10 Minutes to pandas.ipynb

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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "10 Minutes to pandas"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import * \n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Work through of the introduction to pandas found at http://pandas.pydata.org/pandas-docs/stable/10min.html"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# creating a series\n",
      "\n",
      "s = Series([1,3,5,np.nan, 6,8])\n",
      "s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "0     1\n",
        "1     3\n",
        "2     5\n",
        "3   NaN\n",
        "4     6\n",
        "5     8\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# creating a dataframe\n",
      "\n",
      "dates = date_range('20130101', periods = 6)\n",
      "print dates\n",
      "\n",
      "df = DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))\n",
      "print df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2013-01-01, ..., 2013-01-06]\n",
        "Length: 6, Freq: D, Timezone: None\n",
        "                   A         B         C         D\n",
        "2013-01-01  0.042345  0.158389 -0.851400  0.961354\n",
        "2013-01-02 -0.912974  3.206082  2.029802 -0.218770\n",
        "2013-01-03 -1.273348 -0.712104  2.016342  0.789753\n",
        "2013-01-04 -0.699427 -0.506354  0.492272 -0.582514\n",
        "2013-01-05 -0.671727  0.384946 -0.533715  0.252866\n",
        "2013-01-06 -0.373994  0.183951 -0.545556 -0.368279"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2 = DataFrame({ 'A' : 1.,\n",
      "                 'B' : Timestamp('20130102'),\n",
      "                 'C' : Series(1, index=list(range(4)), dtype='float32'),\n",
      "                 'D' : np.array([3] * 4, dtype='int32'),\n",
      "                 'E' : Categorical([\"test\", \"train\", \"test\", \"train\"]),\n",
      "                 'F' : 'foo'})\n",
      "print df2\n",
      "print df2.dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   A          B  C  D      E    F\n",
        "0  1 2013-01-02  1  3   test  foo\n",
        "1  1 2013-01-02  1  3  train  foo\n",
        "2  1 2013-01-02  1  3   test  foo\n",
        "3  1 2013-01-02  1  3  train  foo\n",
        "A           float64\n",
        "B    datetime64[ns]\n",
        "C           float32\n",
        "D             int32\n",
        "E            object\n",
        "F            object\n",
        "dtype: object\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2.pivot_table"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<bound method DataFrame.pivot_table of    A          B  C  D      E    F\n",
        "0  1 2013-01-02  1  3   test  foo\n",
        "1  1 2013-01-02  1  3  train  foo\n",
        "2  1 2013-01-02  1  3   test  foo\n",
        "3  1 2013-01-02  1  3  train  foo>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df.head()\n",
      "print df.tail()\n",
      "print df.index\n",
      "print df.columns\n",
      "print df.values\n",
      "print df.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                   A         B         C         D\n",
        "2013-01-01  0.042345  0.158389 -0.851400  0.961354\n",
        "2013-01-02 -0.912974  3.206082  2.029802 -0.218770\n",
        "2013-01-03 -1.273348 -0.712104  2.016342  0.789753\n",
        "2013-01-04 -0.699427 -0.506354  0.492272 -0.582514\n",
        "2013-01-05 -0.671727  0.384946 -0.533715  0.252866\n",
        "                   A         B         C         D\n",
        "2013-01-02 -0.912974  3.206082  2.029802 -0.218770\n",
        "2013-01-03 -1.273348 -0.712104  2.016342  0.789753\n",
        "2013-01-04 -0.699427 -0.506354  0.492272 -0.582514\n",
        "2013-01-05 -0.671727  0.384946 -0.533715  0.252866\n",
        "2013-01-06 -0.373994  0.183951 -0.545556 -0.368279\n",
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2013-01-01, ..., 2013-01-06]\n",
        "Length: 6, Freq: D, Timezone: None\n",
        "Index([u'A', u'B', u'C', u'D'], dtype='object')\n",
        "[[ 0.04234466  0.15838922 -0.85139955  0.96135399]\n",
        " [-0.91297399  3.20608204  2.02980187 -0.21877017]\n",
        " [-1.27334787 -0.7121038   2.01634198  0.78975337]\n",
        " [-0.69942679 -0.50635425  0.49227177 -0.58251373]\n",
        " [-0.67172682  0.38494621 -0.5337152   0.25286563]\n",
        " [-0.3739941   0.18395134 -0.54555593 -0.36827905]]\n",
        "              A         B         C         D\n",
        "count  6.000000  6.000000  6.000000  6.000000\n",
        "mean  -0.648187  0.452485  0.434624  0.139068\n",
        "std    0.450807  1.415705  1.311643  0.635394\n",
        "min   -1.273348 -0.712104 -0.851400 -0.582514\n",
        "25%   -0.859587 -0.340168 -0.542596 -0.330902\n",
        "50%   -0.685577  0.171170 -0.020722  0.017048\n",
        "75%   -0.448427  0.334697  1.635324  0.655531\n",
        "max    0.042345  3.206082  2.029802  0.961354"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df.T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   2013-01-01  2013-01-02  2013-01-03  2013-01-04  2013-01-05  2013-01-06\n",
        "A    0.042345   -0.912974   -1.273348   -0.699427   -0.671727   -0.373994\n",
        "B    0.158389    3.206082   -0.712104   -0.506354    0.384946    0.183951\n",
        "C   -0.851400    2.029802    2.016342    0.492272   -0.533715   -0.545556\n",
        "D    0.961354   -0.218770    0.789753   -0.582514    0.252866   -0.368279\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df.sort_index(axis=1, ascending = False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                   D         C         B         A\n",
        "2013-01-01  0.961354 -0.851400  0.158389  0.042345\n",
        "2013-01-02 -0.218770  2.029802  3.206082 -0.912974\n",
        "2013-01-03  0.789753  2.016342 -0.712104 -1.273348\n",
        "2013-01-04 -0.582514  0.492272 -0.506354 -0.699427\n",
        "2013-01-05  0.252866 -0.533715  0.384946 -0.671727\n",
        "2013-01-06 -0.368279 -0.545556  0.183951 -0.373994\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.sort(['B'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-03</th>\n",
        "      <td>-1.273348</td>\n",
        "      <td>-0.712104</td>\n",
        "      <td> 2.016342</td>\n",
        "      <td> 0.789753</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-04</th>\n",
        "      <td>-0.699427</td>\n",
        "      <td>-0.506354</td>\n",
        "      <td> 0.492272</td>\n",
        "      <td>-0.582514</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-01</th>\n",
        "      <td> 0.042345</td>\n",
        "      <td> 0.158389</td>\n",
        "      <td>-0.851400</td>\n",
        "      <td> 0.961354</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-06</th>\n",
        "      <td>-0.373994</td>\n",
        "      <td> 0.183951</td>\n",
        "      <td>-0.545556</td>\n",
        "      <td>-0.368279</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-05</th>\n",
        "      <td>-0.671727</td>\n",
        "      <td> 0.384946</td>\n",
        "      <td>-0.533715</td>\n",
        "      <td> 0.252866</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-02</th>\n",
        "      <td>-0.912974</td>\n",
        "      <td> 3.206082</td>\n",
        "      <td> 2.029802</td>\n",
        "      <td>-0.218770</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "                   A         B         C         D\n",
        "2013-01-03 -1.273348 -0.712104  2.016342  0.789753\n",
        "2013-01-04 -0.699427 -0.506354  0.492272 -0.582514\n",
        "2013-01-01  0.042345  0.158389 -0.851400  0.961354\n",
        "2013-01-06 -0.373994  0.183951 -0.545556 -0.368279\n",
        "2013-01-05 -0.671727  0.384946 -0.533715  0.252866\n",
        "2013-01-02 -0.912974  3.206082  2.029802 -0.218770"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['A']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "2013-01-01    0.042345\n",
        "2013-01-02   -0.912974\n",
        "2013-01-03   -1.273348\n",
        "2013-01-04   -0.699427\n",
        "2013-01-05   -0.671727\n",
        "2013-01-06   -0.373994\n",
        "Freq: D, Name: A, dtype: float64"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[0:3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-01</th>\n",
        "      <td> 0.042345</td>\n",
        "      <td> 0.158389</td>\n",
        "      <td>-0.851400</td>\n",
        "      <td> 0.961354</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-02</th>\n",
        "      <td>-0.912974</td>\n",
        "      <td> 3.206082</td>\n",
        "      <td> 2.029802</td>\n",
        "      <td>-0.218770</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-03</th>\n",
        "      <td>-1.273348</td>\n",
        "      <td>-0.712104</td>\n",
        "      <td> 2.016342</td>\n",
        "      <td> 0.789753</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "                   A         B         C         D\n",
        "2013-01-01  0.042345  0.158389 -0.851400  0.961354\n",
        "2013-01-02 -0.912974  3.206082  2.029802 -0.218770\n",
        "2013-01-03 -1.273348 -0.712104  2.016342  0.789753"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['20130102':'20130104']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-02</th>\n",
        "      <td>-0.912974</td>\n",
        "      <td> 3.206082</td>\n",
        "      <td> 2.029802</td>\n",
        "      <td>-0.218770</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-03</th>\n",
        "      <td>-1.273348</td>\n",
        "      <td>-0.712104</td>\n",
        "      <td> 2.016342</td>\n",
        "      <td> 0.789753</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-04</th>\n",
        "      <td>-0.699427</td>\n",
        "      <td>-0.506354</td>\n",
        "      <td> 0.492272</td>\n",
        "      <td>-0.582514</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "                   A         B         C         D\n",
        "2013-01-02 -0.912974  3.206082  2.029802 -0.218770\n",
        "2013-01-03 -1.273348 -0.712104  2.016342  0.789753\n",
        "2013-01-04 -0.699427 -0.506354  0.492272 -0.582514"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.loc[dates[0]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "A    0.042345\n",
        "B    0.158389\n",
        "C   -0.851400\n",
        "D    0.961354\n",
        "Name: 2013-01-01 00:00:00, dtype: float64"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.loc[:, ['A', 'B']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-01</th>\n",
        "      <td> 0.042345</td>\n",
        "      <td> 0.158389</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-02</th>\n",
        "      <td>-0.912974</td>\n",
        "      <td> 3.206082</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-03</th>\n",
        "      <td>-1.273348</td>\n",
        "      <td>-0.712104</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-04</th>\n",
        "      <td>-0.699427</td>\n",
        "      <td>-0.506354</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-05</th>\n",
        "      <td>-0.671727</td>\n",
        "      <td> 0.384946</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-06</th>\n",
        "      <td>-0.373994</td>\n",
        "      <td> 0.183951</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "                   A         B\n",
        "2013-01-01  0.042345  0.158389\n",
        "2013-01-02 -0.912974  3.206082\n",
        "2013-01-03 -1.273348 -0.712104\n",
        "2013-01-04 -0.699427 -0.506354\n",
        "2013-01-05 -0.671727  0.384946\n",
        "2013-01-06 -0.373994  0.183951"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.loc['20130102':'20130104', ['A', 'B']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-02</th>\n",
        "      <td>-0.912974</td>\n",
        "      <td> 3.206082</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-03</th>\n",
        "      <td>-1.273348</td>\n",
        "      <td>-0.712104</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-04</th>\n",
        "      <td>-0.699427</td>\n",
        "      <td>-0.506354</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "                   A         B\n",
        "2013-01-02 -0.912974  3.206082\n",
        "2013-01-03 -1.273348 -0.712104\n",
        "2013-01-04 -0.699427 -0.506354"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.loc['20130102', ['A', 'B']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "A   -0.912974\n",
        "B    3.206082\n",
        "Name: 2013-01-02 00:00:00, dtype: float64"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.at[dates[0], 'A']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "0.042344658501950926"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.iloc[3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "A   -0.699427\n",
        "B   -0.506354\n",
        "C    0.492272\n",
        "D   -0.582514\n",
        "Name: 2013-01-04 00:00:00, dtype: float64"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.iloc[3:5, 0:2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-04</th>\n",
        "      <td>-0.699427</td>\n",
        "      <td>-0.506354</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-05</th>\n",
        "      <td>-0.671727</td>\n",
        "      <td> 0.384946</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "                   A         B\n",
        "2013-01-04 -0.699427 -0.506354\n",
        "2013-01-05 -0.671727  0.384946"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.iloc[1:3, :]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-02</th>\n",
        "      <td>-0.912974</td>\n",
        "      <td> 3.206082</td>\n",
        "      <td> 2.029802</td>\n",
        "      <td>-0.218770</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-03</th>\n",
        "      <td>-1.273348</td>\n",
        "      <td>-0.712104</td>\n",
        "      <td> 2.016342</td>\n",
        "      <td> 0.789753</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "                   A         B         C         D\n",
        "2013-01-02 -0.912974  3.206082  2.029802 -0.218770\n",
        "2013-01-03 -1.273348 -0.712104  2.016342  0.789753"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.iloc[:, 1:3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-01</th>\n",
        "      <td> 0.158389</td>\n",
        "      <td>-0.851400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-02</th>\n",
        "      <td> 3.206082</td>\n",
        "      <td> 2.029802</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-03</th>\n",
        "      <td>-0.712104</td>\n",
        "      <td> 2.016342</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-04</th>\n",
        "      <td>-0.506354</td>\n",
        "      <td> 0.492272</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-05</th>\n",
        "      <td> 0.384946</td>\n",
        "      <td>-0.533715</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-06</th>\n",
        "      <td> 0.183951</td>\n",
        "      <td>-0.545556</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "                   B         C\n",
        "2013-01-01  0.158389 -0.851400\n",
        "2013-01-02  3.206082  2.029802\n",
        "2013-01-03 -0.712104  2.016342\n",
        "2013-01-04 -0.506354  0.492272\n",
        "2013-01-05  0.384946 -0.533715\n",
        "2013-01-06  0.183951 -0.545556"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.iat[1,1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "3.206082041070911"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df.A > 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-01</th>\n",
        "      <td> 0.042345</td>\n",
        "      <td> 0.158389</td>\n",
        "      <td>-0.8514</td>\n",
        "      <td> 0.961354</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "                   A         B       C         D\n",
        "2013-01-01  0.042345  0.158389 -0.8514  0.961354"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df>0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-01</th>\n",
        "      <td> 0.042345</td>\n",
        "      <td> 0.158389</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.961354</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-02</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 3.206082</td>\n",
        "      <td> 2.029802</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-03</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 2.016342</td>\n",
        "      <td> 0.789753</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-04</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.492272</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-05</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.384946</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.252866</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-06</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.183951</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "                   A         B         C         D\n",
        "2013-01-01  0.042345  0.158389       NaN  0.961354\n",
        "2013-01-02       NaN  3.206082  2.029802       NaN\n",
        "2013-01-03       NaN       NaN  2.016342  0.789753\n",
        "2013-01-04       NaN       NaN  0.492272       NaN\n",
        "2013-01-05       NaN  0.384946       NaN  0.252866\n",
        "2013-01-06       NaN  0.183951       NaN       NaN"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2 = df.copy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three']\n",
      "print df2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                   A         B         C         D      E\n",
        "2013-01-01  0.042345  0.158389 -0.851400  0.961354    one\n",
        "2013-01-02 -0.912974  3.206082  2.029802 -0.218770    one\n",
        "2013-01-03 -1.273348 -0.712104  2.016342  0.789753    two\n",
        "2013-01-04 -0.699427 -0.506354  0.492272 -0.582514  three\n",
        "2013-01-05 -0.671727  0.384946 -0.533715  0.252866   four\n",
        "2013-01-06 -0.373994  0.183951 -0.545556 -0.368279  three\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2[df2['E'].isin(['two', 'four'])]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "      <th>E</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-03</th>\n",
        "      <td>-1.273348</td>\n",
        "      <td>-0.712104</td>\n",
        "      <td> 2.016342</td>\n",
        "      <td> 0.789753</td>\n",
        "      <td>  two</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-05</th>\n",
        "      <td>-0.671727</td>\n",
        "      <td> 0.384946</td>\n",
        "      <td>-0.533715</td>\n",
        "      <td> 0.252866</td>\n",
        "      <td> four</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "                   A         B         C         D     E\n",
        "2013-01-03 -1.273348 -0.712104  2.016342  0.789753   two\n",
        "2013-01-05 -0.671727  0.384946 -0.533715  0.252866  four"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s1 = Series([1,2,3,4,5,6], index = date_range('20130102', periods = 6))\n",
      "print s1 \n",
      "df['F'] = s1\n",
      "print df\n",
      "df.at[dates[0], 'A'] = 0\n",
      "df.iat[0,1] = 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2013-01-02    1\n",
        "2013-01-03    2\n",
        "2013-01-04    3\n",
        "2013-01-05    4\n",
        "2013-01-06    5\n",
        "2013-01-07    6\n",
        "Freq: D, dtype: int64\n",
        "                   A         B         C         D   F\n",
        "2013-01-01  0.042345  0.158389 -0.851400  0.961354 NaN\n",
        "2013-01-02 -0.912974  3.206082  2.029802 -0.218770   1\n",
        "2013-01-03 -1.273348 -0.712104  2.016342  0.789753   2\n",
        "2013-01-04 -0.699427 -0.506354  0.492272 -0.582514   3\n",
        "2013-01-05 -0.671727  0.384946 -0.533715  0.252866   4\n",
        "2013-01-06 -0.373994  0.183951 -0.545556 -0.368279   5\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.loc[:, 'D'] = np.array([5] * len(df))\n",
      "print df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                   A         B         C  D   F\n",
        "2013-01-01  0.000000  0.000000 -0.851400  5 NaN\n",
        "2013-01-02 -0.912974  3.206082  2.029802  5   1\n",
        "2013-01-03 -1.273348 -0.712104  2.016342  5   2\n",
        "2013-01-04 -0.699427 -0.506354  0.492272  5   3\n",
        "2013-01-05 -0.671727  0.384946 -0.533715  5   4\n",
        "2013-01-06 -0.373994  0.183951 -0.545556  5   5\n"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2 = df.copy()\n",
      "df2[df2 > 0] = -df2\n",
      "print df2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                   A         B         C  D   F\n",
        "2013-01-01  0.000000  0.000000 -0.851400 -5 NaN\n",
        "2013-01-02 -0.912974 -3.206082 -2.029802 -5  -1\n",
        "2013-01-03 -1.273348 -0.712104 -2.016342 -5  -2\n",
        "2013-01-04 -0.699427 -0.506354 -0.492272 -5  -3\n",
        "2013-01-05 -0.671727 -0.384946 -0.533715 -5  -4\n",
        "2013-01-06 -0.373994 -0.183951 -0.545556 -5  -5\n"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])\n",
      "df1.loc[dates[0]:dates[1], 'E'] = 1\n",
      "df1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "      <th>F</th>\n",
        "      <th>E</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-01</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-0.851400</td>\n",
        "      <td> 5</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-02</th>\n",
        "      <td>-0.912974</td>\n",
        "      <td> 3.206082</td>\n",
        "      <td> 2.029802</td>\n",
        "      <td> 5</td>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-03</th>\n",
        "      <td>-1.273348</td>\n",
        "      <td>-0.712104</td>\n",
        "      <td> 2.016342</td>\n",
        "      <td> 5</td>\n",
        "      <td>  2</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-04</th>\n",
        "      <td>-0.699427</td>\n",
        "      <td>-0.506354</td>\n",
        "      <td> 0.492272</td>\n",
        "      <td> 5</td>\n",
        "      <td>  3</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "                   A         B         C  D   F   E\n",
        "2013-01-01  0.000000  0.000000 -0.851400  5 NaN   1\n",
        "2013-01-02 -0.912974  3.206082  2.029802  5   1   1\n",
        "2013-01-03 -1.273348 -0.712104  2.016342  5   2 NaN\n",
        "2013-01-04 -0.699427 -0.506354  0.492272  5   3 NaN"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1.dropna(how = 'any')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "      <th>F</th>\n",
        "      <th>E</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-02</th>\n",
        "      <td>-0.912974</td>\n",
        "      <td> 3.206082</td>\n",
        "      <td> 2.029802</td>\n",
        "      <td> 5</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "                   A         B         C  D  F  E\n",
        "2013-01-02 -0.912974  3.206082  2.029802  5  1  1"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1.fillna(value = 5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "      <th>F</th>\n",
        "      <th>E</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-01</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-0.851400</td>\n",
        "      <td> 5</td>\n",
        "      <td> 5</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-02</th>\n",
        "      <td>-0.912974</td>\n",
        "      <td> 3.206082</td>\n",
        "      <td> 2.029802</td>\n",
        "      <td> 5</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-03</th>\n",
        "      <td>-1.273348</td>\n",
        "      <td>-0.712104</td>\n",
        "      <td> 2.016342</td>\n",
        "      <td> 5</td>\n",
        "      <td> 2</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-04</th>\n",
        "      <td>-0.699427</td>\n",
        "      <td>-0.506354</td>\n",
        "      <td> 0.492272</td>\n",
        "      <td> 5</td>\n",
        "      <td> 3</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "                   A         B         C  D  F  E\n",
        "2013-01-01  0.000000  0.000000 -0.851400  5  5  1\n",
        "2013-01-02 -0.912974  3.206082  2.029802  5  1  1\n",
        "2013-01-03 -1.273348 -0.712104  2.016342  5  2  5\n",
        "2013-01-04 -0.699427 -0.506354  0.492272  5  3  5"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "isnull(df1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "      <th>F</th>\n",
        "      <th>E</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-01</th>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-02</th>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-03</th>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-04</th>\n",
        "      <td> False</td>\n",
        "      <td> False</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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "                A      B      C      D      F      E\n",
        "2013-01-01  False  False  False  False   True  False\n",
        "2013-01-02  False  False  False  False  False  False\n",
        "2013-01-03  False  False  False  False  False   True\n",
        "2013-01-04  False  False  False  False  False   True"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df.mean()\n",
      "print df.mean(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "A   -0.655245\n",
        "B    0.426087\n",
        "C    0.434624\n",
        "D    5.000000\n",
        "F    3.000000\n",
        "dtype: float64\n",
        "2013-01-01    1.037150\n",
        "2013-01-02    2.064582\n",
        "2013-01-03    1.406178\n",
        "2013-01-04    1.457298\n",
        "2013-01-05    1.635901\n",
        "2013-01-06    1.852880\n",
        "Freq: D, dtype: float64\n"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = Series([1,3,5,np.nan,6,8], index=dates).shift(2)\n",
      "s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "2013-01-01   NaN\n",
        "2013-01-02   NaN\n",
        "2013-01-03     1\n",
        "2013-01-04     3\n",
        "2013-01-05     5\n",
        "2013-01-06   NaN\n",
        "Freq: D, dtype: float64"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.sub(s, axis='index')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "      <th>F</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-01</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-02</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-03</th>\n",
        "      <td>-2.273348</td>\n",
        "      <td>-1.712104</td>\n",
        "      <td> 1.016342</td>\n",
        "      <td>  4</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-04</th>\n",
        "      <td>-3.699427</td>\n",
        "      <td>-3.506354</td>\n",
        "      <td>-2.507728</td>\n",
        "      <td>  2</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-05</th>\n",
        "      <td>-5.671727</td>\n",
        "      <td>-4.615054</td>\n",
        "      <td>-5.533715</td>\n",
        "      <td>  0</td>\n",
        "      <td> -1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-06</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "                   A         B         C   D   F\n",
        "2013-01-01       NaN       NaN       NaN NaN NaN\n",
        "2013-01-02       NaN       NaN       NaN NaN NaN\n",
        "2013-01-03 -2.273348 -1.712104  1.016342   4   1\n",
        "2013-01-04 -3.699427 -3.506354 -2.507728   2   0\n",
        "2013-01-05 -5.671727 -4.615054 -5.533715   0  -1\n",
        "2013-01-06       NaN       NaN       NaN NaN NaN"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.apply(np.cumsum)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "      <th>F</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-01-01</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-0.851400</td>\n",
        "      <td>  5</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-02</th>\n",
        "      <td>-0.912974</td>\n",
        "      <td> 3.206082</td>\n",
        "      <td> 1.178402</td>\n",
        "      <td> 10</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-03</th>\n",
        "      <td>-2.186322</td>\n",
        "      <td> 2.493978</td>\n",
        "      <td> 3.194744</td>\n",
        "      <td> 15</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-04</th>\n",
        "      <td>-2.885749</td>\n",
        "      <td> 1.987624</td>\n",
        "      <td> 3.687016</td>\n",
        "      <td> 20</td>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-05</th>\n",
        "      <td>-3.557475</td>\n",
        "      <td> 2.372570</td>\n",
        "      <td> 3.153301</td>\n",
        "      <td> 25</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-01-06</th>\n",
        "      <td>-3.931470</td>\n",
        "      <td> 2.556522</td>\n",
        "      <td> 2.607745</td>\n",
        "      <td> 30</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "                   A         B         C   D   F\n",
        "2013-01-01  0.000000  0.000000 -0.851400   5 NaN\n",
        "2013-01-02 -0.912974  3.206082  1.178402  10   1\n",
        "2013-01-03 -2.186322  2.493978  3.194744  15   3\n",
        "2013-01-04 -2.885749  1.987624  3.687016  20   6\n",
        "2013-01-05 -3.557475  2.372570  3.153301  25  10\n",
        "2013-01-06 -3.931470  2.556522  2.607745  30  15"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.apply(lambda x: x.max() - x.min())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "A    1.273348\n",
        "B    3.918186\n",
        "C    2.881201\n",
        "D    0.000000\n",
        "F    4.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = Series(np.random.randint(0,7,size=10))\n",
      "print s\n",
      "print s.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0    6\n",
        "1    0\n",
        "2    6\n",
        "3    5\n",
        "4    3\n",
        "5    6\n",
        "6    1\n",
        "7    6\n",
        "8    3\n",
        "9    6\n",
        "dtype: int32\n",
        "6    5\n",
        "3    2\n",
        "5    1\n",
        "1    1\n",
        "0    1\n",
        "dtype: int64\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])\n",
      "s.str.lower()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "0       a\n",
        "1       b\n",
        "2       c\n",
        "3    aaba\n",
        "4    baca\n",
        "5     NaN\n",
        "6    caba\n",
        "7     dog\n",
        "8     cat\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.str.upper()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "0       A\n",
        "1       B\n",
        "2       C\n",
        "3    AABA\n",
        "4    BACA\n",
        "5     NaN\n",
        "6    CABA\n",
        "7     DOG\n",
        "8     CAT\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = DataFrame(np.random.randn(10,4))\n",
      "print df\n",
      "pieces = [df[:3], df[3:7], df[7:]]\n",
      "print concat(pieces)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "          0         1         2         3\n",
        "0  2.204831 -0.166234  0.701033  0.391758\n",
        "1  1.106728 -0.331615  2.024063 -1.628830\n",
        "2  0.278071  1.372734  0.785519  1.065990\n",
        "3 -1.701380 -1.614639 -1.122598 -0.639097\n",
        "4  0.954655 -1.606071  0.390755 -0.162367\n",
        "5 -0.250247  0.755738 -0.345874 -1.245918\n",
        "6 -0.367701  0.423440  1.764583  1.207401\n",
        "7  0.892942  0.032983  0.005047 -0.415439\n",
        "8  0.306126 -0.382189  1.946025  0.267073\n",
        "9  0.691870 -0.561363 -0.352636  0.724232\n",
        "          0         1         2         3\n",
        "0  2.204831 -0.166234  0.701033  0.391758\n",
        "1  1.106728 -0.331615  2.024063 -1.628830\n",
        "2  0.278071  1.372734  0.785519  1.065990\n",
        "3 -1.701380 -1.614639 -1.122598 -0.639097\n",
        "4  0.954655 -1.606071  0.390755 -0.162367\n",
        "5 -0.250247  0.755738 -0.345874 -1.245918\n",
        "6 -0.367701  0.423440  1.764583  1.207401\n",
        "7  0.892942  0.032983  0.005047 -0.415439\n",
        "8  0.306126 -0.382189  1.946025  0.267073\n",
        "9  0.691870 -0.561363 -0.352636  0.724232\n"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "left = DataFrame({'key' : ['foo', 'foo'], 'lval' : [1,2]})\n",
      "right = DataFrame({'key' : ['foo', 'foo'], 'rval' : [4,5]})\n",
      "\n",
      "print left\n",
      "print right\n",
      "\n",
      "print merge(left, right, on = 'key')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   key  lval\n",
        "0  foo     1\n",
        "1  foo     2\n",
        "   key  rval\n",
        "0  foo     4\n",
        "1  foo     5\n",
        "   key  lval  rval\n",
        "0  foo     1     4\n",
        "1  foo     1     5\n",
        "2  foo     2     4\n",
        "3  foo     2     5"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = DataFrame(np.random.randn(8,4), columns = ['A', 'B', 'C', 'D'])\n",
      "print df\n",
      "\n",
      "s = df.iloc[3]\n",
      "print df.append(s, ignore_index = True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "          A         B         C         D\n",
        "0  1.147185 -0.997354 -0.103048 -1.807633\n",
        "1  0.652317 -1.691922 -0.539705 -0.098037\n",
        "2  0.170422 -0.608547 -0.226867 -0.642464\n",
        "3 -0.322477 -0.115725  1.976223  1.218432\n",
        "4 -0.988618  0.327772  0.345339 -0.398525\n",
        "5  1.058921  1.108539 -1.107012 -1.424147\n",
        "6 -0.123521 -0.809905 -0.212632 -0.876551\n",
        "7  0.490039 -0.449509 -1.729609  0.687324\n",
        "          A         B         C         D\n",
        "0  1.147185 -0.997354 -0.103048 -1.807633\n",
        "1  0.652317 -1.691922 -0.539705 -0.098037\n",
        "2  0.170422 -0.608547 -0.226867 -0.642464\n",
        "3 -0.322477 -0.115725  1.976223  1.218432\n",
        "4 -0.988618  0.327772  0.345339 -0.398525\n",
        "5  1.058921  1.108539 -1.107012 -1.424147\n",
        "6 -0.123521 -0.809905 -0.212632 -0.876551\n",
        "7  0.490039 -0.449509 -1.729609  0.687324\n",
        "8 -0.322477 -0.115725  1.976223  1.218432\n"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "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": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> foo</td>\n",
        "      <td>   one</td>\n",
        "      <td>-0.259342</td>\n",
        "      <td>-0.255141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> bar</td>\n",
        "      <td>   one</td>\n",
        "      <td> 2.236683</td>\n",
        "      <td> 0.002345</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> foo</td>\n",
        "      <td>   two</td>\n",
        "      <td>-0.918383</td>\n",
        "      <td> 0.583626</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> bar</td>\n",
        "      <td> three</td>\n",
        "      <td> 0.939925</td>\n",
        "      <td> 1.528471</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> foo</td>\n",
        "      <td>   two</td>\n",
        "      <td>-0.185482</td>\n",
        "      <td> 0.151661</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> bar</td>\n",
        "      <td>   two</td>\n",
        "      <td>-1.656500</td>\n",
        "      <td> 0.416123</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> foo</td>\n",
        "      <td>   one</td>\n",
        "      <td> 1.963015</td>\n",
        "      <td>-0.285482</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> foo</td>\n",
        "      <td> three</td>\n",
        "      <td>-0.235122</td>\n",
        "      <td> 0.407489</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "     A      B         C         D\n",
        "0  foo    one -0.259342 -0.255141\n",
        "1  bar    one  2.236683  0.002345\n",
        "2  foo    two -0.918383  0.583626\n",
        "3  bar  three  0.939925  1.528471\n",
        "4  foo    two -0.185482  0.151661\n",
        "5  bar    two -1.656500  0.416123\n",
        "6  foo    one  1.963015 -0.285482\n",
        "7  foo  three -0.235122  0.407489"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.groupby('A').sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>bar</th>\n",
        "      <td> 1.520107</td>\n",
        "      <td> 1.946940</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>foo</th>\n",
        "      <td> 0.364686</td>\n",
        "      <td> 0.602153</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "            C         D\n",
        "A                      \n",
        "bar  1.520107  1.946940\n",
        "foo  0.364686  0.602153"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df.groupby(['A', 'B']).sum()\n",
      "print df.groupby(['B', 'A']).sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                  C         D\n",
        "A   B                        \n",
        "bar one    2.236683  0.002345\n",
        "    three  0.939925  1.528471\n",
        "    two   -1.656500  0.416123\n",
        "foo one    1.703674 -0.540623\n",
        "    three -0.235122  0.407489\n",
        "    two   -1.103866  0.735287\n",
        "                  C         D\n",
        "B     A                      \n",
        "one   bar  2.236683  0.002345\n",
        "      foo  1.703674 -0.540623\n",
        "three bar  0.939925  1.528471\n",
        "      foo -0.235122  0.407489\n",
        "two   bar -1.656500  0.416123\n",
        "      foo -1.103866  0.735287\n"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tuples = list(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, names=['first', 'second'])\n",
      "df = DataFrame(np.random.randn(8,2), index = index, columns = ['A', 'B'])\n",
      "\n",
      "df2 = df[:4]\n",
      "\n",
      "df2\n",
      "                     "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>first</th>\n",
        "      <th>second</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
        "      <th>one</th>\n",
        "      <td> 1.524655</td>\n",
        "      <td> 0.288257</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>two</th>\n",
        "      <td>-0.513995</td>\n",
        "      <td>-0.989347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
        "      <th>one</th>\n",
        "      <td> 0.293811</td>\n",
        "      <td> 0.870650</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>two</th>\n",
        "      <td>-0.805459</td>\n",
        "      <td> 0.560943</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "                     A         B\n",
        "first second                    \n",
        "bar   one     1.524655  0.288257\n",
        "      two    -0.513995 -0.989347\n",
        "baz   one     0.293811  0.870650\n",
        "      two    -0.805459  0.560943"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stacked = df2.stack()\n",
      "stacked"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "first  second   \n",
        "bar    one     A    1.524655\n",
        "               B    0.288257\n",
        "       two     A   -0.513995\n",
        "               B   -0.989347\n",
        "baz    one     A    0.293811\n",
        "               B    0.870650\n",
        "       two     A   -0.805459\n",
        "               B    0.560943\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stacked.unstack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>first</th>\n",
        "      <th>second</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
        "      <th>one</th>\n",
        "      <td> 1.524655</td>\n",
        "      <td> 0.288257</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>two</th>\n",
        "      <td>-0.513995</td>\n",
        "      <td>-0.989347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
        "      <th>one</th>\n",
        "      <td> 0.293811</td>\n",
        "      <td> 0.870650</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>two</th>\n",
        "      <td>-0.805459</td>\n",
        "      <td> 0.560943</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "                     A         B\n",
        "first second                    \n",
        "bar   one     1.524655  0.288257\n",
        "      two    -0.513995 -0.989347\n",
        "baz   one     0.293811  0.870650\n",
        "      two    -0.805459  0.560943"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stacked.unstack(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>second</th>\n",
        "      <th>one</th>\n",
        "      <th>two</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>first</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
        "      <th>A</th>\n",
        "      <td> 1.524655</td>\n",
        "      <td>-0.513995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td> 0.288257</td>\n",
        "      <td>-0.989347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
        "      <th>A</th>\n",
        "      <td> 0.293811</td>\n",
        "      <td>-0.805459</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td> 0.870650</td>\n",
        "      <td> 0.560943</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "second        one       two\n",
        "first                      \n",
        "bar   A  1.524655 -0.513995\n",
        "      B  0.288257 -0.989347\n",
        "baz   A  0.293811 -0.805459\n",
        "      B  0.870650  0.560943"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stacked.unstack(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>first</th>\n",
        "      <th>bar</th>\n",
        "      <th>baz</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>second</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">one</th>\n",
        "      <th>A</th>\n",
        "      <td> 1.524655</td>\n",
        "      <td> 0.293811</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td> 0.288257</td>\n",
        "      <td> 0.870650</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">two</th>\n",
        "      <th>A</th>\n",
        "      <td>-0.513995</td>\n",
        "      <td>-0.805459</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td>-0.989347</td>\n",
        "      <td> 0.560943</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "first          bar       baz\n",
        "second                      \n",
        "one    A  1.524655  0.293811\n",
        "       B  0.288257  0.870650\n",
        "two    A -0.513995 -0.805459\n",
        "       B -0.989347  0.560943"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,\n",
      "                'B' : ['A', 'B', 'C'] * 4 ,\n",
      "                'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,\n",
      "                'D' : np.random.randn(12),\n",
      "                'E' : np.random.randn(12)})\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "      <th>E</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>   one</td>\n",
        "      <td> A</td>\n",
        "      <td> foo</td>\n",
        "      <td>-0.252614</td>\n",
        "      <td> 0.024820</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>   one</td>\n",
        "      <td> B</td>\n",
        "      <td> foo</td>\n",
        "      <td>-1.153801</td>\n",
        "      <td>-0.032719</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>   two</td>\n",
        "      <td> C</td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.273024</td>\n",
        "      <td>-0.817465</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> three</td>\n",
        "      <td> A</td>\n",
        "      <td> bar</td>\n",
        "      <td>-0.709631</td>\n",
        "      <td>-0.573687</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>   one</td>\n",
        "      <td> B</td>\n",
        "      <td> bar</td>\n",
        "      <td>-1.221847</td>\n",
        "      <td>-1.047370</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>   one</td>\n",
        "      <td> C</td>\n",
        "      <td> bar</td>\n",
        "      <td>-1.677957</td>\n",
        "      <td>-0.188440</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>   two</td>\n",
        "      <td> A</td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.169261</td>\n",
        "      <td> 1.659587</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> three</td>\n",
        "      <td> B</td>\n",
        "      <td> foo</td>\n",
        "      <td>-1.030056</td>\n",
        "      <td> 0.566057</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>   one</td>\n",
        "      <td> C</td>\n",
        "      <td> foo</td>\n",
        "      <td> 0.056438</td>\n",
        "      <td>-0.047879</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>   one</td>\n",
        "      <td> A</td>\n",
        "      <td> bar</td>\n",
        "      <td>-0.558527</td>\n",
        "      <td>-0.047091</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>   two</td>\n",
        "      <td> B</td>\n",
        "      <td> bar</td>\n",
        "      <td> 1.717596</td>\n",
        "      <td>-0.482111</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> three</td>\n",
        "      <td> C</td>\n",
        "      <td> bar</td>\n",
        "      <td>-1.378473</td>\n",
        "      <td>-0.694371</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "        A  B    C         D         E\n",
        "0     one  A  foo -0.252614  0.024820\n",
        "1     one  B  foo -1.153801 -0.032719\n",
        "2     two  C  foo  0.273024 -0.817465\n",
        "3   three  A  bar -0.709631 -0.573687\n",
        "4     one  B  bar -1.221847 -1.047370\n",
        "5     one  C  bar -1.677957 -0.188440\n",
        "6     two  A  foo  0.169261  1.659587\n",
        "7   three  B  foo -1.030056  0.566057\n",
        "8     one  C  foo  0.056438 -0.047879\n",
        "9     one  A  bar -0.558527 -0.047091\n",
        "10    two  B  bar  1.717596 -0.482111\n",
        "11  three  C  bar -1.378473 -0.694371"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pivot_table(df, values = 'D', index = ['A', 'B'], columns = ['C'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>C</th>\n",
        "      <th>bar</th>\n",
        "      <th>foo</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">one</th>\n",
        "      <th>A</th>\n",
        "      <td>-0.558527</td>\n",
        "      <td>-0.252614</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td>-1.221847</td>\n",
        "      <td>-1.153801</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td>-1.677957</td>\n",
        "      <td> 0.056438</td>\n",
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        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">three</th>\n",
        "      <th>A</th>\n",
        "      <td>-0.709631</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>-1.030056</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td>-1.378473</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">two</th>\n",
        "      <th>A</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.169261</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td> 1.717596</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.273024</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "C             bar       foo\n",
        "A     B                    \n",
        "one   A -0.558527 -0.252614\n",
        "      B -1.221847 -1.153801\n",
        "      C -1.677957  0.056438\n",
        "three A -0.709631       NaN\n",
        "      B       NaN -1.030056\n",
        "      C -1.378473       NaN\n",
        "two   A       NaN  0.169261\n",
        "      B  1.717596       NaN\n",
        "      C       NaN  0.273024"
       ]
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     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pivot_table(df, values = ['D','E'], index = ['A', 'B'], columns = ['C'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">D</th>\n",
        "      <th colspan=\"2\" halign=\"left\">E</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>C</th>\n",
        "      <th>bar</th>\n",
        "      <th>foo</th>\n",
        "      <th>bar</th>\n",
        "      <th>foo</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">one</th>\n",
        "      <th>A</th>\n",
        "      <td>-0.558527</td>\n",
        "      <td>-0.252614</td>\n",
        "      <td>-0.047091</td>\n",
        "      <td> 0.024820</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td>-1.221847</td>\n",
        "      <td>-1.153801</td>\n",
        "      <td>-1.047370</td>\n",
        "      <td>-0.032719</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td>-1.677957</td>\n",
        "      <td> 0.056438</td>\n",
        "      <td>-0.188440</td>\n",
        "      <td>-0.047879</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">three</th>\n",
        "      <th>A</th>\n",
        "      <td>-0.709631</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>-0.573687</td>\n",
        "      <td>      NaN</td>\n",
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        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>-1.030056</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.566057</td>\n",
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        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td>-1.378473</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>-0.694371</td>\n",
        "      <td>      NaN</td>\n",
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        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">two</th>\n",
        "      <th>A</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.169261</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 1.659587</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td> 1.717596</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>-0.482111</td>\n",
        "      <td>      NaN</td>\n",
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        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.273024</td>\n",
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        "      <td>-0.817465</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "                D                   E          \n",
        "C             bar       foo       bar       foo\n",
        "A     B                                        \n",
        "one   A -0.558527 -0.252614 -0.047091  0.024820\n",
        "      B -1.221847 -1.153801 -1.047370 -0.032719\n",
        "      C -1.677957  0.056438 -0.188440 -0.047879\n",
        "three A -0.709631       NaN -0.573687       NaN\n",
        "      B       NaN -1.030056       NaN  0.566057\n",
        "      C -1.378473       NaN -0.694371       NaN\n",
        "two   A       NaN  0.169261       NaN  1.659587\n",
        "      B  1.717596       NaN -0.482111       NaN\n",
        "      C       NaN  0.273024       NaN -0.817465"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng = date_range('1/1/2012', periods = 100, freq = 's')\n",
      "ts = Series(np.random.randint(0,500,len(rng)), index=rng)\n",
      "ts.resample('5Min', how='sum')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "2012-01-01    26316\n",
        "Freq: 5T, dtype: int32"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng = date_range('3/6/2012', periods = 5, freq = 'D')\n",
      "ts = Series(np.random.randn(len(rng)), rng)\n",
      "ts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "2012-03-06   -0.663331\n",
        "2012-03-07   -1.645174\n",
        "2012-03-08    0.652372\n",
        "2012-03-09   -0.274692\n",
        "2012-03-10   -0.723085\n",
        "Freq: D, dtype: float64"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ts_utc = ts.tz_localize('UTC')\n",
      "ts_utc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "2012-03-06 00:00:00+00:00   -0.663331\n",
        "2012-03-07 00:00:00+00:00   -1.645174\n",
        "2012-03-08 00:00:00+00:00    0.652372\n",
        "2012-03-09 00:00:00+00:00   -0.274692\n",
        "2012-03-10 00:00:00+00:00   -0.723085\n",
        "Freq: D, dtype: float64"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ts_utc.tz_convert('US/Eastern')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "2012-03-05 19:00:00-05:00   -0.663331\n",
        "2012-03-06 19:00:00-05:00   -1.645174\n",
        "2012-03-07 19:00:00-05:00    0.652372\n",
        "2012-03-08 19:00:00-05:00   -0.274692\n",
        "2012-03-09 19:00:00-05:00   -0.723085\n",
        "Freq: D, dtype: float64"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng = date_range('1/1/2012', periods = 5, freq = 'M')\n",
      "ts = Series(np.random.randn(len(rng)), index = rng)\n",
      "ts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "2012-01-31   -0.700093\n",
        "2012-02-29   -0.851161\n",
        "2012-03-31   -1.607997\n",
        "2012-04-30   -0.149182\n",
        "2012-05-31   -0.568607\n",
        "Freq: M, dtype: float64"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ps = ts.to_period()\n",
      "ps"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "2012-01   -0.700093\n",
        "2012-02   -0.851161\n",
        "2012-03   -1.607997\n",
        "2012-04   -0.149182\n",
        "2012-05   -0.568607\n",
        "Freq: M, dtype: float64"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ps.to_timestamp()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "2012-01-01   -0.700093\n",
        "2012-02-01   -0.851161\n",
        "2012-03-01   -1.607997\n",
        "2012-04-01   -0.149182\n",
        "2012-05-01   -0.568607\n",
        "Freq: MS, dtype: float64"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prng = period_range('1990Q1', '2000Q4', freq = 'Q-NOV')\n",
      "ts = Series(np.random.randn(len(prng)), prng)\n",
      "ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9\n",
      "ts.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "1990-03-01 09:00   -0.467372\n",
        "1990-06-01 09:00    0.731586\n",
        "1990-09-01 09:00   -0.762516\n",
        "1990-12-01 09:00   -0.309462\n",
        "1991-03-01 09:00   -2.166920\n",
        "Freq: H, dtype: float64"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ts = Series(np.random.randn(1000), index = date_range('1/1/2000', periods = 1000))\n",
      "ts = ts.cumsum()\n",
      "ts.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 98,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0xc94bac8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GnHBC9OvYfwg+/dSakTtlimtNzVmKi6ML+iOPWDH5ZtDRLujRZkZ3dsYW9FSJ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\noih5ggq6oihKnqCCriiKkif8fzPsxsCSWitgAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc94b6a0>"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = DataFrame(np.random.randn(1000,4), index=ts.index, columns = ['A', 'B', 'C', 'D'])\n",
      "df = df.cumsum()\n",
      "plt.figure(); df.plot(); plt.legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 99,
       "text": [
        "<matplotlib.legend.Legend at 0xcc7a860>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0xc8ec3c8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QME1iIl3K9Om0PiaGzCiAEKS0caOwn50dRa1aWmofr0sXmnp5UeSvsel/rKyE\n+R9/FB6zMfC2LQZ3+jRFcwcGAn366LXfyJGUjkEmo2vr1IlS51+/TnFzCxfql1jVJKhP0jf3Dw2o\nne+8Q2aW0FDqKHX15kolec8LChhLz65gP/9JGtG6f9cxLAPLLslWb3s8+Tgb8rORXCFjMWaM0I0z\nYqn068fY9/X4Cnmak7U1MT/qQ2IiY23akNL5+uuMrVxJJimeGq7rt2MHTRcsoHY0J958k7E5c8JY\ncjJjTk5kRtNEt261Eht+8glRO2s3+vnn2Z7du9moS5fYjLg4dq5rV6JT1NQwlpLC9uXmqjX1EdHR\nrKS6mjx4AA31JkxgzMHBBCT2RlBYSA+P55jqGrtXVjZvG0yAJUsY+/BDxh58kLEjR3Rv4+2tPXoM\nayB9YVUVvZfV1fRt12ZyGYKaGoEuuGMHjQZ8fJgWB16NESNIRdZEZCR9MLVHgprMJf6n8c3Wh4oK\ncgZrsmymTqU2ubnpQX4y0IuLe83kwt8YTVaDLvTty9gD345jWAaGpVJWVsbYmF/H1DGvJBckM58v\nfFqX6eLnx1hSEquuJnoXf40NUaE1TTENgWf9AMSlXbmSsTlzhHXLltGU53h36CD8t3at6S5RF6ZM\nYWzp0jDGGGO9emkHu0REUBuuXNHY4dAhWvnYY2QQVTW0ysxMLbAv8/lGz55l7NVXGQsOZrmVlQxh\nYWxhQgJDWBgbHRXFlAsWCBdqYdG8NAtd6Nq1bgQJb2O6y4U6z/pwd69foVi2jLGHHxaWGxLomhg4\nkLG//iKut6GormasfXtqW3x8I6Ybd3fasF07YZ1CIbwTXl4ksHfvZmzpUsYeekjgIr73Hk3t7PRq\n16RJwmEZI2oiQIyfBqFU0oYG2GTuWYGektLwhT3zDCNhrvq9+XY1c1vtxlKLUrW2K68uZ1gGNmzT\nsIYP2FzIzyfPkkLBtmwRrm/BgoZ3mz+flMvGoKlQnDtHimnbtow5O5N8rKig/+bOFZRW/mfq1N5l\nNTXsgEpwKhTUiURcrmZp5eVs2jQKyODx5JPUBq1RAu8F5r/2qCimiI1l6//4Qy3QC3hDZZcuNOU4\nxoqLWVF1Nbv9669s7aRJrN/OnWzryJECx27AANNeqD6YPp2xH3/UXvfTT4KQ0UP7ay3wdMXNm01/\n7AkTtIVffSgtJU1eqSQ26JEjjB0/Tvs1yg7NyBBOMniwsD4mRlgfEqL9MWj+lEry/Fpb69X5xsaS\n45iPGzyfBMa8AAAgAElEQVR7lg5TDwtXAB8izRP99UBDAv2us6EzDduut3fD2wYGai9/aSuFp60n\n2jq01VpvaU4GvZOpJ1FRU6H137VrzZdpTo0zZ6jUjESC+HhhtWZtTF1Yt45Mvo3hwAFgtSoHWY8e\nlAv61i2yZT7yCKVLGTeOTL18hr233qJakykpuo+5Ny8PtxvJe/p7Tg6W1zrA7OvXMfbqVTz5tBJm\nZnT8H6SJ8IuIgJMT8OyzQmIxvgwaT0MFINih+VpbISFof+cOXnF3BwCMjIyE06pV9B/vZLCwAC5c\ngIO5OdyOHMGru3bh2fPncSY4mEq8/PBDHbpoi8Damvi24eH0ss6dK/D/bt4kB8JdCqmUaLN8lktT\ngqc91gZfTYiXAePHU+IwKytKoPXbb0BcpBzz5lRr5TXXCd55s24dJWrhceSI4KjS/AB/+IF4sUFB\nxEHkOHpmcjml/mSMKKz1pFgMCqLMjHzR7UGDaBeNVEV1wZjAd6yP92go6pP0zf1DPWyGgweFlBmN\n4fOvKhmWmrOPtx9Va+kh3+u2URy8cZBhGVjw+mCt9V27Nq4paGLZsiYQGN57j4Z0jLHHH6doOsD0\n7DZNbaAhu13v3qTAXLlCASG6gLAw9rRGspicykpWUFXFDufnM4VSyTIqKtQaM2OMKZRKdraoSGCg\neMrJBHm0mvX88UeGsDB1CpXwcNJonJ115K7hh8QaPE2f8HDW68IFFhkczIqtrLQ1KT8/xl58kR7M\n8ePkeOnfn/3TuzcbumZNy5tZNFE7Ygwg2kNICKlwjo6MZWW1XvtMDH1NLrx/CNCOIN2+ndYdO0bL\nTk51b186vFlC7ycbP8nGjRRivWMHUUp5jBpFvEKemlNSImjg5eV17UsbNhDFh3c8/fKLXteoFz75\nRLiwOXP03g13rYbO560EXVV2NiXd14fQDwDjp2XDw9YDi58aBfYBg9Wl1zGzywKd244JHAN7C3tc\nvX0V636/oV7PpxDVJx3t5cuUNHDWLErnqi/YxYuIt+qNlBRiDXzyCVUjqc0EMBaa2gDPRNCFixdp\n9OPvTxq05qiotKYGr6iSQOeqEtAzxuBx9iycw8PxyJUrOFZYiI28KgKgpKYGZidPYnB0NHpzjkC0\nI8bOqcC36Rl43S4Sl8vKAAAFARTcdf06sXFGjBBSAqshkVCDrK3BGMMXaWnIqKrC+sBA9Nm5E3YT\nJlDFp8RESsuYkkIUimXLqBJBWhowezY8CgqQ16sXsU9aC6++WqdIBrt0iSpMDBpExT6PHm2lxrUe\nbG2JgOTiQslH+XTYZ87QOxweTpkSeSaMZpEOH2SifXZ44ydJTaVRnp0dUU4Aeq8iI2moummT0Jg2\nbWje0pKipDQxdiw1aMoUWjZFJRAefBppgKg6JkDrCnT+RoMerJcXUX5OndJv91Ppx9DWSbDLeF/9\nCol/1z9GvLPoDhA3BZ/+IlAYVbJGr+f07bc0PXoUmD9f44+MDAoDY4xy3R4+rN7+8SkM1ecu4uEl\nfdCuHQUS9uhB6ThbG/b29A5rjvYiS0rwnYr+F1VSghtyOXbm5YEDwPcRB/LzcZi3lwBIKC9Xz9/J\nKwayLWH/aC7mJSQgpaIC6NWL9htxGZPnVCIzk4am771Xf9sYY5CcPIm3k5IAAK5SKeUp37aN7AAd\nOlBpHomEQoh5JCUB06fDMToaRTyvrjXRrx9QUYHM5Ms43RZUKUFlPkLXrtQx3SeonQ+9IUil9Nim\nTiUFCaAycDNnUoefkCBYSkaOpMc+BBRmKpGXNXzwo0eJr9utGwn00lIS8PwB+fuvDzR7k5Ej9RdO\n9YExSt3M6+Y8TESlbV2Bzhc7BMlDZ2fg00+FauYNIe1OGmbvmw1ziXAMM7P6zZKff055jZHbFdZ+\nZDRPSKDfI4+QHOZRXg688ELd+gWRkUKYviQ5EaxSZWMeN47I3tu3U+b7MWMApRJ/fZ+P1J2RqKpU\nIh2U2Lt37xbkpOqBgAAgJYVheUoKNmRmYm1GBoJtbHCyVy982K4dZl+/jojiYnzUrh2yBw/GJ+3a\nYV1GBsKLixHWsyc8pFL8W1yMIbY0PCiLKcKgh6uxrTxTfY6sQQNxpUc7DLWRIH/AIRw9m4OY8hJU\nedb/YcpVSbR7qjQml4aEM8/v50vSWFvDwdYWd/RNB9DcsLBAqnkZYt0ArqpKKH3XqRMN1e6aaiUt\nC74fLiqiXOUXLwLPP0/COyiI5Ocbb1B2hb59ARmnqsFb2Uh9g59/pmm/fnSvs7IopURKCikFtdM4\nNIZ9+8gH9umn1AHrKLXXIJRKsucvW0bC7eGHSfDww+hz5yhwxBSozxZj7A/AIwCuAUgA8I6O/xkD\nWOaGA+zfzXFsc++v2Y4vU8hIpEdJ7U3RmxiWgQ34SWAvFBeTUzo+nhhIvDlMoSBOaEAAY+i7ntlP\nm6v24g8YwNiaNdqhuSdO0H+1w4idnYXoVQawoideYCwuTrCDLVwozB84wPLMiDZ1CKPZ119TatIT\nJxq9tBbFpEmMfbC7QCv68tds4vCX19QwC5mMPRETw35R2XqvlpQwhIWxwRcvMoVSyQZfvMi6/vsv\nmxZ9kuFDC+b66Lds15US9bGG71nIOr3ViXwcv73F8MM4hhE/MYSFsc4REfW2K628nNmfOsUqFQqW\n2EDkqBorVxJ5WuUQUSqVDGFhbOzly8bfJBPgz5g/2bipqneDz21bVUW5G1JSiFFxj0NfGzqPDz8U\nPhdra6Lz8iH4ISGUP5wxRlzF5GRy/vBpSeuzZWdl0YfKxxvwNQg2bKD9Ondu8vUxxigFtKHpEnNy\nhAt1c6MAFIDCxI8coYyDetIjGWsFGzrHcWYAvlEJ9SAAT3Mc11XXtjFv/Iz+s4IwM2oBJr8ZQL1V\naCh5lxvApWxKgsQgDFvs7KhDfu454PhxIhIAdMjiYuqgh/fzglySpWbIKBRkzvzuO8HsEh9P2kFU\nFI2Ke7hlARwHZUEhJk4ELkeRRmXxzwGiivD47DM6EACMHQsXBVXuuYFOCAkh08bw4frdw5ZCQABw\nMk3QlA8GB2OaakhqaWYGKzMz/JWbC2+VgT7IxgZD7O2xsUsXSDgOHaysEC+Xo3tNMqCsRF7/eQj1\ns1Vr1udj1uNGnspnUVMCmNsB4yjrmC69dFVqKtZnZODJuDh4tmmDNhIJOmiGBdaHd98lDUhFI+JU\nWtgBDdNQa+JO5R1E84GkvIYulZIWGR1NDAtTaWn3CEaMEOYHDaKCzmZmJPmiojSKQ23eTOaSqCja\n6fXXBfunJkpLgSVLyEbOm1UkEpIn69fTfTeWWdS9u+G0ON7ftGwZCaWdO2k5O5uev50dyTsTFE1u\nLpNLfwCJjLEUxlg1gO0AJujaMKCq1s3heXqNFOBNvvEv2lt4Yk7vOVrrP/lEqDt88iQJ96VLBTtd\nJy8fWHunITWVnIIHD5KVZPJkYjQB5FcLDSXT+LVrgHteLABgCMLBcUCPnGMotPaGZWE2pU87coRi\nfgGgvBwsI1OrTclo36QMa/vz8sAYw+5ru6FkzVPHq0cPQJYgB9Z1RPqgQRjj4qIWhgCwsXNnvODp\niUH29gAACcfhTO/e6KyqlVUoJxrekgPPqfexsKmAmeoYVlIroB3gZesF1BTD3WMg0JZEuVLThqjC\n4ps3MS8hAeeKi3FDwzavF6RSIcYcgLXKdqfrPC2N0qpSOHXsjjlzvcF4JxxAvgD+nTe2nHwrwxAb\nOkDfnV7gTRO7d1NOguefF2rNMUYCdvdu4jlu2kT1azUxZQp1Bs8/T5qaMfDx0a4z2xiUShLcI0dS\nFW8bG6qr+OWX5IhzdBT8QMnJxrUNzSfQfQCkaSynq9Zp4XL3RxBYScLy19fOg335lcDz/PJLbaeB\nBhhj2LsgArExw/BCyPNkF1MVQ+Tznvj7k+N482Yyn733HuVVeP/lIFTbX8PFS1VIShKUpVGjqCOQ\nyagkZrXDNRw4qER6OtDNilT9D5+itmLMGDjJVUJ7yhQoHxoFmeNE7J/5F0ZueRYrNmjk9MjNxZqa\nV6H5DeuDtIoKjIuJwYXiQkz6YxKOJx837ACNQMkY4svKUNQjFwgqBnIs4GlelzQ7yc0NG7p0gbUO\n2sxfsX8h4szLwMW5sJZaY+9YcjbH58UhQEXhKaksQbB7MB7p+AgWDZiP2zbBgHM/2GXvRlZlXYFt\nqRLCHSwt8UFT8qBqoGzoUADAuLtASJZWlWJ8lwn4rW0RSqs0apHytVUBYj306GF8opN7BFIpfeLL\nl5Nircbt22REf/ppWpbLScPiqz07OQmjmYMHqROfNImG4OfP1y06+9BDZLfWYNU1GYYI9GPHqDN6\n5BEhroLHG28QR55XnoKDheLqYWG6Zd/Nm9pJbHSguQS6XipRL8lhLAPwpgWQ1y4cazyUZLLo0AGy\nNWsgO3hQva1MJoNM5YzYfvk3yABE3Eyj4UxSEmRbtkAmk6mLCnt5yXDligw+PuQQjYuToWNHGbxd\nbdDeqT0uJm7B6dMyzNk3B0kFSTiTuRTbt8swfDg5Q5dHdsWFnBXw8QH6OtzAD+iEtIt/qtuzad5n\nGGb/Fa4t2ozDRzgMHy7DgtOuOH7ZFR98AEyXzMXLYzZRsmQzM632174eXcsbDx0CLl3CgOgrgNQR\ne4/sNWj/xpa/2LcPQT/9hDeKY4HOpUByJNatk0FFKtHreE9+9iTyiuKA0hvoUtoFqRF3YFcwFJ+c\n+QTPZWXio+w4BLkFYU2XNXjW/lk4JAp0Gr8sS1RGRSGtogI1SiVkMhnCwsIg5TjcHDAA38vlCE1N\nNcn1HiwoMPp+NXV56KahOJ58HHEX4nA79jY8bT2RXZotbK/i6MrefhvqvTMzW629xiyvWbOmSfu/\n/z4gkWj8/+mnkMXHQ7Z9Oy0XFkLm4yP87+QEWV4eLR87BqxcCZlUCtnNm2oHudb52raFbPFiyM6e\nNf56VQJdFhZW9/8//1SPHGQyGWQPPyz8X1HR8PHt7SHbt4+ouCNGQLZnj/p/mUyGWbNmYdbzz2NZ\nhXZgZB3UZ1w35gdgIIDDGsuLUcsxClAg0NTfZzHpe0L4fkphCnkqAcrMVQtVVy6xWFeVg+G55xjb\nv5/mp01Tb7N5M2PZvn2YIwrYb7/VdSrM2DWD/Rj5I1MoFVqpAzZtVghBDMvAvr9AmbNudX6IPY4/\nmTrcXCplimqNbVU/Z2cKVPL2Nj496Nq0NMFR+XUfhrAwFlZPvode3/dit0tvs8djYtgmVdRTtULB\nrjVQRuVrzeO3K2FmZnQN/v76t1Hz3r1zdBGbPp2xQc9SAJfX517snWPvsJf3v6x2liXK5QxhYex/\niYnsm6v7mOT4Ua3gpPSKCuZ25oxJc+7wx1+blsZKW7hEXHFFMcMysP8d/R+bd2AeW/fvOjb458Hs\nVIpGkiKlUjvfy/DhVJj6Lk4LUB8MdYrWi06dhI+qpIQKcX7+ufC/UknVm+VySnW7aZM6jD9RLmfj\ntZIDNQNsbXUXend21i43xl/DpEmq/NANYPt2SujGFyuNiKib6Gn9esbmzGmVwKJIAIEcxwVwHNcG\nwFMA9ura8OfHv0W1wDzEmVtnyCbt6kpDr1qoXvIOgnhFr7qabCTTp9PQi+OAvDzMHHoTHukX0Q8X\ntMPKVejj1QcXsy4ivThda/3AsTfw7dZojJHsAQDsu7EPuHED7rdjcASjwSwsyKju5QWJuXDr+JHc\n+PEUHHH9uuHMqNrI5sPu5emANw01h1++rLVNUkES3jvxHi5lX8KOa3vxd24uDqmcgIcKCjCFH8Lp\nwJXSUoxW8Sdzz9uq6ZipqcDDP0/B5D8mN9i+agU5cB4NfBS9PHuhh/UY/PYb4GROw92s0iz8cPEH\nLB26VG1bDbC0xHwfH6xq3x5D3fyhlAhUxBqlEl+lpaGbjY2WDd9YRPTuDQB4LTERu/iw+xbC9Xzy\nD13Lv4ZvL3wLK3MreNl6IaNEY8jOcdppWr29yT44cSK9WG+8ofvgZ8/edUFJhtrQdUKhoFiC9HQy\nVaxbRzH/mjmkOY4ij6ytgTt3yA6tso3/W1yMvfn5WK8yi/x5+7bpn3t9ZpeCAuI2c5y283XnTlV+\n6AYQHEz+E/64AweSTUpTBkZFaado1oFmEeiMsRoA8wEcARAH4A/GWHzt7TLezoW11BoXXrwAJ0sS\nLtfzr1OuhcGDUZ2ZXscZqEhNgYIDzvV0oZf67FmyrfHk9d69iRcOYDjCdAr0ft79EJ4WjnNp5zA2\ncKx6fddvu+LPf3pjg+OTAIADCQdQffQIqkaMQQnswfGJWFRBAHv3kh+DLwM3YgRx6etEPzYB2VVV\nGF1zBUhcC7g+qF6vmV/l77i/8dHpjwAAL5+i4W6aiqMbWVKCG+XlqFbqdqZeLSvD0oAA3BwwAK6u\n5DzmTY/H0ndCliJTb8uXwtNEWnEafO18MY0dwJ8jopEVQfbqxfN81dsM9hsMLzvhBTTjOKwLDATH\ncfB3VNnH88LhpijAd5mZOFhQgFlOZrhTcUfPu9Q4BtjbY7KrKwBQkFML4lreNXR26YyTKScBANml\n2ejk0gk38m/o3L5aUQ3m7S0k2PngA+Kp186po1AQOXv0aIrIMwQ85724mITh3Yb8fBLQPj7AvHlk\nXLe0rFsU4dFHaZqaSvLi+eeBZ56BXHV9H6ak4HRREZ6Ki8PjsbHq3T5KTcWKlBTElpUhUteLrQ/a\ntaPELQBFJm7YIPzHxz6cO0dTntHSGAIDiY2xaJH2+mXLhPkNG0i+NYBmCyxijB1ijHVmjHVkjH2i\na5t0BXkK+3r3Re7CXGwYtwErTq1A2M0wlHu4QPrEU5iz63mtfaTpmRi6sj38T12mh3r2LPGb+B4w\nLY2ilAAsxiq4JZ6rc94BvgNQrajGjN0zMLrDaJQtKcO+p/cBAIJyAbvSKozrNA59MoDbS16D7dih\n5KPgi3X26AGA+g1+VVUVJZ4yFbKrqpB/JxGrhpG3yDz9Lwyzt0avyEgwxvB5xDr8Fb9Lvb3EfQQe\nblOEc8XF4GQyHC0sRDVjSNLBFFEwhtiyMnS3sUGAyskSHExK0dsryJd9Ry7HY08U4s4d+l62/6HA\nmXChcwi7GYYA6UA88wwFfbz9NjGJHugj9KCh/qEAoGUr5GFvYQ9EPAnELcfwmqtYkJiIpPJy/Hhi\nPrbFbDP29mlhpGokkmcCWpi+KCwvxLW8axjXaRzuVJLgnNd/HoLcgnD61mmd+ziscsAhuWoUtnYt\naef9+mkX4iwvJw0iM5M0VENyUMjlFMzn40OsAU0huX8/RfQYAV3P2WDcvi1QDjt3pqmuDGEHDlAE\n8uXL9IJ26QJs3YqMqipMd3eHVCLBLyqChRJAfFkZqpVKvHfzJt5PScGrCQnoF9XEojfDhwv3ff58\n4MUXgb//pqjS2Fhy5qakAMOG6ZddDyBtnBfWKgYZvvmGnjdAtnkrK7WyWh9aNVL0X40e0kxihlEd\nSCivOLUCh2N3AwBksi3IeP15YrGUlcGsrByDe0+Et72PUH7Hw4NeRs0eV3VT/GIP1zmvhJNg88TN\nMJeY47FOj8Faao3HOj0GX3tfdMsF7KqAPRlDEfkT4FMCZPTRSOu4f79WBR0epo4yz66qQnxmOEJ9\naDjey8ENI0qOolypRF51NRYW++Ci/Si8NegtHJx2EIG+I2BRKgyCIoqL0cfWFu8kJ+NMLdZEglwO\nV6kUDhqRujxGPZUISdqDUGZ3x4Grp9R8/ad/fB8PHrFUZ6v8OfpnVEdS2jz+tvPZMXc9tQsrh6/E\n40GPN3yRlbkAq4ayOAYMQFXFbZy9dQrJhcbTtzTxkrc3Pm3fXm+Bvjs3t84904XSqtI6I8hjScew\n6J9FcF7tjO8iv0Mf7z4Y4DMAnraecLR0xBNBTyD8Vrg200WF8ppyxLZRac2TJxPTa/BgGmrz2L6d\nuLljxxKFa9cuosZpYvly4l3XBm8SycwkJo0mn/qpp9QVo+Zcv46ERuJAmg23bwvUMz6UlK9kVRs8\n48PBATtyc7EnLw/HCgow2c0N6ZWVOK5iwvSwsUHQhQtYoeFk50drKYZSYwEaoW/cSLJg3z4S3AsX\nUscTFEQC/+xZw6u1HzxIQ/4lSyhLZ3AwjQSmTqXIUl/fRm25db/oFkSkRi4XAGjr0BbvPvguPjr9\nEXJ7ApPOAl3zAJ9tm3ArIgKn35iMgfYMvo6qB8334F5edKF2drScmUkP+9VXYX75os5zD/QdiLIl\n2qHnSqZEN5XJilu4UL3+hl0V1IaEsWNhalzKvoQzt85gbp+5MJeYg+M4ZFRWQFpTgk4OngCS8Jhf\nL0Rd2wyf9g9j+c1koI0z4PoARrRVQOHUD9czYuCcfxVfPvAM3kxKwms+PrCUSLA6LQ3VjOGgKize\n6tQpVCiVmF+b2qVCtjwNfvZ+SI3xBtxjkXt9AvoPK8D59v8AZtWYu2c+suS3EJsTD2nYIGzbRorJ\n8OECM2til4mY2GWi+pj12VaD3ILQy7MX+TLcAFSR/d/UAp3jOATb2OCEnoE7k2Jj8aCDA06FhKDb\n+fPYFhSEHjrsaP5r/PFIx0fw2+Tf1Ovel72PiHQKhCgoL0Bbh7Y4/dxplFTRu25hboHymnJM2zEN\ne58mtxJjDIv+oaH2tQEdgI1zhd6xQwcS4vb2JOCeV41YKyqAIUOI5hseToFJPJYtI7MFr/Dw4M2S\ngYEkIDSzw1lbkwZfVYWfsrLwU1YWjvbogVG6bJb1oEk29Oxssjs/9hgtX7kixBJYWBDXWCO2QAuT\nJ9NIIyAAj58WRj3/qNp8s6ICF/v0QS9bW6y+dQuL+UhDAL1sbeFrYYEv0tPxcbt2sNOh3NSLzp3J\n1DJuHNm6n3mGtHReHk2ZQhTE+vwf9cHBgX7vvkvLWVnUMQDkV1DlRGoIraqh69KYVo5YibcHvY0Y\nD6Dq6y+x+V+ywcbnxeP3vR8hyV6Bhzuo6EB8Zj5V0AsAWvbyohf644/phly6pFd7urp2RY8Cc2iS\nxld+MByZJZlNv0g9sOLUCrx66FXYfWKHTt90wtXb11BYU4P2tk5qLbqvkw9S76SiqKoM32Zl045m\nFrC09sE4lfPzSuohPOlshfCQEHwdGAh31XXYqDjkjDFUqLS52fU4V9LupGH0ID+EtPfHw49nAFI5\ncrt+BCc7a+D8K/gl5mccSz6G4uoiDOntgqlTSfG7fZuUCkMQ+0osljywBNFZ0ZBU5mK6py/CZoYh\nV25656WbVIocHe/b9pwc5FeQtvxVWhoOqALa4uVyyBUKxMnliCkrq7MfQAL796u/a62TSrSHap62\nnpCaSeFspS0Y993Yh7n75gIAkgqTsPosJbQvRDk5NHhNrEsXeofnzqUISYAikr//XhD6Kr69FjTz\n2DBGwyg7O8oLf+MGCVE+901lJf3fsSOWRUerd3v4yhVsyc7Wee0mw9KlJBj5Yd6BA9rBP7Nn1586\n9O23gW3boJRIIOU4WHAcnMzNYWlmhlWqRFxdrK0h4TiM1ci6KX/wQWwLCoKzuTm+ycjAjxqZQ/VC\nQADdf4CEeUgIzfNTZ2fg669pO2OgmRgsMlKvKNdWFehFNTW4Wlp36Ll61GqULi5Fm/kL4JJMN3tY\n2wcRmm2J0CcXoqubziwCdeHrSwEI/I1uCOvW4Z+Zx+FcUiNo+gAUXTo3u0DnhUClohKJBYlYefFX\nuHKV6ObaGRKOw7EePdDb2ReXsi8ho1iI1zJXlOPHQvrwY/r1w+j2I/DbpY0YrErg/6BqmlpRgWql\nErtVaRWtJRL0rMdzm16cjh7+flj0qjvsvW4j4NWXcNPzS6yd9QIe8iGte5DvIHQ8dRrDhgrDPze3\n+pOqNWRbdbNxQ0lVCZ4u24Nf+42Fi5UL8uTUzqisqDpMpKais7U1bsjlUGgEbJQpFHg6Ph6uPwzD\ngN8ex5tJSZh9/Tre8/fHQ46OmK0ySUSXluLZ+HhEpEdgxi4yM/EsHwCoUghOy5yyHAzzH4bVI0lA\ne9h4oDauvnwVc3rPwY9RP2L3td2IzorG2MCxiHk5Bruu7cLoX0ejrErViYwcSYEmR4+SieTsWRJk\nHTvS/w89JBw4KUkQ+rwDOCWFtFgHB3Ig8h15794kyDMzKfCiY0egVy98r/oeWWgoLvTujfc0tNrG\nYLANnT+3jw+xxz78kKL7GrETa0KuUGBffj5szcxQMWwY8ocMAQA84uwMR3NzdUBcsK0tMgYNwgIf\nH1iZmcFCIkGS6h7pkkGNgh+pjxlDZpagILVvzWTgODK3DBtGy3pkiWxVgR5bVoYekZFqzzQPjuNg\n08aGJIRKGFtGXsLCExVoM2yErkPphr70t/PnKY2ihwd5knm72o0bsPMPbH6Bbqat1ZnbtgMnv6Ue\niYx0doaHLQmGeebXMElKzp6nvQPwR14BPKRSdLG2xoiAEVrmiv729sgcNAipFRU4decOJqucxdM8\n6goZHmnFafC194W7jTv+jvsbKfZbAQDd3bvjqwXD4RK9Cvun7QdLfQDjxxt/7S5WpDl52ZKgcbF2\nQb6ctOShm4bC7ys/Pm7BKNiZm6OtpSUiNPws+28TRcy/4zM4z7WFu1SK7Koq9LOzw0ve3timoox9\nnpaGX3NysCZqC7Ze2YoaZQ3m7ift2t/BH2l3hE42uzQbu57ahVf6vQJrqTW9x7XQ3b07Vo8igf/m\nkTdxs+gmurh2QVe3rhjZfiSOJh2F7Se2KCgvoHc4NJSc/uXl2vQ9gAJrHB0pivDECdIMAdLQ166l\nnOy8LTchQRDoHEfaf2Ii2ds//RQIDkYgf3/u3EHvigrkVVfX+T5NhpMnyVb36KNEGbtwgdpsQKGA\nVbduYWJMDDxUo1Ge8trT1haFDzygta23hQXWaJQ5e9bDAwPt7XG1nhFYg/DxIVNX+/Zk3o2N1S9N\nrKHYto06dD1t/a0q0O+oXpTEhhobFUVDRt7eXvuFbgybNze+DZ/j+JtvKG/uU08RLSwwEN523s0u\n0BEeE0EAACAASURBVHltbFrwNCwPXY7k0nzkFt3AqPYCd1XCSXDhxQv4fMT7eLQd2UtfVH2cH7Zr\nBzOOQzundlqUuMqaSrhJzXGnpgaxZWUY5+KCgiFDsL5jey0NUxPpxenwc/DTMhGcmHECvTx7oVNH\nc5Qefgf2UmcUFEAnJVQXGrKtmklIg+LLBLpYuaCgvAAF5QVwsyHnWE6Z7rJfhuJxNzcc0UjWtSeH\nKiuk2oQA3uPRV0627752dhjs4ABpLYXgUCG9g+nF6VAwBQb7DUYX1y4IT6OCC2VVZahSVMHR0hE2\nbWzq+Gg04WDpgCPPHEF+eT6SC5MR4BgACSfBsWePwdOWhtp8Ajo1dAk6jiN60ebNlOSLx4oVlHR/\n/37Kz8/bw1QUTgBkssnIQHRJCaJ69waCgyEpLcWJnj2ByZMh8fBAgLk5bupJ9zTYhp6ZSX6B2bOB\njz6ituqZ7mFGfDw+Sk1FvMp569EEVsL/2rbFzm7dkNFYOl5d4Li6VMrmAsfp3cndFTVFd+TmYr8+\nNfUmTBDqAeqLZ56hnlOTCVBTo52Dmq83yd+0jRuBPynM39vOG1mlBtrYDEByYTLCUsIAkFPW0dIR\nEbevoYuDl1or59HXuy8szS3xpLs7zoSEoKuKydNFNR3qPxSxubGIy41DRnEGLD+yhHSFOWyUpTic\nl43+dnZwkkox9vex8P7SG9xyDldztPOcpBWTUzTYPRgbxhG/NtgjGAD5qDw9SdErLDT8UdSHnLdz\n8N5QqnZhYW4BLzsvuKx2QUpRCto6tMWRxCMmOY9nmzbIV9nRC6urcb60Au6VxHzwSd+EquwjuNy3\nL7wtLNBGIkHVsGHY1bUDJuIGELccxf6z4d/9f4gvTEVqUSqWhy7HE0FP4PjN46hR1mDiHxPR0bmj\n3oFRD3d4GE6WTth/Yz86u3RWr096LQmzQ2Zj1ZlVKKooQmVNIwJn2DAaZfJsmKNHtfnMVlZCJixN\nLdLHB4iMRO+vv8YTt25B3qcPUqVStL19W13wtf3160huChNEH2RlUacSFCSs0/Ol2pqTg6/S0tT+\njQGafjQD4CyVoqCmxiSjwLsBrS7QLTgOH6amqh179eLyZfJ4GwozM4r00QyiGDwYeFxFqWOM7HZt\n2uj0pje3hv7GkTfw7oPk1VYoFbCzcAT8Z8DP3rfefezNzTHEwUFd9KGjir5l28YWM3rMwN9xf+Ni\nlsDuKS1JwqHCEjVTI604TW2nPpJ0BMWVxej5fU9wyzkUlBfA1doVHMdhdMfRsJHawNVa0OrathW+\nv4bK3GmiMduqu407LDQSg2kGe926cwuz9szSslM3FS7m5mpH/NBLl5BUY45HlbHIGTwY6/tNgRkn\nqcNmibj6LXafnAsUkrBMdRmD3/JKkVCQgEDnQHjYeiBPnoeskiz8k/yP1r3SB/18+iGzJBP9fYTU\ng9ZSa7jZuOFY8jE4feoEy48sse/6vvoPEhpK5ot//yWKX//+xDe3sqLeF6AgpNqBEoMGUSQmgOSK\nCnTLzESqpycC4uKI8XLmDNrHxOBCQYFeAq/Ocz58mDSArVt175CZSQLd1hZYvJjW8Y5aPZBfU4Nr\ncjmqhg5VO0ENhYVEgjYch7L7pMhIqwr0rtbWWNW+vXpoW9xQhZkePQR+qqHw8tIO1b1wgexSAL1U\ntrbkIOIdTZq72nohuTAZt+7catq5G8C5tHPYe30vZvaciYWDF2J+//mwd6LkQrP9gxvdn+M4VA0d\nqs5VDgCBLoH4QPYBorKi4G1HLIjx/gMBjkMflbPX3sIes0NmAyDHo8MqB1zJuaJ1XADwtfdF6RJt\nh9Hs2ZS4zpB4FkMxs6cQSPLLxF8AoM5IoilwlUrxZ24uduXmIk6l2QU7eMG9TRu427jjSNKROmaO\nU6kqc1xNMVhoKIZVnEf0nTzklObAz8EPrtauyJPnoaiCeOubJ2w2qE19vfoiyC0IDpbamml5tbZW\nPH77eGQU15Plz9FRKKX122+CliuXC++0ry/wyy/a+4WGQqnxzaVUVGBQfj7MLl4kOuHAgWhnZoYV\nmZlNY7uMGUO0yhkz6ka7AvTt8Tb9V1+lqR4aenJ5Odw1TCxSicSodBEO5uaIlcvvCy29VQV6XP/+\neN3PD9mDB6ObtXXzDe38/YUQWj64gB96RkQ0yILhnVr+a/yx59oekzbrZOpJjO4wGm42blg9ajWG\n+g/FLYkbpjjbYaKvfkweaS1HTGhAKADgXPo5zOs3DyWLS/CYOxHEzatJ6KQUpWDxg4vxWv/XsC1m\nmxYTY92YdQ2eb8YMSrVjSKEOQ22rA3wHoGxJGeLnxePZns9iTu85OJZ8zKBj6EJ71UhmcmwslACQ\n9idCvIjb28ODGAqarJpzaedwLv0cvO28ET2X7NP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uDi8nJ74sKuJ6Pz/6ubpyWafTuxKq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nAAAA\nQElEQVSVSCQSO0EqdIlEIrETpEKXSCQSO0EqdIlEIrETpEKXSCQSO0EqdIlEIrETpEKXSCQSO0Eq\ndIlEIrET/h+PS5bBo4YUagAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xcc7a898>"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}