139871"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "Index([b, e, a, d, f], dtype=object)"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "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['d'] = range(6)\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "  <thead>\n",
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        "      <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",
        "      <td><strong>0</strong></td>\n",
        "      <td>-0.497943</td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.542663</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td>-1.308615</td>\n",
        "      <td> bar</td>\n",
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        "      <td> 1</td>\n",
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        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td>-1.394336</td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.198010</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 1.004886</td>\n",
        "      <td> bar</td>\n",
        "      <td>-2.211485</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td>-1.183105</td>\n",
        "      <td> foo</td>\n",
        "      <td>-0.923296</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>5</strong></td>\n",
        "      <td> 1.007496</td>\n",
        "      <td> bar</td>\n",
        "      <td>-1.349994</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "          a    b         c  d\n",
        "0 -0.497943  foo  0.542663  0\n",
        "1 -1.308615  bar -0.274153  1\n",
        "2 -1.394336  foo  0.198010  2\n",
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        "5  1.007496  bar -1.349994  5"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['b']    # by column"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "0    foo\n",
        "1    bar\n",
        "2    foo\n",
        "3    bar\n",
        "4    foo\n",
        "5    bar\n",
        "Name: b"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[:3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
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        "      <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",
        "      <td><strong>0</strong></td>\n",
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        "      <td> foo</td>\n",
        "      <td> 0.542663</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td>-1.308615</td>\n",
        "      <td> bar</td>\n",
        "      <td>-0.274153</td>\n",
        "      <td> 1</td>\n",
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        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td>-1.394336</td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.198010</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "          a    b         c  d\n",
        "0 -0.497943  foo  0.542663  0\n",
        "1 -1.308615  bar -0.274153  1\n",
        "2 -1.394336  foo  0.198010  2"
       ]
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     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[:-2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\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",
        "      <td><strong>0</strong></td>\n",
        "      <td>-0.497943</td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.542663</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td>-1.308615</td>\n",
        "      <td> bar</td>\n",
        "      <td>-0.274153</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td>-1.394336</td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.198010</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 1.004886</td>\n",
        "      <td> bar</td>\n",
        "      <td>-2.211485</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "          a    b         c  d\n",
        "0 -0.497943  foo  0.542663  0\n",
        "1 -1.308615  bar -0.274153  1\n",
        "2 -1.394336  foo  0.198010  2\n",
        "3  1.004886  bar -2.211485  3"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[-2:]   # the last 2 rows"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\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",
        "      <td><strong>4</strong></td>\n",
        "      <td>-1.183105</td>\n",
        "      <td> foo</td>\n",
        "      <td>-0.923296</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>5</strong></td>\n",
        "      <td> 1.007496</td>\n",
        "      <td> bar</td>\n",
        "      <td>-1.349994</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "          a    b         c  d\n",
        "4 -1.183105  foo -0.923296  4\n",
        "5  1.007496  bar -1.349994  5"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.tile(['foo', 'bar'], 3)   # in numpy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "array(['foo', 'bar', 'foo', 'bar', 'foo', 'bar'], \n",
        "      dtype='|S3')"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.xs(0)   # returning the first row"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "a   -0.4979432\n",
        "b          foo\n",
        "c    0.5426634\n",
        "d            0\n",
        "Name: 0"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[2]    # does equal thing as above. In this case, 3rd row. "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "a    -1.394336\n",
        "b          foo\n",
        "c    0.1980095\n",
        "d            2\n",
        "Name: 2"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[2, 'b']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "'foo'"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.get_value(2, 'b')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "'foo'"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "timeit df.ix[2, 'b']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100000 loops, best of 3: 2.93 us per loop\n"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "timeit df.get_value(2, 'b')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000000 loops, best of 3: 1.6 us per loop\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[2:4, 'b']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "2    foo\n",
        "3    bar\n",
        "4    foo\n",
        "Name: b"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[2:4, ['b', 'c']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "      <th></th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.198010</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> bar</td>\n",
        "      <td>-2.211485</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> foo</td>\n",
        "      <td>-0.923296</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "     b         c\n",
        "2  foo  0.198010\n",
        "3  bar -2.211485\n",
        "4  foo -0.923296"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[2:4, 'b':'c']   # slices by column - essentially the same as above."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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        "      <th></th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.198010</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> bar</td>\n",
        "      <td>-2.211485</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> foo</td>\n",
        "      <td>-0.923296</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "     b         c\n",
        "2  foo  0.198010\n",
        "3  bar -2.211485\n",
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      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[[0, 2, 4], ['b', 'c', 'd']]   # pass a list of rows and columns I want to select out"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.542663</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.198010</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> foo</td>\n",
        "      <td>-0.923296</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "     b         c  d\n",
        "0  foo  0.542663  0\n",
        "2  foo  0.198010  2\n",
        "4  foo -0.923296  4"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['c'] > 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "0     True\n",
        "1    False\n",
        "2     True\n",
        "3    False\n",
        "4    False\n",
        "5    False\n",
        "Name: c"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[df['c'] > 0]   # boolean arrays"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\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",
        "      <td><strong>0</strong></td>\n",
        "      <td>-0.497943</td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.542663</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td>-1.394336</td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.198010</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "          a    b         c  d\n",
        "0 -0.497943  foo  0.542663  0\n",
        "2 -1.394336  foo  0.198010  2"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "Int64Index([0, 1, 2, 3, 4, 5])"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "Index([a, b, c, d], dtype=object)"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "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=DateRange('1/1/2000', periods=6))\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/local/Cellar/python/2.7.2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.8.1-py2.7-macosx-10.5-intel.egg/pandas/core/daterange.py:21: FutureWarning: DateRange is deprecated, use DatetimeIndex instead\n",
        "  FutureWarning)\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>2000-01-03 00:00:00</strong></td>\n",
        "      <td> 1.926781</td>\n",
        "      <td> foo</td>\n",
        "      <td>-0.904502</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2000-01-04 00:00:00</strong></td>\n",
        "      <td> 0.678309</td>\n",
        "      <td> bar</td>\n",
        "      <td> 2.140457</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2000-01-05 00:00:00</strong></td>\n",
        "      <td>-0.846715</td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.620089</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2000-01-06 00:00:00</strong></td>\n",
        "      <td> 0.462977</td>\n",
        "      <td> bar</td>\n",
        "      <td> 1.282020</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2000-01-07 00:00:00</strong></td>\n",
        "      <td>-0.704591</td>\n",
        "      <td> foo</td>\n",
        "      <td> 1.366704</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2000-01-10 00:00:00</strong></td>\n",
        "      <td> 0.465697</td>\n",
        "      <td> bar</td>\n",
        "      <td>-0.575587</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "                   a    b         c\n",
        "2000-01-03  1.926781  foo -0.904502\n",
        "2000-01-04  0.678309  bar  2.140457\n",
        "2000-01-05 -0.846715  foo  0.620089\n",
        "2000-01-06  0.462977  bar  1.282020\n",
        "2000-01-07 -0.704591  foo  1.366704\n",
        "2000-01-10  0.465697  bar -0.575587"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "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\">\n",
        "  <thead>\n",
        "    <tr>\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",
        "      <td><strong>0</strong></td>\n",
        "      <td> 0.598279</td>\n",
        "      <td> foo</td>\n",
        "      <td> 1.155302</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 0.491108</td>\n",
        "      <td> bar</td>\n",
        "      <td>-1.693307</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td>-0.529175</td>\n",
        "      <td> foo</td>\n",
        "      <td>-2.867020</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td>-0.758212</td>\n",
        "      <td> bar</td>\n",
        "      <td> 0.615492</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 0.977810</td>\n",
        "      <td> foo</td>\n",
        "      <td> 1.549035</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>5</strong></td>\n",
        "      <td>-2.104020</td>\n",
        "      <td> bar</td>\n",
        "      <td>-2.671483</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "          a    b         c   d\n",
        "0  0.598279  foo  1.155302 NaN\n",
        "1  0.491108  bar -1.693307 NaN\n",
        "2 -0.529175  foo -2.867020 NaN\n",
        "3 -0.758212  bar  0.615492 NaN\n",
        "4  0.977810  foo  1.549035 NaN\n",
        "5 -2.104020  bar -2.671483 NaN"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "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\">\n",
        "  <thead>\n",
        "    <tr>\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",
        "      <td><strong>0</strong></td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>5</strong></td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 55,
       "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": 55
    },
    {
     "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()   # sorted cols: bar, baz, foo\n",
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "{'bar': {'a': 0.9295182780071557,\n",
        "  'b': 0.629167406292866,\n",
        "  'c': 0.7481003106804884,\n",
        "  'd': -0.7406144327794744},\n",
        " 'baz': {'a': -0.47898235355732777,\n",
        "  'b': 1.3748698924766567,\n",
        "  'c': -0.35506751008916465,\n",
        "  'd': 0.6257300406561229},\n",
        " 'foo': {'a': 1.1424385903258707,\n",
        "  'b': -1.7092715082842402,\n",
        "  'c': 0.6934118811100397,\n",
        "  'd': 0.1536432266628359}}"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "del data ['foo']['c']   # delete an entry by index key (col) and row."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "DataFrame(data)   # pass a dict "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>bar</th>\n",
        "      <th>baz</th>\n",
        "      <th>foo</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>a</strong></td>\n",
        "      <td> 0.929518</td>\n",
        "      <td>-0.478982</td>\n",
        "      <td> 1.142439</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>b</strong></td>\n",
        "      <td> 0.629167</td>\n",
        "      <td> 1.374870</td>\n",
        "      <td>-1.709272</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>c</strong></td>\n",
        "      <td> 0.748100</td>\n",
        "      <td>-0.355068</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>d</strong></td>\n",
        "      <td>-0.740614</td>\n",
        "      <td> 0.625730</td>\n",
        "      <td> 0.153643</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "        bar       baz       foo\n",
        "a  0.929518 -0.478982  1.142439\n",
        "b  0.629167  1.374870 -1.709272\n",
        "c  0.748100 -0.355068       NaN\n",
        "d -0.740614  0.625730  0.153643"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "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": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head -n 10 stock_data.csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ",AA,AAPL,GE,IBM,JNJ,MSFT,PEP,SPX,XOM\r\n",
        "2007-10-29 00:00:00,37.41,185.09,34.46,106.78,57.13,31.78,65.67,1540.98,85.51\r\n",
        "2007-10-30 00:00:00,36.43,187.0,34.39,106.15,56.99,32.7,65.8,1531.02,83.25\r\n",
        "2007-10-31 00:00:00,36.79,189.95,34.97,108.01,57.3,33.84,65.69,1549.38,84.03\r\n",
        "2007-11-01 00:00:00,35.22,187.44,34.27,105.72,56.85,34.07,64.51,1508.44,80.84\r\n",
        "2007-11-02 00:00:00,35.83,187.87,34.27,106.59,56.95,34.07,65.03,1509.65,80.32\r\n",
        "2007-11-05 00:00:00,35.22,186.18,34.15,105.48,56.7,33.77,64.84,1502.17,80.07\r\n",
        "2007-11-06 00:00:00,35.84,191.79,34.14,105.27,56.8,33.47,65.49,1520.27,82.56\r\n",
        "2007-11-07 00:00:00,34.76,186.3,33.2,103.69,56.19,32.65,64.46,1475.62,79.96\r\n",
        "2007-11-08 00:00:00,35.0,175.47,33.15,99.05,56.79,31.94,65.5,1474.77,82.0\r\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "close_px"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 1000 entries, 2007-10-29 00:00:00 to 2011-10-14 00:00:00\n",
        "Data columns:\n",
        "AA      1000  non-null values\n",
        "AAPL    1000  non-null values\n",
        "GE      1000  non-null values\n",
        "IBM     1000  non-null values\n",
        "JNJ     1000  non-null values\n",
        "MSFT    1000  non-null values\n",
        "PEP     1000  non-null values\n",
        "SPX     1000  non-null values\n",
        "XOM     1000  non-null values\n",
        "dtypes: float64(9)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s1 = close_px['AAPL'][-20:]      # the last 20 rows\n",
      "s2 = close_px['AAPL'][-25:-10]   # the last 25 - last 10 = 15 rows.\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          \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                                  \n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s1 + s2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "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"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s1.add(s2, fill_value=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "2011-09-12    379.94\n",
        "2011-09-13    384.62\n",
        "2011-09-14    389.30\n",
        "2011-09-15    392.96\n",
        "2011-09-16    400.50\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    374.60\n",
        "2011-10-04    372.50\n",
        "2011-10-05    378.25\n",
        "2011-10-06    377.37\n",
        "2011-10-07    369.80\n",
        "2011-10-10    388.81\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"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(s1 + s2).dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "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",
        "Name: AAPL"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "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)   # align indexes of s1 and s2. Print 2 objects alongside each other."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "side_by_side(s1.ix[s2.index], s2)   # same as above, supported in newer version of 0.12 ipython."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2011-09-12       NaN    2011-09-12    379.94\n",
        "2011-09-13       NaN    2011-09-13    384.62\n",
        "2011-09-14       NaN    2011-09-14    389.30\n",
        "2011-09-15       NaN    2011-09-15    392.96\n",
        "2011-09-16       NaN    2011-09-16    400.50\n",
        "2011-09-19    411.63    2011-09-19    411.63\n",
        "2011-09-20    413.45    2011-09-20    413.45\n",
        "2011-09-21    412.14    2011-09-21    412.14\n",
        "2011-09-22    401.82    2011-09-22    401.82\n",
        "2011-09-23    404.30    2011-09-23    404.30\n",
        "2011-09-26    403.17    2011-09-26    403.17\n",
        "2011-09-27    399.26    2011-09-27    399.26\n",
        "2011-09-28    397.01    2011-09-28    397.01\n",
        "2011-09-29    390.57    2011-09-29    390.57\n",
        "2011-09-30    381.32    2011-09-30    381.32\n",
        "Name: AAPL              Name: AAPL          \n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "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": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>AAPL</th>\n",
        "      <th>IBM</th>\n",
        "      <th>MSFT</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>2011-10-03 00:00:00</strong></td>\n",
        "      <td> 374.60</td>\n",
        "      <td> 173.29</td>\n",
        "      <td> 24.53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-10-04 00:00:00</strong></td>\n",
        "      <td> 372.50</td>\n",
        "      <td> 174.74</td>\n",
        "      <td> 25.34</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-10-05 00:00:00</strong></td>\n",
        "      <td> 378.25</td>\n",
        "      <td> 176.85</td>\n",
        "      <td> 25.89</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-10-06 00:00:00</strong></td>\n",
        "      <td> 377.37</td>\n",
        "      <td> 181.69</td>\n",
        "      <td> 26.34</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-10-07 00:00:00</strong></td>\n",
        "      <td> 369.80</td>\n",
        "      <td> 182.39</td>\n",
        "      <td> 26.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-10-10 00:00:00</strong></td>\n",
        "      <td> 388.81</td>\n",
        "      <td> 186.62</td>\n",
        "      <td> 26.94</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-10-11 00:00:00</strong></td>\n",
        "      <td> 400.29</td>\n",
        "      <td> 185.00</td>\n",
        "      <td> 27.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-10-12 00:00:00</strong></td>\n",
        "      <td> 402.19</td>\n",
        "      <td> 186.12</td>\n",
        "      <td> 26.96</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-10-13 00:00:00</strong></td>\n",
        "      <td> 408.43</td>\n",
        "      <td> 186.82</td>\n",
        "      <td> 27.18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2011-10-14 00:00:00</strong></td>\n",
        "      <td> 422.00</td>\n",
        "      <td> 190.53</td>\n",
        "      <td> 27.27</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "              AAPL     IBM   MSFT\n",
        "2011-10-03  374.60  173.29  24.53\n",
        "2011-10-04  372.50  174.74  25.34\n",
        "2011-10-05  378.25  176.85  25.89\n",
        "2011-10-06  377.37  181.69  26.34\n",
        "2011-10-07  369.80  182.39  26.25\n",
        "2011-10-10  388.81  186.62  26.94\n",
        "2011-10-11  400.29  185.00  27.00\n",
        "2011-10-12  402.19  186.12  26.96\n",
        "2011-10-13  408.43  186.82  27.18\n",
        "2011-10-14  422.00  190.53  27.27"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2 = df.ix[::2, ['IBM', 'MSFT']]\n",
      "side_by_side(df, df2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "              AAPL     IBM   MSFT                   IBM   MSFT\n",
        "2011-10-03  374.60  173.29  24.53    2011-10-03  173.29  24.53\n",
        "2011-10-04  372.50  174.74  25.34    2011-10-05  176.85  25.89\n",
        "2011-10-05  378.25  176.85  25.89    2011-10-07  182.39  26.25\n",
        "2011-10-06  377.37  181.69  26.34    2011-10-11  185.00  27.00\n",
        "2011-10-07  369.80  182.39  26.25    2011-10-13  186.82  27.18\n",
        "2011-10-10  388.81  186.62  26.94                             \n",
        "2011-10-11  400.29  185.00  27.00                             \n",
        "2011-10-12  402.19  186.12  26.96                             \n",
        "2011-10-13  408.43  186.82  27.18                             \n",
        "2011-10-14  422.00  190.53  27.27                             \n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "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": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>2011-10-03 00:00:00</th>\n",
        "      <th>2011-10-04 00:00:00</th>\n",
        "      <th>2011-10-05 00:00:00</th>\n",
        "      <th>2011-10-06 00:00:00</th>\n",
        "      <th>2011-10-07 00:00:00</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>AAPL</strong></td>\n",
        "      <td> 374.60</td>\n",
        "      <td> 372.50</td>\n",
        "      <td> 378.25</td>\n",
        "      <td> 377.37</td>\n",
        "      <td> 369.80</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>IBM</strong></td>\n",
        "      <td> 173.29</td>\n",
        "      <td> 174.74</td>\n",
        "      <td> 176.85</td>\n",
        "      <td> 181.69</td>\n",
        "      <td> 182.39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>MSFT</strong></td>\n",
        "      <td>  24.53</td>\n",
        "      <td>  25.34</td>\n",
        "      <td>  25.89</td>\n",
        "      <td>  26.34</td>\n",
        "      <td>  26.25</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "      2011-10-03  2011-10-04  2011-10-05  2011-10-06  2011-10-07\n",
        "AAPL      374.60      372.50      378.25      377.37      369.80\n",
        "IBM       173.29      174.74      176.85      181.69      182.39\n",
        "MSFT       24.53       25.34       25.89       26.34       26.25"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "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": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/local/Cellar/python/2.7.2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.8.1-py2.7-macosx-10.5-intel.egg/pandas/core/daterange.py:21: FutureWarning: DateRange is deprecated, use DatetimeIndex instead\n",
        "  FutureWarning)\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>floats</th>\n",
        "      <th>ints</th>\n",
        "      <th>strings</th>\n",
        "      <th>bools</th>\n",
        "      <th>objects</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td>-0.585959</td>\n",
        "      <td> 0</td>\n",
        "      <td> foo</td>\n",
        "      <td> False</td>\n",
        "      <td>2000-01-03 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 0.675488</td>\n",
        "      <td> 1</td>\n",
        "      <td> bar</td>\n",
        "      <td>  True</td>\n",
        "      <td>2000-01-04 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 1.801818</td>\n",
        "      <td> 2</td>\n",
        "      <td> foo</td>\n",
        "      <td>  True</td>\n",
        "      <td>2000-01-05 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 0.665562</td>\n",
        "      <td> 3</td>\n",
        "      <td> bar</td>\n",
        "      <td>  True</td>\n",
        "      <td>2000-01-06 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td>-1.089721</td>\n",
        "      <td> 4</td>\n",
        "      <td> foo</td>\n",
        "      <td> False</td>\n",
        "      <td>2000-01-07 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>5</strong></td>\n",
        "      <td>-0.298291</td>\n",
        "      <td> 5</td>\n",
        "      <td> bar</td>\n",
        "      <td> False</td>\n",
        "      <td>2000-01-10 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>6</strong></td>\n",
        "      <td> 0.657715</td>\n",
        "      <td> 6</td>\n",
        "      <td> foo</td>\n",
        "      <td>  True</td>\n",
        "      <td>2000-01-11 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>7</strong></td>\n",
        "      <td>-0.182239</td>\n",
        "      <td> 7</td>\n",
        "      <td> bar</td>\n",
        "      <td> False</td>\n",
        "      <td>2000-01-12 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>8</strong></td>\n",
        "      <td>-0.145589</td>\n",
        "      <td> 8</td>\n",
        "      <td> foo</td>\n",
        "      <td> False</td>\n",
        "      <td>2000-01-13 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>9</strong></td>\n",
        "      <td> 0.551702</td>\n",
        "      <td> 9</td>\n",
        "      <td> bar</td>\n",
        "      <td>  True</td>\n",
        "      <td>2000-01-14 00:00:00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "     floats  ints strings  bools             objects\n",
        "0 -0.585959     0     foo  False 2000-01-03 00:00:00\n",
        "1  0.675488     1     bar   True 2000-01-04 00:00:00\n",
        "2  1.801818     2     foo   True 2000-01-05 00:00:00\n",
        "3  0.665562     3     bar   True 2000-01-06 00:00:00\n",
        "4 -1.089721     4     foo  False 2000-01-07 00:00:00\n",
        "5 -0.298291     5     bar  False 2000-01-10 00:00:00\n",
        "6  0.657715     6     foo   True 2000-01-11 00:00:00\n",
        "7 -0.182239     7     bar  False 2000-01-12 00:00:00\n",
        "8 -0.145589     8     foo  False 2000-01-13 00:00:00\n",
        "9  0.551702     9     bar   True 2000-01-14 00:00:00"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "foo.dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "floats            float64\n",
        "ints                int64\n",
        "strings            object\n",
        "bools                bool\n",
        "objects    datetime64[ns]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "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": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>floats</th>\n",
        "      <th>ints</th>\n",
        "      <th>strings</th>\n",
        "      <th>bools</th>\n",
        "      <th>objects</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td>-0.5859591</td>\n",
        "      <td> 0</td>\n",
        "      <td> foo</td>\n",
        "      <td> False</td>\n",
        "      <td> 2000-01-03 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td>  0.675488</td>\n",
        "      <td> 1</td>\n",
        "      <td> bar</td>\n",
        "      <td>  True</td>\n",
        "      <td> 2000-01-04 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td>  1.801818</td>\n",
        "      <td> 2</td>\n",
        "      <td> foo</td>\n",
        "      <td>  True</td>\n",
        "      <td> 2000-01-05 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 0.6655621</td>\n",
        "      <td> 3</td>\n",
        "      <td> bar</td>\n",
        "      <td>  True</td>\n",
        "      <td> 2000-01-06 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> -1.089721</td>\n",
        "      <td> 4</td>\n",
        "      <td> foo</td>\n",
        "      <td> False</td>\n",
        "      <td> 2000-01-07 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>5</strong></td>\n",
        "      <td>-0.2982908</td>\n",
        "      <td> 5</td>\n",
        "      <td> bar</td>\n",
        "      <td> False</td>\n",
        "      <td> 2000-01-10 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>6</strong></td>\n",
        "      <td> 0.6577153</td>\n",
        "      <td> 6</td>\n",
        "      <td> foo</td>\n",
        "      <td>  True</td>\n",
        "      <td> 2000-01-11 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>7</strong></td>\n",
        "      <td> -0.182239</td>\n",
        "      <td> 7</td>\n",
        "      <td> bar</td>\n",
        "      <td> False</td>\n",
        "      <td> 2000-01-12 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>8</strong></td>\n",
        "      <td>-0.1455885</td>\n",
        "      <td> 8</td>\n",
        "      <td> foo</td>\n",
        "      <td> False</td>\n",
        "      <td> 2000-01-13 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>9</strong></td>\n",
        "      <td> 0.5517019</td>\n",
        "      <td> 9</td>\n",
        "      <td> bar</td>\n",
        "      <td>  True</td>\n",
        "      <td> 2000-01-14 00:00:00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "      floats ints strings  bools              objects\n",
        "0 -0.5859591    0     foo  False  2000-01-03 00:00:00\n",
        "1   0.675488    1     bar   True  2000-01-04 00:00:00\n",
        "2   1.801818    2     foo   True  2000-01-05 00:00:00\n",
        "3  0.6655621    3     bar   True  2000-01-06 00:00:00\n",
        "4  -1.089721    4     foo  False  2000-01-07 00:00:00\n",
        "5 -0.2982908    5     bar  False  2000-01-10 00:00:00\n",
        "6  0.6577153    6     foo   True  2000-01-11 00:00:00\n",
        "7  -0.182239    7     bar  False  2000-01-12 00:00:00\n",
        "8 -0.1455885    8     foo  False  2000-01-13 00:00:00\n",
        "9  0.5517019    9     bar   True  2000-01-14 00:00:00"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "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": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "AAPL    389.424\n",
        "IBM     182.405\n",
        "MSFT     26.370"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.mean()   # same as above.   df.mean? to look up the function usage."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "AAPL    389.424\n",
        "IBM     182.405\n",
        "MSFT     26.370"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.mean(1)   # get the mean of the rows based on a column. Also excludes missing data (NaN: not a number)."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "2011-10-03    190.806667\n",
        "2011-10-04    190.860000\n",
        "2011-10-05    193.663333\n",
        "2011-10-06    195.133333\n",
        "2011-10-07    192.813333\n",
        "2011-10-10    200.790000\n",
        "2011-10-11    204.096667\n",
        "2011-10-12    205.090000\n",
        "2011-10-13    207.476667\n",
        "2011-10-14    213.266667"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.mean(1, skipna=False)   # flag skipna: to include NaN. In this data, no NaN."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "2011-10-03    190.806667\n",
        "2011-10-04    190.860000\n",
        "2011-10-05    193.663333\n",
        "2011-10-06    195.133333\n",
        "2011-10-07    192.813333\n",
        "2011-10-10    200.790000\n",
        "2011-10-11    204.096667\n",
        "2011-10-12    205.090000\n",
        "2011-10-13    207.476667\n",
        "2011-10-14    213.266667"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.apply(np.mean, axis=1)   # same as above."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "2011-10-03    190.806667\n",
        "2011-10-04    190.860000\n",
        "2011-10-05    193.663333\n",
        "2011-10-06    195.133333\n",
        "2011-10-07    192.813333\n",
        "2011-10-10    200.790000\n",
        "2011-10-11    204.096667\n",
        "2011-10-12    205.090000\n",
        "2011-10-13    207.476667\n",
        "2011-10-14    213.266667"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can get as fancy as you want"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "close_px    # Output: 1000 rows."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 1000 entries, 2007-10-29 00:00:00 to 2011-10-14 00:00:00\n",
        "Data columns:\n",
        "AA      1000  non-null values\n",
        "AAPL    1000  non-null values\n",
        "GE      1000  non-null values\n",
        "IBM     1000  non-null values\n",
        "JNJ     1000  non-null values\n",
        "MSFT    1000  non-null values\n",
        "PEP     1000  non-null values\n",
        "SPX     1000  non-null values\n",
        "XOM     1000  non-null values\n",
        "dtypes: float64(9)"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "close_px.AAPL     # For easier typing, after dot ., use tab to select the name of the stock."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "2007-10-29    185.09\n",
        "2007-10-30    187.00\n",
        "2007-10-31    189.95\n",
        "2007-11-01    187.44\n",
        "2007-11-02    187.87\n",
        "2007-11-05    186.18\n",
        "2007-11-06    191.79\n",
        "2007-11-07    186.30\n",
        "2007-11-08    175.47\n",
        "2007-11-09    165.37\n",
        "2007-11-12    153.76\n",
        "2007-11-13    169.96\n",
        "2007-11-14    166.11\n",
        "2007-11-15    164.30\n",
        "2007-11-16    166.39\n",
        "...\n",
        "2011-09-26    403.17\n",
        "2011-09-27    399.26\n",
        "2011-09-28    397.01\n",
        "2011-09-29    390.57\n",
        "2011-09-30    381.32\n",
        "2011-10-03    374.60\n",
        "2011-10-04    372.50\n",
        "2011-10-05    378.25\n",
        "2011-10-06    377.37\n",
        "2011-10-07    369.80\n",
        "2011-10-10    388.81\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, Length: 1000"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "close_px.AAPL.idxmax() # gives index of its maximum value."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "<Timestamp: 2011-10-14 00:00:00>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "close_px.AAPL.index[close_px.AAPL.argmax()]    # If you are numpy aficionado, does the same thing."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<Timestamp: 2011-10-14 00:00:00>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def peak_date(series):\n",
      "    return series.index[series.argmax()]    # new method: series.idxmax()\n",
      "close_px.apply(peak_date)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "AA      2008-05-19 00:00:00\n",
        "AAPL    2011-10-14 00:00:00\n",
        "GE      2007-10-31 00:00:00\n",
        "IBM     2011-10-14 00:00:00\n",
        "JNJ     2011-07-07 00:00:00\n",
        "MSFT    2007-11-01 00:00:00\n",
        "PEP     2008-01-10 00:00:00\n",
        "SPX     2007-10-31 00:00:00\n",
        "XOM     2008-05-20 00:00:00"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.apply(lambda x: x.max() - x.min()) # np.ptp"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "AAPL    52.20\n",
        "IBM     17.24\n",
        "MSFT     2.74"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.log(close_px)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 1000 entries, 2007-10-29 00:00:00 to 2011-10-14 00:00:00\n",
        "Data columns:\n",
        "AA      1000  non-null values\n",
        "AAPL    1000  non-null values\n",
        "GE      1000  non-null values\n",
        "IBM     1000  non-null values\n",
        "JNJ     1000  non-null values\n",
        "MSFT    1000  non-null values\n",
        "PEP     1000  non-null values\n",
        "SPX     1000  non-null values\n",
        "XOM     1000  non-null values\n",
        "dtypes: float64(9)"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "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()    # Can pass a list of col names to get items, so you don't have to use ix."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 1000 entries, 2007-10-29 00:00:00 to 2011-10-14 00:00:00\n",
        "Data columns:\n",
        "AAPL    1000  non-null values\n",
        "IBM     1000  non-null values\n",
        "MSFT    1000  non-null values\n",
        "XOM     1000  non-null values\n",
        "dtypes: float64(4)"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "close_px[['AAPL', 'IBM', 'MSFT', 'XOM']].plot() "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1068ab190>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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Maoq8vLTbNIHUxYuy6PAffwDPPVfxa8XEACEh2v5RxdnZAa1bSyuEQ4dkGRhz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O2Nw42lOh1Cz59HiLU1dgYFwbraaagNBoSdOweNEPg+JASh1UOsp1Qq2JqaGnME7qdcr8f4pCQM\nsLWF2mDA9YoKvNG7NwxCYFKdHl8NpTodFly/jh+LitBDJsPNQYPgKJNBJwQ+zczEudJSzHF2hrWp\nKZQ6HUY6OMDG1LRR51as1WJoYiK+CAjACAeHet9dLivDT0VFUGg06Gdlhec9PGAQAjohWpzY+9f0\ndFwtL8f2Jt49lKfRYHd+vvH6f+vmhlh7+y713kuVTofkigpkqdXwt7REqLV1lylbV2Ti5cvoZ2WF\n37m7o5+1Ncr0eoScO4cv/f0hMTHBe+np6GVhgd4WFljm4dHqt20wbUcIgWdu3MBXgYEdPtuxS65s\nzjzerOrVCwCMq/LeT12jd5BKkVX9fkGFRoPr5eUAgA0KBabJ5XCQSus5fo0QxnC5r6UlIu0cgZz8\nThVRADDcwQE/hIbidmUl5l67BhNQBKgxIaU1GCCTSOB6+jTytVrIZTJkxMRg9rVruFlRgWfd3SGX\nSrHU3b3ByFpdlDodep85A031g17z1oA/e3mhj6Ul9ubnw1wiwVhHR/ynekZiDeYSCa4PGoSNCgVm\nX7uGRa6ueDc9HfrqYzUkUIUQePXuXZwuKcGB0FDYS6VYeecO/p6RgWyNBh9nZmKKXI4stRoveHjA\n19ISq9PSYGNqiisDB2Jrbi76nTuH9QEBeD8jAxYSCWLs7PBxZiZOqVQwMzGBZfULyt/39cWOvDz8\n1s0NK7y8kFZVhS+zsrBeocBsZ2cMs7d/oD7CbGwQdp8AlJiYwOwhnO0KT09EXbiAd6oTUFMqKpBW\nVYWk8nK4m5mhj6UldublwdfCAn0sLRFgZYUnrl5FhcEAXwsLJEZFPZKp7DohcEypxGmVCiagGU3W\npqYYaGuLYGtrvJ+RAYBWvL6nVuP1Xr0w09n5gaGLXzM1w91/TU9HYmkpvg0ONiaB25ia1nv1V0O5\nd0znYGJi0unpFCykmC6Bg1SKg0VFAChHBQDGOzri3fR0mFR/X8NTrq4ItbZGlcFg3GbZhRK8a1bo\nLdPrITExwWeZmQ3up9Lp4HDqFH7r5oZ8rRbPe3igWKtF0LlzkEul2BkcjIEXLgAAtuXl4SUvL0yR\ny+tFU5LLy3GtogL66heVjnJ0xARHR/SysMCF0lL8xs0NfRMSME0ux3+VSvhYWGBnI9EVUxMTPOfh\ngZTKSqxJS4OPhQVORkZi3rVrGJGYiIXV7+kCaA2XDTk5sDU1xY3qqBIAfFg9fKs2GLAjLw878vJw\nsKgI2/Py8Lm/Pzbn5uL8gAHobWGBD3x9MdbREZMvX8Y8FxdsDgw0JndX6vWwNDWFXggcKirCtwUF\neLN3b/z+5k2EWlvj6Rs3MNbREUlRUQ+s6N+e2EqlOBAaipV37uBKeTnkMhlCbWzwB09PnFSpkFZV\nhTP9+8OvTq7gql69oDEYMC4pCT8UFtabyZlQUgJ/S8t6Q0BqgwHlen2DL1NPKivD9fJy/F9BAWyq\nI4Ov3b2LGDs7uJiZYbSDA3bk5SG5ogIuMhmi7eywXqHAychIeFtYYE9+PvI0GiRGRaF3ddTkhEqF\nf2dnY11WFs7079/ka0MeV4q1WmzOzcXajAxYm5pCgGaBWUgkGGpvj/MDBvBMOqbF8NAe0yVYn52N\nP96+DRMAFdUCabpcju8LC2ElkWCSXI49wcH1fqM2GHCnshJB1tYo0mrxRXY23mzD4mwdgRACPePj\n8W1wsHFl/QXXryO5vBzu5ua4Wl6OLLUayzw9sc7fHwYh8GFmJoba2yPGzg63KirQ28ICf7t3D2+l\npuJ5Dw986e+Pyurp+4MvXkSJXo8RDg44W1KCjJiYBxJb8zUaOJuZoUCrha2paYtmFZbp9cbhtFKd\nDgmlpZh97RpOREQgxNoaPX75BaciI9HPyqrZISKdEPg6JwdLU1KwqlcvvO/rW+97vRDNvmOrhvfT\n0/F6air+4uPT5e71/ezNz8eLt29jjrMzRjk4wNrUFGOTkjDH2Rk7goLwdU4OCrVarFcocKuyEgGW\nlgiytkaApSV8LC2RVFaGXdXiOMbODmV6PU6rVFjm6QkzExPcqKjA4eJiLHZ1hYeZmXEI1yBEi2Yb\nvp+ejh+LirA9KAhbcnIQZmODKd14YeHmyNVosFGhwJXqmXT9bWxwvrQUGwMDEWptjf8qlRjj4NBp\nb3Jguj6cI8V0aWoSBkc6OOCYUokj4eH48N49/FhUhBBra8TY2WH9fcNR3YUNCgXWKxSYKpfjzd69\nYXLsGADg3wEBCLOxQczFizgdGYnBDQxP1aVEp8PopCRcKC0FANhLpbCWSLDQ1RV/8/Xt8Kn6XykU\nWHnnDvwsLWErleJIePhD/f5GRQW8zM3b9NZ2IQQKtNpuMwvqF5UKp1QqbFAokKPRYEPfvph3/Tr8\nLS1xq7ISfSwtscbbG+McHZGlVuPfCgWSKyrgbWGBATY26GtlhfEdNIxUodfD+uRJuMhk8DA3R0pF\nBT7w9cUCF5cWL0isNRhwoawMzjJZl12MtkKvR7ZGg9Bz5zBNLscUuRxjHB3hxXlNzEPCQorp0iSU\nlCD64kW80rMn/nbvHu4NHowlN27gcHExkqKiUKrXY0gzQqOrohcCjqdOoVSvxzxnZxwuLsblOkNS\ndZPum6NMr8dTycnYV1CA4qFDm3y9QUdwtLgYlQYDJjg6NpuzxdSSo9HgVkUFhjk44EZFBdQGA/pU\nL+7amXyRlQUnmQwLXFxwsKgI3+Tm4r9KJS5FRaGHTFYvYb4mn69Iq8UplQpKnQ5vpKbCQiJBgVaL\nqwMHwrOV4uRPt29jS24upsvlMJdI8LGfX5OR04yqKphJJHAzM0N2dW6lk0wGtcGA7woKkKPRIEej\nQUpFBeJLSqDS6/F6r154x8enVeVjGICFFNPFyVSr0fPMGewJDsaca9dQMWwYUiorkVT9hvDuTrFW\nC6dffjEmWnu3Ma+nK80IYx4vhiUmIlejwY6gIIy8dAkf+/nB19ISoy5dwq3oaPifPQsriQQmJibY\nEhiIGT16YNXdu1DpdMbXKrWEK2Vl+I9Cgb5WVvh/9+7hq8BAHFcq8X1hIaJsbTFVLscbqak4Gh4O\nebWoK9HrcUKpxG9u3IABgKe5ObLVaphVCzwAmCKXQ6HRYIqTE7RCIMzGBhOdnGApkfwq3mHHdBws\npJgujdZggNmJE0iNiYFPfLxx5fLHiZfv3MEIBwdMfYzzUJjuz3cFBfhndjaSysqg0GjgIJXCUiKB\nQqNBtJ0dzpaU4OfwcIxxcDCK+SKtFn0TEvBTWFi9mYBVBgOy1WqcUKlQqtPheQ8P3K2qwst37iCh\npASD7OzwY2EhTkZGIrY64pxaWYn/TUtDSkUF0quq4GZmBp0QqDIYkK5Ww0EqxV99fPCUqysulpVh\nkK0tzCQSCCGg0uvrTUxhmPaEhRTT5VHpdLCXSqHU6dgZMkwnk6vR4JRKhTGOjvhKocAQe3scVyox\n3smpwXXFtubm4qXbt/G8hwfe8fFBuV6PiPPnUWUwwMfCAqlVVfA0N8fZkhL0t7HB6f79YWZignS1\nutEIrV4IfJWTAztTU+zKz8d8Z2eUV7/vs7OXOmF+fXSIkPL29oadnR1MTU0hk8mQkJCAoqIizJ8/\nH+np6fD29sauXbvgcN9CeU0ViGEYhumeZKvVCD53DhOdnHBCqcSMHj3wRUAAAOBaeTkulJYi3MYG\nnubm6MGrgDPdjA4RUj4+Prhw4QKc6swqeeWVV9CjRw+88sorWLt2LYqLi/HBBx+0uEAMwzBM9yVL\nrcb2vDxMdHJCCC8lwDxGdJiQOn/+POR1cj4CAwNx/PhxuLq6IicnByNHjsSN6hfKtqRADMMwDMMw\nXY3GdEubBplNTEwwduxYREVFYf369QCA3NxcuFa/DsPV1RW5ubltOQXDMAzDMEyXpU0Zvb/88gvc\n3d2Rn5+PcePGITAwsN73JiYmPEWbYRiGYZjHljYJKXd3dwCAs7MzZs6ciYSEBOOQnpubGxQKBVwa\neWEtAKxevdr498iRIzHyMZzyzjAMwzBM9+PYsWM4Vv0miqZodY5URUUF9Ho9bG1tUV5ejvHjx+Pt\nt9/GkSNHIJfL8eqrr+KDDz6AUqnkZHOGYRiGYbo17Z5snpqaipkzZwIAdDodFi9ejNdeew1FRUWY\nN28eMjIyePkDhmEYhmEeC3hBToZhGIZhmFbSIbP2GIZhGIZhfs2wkGIYhmEYhmklLKQYhmEYhmFa\nCQsphmEYhmGYVsJCimEYhmEYppWwkGIYhmEYhmklnSukdLpOPT3DMAzDMExb6FwhZWcHjBoF5OS0\n7Tg3bgBLlwKRkcBvfgMcOgRoNO1SRIZhGIZhmMbo3AU5y8qAN94AsrOBHTuAc+eAoCBAoQBSU4HY\nWMDWtvZHBQXA3r20//XrwOXLwPTpwNatwO9/T6Ls//4PiI8HTp8G5swBJk8GvLwAf39ApQLCwzvj\nchmGYRiG6cZ03ZXNKyuBsDDA0xM4fhwwNQWkUkAup6jS/PkksPr3B65coSiWtzeJqFdfBQ4cIDE1\nZUr9E9y7B7z5JrB5M322tgZkMhJcS5fS34cOAdHRJLLMzAATk0deDwzDdEN0OqCqCrCx6eySNI8Q\nwPnz5FM9PQFz884uEcN0S7qukAIoAnXoEAmqvn1J9ADApUvAp58Cs2eTcDp1CtizB7C0bPmJFAr6\n380NSEkBPvqIjgEA5eVA797AzZvAP/8JPP98+13gw5CUBCQkAM891znnZ5hfG1VVwJ07FAFXqaiD\nJpEASiVQVkafLS2B/HwgM5O2nT0LHDsGXLsG5OVRh2/hQoqOu7tThywwEPjXv4DCQsDKCti/H+jV\nq2OuITcXKC4mn6nVUmcQAO7eBTZsoM5kfDz5OUtLiuRXVVG5/P3p/23bqGPKMEyzdG0h9ahRqUi4\nTZlCou2jj4CvvwZefx2YN+/RlePOHeCrr4C//pU+l5eTc6v5WyYjJ37yJPDzzyT0hgwhh88wTOMU\nFQGOjrVRZp0OuHoV+O9/gX37SGQolbW5lJ6eJEgOHABcXUk4qdWAkxOJpKQkimYPGwYMHkz7JCYC\nFy8CISEkrhQK6hDNmkVpBrt3Azt3kpBKT6fz2tqSz6npLAJUhv37SXw5OtI+t27R/15eVPZz58gP\nFBZSp1IIQK+vvT6JBJg6FSgpoet88knAxwcYNAjo0QPw9aX9ARJUV6/SNf3znyT8tm6lYwYEAGPH\nkp9hWk9lJdmQmRkQF0f3x82N/pmakt3s2kX2VF5OUcLkZCAigmzxxx9p2wcfUEqLlRUJ+cBAsolf\nEwYD2XlKCj1fsbGAn9+jL4deDxOplIVUo1RVAf/+N/D++/QvNJQcmYUF0K8fYG8PuLiQQavVQM+e\ndHMBMv5nngG+/JKGEdetAwYMoN+++y49ABMnUg/QxATw8AAWL6b/586lPK7wcIq2HTxIju/8eerx\najSAszMZjrU1OW43N+Ctt6gcpqYUro+IoJyyPn1o+6JF9Ftzc2DBAjp/djY5ydYiBE0KMDen+jA1\npe0GAzkJjYZ66CzymM5EpaII9rFjFG2ePZsiN7t3U+Rl2DBg2jQSFwMHAqWlZM8nTtAzN2IEPetC\n1I/yVFTUdnJaik5HPiAoiJ7dDz8kv6DVAk88QdGk4mKaLNO3L/1LSaFnzM+PUhmsrOhzcDAJnB49\nyCdZWtKzZmpKx7t9m3yRvT35p5Y+6y+8QHU1cybg4EDicuNGylkdNerB65FKH64OuhsqFdXH3bsk\nmC0sSNz27t34b06coLpXq0nkqtXky/V6spvhw+ke5uTQP72e/Pno0WR/CgX57sBAagdKS6kN2bqV\nhLNEQm1HRAS1SzNmUCfb1JTK5e9PotvRkSZctXeKSmUlRV0HDGjf4zZEaSm1oXo9dUByc4GjRynw\n4e5OIvP8eWoXo6IoCNK3b9PH1GqBLVuo3uRyqm9XV+pIPPcckJZG92jv3vp1JwTlWu/bR/fz9m2Y\nVFWxkGqWuDhysl5e5OA8PalXUVBAhl9SQsLhf/+XehPnz5NBu7nR/nI53awnn/eFrhEAABXpSURB\nVCRh89xz9AC99ho9BDUP2ldf0TE/+oj2rSExkSJjc+fSA9yjB5Whxnmp1cAf/kAPes+eJACLisjY\nlEp6EG/cAIYOBV56Cfj2W+DMGXKyADmGyZMpd6wuSiXwxz8Cf/lLw8MQBgNFw7ZvrxVwzs7UE79w\ngUSUSkXnnTCBHERsLPX6Fy0icfjmm/SgA8CRI9RgTJtGdVfTWDFMQ1RUkDM0MaHnqW6OT020Zt06\nst34eGpUtm0jx3jnDjVef/oTCZrOpLCQIlw//EBDhpaW9Dk8nBx7ZyDEgw3vt98CS5ZQ/ujw4SRA\nr18HvviC6jcggNIhDhwANm0iXzR1KnUomxKbJSU0ZFpWBhw+TKJAqSS/kJRE4jIkhD67uT38tWi1\ntZEgD4+m9y0sJNFS01m2siLRu2kTCRMXFxoNMBjIfoKDabu7O923qCgSMZmZ5K9nzqRzh4bSbwcP\npuO2BZ2OfKtMRuUwNyf/HhdH902nI8GXnEz+NyuLyhAeXjs07exMZZkzh9qCw4fpd05O5MdNTGj7\n6dNU7uHDKRrp6kp2amcH/Oc/ZK+zZgEZGbTNxYXsY+5cEuB10eupLPb29K8p9Hrgb3+jQEZxMbUv\ns2dTu5eaSs+yhQV1IiZOpL81Gur0vPwy2c2779JnFxfqfISH0/1QKCiN54sv6FoVCrLlixfpOObm\nlDo0bhy1i8OG0X5padQexscDn3xCbdiiRUC/fjCxtmYh1WoMBrphrq4U2fniC7pp8+aRc7awaNgh\nPaooTVxcbc/11i0SLnV7UKWl1BjdvAk89RQZk709GdysWeQ8PvmEnGFlJfUAnnmGBNjixST6vv+e\nHkIHB9qnoIBCzS4udK6cHHqgb96kHu3gwcAvv9Dvc3Pp/G+8QeJq+XKqG7Wa/r34IkUCTU0pdBsZ\nSc6D6frk5ZFoX7IEGDmy+f0NBnL+eXnk0LVacsjJyTT8lZ9P3+Xn07+iIhLeADUMMhlNMhGCxFNx\nMT2XkyaRHZqbk9NrTUPM1FJRAXz8MT2zM2dSp04up9wrJ6fahmvZMqr/rVup4XvjDWDMGGrAAeps\nXr9OPiYujhrIrCwadpw1i3xnXh7lx376KfnQoiLqMHp7U0pDbCyJtcBAEss1w2A5OXQclYrKcfEi\n2YVMRoLAyor8VWgodWSvX6dGMiuLIhRjx9KQrF5PIm/6dODvf3+wM5mXR0OhZ86Q8CsspKE3Gxsq\n75o1ZHOdjU5HQuf69dpIp1wOfP45PUt9+tQOS6tUJDitrCiiM2YMXefRo9Q5vnmTRG1AAEVuw8Ko\nA29rS+1ZQQHVQVwc3aecHBKXtrbUVjg50X14+WW6XyYm9LurV6n9uHSJyufiQsL57bepbGZmLY/8\nCkFtzdKl9Dk6mnzQli3kD7ZuJZ+xbx/ZQ2YmXVdQEJXFxaW2zT5/nuwvOJjO//HHFKiIjyfbq4Zz\npBji5k2KfEmlwPr11HgBZHSHDtHDVF5ODZG1NRlSQQHt29IeVmIiRd5iYughLS2lXkzPnuTQZs8m\nx7x1KzmuBQsoOrVkCUUDFy2ihyslhR7K+4VoQ6KVaT0lJdQIKRQklkND6T7k5JB9lJbSEJCPD92X\nykqKUO7aRXZUXk6O6+WX6fdubnSsnj0pMlBaSg2QjQ3lDF25Qsc3MyNnlZ5ODm/gQGqAXVzof2dn\ncsz9+9f2sH/6iSIhRUXAs8/WNphM+1MznO/uXrtNpyNh269f/c6aEBRN376dfjNrFjW8KSk0JLR4\nMUWss7JIENVEBBoiOZk6q0VF1IBfvEh/JyaSLbm4kM0FBZHtqFTUED/3HNlJURH93tub7O7qVWrw\ng4OpLDY25HNcXel6atIUHlefUlVFwtjJqf2PXVJCbYpcTqI0PZ38u5sbDYfVndhlMJD/6NGDRi28\nvUncDBvWtrrPzyfxOHx4bS7VoUPkN4YMaf2xa9JW6sBCiulcsrPJITdk1KdPU29w5kzqgaanU7Tv\n8mVg9WoaLn36aRo2/OUXilpdvfrIL+GxIi+P6lqjoSFdNzdqoFxda3vd5ubUy0tMpGiDQkETNH78\nkfY9eJB+l51Nkc7sbPperSZneecOHae8nBrPmzdJML/wQm2ytcFA9z88vP6acUz3xGCgHE6tlnKs\nhg1r3yUiqqqo0awZCWCYRwgLKaZr8+mnNC596hSFfJcsoSGjDz+kXmlGBoV+LS0ph2PSJOqBeHhQ\n77Imz0qloka6blJsURFFQYYMaT5Z1mCgXnOdcG6XJC+PROXYsY0LkKoqqjcvL4raKJU0xLJrV20P\nzsaGepCzZzd/zvh4ElWxsbywLcMwvzpYSDFdGyEoByE2tnZbaSlFp86do9yK8eNp+2efUbTj7l2a\nNr58OYmCqiqKjAAUSfn8cxJoR4/ScMMTT1Di/yef0FIXLi60r7k5CZM//5miLRoNRcFmzaJoioMD\nfS4tfTApWKMBvvmGIjP9+pGwa2t+l05XWxd5ebXJkjY2lL8xdy7wzju1awXZ2tI5o6LoGg0GGv46\neZJy4aqqKKR+5w7V4cqVNIx2f5IowzAM0ygspJjHD4OBkhY/+ojyrQBaHd/GhvI4Xn2Vtm3fTkNO\nixfTsGB4OE0eUKtpqLGyksbCly+nGZm7d9NSFhYWJEbc3WlYCiDBl5ZG35WWUlK+ECRosrJInC1e\nTHlGo0a1bLFDvZ7E4e7dNO3dzY1msvToQSLoySfp73ffpf2lUhJbp05R2ezsaChlzx7aptdTDsKi\nRSSWzp2jiF1oKC9PwTAM00pYSDGPN998QwKmrk1duUIzT5pLNqxZ3LCpYb9Tpyjfo2atn5pozqpV\nNARZMz0/I4PKcuUKJdtGRABr11K0qCZ58eefKZq0cCEJv5MnaQaNtzdFni5coGE7c3MSbHI5natm\nYbqMDBJsD7PCP8MwDNMmWEgxjz8dPZsvPp7ES1AQCSVPz6bX/1GpKMr0+us09FZSQtPDv/iCcpKu\nXKEVp2fMoET6x32xQ4ZhmG4MCymG6SwMBpr1dukSRalcXGhmE8MwDNNtYCHFMAzDMAzTShrTLZx5\nyjAMwzAM00pYSDEMwzAMw7QSFlIMwzAMwzCthIUUwzAMwzBMK2EhxTAMwzAM00o6REgdPHgQgYGB\n8Pf3x9q1axvd79ixYx1x+m4P10vjcN00DNdL43DdNAzXS+Nw3TQM10vDtLuQ0uv1+MMf/oCDBw/i\n+vXr2L59O5KTkxvcl29Kw3C9NA7XTcNwvTQO103DcL00DtdNw3C9NEy7C6mEhAT4+fnB29sbMpkM\nCxYswHfffdfep2EYhmEYhul02l1IZWVloWfPnsbPXl5eyMrKau/TMAzDMAzDdDrtvrL53r17cfDg\nQaxfvx4AsHXrVpw9exafffZZ/RN35DvRGIZhGIZh2pmGJFO7vyXV09MT9+7dM36+d+8evLy8WlQY\nhmEYhmGY7kS7D+1FRUXh1q1bSEtLg0ajwc6dOzF9+vT2Pg3DMAzDMEyn0+4RKalUinXr1mHChAnQ\n6/VYsmQJ+vXr196nYRiGYRiG6XSajUhJJBKsXLnS+Pkf//gH1qxZ0+RvJk2ahJSUFNy+fRuvvfZa\ng/t8+OGHCA4ORnh4OMaOHYuMjAzjd19//TUCAgIQEBCAzZs3G7enpqYiOjoa/v7+WLBgAbRarbFM\nkZGRiIyMRGhoKKRSKZRKZXOX1mokEgmeeuop42edTgdnZ2dMmzatzcf++eefERUVhbCwMERFRSEu\nLs743YULFxAaGgp/f3/88Y9/NG5Xq9WYP38+/P39ERMTg/T0dAA0fLpixQoEBwcjKCio3m86ktbY\nTEtoT5spKCjAxIkTERERgZCQEGzatKnN5WuK7mIzAPDqq68iNDQUoaGh2LVrV5vL1xK6g80UFxdj\n5syZCA8PR3R0NK5du9bm8jVFV7OZEydOoH///pDJZNi7d2+94zVWlx1JV7KZdevWwc/PDxKJBEVF\nRfWOt2LFCvj7+yM8PByJiYltLl9TdCebmThxIhwdHdulbJ2OaAZzc3Ph6+srCgoKhBBC/OMf/xCr\nV69u7mfNEhcXJyorK4UQQnz55Zdi/vz5QgghCgsLha+vryguLhbFxcXC19dXKJVKIYQQc+fOFTt3\n7hRCCPHCCy+IL7/88oHj7t+/X4wZM6bN5WsKGxsbERkZaSz/jz/+KCIiIsS0adPafOzExEShUCiE\nEEJcvXpVeHp6Gr8bOHCgOHv2rBBCiEmTJomffvpJCCHE559/Lv7nf/5HCCHEjh07jHUZFxcnhgwZ\nIgwGg9Dr9WLw4MHi2LFjbS5jc3QHm3n77bfFqlWrhBBC5OfnCycnJ6HVattcxsboLjZz4MABMW7c\nOKHX60V5ebkYOHCgKCkpaXMZm6M72MzKlSvFO++8I4QQ4saNG786P5OWliYuX74snn76abFnzx7j\n/g3VZXFxcZvL2BxdwWZqrjMxMVGkpaUJb29vUVhYaDzWDz/8ICZNmiSEECI+Pl5ER0e3uXxN0V1s\nRgghjh49Kvbv3y+mTp3a5rJ1Ns1GpGQyGX73u9/ho48+euC7tLQ0jB492qjc7927B5VKBW9vb+M+\n5eXl6NWrF/R6fb3fjhw5EhYWFgCA6OhoZGZmAgAOHTqE8ePHw8HBAQ4ODhg3bhx++uknCCEQFxeH\nOXPmAACeeeYZ7Nu374EyffPNN1i4cGHLlWQrmTx5Mn744QcAwPbt27Fw4UJjAn1CQgJiY2PRv39/\nDBkyBDdv3gQAjBgxAklJScZjDB06FFeuXKl33IiICLi5uQEAgoKCUFlZCa1WC4VCgdLSUgwaNAgA\n8PTTTxuv//vvv8czzzwDAJg9ezaOHj0KAHBxcYFGo4FarTYep+bYHUl3sBl3d3eUlJQAAEpKSiCX\nyyGVtvtIdz26g80kJydj+PDhkEgksLKyQlhYGA4ePNhRVWKkO9hMcnIyRo0aBQDo27cv0tLSkJ+f\n3+51UZeuZDO9e/dGaGgoJJL6zUZDdflrsZma64yIiEDv3r0fKEfd5yw6OhpKpRK5ubntcv2N0R1s\nBgBGjx4NGxubdr76zqFFyea///3vsW3bNmPDU8Py5cvx7LPPIikpCYsXL8aKFStgb2+PiIgI4wqo\nBw4cwMSJE2Fqatro8Tds2IDJkycDALKzs+vN8qtZh6qoqAgODg7GG+Lp6fnA+lQVFRU4dOgQZs+e\n3ZLLahPz58/Hjh07oFarceXKFURHRxu/69evH06ePImLFy9izZo1eP311wEAS5YsMQ4h3bx5E2q1\nGqGhoY2eY+/evRgwYABkMhmysrLq1Uvd66+7dpdUKoW9vT2KiooQFBSE8ePHw93dHZ6enpg4cSL6\n9u3b3lXRIF3dZpYuXYpr167Bw8MD4eHh+OSTT9rz8hukO9hMeHg4Dh48iMrKShQUFCAuLs7YkHQ0\nXd1mwsPD8e233wKgBik9Pb3D66Yr2UxjNFaXj4KuYDNN0dC6imwzjx8tElK2trZ4+umn8emnn9bb\nHh8fj0WLFgEAnnzySZw6dQoA3cidO3cCAHbs2IH58+c3euytW7fi4sWLePnllxvdp6VrTu3fvx9D\nhw6Fg4NDi/ZvC6GhoUhLS8P27dsxZcqUet8plUrMmTMHoaGh+POf/2zMpZgzZw4OHDgAnU6HjRs3\n4tlnn230+NeuXcOqVavwr3/9q9VlPHHiBOLi4pCVlYWsrCwcPXrUeI86mq5uM++//z4iIiKQnZ2N\nS5cuYdmyZSgtLW3RtbWW7mAz48aNw+TJkxEbG4tFixZh8ODBDfYmO4KubjOrVq2CUqlEZGQk1q1b\nh8jIyCYb4fagO9hMZ9LZNtMSxH1L/XT0GopsM4+eFnvIF198ERs2bEB5eXm97fcbCQBMmzYNBw8e\nRHFxMS5evIjRo0c3eMwjR47gvffew/fffw+ZTAag4XWoPD094eTkBKVSCYPBAADIzMyEp6dnvePt\n2LHjkQzr1TB9+nSsXLmyXugUAN566y2MGTMGV65cwf79+1FVVQUAsLKywrhx47Bv3z7s3r0bixcv\nbvC4mZmZmDVrFrZs2QIfHx8AVC91ezKZmZnGXoCnp6cxIVKn00GlUsHJyQlnzpzBpEmTYGVlBWtr\na0yaNAlnzpzpkLpoiK5oMzV1dvr0acydOxcA0KdPH/j4+CAlJaXtF90MXd1mAOD1119HYmIiDh8+\nDCHEI4tiAl3TZmr8jK2tLTZu3IjExERs3rwZ+fn58PX1bZfrborOtpn7/SxQXwy0dO3AjqIzbaa5\n67z/N43VZ3vT1W2mqW3dkRYLKUdHR8ybNw8bNmwwXnxsbCx27NgBANi2bRuGDx8OALCxscHAgQOx\nYsUKTJs2rcHKSkxMxAsvvID9+/ejR48exu0TJkzA4cOHoVQqUVxcjJ9//hkTJkyAiYkJRo0ahd27\ndwOg2RNPPPGE8XcqlQonTpzAjBkzWlENreO3v/0tVq9ejeDg4HrbS0pK4OHhAQD46quv6n23dOlS\nrFixAoMGDYK9vf0Dx1QqlZgyZQrWrl2LwYMHG7e7u7vDzs4OZ8+ehRACW7ZsMV7r9OnT8fXXXwMA\n9uzZgzFjxgCgMO7x48eh1+uh1Wpx/PhxBAUFtV8FNENXtJmaOgsMDMSRI0cAALm5uUhJSXkkjWJX\ntxmDwYDCwkIAwOXLl3H58mWMHz++na6+ebqizdT4GZVKBY1GAwBYv349RowY8UhyPDrbZur6WYAE\nSt3GubG6fFR0ts3cT926mT59unF2X3x8PBwcHODq6tp+F98IXd1m6m5/LGguG93W1tb4d25urrCy\nshJr1qwRQgiRnp4uRo8eLcLCwsTYsWPFvXv3jPvu2bNHSCQSceLEiQaPO3bsWOHm5iYiIiJERESE\nmDFjhvG7jRs3Cj8/P+Hn5yc2bdpk3H737l0xaNAg4efnJ+bNmyc0Go3xu02bNomFCxc2dzntQt06\nqeHYsWPGmRFnzpwRAQEBIjIyUrz55pvCx8en3r6BgYHi0KFDDR77L3/5i7C2tjbWS0REhMjPzxdC\nCHH+/HkREhIi+vTpI5YvX278TVVVlZg7d67w8/MT0dHRIjU11fjdiy++KIKDg0VQUJB46aWX2nrp\nLaI72Ex+fr6YOnWqCAsLEyEhIWLbtm3tWgf3011sprKyUgQFBYmgoCAxePBgkZSU1B6X3yzdwWZq\n7lHfvn3F7NmzjbP8OoquZjMJCQnCy8tLWFtbC7lcLkJCQozfNVaXHUlXsplPPvlEeHl5CZlMJjw8\nPMRzzz1n/G7ZsmWiT58+IiwsTFy4cKHdrr8hupPNDB06VDg7OwtLS0vh5eUlDh8+3Obr7yza/V17\nTNNkZ2dj1KhRj2QYiXk8YJthHha2GeZhYZtpPY8mi5QBAGzevBkxMTF47733OrsoTDeBbYZ5WNhm\nmIeFbaZtcESKYRiGYRimlXBEimEYhmEYppWwkGIYhmEYhmklLKQYhmEYhmFaCQsphmEYhmGYVsJC\nimEYhmEYppWwkGIYhmEYhmkl/x/JoA4x0cI6VQAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "close_px.ix[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "AA        10.26\n",
        "AAPL     422.00\n",
        "GE        16.60\n",
        "IBM      190.53\n",
        "JNJ       64.72\n",
        "MSFT      27.27\n",
        "PEP       62.24\n",
        "SPX     1224.58\n",
        "XOM       78.11\n",
        "Name: 2011-10-14 00:00:00"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "close_px.ix[-1].plot(kind='bar')    # the last row of the data.\n",
      "title('Prices on %s' % close_px.index[-1])\n",
      "axhline(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "<matplotlib.lines.Line2D at 0x107675c90>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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7DQV5nc9tmcnrbG7L6+ezugBT2XGNabAAAADCjBksAAAigBks52MGCwAAwEQ0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\nAIAw+3+MMmHbHD5mQQAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 29
    },
    {
     "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": {}
  }
 ]
}