2_pandas.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Pandas"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "from pandas import Series, DataFrame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj = Series([3,6,9,12])\n",
      "obj"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "0     3\n",
        "1     6\n",
        "2     9\n",
        "3    12\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "array([ 3,  6,  9, 12], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "Int64Index([0, 1, 2, 3], dtype='int64')"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ww2_cas = Series([8700000,4200000,3000000,2100000,400000],index=['USSR','Germain','China','Japan','USA'])\n",
      "ww2_cas"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "USSR       8700000\n",
        "Germain    4200000\n",
        "China      3000000\n",
        "Japan      2100000\n",
        "USA         400000\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ww2_cas['USA']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "400000"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#cechk with country has cas greater than 4 mil\n",
      "ww2_cas[ww2_cas> 4000000]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "USSR       8700000\n",
        "Germain    4200000\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'USSR' in ww2_cas"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ww2_dict=ww2_cas.to_dict()\n",
      "ww2_dict"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "{'China': 3000000,\n",
        " 'Germain': 4200000,\n",
        " 'Japan': 2100000,\n",
        " 'USA': 400000,\n",
        " 'USSR': 8700000}"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ww2_s = Series(ww2_dict)\n",
      "ww2_s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "China      3000000\n",
        "Germain    4200000\n",
        "Japan      2100000\n",
        "USA         400000\n",
        "USSR       8700000\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "countries = ['China','Germain','Japan','USA','USSR','Argentina']\n",
      "obj2 = Series(ww2_dict,index=countries)\n",
      "obj2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "China        3000000\n",
        "Germain      4200000\n",
        "Japan        2100000\n",
        "USA           400000\n",
        "USSR         8700000\n",
        "Argentina        NaN\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.isnull(obj2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "China        False\n",
        "Germain      False\n",
        "Japan        False\n",
        "USA          False\n",
        "USSR         False\n",
        "Argentina     True\n",
        "dtype: bool"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ww2_s + obj2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "Argentina         NaN\n",
        "China         6000000\n",
        "Germain       8400000\n",
        "Japan         4200000\n",
        "USA            800000\n",
        "USSR         17400000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2.name = \"WW 2 casualties\"\n",
      "obj2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "China        3000000\n",
        "Germain      4200000\n",
        "Japan        2100000\n",
        "USA           400000\n",
        "USSR         8700000\n",
        "Argentina        NaN\n",
        "Name: WW 2 casualties, dtype: float64"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2.index.name=\"countries\"\n",
      "obj2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "countries\n",
        "China        3000000\n",
        "Germain      4200000\n",
        "Japan        2100000\n",
        "USA           400000\n",
        "USSR         8700000\n",
        "Argentina        NaN\n",
        "Name: WW 2 casualties, dtype: float64"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Lecture 2"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import webbrowser\n",
      "website = \"http://en.wikipedia.org/wiki/NFL_win-loss_records\"\n",
      "webbrowser.open(website)\n",
      "#copy from the browser"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nfl=pd.read_clipboard()\n",
      "nfl"
     ],
     "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>Rank</th>\n",
        "      <th>Team</th>\n",
        "      <th>Won</th>\n",
        "      <th>Lost</th>\n",
        "      <th>Tied*</th>\n",
        "      <th>Pct.</th>\n",
        "      <th>First Season</th>\n",
        "      <th>Total Games</th>\n",
        "      <th>Conference</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>  1</td>\n",
        "      <td>       Dallas Cowboys</td>\n",
        "      <td> 510</td>\n",
        "      <td> 378</td>\n",
        "      <td>  6</td>\n",
        "      <td> 0.574</td>\n",
        "      <td> 1960</td>\n",
        "      <td>  894</td>\n",
        "      <td>  NFC East</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>  2</td>\n",
        "      <td>        Chicago Bears</td>\n",
        "      <td> 752</td>\n",
        "      <td> 563</td>\n",
        "      <td> 42</td>\n",
        "      <td> 0.570</td>\n",
        "      <td> 1920</td>\n",
        "      <td> 1357</td>\n",
        "      <td> NFC North</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>  3</td>\n",
        "      <td>    Green Bay Packers</td>\n",
        "      <td> 741</td>\n",
        "      <td> 561</td>\n",
        "      <td> 37</td>\n",
        "      <td> 0.567</td>\n",
        "      <td> 1921</td>\n",
        "      <td> 1339</td>\n",
        "      <td> NFC North</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>  4</td>\n",
        "      <td>       Miami Dolphins</td>\n",
        "      <td> 443</td>\n",
        "      <td> 345</td>\n",
        "      <td>  4</td>\n",
        "      <td> 0.562</td>\n",
        "      <td> 1966</td>\n",
        "      <td>  792</td>\n",
        "      <td>  AFC East</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>  5</td>\n",
        "      <td>     Baltimore Ravens</td>\n",
        "      <td> 182</td>\n",
        "      <td> 143</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0.560</td>\n",
        "      <td> 1996</td>\n",
        "      <td>  326</td>\n",
        "      <td> AFC North</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>  6</td>\n",
        "      <td>  San Francisco 49ers</td>\n",
        "      <td> 545</td>\n",
        "      <td> 444</td>\n",
        "      <td> 14</td>\n",
        "      <td> 0.550</td>\n",
        "      <td> 1950</td>\n",
        "      <td> 1003</td>\n",
        "      <td>  NFC West</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>  7</td>\n",
        "      <td> New England Patriots</td>\n",
        "      <td> 478</td>\n",
        "      <td> 395</td>\n",
        "      <td>  9</td>\n",
        "      <td> 0.547</td>\n",
        "      <td> 1960</td>\n",
        "      <td>  882</td>\n",
        "      <td>  AFC East</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>  8</td>\n",
        "      <td>      New York Giants</td>\n",
        "      <td> 691</td>\n",
        "      <td> 581</td>\n",
        "      <td> 33</td>\n",
        "      <td> 0.542</td>\n",
        "      <td> 1925</td>\n",
        "      <td> 1305</td>\n",
        "      <td>  NFC East</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>  9</td>\n",
        "      <td>       Denver Broncos</td>\n",
        "      <td> 464</td>\n",
        "      <td> 400</td>\n",
        "      <td> 10</td>\n",
        "      <td> 0.537</td>\n",
        "      <td> 1960</td>\n",
        "      <td>  874</td>\n",
        "      <td>  AFC West</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 10</td>\n",
        "      <td>      Oakland Raiders</td>\n",
        "      <td> 462</td>\n",
        "      <td> 406</td>\n",
        "      <td> 11</td>\n",
        "      <td> 0.532</td>\n",
        "      <td> 1960</td>\n",
        "      <td>  879</td>\n",
        "      <td>  AFC West</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 11</td>\n",
        "      <td>    Minnesota Vikings</td>\n",
        "      <td> 457</td>\n",
        "      <td> 401</td>\n",
        "      <td> 10</td>\n",
        "      <td> 0.532</td>\n",
        "      <td> 1961</td>\n",
        "      <td>  868</td>\n",
        "      <td> NFC North</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "    Rank                  Team  Won  Lost  Tied*   Pct.  First Season  \\\n",
        "0      1        Dallas Cowboys  510   378      6  0.574          1960   \n",
        "1      2         Chicago Bears  752   563     42  0.570          1920   \n",
        "2      3     Green Bay Packers  741   561     37  0.567          1921   \n",
        "3      4        Miami Dolphins  443   345      4  0.562          1966   \n",
        "4      5      Baltimore Ravens  182   143      1  0.560          1996   \n",
        "5      6   San Francisco 49ers  545   444     14  0.550          1950   \n",
        "6      7  New England Patriots  478   395      9  0.547          1960   \n",
        "7      8       New York Giants  691   581     33  0.542          1925   \n",
        "8      9        Denver Broncos  464   400     10  0.537          1960   \n",
        "9     10       Oakland Raiders  462   406     11  0.532          1960   \n",
        "10    11     Minnesota Vikings  457   401     10  0.532          1961   \n",
        "\n",
        "    Total Games Conference  \n",
        "0           894   NFC East  \n",
        "1          1357  NFC North  \n",
        "2          1339  NFC North  \n",
        "3           792   AFC East  \n",
        "4           326  AFC North  \n",
        "5          1003   NFC West  \n",
        "6           882   AFC East  \n",
        "7          1305   NFC East  \n",
        "8           874   AFC West  \n",
        "9           879   AFC West  \n",
        "10          868  NFC North  "
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nfl.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "Index([u'Rank', u'Team', u'Won', u'Lost', u'Tied*', u'Pct.', u'First Season', u'Total Games', u'Conference'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nfl.Rank"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "0    1\n",
        "1    2\n",
        "2    3\n",
        "3    4\n",
        "Name: Rank, dtype: int64"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nfl['First Season']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "0    1960\n",
        "1    1920\n",
        "2    1921\n",
        "3    1966\n",
        "Name: First Season, dtype: int64"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "DataFrame(nfl,columns=['Team','First Season','Total Games'])"
     ],
     "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>Team</th>\n",
        "      <th>First Season</th>\n",
        "      <th>Total Games</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>    Dallas Cowboys</td>\n",
        "      <td> 1960</td>\n",
        "      <td>  894</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>     Chicago Bears</td>\n",
        "      <td> 1920</td>\n",
        "      <td> 1357</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> Green Bay Packers</td>\n",
        "      <td> 1921</td>\n",
        "      <td> 1339</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>    Miami Dolphins</td>\n",
        "      <td> 1966</td>\n",
        "      <td>  792</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "                Team  First Season  Total Games\n",
        "0     Dallas Cowboys          1960          894\n",
        "1      Chicago Bears          1920         1357\n",
        "2  Green Bay Packers          1921         1339\n",
        "3     Miami Dolphins          1966          792"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "DataFrame(nfl,columns=['Team','First Season','Total Games','Stadium'])"
     ],
     "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>Team</th>\n",
        "      <th>First Season</th>\n",
        "      <th>Total Games</th>\n",
        "      <th>Stadium</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>    Dallas Cowboys</td>\n",
        "      <td> 1960</td>\n",
        "      <td>  894</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>     Chicago Bears</td>\n",
        "      <td> 1920</td>\n",
        "      <td> 1357</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> Green Bay Packers</td>\n",
        "      <td> 1921</td>\n",
        "      <td> 1339</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>    Miami Dolphins</td>\n",
        "      <td> 1966</td>\n",
        "      <td>  792</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "                Team  First Season  Total Games  Stadium\n",
        "0     Dallas Cowboys          1960          894      NaN\n",
        "1      Chicago Bears          1920         1357      NaN\n",
        "2  Green Bay Packers          1921         1339      NaN\n",
        "3     Miami Dolphins          1966          792      NaN"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nfl.head(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>Rank</th>\n",
        "      <th>Team</th>\n",
        "      <th>Won</th>\n",
        "      <th>Lost</th>\n",
        "      <th>Tied*</th>\n",
        "      <th>Pct.</th>\n",
        "      <th>First Season</th>\n",
        "      <th>Total Games</th>\n",
        "      <th>Conference</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> Dallas Cowboys</td>\n",
        "      <td> 510</td>\n",
        "      <td> 378</td>\n",
        "      <td>  6</td>\n",
        "      <td> 0.574</td>\n",
        "      <td> 1960</td>\n",
        "      <td>  894</td>\n",
        "      <td>  NFC East</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td>  Chicago Bears</td>\n",
        "      <td> 752</td>\n",
        "      <td> 563</td>\n",
        "      <td> 42</td>\n",
        "      <td> 0.570</td>\n",
        "      <td> 1920</td>\n",
        "      <td> 1357</td>\n",
        "      <td> NFC North</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "   Rank            Team  Won  Lost  Tied*   Pct.  First Season  Total Games  \\\n",
        "0     1  Dallas Cowboys  510   378      6  0.574          1960          894   \n",
        "1     2   Chicago Bears  752   563     42  0.570          1920         1357   \n",
        "\n",
        "  Conference  \n",
        "0   NFC East  \n",
        "1  NFC North  "
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nfl.tail(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>Rank</th>\n",
        "      <th>Team</th>\n",
        "      <th>Won</th>\n",
        "      <th>Lost</th>\n",
        "      <th>Tied*</th>\n",
        "      <th>Pct.</th>\n",
        "      <th>First Season</th>\n",
        "      <th>Total Games</th>\n",
        "      <th>Conference</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td> Green Bay Packers</td>\n",
        "      <td> 741</td>\n",
        "      <td> 561</td>\n",
        "      <td> 37</td>\n",
        "      <td> 0.567</td>\n",
        "      <td> 1921</td>\n",
        "      <td> 1339</td>\n",
        "      <td> NFC North</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td>    Miami Dolphins</td>\n",
        "      <td> 443</td>\n",
        "      <td> 345</td>\n",
        "      <td>  4</td>\n",
        "      <td> 0.562</td>\n",
        "      <td> 1966</td>\n",
        "      <td>  792</td>\n",
        "      <td>  AFC East</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "   Rank               Team  Won  Lost  Tied*   Pct.  First Season  \\\n",
        "2     3  Green Bay Packers  741   561     37  0.567          1921   \n",
        "3     4     Miami Dolphins  443   345      4  0.562          1966   \n",
        "\n",
        "   Total Games Conference  \n",
        "2         1339  NFC North  \n",
        "3          792   AFC East  "
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nfl.ix[3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "Rank                         4\n",
        "Team            Miami Dolphins\n",
        "Won                        443\n",
        "Lost                       345\n",
        "Tied*                        4\n",
        "Pct.                     0.562\n",
        "First Season              1966\n",
        "Total Games                792\n",
        "Conference            AFC East\n",
        "Name: 3, dtype: object"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nfl['Stadium'] = \"Levis's stadium\"\n",
      "nfl"
     ],
     "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>Rank</th>\n",
        "      <th>Team</th>\n",
        "      <th>Won</th>\n",
        "      <th>Lost</th>\n",
        "      <th>Tied*</th>\n",
        "      <th>Pct.</th>\n",
        "      <th>First Season</th>\n",
        "      <th>Total Games</th>\n",
        "      <th>Conference</th>\n",
        "      <th>Stadium</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td>    Dallas Cowboys</td>\n",
        "      <td> 510</td>\n",
        "      <td> 378</td>\n",
        "      <td>  6</td>\n",
        "      <td> 0.574</td>\n",
        "      <td> 1960</td>\n",
        "      <td>  894</td>\n",
        "      <td>  NFC East</td>\n",
        "      <td> Levis's stadium</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td>     Chicago Bears</td>\n",
        "      <td> 752</td>\n",
        "      <td> 563</td>\n",
        "      <td> 42</td>\n",
        "      <td> 0.570</td>\n",
        "      <td> 1920</td>\n",
        "      <td> 1357</td>\n",
        "      <td> NFC North</td>\n",
        "      <td> Levis's stadium</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td> Green Bay Packers</td>\n",
        "      <td> 741</td>\n",
        "      <td> 561</td>\n",
        "      <td> 37</td>\n",
        "      <td> 0.567</td>\n",
        "      <td> 1921</td>\n",
        "      <td> 1339</td>\n",
        "      <td> NFC North</td>\n",
        "      <td> Levis's stadium</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td>    Miami Dolphins</td>\n",
        "      <td> 443</td>\n",
        "      <td> 345</td>\n",
        "      <td>  4</td>\n",
        "      <td> 0.562</td>\n",
        "      <td> 1966</td>\n",
        "      <td>  792</td>\n",
        "      <td>  AFC East</td>\n",
        "      <td> Levis's stadium</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "   Rank               Team  Won  Lost  Tied*   Pct.  First Season  \\\n",
        "0     1     Dallas Cowboys  510   378      6  0.574          1960   \n",
        "1     2      Chicago Bears  752   563     42  0.570          1920   \n",
        "2     3  Green Bay Packers  741   561     37  0.567          1921   \n",
        "3     4     Miami Dolphins  443   345      4  0.562          1966   \n",
        "\n",
        "   Total Games Conference          Stadium  \n",
        "0          894   NFC East  Levis's stadium  \n",
        "1         1357  NFC North  Levis's stadium  \n",
        "2         1339  NFC North  Levis's stadium  \n",
        "3          792   AFC East  Levis's stadium  "
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nfl['Stadium'] = np.arange(4)\n",
      "nfl"
     ],
     "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>Rank</th>\n",
        "      <th>Team</th>\n",
        "      <th>Won</th>\n",
        "      <th>Lost</th>\n",
        "      <th>Tied*</th>\n",
        "      <th>Pct.</th>\n",
        "      <th>First Season</th>\n",
        "      <th>Total Games</th>\n",
        "      <th>Conference</th>\n",
        "      <th>Stadium</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td>    Dallas Cowboys</td>\n",
        "      <td> 510</td>\n",
        "      <td> 378</td>\n",
        "      <td>  6</td>\n",
        "      <td> 0.574</td>\n",
        "      <td> 1960</td>\n",
        "      <td>  894</td>\n",
        "      <td>  NFC East</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td>     Chicago Bears</td>\n",
        "      <td> 752</td>\n",
        "      <td> 563</td>\n",
        "      <td> 42</td>\n",
        "      <td> 0.570</td>\n",
        "      <td> 1920</td>\n",
        "      <td> 1357</td>\n",
        "      <td> NFC North</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td> Green Bay Packers</td>\n",
        "      <td> 741</td>\n",
        "      <td> 561</td>\n",
        "      <td> 37</td>\n",
        "      <td> 0.567</td>\n",
        "      <td> 1921</td>\n",
        "      <td> 1339</td>\n",
        "      <td> NFC North</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td>    Miami Dolphins</td>\n",
        "      <td> 443</td>\n",
        "      <td> 345</td>\n",
        "      <td>  4</td>\n",
        "      <td> 0.562</td>\n",
        "      <td> 1966</td>\n",
        "      <td>  792</td>\n",
        "      <td>  AFC East</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "   Rank               Team  Won  Lost  Tied*   Pct.  First Season  \\\n",
        "0     1     Dallas Cowboys  510   378      6  0.574          1960   \n",
        "1     2      Chicago Bears  752   563     42  0.570          1920   \n",
        "2     3  Green Bay Packers  741   561     37  0.567          1921   \n",
        "3     4     Miami Dolphins  443   345      4  0.562          1966   \n",
        "\n",
        "   Total Games Conference  Stadium  \n",
        "0          894   NFC East        0  \n",
        "1         1357  NFC North        1  \n",
        "2         1339  NFC North        2  \n",
        "3          792   AFC East        3  "
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stadiums = Series([\"Levi's Stadium\",\"AT&T Stadium\"],index=[2,0])\n",
      "stadiums"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "2    Levi's Stadium\n",
        "0      AT&T Stadium\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nfl['Stadium'] = stadiums\n",
      "nfl"
     ],
     "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>Rank</th>\n",
        "      <th>Team</th>\n",
        "      <th>Won</th>\n",
        "      <th>Lost</th>\n",
        "      <th>Tied*</th>\n",
        "      <th>Pct.</th>\n",
        "      <th>First Season</th>\n",
        "      <th>Total Games</th>\n",
        "      <th>Conference</th>\n",
        "      <th>Stadium</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td>    Dallas Cowboys</td>\n",
        "      <td> 510</td>\n",
        "      <td> 378</td>\n",
        "      <td>  6</td>\n",
        "      <td> 0.574</td>\n",
        "      <td> 1960</td>\n",
        "      <td>  894</td>\n",
        "      <td>  NFC East</td>\n",
        "      <td>   AT&amp;T Stadium</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td>     Chicago Bears</td>\n",
        "      <td> 752</td>\n",
        "      <td> 563</td>\n",
        "      <td> 42</td>\n",
        "      <td> 0.570</td>\n",
        "      <td> 1920</td>\n",
        "      <td> 1357</td>\n",
        "      <td> NFC North</td>\n",
        "      <td>            NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td> Green Bay Packers</td>\n",
        "      <td> 741</td>\n",
        "      <td> 561</td>\n",
        "      <td> 37</td>\n",
        "      <td> 0.567</td>\n",
        "      <td> 1921</td>\n",
        "      <td> 1339</td>\n",
        "      <td> NFC North</td>\n",
        "      <td> Levi's Stadium</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td>    Miami Dolphins</td>\n",
        "      <td> 443</td>\n",
        "      <td> 345</td>\n",
        "      <td>  4</td>\n",
        "      <td> 0.562</td>\n",
        "      <td> 1966</td>\n",
        "      <td>  792</td>\n",
        "      <td>  AFC East</td>\n",
        "      <td>            NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "   Rank               Team  Won  Lost  Tied*   Pct.  First Season  \\\n",
        "0     1     Dallas Cowboys  510   378      6  0.574          1960   \n",
        "1     2      Chicago Bears  752   563     42  0.570          1920   \n",
        "2     3  Green Bay Packers  741   561     37  0.567          1921   \n",
        "3     4     Miami Dolphins  443   345      4  0.562          1966   \n",
        "\n",
        "   Total Games Conference         Stadium  \n",
        "0          894   NFC East    AT&T Stadium  \n",
        "1         1357  NFC North             NaN  \n",
        "2         1339  NFC North  Levi's Stadium  \n",
        "3          792   AFC East             NaN  "
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "del nfl['Stadium']\n",
      "nfl"
     ],
     "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>Rank</th>\n",
        "      <th>Team</th>\n",
        "      <th>Won</th>\n",
        "      <th>Lost</th>\n",
        "      <th>Tied*</th>\n",
        "      <th>Pct.</th>\n",
        "      <th>First Season</th>\n",
        "      <th>Total Games</th>\n",
        "      <th>Conference</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td>    Dallas Cowboys</td>\n",
        "      <td> 510</td>\n",
        "      <td> 378</td>\n",
        "      <td>  6</td>\n",
        "      <td> 0.574</td>\n",
        "      <td> 1960</td>\n",
        "      <td>  894</td>\n",
        "      <td>  NFC East</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td>     Chicago Bears</td>\n",
        "      <td> 752</td>\n",
        "      <td> 563</td>\n",
        "      <td> 42</td>\n",
        "      <td> 0.570</td>\n",
        "      <td> 1920</td>\n",
        "      <td> 1357</td>\n",
        "      <td> NFC North</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td> Green Bay Packers</td>\n",
        "      <td> 741</td>\n",
        "      <td> 561</td>\n",
        "      <td> 37</td>\n",
        "      <td> 0.567</td>\n",
        "      <td> 1921</td>\n",
        "      <td> 1339</td>\n",
        "      <td> NFC North</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td>    Miami Dolphins</td>\n",
        "      <td> 443</td>\n",
        "      <td> 345</td>\n",
        "      <td>  4</td>\n",
        "      <td> 0.562</td>\n",
        "      <td> 1966</td>\n",
        "      <td>  792</td>\n",
        "      <td>  AFC East</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "   Rank               Team  Won  Lost  Tied*   Pct.  First Season  \\\n",
        "0     1     Dallas Cowboys  510   378      6  0.574          1960   \n",
        "1     2      Chicago Bears  752   563     42  0.570          1920   \n",
        "2     3  Green Bay Packers  741   561     37  0.567          1921   \n",
        "3     4     Miami Dolphins  443   345      4  0.562          1966   \n",
        "\n",
        "   Total Games Conference  \n",
        "0          894   NFC East  \n",
        "1         1357  NFC North  \n",
        "2         1339  NFC North  \n",
        "3          792   AFC East  "
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = {'City':['SF','LA','NYC'],\n",
      "        'Population':[837000,3880000,8400000]}\n",
      "\n",
      "city=DataFrame(data)\n",
      "\n",
      "city"
     ],
     "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>City</th>\n",
        "      <th>Population</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  SF</td>\n",
        "      <td>  837000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  LA</td>\n",
        "      <td> 3880000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> NYC</td>\n",
        "      <td> 8400000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "  City  Population\n",
        "0   SF      837000\n",
        "1   LA     3880000\n",
        "2  NYC     8400000"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#For full list of ways to create DataFrames from various sources go to teh documentation for pandas:\n",
      "website = 'http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.html'\n",
      "webbrowser.open(website)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Lecture 3"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_ser = Series([1,2,3,4], index=['A','B','C','D'])\n",
      "my_ser"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "A    1\n",
        "B    2\n",
        "C    3\n",
        "D    4\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_index = my_ser.index\n",
      "my_index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "Index([u'A', u'B', u'C', u'D'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_index[2:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "Index([u'C', u'D'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_index[0] = 'Z'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "'<class 'pandas.core.index.Index'>' does not support mutable operations.",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-9-92ae0de335f8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmy_index\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Z'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32mC:\\Users\\Fabio\\Anaconda\\lib\\site-packages\\pandas\\core\\base.pyc\u001b[0m in \u001b[0;36m_disabled\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    180\u001b[0m         \u001b[1;34m\"\"\"This method will not function because object is immutable.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    181\u001b[0m         raise TypeError(\"'%s' does not support mutable operations.\" %\n\u001b[1;32m--> 182\u001b[1;33m                         self.__class__)\n\u001b[0m\u001b[0;32m    183\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    184\u001b[0m     \u001b[0m__setitem__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m__setslice__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m__delitem__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m__delslice__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_disabled\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mTypeError\u001b[0m: '<class 'pandas.core.index.Index'>' does not support mutable operations."
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser_2 = my_ser.reindex(['A','B','C','D','E','F'])\n",
      "ser_2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "A     1\n",
        "B     2\n",
        "C     3\n",
        "D     4\n",
        "E   NaN\n",
        "F   NaN\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser_2.reindex(['A','B','C','D','E','F','G'],fill_value=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "A     1\n",
        "B     2\n",
        "C     3\n",
        "D     4\n",
        "E   NaN\n",
        "F   NaN\n",
        "G     0\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser_3 = Series(['USA','Mexico','Canada'],index=[0,3,10])\n",
      "ser_3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "0        USA\n",
        "3     Mexico\n",
        "10    Canada\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#forward fill -> \n",
      "ser_3.reindex(range(15),method='ffill')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "0        USA\n",
        "1        USA\n",
        "2        USA\n",
        "3     Mexico\n",
        "4     Mexico\n",
        "5     Mexico\n",
        "6     Mexico\n",
        "7     Mexico\n",
        "8     Mexico\n",
        "9     Mexico\n",
        "10    Canada\n",
        "11    Canada\n",
        "12    Canada\n",
        "13    Canada\n",
        "14    Canada\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dframe = DataFrame(randn(25).reshape((5,5)),index=['A','B','D','E','F'],columns=['col1','col2','col3','col4','col5'])\n",
      "\n",
      "#Show\n",
      "dframe"
     ],
     "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>col1</th>\n",
        "      <th>col2</th>\n",
        "      <th>col3</th>\n",
        "      <th>col4</th>\n",
        "      <th>col5</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <td> 0.124991</td>\n",
        "      <td> 0.711871</td>\n",
        "      <td>-0.334201</td>\n",
        "      <td>-1.375417</td>\n",
        "      <td> 0.085972</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td>-1.973487</td>\n",
        "      <td>-0.447761</td>\n",
        "      <td>-1.127489</td>\n",
        "      <td>-1.297472</td>\n",
        "      <td> 0.769919</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>D</th>\n",
        "      <td> 0.331138</td>\n",
        "      <td>-1.182104</td>\n",
        "      <td>-0.652455</td>\n",
        "      <td>-0.787272</td>\n",
        "      <td> 0.584078</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>E</th>\n",
        "      <td> 0.293480</td>\n",
        "      <td>-1.400592</td>\n",
        "      <td> 0.652881</td>\n",
        "      <td> 1.073671</td>\n",
        "      <td> 0.151215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>F</th>\n",
        "      <td> 0.855380</td>\n",
        "      <td> 1.112555</td>\n",
        "      <td> 0.857606</td>\n",
        "      <td> 0.105873</td>\n",
        "      <td> 0.962984</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "       col1      col2      col3      col4      col5\n",
        "A  0.124991  0.711871 -0.334201 -1.375417  0.085972\n",
        "B -1.973487 -0.447761 -1.127489 -1.297472  0.769919\n",
        "D  0.331138 -1.182104 -0.652455 -0.787272  0.584078\n",
        "E  0.293480 -1.400592  0.652881  1.073671  0.151215\n",
        "F  0.855380  1.112555  0.857606  0.105873  0.962984"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dframe2 = dframe.reindex(['A','B','C','D','E','F'])\n",
      "dframe2"
     ],
     "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>col1</th>\n",
        "      <th>col2</th>\n",
        "      <th>col3</th>\n",
        "      <th>col4</th>\n",
        "      <th>col5</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <td> 0.124991</td>\n",
        "      <td> 0.711871</td>\n",
        "      <td>-0.334201</td>\n",
        "      <td>-1.375417</td>\n",
        "      <td> 0.085972</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td>-1.973487</td>\n",
        "      <td>-0.447761</td>\n",
        "      <td>-1.127489</td>\n",
        "      <td>-1.297472</td>\n",
        "      <td> 0.769919</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</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>D</th>\n",
        "      <td> 0.331138</td>\n",
        "      <td>-1.182104</td>\n",
        "      <td>-0.652455</td>\n",
        "      <td>-0.787272</td>\n",
        "      <td> 0.584078</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>E</th>\n",
        "      <td> 0.293480</td>\n",
        "      <td>-1.400592</td>\n",
        "      <td> 0.652881</td>\n",
        "      <td> 1.073671</td>\n",
        "      <td> 0.151215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>F</th>\n",
        "      <td> 0.855380</td>\n",
        "      <td> 1.112555</td>\n",
        "      <td> 0.857606</td>\n",
        "      <td> 0.105873</td>\n",
        "      <td> 0.962984</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "       col1      col2      col3      col4      col5\n",
        "A  0.124991  0.711871 -0.334201 -1.375417  0.085972\n",
        "B -1.973487 -0.447761 -1.127489 -1.297472  0.769919\n",
        "C       NaN       NaN       NaN       NaN       NaN\n",
        "D  0.331138 -1.182104 -0.652455 -0.787272  0.584078\n",
        "E  0.293480 -1.400592  0.652881  1.073671  0.151215\n",
        "F  0.855380  1.112555  0.857606  0.105873  0.962984"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "new_columns = ['col1','col2','col3','col4','col5','col6']\n",
      "\n",
      "dframe2.reindex(columns=new_columns)"
     ],
     "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>col1</th>\n",
        "      <th>col2</th>\n",
        "      <th>col3</th>\n",
        "      <th>col4</th>\n",
        "      <th>col5</th>\n",
        "      <th>col6</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <td> 0.124991</td>\n",
        "      <td> 0.711871</td>\n",
        "      <td>-0.334201</td>\n",
        "      <td>-1.375417</td>\n",
        "      <td> 0.085972</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td>-1.973487</td>\n",
        "      <td>-0.447761</td>\n",
        "      <td>-1.127489</td>\n",
        "      <td>-1.297472</td>\n",
        "      <td> 0.769919</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td>      NaN</td>\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>D</th>\n",
        "      <td> 0.331138</td>\n",
        "      <td>-1.182104</td>\n",
        "      <td>-0.652455</td>\n",
        "      <td>-0.787272</td>\n",
        "      <td> 0.584078</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>E</th>\n",
        "      <td> 0.293480</td>\n",
        "      <td>-1.400592</td>\n",
        "      <td> 0.652881</td>\n",
        "      <td> 1.073671</td>\n",
        "      <td> 0.151215</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>F</th>\n",
        "      <td> 0.855380</td>\n",
        "      <td> 1.112555</td>\n",
        "      <td> 0.857606</td>\n",
        "      <td> 0.105873</td>\n",
        "      <td> 0.962984</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "       col1      col2      col3      col4      col5  col6\n",
        "A  0.124991  0.711871 -0.334201 -1.375417  0.085972   NaN\n",
        "B -1.973487 -0.447761 -1.127489 -1.297472  0.769919   NaN\n",
        "C       NaN       NaN       NaN       NaN       NaN   NaN\n",
        "D  0.331138 -1.182104 -0.652455 -0.787272  0.584078   NaN\n",
        "E  0.293480 -1.400592  0.652881  1.073671  0.151215   NaN\n",
        "F  0.855380  1.112555  0.857606  0.105873  0.962984   NaN"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#reindex\n",
      "dframe.ix[['A','B','C','D','E','F'],new_columns]"
     ],
     "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>col1</th>\n",
        "      <th>col2</th>\n",
        "      <th>col3</th>\n",
        "      <th>col4</th>\n",
        "      <th>col5</th>\n",
        "      <th>col6</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <td> 0.124991</td>\n",
        "      <td> 0.711871</td>\n",
        "      <td>-0.334201</td>\n",
        "      <td>-1.375417</td>\n",
        "      <td> 0.085972</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td>-1.973487</td>\n",
        "      <td>-0.447761</td>\n",
        "      <td>-1.127489</td>\n",
        "      <td>-1.297472</td>\n",
        "      <td> 0.769919</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td>      NaN</td>\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>D</th>\n",
        "      <td> 0.331138</td>\n",
        "      <td>-1.182104</td>\n",
        "      <td>-0.652455</td>\n",
        "      <td>-0.787272</td>\n",
        "      <td> 0.584078</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>E</th>\n",
        "      <td> 0.293480</td>\n",
        "      <td>-1.400592</td>\n",
        "      <td> 0.652881</td>\n",
        "      <td> 1.073671</td>\n",
        "      <td> 0.151215</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>F</th>\n",
        "      <td> 0.855380</td>\n",
        "      <td> 1.112555</td>\n",
        "      <td> 0.857606</td>\n",
        "      <td> 0.105873</td>\n",
        "      <td> 0.962984</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "       col1      col2      col3      col4      col5  col6\n",
        "A  0.124991  0.711871 -0.334201 -1.375417  0.085972   NaN\n",
        "B -1.973487 -0.447761 -1.127489 -1.297472  0.769919   NaN\n",
        "C       NaN       NaN       NaN       NaN       NaN   NaN\n",
        "D  0.331138 -1.182104 -0.652455 -0.787272  0.584078   NaN\n",
        "E  0.293480 -1.400592  0.652881  1.073671  0.151215   NaN\n",
        "F  0.855380  1.112555  0.857606  0.105873  0.962984   NaN"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Drop entry"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser1 = Series(np.arange(3),index=['a','b','c'])\n",
      "ser1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "a    0\n",
        "b    1\n",
        "c    2\n",
        "dtype: int32"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Now let's drop an index\n",
      "ser1.drop('b')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "a    0\n",
        "c    2\n",
        "dtype: int32"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dframe1 = DataFrame(np.arange(9).reshape((3,3)),index=['SF','LA','NY'],columns=['pop','size','year'])\n",
      "\n",
      "#Show (remember just random values)\n",
      "dframe1"
     ],
     "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>pop</th>\n",
        "      <th>size</th>\n",
        "      <th>year</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>SF</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LA</th>\n",
        "      <td> 3</td>\n",
        "      <td> 4</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NY</th>\n",
        "      <td> 6</td>\n",
        "      <td> 7</td>\n",
        "      <td> 8</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "    pop  size  year\n",
        "SF    0     1     2\n",
        "LA    3     4     5\n",
        "NY    6     7     8"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Now dropping a row\n",
      "# to be permanet you have to reassign the dataframe to another dataframe\n",
      "dframe1.drop('LA')"
     ],
     "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>pop</th>\n",
        "      <th>size</th>\n",
        "      <th>year</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>SF</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NY</th>\n",
        "      <td> 6</td>\n",
        "      <td> 7</td>\n",
        "      <td> 8</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "    pop  size  year\n",
        "SF    0     1     2\n",
        "NY    6     7     8"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Or we could drop a column\n",
      "\n",
      "#Need to specify that axis is 1, not 0\n",
      "dframe1.drop('year',axis=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>pop</th>\n",
        "      <th>size</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>SF</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LA</th>\n",
        "      <td> 3</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NY</th>\n",
        "      <td> 6</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "    pop  size\n",
        "SF    0     1\n",
        "LA    3     4\n",
        "NY    6     7"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Selecting entries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Lets try some Series indexing\n",
      "ser1 = Series(np.arange(3),index=['A','B','C'])\n",
      "\n",
      "#multiply all values by 2, to avoid confusion in future\n",
      "ser1 = 2*ser1\n",
      "\n",
      "#Show\n",
      "ser1 "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "A    0\n",
        "B    2\n",
        "C    4\n",
        "dtype: int32"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Can grab entry by index name\n",
      "ser1['B']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "2"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser1[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "2"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser1[0:3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "A    0\n",
        "B    2\n",
        "C    4\n",
        "dtype: int32"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Or grab range by range of index values\n",
      "ser1[['A','B','C']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "A    0\n",
        "B    2\n",
        "C    4\n",
        "dtype: int32"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser1[ser1>3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "C    4\n",
        "dtype: int32"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser1[ser1>3] = 10\n",
      "ser1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "A     0\n",
        "B     2\n",
        "C    10\n",
        "dtype: int32"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dframe = DataFrame(np.arange(25).reshape((5,5)),index=['NYC','LA','SF','DC','Chi'],columns=['A','B','C','D','E'])\n",
      "\n",
      "#Show\n",
      "dframe"
     ],
     "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>NYC</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LA</th>\n",
        "      <td>  5</td>\n",
        "      <td>  6</td>\n",
        "      <td>  7</td>\n",
        "      <td>  8</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SF</th>\n",
        "      <td> 10</td>\n",
        "      <td> 11</td>\n",
        "      <td> 12</td>\n",
        "      <td> 13</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DC</th>\n",
        "      <td> 15</td>\n",
        "      <td> 16</td>\n",
        "      <td> 17</td>\n",
        "      <td> 18</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Chi</th>\n",
        "      <td> 20</td>\n",
        "      <td> 21</td>\n",
        "      <td> 22</td>\n",
        "      <td> 23</td>\n",
        "      <td> 24</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "      A   B   C   D   E\n",
        "NYC   0   1   2   3   4\n",
        "LA    5   6   7   8   9\n",
        "SF   10  11  12  13  14\n",
        "DC   15  16  17  18  19\n",
        "Chi  20  21  22  23  24"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Select by column name\n",
      "dframe['B']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "NYC     1\n",
        "LA      6\n",
        "SF     11\n",
        "DC     16\n",
        "Chi    21\n",
        "Name: B, dtype: int32"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Select by multiple columns\n",
      "dframe[['B','E']]"
     ],
     "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>E</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>NYC</th>\n",
        "      <td>  1</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LA</th>\n",
        "      <td>  6</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SF</th>\n",
        "      <td> 11</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DC</th>\n",
        "      <td> 16</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Chi</th>\n",
        "      <td> 21</td>\n",
        "      <td> 24</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "      B   E\n",
        "NYC   1   4\n",
        "LA    6   9\n",
        "SF   11  14\n",
        "DC   16  19\n",
        "Chi  21  24"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Can also use boolean\n",
      "dframe[dframe['C']>8]"
     ],
     "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>SF</th>\n",
        "      <td> 10</td>\n",
        "      <td> 11</td>\n",
        "      <td> 12</td>\n",
        "      <td> 13</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DC</th>\n",
        "      <td> 15</td>\n",
        "      <td> 16</td>\n",
        "      <td> 17</td>\n",
        "      <td> 18</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Chi</th>\n",
        "      <td> 20</td>\n",
        "      <td> 21</td>\n",
        "      <td> 22</td>\n",
        "      <td> 23</td>\n",
        "      <td> 24</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "      A   B   C   D   E\n",
        "SF   10  11  12  13  14\n",
        "DC   15  16  17  18  19\n",
        "Chi  20  21  22  23  24"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Can also just shoe a boolean DataFrame\n",
      "dframe> 10"
     ],
     "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>NYC</th>\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>LA</th>\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>SF</th>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DC</th>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Chi</th>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "         A      B      C      D      E\n",
        "NYC  False  False  False  False  False\n",
        "LA   False  False  False  False  False\n",
        "SF   False   True   True   True   True\n",
        "DC    True   True   True   True   True\n",
        "Chi   True   True   True   True   True"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Can alos use ix as previously discussed to label-index\n",
      "dframe.ix['LA']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "A    5\n",
        "B    6\n",
        "C    7\n",
        "D    8\n",
        "E    9\n",
        "Name: LA, dtype: int32"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Another example\n",
      "dframe.ix[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "A    5\n",
        "B    6\n",
        "C    7\n",
        "D    8\n",
        "E    9\n",
        "Name: LA, dtype: int32"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Data Allignment"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Lets start by making two Series\n",
      "\n",
      "ser1 = Series([0,1,2],index=['A','B','C'])\n",
      "\n",
      "#Show\n",
      "ser1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "A    0\n",
        "B    1\n",
        "C    2\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Now second Series 2\n",
      "ser2 = Series([3,4,5,6],index=['A','B','C','D'])\n",
      "\n",
      "#Show \n",
      "ser2 "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "A    3\n",
        "B    4\n",
        "C    5\n",
        "D    6\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#So what happens when we add these together\n",
      "ser1 + ser2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "A     3\n",
        "B     5\n",
        "C     7\n",
        "D   NaN\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now let's try it with DataFrames!\n",
      "dframe1 = DataFrame(np.arange(4).reshape(2,2),columns=list('AB'),index=['NYC','LA'])\n",
      "\n",
      "#Show\n",
      "dframe1"
     ],
     "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>NYC</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LA</th>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "     A  B\n",
        "NYC  0  1\n",
        "LA   2  3"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Second DataFrame\n",
      "dframe2 = DataFrame(np.arange(9).reshape(3,3),columns=list('ADC'),index=['NYC','SF','LA'])\n",
      "\n",
      "#Show\n",
      "dframe2"
     ],
     "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>D</th>\n",
        "      <th>C</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>NYC</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SF</th>\n",
        "      <td> 3</td>\n",
        "      <td> 4</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LA</th>\n",
        "      <td> 6</td>\n",
        "      <td> 7</td>\n",
        "      <td> 8</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "     A  D  C\n",
        "NYC  0  1  2\n",
        "SF   3  4  5\n",
        "LA   6  7  8"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#What happens when we add them together?\n",
      "\n",
      "dframe1 + dframe2"
     ],
     "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>LA</th>\n",
        "      <td>  8</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NYC</th>\n",
        "      <td>  0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SF</th>\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": 63,
       "text": [
        "      A   B   C   D\n",
        "LA    8 NaN NaN NaN\n",
        "NYC   0 NaN NaN NaN\n",
        "SF  NaN NaN NaN NaN"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#What if we want to replace the NaN values\n",
      "# Then we can use .add()\n",
      "\n",
      "dframe1.add(dframe2,fill_value=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>LA</th>\n",
        "      <td> 8</td>\n",
        "      <td>  3</td>\n",
        "      <td> 8</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NYC</th>\n",
        "      <td> 0</td>\n",
        "      <td>  1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SF</th>\n",
        "      <td> 3</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 5</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "     A   B  C  D\n",
        "LA   8   3  8  7\n",
        "NYC  0   1  2  1\n",
        "SF   3 NaN  5  4"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "dframe2"
     ],
     "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>D</th>\n",
        "      <th>C</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>NYC</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SF</th>\n",
        "      <td> 3</td>\n",
        "      <td> 4</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LA</th>\n",
        "      <td> 6</td>\n",
        "      <td> 7</td>\n",
        "      <td> 8</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "     A  D  C\n",
        "NYC  0  1  2\n",
        "SF   3  4  5\n",
        "LA   6  7  8"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Create a Series from DataFrame's 0 row\n",
      "ser3 = dframe2.ix[0]\n",
      "\n",
      "#Show\n",
      "ser3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "A    0\n",
        "D    1\n",
        "C    2\n",
        "Name: NYC, dtype: int32"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Now we can use arithmetic operations\n",
      "dframe2-ser3"
     ],
     "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>D</th>\n",
        "      <th>C</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>NYC</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SF</th>\n",
        "      <td> 3</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LA</th>\n",
        "      <td> 6</td>\n",
        "      <td> 6</td>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "     A  D  C\n",
        "NYC  0  0  0\n",
        "SF   3  3  3\n",
        "LA   6  6  6"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#sort and rank"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Sorting by index\n",
      "ser1 = Series(range(3),index=['C','A','B'])\n",
      "\n",
      "#show\n",
      "ser1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "C    0\n",
        "A    1\n",
        "B    2\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Now sort_index\n",
      "ser1.sort_index()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "A    1\n",
        "B    2\n",
        "C    0\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Can sort a Series by its values\n",
      "ser1.order()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "C    0\n",
        "A    1\n",
        "B    2\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Lets see how ranking works\n",
      "\n",
      "from numpy.random import randn\n",
      "ser2 = Series(randn(10))\n",
      "\n",
      "#Show\n",
      "ser2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "0    0.769689\n",
        "1   -1.856003\n",
        "2    1.977446\n",
        "3   -0.465335\n",
        "4   -1.633333\n",
        "5   -0.382643\n",
        "6    2.435465\n",
        "7    0.238341\n",
        "8   -0.543855\n",
        "9    0.287768\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser2.rank()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 78,
       "text": [
        "0     8\n",
        "1     1\n",
        "2     9\n",
        "3     4\n",
        "4     2\n",
        "5     5\n",
        "6    10\n",
        "7     6\n",
        "8     3\n",
        "9     7\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser2.sort()\n",
      "ser2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "1   -1.856003\n",
        "4   -1.633333\n",
        "8   -0.543855\n",
        "3   -0.465335\n",
        "5   -0.382643\n",
        "7    0.238341\n",
        "9    0.287768\n",
        "0    0.769689\n",
        "2    1.977446\n",
        "6    2.435465\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser2.rank()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "1     1\n",
        "4     2\n",
        "8     3\n",
        "3     4\n",
        "5     5\n",
        "7     6\n",
        "9     7\n",
        "0     8\n",
        "2     9\n",
        "6    10\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Summary statistics"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Let's create a dataframe to work with\n",
      "arr = np.array([[1,2,np.nan],[np.nan,3,4]]) # null values\n",
      "dframe1 = DataFrame(arr,index=['A','B'],columns = ['One','Two','Three'])\n",
      "\n",
      "#Show\n",
      "dframe1"
     ],
     "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>One</th>\n",
        "      <th>Two</th>\n",
        "      <th>Three</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <td>  1</td>\n",
        "      <td> 2</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 3</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "   One  Two  Three\n",
        "A    1    2    NaN\n",
        "B  NaN    3      4"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#ignore null value :)\n",
      "dframe1.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "One      1\n",
        "Two      5\n",
        "Three    4\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dframe1.sum(axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "A    3\n",
        "B    7\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Can also grab min and max values of dataframe\n",
      "dframe1.min()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "One      1\n",
        "Two      2\n",
        "Three    4\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#As well as there index\n",
      "# index of each of the minumum value for each column\n",
      "dframe1.idxmin()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "One      A\n",
        "Two      A\n",
        "Three    B\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Show\n",
      "dframe1"
     ],
     "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>One</th>\n",
        "      <th>Two</th>\n",
        "      <th>Three</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <td>  1</td>\n",
        "      <td> 2</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 3</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "   One  Two  Three\n",
        "A    1    2    NaN\n",
        "B  NaN    3      4"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Can also do an accumulation sum\n",
      "# 3 -> 5\n",
      "dframe1.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>One</th>\n",
        "      <th>Two</th>\n",
        "      <th>Three</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <td>  1</td>\n",
        "      <td> 2</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 5</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "   One  Two  Three\n",
        "A    1    2    NaN\n",
        "B  NaN    5      4"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#A very useful feature is describe, which provides summary statistics\n",
      "dframe1.describe()"
     ],
     "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>One</th>\n",
        "      <th>Two</th>\n",
        "      <th>Three</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>  1</td>\n",
        "      <td> 2.000000</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>  1</td>\n",
        "      <td> 2.500000</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 0.707107</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>  1</td>\n",
        "      <td> 2.000000</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>  1</td>\n",
        "      <td> 2.250000</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>  1</td>\n",
        "      <td> 2.500000</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>  1</td>\n",
        "      <td> 2.750000</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>  1</td>\n",
        "      <td> 3.000000</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 89,
       "text": [
        "       One       Two  Three\n",
        "count    1  2.000000      1\n",
        "mean     1  2.500000      4\n",
        "std    NaN  0.707107    NaN\n",
        "min      1  2.000000      4\n",
        "25%      1  2.250000      4\n",
        "50%      1  2.500000      4\n",
        "75%      1  2.750000      4\n",
        "max      1  3.000000      4"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import YouTubeVideo\n",
      "# For more information about Covariaance and Correlation\n",
      "# Check out these great videos!\n",
      "# Video credit: Brandon Foltz.\n",
      "\n",
      "#CoVariance\n",
      "YouTubeVideo('xGbpuFNR1ME')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"400\"\n",
        "            height=300\"\n",
        "            src=\"https://www.youtube.com/embed/xGbpuFNR1ME\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 90,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0xd16f940>"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Correlation\n",
      "YouTubeVideo('4EXNedimDMs')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"400\"\n",
        "            height=300\"\n",
        "            src=\"https://www.youtube.com/embed/4EXNedimDMs\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 91,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0xd16fa20>"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Pandas can get info off the web\n",
      "import pandas.io.data as pdweb\n",
      "\n",
      "#Set datetime for date input\n",
      "import datetime\n",
      "\n",
      "#Get the closing prices\n",
      "\n",
      "prices = pdweb.get_data_yahoo(['CVX','XOM','BP'], \n",
      "                               start=datetime.datetime(2010, 1, 1), \n",
      "                               end=datetime.datetime(2013, 1, 1))['Adj Close']\n",
      "#Show preview\n",
      "prices.head()"
     ],
     "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>BP</th>\n",
        "      <th>CVX</th>\n",
        "      <th>XOM</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2010-01-04</th>\n",
        "      <td> 47.088648</td>\n",
        "      <td> 65.512958</td>\n",
        "      <td> 59.769500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-05</th>\n",
        "      <td> 47.423003</td>\n",
        "      <td> 65.977006</td>\n",
        "      <td> 60.002871</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-06</th>\n",
        "      <td> 47.669792</td>\n",
        "      <td> 65.985288</td>\n",
        "      <td> 60.521477</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-07</th>\n",
        "      <td> 47.653870</td>\n",
        "      <td> 65.736697</td>\n",
        "      <td> 60.331326</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-08</th>\n",
        "      <td> 47.765322</td>\n",
        "      <td> 65.852707</td>\n",
        "      <td> 60.089304</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 92,
       "text": [
        "                   BP        CVX        XOM\n",
        "Date                                       \n",
        "2010-01-04  47.088648  65.512958  59.769500\n",
        "2010-01-05  47.423003  65.977006  60.002871\n",
        "2010-01-06  47.669792  65.985288  60.521477\n",
        "2010-01-07  47.653870  65.736697  60.331326\n",
        "2010-01-08  47.765322  65.852707  60.089304"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Now lets get the volume trades\n",
      "\n",
      "volume = pdweb.get_data_yahoo(['CVX','XOM','BP'], \n",
      "                               start=datetime.datetime(2010, 1, 1), \n",
      "                               end=datetime.datetime(2013, 1, 1))['Volume']\n",
      "\n",
      "#Show preview\n",
      "volume.head()"
     ],
     "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>BP</th>\n",
        "      <th>CVX</th>\n",
        "      <th>XOM</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2010-01-04</th>\n",
        "      <td> 3956100</td>\n",
        "      <td> 10173800</td>\n",
        "      <td> 27809100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-05</th>\n",
        "      <td> 4109600</td>\n",
        "      <td> 10593700</td>\n",
        "      <td> 30174700</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-06</th>\n",
        "      <td> 6227900</td>\n",
        "      <td> 11014600</td>\n",
        "      <td> 35044700</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-07</th>\n",
        "      <td> 4431300</td>\n",
        "      <td>  9626900</td>\n",
        "      <td> 27192100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-08</th>\n",
        "      <td> 3786100</td>\n",
        "      <td>  5624300</td>\n",
        "      <td> 24891800</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 93,
       "text": [
        "                 BP       CVX       XOM\n",
        "Date                                   \n",
        "2010-01-04  3956100  10173800  27809100\n",
        "2010-01-05  4109600  10593700  30174700\n",
        "2010-01-06  6227900  11014600  35044700\n",
        "2010-01-07  4431300   9626900  27192100\n",
        "2010-01-08  3786100   5624300  24891800"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Lets get the return\n",
      "#percent change\n",
      "rets = prices.pct_change()\n",
      "rets"
     ],
     "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>BP</th>\n",
        "      <th>CVX</th>\n",
        "      <th>XOM</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2010-01-04</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-05</th>\n",
        "      <td> 0.007101</td>\n",
        "      <td> 0.007083</td>\n",
        "      <td> 0.003905</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-06</th>\n",
        "      <td> 0.005204</td>\n",
        "      <td> 0.000126</td>\n",
        "      <td> 0.008643</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-07</th>\n",
        "      <td>-0.000334</td>\n",
        "      <td>-0.003767</td>\n",
        "      <td>-0.003142</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-08</th>\n",
        "      <td> 0.002339</td>\n",
        "      <td> 0.001765</td>\n",
        "      <td>-0.004012</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-11</th>\n",
        "      <td> 0.031500</td>\n",
        "      <td> 0.017743</td>\n",
        "      <td> 0.011220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-12</th>\n",
        "      <td>-0.006301</td>\n",
        "      <td>-0.005811</td>\n",
        "      <td>-0.004979</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-13</th>\n",
        "      <td> 0.004878</td>\n",
        "      <td>-0.007586</td>\n",
        "      <td>-0.004003</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-14</th>\n",
        "      <td>-0.001133</td>\n",
        "      <td>-0.003133</td>\n",
        "      <td> 0.000144</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-15</th>\n",
        "      <td>-0.001458</td>\n",
        "      <td>-0.004023</td>\n",
        "      <td>-0.008180</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-19</th>\n",
        "      <td> 0.011032</td>\n",
        "      <td> 0.005680</td>\n",
        "      <td> 0.002315</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-20</th>\n",
        "      <td>-0.020218</td>\n",
        "      <td>-0.019202</td>\n",
        "      <td>-0.017901</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-21</th>\n",
        "      <td>-0.024402</td>\n",
        "      <td>-0.024440</td>\n",
        "      <td>-0.019550</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-22</th>\n",
        "      <td>-0.028538</td>\n",
        "      <td>-0.021642</td>\n",
        "      <td>-0.008995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-25</th>\n",
        "      <td> 0.011750</td>\n",
        "      <td>-0.000536</td>\n",
        "      <td>-0.003782</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-26</th>\n",
        "      <td>-0.001025</td>\n",
        "      <td>-0.005366</td>\n",
        "      <td> 0.001063</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-27</th>\n",
        "      <td>-0.007352</td>\n",
        "      <td>-0.006069</td>\n",
        "      <td>-0.005765</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-28</th>\n",
        "      <td>-0.012573</td>\n",
        "      <td>-0.006241</td>\n",
        "      <td>-0.008850</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-29</th>\n",
        "      <td>-0.021106</td>\n",
        "      <td>-0.015292</td>\n",
        "      <td>-0.008159</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-02-01</th>\n",
        "      <td> 0.019779</td>\n",
        "      <td> 0.020244</td>\n",
        "      <td> 0.027161</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-02-02</th>\n",
        "      <td>-0.030928</td>\n",
        "      <td> 0.011824</td>\n",
        "      <td> 0.011786</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-02-03</th>\n",
        "      <td>-0.005229</td>\n",
        "      <td>-0.016655</td>\n",
        "      <td>-0.005376</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-02-04</th>\n",
        "      <td>-0.030633</td>\n",
        "      <td>-0.025133</td>\n",
        "      <td>-0.028228</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-02-05</th>\n",
        "      <td>-0.005610</td>\n",
        "      <td>-0.002662</td>\n",
        "      <td> 0.001236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-02-08</th>\n",
        "      <td>-0.014103</td>\n",
        "      <td>-0.014751</td>\n",
        "      <td>-0.000466</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-02-09</th>\n",
        "      <td> 0.022506</td>\n",
        "      <td> 0.016826</td>\n",
        "      <td> 0.013209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-02-10</th>\n",
        "      <td> 0.000746</td>\n",
        "      <td>-0.007853</td>\n",
        "      <td>-0.005368</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-02-11</th>\n",
        "      <td> 0.021435</td>\n",
        "      <td> 0.013852</td>\n",
        "      <td> 0.006014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-02-12</th>\n",
        "      <td>-0.002372</td>\n",
        "      <td>-0.000563</td>\n",
        "      <td>-0.006744</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-02-16</th>\n",
        "      <td> 0.023413</td>\n",
        "      <td> 0.027883</td>\n",
        "      <td> 0.022839</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-16</th>\n",
        "      <td>-0.006700</td>\n",
        "      <td> 0.007676</td>\n",
        "      <td> 0.003599</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-19</th>\n",
        "      <td> 0.029978</td>\n",
        "      <td> 0.019043</td>\n",
        "      <td> 0.014112</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-20</th>\n",
        "      <td>-0.000242</td>\n",
        "      <td>-0.007571</td>\n",
        "      <td>-0.001939</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-21</th>\n",
        "      <td> 0.009219</td>\n",
        "      <td> 0.005214</td>\n",
        "      <td> 0.005829</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-23</th>\n",
        "      <td> 0.010096</td>\n",
        "      <td> 0.013160</td>\n",
        "      <td> 0.012271</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-26</th>\n",
        "      <td>-0.009995</td>\n",
        "      <td>-0.004551</td>\n",
        "      <td>-0.005275</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-27</th>\n",
        "      <td>-0.006010</td>\n",
        "      <td>-0.015335</td>\n",
        "      <td>-0.014331</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-28</th>\n",
        "      <td> 0.003144</td>\n",
        "      <td> 0.021281</td>\n",
        "      <td> 0.008586</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-29</th>\n",
        "      <td>-0.004339</td>\n",
        "      <td> 0.001989</td>\n",
        "      <td> 0.000227</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-30</th>\n",
        "      <td> 0.011138</td>\n",
        "      <td>-0.000945</td>\n",
        "      <td> 0.000227</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-03</th>\n",
        "      <td>-0.013649</td>\n",
        "      <td>-0.009745</td>\n",
        "      <td>-0.006013</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-04</th>\n",
        "      <td>-0.004613</td>\n",
        "      <td>-0.006688</td>\n",
        "      <td>-0.004794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-05</th>\n",
        "      <td> 0.007561</td>\n",
        "      <td> 0.011639</td>\n",
        "      <td> 0.006193</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-06</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.012171</td>\n",
        "      <td> 0.003078</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-07</th>\n",
        "      <td>-0.005810</td>\n",
        "      <td> 0.005073</td>\n",
        "      <td> 0.006818</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-10</th>\n",
        "      <td>-0.001948</td>\n",
        "      <td>-0.000280</td>\n",
        "      <td>-0.002144</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-11</th>\n",
        "      <td> 0.004391</td>\n",
        "      <td> 0.007292</td>\n",
        "      <td> 0.006560</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-12</th>\n",
        "      <td> 0.010930</td>\n",
        "      <td> 0.003156</td>\n",
        "      <td> 0.005169</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-13</th>\n",
        "      <td>-0.003604</td>\n",
        "      <td>-0.001573</td>\n",
        "      <td>-0.009726</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-14</th>\n",
        "      <td>-0.001929</td>\n",
        "      <td>-0.000834</td>\n",
        "      <td>-0.005645</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-17</th>\n",
        "      <td> 0.007731</td>\n",
        "      <td> 0.007976</td>\n",
        "      <td> 0.008969</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-18</th>\n",
        "      <td> 0.007432</td>\n",
        "      <td> 0.013066</td>\n",
        "      <td> 0.007764</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-19</th>\n",
        "      <td> 0.002142</td>\n",
        "      <td>-0.001726</td>\n",
        "      <td>-0.012506</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-20</th>\n",
        "      <td> 0.002850</td>\n",
        "      <td> 0.004276</td>\n",
        "      <td> 0.005088</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-21</th>\n",
        "      <td>-0.002605</td>\n",
        "      <td>-0.006070</td>\n",
        "      <td>-0.018675</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-24</th>\n",
        "      <td>-0.009497</td>\n",
        "      <td>-0.009844</td>\n",
        "      <td>-0.003554</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-26</th>\n",
        "      <td>-0.000959</td>\n",
        "      <td>-0.001565</td>\n",
        "      <td> 0.001726</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-27</th>\n",
        "      <td>-0.001440</td>\n",
        "      <td> 0.000553</td>\n",
        "      <td>-0.002412</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-28</th>\n",
        "      <td>-0.009611</td>\n",
        "      <td>-0.019075</td>\n",
        "      <td>-0.020263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-31</th>\n",
        "      <td> 0.010189</td>\n",
        "      <td> 0.015876</td>\n",
        "      <td> 0.017039</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>754 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 95,
       "text": [
        "                  BP       CVX       XOM\n",
        "Date                                    \n",
        "2010-01-04       NaN       NaN       NaN\n",
        "2010-01-05  0.007101  0.007083  0.003905\n",
        "2010-01-06  0.005204  0.000126  0.008643\n",
        "2010-01-07 -0.000334 -0.003767 -0.003142\n",
        "2010-01-08  0.002339  0.001765 -0.004012\n",
        "2010-01-11  0.031500  0.017743  0.011220\n",
        "2010-01-12 -0.006301 -0.005811 -0.004979\n",
        "2010-01-13  0.004878 -0.007586 -0.004003\n",
        "2010-01-14 -0.001133 -0.003133  0.000144\n",
        "2010-01-15 -0.001458 -0.004023 -0.008180\n",
        "2010-01-19  0.011032  0.005680  0.002315\n",
        "2010-01-20 -0.020218 -0.019202 -0.017901\n",
        "2010-01-21 -0.024402 -0.024440 -0.019550\n",
        "2010-01-22 -0.028538 -0.021642 -0.008995\n",
        "2010-01-25  0.011750 -0.000536 -0.003782\n",
        "2010-01-26 -0.001025 -0.005366  0.001063\n",
        "2010-01-27 -0.007352 -0.006069 -0.005765\n",
        "2010-01-28 -0.012573 -0.006241 -0.008850\n",
        "2010-01-29 -0.021106 -0.015292 -0.008159\n",
        "2010-02-01  0.019779  0.020244  0.027161\n",
        "2010-02-02 -0.030928  0.011824  0.011786\n",
        "2010-02-03 -0.005229 -0.016655 -0.005376\n",
        "2010-02-04 -0.030633 -0.025133 -0.028228\n",
        "2010-02-05 -0.005610 -0.002662  0.001236\n",
        "2010-02-08 -0.014103 -0.014751 -0.000466\n",
        "2010-02-09  0.022506  0.016826  0.013209\n",
        "2010-02-10  0.000746 -0.007853 -0.005368\n",
        "2010-02-11  0.021435  0.013852  0.006014\n",
        "2010-02-12 -0.002372 -0.000563 -0.006744\n",
        "2010-02-16  0.023413  0.027883  0.022839\n",
        "...              ...       ...       ...\n",
        "2012-11-16 -0.006700  0.007676  0.003599\n",
        "2012-11-19  0.029978  0.019043  0.014112\n",
        "2012-11-20 -0.000242 -0.007571 -0.001939\n",
        "2012-11-21  0.009219  0.005214  0.005829\n",
        "2012-11-23  0.010096  0.013160  0.012271\n",
        "2012-11-26 -0.009995 -0.004551 -0.005275\n",
        "2012-11-27 -0.006010 -0.015335 -0.014331\n",
        "2012-11-28  0.003144  0.021281  0.008586\n",
        "2012-11-29 -0.004339  0.001989  0.000227\n",
        "2012-11-30  0.011138 -0.000945  0.000227\n",
        "2012-12-03 -0.013649 -0.009745 -0.006013\n",
        "2012-12-04 -0.004613 -0.006688 -0.004794\n",
        "2012-12-05  0.007561  0.011639  0.006193\n",
        "2012-12-06  0.000000  0.012171  0.003078\n",
        "2012-12-07 -0.005810  0.005073  0.006818\n",
        "2012-12-10 -0.001948 -0.000280 -0.002144\n",
        "2012-12-11  0.004391  0.007292  0.006560\n",
        "2012-12-12  0.010930  0.003156  0.005169\n",
        "2012-12-13 -0.003604 -0.001573 -0.009726\n",
        "2012-12-14 -0.001929 -0.000834 -0.005645\n",
        "2012-12-17  0.007731  0.007976  0.008969\n",
        "2012-12-18  0.007432  0.013066  0.007764\n",
        "2012-12-19  0.002142 -0.001726 -0.012506\n",
        "2012-12-20  0.002850  0.004276  0.005088\n",
        "2012-12-21 -0.002605 -0.006070 -0.018675\n",
        "2012-12-24 -0.009497 -0.009844 -0.003554\n",
        "2012-12-26 -0.000959 -0.001565  0.001726\n",
        "2012-12-27 -0.001440  0.000553 -0.002412\n",
        "2012-12-28 -0.009611 -0.019075 -0.020263\n",
        "2012-12-31  0.010189  0.015876  0.017039\n",
        "\n",
        "[754 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Get the correlation of the stocks\n",
      "corr = rets.corr\n",
      "corr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 96,
       "text": [
        "<bound method DataFrame.corr of                   BP       CVX       XOM\n",
        "Date                                    \n",
        "2010-01-04       NaN       NaN       NaN\n",
        "2010-01-05  0.007101  0.007083  0.003905\n",
        "2010-01-06  0.005204  0.000126  0.008643\n",
        "2010-01-07 -0.000334 -0.003767 -0.003142\n",
        "2010-01-08  0.002339  0.001765 -0.004012\n",
        "2010-01-11  0.031500  0.017743  0.011220\n",
        "2010-01-12 -0.006301 -0.005811 -0.004979\n",
        "2010-01-13  0.004878 -0.007586 -0.004003\n",
        "2010-01-14 -0.001133 -0.003133  0.000144\n",
        "2010-01-15 -0.001458 -0.004023 -0.008180\n",
        "2010-01-19  0.011032  0.005680  0.002315\n",
        "2010-01-20 -0.020218 -0.019202 -0.017901\n",
        "2010-01-21 -0.024402 -0.024440 -0.019550\n",
        "2010-01-22 -0.028538 -0.021642 -0.008995\n",
        "2010-01-25  0.011750 -0.000536 -0.003782\n",
        "2010-01-26 -0.001025 -0.005366  0.001063\n",
        "2010-01-27 -0.007352 -0.006069 -0.005765\n",
        "2010-01-28 -0.012573 -0.006241 -0.008850\n",
        "2010-01-29 -0.021106 -0.015292 -0.008159\n",
        "2010-02-01  0.019779  0.020244  0.027161\n",
        "2010-02-02 -0.030928  0.011824  0.011786\n",
        "2010-02-03 -0.005229 -0.016655 -0.005376\n",
        "2010-02-04 -0.030633 -0.025133 -0.028228\n",
        "2010-02-05 -0.005610 -0.002662  0.001236\n",
        "2010-02-08 -0.014103 -0.014751 -0.000466\n",
        "2010-02-09  0.022506  0.016826  0.013209\n",
        "2010-02-10  0.000746 -0.007853 -0.005368\n",
        "2010-02-11  0.021435  0.013852  0.006014\n",
        "2010-02-12 -0.002372 -0.000563 -0.006744\n",
        "2010-02-16  0.023413  0.027883  0.022839\n",
        "...              ...       ...       ...\n",
        "2012-11-16 -0.006700  0.007676  0.003599\n",
        "2012-11-19  0.029978  0.019043  0.014112\n",
        "2012-11-20 -0.000242 -0.007571 -0.001939\n",
        "2012-11-21  0.009219  0.005214  0.005829\n",
        "2012-11-23  0.010096  0.013160  0.012271\n",
        "2012-11-26 -0.009995 -0.004551 -0.005275\n",
        "2012-11-27 -0.006010 -0.015335 -0.014331\n",
        "2012-11-28  0.003144  0.021281  0.008586\n",
        "2012-11-29 -0.004339  0.001989  0.000227\n",
        "2012-11-30  0.011138 -0.000945  0.000227\n",
        "2012-12-03 -0.013649 -0.009745 -0.006013\n",
        "2012-12-04 -0.004613 -0.006688 -0.004794\n",
        "2012-12-05  0.007561  0.011639  0.006193\n",
        "2012-12-06  0.000000  0.012171  0.003078\n",
        "2012-12-07 -0.005810  0.005073  0.006818\n",
        "2012-12-10 -0.001948 -0.000280 -0.002144\n",
        "2012-12-11  0.004391  0.007292  0.006560\n",
        "2012-12-12  0.010930  0.003156  0.005169\n",
        "2012-12-13 -0.003604 -0.001573 -0.009726\n",
        "2012-12-14 -0.001929 -0.000834 -0.005645\n",
        "2012-12-17  0.007731  0.007976  0.008969\n",
        "2012-12-18  0.007432  0.013066  0.007764\n",
        "2012-12-19  0.002142 -0.001726 -0.012506\n",
        "2012-12-20  0.002850  0.004276  0.005088\n",
        "2012-12-21 -0.002605 -0.006070 -0.018675\n",
        "2012-12-24 -0.009497 -0.009844 -0.003554\n",
        "2012-12-26 -0.000959 -0.001565  0.001726\n",
        "2012-12-27 -0.001440  0.000553 -0.002412\n",
        "2012-12-28 -0.009611 -0.019075 -0.020263\n",
        "2012-12-31  0.010189  0.015876  0.017039\n",
        "\n",
        "[754 rows x 3 columns]>"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Lets see the prices over time to get a very rough idea of the correlation between the stock prices\n",
      "prices.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0xd196358>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HwNvZGzFdYG5mbvR45fG7Mjczp6ptVbbd2AbISUyAtKw0vn/me77q/VWJx1YVuRHExcUx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\n43JCGQUxjDY6J1PihFEuJ1PihFWueKRqIx8ALMDydwLcAry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i5osv2nhAs2bw9deRfR9/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\nOf8PbuwWEftLleMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd16f898>"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import seaborn as sns\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "#As expected pretty strong correlations with eachother\n",
      "sns.heatmap(rets.corr())\n",
      "\n",
      "#We'll learn much more about seaborn later!"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 100,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0xf5a0978>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FxEnAoZm5qg/jk6T10rg2BXA6cGZmnte+MiKOBs4ADpnCcUnS5NQ0jDv1hXccG8QAmXkB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\nLg6iJQomAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xf52d240>"
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.corrplot(rets,annot=False,diag_names=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 102,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0xfa0ba20>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xfa27390>"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We can also check for unique values and their counts \n",
      "\n",
      "#For example\n",
      "ser1 = Series(['w','w','x', 'y', 'z' ,'w' ,'w' ,'x' ,'x' ,'y' ,'a' ,'z' ])\n",
      "\n",
      "#Show\n",
      "ser1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 103,
       "text": [
        "0     w\n",
        "1     w\n",
        "2     x\n",
        "3     y\n",
        "4     z\n",
        "5     w\n",
        "6     w\n",
        "7     x\n",
        "8     x\n",
        "9     y\n",
        "10    a\n",
        "11    z\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Grab the unique values\n",
      "ser1.unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 105,
       "text": [
        "array(['w', 'x', 'y', 'z', 'a'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Now get the count of the unique values\n",
      "ser1.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 107,
       "text": [
        "w    4\n",
        "x    3\n",
        "z    2\n",
        "y    2\n",
        "a    1\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Missing data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = Series(['one','two', np.nan, 'four'])\n",
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 108,
       "text": [
        "0     one\n",
        "1     two\n",
        "2     NaN\n",
        "3    four\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Find the missing values\n",
      "data.isnull()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 109,
       "text": [
        "0    False\n",
        "1    False\n",
        "2     True\n",
        "3    False\n",
        "dtype: bool"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#We can simply drop the NAN \n",
      "data.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 110,
       "text": [
        "0     one\n",
        "1     two\n",
        "3    four\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# In a DataFrame we need to be a little more careful!\n",
      "\n",
      "dframe = DataFrame([[1,2,3],[np.nan,5,6],[7,np.nan,9],[np.nan,np.nan,np.nan]])\n",
      "dframe"
     ],
     "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>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>NaN</td>\n",
        "      <td>  5</td>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  7</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\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": 112,
       "text": [
        "    0   1   2\n",
        "0   1   2   3\n",
        "1 NaN   5   6\n",
        "2   7 NaN   9\n",
        "3 NaN NaN NaN"
       ]
      }
     ],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clean_dframe = dframe.dropna()\n",
      "clean_dframe"
     ],
     "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>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 113,
       "text": [
        "   0  1  2\n",
        "0  1  2  3"
       ]
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#We can also specify to only drop rows that are complete missing all data\n",
      "dframe.dropna(how='all')"
     ],
     "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>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>NaN</td>\n",
        "      <td>  5</td>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  7</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 9</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 114,
       "text": [
        "    0   1  2\n",
        "0   1   2  3\n",
        "1 NaN   5  6\n",
        "2   7 NaN  9"
       ]
      }
     ],
     "prompt_number": 114
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Or we can specify to drop columns with missing data\n",
      "dframe.dropna(axis=1)\n",
      "\n",
      "#This should drop all columns out since every column contains at least 1 NAN"
     ],
     "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",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 115,
       "text": [
        "Empty DataFrame\n",
        "Columns: []\n",
        "Index: [0, 1, 2, 3]"
       ]
      }
     ],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#We can also threshold teh missing data as well\n",
      "\n",
      "#For example if we only want rows with at least 3 data points\n",
      "dframe2 = DataFrame([[1,2,3,np.nan],[2,np.nan,5,6],[np.nan,7,np.nan,9],[1,np.nan,np.nan,np.nan]])\n",
      "\n",
      "#Show\n",
      "dframe2"
     ],
     "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>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  2</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  5</td>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>NaN</td>\n",
        "      <td>  7</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  1</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": 116,
       "text": [
        "    0   1   2   3\n",
        "0   1   2   3 NaN\n",
        "1   2 NaN   5   6\n",
        "2 NaN   7 NaN   9\n",
        "3   1 NaN NaN NaN"
       ]
      }
     ],
     "prompt_number": 116
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Droppin any rows tht dont have at least 2 data points\n",
      "dframe2.dropna(thresh=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>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  2</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  5</td>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>NaN</td>\n",
        "      <td>  7</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 117,
       "text": [
        "    0   1   2   3\n",
        "0   1   2   3 NaN\n",
        "1   2 NaN   5   6\n",
        "2 NaN   7 NaN   9"
       ]
      }
     ],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Dropiing rows without at least 3 data points\n",
      "dframe2.dropna(thresh=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>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td>  2</td>\n",
        "      <td> 3</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 5</td>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 119,
       "text": [
        "   0   1  2   3\n",
        "0  1   2  3 NaN\n",
        "1  2 NaN  5   6"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#We can also fill any NAN\n",
      "dframe2.fillna(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>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5</td>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1</td>\n",
        "      <td> 7</td>\n",
        "      <td> 1</td>\n",
        "      <td> 9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 120,
       "text": [
        "   0  1  2  3\n",
        "0  1  2  3  1\n",
        "1  2  1  5  6\n",
        "2  1  7  1  9\n",
        "3  1  1  1  1"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Can also fill in diff values for diff columns\n",
      "dframe2.fillna({0:0,1:1,2:2,3:3})"
     ],
     "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>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5</td>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0</td>\n",
        "      <td> 7</td>\n",
        "      <td> 2</td>\n",
        "      <td> 9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 121,
       "text": [
        "   0  1  2  3\n",
        "0  1  2  3  3\n",
        "1  2  1  5  6\n",
        "2  0  7  2  9\n",
        "3  1  1  2  3"
       ]
      }
     ],
     "prompt_number": 121
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dframe2"
     ],
     "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>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  2</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  5</td>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>NaN</td>\n",
        "      <td>  7</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  1</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": 122,
       "text": [
        "    0   1   2   3\n",
        "0   1   2   3 NaN\n",
        "1   2 NaN   5   6\n",
        "2 NaN   7 NaN   9\n",
        "3   1 NaN NaN NaN"
       ]
      }
     ],
     "prompt_number": 122
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#If we want to modify the exsisting object, use inplace\n",
      "dframe2.fillna(0,inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dframe2"
     ],
     "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>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0</td>\n",
        "      <td> 7</td>\n",
        "      <td> 0</td>\n",
        "      <td> 9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 124,
       "text": [
        "   0  1  2  3\n",
        "0  1  2  3  0\n",
        "1  2  0  5  6\n",
        "2  0  7  0  9\n",
        "3  1  0  0  0"
       ]
      }
     ],
     "prompt_number": 124
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Index Hierarchy"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Now we'll learn about Index Hierarchy\n",
      "\n",
      "#pandas allows you to have multiple index levels, which is very clear with this example:\n",
      "\n",
      "ser = Series(np.random.randn(6),index=[[1,1,1,2,2,2],['a','b','c','a','b','c']])\n",
      "ser"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 126,
       "text": [
        "1  a   -1.466525\n",
        "   b   -0.305346\n",
        "   c    0.545888\n",
        "2  a    0.468509\n",
        "   b   -2.165480\n",
        "   c   -0.780303\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We can check the multiple levels\n",
      "ser.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 127,
       "text": [
        "MultiIndex(levels=[[1, 2], [u'a', u'b', u'c']],\n",
        "           labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Now we can sleect specific subsets\n",
      "ser[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 128,
       "text": [
        "a   -1.466525\n",
        "b   -0.305346\n",
        "c    0.545888\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We can also select from an internal index level\n",
      "ser[:,'a']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 129,
       "text": [
        "1   -1.466525\n",
        "2    0.468509\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 129
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We can also create Data Frames from Series with multiple levels\n",
      "dframe = ser.unstack()\n",
      "\n",
      "#Show\n",
      "dframe"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>-1.466525</td>\n",
        "      <td>-0.305346</td>\n",
        "      <td> 0.545888</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.468509</td>\n",
        "      <td>-2.165480</td>\n",
        "      <td>-0.780303</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 130,
       "text": [
        "          a         b         c\n",
        "1 -1.466525 -0.305346  0.545888\n",
        "2  0.468509 -2.165480 -0.780303"
       ]
      }
     ],
     "prompt_number": 130
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Can also reverse\n",
      "dframe.unstack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 131,
       "text": [
        "a  1   -1.466525\n",
        "   2    0.468509\n",
        "b  1   -0.305346\n",
        "   2   -2.165480\n",
        "c  1    0.545888\n",
        "   2   -0.780303\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We can also apply multiple level indexing to DataFrames\n",
      "dframe2 = DataFrame(np.arange(16).reshape(4,4),\n",
      "                    index=[['a','a','b','b'],[1,2,1,2]],\n",
      "                    columns=[['NY','NY','LA','SF'],['cold','hot','hot','cold']])\n",
      "                                                   \n",
      "dframe2    "
     ],
     "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\">NY</th>\n",
        "      <th>LA</th>\n",
        "      <th>SF</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>cold</th>\n",
        "      <th>hot</th>\n",
        "      <th>hot</th>\n",
        "      <th>cold</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
        "      <th>1</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  4</td>\n",
        "      <td>  5</td>\n",
        "      <td>  6</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
        "      <th>1</th>\n",
        "      <td>  8</td>\n",
        "      <td>  9</td>\n",
        "      <td> 10</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 12</td>\n",
        "      <td> 13</td>\n",
        "      <td> 14</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 132,
       "text": [
        "       NY        LA    SF\n",
        "     cold  hot  hot  cold\n",
        "a 1     0    1    2     3\n",
        "  2     4    5    6     7\n",
        "b 1     8    9   10    11\n",
        "  2    12   13   14    15"
       ]
      }
     ],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We can also give these index levels names\n",
      "\n",
      "#Name the index levels\n",
      "dframe2.index.names = ['INDEX_1','INDEX_2']\n",
      "\n",
      "#Name the column levels\n",
      "dframe2.columns.names = ['Cities','Temp']\n",
      "\n",
      "dframe2"
     ],
     "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>Cities</th>\n",
        "      <th colspan=\"2\" halign=\"left\">NY</th>\n",
        "      <th>LA</th>\n",
        "      <th>SF</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>Temp</th>\n",
        "      <th>cold</th>\n",
        "      <th>hot</th>\n",
        "      <th>hot</th>\n",
        "      <th>cold</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>INDEX_1</th>\n",
        "      <th>INDEX_2</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
        "      <th>1</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  4</td>\n",
        "      <td>  5</td>\n",
        "      <td>  6</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
        "      <th>1</th>\n",
        "      <td>  8</td>\n",
        "      <td>  9</td>\n",
        "      <td> 10</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 12</td>\n",
        "      <td> 13</td>\n",
        "      <td> 14</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 133,
       "text": [
        "Cities             NY        LA    SF\n",
        "Temp             cold  hot  hot  cold\n",
        "INDEX_1 INDEX_2                      \n",
        "a       1           0    1    2     3\n",
        "        2           4    5    6     7\n",
        "b       1           8    9   10    11\n",
        "        2          12   13   14    15"
       ]
      }
     ],
     "prompt_number": 133
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We can also interchange level orders (note the axis=1 for columns)\n",
      "dframe2.swaplevel('Cities','Temp',axis=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>\n",
        "      <th></th>\n",
        "      <th>Temp</th>\n",
        "      <th>cold</th>\n",
        "      <th colspan=\"2\" halign=\"left\">hot</th>\n",
        "      <th>cold</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>Cities</th>\n",
        "      <th>NY</th>\n",
        "      <th>NY</th>\n",
        "      <th>LA</th>\n",
        "      <th>SF</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>INDEX_1</th>\n",
        "      <th>INDEX_2</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
        "      <th>1</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  4</td>\n",
        "      <td>  5</td>\n",
        "      <td>  6</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
        "      <th>1</th>\n",
        "      <td>  8</td>\n",
        "      <td>  9</td>\n",
        "      <td> 10</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 12</td>\n",
        "      <td> 13</td>\n",
        "      <td> 14</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 134,
       "text": [
        "Temp             cold  hot      cold\n",
        "Cities             NY   NY  LA    SF\n",
        "INDEX_1 INDEX_2                     \n",
        "a       1           0    1   2     3\n",
        "        2           4    5   6     7\n",
        "b       1           8    9  10    11\n",
        "        2          12   13  14    15"
       ]
      }
     ],
     "prompt_number": 134
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#We can also sort levels\n",
      "dframe2.sortlevel(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>\n",
        "      <th></th>\n",
        "      <th>Cities</th>\n",
        "      <th colspan=\"2\" halign=\"left\">NY</th>\n",
        "      <th>LA</th>\n",
        "      <th>SF</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>Temp</th>\n",
        "      <th>cold</th>\n",
        "      <th>hot</th>\n",
        "      <th>hot</th>\n",
        "      <th>cold</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>INDEX_1</th>\n",
        "      <th>INDEX_2</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>a</th>\n",
        "      <th>1</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <th>1</th>\n",
        "      <td>  8</td>\n",
        "      <td>  9</td>\n",
        "      <td> 10</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>a</th>\n",
        "      <th>2</th>\n",
        "      <td>  4</td>\n",
        "      <td>  5</td>\n",
        "      <td>  6</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <th>2</th>\n",
        "      <td> 12</td>\n",
        "      <td> 13</td>\n",
        "      <td> 14</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 135,
       "text": [
        "Cities             NY        LA    SF\n",
        "Temp             cold  hot  hot  cold\n",
        "INDEX_1 INDEX_2                      \n",
        "a       1           0    1    2     3\n",
        "b       1           8    9   10    11\n",
        "a       2           4    5    6     7\n",
        "b       2          12   13   14    15"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#We can also sort levels\n",
      "dframe2.sortlevel(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>\n",
        "      <th></th>\n",
        "      <th>Cities</th>\n",
        "      <th colspan=\"2\" halign=\"left\">NY</th>\n",
        "      <th>LA</th>\n",
        "      <th>SF</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>Temp</th>\n",
        "      <th>cold</th>\n",
        "      <th>hot</th>\n",
        "      <th>hot</th>\n",
        "      <th>cold</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>INDEX_1</th>\n",
        "      <th>INDEX_2</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
        "      <th>1</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  4</td>\n",
        "      <td>  5</td>\n",
        "      <td>  6</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
        "      <th>1</th>\n",
        "      <td>  8</td>\n",
        "      <td>  9</td>\n",
        "      <td> 10</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 12</td>\n",
        "      <td> 13</td>\n",
        "      <td> 14</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 136,
       "text": [
        "Cities             NY        LA    SF\n",
        "Temp             cold  hot  hot  cold\n",
        "INDEX_1 INDEX_2                      \n",
        "a       1           0    1    2     3\n",
        "        2           4    5    6     7\n",
        "b       1           8    9   10    11\n",
        "        2          12   13   14    15"
       ]
      }
     ],
     "prompt_number": 136
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#We can also perform operations on particular levels\n",
      "dframe2.sum(level='Temp',axis=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>Temp</th>\n",
        "      <th>cold</th>\n",
        "      <th>hot</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>INDEX_1</th>\n",
        "      <th>INDEX_2</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
        "      <th>1</th>\n",
        "      <td>  3</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 11</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
        "      <th>1</th>\n",
        "      <td> 19</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 27</td>\n",
        "      <td> 27</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 137,
       "text": [
        "Temp             cold  hot\n",
        "INDEX_1 INDEX_2           \n",
        "a       1           3    3\n",
        "        2          11   11\n",
        "b       1          19   19\n",
        "        2          27   27"
       ]
      }
     ],
     "prompt_number": 137
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#We can also perform operations on particular levels\n",
      "dframe2.sum(level='INDEX_1',axis=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>\n",
        "      <th>Cities</th>\n",
        "      <th colspan=\"2\" halign=\"left\">NY</th>\n",
        "      <th>LA</th>\n",
        "      <th>SF</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Temp</th>\n",
        "      <th>cold</th>\n",
        "      <th>hot</th>\n",
        "      <th>hot</th>\n",
        "      <th>cold</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>INDEX_1</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>a</th>\n",
        "      <td>  4</td>\n",
        "      <td>  6</td>\n",
        "      <td>  8</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <td> 20</td>\n",
        "      <td> 22</td>\n",
        "      <td> 24</td>\n",
        "      <td> 26</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 138,
       "text": [
        "Cities     NY        LA    SF\n",
        "Temp     cold  hot  hot  cold\n",
        "INDEX_1                      \n",
        "a           4    6    8    10\n",
        "b          20   22   24    26"
       ]
      }
     ],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}