{
 "metadata": {
  "email": "sukh @ yahoo",
  "name": "",
  "signature": "sha256:39e320de04253b196bceeb4b4163e70a6ef6de02b83f1a9939dff214c28d684f"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "#\n",
      "# data source:\n",
      "# https://raw.githubusercontent.com/datameet/india-election-data/master/affidavits/myneta.2014.csv\n",
      "# https://github.com/datameet/india-election-data\n",
      "#\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import matplotlib\n",
      "\n",
      "%matplotlib inline\n",
      "\n",
      "matplotlib.rc('figure', figsize=(10, 5))\n",
      "matplotlib.rc('font', weight='bold', size=12)\n",
      "\n",
      "\n",
      "fname=r\"/Users/Sukhbinder/myneta2014.csv\"\n",
      "\n",
      "\n",
      "data =pd.read_csv(fname)\n",
      "data.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>Criminal Cases</th>\n",
        "      <th>ID</th>\n",
        "      <th>Sno</th>\n",
        "      <th>Total Assets</th>\n",
        "      <th>Total Liabilities</th>\n",
        "      <th>Year</th>\n",
        "      <th>PC_CODE</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 8163.000000</td>\n",
        "      <td> 8163.000000</td>\n",
        "      <td> 8163.000000</td>\n",
        "      <td> 8.163000e+03</td>\n",
        "      <td> 8.163000e+03</td>\n",
        "      <td> 8163</td>\n",
        "      <td> 8163.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>    0.544653</td>\n",
        "      <td> 4931.800196</td>\n",
        "      <td> 4082.000000</td>\n",
        "      <td> 4.931083e+07</td>\n",
        "      <td> 3.987124e+06</td>\n",
        "      <td> 2014</td>\n",
        "      <td>   19.544898</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>    6.224428</td>\n",
        "      <td> 2830.910900</td>\n",
        "      <td> 2356.599457</td>\n",
        "      <td> 9.335788e+08</td>\n",
        "      <td> 4.180161e+07</td>\n",
        "      <td>    0</td>\n",
        "      <td>   17.105895</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>    0.000000</td>\n",
        "      <td>    3.000000</td>\n",
        "      <td>    1.000000</td>\n",
        "      <td> 0.000000e+00</td>\n",
        "      <td> 0.000000e+00</td>\n",
        "      <td> 2014</td>\n",
        "      <td>    1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>    0.000000</td>\n",
        "      <td> 2530.500000</td>\n",
        "      <td> 2041.500000</td>\n",
        "      <td> 4.422500e+05</td>\n",
        "      <td> 0.000000e+00</td>\n",
        "      <td> 2014</td>\n",
        "      <td>    7.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>    0.000000</td>\n",
        "      <td> 4857.000000</td>\n",
        "      <td> 4082.000000</td>\n",
        "      <td> 2.425000e+06</td>\n",
        "      <td> 0.000000e+00</td>\n",
        "      <td> 2014</td>\n",
        "      <td>   15.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>    0.000000</td>\n",
        "      <td> 7410.500000</td>\n",
        "      <td> 6122.500000</td>\n",
        "      <td> 1.150914e+07</td>\n",
        "      <td> 4.000000e+05</td>\n",
        "      <td> 2014</td>\n",
        "      <td>   28.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>  382.000000</td>\n",
        "      <td> 9828.000000</td>\n",
        "      <td> 8163.000000</td>\n",
        "      <td> 7.710296e+10</td>\n",
        "      <td> 2.218003e+09</td>\n",
        "      <td> 2014</td>\n",
        "      <td>   80.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 227,
       "text": [
        "       Criminal Cases           ID          Sno  Total Assets  \\\n",
        "count     8163.000000  8163.000000  8163.000000  8.163000e+03   \n",
        "mean         0.544653  4931.800196  4082.000000  4.931083e+07   \n",
        "std          6.224428  2830.910900  2356.599457  9.335788e+08   \n",
        "min          0.000000     3.000000     1.000000  0.000000e+00   \n",
        "25%          0.000000  2530.500000  2041.500000  4.422500e+05   \n",
        "50%          0.000000  4857.000000  4082.000000  2.425000e+06   \n",
        "75%          0.000000  7410.500000  6122.500000  1.150914e+07   \n",
        "max        382.000000  9828.000000  8163.000000  7.710296e+10   \n",
        "\n",
        "       Total Liabilities  Year      PC_CODE  \n",
        "count       8.163000e+03  8163  8163.000000  \n",
        "mean        3.987124e+06  2014    19.544898  \n",
        "std         4.180161e+07     0    17.105895  \n",
        "min         0.000000e+00  2014     1.000000  \n",
        "25%         0.000000e+00  2014     7.000000  \n",
        "50%         0.000000e+00  2014    15.000000  \n",
        "75%         4.000000e+05  2014    28.000000  \n",
        "max         2.218003e+09  2014    80.000000  \n",
        "\n",
        "[8 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 227
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# No of party contesting elections\n",
      "print \"Total number of Parties:\",np.size(data[\"Party\"].unique())\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Total number of Parties: 491\n"
       ]
      }
     ],
     "prompt_number": 228
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# how many candidates per party only choosing parties that are known \n",
      "mydata= data[data[\"Party\"].isin([\"AAP\",\"BJP\",\"INC\",\"IND\",\"BSP\",\"SP\",\"JDU\",\"CPI(M)\"])]\n",
      "p=mydata[[\"Party\",\"Candidate\"]].groupby([\"Party\"]).aggregate(lambda x: len(x.unique())).sort([\"Candidate\"],ascending=False)\n",
      "print p\n",
      "ip=p.plot(kind=\"bar\",color=[\"lightgreen\"],grid=\"off\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "        Candidate\n",
        "Party            \n",
        "IND          3111\n",
        "BSP           496\n",
        "INC           461\n",
        "AAP           425\n",
        "BJP           423\n",
        "SP            194\n",
        "CPI(M)         93\n",
        "\n",
        "[7 rows x 1 columns]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3btT58+f14YcfavTo0Zo+fboeeOABBQYGKicnxxHIqv+UpKCgIAUGBtbaX/340vbS0tI6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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10cf53fd0>"
       ]
      }
     ],
     "prompt_number": 229
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get education background\n",
      "pv =pd.pivot_table(data,values=[\"Candidate\"],aggfunc=lambda x: len(x.unique()),rows=[\"Education\"])\n",
      "pv=pv.sort([\"Candidate\"],ascending=False)\n",
      "\n",
      "a=pv.plot(kind=\"bar\",color=[\"lightgreen\"],grid=\"off\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Y4uLi2LRpE9deey3l5eV8/vnndOvWjezsbADGjRvHE088QevWrUlOTmb69OlUVVWRl5dH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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107562e10>"
       ]
      }
     ],
     "prompt_number": 230
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Education Data by Party for knowm parties \n",
      "mydata= data[data[\"Party\"].isin([\"AAP\",\"BJP\",\"INC\",\"BSP\",\"SP\",\"JDU\",\"CPI(M)\",\"IND\"])]\n",
      "pv =pd.pivot_table(mydata,values=[\"Candidate\"],aggfunc=lambda x: len(x.unique()),rows=[\"Education\"],cols=[\"Party\"])\n",
      "print pv.fillna(0)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                       Candidate                                \n",
        "Party                        AAP  BJP  BSP  CPI(M)  INC  IND  SP\n",
        "Education                                                       \n",
        "10th Pass                     33   43   79       8   23  599  16\n",
        "12th Pass                     33   43   78      11   45  481  29\n",
        "5th Pass                       2    6   20       1    3  246  11\n",
        "8th Pass                       7    8   43       3    7  360  13\n",
        "Doctorate                     25   30   11       6   33   42   5\n",
        "Graduate                      81  106   82      23  114  480  48\n",
        "Graduate Professional         76   82   58      16   90  253  22\n",
        "Illiterate                     0    0    6       0    0   63   1\n",
        "Literate                       2    3   17       1    2  150   5\n",
        "Not Given                      5    0    0       0    1   61   3\n",
        "Others                        16    7   14       1    5   71   2\n",
        "Post Graduate                146   97   91      23  139  363  39\n",
        "\n",
        "[12 rows x 7 columns]\n"
       ]
      }
     ],
     "prompt_number": 231
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#\n",
      "# Top Ten Criminal Cases Registered Candidate (Top 10)\n",
      "#\n",
      "mydata = data[[\"Candidate\",\"Constituency\",\"Party\",\"Criminal Cases\"]]\n",
      "print  mydata.sort('Criminal Cases', ascending=False).head(10)\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                          Candidate   Constituency   Party  Criminal Cases\n",
        "7686                Udayakumar S.P.  KANNIYAKUMARI     AAP             382\n",
        "3669                  M.Pushparayan   THOOTHUKKUDI     AAP             380\n",
        "7122            Sridip Bhattacharya         HOWRAH  CPI(M)              57\n",
        "3136               Kameshwar Baitha        PALAMAU    AITC              48\n",
        "738                      Atiq Ahmad      SHRAWASTI      SP              42\n",
        "5041                   Pawan Pandey      SULTANPUR     BSP              32\n",
        "4646                   Nepal Mahata        PURULIA     INC              25\n",
        "5591  Rajesh Ranjan Urf Pappu Yadav      MADHEPURA     RJD              24\n",
        "1508                    Dadan Yadav          BUXAR     BSP              22\n",
        "360       Amrishbhai Rasiklal Patel          DHULE     INC              21\n",
        "\n",
        "[10 rows x 4 columns]\n"
       ]
      }
     ],
     "prompt_number": 232
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#\n",
      "#  Party with most criminals cases (Top 10)\n",
      "# \n",
      "a=mydata.groupby(\"Party\").aggregate(np.sum).sort(\"Criminal Cases\",ascending=False).head(10).plot(kind='bar',color=[\"lightgreen\"],grid=\"off\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AYDECGAAAgMUIYAAAABYjgAEAAFjM5QC2detW9e3bVw0aNJCvr686deqkN954w9m+aNEi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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108b8bfd0>"
       ]
      }
     ],
     "prompt_number": 233
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#\n",
      "# Party with most Total Assets (top 10)\n",
      "#\n",
      "mydata = data[[\"Candidate\",\"Constituency\",\"Party\",\"Total Assets\"]]\n",
      "a=mydata.groupby(\"Party\").aggregate(np.sum).sort(\"Total Assets\",ascending=False).head(10).plot(kind='bar',color=[\"lightgreen\"],grid=\"off\")\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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R2rlzp9avX69WrVpZLBcAAMDzuH0MVUmYkuTslfLx8VH9+vXd/VIAAAAe4YJd5XfkyBEN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\nPn1akvTiiy+qffv2Sk1NVbdu3XTo0CHDCQGYxCU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       "text": [
        "<matplotlib.figure.Figure at 0x10beddcd0>"
       ]
      }
     ],
     "prompt_number": 235
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}