{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's say hello to Pandas. The convention is use pd in the import. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "#Pandas Series Object"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Array/list, one dimensional object. The easiest way is to initialize it with a list."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cities = ['London', 'New York', 'Berlin', 'Toronto']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s =pd.Series(cities)\n",
      "s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "0      London\n",
        "1    New York\n",
        "2      Berlin\n",
        "3     Toronto\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We see that we have the values in the list, but also and index, in this case going from 0 to 3. That is the default index."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.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": [
      "s.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "array(['London', 'New York', 'Berlin', 'Toronto'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can reference the values by using the appropiate index. In this case there is not much difference with a numpy array."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "'New York'"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can assign you **own** index. Which brings the power to the structure. Later it will be clear why."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = pd.Series(cities, index = ['A', 'B', 'C', 'D'])\n",
      "s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "A      London\n",
        "B    New York\n",
        "C      Berlin\n",
        "D     Toronto\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this case we have used labels A to D. And we can now reference the data in the Series by using the label."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s['D']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "'Toronto'"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Bogot\u00e1###\n",
      "<center>\n",
      "<img src=\"files/bogota.jpg\" width=\"400\">\n",
      "</center>\n",
      "[Bogot\u00e1 Image by Jorge D\u00edaz](http://en.wikipedia.org/wiki/File:Bogot%C3%A1_de_noche.jpg)\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let us try to look at a more meaningful series. The population of Bogot\u00e1 (the capital of Colombia) as evolving in time. Data taken from wikipedia. In this case it makes sense that the label is the year, and the value is the corresponding population. Then we can easily retrieve by year. Previously we used a list, but we can also use a dictionary to initialize a Series Object. In this case the keys will be the index, and the value will be the population in that year:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "population_bogota = {1800:21964, \n",
      "                     1912:121257, \n",
      "                     1951:715250, \n",
      "                     1964:1697311, \n",
      "                     1973:2855065, \n",
      "                     1985:4236490, \n",
      "                     1999:6276428, \n",
      "                     2012:7571345}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota = pd.Series(population_bogota)\n",
      "series_bogota"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "1800      21964\n",
        "1912     121257\n",
        "1951     715250\n",
        "1964    1697311\n",
        "1973    2855065\n",
        "1985    4236490\n",
        "1999    6276428\n",
        "2012    7571345\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "When you are working with different series it may be useful to include some meta-data on the series. Like the name of the series itself, and of the index."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota.name = 'Bogota population'\n",
      "series_bogota.index.name = 'year'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "year\n",
        "1800      21964\n",
        "1912     121257\n",
        "1951     715250\n",
        "1964    1697311\n",
        "1973    2855065\n",
        "1985    4236490\n",
        "1999    6276428\n",
        "2012    7571345\n",
        "Name: Bogota population, dtype: int64"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "Int64Index([1800, 1912, 1951, 1964, 1973, 1985, 1999, 2012], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "array([  21964,  121257,  715250, 1697311, 2855065, 4236490, 6276428,\n",
        "       7571345])"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can obviously reference specific values by using the index"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota[1800]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "21964"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that the values in the series are numpy arrays. This is part of what makes pandas fast and can be very useful when working with other libraries."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(series_bogota.values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "numpy.ndarray"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The index, on the other hand is a pandas Object. There is a hierarchy of Index objects that includes specific types for time indexes, hierichical index and other types of indexes."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(series_bogota.index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "pandas.core.index.Int64Index"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can query som information based on values. When did the population of bogota went above 1.000.000"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota[series_bogota > 1000000]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "year\n",
        "1964    1697311\n",
        "1973    2855065\n",
        "1985    4236490\n",
        "1999    6276428\n",
        "2012    7571345\n",
        "Name: Bogota population, dtype: int64"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I need the population from the 60's on"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota[series_bogota.index > 1960]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "year\n",
        "1964    1697311\n",
        "1973    2855065\n",
        "1985    4236490\n",
        "1999    6276428\n",
        "2012    7571345\n",
        "Name: Bogota population, dtype: int64"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What is actually going on with this way of querying information? Let us see what the bit inside the brackets yields."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota.index > 1965"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "array([False, False, False, False,  True,  True,  True,  True], dtype=bool)"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This means that we can also query using an array of booleans, perhaps handy if we have complex programatic conditions."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota[[True, False, True]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "year\n",
        "1800     21964\n",
        "1951    715250\n",
        "Name: Bogota population, dtype: int64"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can also query based on the index value itself."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota[[1973, 2012, 2011]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "1973    2855065\n",
        "2012    7571345\n",
        "2011        NaN\n",
        "Name: Bogota population, dtype: float64"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that we get NaN, for 2011, because this label is not in the original index. This is part of the automatic handling of missing values, and will turn out to be extremely valuable when working with real data."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Lets add a ficticious value for 2011."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota = series_bogota.set_value(2011, 6500000)\n",
      "series_bogota"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "year\n",
        "1800      21964\n",
        "1912     121257\n",
        "1951     715250\n",
        "1964    1697311\n",
        "1973    2855065\n",
        "1985    4236490\n",
        "1999    6276428\n",
        "2012    7571345\n",
        "2011    6500000\n",
        "Name: Bogota population, dtype: int64"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can apply functions to each element of the series. Remember, this is close to numpy."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "millions = lambda x: x/1000000.0\n",
      "series_bogota.apply(millions)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "year\n",
        "1800    0.021964\n",
        "1912    0.121257\n",
        "1951    0.715250\n",
        "1964    1.697311\n",
        "1973    2.855065\n",
        "1985    4.236490\n",
        "1999    6.276428\n",
        "2012    7.571345\n",
        "2011    6.500000\n",
        "Name: Bogota population, dtype: float64"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally, sometimes we need to have a quick idea of what the data looks like. For this we can use the function describe."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "count          9.000000\n",
        "mean     3332790.000000\n",
        "std      2926351.983191\n",
        "min        21964.000000\n",
        "25%       715250.000000\n",
        "50%      2855065.000000\n",
        "75%      6276428.000000\n",
        "max      7571345.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or even cooler, plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.Series.plot(series_bogota, kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10993e150>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mMLe/cRmjkOfGfW6tPH3/bmhogF9+B7RIfvWrX+Xvf/97kuTzzz/PoqIiFhUVqXMVxcXF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\nwH6+DwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1091a8dd0>"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota.sort()\n",
      "pd.Series.plot(series_bogota/1000000.0, kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x109a4b350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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C2NLSgv79+wMABg8ejG+++QaA55/os2bN0t4zevRojBw5EldcccVptz355JPa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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10992ed90>"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What about population change?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota.pct_change()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "year\n",
        "1800         NaN\n",
        "1912    4.520716\n",
        "1951    4.898629\n",
        "1964    1.373032\n",
        "1973    0.682111\n",
        "1985    0.483851\n",
        "1999    0.481516\n",
        "2011    0.035621\n",
        "2012    0.164822\n",
        "Name: Bogota population, dtype: float64"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota.pct_change().plot(kind='bar')\n",
      "plt.ylabel('percentage change')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "<matplotlib.text.Text at 0x109d0ccd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FeXm5PV///v0tz3PhmrW1teKGG24QQghx4sQJ+31npZ49e4rDhw87vKxHjx4W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\nfx4/0QCnjTsvAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1099d56d0>"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that there is a lot of missing data. Is there a way to solve this quickly?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Reindex + Interpolation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota = series_bogota.reindex(range(1800, 2014))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "1800    21964\n",
        "1801      NaN\n",
        "1802      NaN\n",
        "1803      NaN\n",
        "1804      NaN\n",
        "1805      NaN\n",
        "1806      NaN\n",
        "1807      NaN\n",
        "1808      NaN\n",
        "1809      NaN\n",
        "1810      NaN\n",
        "1811      NaN\n",
        "1812      NaN\n",
        "1813      NaN\n",
        "1814      NaN\n",
        "...\n",
        "1999    6276428\n",
        "2000        NaN\n",
        "2001        NaN\n",
        "2002        NaN\n",
        "2003        NaN\n",
        "2004        NaN\n",
        "2005        NaN\n",
        "2006        NaN\n",
        "2007        NaN\n",
        "2008        NaN\n",
        "2009        NaN\n",
        "2010        NaN\n",
        "2011    6500000\n",
        "2012    7571345\n",
        "2013        NaN\n",
        "Name: Bogota population, Length: 214, dtype: float64"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota = series_bogota.interpolate('values')\n",
      "series_bogota"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "1800    21964.000000\n",
        "1801    22850.544643\n",
        "1802    23737.089286\n",
        "1803    24623.633929\n",
        "1804    25510.178571\n",
        "1805    26396.723214\n",
        "1806    27283.267857\n",
        "1807    28169.812500\n",
        "1808    29056.357143\n",
        "1809    29942.901786\n",
        "1810    30829.446429\n",
        "1811    31715.991071\n",
        "1812    32602.535714\n",
        "1813    33489.080357\n",
        "1814    34375.625000\n",
        "...\n",
        "1999    6276428\n",
        "2000    6295059\n",
        "2001    6313690\n",
        "2002    6332321\n",
        "2003    6350952\n",
        "2004    6369583\n",
        "2005    6388214\n",
        "2006    6406845\n",
        "2007    6425476\n",
        "2008    6444107\n",
        "2009    6462738\n",
        "2010    6481369\n",
        "2011    6500000\n",
        "2012    7571345\n",
        "2013    7571345\n",
        "Name: Bogota population, Length: 214, dtype: float64"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(series_bogota/1000000.0).plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x109dc0750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10993ead0>"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(series_bogota).pct_change().plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x109dc6e90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3ikUYR/QnfXcdgYNGA/zwh8Arr0h625AwBVAjAIDvfx/oUcoDwNNLFy8C/fu7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\nhEQ5CgSEEBLlKBAQQkiUo0BACCFR7v8DQLtUuIfPK28AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109d17910>"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally, let us look at adding series. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Cali###\n",
      "<center>\n",
      "<img src=\"files/cali.jpg\" width=\"600\">\n",
      "</center>\n",
      "[Cali Image by Carlos Duss\u00e1n G\u00f3mez](http://en.wikipedia.org/wiki/File:Gatonegro_Cali_Noche_desde_Cristo_Rey.jpg)\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "population_cali = {1809: 7546, 1938:101883, 1973:991549, 1985:1429026, 2013:2319684}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_cali = pd.Series(population_cali)\n",
      "series_cali.name = 'Cali (Colombia) Population'\n",
      "series_cali.index.name = 'year'\n",
      "series_cali"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "year\n",
        "1809       7546\n",
        "1938     101883\n",
        "1973     991549\n",
        "1985    1429026\n",
        "2013    2319684\n",
        "Name: Cali (Colombia) Population, dtype: int64"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(series_cali + series_bogota)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "1800             NaN\n",
        "1801             NaN\n",
        "1802             NaN\n",
        "1803             NaN\n",
        "1804             NaN\n",
        "1805             NaN\n",
        "1806             NaN\n",
        "1807             NaN\n",
        "1808             NaN\n",
        "1809    37488.901786\n",
        "1810             NaN\n",
        "1811             NaN\n",
        "1812             NaN\n",
        "1813             NaN\n",
        "1814             NaN\n",
        "...\n",
        "1999        NaN\n",
        "2000        NaN\n",
        "2001        NaN\n",
        "2002        NaN\n",
        "2003        NaN\n",
        "2004        NaN\n",
        "2005        NaN\n",
        "2006        NaN\n",
        "2007        NaN\n",
        "2008        NaN\n",
        "2009        NaN\n",
        "2010        NaN\n",
        "2011        NaN\n",
        "2012        NaN\n",
        "2013    9891029\n",
        "Length: 214, dtype: float64"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Probably the most meaningful way to add the series is if they share an index. So I can reindex cali with the index of Bogot\u00e1"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_cali"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "year\n",
        "1809       7546\n",
        "1938     101883\n",
        "1973     991549\n",
        "1985    1429026\n",
        "2013    2319684\n",
        "Name: Cali (Colombia) Population, dtype: int64"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_cali = series_cali.reindex(series_bogota.index)\n",
      "series_cali"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "1800     NaN\n",
        "1801     NaN\n",
        "1802     NaN\n",
        "1803     NaN\n",
        "1804     NaN\n",
        "1805     NaN\n",
        "1806     NaN\n",
        "1807     NaN\n",
        "1808     NaN\n",
        "1809    7546\n",
        "1810     NaN\n",
        "1811     NaN\n",
        "1812     NaN\n",
        "1813     NaN\n",
        "1814     NaN\n",
        "...\n",
        "1999        NaN\n",
        "2000        NaN\n",
        "2001        NaN\n",
        "2002        NaN\n",
        "2003        NaN\n",
        "2004        NaN\n",
        "2005        NaN\n",
        "2006        NaN\n",
        "2007        NaN\n",
        "2008        NaN\n",
        "2009        NaN\n",
        "2010        NaN\n",
        "2011        NaN\n",
        "2012        NaN\n",
        "2013    2319684\n",
        "Name: Cali (Colombia) Population, Length: 214, dtype: float64"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(series_bogota) == len(series_cali)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.alltrue(series_bogota.index == series_cali.index) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 73,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_cali = series_cali.interpolate('values')\n",
      "series_cali"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "1800             NaN\n",
        "1801             NaN\n",
        "1802             NaN\n",
        "1803             NaN\n",
        "1804             NaN\n",
        "1805             NaN\n",
        "1806             NaN\n",
        "1807             NaN\n",
        "1808             NaN\n",
        "1809     7546.000000\n",
        "1810     8277.294574\n",
        "1811     9008.589147\n",
        "1812     9739.883721\n",
        "1813    10471.178295\n",
        "1814    11202.472868\n",
        "...\n",
        "1999    1874355.000000\n",
        "2000    1906164.214286\n",
        "2001    1937973.428571\n",
        "2002    1969782.642857\n",
        "2003    2001591.857143\n",
        "2004    2033401.071429\n",
        "2005    2065210.285714\n",
        "2006    2097019.500000\n",
        "2007    2128828.714286\n",
        "2008    2160637.928571\n",
        "2009    2192447.142857\n",
        "2010    2224256.357143\n",
        "2011    2256065.571429\n",
        "2012    2287874.785714\n",
        "2013    2319684.000000\n",
        "Name: Cali (Colombia) Population, Length: 214, dtype: float64"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_cali = series_cali.fillna(0.0)\n",
      "series_cali"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "1800        0.000000\n",
        "1801        0.000000\n",
        "1802        0.000000\n",
        "1803        0.000000\n",
        "1804        0.000000\n",
        "1805        0.000000\n",
        "1806        0.000000\n",
        "1807        0.000000\n",
        "1808        0.000000\n",
        "1809     7546.000000\n",
        "1810     8277.294574\n",
        "1811     9008.589147\n",
        "1812     9739.883721\n",
        "1813    10471.178295\n",
        "1814    11202.472868\n",
        "...\n",
        "1999    1874355.000000\n",
        "2000    1906164.214286\n",
        "2001    1937973.428571\n",
        "2002    1969782.642857\n",
        "2003    2001591.857143\n",
        "2004    2033401.071429\n",
        "2005    2065210.285714\n",
        "2006    2097019.500000\n",
        "2007    2128828.714286\n",
        "2008    2160637.928571\n",
        "2009    2192447.142857\n",
        "2010    2224256.357143\n",
        "2011    2256065.571429\n",
        "2012    2287874.785714\n",
        "2013    2319684.000000\n",
        "Name: Cali (Colombia) Population, Length: 214, dtype: float64"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now finally add the two series"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota + series_cali"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "1800    21964.000000\n",
        "1801    22850.544643\n",
        "1802    23737.089286\n",
        "1803    24623.633929\n",
        "1804    25510.178571\n",
        "1805    26396.723214\n",
        "1806    27283.267857\n",
        "1807    28169.812500\n",
        "1808    29056.357143\n",
        "1809    37488.901786\n",
        "1810    39106.741002\n",
        "1811    40724.580219\n",
        "1812    42342.419435\n",
        "1813    43960.258652\n",
        "1814    45578.097868\n",
        "...\n",
        "1999    8150783.000000\n",
        "2000    8201223.214286\n",
        "2001    8251663.428571\n",
        "2002    8302103.642857\n",
        "2003    8352543.857143\n",
        "2004    8402984.071429\n",
        "2005    8453424.285714\n",
        "2006    8503864.500000\n",
        "2007    8554304.714286\n",
        "2008    8604744.928571\n",
        "2009    8655185.142857\n",
        "2010    8705625.357143\n",
        "2011    8756065.571429\n",
        "2012    9859219.785714\n",
        "2013    9891029.000000\n",
        "Length: 214, dtype: float64"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_bogota.plot(label='Bogota population')\n",
      "series_cali.plot(label='Cali population')\n",
      "plt.legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "<matplotlib.legend.Legend at 0x109fd1950>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RkL+/P2m1WiIiSkxMpPz8fCIiSk9Pp+zs7C7x9vT1Bvi1GRsQ3r+cy9NPE2VmWjsK8zHn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\nERERJ74AqMZe/8me7DEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109f70a90>"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I do not vouch for the statistics here, it is just an example of the tool."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Pandas Data Frame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame({'bogot\u00e1':series_bogota, 'cali':series_cali})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 214 entries, 1800 to 2013\n",
        "Data columns (total 2 columns):\n",
        "bogot\u00e1    214  non-null values\n",
        "cali      214  non-null values\n",
        "dtypes: float64(2)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 214 entries, 1800 to 2013\n",
        "Data columns (total 2 columns):\n",
        "bogot\u00e1    214  non-null values\n",
        "cali      214  non-null values\n",
        "dtypes: float64(2)"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.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>bogot\u00e1</th>\n",
        "      <th>cali</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1800</th>\n",
        "      <td> 21964.000000</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1801</th>\n",
        "      <td> 22850.544643</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1802</th>\n",
        "      <td> 23737.089286</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1803</th>\n",
        "      <td> 24623.633929</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1804</th>\n",
        "      <td> 25510.178571</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "            bogot\u00e1  cali\n",
        "1800  21964.000000     0\n",
        "1801  22850.544643     0\n",
        "1802  23737.089286     0\n",
        "1803  24623.633929     0\n",
        "1804  25510.178571     0"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.options.display.float_format = '{:20,.2f}'.format\n",
      "df.index.name = 'year'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.tail()"
     ],
     "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>bogot\u00e1</th>\n",
        "      <th>cali</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td>        6,462,738.00</td>\n",
        "      <td>        2,192,447.14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td>        6,481,369.00</td>\n",
        "      <td>        2,224,256.36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td>        6,500,000.00</td>\n",
        "      <td>        2,256,065.57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012</th>\n",
        "      <td>        7,571,345.00</td>\n",
        "      <td>        2,287,874.79</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013</th>\n",
        "      <td>        7,571,345.00</td>\n",
        "      <td>        2,319,684.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "                   bogot\u00e1                 cali\n",
        "year                                          \n",
        "2009         6,462,738.00         2,192,447.14\n",
        "2010         6,481,369.00         2,224,256.36\n",
        "2011         6,500,000.00         2,256,065.57\n",
        "2012         7,571,345.00         2,287,874.79\n",
        "2013         7,571,345.00         2,319,684.00"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Elements of DataFrame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "Int64Index([1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835, 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "Index([u'bogot\u00e1', u'cali'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.shape(df.values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "(214, 2)"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['population difference'] = df['bogot\u00e1'] - df['cali']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.tail()"
     ],
     "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>bogot\u00e1</th>\n",
        "      <th>cali</th>\n",
        "      <th>population difference</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td>        6,462,738.00</td>\n",
        "      <td>        2,192,447.14</td>\n",
        "      <td>        4,270,290.86</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td>        6,481,369.00</td>\n",
        "      <td>        2,224,256.36</td>\n",
        "      <td>        4,257,112.64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td>        6,500,000.00</td>\n",
        "      <td>        2,256,065.57</td>\n",
        "      <td>        4,243,934.43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012</th>\n",
        "      <td>        7,571,345.00</td>\n",
        "      <td>        2,287,874.79</td>\n",
        "      <td>        5,283,470.21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013</th>\n",
        "      <td>        7,571,345.00</td>\n",
        "      <td>        2,319,684.00</td>\n",
        "      <td>        5,251,661.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "                   bogot\u00e1                 cali  population difference\n",
        "year                                                                 \n",
        "2009         6,462,738.00         2,192,447.14           4,270,290.86\n",
        "2010         6,481,369.00         2,224,256.36           4,257,112.64\n",
        "2011         6,500,000.00         2,256,065.57           4,243,934.43\n",
        "2012         7,571,345.00         2,287,874.79           5,283,470.21\n",
        "2013         7,571,345.00         2,319,684.00           5,251,661.00"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.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>bogot\u00e1</th>\n",
        "      <th>cali</th>\n",
        "      <th>population difference</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>              214.00</td>\n",
        "      <td>              214.00</td>\n",
        "      <td>              214.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>        1,269,633.63</td>\n",
        "      <td>          440,945.03</td>\n",
        "      <td>          828,688.61</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>        2,057,142.86</td>\n",
        "      <td>          658,404.11</td>\n",
        "      <td>        1,404,249.56</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>           21,964.00</td>\n",
        "      <td>                0.00</td>\n",
        "      <td>           21,964.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>           69,172.50</td>\n",
        "      <td>           39,905.78</td>\n",
        "      <td>           29,266.72</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>          116,381.00</td>\n",
        "      <td>           78,847.22</td>\n",
        "      <td>           37,533.78</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>        1,376,252.60</td>\n",
        "      <td>          654,746.87</td>\n",
        "      <td>          721,505.72</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>        7,571,345.00</td>\n",
        "      <td>        2,319,684.00</td>\n",
        "      <td>        5,283,470.21</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "                    bogot\u00e1                 cali  population difference\n",
        "count               214.00               214.00                 214.00\n",
        "mean          1,269,633.63           440,945.03             828,688.61\n",
        "std           2,057,142.86           658,404.11           1,404,249.56\n",
        "min              21,964.00                 0.00              21,964.00\n",
        "25%              69,172.50            39,905.78              29,266.72\n",
        "50%             116,381.00            78,847.22              37,533.78\n",
        "75%           1,376,252.60           654,746.87             721,505.72\n",
        "max           7,571,345.00         2,319,684.00           5,283,470.21"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['population difference'].tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 89,
       "text": [
        "year\n",
        "2009           4,270,290.86\n",
        "2010           4,257,112.64\n",
        "2011           4,243,934.43\n",
        "2012           5,283,470.21\n",
        "2013           5,251,661.00\n",
        "Name: population difference, dtype: float64"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df.index >1990]"
     ],
     "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>bogot\u00e1</th>\n",
        "      <th>cali</th>\n",
        "      <th>population difference</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1991</th>\n",
        "      <td>        5,110,749.14</td>\n",
        "      <td>        1,619,881.29</td>\n",
        "      <td>        3,490,867.86</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1992</th>\n",
        "      <td>        5,256,459.00</td>\n",
        "      <td>        1,651,690.50</td>\n",
        "      <td>        3,604,768.50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1993</th>\n",
        "      <td>        5,402,168.86</td>\n",
        "      <td>        1,683,499.71</td>\n",
        "      <td>        3,718,669.14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1994</th>\n",
        "      <td>        5,547,878.71</td>\n",
        "      <td>        1,715,308.93</td>\n",
        "      <td>        3,832,569.79</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1995</th>\n",
        "      <td>        5,693,588.57</td>\n",
        "      <td>        1,747,118.14</td>\n",
        "      <td>        3,946,470.43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1996</th>\n",
        "      <td>        5,839,298.43</td>\n",
        "      <td>        1,778,927.36</td>\n",
        "      <td>        4,060,371.07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1997</th>\n",
        "      <td>        5,985,008.29</td>\n",
        "      <td>        1,810,736.57</td>\n",
        "      <td>        4,174,271.71</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1998</th>\n",
        "      <td>        6,130,718.14</td>\n",
        "      <td>        1,842,545.79</td>\n",
        "      <td>        4,288,172.36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1999</th>\n",
        "      <td>        6,276,428.00</td>\n",
        "      <td>        1,874,355.00</td>\n",
        "      <td>        4,402,073.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2000</th>\n",
        "      <td>        6,295,059.00</td>\n",
        "      <td>        1,906,164.21</td>\n",
        "      <td>        4,388,894.79</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2001</th>\n",
        "      <td>        6,313,690.00</td>\n",
        "      <td>        1,937,973.43</td>\n",
        "      <td>        4,375,716.57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2002</th>\n",
        "      <td>        6,332,321.00</td>\n",
        "      <td>        1,969,782.64</td>\n",
        "      <td>        4,362,538.36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2003</th>\n",
        "      <td>        6,350,952.00</td>\n",
        "      <td>        2,001,591.86</td>\n",
        "      <td>        4,349,360.14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004</th>\n",
        "      <td>        6,369,583.00</td>\n",
        "      <td>        2,033,401.07</td>\n",
        "      <td>        4,336,181.93</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2005</th>\n",
        "      <td>        6,388,214.00</td>\n",
        "      <td>        2,065,210.29</td>\n",
        "      <td>        4,323,003.71</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td>        6,406,845.00</td>\n",
        "      <td>        2,097,019.50</td>\n",
        "      <td>        4,309,825.50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td>        6,425,476.00</td>\n",
        "      <td>        2,128,828.71</td>\n",
        "      <td>        4,296,647.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td>        6,444,107.00</td>\n",
        "      <td>        2,160,637.93</td>\n",
        "      <td>        4,283,469.07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td>        6,462,738.00</td>\n",
        "      <td>        2,192,447.14</td>\n",
        "      <td>        4,270,290.86</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td>        6,481,369.00</td>\n",
        "      <td>        2,224,256.36</td>\n",
        "      <td>        4,257,112.64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td>        6,500,000.00</td>\n",
        "      <td>        2,256,065.57</td>\n",
        "      <td>        4,243,934.43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012</th>\n",
        "      <td>        7,571,345.00</td>\n",
        "      <td>        2,287,874.79</td>\n",
        "      <td>        5,283,470.21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013</th>\n",
        "      <td>        7,571,345.00</td>\n",
        "      <td>        2,319,684.00</td>\n",
        "      <td>        5,251,661.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 90,
       "text": [
        "                   bogot\u00e1                 cali  population difference\n",
        "year                                                                 \n",
        "1991         5,110,749.14         1,619,881.29           3,490,867.86\n",
        "1992         5,256,459.00         1,651,690.50           3,604,768.50\n",
        "1993         5,402,168.86         1,683,499.71           3,718,669.14\n",
        "1994         5,547,878.71         1,715,308.93           3,832,569.79\n",
        "1995         5,693,588.57         1,747,118.14           3,946,470.43\n",
        "1996         5,839,298.43         1,778,927.36           4,060,371.07\n",
        "1997         5,985,008.29         1,810,736.57           4,174,271.71\n",
        "1998         6,130,718.14         1,842,545.79           4,288,172.36\n",
        "1999         6,276,428.00         1,874,355.00           4,402,073.00\n",
        "2000         6,295,059.00         1,906,164.21           4,388,894.79\n",
        "2001         6,313,690.00         1,937,973.43           4,375,716.57\n",
        "2002         6,332,321.00         1,969,782.64           4,362,538.36\n",
        "2003         6,350,952.00         2,001,591.86           4,349,360.14\n",
        "2004         6,369,583.00         2,033,401.07           4,336,181.93\n",
        "2005         6,388,214.00         2,065,210.29           4,323,003.71\n",
        "2006         6,406,845.00         2,097,019.50           4,309,825.50\n",
        "2007         6,425,476.00         2,128,828.71           4,296,647.29\n",
        "2008         6,444,107.00         2,160,637.93           4,283,469.07\n",
        "2009         6,462,738.00         2,192,447.14           4,270,290.86\n",
        "2010         6,481,369.00         2,224,256.36           4,257,112.64\n",
        "2011         6,500,000.00         2,256,065.57           4,243,934.43\n",
        "2012         7,571,345.00         2,287,874.79           5,283,470.21\n",
        "2013         7,571,345.00         2,319,684.00           5,251,661.00"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 91,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a2c04d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WW24BAKjVagCW6fn/8Ic/4Jlnnrn4MbvR2JIQjq66GrjiCsDd3d41cUzSwFxEaxsGWyku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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1013fa950>"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls *.csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "cali_and_bogota.csv metropolitan.csv    patients.csv\r\n"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.to_csv('cali_and_bogota.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head cali_and_bogota.csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "year,bogot\u00e1,cali,population difference\r\n",
        "1800,21964.0,0.0,21964.0\r\n",
        "1801,22850.54464285714,0.0,22850.54464285714\r\n",
        "1802,23737.089285714286,0.0,23737.089285714286\r\n",
        "1803,24623.633928571428,0.0,24623.633928571428\r\n",
        "1804,25510.178571428572,0.0,25510.178571428572\r\n",
        "1805,26396.723214285714,0.0,26396.723214285714\r\n",
        "1806,27283.267857142855,0.0,27283.267857142855\r\n",
        "1807,28169.8125,0.0,28169.8125\r\n",
        "1808,29056.357142857145,0.0,29056.357142857145\r\n"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are many ways to initialize DataFrames"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Let's be more cosmopolitan (if we still have 7 more minutes)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!cat metropolitan.csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Metropolitan area,Country,Population,Area\r\n",
        "Tokyo,Japan,32450000,8014\r\n",
        "Seoul,South Korea,20550000,5076\r\n",
        "Mexico City,Mexico,20450000,7346\r\n",
        "New York City,United States,19750000,17884\r\n",
        "Mumbai-Bombay,India,19200000,2350\r\n",
        "Jakarta,Indonesia,18900000,5100\r\n",
        "S\u00e3o Paulo,Brazil,18850000,8479\r\n",
        "New Delhi,India,18600000,3182\r\n",
        "Osaka-Kobe-Kyoto,Japan,17375000,6930\r\n",
        "Shanghai,China,16650000,5177\r\n",
        "Manila,Philippines,16300000,2521\r\n",
        "Hong Kong,Hong Kong/China,15800000,3051\r\n",
        "Los Angeles,United States,15250000,10780\r\n",
        "Kolkata,India,15100000,1785\r\n",
        "Moscow,Russia,15000000,14925\r\n",
        "Cairo,Egypt,14450000,1600\r\n",
        "Buenos Aires,Argentina,13170000,10888\r\n",
        "London,United Kingdom,12875000,11391\r\n",
        "Beijing,China,12500000,6562\r\n",
        "Karachi,Pakistan,11800000,1100"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv('metropolitan.csv')\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Metropolitan area</th>\n",
        "      <th>Country</th>\n",
        "      <th>Population</th>\n",
        "      <th>Area</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>            Tokyo</td>\n",
        "      <td>           Japan</td>\n",
        "      <td> 32450000</td>\n",
        "      <td>  8014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>            Seoul</td>\n",
        "      <td>     South Korea</td>\n",
        "      <td> 20550000</td>\n",
        "      <td>  5076</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>      Mexico City</td>\n",
        "      <td>          Mexico</td>\n",
        "      <td> 20450000</td>\n",
        "      <td>  7346</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>    New York City</td>\n",
        "      <td>   United States</td>\n",
        "      <td> 19750000</td>\n",
        "      <td> 17884</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>    Mumbai-Bombay</td>\n",
        "      <td>           India</td>\n",
        "      <td> 19200000</td>\n",
        "      <td>  2350</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>          Jakarta</td>\n",
        "      <td>       Indonesia</td>\n",
        "      <td> 18900000</td>\n",
        "      <td>  5100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>        S\u00e3o Paulo</td>\n",
        "      <td>          Brazil</td>\n",
        "      <td> 18850000</td>\n",
        "      <td>  8479</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>        New Delhi</td>\n",
        "      <td>           India</td>\n",
        "      <td> 18600000</td>\n",
        "      <td>  3182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> Osaka-Kobe-Kyoto</td>\n",
        "      <td>           Japan</td>\n",
        "      <td> 17375000</td>\n",
        "      <td>  6930</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>         Shanghai</td>\n",
        "      <td>           China</td>\n",
        "      <td> 16650000</td>\n",
        "      <td>  5177</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>           Manila</td>\n",
        "      <td>     Philippines</td>\n",
        "      <td> 16300000</td>\n",
        "      <td>  2521</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>        Hong Kong</td>\n",
        "      <td> Hong Kong/China</td>\n",
        "      <td> 15800000</td>\n",
        "      <td>  3051</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>      Los Angeles</td>\n",
        "      <td>   United States</td>\n",
        "      <td> 15250000</td>\n",
        "      <td> 10780</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>          Kolkata</td>\n",
        "      <td>           India</td>\n",
        "      <td> 15100000</td>\n",
        "      <td>  1785</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>           Moscow</td>\n",
        "      <td>          Russia</td>\n",
        "      <td> 15000000</td>\n",
        "      <td> 14925</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>            Cairo</td>\n",
        "      <td>           Egypt</td>\n",
        "      <td> 14450000</td>\n",
        "      <td>  1600</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>     Buenos Aires</td>\n",
        "      <td>       Argentina</td>\n",
        "      <td> 13170000</td>\n",
        "      <td> 10888</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>           London</td>\n",
        "      <td>  United Kingdom</td>\n",
        "      <td> 12875000</td>\n",
        "      <td> 11391</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>          Beijing</td>\n",
        "      <td>           China</td>\n",
        "      <td> 12500000</td>\n",
        "      <td>  6562</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>          Karachi</td>\n",
        "      <td>        Pakistan</td>\n",
        "      <td> 11800000</td>\n",
        "      <td>  1100</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 96,
       "text": [
        "   Metropolitan area          Country  Population   Area\n",
        "0              Tokyo            Japan    32450000   8014\n",
        "1              Seoul      South Korea    20550000   5076\n",
        "2        Mexico City           Mexico    20450000   7346\n",
        "3      New York City    United States    19750000  17884\n",
        "4      Mumbai-Bombay            India    19200000   2350\n",
        "5            Jakarta        Indonesia    18900000   5100\n",
        "6          S\u00e3o Paulo           Brazil    18850000   8479\n",
        "7          New Delhi            India    18600000   3182\n",
        "8   Osaka-Kobe-Kyoto            Japan    17375000   6930\n",
        "9           Shanghai            China    16650000   5177\n",
        "10            Manila      Philippines    16300000   2521\n",
        "11         Hong Kong  Hong Kong/China    15800000   3051\n",
        "12       Los Angeles    United States    15250000  10780\n",
        "13           Kolkata            India    15100000   1785\n",
        "14            Moscow           Russia    15000000  14925\n",
        "15             Cairo            Egypt    14450000   1600\n",
        "16      Buenos Aires        Argentina    13170000  10888\n",
        "17            London   United Kingdom    12875000  11391\n",
        "18           Beijing            China    12500000   6562\n",
        "19           Karachi         Pakistan    11800000   1100"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv('metropolitan.csv', index_col='Metropolitan area')\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Country</th>\n",
        "      <th>Population</th>\n",
        "      <th>Area</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Metropolitan area</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Tokyo</th>\n",
        "      <td>           Japan</td>\n",
        "      <td> 32450000</td>\n",
        "      <td>  8014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Seoul</th>\n",
        "      <td>     South Korea</td>\n",
        "      <td> 20550000</td>\n",
        "      <td>  5076</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mexico City</th>\n",
        "      <td>          Mexico</td>\n",
        "      <td> 20450000</td>\n",
        "      <td>  7346</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New York City</th>\n",
        "      <td>   United States</td>\n",
        "      <td> 19750000</td>\n",
        "      <td> 17884</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mumbai-Bombay</th>\n",
        "      <td>           India</td>\n",
        "      <td> 19200000</td>\n",
        "      <td>  2350</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jakarta</th>\n",
        "      <td>       Indonesia</td>\n",
        "      <td> 18900000</td>\n",
        "      <td>  5100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S\u00e3o Paulo</th>\n",
        "      <td>          Brazil</td>\n",
        "      <td> 18850000</td>\n",
        "      <td>  8479</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New Delhi</th>\n",
        "      <td>           India</td>\n",
        "      <td> 18600000</td>\n",
        "      <td>  3182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Osaka-Kobe-Kyoto</th>\n",
        "      <td>           Japan</td>\n",
        "      <td> 17375000</td>\n",
        "      <td>  6930</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Shanghai</th>\n",
        "      <td>           China</td>\n",
        "      <td> 16650000</td>\n",
        "      <td>  5177</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Manila</th>\n",
        "      <td>     Philippines</td>\n",
        "      <td> 16300000</td>\n",
        "      <td>  2521</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hong Kong</th>\n",
        "      <td> Hong Kong/China</td>\n",
        "      <td> 15800000</td>\n",
        "      <td>  3051</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Los Angeles</th>\n",
        "      <td>   United States</td>\n",
        "      <td> 15250000</td>\n",
        "      <td> 10780</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Kolkata</th>\n",
        "      <td>           India</td>\n",
        "      <td> 15100000</td>\n",
        "      <td>  1785</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Moscow</th>\n",
        "      <td>          Russia</td>\n",
        "      <td> 15000000</td>\n",
        "      <td> 14925</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cairo</th>\n",
        "      <td>           Egypt</td>\n",
        "      <td> 14450000</td>\n",
        "      <td>  1600</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Buenos Aires</th>\n",
        "      <td>       Argentina</td>\n",
        "      <td> 13170000</td>\n",
        "      <td> 10888</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>London</th>\n",
        "      <td>  United Kingdom</td>\n",
        "      <td> 12875000</td>\n",
        "      <td> 11391</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Beijing</th>\n",
        "      <td>           China</td>\n",
        "      <td> 12500000</td>\n",
        "      <td>  6562</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Karachi</th>\n",
        "      <td>        Pakistan</td>\n",
        "      <td> 11800000</td>\n",
        "      <td>  1100</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "                           Country  Population   Area\n",
        "Metropolitan area                                    \n",
        "Tokyo                        Japan    32450000   8014\n",
        "Seoul                  South Korea    20550000   5076\n",
        "Mexico City                 Mexico    20450000   7346\n",
        "New York City        United States    19750000  17884\n",
        "Mumbai-Bombay                India    19200000   2350\n",
        "Jakarta                  Indonesia    18900000   5100\n",
        "S\u00e3o Paulo                   Brazil    18850000   8479\n",
        "New Delhi                    India    18600000   3182\n",
        "Osaka-Kobe-Kyoto             Japan    17375000   6930\n",
        "Shanghai                     China    16650000   5177\n",
        "Manila                 Philippines    16300000   2521\n",
        "Hong Kong          Hong Kong/China    15800000   3051\n",
        "Los Angeles          United States    15250000  10780\n",
        "Kolkata                      India    15100000   1785\n",
        "Moscow                      Russia    15000000  14925\n",
        "Cairo                        Egypt    14450000   1600\n",
        "Buenos Aires             Argentina    13170000  10888\n",
        "London              United Kingdom    12875000  11391\n",
        "Beijing                      China    12500000   6562\n",
        "Karachi                   Pakistan    11800000   1100"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.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>Population</th>\n",
        "      <th>Area</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>               20.00</td>\n",
        "      <td>               20.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>       17,251,000.00</td>\n",
        "      <td>            6,707.05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>        4,485,562.40</td>\n",
        "      <td>            4,619.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>       11,800,000.00</td>\n",
        "      <td>            1,100.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>       14,862,500.00</td>\n",
        "      <td>            2,918.50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>       16,475,000.00</td>\n",
        "      <td>            5,869.50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>       18,975,000.00</td>\n",
        "      <td>            9,054.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>       32,450,000.00</td>\n",
        "      <td>           17,884.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 98,
       "text": [
        "                Population                 Area\n",
        "count                20.00                20.00\n",
        "mean         17,251,000.00             6,707.05\n",
        "std           4,485,562.40             4,619.33\n",
        "min          11,800,000.00             1,100.00\n",
        "25%          14,862,500.00             2,918.50\n",
        "50%          16,475,000.00             5,869.50\n",
        "75%          18,975,000.00             9,054.25\n",
        "max          32,450,000.00            17,884.00"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 99,
       "text": [
        "Country       object\n",
        "Population     int64\n",
        "Area           int64\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Population']= df['Population'].apply(float)\n",
      "df['Area']= df['Area'].apply(float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 101,
       "text": [
        "Country        object\n",
        "Population    float64\n",
        "Area          float64\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Three biggest metro areas"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.sort('Population')\n",
      "df.head(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>Country</th>\n",
        "      <th>Population</th>\n",
        "      <th>Area</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Metropolitan area</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Tokyo</th>\n",
        "      <td>       Japan</td>\n",
        "      <td>       32,450,000.00</td>\n",
        "      <td>            8,014.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Seoul</th>\n",
        "      <td> South Korea</td>\n",
        "      <td>       20,550,000.00</td>\n",
        "      <td>            5,076.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mexico City</th>\n",
        "      <td>      Mexico</td>\n",
        "      <td>       20,450,000.00</td>\n",
        "      <td>            7,346.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 102,
       "text": [
        "                       Country           Population                 Area\n",
        "Metropolitan area                                                       \n",
        "Tokyo                    Japan        32,450,000.00             8,014.00\n",
        "Seoul              South Korea        20,550,000.00             5,076.00\n",
        "Mexico City             Mexico        20,450,000.00             7,346.00"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Area largest than 10.0000 km2"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df['Area']> 10000]"
     ],
     "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>Country</th>\n",
        "      <th>Population</th>\n",
        "      <th>Area</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Metropolitan area</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>New York City</th>\n",
        "      <td>  United States</td>\n",
        "      <td>       19,750,000.00</td>\n",
        "      <td>           17,884.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Los Angeles</th>\n",
        "      <td>  United States</td>\n",
        "      <td>       15,250,000.00</td>\n",
        "      <td>           10,780.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Moscow</th>\n",
        "      <td>         Russia</td>\n",
        "      <td>       15,000,000.00</td>\n",
        "      <td>           14,925.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Buenos Aires</th>\n",
        "      <td>      Argentina</td>\n",
        "      <td>       13,170,000.00</td>\n",
        "      <td>           10,888.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>London</th>\n",
        "      <td> United Kingdom</td>\n",
        "      <td>       12,875,000.00</td>\n",
        "      <td>           11,391.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 103,
       "text": [
        "                          Country           Population                 Area\n",
        "Metropolitan area                                                          \n",
        "New York City       United States        19,750,000.00            17,884.00\n",
        "Los Angeles         United States        15,250,000.00            10,780.00\n",
        "Moscow                     Russia        15,000,000.00            14,925.00\n",
        "Buenos Aires            Argentina        13,170,000.00            10,888.00\n",
        "London             United Kingdom        12,875,000.00            11,391.00"
       ]
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[(df['Area']> 10000) & (df['Population'] > 20000)]"
     ],
     "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>Country</th>\n",
        "      <th>Population</th>\n",
        "      <th>Area</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Metropolitan area</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>New York City</th>\n",
        "      <td>  United States</td>\n",
        "      <td>       19,750,000.00</td>\n",
        "      <td>           17,884.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Los Angeles</th>\n",
        "      <td>  United States</td>\n",
        "      <td>       15,250,000.00</td>\n",
        "      <td>           10,780.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Moscow</th>\n",
        "      <td>         Russia</td>\n",
        "      <td>       15,000,000.00</td>\n",
        "      <td>           14,925.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Buenos Aires</th>\n",
        "      <td>      Argentina</td>\n",
        "      <td>       13,170,000.00</td>\n",
        "      <td>           10,888.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>London</th>\n",
        "      <td> United Kingdom</td>\n",
        "      <td>       12,875,000.00</td>\n",
        "      <td>           11,391.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 104,
       "text": [
        "                          Country           Population                 Area\n",
        "Metropolitan area                                                          \n",
        "New York City       United States        19,750,000.00            17,884.00\n",
        "Los Angeles         United States        15,250,000.00            10,780.00\n",
        "Moscow                     Russia        15,000,000.00            14,925.00\n",
        "Buenos Aires            Argentina        13,170,000.00            10,888.00\n",
        "London             United Kingdom        12,875,000.00            11,391.00"
       ]
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Density'] = df['Population']/df['Area']\n",
      "df.sort('Density', ascending=False)['Density'].plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 105,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x109faac10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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YAT0M/vIytmcNrIE16K4P1qBAD4M/wzAMowrO+ZeavX5q0IaP8qlBGz5YA2so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\nt9v2EdKV2NhYnDt3Dg4ODjh79iyKi4sRERHBd1hEQCj5C0B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       "text": [
        "<matplotlib.figure.Figure at 0x10a2ceb90>"
       ]
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.groupby('Country').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></th>\n",
        "      <th>Country</th>\n",
        "      <th>Population</th>\n",
        "      <th>Area</th>\n",
        "      <th>Density</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Country</th>\n",
        "      <th>Metropolitan area</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Argentina</th>\n",
        "      <th>Buenos Aires</th>\n",
        "      <td>       Argentina</td>\n",
        "      <td>       13,170,000.00</td>\n",
        "      <td>           10,888.00</td>\n",
        "      <td>            1,209.59</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Brazil</th>\n",
        "      <th>S\u00e3o Paulo</th>\n",
        "      <td>          Brazil</td>\n",
        "      <td>       18,850,000.00</td>\n",
        "      <td>            8,479.00</td>\n",
        "      <td>            2,223.14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">China</th>\n",
        "      <th>Shanghai</th>\n",
        "      <td>           China</td>\n",
        "      <td>       16,650,000.00</td>\n",
        "      <td>            5,177.00</td>\n",
        "      <td>            3,216.15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Beijing</th>\n",
        "      <td>           China</td>\n",
        "      <td>       12,500,000.00</td>\n",
        "      <td>            6,562.00</td>\n",
        "      <td>            1,904.91</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Egypt</th>\n",
        "      <th>Cairo</th>\n",
        "      <td>           Egypt</td>\n",
        "      <td>       14,450,000.00</td>\n",
        "      <td>            1,600.00</td>\n",
        "      <td>            9,031.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hong Kong/China</th>\n",
        "      <th>Hong Kong</th>\n",
        "      <td> Hong Kong/China</td>\n",
        "      <td>       15,800,000.00</td>\n",
        "      <td>            3,051.00</td>\n",
        "      <td>            5,178.63</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">India</th>\n",
        "      <th>Mumbai-Bombay</th>\n",
        "      <td>           India</td>\n",
        "      <td>       19,200,000.00</td>\n",
        "      <td>            2,350.00</td>\n",
        "      <td>            8,170.21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New Delhi</th>\n",
        "      <td>           India</td>\n",
        "      <td>       18,600,000.00</td>\n",
        "      <td>            3,182.00</td>\n",
        "      <td>            5,845.38</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Kolkata</th>\n",
        "      <td>           India</td>\n",
        "      <td>       15,100,000.00</td>\n",
        "      <td>            1,785.00</td>\n",
        "      <td>            8,459.38</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Indonesia</th>\n",
        "      <th>Jakarta</th>\n",
        "      <td>       Indonesia</td>\n",
        "      <td>       18,900,000.00</td>\n",
        "      <td>            5,100.00</td>\n",
        "      <td>            3,705.88</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Japan</th>\n",
        "      <th>Tokyo</th>\n",
        "      <td>           Japan</td>\n",
        "      <td>       32,450,000.00</td>\n",
        "      <td>            8,014.00</td>\n",
        "      <td>            4,049.16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Osaka-Kobe-Kyoto</th>\n",
        "      <td>           Japan</td>\n",
        "      <td>       17,375,000.00</td>\n",
        "      <td>            6,930.00</td>\n",
        "      <td>            2,507.22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mexico</th>\n",
        "      <th>Mexico City</th>\n",
        "      <td>          Mexico</td>\n",
        "      <td>       20,450,000.00</td>\n",
        "      <td>            7,346.00</td>\n",
        "      <td>            2,783.83</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pakistan</th>\n",
        "      <th>Karachi</th>\n",
        "      <td>        Pakistan</td>\n",
        "      <td>       11,800,000.00</td>\n",
        "      <td>            1,100.00</td>\n",
        "      <td>           10,727.27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Philippines</th>\n",
        "      <th>Manila</th>\n",
        "      <td>     Philippines</td>\n",
        "      <td>       16,300,000.00</td>\n",
        "      <td>            2,521.00</td>\n",
        "      <td>            6,465.69</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Russia</th>\n",
        "      <th>Moscow</th>\n",
        "      <td>          Russia</td>\n",
        "      <td>       15,000,000.00</td>\n",
        "      <td>           14,925.00</td>\n",
        "      <td>            1,005.03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>South Korea</th>\n",
        "      <th>Seoul</th>\n",
        "      <td>     South Korea</td>\n",
        "      <td>       20,550,000.00</td>\n",
        "      <td>            5,076.00</td>\n",
        "      <td>            4,048.46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>United Kingdom</th>\n",
        "      <th>London</th>\n",
        "      <td>  United Kingdom</td>\n",
        "      <td>       12,875,000.00</td>\n",
        "      <td>           11,391.00</td>\n",
        "      <td>            1,130.28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">United States</th>\n",
        "      <th>New York City</th>\n",
        "      <td>   United States</td>\n",
        "      <td>       19,750,000.00</td>\n",
        "      <td>           17,884.00</td>\n",
        "      <td>            1,104.34</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Los Angeles</th>\n",
        "      <td>   United States</td>\n",
        "      <td>       15,250,000.00</td>\n",
        "      <td>           10,780.00</td>\n",
        "      <td>            1,414.66</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 106,
       "text": [
        "                                           Country           Population  \\\n",
        "Country         Metropolitan area                                         \n",
        "Argentina       Buenos Aires             Argentina        13,170,000.00   \n",
        "Brazil          S\u00e3o Paulo                   Brazil        18,850,000.00   \n",
        "China           Shanghai                     China        16,650,000.00   \n",
        "                Beijing                      China        12,500,000.00   \n",
        "Egypt           Cairo                        Egypt        14,450,000.00   \n",
        "Hong Kong/China Hong Kong          Hong Kong/China        15,800,000.00   \n",
        "India           Mumbai-Bombay                India        19,200,000.00   \n",
        "                New Delhi                    India        18,600,000.00   \n",
        "                Kolkata                      India        15,100,000.00   \n",
        "Indonesia       Jakarta                  Indonesia        18,900,000.00   \n",
        "Japan           Tokyo                        Japan        32,450,000.00   \n",
        "                Osaka-Kobe-Kyoto             Japan        17,375,000.00   \n",
        "Mexico          Mexico City                 Mexico        20,450,000.00   \n",
        "Pakistan        Karachi                   Pakistan        11,800,000.00   \n",
        "Philippines     Manila                 Philippines        16,300,000.00   \n",
        "Russia          Moscow                      Russia        15,000,000.00   \n",
        "South Korea     Seoul                  South Korea        20,550,000.00   \n",
        "United Kingdom  London              United Kingdom        12,875,000.00   \n",
        "United States   New York City        United States        19,750,000.00   \n",
        "                Los Angeles          United States        15,250,000.00   \n",
        "\n",
        "                                                  Area              Density  \n",
        "Country         Metropolitan area                                            \n",
        "Argentina       Buenos Aires                 10,888.00             1,209.59  \n",
        "Brazil          S\u00e3o Paulo                     8,479.00             2,223.14  \n",
        "China           Shanghai                      5,177.00             3,216.15  \n",
        "                Beijing                       6,562.00             1,904.91  \n",
        "Egypt           Cairo                         1,600.00             9,031.25  \n",
        "Hong Kong/China Hong Kong                     3,051.00             5,178.63  \n",
        "India           Mumbai-Bombay                 2,350.00             8,170.21  \n",
        "                New Delhi                     3,182.00             5,845.38  \n",
        "                Kolkata                       1,785.00             8,459.38  \n",
        "Indonesia       Jakarta                       5,100.00             3,705.88  \n",
        "Japan           Tokyo                         8,014.00             4,049.16  \n",
        "                Osaka-Kobe-Kyoto              6,930.00             2,507.22  \n",
        "Mexico          Mexico City                   7,346.00             2,783.83  \n",
        "Pakistan        Karachi                       1,100.00            10,727.27  \n",
        "Philippines     Manila                        2,521.00             6,465.69  \n",
        "Russia          Moscow                       14,925.00             1,005.03  \n",
        "South Korea     Seoul                         5,076.00             4,048.46  \n",
        "United Kingdom  London                       11,391.00             1,130.28  \n",
        "United States   New York City                17,884.00             1,104.34  \n",
        "                Los Angeles                  10,780.00             1,414.66  "
       ]
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(df.groupby('Country'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 107,
       "text": [
        "pandas.core.groupby.DataFrameGroupBy"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df.groupby('Country').sum().sort('Density', ascending=False).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                      Population                 Area              Density\n",
        "Country                                                                   \n",
        "India              52,900,000.00             7,317.00            22,474.98\n",
        "Pakistan           11,800,000.00             1,100.00            10,727.27\n",
        "Egypt              14,450,000.00             1,600.00             9,031.25\n",
        "Japan              49,825,000.00            14,944.00             6,556.38\n",
        "Philippines        16,300,000.00             2,521.00             6,465.69\n"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "All the data in this notebook was taken from wikipedia."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}