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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "# Lets Create a Dataframe"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Lets Create a Dataframe\n",
      "The code below creates a pandas dataframe that we can use to pivot out some data. You will have to download the notebook and run it to see the final result as NBConvert cannot render it and it has to run against an open kernel. You can download the notebook at [here](https://raw.githubusercontent.com/Tooblippe/insightstack-blog/gh-pages/downloads/notebooks/pandas_pivot.ipynb)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import Series, DataFrame, pivot_table\n",
      "import numpy as np\n",
      "import numpy\n",
      "\n",
      "d = { 'Class'   : Series( ['a',  'b', 'b','a','a',  'b', 'b','a','a',  'b', 'b','a','a','b','b','b']),\n",
      "      'Area'   : Series( ['North','East', 'South', 'West','North','East', 'South', 'West','North','East', 'South', 'West','South', 'West','South', 'West']),\n",
      "      'Type' : Series( ['square', 'round','square', 'round', 'round', 'square', 'round', 'square', 'round', 'square','round', 'square',]),\n",
      "      'Web'  : Series( ['Y','N','N','Y','Y','N','N','Y','Y','N','N','Y','Y','N','N','Y']),\n",
      "      'Agent'   : Series( ['Mike',  'John', 'Pete','Mike',  'John', 'Pete','Mike',  'John', 'Pete','Mike',  'John', 'Pete','John', 'Pete','John', 'Pete']),\n",
      "      'Income'   : Series( [20., 40., 90., 20.]),\n",
      "      'Profit' : Series( [1., 2., 3., 4.,1., 2., 3., 4.,1., 2., 3., 4.,1., 2., 3., 4.]),\n",
      "      'Stock' : Series( [20., 23., 33., 43.,12., 21., 310., 41.,11., 21., 31., 41.,11., 22., 34., 54.] )\n",
      "     }\n",
      "df = DataFrame(d)\n",
      "print df\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   Agent   Area Class  Income  Profit  Stock    Type Web\n",
        "0   Mike  North     a      20       1     20  square   Y\n",
        "1   John   East     b      40       2     23   round   N\n",
        "2   Pete  South     b      90       3     33  square   N\n",
        "3   Mike   West     a      20       4     43   round   Y\n",
        "4   John  North     a     NaN       1     12   round   Y\n",
        "5   Pete   East     b     NaN       2     21  square   N\n",
        "6   Mike  South     b     NaN       3    310   round   N\n",
        "7   John   West     a     NaN       4     41  square   Y\n",
        "8   Pete  North     a     NaN       1     11   round   Y\n",
        "9   Mike   East     b     NaN       2     21  square   N\n",
        "10  John  South     b     NaN       3     31   round   N\n",
        "11  Pete   West     a     NaN       4     41  square   Y\n",
        "12  John  South     a     NaN       1     11     NaN   Y\n",
        "13  Pete   West     b     NaN       2     22     NaN   N\n",
        "14  John  South     b     NaN       3     34     NaN   N\n",
        "15  Pete   West     b     NaN       4     54     NaN   Y\n",
        "\n",
        "[16 rows x 8 columns]\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now lets just create a nice pivot to see if everything is working. We call the sum function on the values for 'Profit' and 'Stock' for the 'Area' and 'Class' rows."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pivot_table( df, rows=['Area','Class'], values = ['Profit','Stock'], aggfunc=numpy.sum)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>Profit</th>\n",
        "      <th>Stock</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Area</th>\n",
        "      <th>Class</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>East</th>\n",
        "      <th>b</th>\n",
        "      <td>  6</td>\n",
        "      <td>  65</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>North</th>\n",
        "      <th>a</th>\n",
        "      <td>  3</td>\n",
        "      <td>  43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">South</th>\n",
        "      <th>a</th>\n",
        "      <td>  1</td>\n",
        "      <td>  11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <td> 12</td>\n",
        "      <td> 408</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">West</th>\n",
        "      <th>a</th>\n",
        "      <td> 12</td>\n",
        "      <td> 125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <td>  6</td>\n",
        "      <td>  76</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>6 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "             Profit  Stock\n",
        "Area  Class               \n",
        "East  b           6     65\n",
        "North a           3     43\n",
        "South a           1     11\n",
        "      b          12    408\n",
        "West  a          12    125\n",
        "      b           6     76\n",
        "\n",
        "[6 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To make widgets to interact with we need to first create a function that would return 'visualy' to the workbook what we want. I included a plot for fun. We should be able to pass a matplotlib function like bar to it using the widget aswell. I struggled to pass the dataframe 'df' to the function so hardcoded it for now."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "def my_pivot( rows, values, aggfunc, plt=False):\n",
      "    dfp = df\n",
      "    piv = pivot_table( dfp, rows=rows, values=values, aggfunc=aggfunc)\n",
      "    print piv\n",
      "    if plt: plot( piv )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Lets just test this function to see if it works."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_pivot(rows=['Area','Class'], values = ['Profit','Stock'], aggfunc=numpy.sum, plt=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "             Profit  Stock\n",
        "Area  Class               \n",
        "East  b           6     65\n",
        "North a           3     43\n",
        "South a           1     11\n",
        "      b          12    408\n",
        "West  a          12    125\n",
        "      b           6     76\n",
        "\n",
        "[6 rows x 2 columns]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rOHrj7zu8a9asaXWdDg2/FBcXq+/v2bNHPTMmJCQEiYmJqK2tRX5+Pi5fvgwf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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x103ad0cd0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Lets import some goodies to enable us to use the interactive widget. When you run this code for the first time you will see the selectors and then some error. This is because I did not set the default selector values. Select a column with only text values and then for 'values' a column like 'Income', 'Profit' or 'Stock'. You can also select if you want to sum or average them out. There is also a radio button to show or hide the plot. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.html.widgets import interact, SelectWidget, CheckboxWidget, RadioButtonsWidget\n",
      "from IPython.display import display"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = interact( my_pivot,\n",
      "                 rows    = SelectWidget(values=list(df.columns)), \n",
      "                 values  = SelectWidget(values=list(df.columns)),\n",
      "                 aggfunc = SelectWidget( values={ 'sum' : numpy.sum, 'ave' : numpy.average }),\n",
      "                 plt     = RadioButtonsWidget( values = { 'Plot': True, 'False' : False } )\n",
      "             )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Class\n",
        "a        16\n",
        "b        24\n",
        "Name: Profit, dtype: float64\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x103ac7b50>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "    \n"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}