WBData.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WB data analysis and PCA with census data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import math\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Load WB census data, this has some aggregate points with village id = 0\n",
    "data = pd.read_csv('/home/armaan/BTP/census/WestBengal/WB-combined-dataset.csv', sep=\",\",header=None,index_col=False,dtype={0: str,2: str,4: str,6: str,7: str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>37973.000000</td>\n",
       "      <td>37973.000000</td>\n",
       "      <td>37973.000000</td>\n",
       "      <td>37973.000000</td>\n",
       "      <td>37973.000000</td>\n",
       "      <td>37973.000000</td>\n",
       "      <td>37973.000000</td>\n",
       "      <td>37973.000000</td>\n",
       "      <td>37973.000000</td>\n",
       "      <td>37973.000000</td>\n",
       "      <td>37973.000000</td>\n",
       "      <td>37973.000000</td>\n",
       "      <td>37973.000000</td>\n",
       "      <td>37973.000000</td>\n",
       "      <td>37973.000000</td>\n",
       "      <td>37973.000000</td>\n",
       "      <td>37973.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31.886627</td>\n",
       "      <td>51.996010</td>\n",
       "      <td>1.011161</td>\n",
       "      <td>2.085350</td>\n",
       "      <td>56.072328</td>\n",
       "      <td>28.356796</td>\n",
       "      <td>5.987791</td>\n",
       "      <td>3.664804</td>\n",
       "      <td>0.605135</td>\n",
       "      <td>0.904019</td>\n",
       "      <td>6.552429</td>\n",
       "      <td>41.167577</td>\n",
       "      <td>37.780299</td>\n",
       "      <td>18.604782</td>\n",
       "      <td>6.120451</td>\n",
       "      <td>1.138717</td>\n",
       "      <td>28.245324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.584443</td>\n",
       "      <td>23.858956</td>\n",
       "      <td>3.726864</td>\n",
       "      <td>7.435363</td>\n",
       "      <td>21.923261</td>\n",
       "      <td>15.052955</td>\n",
       "      <td>6.519636</td>\n",
       "      <td>5.820493</td>\n",
       "      <td>1.828204</td>\n",
       "      <td>2.285621</td>\n",
       "      <td>17.939338</td>\n",
       "      <td>27.526587</td>\n",
       "      <td>31.757275</td>\n",
       "      <td>16.033182</td>\n",
       "      <td>6.923252</td>\n",
       "      <td>3.815406</td>\n",
       "      <td>23.125068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.200000</td>\n",
       "      <td>36.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.100000</td>\n",
       "      <td>18.300000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.700000</td>\n",
       "      <td>51.700000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>57.200000</td>\n",
       "      <td>28.800000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.100000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>22.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>45.900000</td>\n",
       "      <td>68.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>71.400000</td>\n",
       "      <td>37.900000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>61.400000</td>\n",
       "      <td>63.600000</td>\n",
       "      <td>26.800000</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>43.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>140.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 9             10            11            12            13  \\\n",
       "count  37973.000000  37973.000000  37973.000000  37973.000000  37973.000000   \n",
       "mean      31.886627     51.996010      1.011161      2.085350     56.072328   \n",
       "std       24.584443     23.858956      3.726864      7.435363     21.923261   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%       12.200000     36.300000      0.000000      0.000000     42.100000   \n",
       "50%       27.700000     51.700000      0.100000      0.300000     57.200000   \n",
       "75%       45.900000     68.200000      0.800000      1.200000     71.400000   \n",
       "max      100.000000    100.000000    100.000000    100.000000    100.000000   \n",
       "\n",
       "                 14            15            16            17            18  \\\n",
       "count  37973.000000  37973.000000  37973.000000  37973.000000  37973.000000   \n",
       "mean      28.356796      5.987791      3.664804      0.605135      0.904019   \n",
       "std       15.052955      6.519636      5.820493      1.828204      2.285621   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%       18.300000      1.900000      0.100000      0.000000      0.000000   \n",
       "50%       28.800000      4.700000      1.800000      0.000000      0.300000   \n",
       "75%       37.900000      8.100000      4.500000      0.700000      1.100000   \n",
       "max      100.000000    100.000000    100.000000    100.000000    100.000000   \n",
       "\n",
       "                 19            20            21            22            23  \\\n",
       "count  37973.000000  37973.000000  37973.000000  37973.000000  37973.000000   \n",
       "mean       6.552429     41.167577     37.780299     18.604782      6.120451   \n",
       "std       17.939338     27.526587     31.757275     16.033182      6.923252   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000     19.200000      8.700000      6.500000      2.000000   \n",
       "50%        0.000000     39.100000     30.500000     15.000000      4.500000   \n",
       "75%        2.000000     61.400000     63.600000     26.800000      8.200000   \n",
       "max      100.000000    100.000000    100.000000    129.000000    136.000000   \n",
       "\n",
       "                 24            25  \n",
       "count  37973.000000  37973.000000  \n",
       "mean       1.138717     28.245324  \n",
       "std        3.815406     23.125068  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.000000      9.100000  \n",
       "50%        0.500000     22.800000  \n",
       "75%        1.300000     43.300000  \n",
       "max      137.000000    140.000000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>00000</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>000000</td>\n",
       "      <td>0000</td>\n",
       "      <td>District - Darjiling</td>\n",
       "      <td>45.9</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>21</td>\n",
       "      <td>77.7</td>\n",
       "      <td>65.7</td>\n",
       "      <td>58.3</td>\n",
       "      <td>10.4</td>\n",
       "      <td>4</td>\n",
       "      <td>50.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0            1    2           3      4           5       6     7   \\\n",
       "0  19  WEST BENGAL  327  Darjiling   00000  Darjiling   000000  0000   \n",
       "\n",
       "                      8     9   ...   16   17   18  19    20    21    22  \\\n",
       "0  District - Darjiling   45.9  ...   12  3.7  3.1  21  77.7  65.7  58.3   \n",
       "\n",
       "     23  24    25  \n",
       "0  10.4   4  50.2  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ls_headings=['state-code', 'state-name', 'district-code', 'district-name', 'teh-code', 'teh-name', 'vill-code', 'ward-no', 'area-name', 'res_good', 'res_bad', 'res-other_good', 'res-other_bad', '1-room', '2-room', '3-room', '4-room', '5-room', '6-room', 'water_treated', 'lighting_electric', 'lat-facility', 'tv', 'bicycle', 'car', 'permanent-house']\n",
    "feature_columns = ls_headings[9:]\n",
    "positive_feature_columns=['res_good', 'res-other_good', '4-room', '5-room', '6-room', 'water_treated', 'lighting_electric', 'lat-facility', 'tv', 'bicycle', 'car', 'permanent-house']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.columns=ls_headings\n",
    "data['census_id'] = data['state-code']+data['district-code']+data['teh-code']+data['vill-code']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data for Punjab and Haryana villages (23997 villages) into python, assign headers.\n",
    "1-9 : meta data columns\n",
    "10-26 : feature columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state-code</th>\n",
       "      <th>state-name</th>\n",
       "      <th>district-code</th>\n",
       "      <th>district-name</th>\n",
       "      <th>teh-code</th>\n",
       "      <th>teh-name</th>\n",
       "      <th>vill-code</th>\n",
       "      <th>ward-no</th>\n",
       "      <th>area-name</th>\n",
       "      <th>res_good</th>\n",
       "      <th>...</th>\n",
       "      <th>5-room</th>\n",
       "      <th>6-room</th>\n",
       "      <th>water_treated</th>\n",
       "      <th>lighting_electric</th>\n",
       "      <th>lat-facility</th>\n",
       "      <th>tv</th>\n",
       "      <th>bicycle</th>\n",
       "      <th>car</th>\n",
       "      <th>permanent-house</th>\n",
       "      <th>census_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>00000</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>000000</td>\n",
       "      <td>0000</td>\n",
       "      <td>District - Darjiling</td>\n",
       "      <td>45.9</td>\n",
       "      <td>...</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>77.7</td>\n",
       "      <td>65.7</td>\n",
       "      <td>58.3</td>\n",
       "      <td>10.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>50.2</td>\n",
       "      <td>1932700000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>00000</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>000000</td>\n",
       "      <td>0000</td>\n",
       "      <td>District - Darjiling</td>\n",
       "      <td>36.6</td>\n",
       "      <td>...</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>7.5</td>\n",
       "      <td>69.5</td>\n",
       "      <td>52.3</td>\n",
       "      <td>46.5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>35.7</td>\n",
       "      <td>1932700000000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  state-code   state-name district-code district-name teh-code    teh-name  \\\n",
       "0         19  WEST BENGAL           327    Darjiling     00000  Darjiling    \n",
       "1         19  WEST BENGAL           327    Darjiling     00000  Darjiling    \n",
       "\n",
       "  vill-code ward-no              area-name  res_good        ...         \\\n",
       "0    000000    0000  District - Darjiling       45.9        ...          \n",
       "1    000000    0000  District - Darjiling       36.6        ...          \n",
       "\n",
       "   5-room  6-room  water_treated  lighting_electric  lat-facility    tv  \\\n",
       "0     3.7     3.1           21.0               77.7          65.7  58.3   \n",
       "1     3.2     2.4            7.5               69.5          52.3  46.5   \n",
       "\n",
       "   bicycle  car  permanent-house         census_id  \n",
       "0     10.4  4.0             50.2  1932700000000000  \n",
       "1      5.6  2.2             35.7  1932700000000000  \n",
       "\n",
       "[2 rows x 27 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:1: FutureWarning: irow(i) is deprecated. Please use .iloc[i]\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "state-code                              19\n",
       "state-name                     WEST BENGAL\n",
       "district-code                          327\n",
       "district-name                   Darjiling \n",
       "teh-code                             00000\n",
       "teh-name                        Darjiling \n",
       "vill-code                           000000\n",
       "ward-no                               0000\n",
       "area-name            District - Darjiling \n",
       "res_good                              45.9\n",
       "res_bad                               44.3\n",
       "res-other_good                         1.7\n",
       "res-other_bad                          1.2\n",
       "1-room                                28.4\n",
       "2-room                                32.1\n",
       "3-room                                19.6\n",
       "4-room                                  12\n",
       "5-room                                 3.7\n",
       "6-room                                 3.1\n",
       "water_treated                           21\n",
       "lighting_electric                     77.7\n",
       "lat-facility                          65.7\n",
       "tv                                    58.3\n",
       "bicycle                               10.4\n",
       "car                                      4\n",
       "permanent-house                       50.2\n",
       "census_id                 1932700000000000\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.irow(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = data[data['vill-code'].astype(int)!=0] #Remove aggregate information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:1: FutureWarning: irow(i) is deprecated. Please use .iloc[i]\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "state-code                            19\n",
       "state-name                   WEST BENGAL\n",
       "district-code                        327\n",
       "district-name                 Darjiling \n",
       "teh-code                           02154\n",
       "teh-name             Darjeeling Pulbazar\n",
       "vill-code                         306145\n",
       "ward-no                             0000\n",
       "area-name               Singalila Forest\n",
       "res_good                            16.4\n",
       "res_bad                             77.3\n",
       "res-other_good                         0\n",
       "res-other_bad                        0.6\n",
       "1-room                              12.4\n",
       "2-room                              67.6\n",
       "3-room                                17\n",
       "4-room                               0.9\n",
       "5-room                                 0\n",
       "6-room                                 0\n",
       "water_treated                        9.7\n",
       "lighting_electric                   14.5\n",
       "lat-facility                        53.3\n",
       "tv                                   4.2\n",
       "bicycle                                0\n",
       "car                                    0\n",
       "permanent-house                       27\n",
       "census_id               1932702154306145\n",
       "Name: 4, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.irow(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37235, 27)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sampleData=data[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state-code</th>\n",
       "      <th>state-name</th>\n",
       "      <th>district-code</th>\n",
       "      <th>district-name</th>\n",
       "      <th>teh-code</th>\n",
       "      <th>teh-name</th>\n",
       "      <th>vill-code</th>\n",
       "      <th>ward-no</th>\n",
       "      <th>area-name</th>\n",
       "      <th>res_good</th>\n",
       "      <th>...</th>\n",
       "      <th>5-room</th>\n",
       "      <th>6-room</th>\n",
       "      <th>water_treated</th>\n",
       "      <th>lighting_electric</th>\n",
       "      <th>lat-facility</th>\n",
       "      <th>tv</th>\n",
       "      <th>bicycle</th>\n",
       "      <th>car</th>\n",
       "      <th>permanent-house</th>\n",
       "      <th>census_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306145</td>\n",
       "      <td>0000</td>\n",
       "      <td>Singalila Forest</td>\n",
       "      <td>16.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>14.5</td>\n",
       "      <td>53.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1932702154306145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306146</td>\n",
       "      <td>0000</td>\n",
       "      <td>Rimbick</td>\n",
       "      <td>33.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>61.6</td>\n",
       "      <td>69.4</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>1932702154306146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306147</td>\n",
       "      <td>0000</td>\n",
       "      <td>Namla</td>\n",
       "      <td>38.1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.4</td>\n",
       "      <td>66.6</td>\n",
       "      <td>50.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1932702154306147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  state-code   state-name district-code district-name teh-code  \\\n",
       "4         19  WEST BENGAL           327    Darjiling     02154   \n",
       "5         19  WEST BENGAL           327    Darjiling     02154   \n",
       "6         19  WEST BENGAL           327    Darjiling     02154   \n",
       "\n",
       "              teh-name vill-code ward-no         area-name  res_good  \\\n",
       "4  Darjeeling Pulbazar    306145    0000  Singalila Forest      16.4   \n",
       "5  Darjeeling Pulbazar    306146    0000           Rimbick      33.0   \n",
       "6  Darjeeling Pulbazar    306147    0000             Namla      38.1   \n",
       "\n",
       "         ...         5-room  6-room  water_treated  lighting_electric  \\\n",
       "4        ...            0.0     0.0            9.7               14.5   \n",
       "5        ...            4.6     2.7            1.6               61.6   \n",
       "6        ...            3.8     1.5            0.0               88.4   \n",
       "\n",
       "   lat-facility    tv  bicycle  car  permanent-house         census_id  \n",
       "4          53.3   4.2      0.0  0.0             27.0  1932702154306145  \n",
       "5          69.4  37.0      0.4  1.4             18.6  1932702154306146  \n",
       "6          66.6  50.9      0.7  0.7             25.5  1932702154306147  \n",
       "\n",
       "[3 rows x 27 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampleData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['res_good', 'res_bad', 'res-other_good', 'res-other_bad', '1-room', '2-room', '3-room', '4-room', '5-room', '6-room', 'water_treated', 'lighting_electric', 'lat-facility', 'tv', 'bicycle', 'car', 'permanent-house']\n",
      "['res_good', 'res-other_good', '4-room', '5-room', '6-room', 'water_treated', 'lighting_electric', 'lat-facility', 'tv', 'bicycle', 'car', 'permanent-house']\n"
     ]
    }
   ],
   "source": [
    "feature_columns = ls_headings[9:]\n",
    "print feature_columns\n",
    "positive_feature_columns=['res_good', 'res-other_good', '4-room', '5-room', '6-room', 'water_treated', 'lighting_electric', 'lat-facility', 'tv', 'bicycle', 'car', 'permanent-house']\n",
    "print positive_feature_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>res_good</th>\n",
       "      <th>res-other_good</th>\n",
       "      <th>4-room</th>\n",
       "      <th>5-room</th>\n",
       "      <th>6-room</th>\n",
       "      <th>water_treated</th>\n",
       "      <th>lighting_electric</th>\n",
       "      <th>lat-facility</th>\n",
       "      <th>tv</th>\n",
       "      <th>bicycle</th>\n",
       "      <th>car</th>\n",
       "      <th>permanent-house</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>14.5</td>\n",
       "      <td>53.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>13.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>61.6</td>\n",
       "      <td>69.4</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>18.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>38.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>16.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.4</td>\n",
       "      <td>66.6</td>\n",
       "      <td>50.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   res_good  res-other_good  4-room  5-room  6-room  water_treated  \\\n",
       "4      16.4             0.0     0.9     0.0     0.0            9.7   \n",
       "5      33.0             3.8    13.1     4.6     2.7            1.6   \n",
       "6      38.1             1.1    16.9     3.8     1.5            0.0   \n",
       "\n",
       "   lighting_electric  lat-facility    tv  bicycle  car  permanent-house  \n",
       "4               14.5          53.3   4.2      0.0  0.0             27.0  \n",
       "5               61.6          69.4  37.0      0.4  1.4             18.6  \n",
       "6               88.4          66.6  50.9      0.7  0.7             25.5  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampleData[positive_feature_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import math\n",
    "sampleData[positive_feature_columns]=sampleData[positive_feature_columns].apply(lambda x: (x-x.mean())/math.sqrt(np.var(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>res_good</th>\n",
       "      <th>res-other_good</th>\n",
       "      <th>4-room</th>\n",
       "      <th>5-room</th>\n",
       "      <th>6-room</th>\n",
       "      <th>water_treated</th>\n",
       "      <th>lighting_electric</th>\n",
       "      <th>lat-facility</th>\n",
       "      <th>tv</th>\n",
       "      <th>bicycle</th>\n",
       "      <th>car</th>\n",
       "      <th>permanent-house</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.378037</td>\n",
       "      <td>-1.023056</td>\n",
       "      <td>-1.37720</td>\n",
       "      <td>-1.395357</td>\n",
       "      <td>-1.267500</td>\n",
       "      <td>1.397379</td>\n",
       "      <td>-1.320386</td>\n",
       "      <td>-1.395357</td>\n",
       "      <td>-1.353510</td>\n",
       "      <td>-1.278724</td>\n",
       "      <td>-1.224745e+00</td>\n",
       "      <td>0.902165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.413771</td>\n",
       "      <td>1.357115</td>\n",
       "      <td>0.41023</td>\n",
       "      <td>0.897015</td>\n",
       "      <td>1.176965</td>\n",
       "      <td>-0.510279</td>\n",
       "      <td>0.221519</td>\n",
       "      <td>0.897015</td>\n",
       "      <td>0.321778</td>\n",
       "      <td>0.116248</td>\n",
       "      <td>1.224745e+00</td>\n",
       "      <td>-1.394255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.964266</td>\n",
       "      <td>-0.334059</td>\n",
       "      <td>0.96697</td>\n",
       "      <td>0.498342</td>\n",
       "      <td>0.090536</td>\n",
       "      <td>-0.887100</td>\n",
       "      <td>1.098867</td>\n",
       "      <td>0.498342</td>\n",
       "      <td>1.031732</td>\n",
       "      <td>1.162476</td>\n",
       "      <td>1.110043e-16</td>\n",
       "      <td>0.492090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   res_good  res-other_good   4-room    5-room    6-room  water_treated  \\\n",
       "4 -1.378037       -1.023056 -1.37720 -1.395357 -1.267500       1.397379   \n",
       "5  0.413771        1.357115  0.41023  0.897015  1.176965      -0.510279   \n",
       "6  0.964266       -0.334059  0.96697  0.498342  0.090536      -0.887100   \n",
       "\n",
       "   lighting_electric  lat-facility        tv   bicycle           car  \\\n",
       "4          -1.320386     -1.395357 -1.353510 -1.278724 -1.224745e+00   \n",
       "5           0.221519      0.897015  0.321778  0.116248  1.224745e+00   \n",
       "6           1.098867      0.498342  1.031732  1.162476  1.110043e-16   \n",
       "\n",
       "   permanent-house  \n",
       "4         0.902165  \n",
       "5        -1.394255  \n",
       "6         0.492090  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampleData[positive_feature_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data[feature_columns] = data[feature_columns].apply(lambda x: (x - x.mean()) / math.sqrt((np.var(x))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'apply'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-7c832d2c8780>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'apply'"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "x=np.array([[1,2,1],[1.1,1,1],[1,3,1.1]])\n",
    "x.apply(lambda x: (x-x.mean())/(np.var(x)))\n",
    "print x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize feature columns to mean 0 variance 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state-code</th>\n",
       "      <th>state-name</th>\n",
       "      <th>district-code</th>\n",
       "      <th>district-name</th>\n",
       "      <th>teh-code</th>\n",
       "      <th>teh-name</th>\n",
       "      <th>vill-code</th>\n",
       "      <th>ward-no</th>\n",
       "      <th>area-name</th>\n",
       "      <th>res_good</th>\n",
       "      <th>...</th>\n",
       "      <th>5-room</th>\n",
       "      <th>6-room</th>\n",
       "      <th>water_treated</th>\n",
       "      <th>lighting_electric</th>\n",
       "      <th>lat-facility</th>\n",
       "      <th>tv</th>\n",
       "      <th>bicycle</th>\n",
       "      <th>car</th>\n",
       "      <th>permanent-house</th>\n",
       "      <th>census_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306145</td>\n",
       "      <td>0000</td>\n",
       "      <td>Singalila Forest</td>\n",
       "      <td>-0.054031</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.588596</td>\n",
       "      <td>-0.408171</td>\n",
       "      <td>-0.015668</td>\n",
       "      <td>-0.055428</td>\n",
       "      <td>-0.038476</td>\n",
       "      <td>-0.077265</td>\n",
       "      <td>-0.152444</td>\n",
       "      <td>-0.159045</td>\n",
       "      <td>-0.050728</td>\n",
       "      <td>1932702154306145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306146</td>\n",
       "      <td>0000</td>\n",
       "      <td>Rimbick</td>\n",
       "      <td>-0.051116</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087115</td>\n",
       "      <td>0.051847</td>\n",
       "      <td>-0.021547</td>\n",
       "      <td>-0.053413</td>\n",
       "      <td>-0.036213</td>\n",
       "      <td>-0.070742</td>\n",
       "      <td>-0.123653</td>\n",
       "      <td>0.006165</td>\n",
       "      <td>-0.055021</td>\n",
       "      <td>1932702154306146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306147</td>\n",
       "      <td>0000</td>\n",
       "      <td>Namla</td>\n",
       "      <td>-0.050220</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030400</td>\n",
       "      <td>-0.152605</td>\n",
       "      <td>-0.022708</td>\n",
       "      <td>-0.052266</td>\n",
       "      <td>-0.036607</td>\n",
       "      <td>-0.067977</td>\n",
       "      <td>-0.102060</td>\n",
       "      <td>-0.076440</td>\n",
       "      <td>-0.051494</td>\n",
       "      <td>1932702154306147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  state-code   state-name district-code district-name teh-code  \\\n",
       "4         19  WEST BENGAL           327    Darjiling     02154   \n",
       "5         19  WEST BENGAL           327    Darjiling     02154   \n",
       "6         19  WEST BENGAL           327    Darjiling     02154   \n",
       "\n",
       "              teh-name vill-code ward-no         area-name  res_good  \\\n",
       "4  Darjeeling Pulbazar    306145    0000  Singalila Forest -0.054031   \n",
       "5  Darjeeling Pulbazar    306146    0000           Rimbick -0.051116   \n",
       "6  Darjeeling Pulbazar    306147    0000             Namla -0.050220   \n",
       "\n",
       "         ...           5-room    6-room  water_treated  lighting_electric  \\\n",
       "4        ...        -0.588596 -0.408171      -0.015668          -0.055428   \n",
       "5        ...         0.087115  0.051847      -0.021547          -0.053413   \n",
       "6        ...        -0.030400 -0.152605      -0.022708          -0.052266   \n",
       "\n",
       "   lat-facility        tv   bicycle       car  permanent-house  \\\n",
       "4     -0.038476 -0.077265 -0.152444 -0.159045        -0.050728   \n",
       "5     -0.036213 -0.070742 -0.123653  0.006165        -0.055021   \n",
       "6     -0.036607 -0.067977 -0.102060 -0.076440        -0.051494   \n",
       "\n",
       "          census_id  \n",
       "4  1932702154306145  \n",
       "5  1932702154306146  \n",
       "6  1932702154306147  \n",
       "\n",
       "[3 rows x 27 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import decomposition\n",
    "pca2 = sklearn.decomposition.PCA(n_components=2, copy=True)\n",
    "pca_full = sklearn.decomposition.PCA()\n",
    "pca2_positive = sklearn.decomposition.PCA(n_components=2, copy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, n_components=None, whiten=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca2.fit(data[feature_columns])\n",
    "pca2_positive.fit(data[positive_feature_columns])\n",
    "pca_full.fit(data[positive_feature_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.58009758  0.13831958]\n",
      "[ 0.61668955  0.14821277]\n",
      "[ 0.61668955  0.14821277  0.10157414  0.06558384  0.03131937  0.02163427\n",
      "  0.0053496   0.00398901  0.0020503   0.00174822  0.00103831  0.00081061]\n"
     ]
    }
   ],
   "source": [
    "print pca2.explained_variance_ratio_\n",
    "print pca2_positive.explained_variance_ratio_\n",
    "print pca_full.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do PCA, keep top 2 principal components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_positive = pca2_positive.fit_transform(data[positive_feature_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = pca2.fit_transform(data[feature_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_positive' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-c6eb76c34add>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mY\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_positive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'X_positive' is not defined"
     ]
    }
   ],
   "source": [
    "Y=X_positive\n",
    "f = plt.figure()\n",
    "plt.scatter(Y[:,0],Y[:,1])\n",
    "plt.plot(np.zeros(len(Y)),Y[:,1])\n",
    "plt.plot(Y[:,0],np.zeros(len(Y)))\n",
    "plt.xlim([-200,200])\n",
    "plt.xlabel('First PC')\n",
    "plt.ylabel('Second PC')\n",
    "plt.title('2-D visualization of data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "len(X_positive)\n",
    "len(data)\n",
    "data['X1']=X_positive[:,0]\n",
    "data['X2']=X_positive[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state-code</th>\n",
       "      <th>state-name</th>\n",
       "      <th>district-code</th>\n",
       "      <th>district-name</th>\n",
       "      <th>teh-code</th>\n",
       "      <th>teh-name</th>\n",
       "      <th>vill-code</th>\n",
       "      <th>ward-no</th>\n",
       "      <th>area-name</th>\n",
       "      <th>res_good</th>\n",
       "      <th>...</th>\n",
       "      <th>lighting_electric</th>\n",
       "      <th>lat-facility</th>\n",
       "      <th>tv</th>\n",
       "      <th>bicycle</th>\n",
       "      <th>car</th>\n",
       "      <th>permanent-house</th>\n",
       "      <th>census_id</th>\n",
       "      <th>newX</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306145</td>\n",
       "      <td>0000</td>\n",
       "      <td>Singalila Forest</td>\n",
       "      <td>-15.439444</td>\n",
       "      <td>...</td>\n",
       "      <td>-26.5919</td>\n",
       "      <td>15.712343</td>\n",
       "      <td>-14.29081</td>\n",
       "      <td>-6.107522</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>-1.037484</td>\n",
       "      <td>1932702154306145</td>\n",
       "      <td>-12.026055</td>\n",
       "      <td>-12.026055</td>\n",
       "      <td>34.015382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306146</td>\n",
       "      <td>0000</td>\n",
       "      <td>Rimbick</td>\n",
       "      <td>1.160556</td>\n",
       "      <td>...</td>\n",
       "      <td>20.5081</td>\n",
       "      <td>31.812343</td>\n",
       "      <td>18.50919</td>\n",
       "      <td>-5.707522</td>\n",
       "      <td>0.266599</td>\n",
       "      <td>-9.437484</td>\n",
       "      <td>1932702154306146</td>\n",
       "      <td>32.794807</td>\n",
       "      <td>32.794807</td>\n",
       "      <td>7.041035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306147</td>\n",
       "      <td>0000</td>\n",
       "      <td>Namla</td>\n",
       "      <td>6.260556</td>\n",
       "      <td>...</td>\n",
       "      <td>47.3081</td>\n",
       "      <td>29.012343</td>\n",
       "      <td>32.40919</td>\n",
       "      <td>-5.407522</td>\n",
       "      <td>-0.433401</td>\n",
       "      <td>-2.537484</td>\n",
       "      <td>1932702154306147</td>\n",
       "      <td>52.542293</td>\n",
       "      <td>52.542293</td>\n",
       "      <td>-12.130106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306148</td>\n",
       "      <td>0000</td>\n",
       "      <td>Lodhama</td>\n",
       "      <td>-31.139444</td>\n",
       "      <td>...</td>\n",
       "      <td>56.9081</td>\n",
       "      <td>-15.787657</td>\n",
       "      <td>-12.39081</td>\n",
       "      <td>-6.107522</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>-18.537484</td>\n",
       "      <td>1932702154306148</td>\n",
       "      <td>-1.343902</td>\n",
       "      <td>-1.343902</td>\n",
       "      <td>-15.780446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306149</td>\n",
       "      <td>0000</td>\n",
       "      <td>Hatta</td>\n",
       "      <td>-3.339444</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.2919</td>\n",
       "      <td>4.112343</td>\n",
       "      <td>3.50919</td>\n",
       "      <td>-5.007522</td>\n",
       "      <td>-0.833401</td>\n",
       "      <td>-5.737484</td>\n",
       "      <td>1932702154306149</td>\n",
       "      <td>-4.278414</td>\n",
       "      <td>-4.278414</td>\n",
       "      <td>7.846165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306150</td>\n",
       "      <td>0000</td>\n",
       "      <td>Dangia</td>\n",
       "      <td>-21.239444</td>\n",
       "      <td>...</td>\n",
       "      <td>-17.9919</td>\n",
       "      <td>29.712343</td>\n",
       "      <td>-9.69081</td>\n",
       "      <td>-5.807522</td>\n",
       "      <td>-0.333401</td>\n",
       "      <td>-21.737484</td>\n",
       "      <td>1932702154306150</td>\n",
       "      <td>-7.263655</td>\n",
       "      <td>-7.263655</td>\n",
       "      <td>41.090386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306151</td>\n",
       "      <td>0000</td>\n",
       "      <td>Singbhumdera</td>\n",
       "      <td>23.560556</td>\n",
       "      <td>...</td>\n",
       "      <td>-40.8919</td>\n",
       "      <td>3.212343</td>\n",
       "      <td>-10.59081</td>\n",
       "      <td>-6.107522</td>\n",
       "      <td>-0.733401</td>\n",
       "      <td>-20.637484</td>\n",
       "      <td>1932702154306151</td>\n",
       "      <td>-24.413896</td>\n",
       "      <td>-24.413896</td>\n",
       "      <td>8.498283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306152</td>\n",
       "      <td>0000</td>\n",
       "      <td>Majua</td>\n",
       "      <td>15.660556</td>\n",
       "      <td>...</td>\n",
       "      <td>-40.6919</td>\n",
       "      <td>29.312343</td>\n",
       "      <td>-11.09081</td>\n",
       "      <td>-6.107522</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>-12.337484</td>\n",
       "      <td>1932702154306152</td>\n",
       "      <td>-5.890732</td>\n",
       "      <td>-5.890732</td>\n",
       "      <td>29.876810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306153</td>\n",
       "      <td>0000</td>\n",
       "      <td>Kankibong</td>\n",
       "      <td>4.560556</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0081</td>\n",
       "      <td>19.512343</td>\n",
       "      <td>3.50919</td>\n",
       "      <td>-4.807522</td>\n",
       "      <td>0.066599</td>\n",
       "      <td>-20.437484</td>\n",
       "      <td>1932702154306153</td>\n",
       "      <td>24.406992</td>\n",
       "      <td>24.406992</td>\n",
       "      <td>-7.306212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306154</td>\n",
       "      <td>0000</td>\n",
       "      <td>Jhepi</td>\n",
       "      <td>-11.339444</td>\n",
       "      <td>...</td>\n",
       "      <td>40.4081</td>\n",
       "      <td>7.512343</td>\n",
       "      <td>8.70919</td>\n",
       "      <td>-5.507522</td>\n",
       "      <td>-0.233401</td>\n",
       "      <td>-19.037484</td>\n",
       "      <td>1932702154306154</td>\n",
       "      <td>17.125519</td>\n",
       "      <td>17.125519</td>\n",
       "      <td>-9.052262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306155</td>\n",
       "      <td>0000</td>\n",
       "      <td>Lamagaon</td>\n",
       "      <td>-27.339444</td>\n",
       "      <td>...</td>\n",
       "      <td>36.0081</td>\n",
       "      <td>26.612343</td>\n",
       "      <td>20.60919</td>\n",
       "      <td>-5.207522</td>\n",
       "      <td>-0.633401</td>\n",
       "      <td>-11.337484</td>\n",
       "      <td>1932702154306155</td>\n",
       "      <td>29.479325</td>\n",
       "      <td>29.479325</td>\n",
       "      <td>12.941563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306156</td>\n",
       "      <td>0000</td>\n",
       "      <td>Relling</td>\n",
       "      <td>29.560556</td>\n",
       "      <td>...</td>\n",
       "      <td>27.2081</td>\n",
       "      <td>19.312343</td>\n",
       "      <td>20.50919</td>\n",
       "      <td>-4.207522</td>\n",
       "      <td>0.166599</td>\n",
       "      <td>-11.237484</td>\n",
       "      <td>1932702154306156</td>\n",
       "      <td>35.037839</td>\n",
       "      <td>35.037839</td>\n",
       "      <td>-20.694939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306157</td>\n",
       "      <td>0000</td>\n",
       "      <td>Kaijalia</td>\n",
       "      <td>-20.639444</td>\n",
       "      <td>...</td>\n",
       "      <td>43.7081</td>\n",
       "      <td>8.412343</td>\n",
       "      <td>14.50919</td>\n",
       "      <td>-5.707522</td>\n",
       "      <td>-0.033401</td>\n",
       "      <td>-6.937484</td>\n",
       "      <td>1932702154306157</td>\n",
       "      <td>23.362977</td>\n",
       "      <td>23.362977</td>\n",
       "      <td>-5.215708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306158</td>\n",
       "      <td>0000</td>\n",
       "      <td>Samalbong</td>\n",
       "      <td>2.160556</td>\n",
       "      <td>...</td>\n",
       "      <td>34.5081</td>\n",
       "      <td>13.212343</td>\n",
       "      <td>31.20919</td>\n",
       "      <td>-4.407522</td>\n",
       "      <td>-0.033401</td>\n",
       "      <td>-5.337484</td>\n",
       "      <td>1932702154306158</td>\n",
       "      <td>33.409243</td>\n",
       "      <td>33.409243</td>\n",
       "      <td>-14.248607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306159</td>\n",
       "      <td>0000</td>\n",
       "      <td>Kolbong</td>\n",
       "      <td>9.760556</td>\n",
       "      <td>...</td>\n",
       "      <td>47.1081</td>\n",
       "      <td>47.212343</td>\n",
       "      <td>27.60919</td>\n",
       "      <td>-6.107522</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>-19.637484</td>\n",
       "      <td>1932702154306159</td>\n",
       "      <td>57.562330</td>\n",
       "      <td>57.562330</td>\n",
       "      <td>-2.365009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306160</td>\n",
       "      <td>0000</td>\n",
       "      <td>Murmidong</td>\n",
       "      <td>-30.439444</td>\n",
       "      <td>...</td>\n",
       "      <td>24.5081</td>\n",
       "      <td>-13.187657</td>\n",
       "      <td>-2.09081</td>\n",
       "      <td>-6.107522</td>\n",
       "      <td>-0.933401</td>\n",
       "      <td>-25.337484</td>\n",
       "      <td>1932702154306160</td>\n",
       "      <td>-15.237671</td>\n",
       "      <td>-15.237671</td>\n",
       "      <td>-2.379982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306161</td>\n",
       "      <td>0000</td>\n",
       "      <td>Karmi</td>\n",
       "      <td>-27.439444</td>\n",
       "      <td>...</td>\n",
       "      <td>25.6081</td>\n",
       "      <td>-8.687657</td>\n",
       "      <td>6.00919</td>\n",
       "      <td>-5.107522</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>-24.137484</td>\n",
       "      <td>1932702154306161</td>\n",
       "      <td>-8.097329</td>\n",
       "      <td>-8.097329</td>\n",
       "      <td>-3.143992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306162</td>\n",
       "      <td>0000</td>\n",
       "      <td>Goke</td>\n",
       "      <td>-15.439444</td>\n",
       "      <td>...</td>\n",
       "      <td>32.1081</td>\n",
       "      <td>12.012343</td>\n",
       "      <td>9.00919</td>\n",
       "      <td>-4.307522</td>\n",
       "      <td>-0.433401</td>\n",
       "      <td>-20.037484</td>\n",
       "      <td>1932702154306162</td>\n",
       "      <td>15.321560</td>\n",
       "      <td>15.321560</td>\n",
       "      <td>-0.655927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306163</td>\n",
       "      <td>0000</td>\n",
       "      <td>Bijanbari</td>\n",
       "      <td>-3.039444</td>\n",
       "      <td>...</td>\n",
       "      <td>37.5081</td>\n",
       "      <td>3.712343</td>\n",
       "      <td>41.50919</td>\n",
       "      <td>-3.707522</td>\n",
       "      <td>1.366599</td>\n",
       "      <td>5.862516</td>\n",
       "      <td>1932702154306163</td>\n",
       "      <td>34.769012</td>\n",
       "      <td>34.769012</td>\n",
       "      <td>-20.149832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306164</td>\n",
       "      <td>0000</td>\n",
       "      <td>Liza Hill Tea Garden</td>\n",
       "      <td>-26.539444</td>\n",
       "      <td>...</td>\n",
       "      <td>51.1081</td>\n",
       "      <td>40.312343</td>\n",
       "      <td>38.10919</td>\n",
       "      <td>-3.007522</td>\n",
       "      <td>-0.533401</td>\n",
       "      <td>-14.337484</td>\n",
       "      <td>1932702154306164</td>\n",
       "      <td>50.973678</td>\n",
       "      <td>50.973678</td>\n",
       "      <td>10.261460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306165</td>\n",
       "      <td>0000</td>\n",
       "      <td>Barnesbeg Tea Garden</td>\n",
       "      <td>-30.539444</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0081</td>\n",
       "      <td>34.212343</td>\n",
       "      <td>39.10919</td>\n",
       "      <td>-2.907522</td>\n",
       "      <td>0.766599</td>\n",
       "      <td>-23.637484</td>\n",
       "      <td>1932702154306165</td>\n",
       "      <td>33.455300</td>\n",
       "      <td>33.455300</td>\n",
       "      <td>16.418640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306166</td>\n",
       "      <td>0000</td>\n",
       "      <td>Singla Tea Garden</td>\n",
       "      <td>-6.239444</td>\n",
       "      <td>...</td>\n",
       "      <td>50.3081</td>\n",
       "      <td>6.212343</td>\n",
       "      <td>22.90919</td>\n",
       "      <td>-4.307522</td>\n",
       "      <td>-0.333401</td>\n",
       "      <td>-26.737484</td>\n",
       "      <td>1932702154306166</td>\n",
       "      <td>23.830711</td>\n",
       "      <td>23.830711</td>\n",
       "      <td>-20.402100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306167</td>\n",
       "      <td>0000</td>\n",
       "      <td>Rangli Forest</td>\n",
       "      <td>62.560556</td>\n",
       "      <td>...</td>\n",
       "      <td>3.3081</td>\n",
       "      <td>62.412343</td>\n",
       "      <td>14.80919</td>\n",
       "      <td>-6.107522</td>\n",
       "      <td>4.466599</td>\n",
       "      <td>10.862516</td>\n",
       "      <td>1932702154306167</td>\n",
       "      <td>65.983693</td>\n",
       "      <td>65.983693</td>\n",
       "      <td>1.752812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306168</td>\n",
       "      <td>0000</td>\n",
       "      <td>Banockburn Tea Garden</td>\n",
       "      <td>-22.939444</td>\n",
       "      <td>...</td>\n",
       "      <td>57.2081</td>\n",
       "      <td>37.612343</td>\n",
       "      <td>40.90919</td>\n",
       "      <td>-2.607522</td>\n",
       "      <td>0.266599</td>\n",
       "      <td>-18.537484</td>\n",
       "      <td>1932702154306168</td>\n",
       "      <td>53.116522</td>\n",
       "      <td>53.116522</td>\n",
       "      <td>2.200021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306169</td>\n",
       "      <td>0000</td>\n",
       "      <td>Phubsering Tea Garden</td>\n",
       "      <td>7.860556</td>\n",
       "      <td>...</td>\n",
       "      <td>57.4081</td>\n",
       "      <td>28.012343</td>\n",
       "      <td>50.40919</td>\n",
       "      <td>-4.907522</td>\n",
       "      <td>-1.033401</td>\n",
       "      <td>-11.737484</td>\n",
       "      <td>1932702154306169</td>\n",
       "      <td>61.340272</td>\n",
       "      <td>61.340272</td>\n",
       "      <td>-23.841905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306170</td>\n",
       "      <td>0000</td>\n",
       "      <td>Rungneet Tea Garden</td>\n",
       "      <td>36.560556</td>\n",
       "      <td>...</td>\n",
       "      <td>54.9081</td>\n",
       "      <td>53.112343</td>\n",
       "      <td>65.20919</td>\n",
       "      <td>-3.407522</td>\n",
       "      <td>0.566599</td>\n",
       "      <td>12.162516</td>\n",
       "      <td>1932702154306170</td>\n",
       "      <td>96.031271</td>\n",
       "      <td>96.031271</td>\n",
       "      <td>-23.415666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306171</td>\n",
       "      <td>0000</td>\n",
       "      <td>Pattabong Tea Garden</td>\n",
       "      <td>3.960556</td>\n",
       "      <td>...</td>\n",
       "      <td>50.4081</td>\n",
       "      <td>39.112343</td>\n",
       "      <td>28.00919</td>\n",
       "      <td>-0.907522</td>\n",
       "      <td>3.266599</td>\n",
       "      <td>-5.937484</td>\n",
       "      <td>1932702154306171</td>\n",
       "      <td>59.266305</td>\n",
       "      <td>59.266305</td>\n",
       "      <td>-6.614909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306172</td>\n",
       "      <td>0000</td>\n",
       "      <td>Patliabas Forest</td>\n",
       "      <td>38.960556</td>\n",
       "      <td>...</td>\n",
       "      <td>58.9081</td>\n",
       "      <td>62.412343</td>\n",
       "      <td>77.30919</td>\n",
       "      <td>18.892478</td>\n",
       "      <td>32.166599</td>\n",
       "      <td>-11.337484</td>\n",
       "      <td>1932702154306172</td>\n",
       "      <td>101.143006</td>\n",
       "      <td>101.143006</td>\n",
       "      <td>-24.911825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306173</td>\n",
       "      <td>0000</td>\n",
       "      <td>Tukvar Tea Garden</td>\n",
       "      <td>-5.439444</td>\n",
       "      <td>...</td>\n",
       "      <td>57.5081</td>\n",
       "      <td>16.812343</td>\n",
       "      <td>41.70919</td>\n",
       "      <td>-1.707522</td>\n",
       "      <td>0.366599</td>\n",
       "      <td>-17.437484</td>\n",
       "      <td>1932702154306173</td>\n",
       "      <td>44.412098</td>\n",
       "      <td>44.412098</td>\n",
       "      <td>-21.046106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>327</td>\n",
       "      <td>Darjiling</td>\n",
       "      <td>02154</td>\n",
       "      <td>Darjeeling Pulbazar</td>\n",
       "      <td>306174</td>\n",
       "      <td>0000</td>\n",
       "      <td>Lebong Tea Garden</td>\n",
       "      <td>-26.039444</td>\n",
       "      <td>...</td>\n",
       "      <td>56.6081</td>\n",
       "      <td>-17.187657</td>\n",
       "      <td>29.30919</td>\n",
       "      <td>-4.407522</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>-23.237484</td>\n",
       "      <td>1932702154306174</td>\n",
       "      <td>10.131013</td>\n",
       "      <td>10.131013</td>\n",
       "      <td>-28.414187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37942</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347102</td>\n",
       "      <td>0000</td>\n",
       "      <td>Chak Pratappur</td>\n",
       "      <td>-21.839444</td>\n",
       "      <td>...</td>\n",
       "      <td>-41.0919</td>\n",
       "      <td>52.412343</td>\n",
       "      <td>-8.49081</td>\n",
       "      <td>-6.107522</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>-18.037484</td>\n",
       "      <td>1934502494347102</td>\n",
       "      <td>-2.757826</td>\n",
       "      <td>-2.757826</td>\n",
       "      <td>66.846338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37943</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347103</td>\n",
       "      <td>0000</td>\n",
       "      <td>Islampur</td>\n",
       "      <td>-12.839444</td>\n",
       "      <td>...</td>\n",
       "      <td>-20.8919</td>\n",
       "      <td>60.712343</td>\n",
       "      <td>-7.59081</td>\n",
       "      <td>-1.607522</td>\n",
       "      <td>0.066599</td>\n",
       "      <td>0.762516</td>\n",
       "      <td>1934502494347103</td>\n",
       "      <td>23.147095</td>\n",
       "      <td>23.147095</td>\n",
       "      <td>58.241975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37944</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347104</td>\n",
       "      <td>0000</td>\n",
       "      <td>Satbatia</td>\n",
       "      <td>9.360556</td>\n",
       "      <td>...</td>\n",
       "      <td>31.1081</td>\n",
       "      <td>56.212343</td>\n",
       "      <td>12.40919</td>\n",
       "      <td>-0.907522</td>\n",
       "      <td>2.966599</td>\n",
       "      <td>30.762516</td>\n",
       "      <td>1934502494347104</td>\n",
       "      <td>70.162636</td>\n",
       "      <td>70.162636</td>\n",
       "      <td>16.179167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37945</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347105</td>\n",
       "      <td>0000</td>\n",
       "      <td>Ghol</td>\n",
       "      <td>-8.539444</td>\n",
       "      <td>...</td>\n",
       "      <td>6.7081</td>\n",
       "      <td>48.412343</td>\n",
       "      <td>6.30919</td>\n",
       "      <td>0.392478</td>\n",
       "      <td>-0.333401</td>\n",
       "      <td>15.362516</td>\n",
       "      <td>1934502494347105</td>\n",
       "      <td>40.061352</td>\n",
       "      <td>40.061352</td>\n",
       "      <td>33.236581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37946</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347106</td>\n",
       "      <td>0000</td>\n",
       "      <td>Banbar</td>\n",
       "      <td>-25.839444</td>\n",
       "      <td>...</td>\n",
       "      <td>9.6081</td>\n",
       "      <td>47.512343</td>\n",
       "      <td>-0.59081</td>\n",
       "      <td>-6.107522</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>13.762516</td>\n",
       "      <td>1934502494347106</td>\n",
       "      <td>33.380036</td>\n",
       "      <td>33.380036</td>\n",
       "      <td>42.874213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37947</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347107</td>\n",
       "      <td>0000</td>\n",
       "      <td>Uttar Shitala</td>\n",
       "      <td>22.060556</td>\n",
       "      <td>...</td>\n",
       "      <td>13.2081</td>\n",
       "      <td>60.412343</td>\n",
       "      <td>13.10919</td>\n",
       "      <td>-0.207522</td>\n",
       "      <td>0.466599</td>\n",
       "      <td>20.762516</td>\n",
       "      <td>1934502494347107</td>\n",
       "      <td>67.740329</td>\n",
       "      <td>67.740329</td>\n",
       "      <td>16.277853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37948</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347108</td>\n",
       "      <td>0000</td>\n",
       "      <td>Dakshin Shitala</td>\n",
       "      <td>7.260556</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.7919</td>\n",
       "      <td>55.912343</td>\n",
       "      <td>2.60919</td>\n",
       "      <td>-0.007522</td>\n",
       "      <td>0.266599</td>\n",
       "      <td>16.862516</td>\n",
       "      <td>1934502494347108</td>\n",
       "      <td>39.693382</td>\n",
       "      <td>39.693382</td>\n",
       "      <td>37.586408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37949</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347109</td>\n",
       "      <td>0000</td>\n",
       "      <td>Keishnapur</td>\n",
       "      <td>-29.139444</td>\n",
       "      <td>...</td>\n",
       "      <td>-32.9919</td>\n",
       "      <td>-21.387657</td>\n",
       "      <td>-13.09081</td>\n",
       "      <td>4.692478</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>-1.037484</td>\n",
       "      <td>1934502494347109</td>\n",
       "      <td>-42.518194</td>\n",
       "      <td>-42.518194</td>\n",
       "      <td>20.201546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37950</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347110</td>\n",
       "      <td>0000</td>\n",
       "      <td>Chhota Chaulkhola</td>\n",
       "      <td>-26.539444</td>\n",
       "      <td>...</td>\n",
       "      <td>-35.7919</td>\n",
       "      <td>-32.287657</td>\n",
       "      <td>-18.49081</td>\n",
       "      <td>-6.107522</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>-22.737484</td>\n",
       "      <td>1934502494347110</td>\n",
       "      <td>-61.416000</td>\n",
       "      <td>-61.416000</td>\n",
       "      <td>13.822319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37951</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347111</td>\n",
       "      <td>0000</td>\n",
       "      <td>Keshabpur</td>\n",
       "      <td>-31.839444</td>\n",
       "      <td>...</td>\n",
       "      <td>-14.1919</td>\n",
       "      <td>-24.587657</td>\n",
       "      <td>-12.89081</td>\n",
       "      <td>-3.307522</td>\n",
       "      <td>0.766599</td>\n",
       "      <td>-16.037484</td>\n",
       "      <td>1934502494347111</td>\n",
       "      <td>-42.241019</td>\n",
       "      <td>-42.241019</td>\n",
       "      <td>11.061209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37952</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347112</td>\n",
       "      <td>0000</td>\n",
       "      <td>Kalindi</td>\n",
       "      <td>4.760556</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.7919</td>\n",
       "      <td>53.112343</td>\n",
       "      <td>0.40919</td>\n",
       "      <td>0.992478</td>\n",
       "      <td>-0.033401</td>\n",
       "      <td>13.562516</td>\n",
       "      <td>1934502494347112</td>\n",
       "      <td>38.503182</td>\n",
       "      <td>38.503182</td>\n",
       "      <td>34.696728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37953</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347113</td>\n",
       "      <td>0000</td>\n",
       "      <td>Suberia</td>\n",
       "      <td>0.060556</td>\n",
       "      <td>...</td>\n",
       "      <td>5.6081</td>\n",
       "      <td>50.912343</td>\n",
       "      <td>1.10919</td>\n",
       "      <td>-0.207522</td>\n",
       "      <td>-0.033401</td>\n",
       "      <td>5.262516</td>\n",
       "      <td>1934502494347113</td>\n",
       "      <td>37.908059</td>\n",
       "      <td>37.908059</td>\n",
       "      <td>30.906854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37954</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347114</td>\n",
       "      <td>0000</td>\n",
       "      <td>Purbba Bar</td>\n",
       "      <td>-1.739444</td>\n",
       "      <td>...</td>\n",
       "      <td>22.7081</td>\n",
       "      <td>57.212343</td>\n",
       "      <td>2.90919</td>\n",
       "      <td>5.692478</td>\n",
       "      <td>0.166599</td>\n",
       "      <td>13.862516</td>\n",
       "      <td>1934502494347114</td>\n",
       "      <td>54.511317</td>\n",
       "      <td>54.511317</td>\n",
       "      <td>27.969896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37955</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347115</td>\n",
       "      <td>0000</td>\n",
       "      <td>Purbba Gadhadharpur</td>\n",
       "      <td>55.660556</td>\n",
       "      <td>...</td>\n",
       "      <td>15.7081</td>\n",
       "      <td>61.312343</td>\n",
       "      <td>0.80919</td>\n",
       "      <td>-2.707522</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>-6.437484</td>\n",
       "      <td>1934502494347115</td>\n",
       "      <td>62.372846</td>\n",
       "      <td>62.372846</td>\n",
       "      <td>-1.215772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37956</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347116</td>\n",
       "      <td>0000</td>\n",
       "      <td>Ramchandra Nagar</td>\n",
       "      <td>29.260556</td>\n",
       "      <td>...</td>\n",
       "      <td>5.9081</td>\n",
       "      <td>59.012343</td>\n",
       "      <td>-3.69081</td>\n",
       "      <td>1.292478</td>\n",
       "      <td>2.866599</td>\n",
       "      <td>18.962516</td>\n",
       "      <td>1934502494347116</td>\n",
       "      <td>54.729566</td>\n",
       "      <td>54.729566</td>\n",
       "      <td>20.867722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37957</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347117</td>\n",
       "      <td>0000</td>\n",
       "      <td>Purbba Ramchandrapur</td>\n",
       "      <td>-19.339444</td>\n",
       "      <td>...</td>\n",
       "      <td>13.1081</td>\n",
       "      <td>62.412343</td>\n",
       "      <td>-5.99081</td>\n",
       "      <td>2.192478</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>46.962516</td>\n",
       "      <td>1934502494347117</td>\n",
       "      <td>58.272975</td>\n",
       "      <td>58.272975</td>\n",
       "      <td>49.345391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37958</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347118</td>\n",
       "      <td>0000</td>\n",
       "      <td>Lachhandrapur</td>\n",
       "      <td>16.360556</td>\n",
       "      <td>...</td>\n",
       "      <td>-17.2919</td>\n",
       "      <td>59.412343</td>\n",
       "      <td>-1.99081</td>\n",
       "      <td>1.192478</td>\n",
       "      <td>-0.533401</td>\n",
       "      <td>12.162516</td>\n",
       "      <td>1934502494347118</td>\n",
       "      <td>37.854509</td>\n",
       "      <td>37.854509</td>\n",
       "      <td>38.317475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37959</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347119</td>\n",
       "      <td>0000</td>\n",
       "      <td>Bishnupur</td>\n",
       "      <td>-24.339444</td>\n",
       "      <td>...</td>\n",
       "      <td>15.6081</td>\n",
       "      <td>62.412343</td>\n",
       "      <td>-10.99081</td>\n",
       "      <td>-1.607522</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>-1.137484</td>\n",
       "      <td>1934502494347119</td>\n",
       "      <td>38.098080</td>\n",
       "      <td>38.098080</td>\n",
       "      <td>49.907734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37960</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347120</td>\n",
       "      <td>0000</td>\n",
       "      <td>Haurburi</td>\n",
       "      <td>-23.139444</td>\n",
       "      <td>...</td>\n",
       "      <td>19.8081</td>\n",
       "      <td>62.412343</td>\n",
       "      <td>-9.79081</td>\n",
       "      <td>-6.107522</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>-19.337484</td>\n",
       "      <td>1934502494347120</td>\n",
       "      <td>33.518084</td>\n",
       "      <td>33.518084</td>\n",
       "      <td>46.598296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37961</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347121</td>\n",
       "      <td>0000</td>\n",
       "      <td>Kismat Haurburi</td>\n",
       "      <td>10.660556</td>\n",
       "      <td>...</td>\n",
       "      <td>-41.0919</td>\n",
       "      <td>61.012343</td>\n",
       "      <td>-10.29081</td>\n",
       "      <td>0.692478</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>4.862516</td>\n",
       "      <td>1934502494347121</td>\n",
       "      <td>20.103867</td>\n",
       "      <td>20.103867</td>\n",
       "      <td>54.621800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37962</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347122</td>\n",
       "      <td>0000</td>\n",
       "      <td>Teghari</td>\n",
       "      <td>7.960556</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.7919</td>\n",
       "      <td>56.312343</td>\n",
       "      <td>3.30919</td>\n",
       "      <td>-0.007522</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>2.662516</td>\n",
       "      <td>1934502494347122</td>\n",
       "      <td>36.193905</td>\n",
       "      <td>36.193905</td>\n",
       "      <td>35.555259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37963</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347123</td>\n",
       "      <td>0000</td>\n",
       "      <td>Purbba Purushottampur</td>\n",
       "      <td>-15.339444</td>\n",
       "      <td>...</td>\n",
       "      <td>-17.0919</td>\n",
       "      <td>61.712343</td>\n",
       "      <td>-6.49081</td>\n",
       "      <td>-2.007522</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>-1.837484</td>\n",
       "      <td>1934502494347123</td>\n",
       "      <td>24.284504</td>\n",
       "      <td>24.284504</td>\n",
       "      <td>58.392557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37964</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347125</td>\n",
       "      <td>0000</td>\n",
       "      <td>Daudpur</td>\n",
       "      <td>-31.839444</td>\n",
       "      <td>...</td>\n",
       "      <td>7.2081</td>\n",
       "      <td>45.212343</td>\n",
       "      <td>-4.69081</td>\n",
       "      <td>-6.107522</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>-10.837484</td>\n",
       "      <td>1934502494347125</td>\n",
       "      <td>18.373361</td>\n",
       "      <td>18.373361</td>\n",
       "      <td>45.653567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37965</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347126</td>\n",
       "      <td>0000</td>\n",
       "      <td>Dhunia Baraj</td>\n",
       "      <td>32.260556</td>\n",
       "      <td>...</td>\n",
       "      <td>13.6081</td>\n",
       "      <td>57.712343</td>\n",
       "      <td>9.60919</td>\n",
       "      <td>6.392478</td>\n",
       "      <td>1.966599</td>\n",
       "      <td>15.762516</td>\n",
       "      <td>1934502494347126</td>\n",
       "      <td>61.634873</td>\n",
       "      <td>61.634873</td>\n",
       "      <td>11.801251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37966</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347127</td>\n",
       "      <td>0000</td>\n",
       "      <td>Dera</td>\n",
       "      <td>-15.239444</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.6919</td>\n",
       "      <td>49.312343</td>\n",
       "      <td>-5.89081</td>\n",
       "      <td>0.492478</td>\n",
       "      <td>0.366599</td>\n",
       "      <td>-2.037484</td>\n",
       "      <td>1934502494347127</td>\n",
       "      <td>22.364077</td>\n",
       "      <td>22.364077</td>\n",
       "      <td>45.245088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37967</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347129</td>\n",
       "      <td>0000</td>\n",
       "      <td>Sona Muhi</td>\n",
       "      <td>-30.239444</td>\n",
       "      <td>...</td>\n",
       "      <td>-41.0919</td>\n",
       "      <td>45.212343</td>\n",
       "      <td>-12.79081</td>\n",
       "      <td>-4.507522</td>\n",
       "      <td>0.466599</td>\n",
       "      <td>-7.537484</td>\n",
       "      <td>1934502494347129</td>\n",
       "      <td>-6.567331</td>\n",
       "      <td>-6.567331</td>\n",
       "      <td>68.053487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37968</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347130</td>\n",
       "      <td>0000</td>\n",
       "      <td>Rania</td>\n",
       "      <td>-10.439444</td>\n",
       "      <td>...</td>\n",
       "      <td>-14.7919</td>\n",
       "      <td>50.812343</td>\n",
       "      <td>-8.09081</td>\n",
       "      <td>-1.207522</td>\n",
       "      <td>-0.333401</td>\n",
       "      <td>1.262516</td>\n",
       "      <td>1934502494347130</td>\n",
       "      <td>25.333249</td>\n",
       "      <td>25.333249</td>\n",
       "      <td>44.409309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37969</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347131</td>\n",
       "      <td>0000</td>\n",
       "      <td>Dadanpatra</td>\n",
       "      <td>4.560556</td>\n",
       "      <td>...</td>\n",
       "      <td>-34.6919</td>\n",
       "      <td>49.312343</td>\n",
       "      <td>-15.09081</td>\n",
       "      <td>1.992478</td>\n",
       "      <td>-0.833401</td>\n",
       "      <td>23.162516</td>\n",
       "      <td>1934502494347131</td>\n",
       "      <td>19.720165</td>\n",
       "      <td>19.720165</td>\n",
       "      <td>49.410243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37970</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347132</td>\n",
       "      <td>0000</td>\n",
       "      <td>Mania</td>\n",
       "      <td>-31.839444</td>\n",
       "      <td>...</td>\n",
       "      <td>-41.0919</td>\n",
       "      <td>-37.587657</td>\n",
       "      <td>-18.49081</td>\n",
       "      <td>-6.107522</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>-28.037484</td>\n",
       "      <td>1934502494347132</td>\n",
       "      <td>-70.955012</td>\n",
       "      <td>-70.955012</td>\n",
       "      <td>15.566596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37971</th>\n",
       "      <td>19</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>345</td>\n",
       "      <td>Purba Medinipur</td>\n",
       "      <td>02494</td>\n",
       "      <td>Ramnagar - II</td>\n",
       "      <td>347133</td>\n",
       "      <td>0000</td>\n",
       "      <td>Dakshin Purushottampur</td>\n",
       "      <td>-25.939444</td>\n",
       "      <td>...</td>\n",
       "      <td>-29.5919</td>\n",
       "      <td>46.212343</td>\n",
       "      <td>-13.99081</td>\n",
       "      <td>-5.107522</td>\n",
       "      <td>-1.133401</td>\n",
       "      <td>-10.637484</td>\n",
       "      <td>1934502494347133</td>\n",
       "      <td>-0.699504</td>\n",
       "      <td>-0.699504</td>\n",
       "      <td>61.234581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37235 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      state-code   state-name district-code    district-name teh-code  \\\n",
       "4             19  WEST BENGAL           327       Darjiling     02154   \n",
       "5             19  WEST BENGAL           327       Darjiling     02154   \n",
       "6             19  WEST BENGAL           327       Darjiling     02154   \n",
       "7             19  WEST BENGAL           327       Darjiling     02154   \n",
       "8             19  WEST BENGAL           327       Darjiling     02154   \n",
       "9             19  WEST BENGAL           327       Darjiling     02154   \n",
       "10            19  WEST BENGAL           327       Darjiling     02154   \n",
       "11            19  WEST BENGAL           327       Darjiling     02154   \n",
       "12            19  WEST BENGAL           327       Darjiling     02154   \n",
       "13            19  WEST BENGAL           327       Darjiling     02154   \n",
       "14            19  WEST BENGAL           327       Darjiling     02154   \n",
       "15            19  WEST BENGAL           327       Darjiling     02154   \n",
       "16            19  WEST BENGAL           327       Darjiling     02154   \n",
       "17            19  WEST BENGAL           327       Darjiling     02154   \n",
       "18            19  WEST BENGAL           327       Darjiling     02154   \n",
       "19            19  WEST BENGAL           327       Darjiling     02154   \n",
       "20            19  WEST BENGAL           327       Darjiling     02154   \n",
       "21            19  WEST BENGAL           327       Darjiling     02154   \n",
       "22            19  WEST BENGAL           327       Darjiling     02154   \n",
       "23            19  WEST BENGAL           327       Darjiling     02154   \n",
       "24            19  WEST BENGAL           327       Darjiling     02154   \n",
       "25            19  WEST BENGAL           327       Darjiling     02154   \n",
       "26            19  WEST BENGAL           327       Darjiling     02154   \n",
       "27            19  WEST BENGAL           327       Darjiling     02154   \n",
       "28            19  WEST BENGAL           327       Darjiling     02154   \n",
       "29            19  WEST BENGAL           327       Darjiling     02154   \n",
       "30            19  WEST BENGAL           327       Darjiling     02154   \n",
       "31            19  WEST BENGAL           327       Darjiling     02154   \n",
       "32            19  WEST BENGAL           327       Darjiling     02154   \n",
       "33            19  WEST BENGAL           327       Darjiling     02154   \n",
       "...          ...          ...           ...              ...      ...   \n",
       "37942         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37943         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37944         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37945         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37946         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37947         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37948         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37949         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37950         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37951         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37952         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37953         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37954         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37955         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37956         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37957         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37958         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37959         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37960         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37961         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37962         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37963         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37964         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37965         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37966         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37967         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37968         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37969         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37970         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "37971         19  WEST BENGAL           345  Purba Medinipur    02494   \n",
       "\n",
       "                  teh-name vill-code ward-no               area-name  \\\n",
       "4      Darjeeling Pulbazar    306145    0000        Singalila Forest   \n",
       "5      Darjeeling Pulbazar    306146    0000                 Rimbick   \n",
       "6      Darjeeling Pulbazar    306147    0000                   Namla   \n",
       "7      Darjeeling Pulbazar    306148    0000                 Lodhama   \n",
       "8      Darjeeling Pulbazar    306149    0000                   Hatta   \n",
       "9      Darjeeling Pulbazar    306150    0000                  Dangia   \n",
       "10     Darjeeling Pulbazar    306151    0000            Singbhumdera   \n",
       "11     Darjeeling Pulbazar    306152    0000                   Majua   \n",
       "12     Darjeeling Pulbazar    306153    0000               Kankibong   \n",
       "13     Darjeeling Pulbazar    306154    0000                   Jhepi   \n",
       "14     Darjeeling Pulbazar    306155    0000                Lamagaon   \n",
       "15     Darjeeling Pulbazar    306156    0000                 Relling   \n",
       "16     Darjeeling Pulbazar    306157    0000                Kaijalia   \n",
       "17     Darjeeling Pulbazar    306158    0000               Samalbong   \n",
       "18     Darjeeling Pulbazar    306159    0000                 Kolbong   \n",
       "19     Darjeeling Pulbazar    306160    0000               Murmidong   \n",
       "20     Darjeeling Pulbazar    306161    0000                   Karmi   \n",
       "21     Darjeeling Pulbazar    306162    0000                    Goke   \n",
       "22     Darjeeling Pulbazar    306163    0000               Bijanbari   \n",
       "23     Darjeeling Pulbazar    306164    0000    Liza Hill Tea Garden   \n",
       "24     Darjeeling Pulbazar    306165    0000    Barnesbeg Tea Garden   \n",
       "25     Darjeeling Pulbazar    306166    0000       Singla Tea Garden   \n",
       "26     Darjeeling Pulbazar    306167    0000           Rangli Forest   \n",
       "27     Darjeeling Pulbazar    306168    0000   Banockburn Tea Garden   \n",
       "28     Darjeeling Pulbazar    306169    0000   Phubsering Tea Garden   \n",
       "29     Darjeeling Pulbazar    306170    0000     Rungneet Tea Garden   \n",
       "30     Darjeeling Pulbazar    306171    0000    Pattabong Tea Garden   \n",
       "31     Darjeeling Pulbazar    306172    0000        Patliabas Forest   \n",
       "32     Darjeeling Pulbazar    306173    0000       Tukvar Tea Garden   \n",
       "33     Darjeeling Pulbazar    306174    0000       Lebong Tea Garden   \n",
       "...                    ...       ...     ...                     ...   \n",
       "37942        Ramnagar - II    347102    0000          Chak Pratappur   \n",
       "37943        Ramnagar - II    347103    0000                Islampur   \n",
       "37944        Ramnagar - II    347104    0000                Satbatia   \n",
       "37945        Ramnagar - II    347105    0000                    Ghol   \n",
       "37946        Ramnagar - II    347106    0000                  Banbar   \n",
       "37947        Ramnagar - II    347107    0000           Uttar Shitala   \n",
       "37948        Ramnagar - II    347108    0000         Dakshin Shitala   \n",
       "37949        Ramnagar - II    347109    0000              Keishnapur   \n",
       "37950        Ramnagar - II    347110    0000       Chhota Chaulkhola   \n",
       "37951        Ramnagar - II    347111    0000               Keshabpur   \n",
       "37952        Ramnagar - II    347112    0000                 Kalindi   \n",
       "37953        Ramnagar - II    347113    0000                 Suberia   \n",
       "37954        Ramnagar - II    347114    0000              Purbba Bar   \n",
       "37955        Ramnagar - II    347115    0000     Purbba Gadhadharpur   \n",
       "37956        Ramnagar - II    347116    0000        Ramchandra Nagar   \n",
       "37957        Ramnagar - II    347117    0000    Purbba Ramchandrapur   \n",
       "37958        Ramnagar - II    347118    0000           Lachhandrapur   \n",
       "37959        Ramnagar - II    347119    0000               Bishnupur   \n",
       "37960        Ramnagar - II    347120    0000                Haurburi   \n",
       "37961        Ramnagar - II    347121    0000         Kismat Haurburi   \n",
       "37962        Ramnagar - II    347122    0000                 Teghari   \n",
       "37963        Ramnagar - II    347123    0000   Purbba Purushottampur   \n",
       "37964        Ramnagar - II    347125    0000                 Daudpur   \n",
       "37965        Ramnagar - II    347126    0000            Dhunia Baraj   \n",
       "37966        Ramnagar - II    347127    0000                    Dera   \n",
       "37967        Ramnagar - II    347129    0000               Sona Muhi   \n",
       "37968        Ramnagar - II    347130    0000                   Rania   \n",
       "37969        Ramnagar - II    347131    0000              Dadanpatra   \n",
       "37970        Ramnagar - II    347132    0000                   Mania   \n",
       "37971        Ramnagar - II    347133    0000  Dakshin Purushottampur   \n",
       "\n",
       "        res_good    ...      lighting_electric  lat-facility        tv  \\\n",
       "4     -15.439444    ...               -26.5919     15.712343 -14.29081   \n",
       "5       1.160556    ...                20.5081     31.812343  18.50919   \n",
       "6       6.260556    ...                47.3081     29.012343  32.40919   \n",
       "7     -31.139444    ...                56.9081    -15.787657 -12.39081   \n",
       "8      -3.339444    ...                -8.2919      4.112343   3.50919   \n",
       "9     -21.239444    ...               -17.9919     29.712343  -9.69081   \n",
       "10     23.560556    ...               -40.8919      3.212343 -10.59081   \n",
       "11     15.660556    ...               -40.6919     29.312343 -11.09081   \n",
       "12      4.560556    ...                35.0081     19.512343   3.50919   \n",
       "13    -11.339444    ...                40.4081      7.512343   8.70919   \n",
       "14    -27.339444    ...                36.0081     26.612343  20.60919   \n",
       "15     29.560556    ...                27.2081     19.312343  20.50919   \n",
       "16    -20.639444    ...                43.7081      8.412343  14.50919   \n",
       "17      2.160556    ...                34.5081     13.212343  31.20919   \n",
       "18      9.760556    ...                47.1081     47.212343  27.60919   \n",
       "19    -30.439444    ...                24.5081    -13.187657  -2.09081   \n",
       "20    -27.439444    ...                25.6081     -8.687657   6.00919   \n",
       "21    -15.439444    ...                32.1081     12.012343   9.00919   \n",
       "22     -3.039444    ...                37.5081      3.712343  41.50919   \n",
       "23    -26.539444    ...                51.1081     40.312343  38.10919   \n",
       "24    -30.539444    ...                33.0081     34.212343  39.10919   \n",
       "25     -6.239444    ...                50.3081      6.212343  22.90919   \n",
       "26     62.560556    ...                 3.3081     62.412343  14.80919   \n",
       "27    -22.939444    ...                57.2081     37.612343  40.90919   \n",
       "28      7.860556    ...                57.4081     28.012343  50.40919   \n",
       "29     36.560556    ...                54.9081     53.112343  65.20919   \n",
       "30      3.960556    ...                50.4081     39.112343  28.00919   \n",
       "31     38.960556    ...                58.9081     62.412343  77.30919   \n",
       "32     -5.439444    ...                57.5081     16.812343  41.70919   \n",
       "33    -26.039444    ...                56.6081    -17.187657  29.30919   \n",
       "...          ...    ...                    ...           ...       ...   \n",
       "37942 -21.839444    ...               -41.0919     52.412343  -8.49081   \n",
       "37943 -12.839444    ...               -20.8919     60.712343  -7.59081   \n",
       "37944   9.360556    ...                31.1081     56.212343  12.40919   \n",
       "37945  -8.539444    ...                 6.7081     48.412343   6.30919   \n",
       "37946 -25.839444    ...                 9.6081     47.512343  -0.59081   \n",
       "37947  22.060556    ...                13.2081     60.412343  13.10919   \n",
       "37948   7.260556    ...               -10.7919     55.912343   2.60919   \n",
       "37949 -29.139444    ...               -32.9919    -21.387657 -13.09081   \n",
       "37950 -26.539444    ...               -35.7919    -32.287657 -18.49081   \n",
       "37951 -31.839444    ...               -14.1919    -24.587657 -12.89081   \n",
       "37952   4.760556    ...                -4.7919     53.112343   0.40919   \n",
       "37953   0.060556    ...                 5.6081     50.912343   1.10919   \n",
       "37954  -1.739444    ...                22.7081     57.212343   2.90919   \n",
       "37955  55.660556    ...                15.7081     61.312343   0.80919   \n",
       "37956  29.260556    ...                 5.9081     59.012343  -3.69081   \n",
       "37957 -19.339444    ...                13.1081     62.412343  -5.99081   \n",
       "37958  16.360556    ...               -17.2919     59.412343  -1.99081   \n",
       "37959 -24.339444    ...                15.6081     62.412343 -10.99081   \n",
       "37960 -23.139444    ...                19.8081     62.412343  -9.79081   \n",
       "37961  10.660556    ...               -41.0919     61.012343 -10.29081   \n",
       "37962   7.960556    ...                -7.7919     56.312343   3.30919   \n",
       "37963 -15.339444    ...               -17.0919     61.712343  -6.49081   \n",
       "37964 -31.839444    ...                 7.2081     45.212343  -4.69081   \n",
       "37965  32.260556    ...                13.6081     57.712343   9.60919   \n",
       "37966 -15.239444    ...               -10.6919     49.312343  -5.89081   \n",
       "37967 -30.239444    ...               -41.0919     45.212343 -12.79081   \n",
       "37968 -10.439444    ...               -14.7919     50.812343  -8.09081   \n",
       "37969   4.560556    ...               -34.6919     49.312343 -15.09081   \n",
       "37970 -31.839444    ...               -41.0919    -37.587657 -18.49081   \n",
       "37971 -25.939444    ...               -29.5919     46.212343 -13.99081   \n",
       "\n",
       "         bicycle        car  permanent-house         census_id        newX  \\\n",
       "4      -6.107522  -1.133401        -1.037484  1932702154306145  -12.026055   \n",
       "5      -5.707522   0.266599        -9.437484  1932702154306146   32.794807   \n",
       "6      -5.407522  -0.433401        -2.537484  1932702154306147   52.542293   \n",
       "7      -6.107522  -1.133401       -18.537484  1932702154306148   -1.343902   \n",
       "8      -5.007522  -0.833401        -5.737484  1932702154306149   -4.278414   \n",
       "9      -5.807522  -0.333401       -21.737484  1932702154306150   -7.263655   \n",
       "10     -6.107522  -0.733401       -20.637484  1932702154306151  -24.413896   \n",
       "11     -6.107522  -1.133401       -12.337484  1932702154306152   -5.890732   \n",
       "12     -4.807522   0.066599       -20.437484  1932702154306153   24.406992   \n",
       "13     -5.507522  -0.233401       -19.037484  1932702154306154   17.125519   \n",
       "14     -5.207522  -0.633401       -11.337484  1932702154306155   29.479325   \n",
       "15     -4.207522   0.166599       -11.237484  1932702154306156   35.037839   \n",
       "16     -5.707522  -0.033401        -6.937484  1932702154306157   23.362977   \n",
       "17     -4.407522  -0.033401        -5.337484  1932702154306158   33.409243   \n",
       "18     -6.107522  -1.133401       -19.637484  1932702154306159   57.562330   \n",
       "19     -6.107522  -0.933401       -25.337484  1932702154306160  -15.237671   \n",
       "20     -5.107522  -1.133401       -24.137484  1932702154306161   -8.097329   \n",
       "21     -4.307522  -0.433401       -20.037484  1932702154306162   15.321560   \n",
       "22     -3.707522   1.366599         5.862516  1932702154306163   34.769012   \n",
       "23     -3.007522  -0.533401       -14.337484  1932702154306164   50.973678   \n",
       "24     -2.907522   0.766599       -23.637484  1932702154306165   33.455300   \n",
       "25     -4.307522  -0.333401       -26.737484  1932702154306166   23.830711   \n",
       "26     -6.107522   4.466599        10.862516  1932702154306167   65.983693   \n",
       "27     -2.607522   0.266599       -18.537484  1932702154306168   53.116522   \n",
       "28     -4.907522  -1.033401       -11.737484  1932702154306169   61.340272   \n",
       "29     -3.407522   0.566599        12.162516  1932702154306170   96.031271   \n",
       "30     -0.907522   3.266599        -5.937484  1932702154306171   59.266305   \n",
       "31     18.892478  32.166599       -11.337484  1932702154306172  101.143006   \n",
       "32     -1.707522   0.366599       -17.437484  1932702154306173   44.412098   \n",
       "33     -4.407522  -1.133401       -23.237484  1932702154306174   10.131013   \n",
       "...          ...        ...              ...               ...         ...   \n",
       "37942  -6.107522  -1.133401       -18.037484  1934502494347102   -2.757826   \n",
       "37943  -1.607522   0.066599         0.762516  1934502494347103   23.147095   \n",
       "37944  -0.907522   2.966599        30.762516  1934502494347104   70.162636   \n",
       "37945   0.392478  -0.333401        15.362516  1934502494347105   40.061352   \n",
       "37946  -6.107522  -1.133401        13.762516  1934502494347106   33.380036   \n",
       "37947  -0.207522   0.466599        20.762516  1934502494347107   67.740329   \n",
       "37948  -0.007522   0.266599        16.862516  1934502494347108   39.693382   \n",
       "37949   4.692478  -1.133401        -1.037484  1934502494347109  -42.518194   \n",
       "37950  -6.107522  -1.133401       -22.737484  1934502494347110  -61.416000   \n",
       "37951  -3.307522   0.766599       -16.037484  1934502494347111  -42.241019   \n",
       "37952   0.992478  -0.033401        13.562516  1934502494347112   38.503182   \n",
       "37953  -0.207522  -0.033401         5.262516  1934502494347113   37.908059   \n",
       "37954   5.692478   0.166599        13.862516  1934502494347114   54.511317   \n",
       "37955  -2.707522  -1.133401        -6.437484  1934502494347115   62.372846   \n",
       "37956   1.292478   2.866599        18.962516  1934502494347116   54.729566   \n",
       "37957   2.192478  -1.133401        46.962516  1934502494347117   58.272975   \n",
       "37958   1.192478  -0.533401        12.162516  1934502494347118   37.854509   \n",
       "37959  -1.607522  -1.133401        -1.137484  1934502494347119   38.098080   \n",
       "37960  -6.107522  -1.133401       -19.337484  1934502494347120   33.518084   \n",
       "37961   0.692478  -1.133401         4.862516  1934502494347121   20.103867   \n",
       "37962  -0.007522  -1.133401         2.662516  1934502494347122   36.193905   \n",
       "37963  -2.007522  -1.133401        -1.837484  1934502494347123   24.284504   \n",
       "37964  -6.107522  -1.133401       -10.837484  1934502494347125   18.373361   \n",
       "37965   6.392478   1.966599        15.762516  1934502494347126   61.634873   \n",
       "37966   0.492478   0.366599        -2.037484  1934502494347127   22.364077   \n",
       "37967  -4.507522   0.466599        -7.537484  1934502494347129   -6.567331   \n",
       "37968  -1.207522  -0.333401         1.262516  1934502494347130   25.333249   \n",
       "37969   1.992478  -0.833401        23.162516  1934502494347131   19.720165   \n",
       "37970  -6.107522  -1.133401       -28.037484  1934502494347132  -70.955012   \n",
       "37971  -5.107522  -1.133401       -10.637484  1934502494347133   -0.699504   \n",
       "\n",
       "               X1         X2  \n",
       "4      -12.026055  34.015382  \n",
       "5       32.794807   7.041035  \n",
       "6       52.542293 -12.130106  \n",
       "7       -1.343902 -15.780446  \n",
       "8       -4.278414   7.846165  \n",
       "9       -7.263655  41.090386  \n",
       "10     -24.413896   8.498283  \n",
       "11      -5.890732  29.876810  \n",
       "12      24.406992  -7.306212  \n",
       "13      17.125519  -9.052262  \n",
       "14      29.479325  12.941563  \n",
       "15      35.037839 -20.694939  \n",
       "16      23.362977  -5.215708  \n",
       "17      33.409243 -14.248607  \n",
       "18      57.562330  -2.365009  \n",
       "19     -15.237671  -2.379982  \n",
       "20      -8.097329  -3.143992  \n",
       "21      15.321560  -0.655927  \n",
       "22      34.769012 -20.149832  \n",
       "23      50.973678  10.261460  \n",
       "24      33.455300  16.418640  \n",
       "25      23.830711 -20.402100  \n",
       "26      65.983693   1.752812  \n",
       "27      53.116522   2.200021  \n",
       "28      61.340272 -23.841905  \n",
       "29      96.031271 -23.415666  \n",
       "30      59.266305  -6.614909  \n",
       "31     101.143006 -24.911825  \n",
       "32      44.412098 -21.046106  \n",
       "33      10.131013 -28.414187  \n",
       "...           ...        ...  \n",
       "37942   -2.757826  66.846338  \n",
       "37943   23.147095  58.241975  \n",
       "37944   70.162636  16.179167  \n",
       "37945   40.061352  33.236581  \n",
       "37946   33.380036  42.874213  \n",
       "37947   67.740329  16.277853  \n",
       "37948   39.693382  37.586408  \n",
       "37949  -42.518194  20.201546  \n",
       "37950  -61.416000  13.822319  \n",
       "37951  -42.241019  11.061209  \n",
       "37952   38.503182  34.696728  \n",
       "37953   37.908059  30.906854  \n",
       "37954   54.511317  27.969896  \n",
       "37955   62.372846  -1.215772  \n",
       "37956   54.729566  20.867722  \n",
       "37957   58.272975  49.345391  \n",
       "37958   37.854509  38.317475  \n",
       "37959   38.098080  49.907734  \n",
       "37960   33.518084  46.598296  \n",
       "37961   20.103867  54.621800  \n",
       "37962   36.193905  35.555259  \n",
       "37963   24.284504  58.392557  \n",
       "37964   18.373361  45.653567  \n",
       "37965   61.634873  11.801251  \n",
       "37966   22.364077  45.245088  \n",
       "37967   -6.567331  68.053487  \n",
       "37968   25.333249  44.409309  \n",
       "37969   19.720165  49.410243  \n",
       "37970  -70.955012  15.566596  \n",
       "37971   -0.699504  61.234581  \n",
       "\n",
       "[37235 rows x 30 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataN=data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "toSaveData=dataN.drop(feature_columns,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "toSaveData.to_csv(\"PCAAssetOwnershipPositive.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No clusters can be observed, a continuum of feature values for villages is observed. \n",
    "Next we'll do a 3D plot to see if this extends in 3 dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.3596259   0.12682423  0.11600016  0.1061766   0.06481167  0.0592992\n",
      "  0.04809804  0.0452674   0.02710791  0.01420802  0.01148861  0.00790677\n",
      "  0.00487153  0.00385105  0.00304265  0.00089605  0.0005242 ]\n"
     ]
    }
   ],
   "source": [
    "pca_full = sklearn.decomposition.PCA()\n",
    "pca_full.fit(data[feature_columns])\n",
    "print pca_full.explained_variance_ratio_\n",
    "X_full = pca_full.fit_transform(data[feature_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f88c49c6290>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7CO8VjuMQCoXgcDh4t2i6rncm4hjZABNrjJxFKNIoYpFGLWlA3c5Ip9OpOIZK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DcRDhehabvQtFj69LmHqqrXKSz8hFBoIuXlU9C0MkKhKAKBGILBYoLBEDk5mXz++TLq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\nCFGBSbImhBBCCFGB/T/iEpV7C/5okQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88c4da9550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "#fig = plt.figure()\n",
    "import pylab\n",
    "fig = pylab.figure()\n",
    "ax = Axes3D(fig)\n",
    "ax.scatter(X_full[:,0],X_full[:,1],X_full[:,2])\n",
    "ax.set_xlabel('First PC')\n",
    "ax.set_ylabel('Second PC')\n",
    "ax.set_zlabel('Third PC')\n",
    "ax.set_title('3D visualization of village data')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, no clusters can be observed here too.\n",
    "So now our aim is to use a samping strategy with the following properties :\n",
    "-select villages with maximum variance, i.e. we want villages farther from the mean to have a higher probability of getting picked by our sampling algorithm.\n",
    "-for this we will assign weights to each village proportional to it's distance from the centre point (for the first two principal components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import math\n",
    "distance_vec = np.sqrt(np.mean(X*X, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sampledData = data.sample(n=10100, weights = distance_vec)\n",
    "sampledData.shape\n",
    "uniformlySampledData = data.sample(n=10100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sampledData uses the above sampling strategy while uniformlySampledData uses uniform sampling (we will compare the results below)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state-code</th>\n",
       "      <th>state-name</th>\n",
       "      <th>district-code</th>\n",
       "      <th>district-name</th>\n",
       "      <th>teh-code</th>\n",
       "      <th>teh-name</th>\n",
       "      <th>vill-code</th>\n",
       "      <th>ward-no</th>\n",
       "      <th>area-name</th>\n",
       "      <th>res_good</th>\n",
       "      <th>...</th>\n",
       "      <th>5-room</th>\n",
       "      <th>6-room</th>\n",
       "      <th>water_treated</th>\n",
       "      <th>lighting_electric</th>\n",
       "      <th>lat-facility</th>\n",
       "      <th>tv</th>\n",
       "      <th>bicycle</th>\n",
       "      <th>car</th>\n",
       "      <th>permanent-house</th>\n",
       "      <th>census_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8438</th>\n",
       "      <td>03</td>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>047</td>\n",
       "      <td>Mansa</td>\n",
       "      <td>00249</td>\n",
       "      <td>Mansa</td>\n",
       "      <td>036085</td>\n",
       "      <td>0000</td>\n",
       "      <td>Khiala Khurd (53)</td>\n",
       "      <td>0.094864</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063014</td>\n",
       "      <td>0.125653</td>\n",
       "      <td>0.054714</td>\n",
       "      <td>0.051316</td>\n",
       "      <td>-0.011781</td>\n",
       "      <td>0.023811</td>\n",
       "      <td>-0.047376</td>\n",
       "      <td>-0.050320</td>\n",
       "      <td>-0.036104</td>\n",
       "      <td>0304700249036085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12074</th>\n",
       "      <td>03</td>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>053</td>\n",
       "      <td>Sangrur</td>\n",
       "      <td>00272</td>\n",
       "      <td>Sunam</td>\n",
       "      <td>039830</td>\n",
       "      <td>0000</td>\n",
       "      <td>Ladbanjara Kalan (147)</td>\n",
       "      <td>-0.057215</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.068461</td>\n",
       "      <td>-0.068597</td>\n",
       "      <td>-0.027717</td>\n",
       "      <td>0.022223</td>\n",
       "      <td>0.011079</td>\n",
       "      <td>0.004900</td>\n",
       "      <td>-0.022989</td>\n",
       "      <td>-0.037544</td>\n",
       "      <td>0.034971</td>\n",
       "      <td>0305300272039830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15472</th>\n",
       "      <td>06</td>\n",
       "      <td>HARYANA</td>\n",
       "      <td>077</td>\n",
       "      <td>Jind</td>\n",
       "      <td>00383</td>\n",
       "      <td>Jind</td>\n",
       "      <td>060156</td>\n",
       "      <td>0000</td>\n",
       "      <td>Shamlo Khurd (63)</td>\n",
       "      <td>-0.064763</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225220</td>\n",
       "      <td>-0.135469</td>\n",
       "      <td>0.061598</td>\n",
       "      <td>0.032667</td>\n",
       "      <td>-0.030168</td>\n",
       "      <td>0.000729</td>\n",
       "      <td>-0.071067</td>\n",
       "      <td>-0.128570</td>\n",
       "      <td>-0.157725</td>\n",
       "      <td>0607700383060156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      state-code state-name district-code district-name teh-code teh-name  \\\n",
       "8438          03     PUNJAB           047         Mansa    00249    Mansa   \n",
       "12074         03     PUNJAB           053       Sangrur    00272    Sunam   \n",
       "15472         06    HARYANA           077          Jind    00383     Jind   \n",
       "\n",
       "      vill-code ward-no               area-name  res_good        ...         \\\n",
       "8438     036085    0000       Khiala Khurd (53)  0.094864        ...          \n",
       "12074    039830    0000  Ladbanjara Kalan (147) -0.057215        ...          \n",
       "15472    060156    0000       Shamlo Khurd (63) -0.064763        ...          \n",
       "\n",
       "         5-room    6-room  water_treated  lighting_electric  lat-facility  \\\n",
       "8438   0.063014  0.125653       0.054714           0.051316     -0.011781   \n",
       "12074 -0.068461 -0.068597      -0.027717           0.022223      0.011079   \n",
       "15472 -0.225220 -0.135469       0.061598           0.032667     -0.030168   \n",
       "\n",
       "             tv   bicycle       car  permanent-house         census_id  \n",
       "8438   0.023811 -0.047376 -0.050320        -0.036104  0304700249036085  \n",
       "12074  0.004900 -0.022989 -0.037544         0.034971  0305300272039830  \n",
       "15472  0.000729 -0.071067 -0.128570        -0.157725  0607700383060156  \n",
       "\n",
       "[3 rows x 27 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampledData.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_new = pca_full.fit_transform(sampledData[feature_columns])\n",
    "X_uniformly = pca_full.transform(uniformlySampledData[feature_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f88c48d5150>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Jl6mpuKZ1+WhfQ00SocLuo51gMzkBa6aRiRN9LAlWgdCW3my6xzK9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wZWI2GRkZCAsLg0gkwo4dO9CyZUvdD4pHNA3W7t+/z/Th0dOXQEVmq127dli5cqVOn0/3hnK/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\nEAgEAUOCNQKBQCAQCAQB83/i1leAnLhirAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88c47c2510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "#fig = plt.figure()\n",
    "import pylab\n",
    "fig = pylab.figure()\n",
    "ax = Axes3D(fig)\n",
    "ax.scatter(X_new[:,0],X_new[:,1],X_new[:,2])\n",
    "ax.set_xlabel('First PC')\n",
    "ax.set_ylabel('Second PC')\n",
    "ax.set_zlabel('Third PC')\n",
    "ax.set_title('3D visualization of weight-sampled data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's compare the villages sampled by the 2 different sampling strategies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1, 2.5)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ixYuntT0iIiLNTsGWiIiUZcmSJfT19dPWtgE/lPB+YJC2tivp6+vXEEIREZEYBVsiIlK2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\nplcLbFzhtUMem89LCkHNkdnvq714HbD+7H7kg5+rkn3T+1H0/UmHMJb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LUMQM6x133FFQ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wGFeVEvkfQG1ReMKmvn4bS/mdiGayVtLdnmaxNwnpaN474V+YvscY2kmxWC68cVPiNG2ACY1gi2\nAGB6iJmq1QoB1XuT9+6U9D8KwwGfI+mG3L77lGW2CrIxzXxb0nPN7IhcdmvJGI+TKivwcIPC8Leo\nZnFlDwVAvi7pL7PKgbdK+kNJb1MIwmZKOtvdDxWBMLP1h9HORmaY2dNz93NZ9vNb9ZtLCsVBHpHU\n5+6fa3L8bytkJPOe2ULbHlL4PhR9Rvn9X6LW71vZ5/abCsMvX5u+aGYLRGYLwDTHX5QAYHr4skIH\n+lxJv6wks5UFQV9RGJp2lGqHEErS9Qod5bcUHTibY1NmWNITJB0qG57NPXqdWqiKVyJWD5yfvuju\nD7r755LHLdn55mZD1VJfz85/RPb8seznoe2yzv4rxtnGVpyfe/56hflthYGUhzXMPiHp5WZ2Yv59\nM3ti8nSHpOPN7KXJ+7Ml/UGzRrn7YwrZqpdl2c64/8mSXpDbPGbyWrlvjyj3mSXHqMn8mdlGheGM\nADCtkdkCgGnA3X9hZnsUMluPKmSzUl9UqGCXn68ldx/JSr+/zcI6V5+S9BOFog+/IelvFcqQF/mX\n7FzvM7NlCvOSfl1h7pE0joDL3R8xs4qkjWZ2n8Kws7uzQiBF1kl6r5l9XKH0/ExJr1QY7vav2TbD\nkt4laYeZbVEoCf8ahSIgT6474uH7maSXZsUk9iiUPn+hpMsL5qyl3iRpjaQ7snbeI+kYSacrfLbH\nZdtdrRDQXpfNV3tQ4Zobza9L/YXCsgBfMLMPKczhOl/Sf0r61WS7sdy3OyW92sKaaPskPejuuyV9\nRtJlZvZ3km6TdKqkjZK+KQCY5gi2AGD6+LxCRcIvpxXgMl+Q9EaFznjdvB93/0szu0fSHyt0xKUw\nP+cGhc5yzebJfo+b2YsU1nr6fYUM0r8oDN27VSHwK9y3yeuvyo75XoXs1J8rBB5FvqJQre8lClm9\nR7JrXO/ud2XtvMfMfkthLakrFBZO/luFoPLqgrYUtbPR63k/V6jUeJWk31aomPgX7v6XjY6ZzS87\nQyHL+DKF4Gq/Qqbu0mS7R8zs+Qrzni7IruOjCnOi8sNE6xvs/lUz26BwLy5XGM53mUJJ9l9NthvL\nfXurwvpmlyosMXCzQtXCtyuUpf8dSb+rkIXdIOk9Gn/2EwC6grnz3zkAwMTKOujbJa109z2dbs9k\nMrOPKsxxajT8EgDQA7puzpaZ/ZmZ3WFm/21mD5nZJ8xsaZN9Xmlmj5vZwezn42b208lqMwBMZ/m1\nrbL5U+crZHO+2pFGAQAwBXTjMMLVCkMYvqzQ/ndK2mlmJ2YLOpZ5WNJSVSfpktIDgIlxZbZQ7u0K\nxTJ+S2FNpksKhjMCANAzui7Ycveh9LmZ/Z6kH0haofoKW7ld/YdtbBoA9KqbFdZgerFCsDUi6XXu\nnp/T00v4gx4AoPuCrQLzFf6nNtpkuzlm9i2FoZN3SbrM3f+rzW0DgGnP3T8m6WOdbsdU4e6bOt0G\nAMDU0NUFMrK1XD4t6Wh3H2iw3UqFxR/vljRP0iUKpXVPcvfvFWy/UKGK1LdUX0kLAAAAQO94gqSn\nSxp29/1j2bHbg60PKQRFZ7n798ew3wyFEsLXuXvdIp5mdo74Ky0AAACAqnPd/bqx7NC1wwjN7AOS\nhiStHkugJUnu/piZfUUh21XkW5K0detWnXjiiYfVTnSviy66SO9973s73Qx0EN8BSHwPwHcAfAd6\n3T333KPzzjtPymKEsejKYCsLtF4qacDd7x/H/n2STlH5wo+PStKJJ56o5cuXj7ud6G7z5s3j8+9x\nfAcg8T0A3wHwHcAhY55e1HXBlpldKWmjpJdIesTMjs3eetjdH822uVbSd939suz5n0u6TdJehYIa\nb5L0VEl/N8nNBwAAANAjui7YkvRaheqDt+Re/31J/5j9frykg8l7CyRdI+k4SQck3SnpTHf/Rltb\nCgAAAKBndV2w5e59LWwzmHv+RklvbFujAAAAACCnaeAC9KqNGzd2ugnoML4DkPgegO8A+A5g/Lq6\n9Hu7mNlySXfeeeedTIYEAAAAethdd92lFStWSNIKd79rLPuS2QIAAACANiDYAgAAAIA2INgCAAAA\ngDYg2AIAAACANiDYAgAAAIA2INgCAAAAgDYg2AIAAACANiDYAgAAAIA2INgCAAAAgDYg2AIAAACA\nNiDYAgAAAIA2INgCAAAAgDYg2AIAAACANiDYAgAAAIA2INgCAAAAgDYg2AIAAACANiDYAgAAAIA2\nINgCAAAAgDYg2AIAAACANiDYAgAAAIA2INgCAAAAgDaY0ekGAAC6T6VS0b59+7R48WItWbKk080B\nAGBKIrMFAGjZ6OioNmw4W8uWLdPQ0JCWLl2qDRvO1oEDBzrdNAAAphyCLQBAy845Z5N27bpN0lZJ\n90vaql27btPGjed1uGUAAEw9DCMEALSkUqloeHiHQqB1bvbquTp40DU8vEkjIyMMKQQAIEFmCwDQ\nkn379mW/rcm9MyBJ2rt376S2BwCAqY5gCwDQkkWLFmW/3Zp7Z7ckafHixZPaHgAApjqCLQBAS5Yu\nXar164fU33+BwlDCByRtVX//hVq/foghhAAA5BBsAQBatm3bVq1du1LSJklPlbRJa9eu1LZtWzvc\nMgAAph4KZAAAWrZgwQLddNMNGhkZ0d69e1lnCwCABgi2AABjtmTJEoIsAACaYBghAAAAALQBwRYA\nAAAAtAHBFgAAAAC0AcEWAAAAALQBwRYAAAAAtAHBFgAAAAC0QdcFW2b2Z2Z2h5n9t5k9ZGafMLOl\nLez322Z2j5n9zMy+ZmYvmoz2AgAAAOhNXRdsSVot6W8lPUfSWkkzJe00syPLdjCz50q6TtIWSadJ\n+qSkfzOzk9rfXAAAAAC9qOsWNXb3ofS5mf2epB9IWiHp8yW7XSDpRnd/T/b8L8xsnaTzJb2uTU0F\nAAAA0MO6MbOVN1+SSxptsM2ZknblXhvOXgcAAACACdfVwZaZmaS/kfR5d/+vBpseJ+mh3GsPZa8D\nAAAAwITrumGEOVdKOknSWZ1uCAAAAACkujbYMrMPSBqStNrdv99k8wclHZt77djs9VIXXXSR5s2b\nV/Paxo0btXHjxjG2FgAAAMBUt23bNm3btq3mtYcffnjcxzN3P9w2Tbos0HqppAF3v6+F7f9J0pHu\n/tLktS9I+pq71xXIMLPlku688847tXz58glsOQAAAIBuctddd2nFihWStMLd7xrLvl2X2TKzKyVt\nlPQSSY+YWcxYPezuj2bbXCvpu+5+Wfbe+yTdYmZvlHRDtv8KSa+Z1MYDAAAA6BndWCDjtZLmSrpF\n0veSx8uTbY5XUvzC3b8k6RxJfyjpq5JeJumlTYpqAAAAAMC4dV1my92bBojuPljw2vWSrm9LowAA\nAAAgpxszWwAAAAAw5RFsAQAAAEAbEGwBAAAAQBsQbAEAAABAGxBsAQAAAEAbEGwBAAAAQBsQbAEA\nAABAGxBsAQAAAEAbEGwBAAAAQBsQbAEAAABAGxBsAQAAAEAbEGwBAAAAQBvM6HQDAGC6qVQq2rdv\nnxYvXqwlS5Z0ujkAAKBDyGwBwAQZHR3Vhg1na9myZRoaGtLSpUu1YcPZOnDgQKebBgAAOoBgCwAm\nyDnnbNKuXbdJ2irpfklbtWvXbdq48bwOtwwAAHQCwwgBYAJUKhUND+9QCLTOzV49VwcPuoaHN2lk\nZIQhhQAA9BgyWwAwAfbt25f9tib3zoAkae/evZPanvGoVCq68cYbNTIy0ummAAAwLRBsAcAEWLRo\nUfbbrbl3dkuSFi9ePKntGQvmmgEA0B4EWwAwAZYuXar164fU33+BwlDCByRtVX//hVq/fmhKDyFk\nrhkAAO1BsAUAE2Tbtq1au3alpE2Snippk9auXalt27Z2uGXl4lyzgwffrzDX7HiFuWbv0/DwDoYU\nAgBwGCiQAQATZMGCBbrpphs0MjKivXv3dsU6W63MNZvq1wAAwFRFsAUAE2zJkiVdE6DUzjU7N3ln\n6s81AwBgqmMYIQD0sG6eawYAwFRHsAUAPa4b55oBANANGEYIAD2uG+eaAQDQDQi2ALRNpVLRvn37\n6Lx3iW6aawYAQDdgGCGACcciuQAAAARbANqARXIBAAAYRghggsVFckOgFUuJn6uDB13Dw5s0MjLC\nUDUAANATyGwBmFCtLJILAADQCwi2AEyo2kVyUyySCwAAegvBFoAJxSK5AAAAAcEWgAnHIrkAAAAU\nyADQBiySCwAAQLAFoI1YJBcAAPQyhhECAAAAQBsQbAEAAABAGxBsAQAAAEAbEGwBAAAAQBt0XbBl\nZqvN7FNm9l0ze9zMXtJk+4Fsu/Rx0MyePFltBgAAANB7ui7YkjRb0lclve7/s/fu8XVd5Z33bx/Z\nUWLLulhJHAoJEFt27FwsZDtxE12walWOmJS2gfI6iZO35PIWAgkwccLQdooxLW9iFwItYFtNghsT\nVxCGKRTFEn55LchAYjUOzXSm4+PYbZMOHUqlECiFtmM/88fay3vttde+HR0dnXP0+34++3N09mXt\ntS9O1u88z/o9ACTjMQKgA8BF/vIaEfmH2ekeIYQQQgghhNSg9buIHAJwCAA8z/NyHPoDEfnR7PSK\nEEIIIYQQQsLUYmSrFDwA3/U873ue5417nnftXHeIEEIIIYQQUt/MB7H19wD+HwA3AvhVAC8DOOJ5\nXuec9ooQQgghhBBS19RcGmFeRKQIoGisesbzvOUA3g/gtrnpFSGEEEIIIaTeqXuxFcNRANel7fT+\n978fLS0toXVbt27F1q1bZ6tfhBBCCCGEkDni4MGDOHjwYGjdq6++WnJ7nkhWQ7/qw/O8MwB+WUS+\nkvO4cQA/EpG3xWzvAvDcc889h66urjL0lBBCCCGEEFKLHDt2DOvWrQOAdSJyLM+xNRfZ8jxvMYAV\nUKYXAHCp53lrAUyLyMue530MwM+JyG3+/vcC+GsA/w3AuQDuBLAJwEDFO08IKTvFYhEnT57EihUr\n0NHRMdfdIYQQQgg5S82JLQDrAfz/ULWzBMDv++v3A3gnVB2ti439z/H3+TkA/wzgBQC/ICLfrFSH\nCSHlZ3p6GjfdtA1jY6Nn1w0ODuHgwQNoa2ubw54RQgghhChqTmyJyAQSXBRF5Net77sA7Jrtfs1H\nGFEgc8lNN23D4cPPADgAoBfAN3H48D3YuvUWHDr0tTnuHSGEEELI/LB+J2VmenoaW7a8BatWrcLQ\n0BBWrlyJLVvegldeeWWuu0bmCcViEWNjozh9+lMAboYKZt+M06c/ibGxUZw4cWKOe0gIIYQQQrFF\nSiAcUXgJwAEcPvwMtm69ZY57RuYLJ0+e9P/qtbb0AQBefPHFivaHEEIIIcQFxRbJBSMKpBpYvny5\n/5c99XICALBixYqK9ocQQgghxAXFFskFIwqkGli5ciUGB4fQ0HAPVIT1ZQAH0NBwLwYHhziHkBBC\nCCFVAcUWyQUjCqRaOHjwADZv3ghgG4BLAGzD5s0bcfDggTnuGSGEEEKIoubcCMncoiMKhw/fg9On\nBSqiNYGGhnuxeXN8RIHOhaTctLW14dChr+HEiRN48cUX+W4RQgghpOqg2CK5OXjwALZuvQVjY9vO\nrtu8ecgZUWAtJDLbdHR0UGQRQgghpCqh2CK5yRNRYC0kQgghhBAyX6HYIiWTFlHQzoVKaN3sr70Z\np08Lxsa24cSJE4xIEEIIIYSQuoUGGWTWoHNhbVMsFvHUU0/Rzp8QQgghpEQotsisQefC2mR6ehpb\ntrwFq1atwtDQEFauXIktW96CV155Za67RgghhBBSU1BskVmDtZBqk/A8u5cAHMDhw89g69Zb5rhn\nhBBCCCG1BcUWmVVYC6m20PPsTp/+FNQ8u4uh5tl9EmNjo0wpJIQQQgjJAQ0yyKzCWki1RZZ5dnx+\nhBBCCCHZoNgiFYG1kGqD8Dy7m40tnGdHCCGEEJIXphGSeQmd9txwnh0hhBBCSPmg2CLzCjrtpcN5\ndoQQQggh5YFphGReEXba6wXwTRw+fA+2br0Fhw59bY57Vx1wnh0hhBBCSHmg2BGoEWgAACAASURB\nVCLzBu20p4SWno90M06fFoyNbcOJEycoKgw4z44QQgghZGYwjZDMG7I47RFCCCGEEFIuKLbIvCHs\ntGdCpz1CCCGEEFJ+KLbIvIFOe4QQQgghpJJQbJF5BZ32CCGEEEJIpaBBBimJYrGIkydP1pxTHZ32\nCCGEEEJIpaDYIrmYnp7GTTdt8139FIODQzh48ADa2trOrqt2MUanveqi2t8XQgghhJBSYBohyUW4\nTtVLAA7g8OFnsHXrLQBYNJjkg+8LIYQQQuoZii2SGV2n6vTpT0HVqboYqk7VJzE2NooTJ06kijFC\nTPi+EEIIIaSeYRohyUxanaojR46waDDJDItME0IIIaTeYWSLZCatTpXnef53Fg0m6bDINCGEEELq\nHYotkpm0OlW9vXrQzKLBs0WxWMRTTz2FEydOzHVXZgyLTBNCCCGk3qHYIrlIqlPFosGzRz0aSfB9\nIYQQQki9Q7FFcqHrVBWLRYyOjqJYLOLQoa+dtX1n0eDZoV6NJPi+EEIIIaSe8URkrvtQdXie1wXg\nueeeew5dXV1z3Z2aZK6LBtdT3aZisYhVq1YhbCQB//s2FIvFObvGct3nuX5fCCGEEELiOHbsGNat\nWwcA60TkWJ5j6UZIZoW5KhqctejyXFGKOMniAllpoVLu+8wi04QQQgipR5hGSOqKak23m8mcqzQj\nibvuuqvi87iq9T4TQgghhFQTFFukbshSdHmu+jUwMIivf/2/oBRxEmck4XnvBdBYUpszoVrvMyGE\nEEJItZFZbHme1+F53kHP85od21o8z3vC87xLy9s9QrJTbXWbzGjWsWN/jjNnXgXwBIAm5BUnLiMJ\nkR8BqLzgqbb7TAghhBBSreSJbG0H8LKoEV4IEXkV6uf27eXqGCF5qba6Ta5UO+AZADrylF2c2C6Q\nw8PDAM4AuN7ac/YFT7XdZ0IIIYSQaiWPQUYfglGiiy9A/WxPaoB6cuvT6HS7w4fvwenTAvXKTqCh\n4V5s3lzZuk061S7sIHgzAIGKTp0A8CyAfOJEG0kUi0V/zTcRdiicfcFTzvtcj+8hIYQQQogmT2Tr\nEgD/kLD9H6FymUgV4zJq6O19c00XxzWplrpNaal2wBYUCu8tuXjvXBcEnul9rscizYQQQgghNnnE\n1qsAlidsXwEgkmJYbjzP6/E87yue5/1Pz/POeJ73SxmOebPnec95nvczz/OKnufdNtv9rFZcqW3f\n+tZ30dGxui4GumlFlytFWqod8A9oa2uckQicS2GZ9T4Xi0U89dRTkTlkdDMkhBBCyHwgc1Fjz/O+\nAGChiPxKzPY/BfCvIvL2MvbPdZ4tAK4F8ByA/wTgV0TkKwn7vwHAXwL4DIBHAGwG8DCAIRH5eswx\ndVnUOK04bnd3H771rSNz0rd65PzzL8LU1M8A/CF0qh3wHgDnAtiNchUkrsaCwEl1uH7wgx/MqEgz\nUw8JIYQQUklmUtQ4T2TrYwCu9zzvSc/zrvYdCFs8z7vG87wvARj095lVROSQiPxHEflTAF6GQ94F\n4JSI3C8ix0Xk0wCeBPD+We1oFZKW2vb00xPzzrY7LvJS6n7m/lNT3wfwRpiRJ/X9+/738hhZdHR0\n4Prrrz87lytPP2eLpMhVqW6GTD0khBBCSK2RWWyJyPMA3gY1QvoOgGl/+TaAHgC/llfpVYiNAA5b\n68YA/Pwc9GVOSU9tmz+23VkH7qUO8ANB8RUARQCj/qcOwn4VQPmMLKpJiKTV4WpoaPD3zOdmOJPU\nw2oRoYQQQgiZX+Qqaiwifwbg9VCi64MA/gOAGwG8ISmVb465CCqUYPJ9AM2e5zXOQX/mjJUrV6K7\nuw/A3TBNFYB7AXQCCA9063mAmnXgXuoAPyxsO6As2jugBUWhMFxWI4tqmgOVFrk6ffp0bnOPUgsp\nV5MIJYQQQsg8REQyL1Bpex0ALgewIM+xs7FAFRr6pZR9jgN4wFp3PYDTABpjjukCIL29vXLDDTeE\nlieeeEJqmenpaWlvXyZQHuT+0imFQqsMDg6JiMjU1JQMDg6F9hkcHJLp6ek57n15OH78uH9dBwQQ\nY3lcAEixWMy1XxyDg0PS0LDU3/8l/7NFgEJZ7+dM+6nbGB0dzbRvOfozPT3tfMeOHj3q7Mfo6Ki/\n30tWmy8JABkdHXX2JXgGB/x9D0hDw9Kz7zohhBBCiMkTTzwRGf/39vbq8UqX5NUrmXdUk03+qy9S\nTgP4WwDr856wnEtGsTUB4OPWuv8bwCsJx3QBkOeee25GD6samZqakv7+AQG80EC3v3/g7OC/3geo\nWQfupQ7wNS5B0dW1QSYnJ+fkelzMlrB2CU3XO1QsFmV0dFSOHj2a2I9SBGU5RCghhBBCyHPPPVey\n2MqTRrgLqgjyLVBphH8HYF+O4+eK7wD4BWvdL/rr6xpXGuBNN23DxMRzAB6H0qHbUSi0YOHChWhr\nays5XauWSJu7plMps+4Xh8se/bnnjmL9+vWld95Bnn7a78RspR9mtaXX5h6//dsfTuxHKXXFSjXi\nIIQQQggpG1lVGYD/BaDb+P4aqAjX4rwKbyYLgMUA1kJNMjoD4H3+94v97R8DsN/Y/w0AfgzgQQCr\nALwbwL8C2JxwjpqObMVFK44ePZr6S39alGTfvn1lSzebS7JGXrLuN9ek9dP1TnR396a+DzNFv1NJ\nbWWNQMWlHsZF4RjZIoQQQkg5mElkK2/K3jJr3T8BeGPek85kgfpZ+gyCdEa9POpvfwzAN6xjeqHq\ncv0UwAkA21LOUdNiKy4NsKtrfWq6WfwA9bMCFMqebjZXZB245x3gzxZpc6rS+ul6JwqFptT3oRLk\nTYPMIuA0tSKWCSGEEFK9VEpsnQawHECzsfwIwFXmurwdqMallsVW2q/5WX7pdxs7NIrntUYEXC0N\nWl2CJevAPc8Av5zknVPl6mf8O/FQVUR+ZjMCVS1imRBCCCG1SyUjW3Y06Yz9d94OVONSy2IrLUrQ\n1bUh9Zd+1wC1GgblpZKUVlnNKZHHjx+Xrq71Uii0zEjkJr8TBSkU2uY88jPbEai5EsuEEEIIqX0q\nZZCxCUC/tWxy/E3mkDSzhL17P5NqXGAbOwwPD/tbZtdowGXoUY5aXy4TiPHxb+PqqzdWZe0lszbU\nsWN/jjNnXgXwBIAmABtw+vQ7c5mVJL8TZ3DddVchzchitslqqFEq2oijXHXNCCGEEEIykVedzYcF\nNRzZEskWJcjzS/9sGw24Ik+bNm32LepnZpuenlY5UXUpka75VUCrAMsi9yNrOlzaO1EtkZ9y96Oc\n9cMIIYQQMj+pSBrhfFpqXWzNxjyV2UzzGhwc8lPltp8VP9E5YrsEOFcASHd3X+ZrSUurBEarKiUy\nXhx2iiqIHE0nzCIoZvpO1JpoqffC3IQQQgipHBRbFFtOyhklmC2jgWeffVbsAstAnyE4pgQYsLYX\npLm5LdO50yNbxZD4qpQDXxxucZjF9CTbM8n7TtSqaKn3wtykMtTajwyEEEJmB4otiq2KUU4BNzU1\nJc3NbZZg0BGcgj9IHvJT6MyUOrW9tfX8TIN+t7timy/qRn3BVc2RrbTo3PZZExTd3X2+gUbtiBbW\n1yIzpVZ/ZCCEEDI7VMogg5CyGg289a2/ih/96AxM4wr1+UYoc8tPAxgF8IcAbgZwsf/5hwDO4Ic/\n/Bne+tZfOdtenJmGy3xB1bmeADAEYCWAO9DfPzDnBgorV67E4OAQGhrugbofLwP4S3+r2/QEuBP6\n3pw+/clc5hlxTE9Po6enD08/PYEzZ/4A5v0v1zlmi5MnT/p/za6hC6lfXKY6hw8/g61bb5njnhFC\nCKk1KLbInFAsFvH00xNQgsoUUp8E8F1/r4f9T/egGfgNfOtbE5icnDzr3udyF7TdFTduvBae1wRz\nIOV5583exeZk584PY+3a5QjE4f1ob19mCbADAN4DZQBqCsTyCIqbbtqGb3/7mP+ttkRLmiPnihUr\nKtofUlsUi0WMjY3i9OlPoZZ+ZCCEEFKdZBJbnuf9p6zLbHeYVA8zsWVPiz4AwNq1a/y/4iI6NwAA\nfuM33p3pV+iOjg4sX74czzzzbYiEo2Uif4BvfOPrczqQ0pbvV199NY4dmwQAdHWtx+TkJE6c+KuY\n6Nw7rFZmLijGxsYwNjaKM2fu9tfUlmhxRwcPoKHhXgwODs159JJUN4yMEkIIKSdZI1uvGsuPAPwC\ngPXG9nX+ulfL2jtSlZh1oHQkqafnzfjCF75QptpPQHd3H7773WPYuPE6AHcjHNG5Fyr97yUAwLFj\nf575V+hqHki5Upf+4i9O4bd+63ci0blisYjBwS1oaPgPcAkKEckthPVz3bJli7/mQQDLAIRFS6Fw\nT9WLltmu20XqF0ZGCSGElJW8k7ygRmDDABqMdQ0A9gLYlbe9alxAg4xE3HWgtKlF9onkbuOKFmlv\nX3b2+Onp6Ui9LaBfgD3S0LBUuro2JJpH2O6C1WqeUEq/XA6Rrvpk9vOIc1jLWt+rpye79f5cUy31\nw0htMZulLgghhNQeFXUjBPADAKsc61cBmMrbXjUu81lspVkdp1up7848KHGJhbiB/OTkpFx++RUR\nEXH06NHcIqUaB1Jp9cBGR0djn40pKJIsz5Mc1tKf6/1SKDRJd3ffjK91ruy0y31esz1ahNcXs1Xq\nghBCSG1SabH1CoC3Ota/FcAredurxqXWxVYpA7+sVsfZigTnixLliT4Ui0XZt2+f7Ny5U4aHhy2B\nkU08VeNAKk3s9PT0pfY3SxtxQiz9uc78Hj377LNGJLJy973cNt7R9gpV9S6R8sHIKCGEEJHKi62P\nA/hHAB8A0O0v/96PeH08b3vVuNSq2JrJoDJrEdhsRYKzFQjOKwqnpqYcKYUF6el5c2rqnItyDqTK\nEdmIE43t7csyPZssginuuY2NjSVuHx8fL/m6gveyICrdtLI1u8pd4DjcXr+omm21U4eMEEIIIfmo\ntNgqALgfwP+EKoZ0xv/7fhjzuGp5qVWxVeqgMu98Ifdcq6WiChDHH6cpVRR2d/f5g/VdAuwXYLc/\n0F0oXV3rZXx8vOK/QruupatrvUxOTuZuyxVx6+7uzfxs0oVwvBDbt2+fdHWtl0KhJSL2ZiocBgeH\n/HYrP1eu3HP0wu1V5/w/QgghhJSXioqt0MFAM4DmmbRRjUstiq2ZDCqzzBfSBNGlcOoUcKUAL2Qa\noOcVhVNTU4bo6LTO25pbtJWL48ePS1fXBv9a9oiKcsy8L2bELc+zEYmPjsWLts86nmWhbPczeC+3\n57qOcpH3/uVrL7ntffv2yejoqIyNjZX0IwDngRFCCCHVwZyJrXpdalFs5RlU2oO4PEItLJQm/EH0\nkswD9FJEoYqMNPnncLkgdmYSbeUiGs06ICqqF+5bodAmXV0bZjRYznu/kuajtbcv8++XGZFsFFdq\n30z7rQneyyO5n3s5mJvIlilg88/nKvccs6zXRWFHCCGEuKl0GuEyAI8D+B6A/w3gtLnkba8al1oU\nW1kGlUmDuCwmE2nn0IYVSeSNNATnvD/x3Gqu2OwP3kVMwamjNclCYiaDZXX9BVHpkqZIahOgELnO\nuGf8xS9+0f++OrStFCGSZ2Aefme0IK2sC2S53SfD7ek5W2EB63mton4EyJ/WW+45ZknMhbAjhBBC\nao1Ki62nAPw3AO8C8MtQLoRnl7ztVeNSi2JLJH1QmTSIy+LQV46UrLyRhuCc+xPPrVK68vWlFNyR\njeQUOWB7yYPl4PrDKYr6e3waYfCM1cDfjLD0CTAiwHBiv3fs2BF6HqUOzIM+lS/VMgtaFE5OTpZV\nUET/rdhpmBA1r7A0IVvJCGAlhR0hhBBSq1RabP0YQGfe42ppqVWxlSSYsg7ikhz6yjUQzBNpCM6Z\nPHgFxnzBtWtWBqWaqOAcEpWGlxZ1e0gAyGOPPZbrfOF7XvSvsei85+kGGRP+Nm1mksVQwxX9zDcw\nd72XpZqIZCFOFE5OTpY1Vc78t6L/Hh7WAjb5x4G4HwPKPccsibT35ZprrpGPfexjZTsfIYQQUqtU\nWmz9dwBvyntcLS21KrY0LsFUrkFcOVKy8ta5Cs6p07LMlK0lYptktLcvS41aHDp0SHbs2JHZ0lzv\n/+ijj1oD1GlfuGhbc9uhcUCA3kj/Tp06lfl+ZU3x3LFjR+IzDqJ/WkztkiDlzex3dB5cFlfEtPTC\nStUsKne0prS0yeqPbLn/mzAldvTR8xbKxMRE2c5LCCGE1BqVFlu/CGAMwBvyHlsrS62LLRflGsSV\nsyBw1sF3+JzhlK2FC8+TPLWbxsfHpbm5LbP4efHFF31jCdc5bdHXENoP2CzAhda6TgGapb19Web7\nlHTPw1EcL/EZB/Pagrpb/f0DjtplnaJE5HExo4VJQu7yy68oyzsxU8opVmaeNhn9cSDfnK3Zndvm\nvldDYtcNA1rE8xaW9dyEEEJILVFpsfUKgH+BMsT4MYBpc8nbXjUu9Si2RMo7iJutKIUrihBXy+rJ\nJ5+UIIqg0+vcA+ukwrpAS6z4CRz89P57BFgYEX0qgrXnrPBQdaVWO86lB+HxhYLjIimuex4d2Lui\nf20S1EAL7o95/mKxaETGXvD3N69PX2+ckGuSOMHrcr+crQhXOdPwypM2md+NsJw/aKQR/m/CkcRn\nPJ9SCunOSAghxKTSYuu2pCVve9W41KvYquQgLi/ZnBJNW/UWecMbLnWIniFfLIQH1lkK69ri59Ch\nQ/7+2w0hNyRAs0TrfS07e95PfOITsmbNlSniRBlQ6EHd2NiYjIyM+IWbsz0fd8qaTms0+9YoSgjG\npyDqPgSRrajFfmvr+Y5i1m3+M3BfZ09P+HrsKGG5379yRbbK0Y4Wx6UW265E2qXrvwlxQnXTpk2z\n1o9qge6MhBBCXLDOFsVWLio1dyYPUUG1SwqFJlm3boM16J0yxIQrShVEjqJ1xG5MHEzu2LHjbH+m\npqZk+fKV1iBUCwe3GHHbqsfNn4LRvikYbfHYKBs3Xuu8Z8lOjcWz67u6NoTadKcgqqW1tT1RZNhi\nENjgOLe+zoI/H8y+T2skT6So9Hep9AhuJY0qqoFisSi33npr4rOfD5EtujMSQghxUXGxBaABwI0A\nfstffgVAQyltVeNS72Kr2ghHEUwxZQqQr4hKFezzhU6yAUFra/tZQbF2bZfVnp6TFD7GjGypSJg9\nd0VHcZIiVg0C7Jb02ltaKJrCTddsip6zu7sv8ut6HjOG5BREM2LYlCoyAse9CYl3NHwo5fr7/GeQ\nvUZbVsoRwa20BXtSPyr5w4jnLZTofMT5MWerWp45IYSQ6qPSaYQrABQB/ATAMX/5CYD/AWB53vaq\ncalnsVWNcxHCUQRd+NacI9VoiaU1oupExYsCz1ssg4ND/qA7OkcrcNtTg0lzzlaQPrg7RijY5406\nuKm0QpdDYYsERhpmoWazZlcxck7PW+T8dT1Ij1wudnHdpF/k4weWySJJuw6G94kWKwYWJT4fNcdr\nSMxInymK8rpFurAFZt53v1JGFS7mKp1tYmLCF1zBeeeLG+F8i2YSQgjJTqXF1ihUYeOlxrp2f93X\n8rZXjUs9iq1qnovgjtBoJ7w+cbmjAecnioKwg15yHamlSy+UU6dOOe+REgQ6CvZSTJvawW2XqNS9\n3X4fPYmmBRYEuMNa/2kBrow5rz7n7WfFjmZqaspyEgyfq6trQ6iWlSk2kgeWBT+qFy8ywkLkBYnO\nYbss4/PRn6oGmOc1+26PQVt5rfJtSn3353KO41yns33sYx+TTZs2zYvUQQ0jW4QQQuKotNj6CYAr\nHevXAvinvO1V41KPYmuuB29pqAiNTmGzo0RxA3ZP3JGjPksYxUVXbhRgiXR1bZBisei8R0EBYPO8\nZsRKpwvaYkN/nxAlvpoksGbfKMoq3p7j1ekLF/O8+pwHBFDpduY9s/vreS2yZEm47timTZsj9u7B\n3Ktsxha2yHAJke7uPvngBz9o3HMtQu3aY2ZES0e49PlbJatbZNZI1Uzf/UrPceSgf+6Yy2gmIYSQ\n6qXSYmsawLWO9deB1u9VSTUN3uIGyCdPnvTNGQoSRLJcxg+mWHqvL1zs9L1gLlCyWCuKHeVKjsS0\n+P3z5LLLLjeOKUjUMGOpv94uJGymQmYRdkvErtUVtr13px0G88a2S1C4OCw22tuXJQ4ss4gMV6pe\ncB+nJTAVsSN2up8bJYgcHpKkZ6BTCvNEqqrp3c/KTNLZqjFNuJaoZsdWQgghc0elxdYfA/hLANcA\n8PxlI4D/CuBzedurxqXexFY1zEVIGyCrbXqej54rlTxQjg7kPQEOCvBZCeZ5ueZNmaJGC7frEu9R\nEHHbK0CL9PT0SbFYlJ07d6b0cdz/fkTCfU0Tgfq8zaKiP++RqLA00wb7RM1jm/C/b8h0rvXr3U6F\nMyEaHVjtP9tdZ5+BSlO0LeN1na9kt8g8kapqePfzUopArOY04VqkGh1bCSGEzB2VFlutAP4UwBmo\n4sa6wPGXAbTkba8al3oTW9Xw6/6mTZslanTRKP39AzI+Pi6BaYQdAXGZL7QIcIG47dcLErYcd80n\nMudhZY1sPSoqSjUmwH1n71vaYF4JIHse2FrHMccliOSNGufVqYcum/s2UQLQvHb491Lfm7TooEr/\nGxkZyfQeZImcuKIDrvpa/f0DlijbnvgMxsfHc7/L1fDul0LedLZqTxMmhBBCapk5qbMF5Up4g7+s\nKLWdalzqTWyJzO1cBDXgNdMDw7bmixe3OISEjj5NO8RSmv26a9suX7g0SzTKpe3X9Xwhl7CzI0nK\ngOLo0aMpfVntuG6dYhhndb9aCoVW474kW7uH0yHvt/ZNiw7uloaGpdLd3ZsookqJnNjRAfu7S5Qp\nc4zoM9BztkqJVPX3D/jiO2jT81qlv39gpq/2rJEnna1WBSUhhBBSK7CoMcVWKnM5F2Hfvn0liCNz\nrlSbAO3Gvjrak5TyF7ctzvXPFEe2sLPrYYUjB6posEuk6X7GXdsSZ7tAi7S2mm6LadGzUeNvVyqe\nKzqoxeyU2GK2p6cvYr8eiPXAcbFcYt0UYadOnYpEwUw3wlKEhTIHcUdVszCX86CypLPVYqokIYQQ\nUktUOo3wSwAecKy/H8AX87ZXjUs9ii3NTOcilDLwDMRWKeIIolLlpkVFcEZFzZvKIt60fbwZ+dEF\nefdL2FhCn+/ThkgKIk1J51NmFbbFe6MoA4+0a0u7jpckrQZX+PrGHG26ooN2mmZY7Nm1ltT1XWGt\nU23Ohgh59NFH5YYbbpD3ve99kfbzRGnD4ky/P8H9SqrjVSvzoBjZIoQQQmaXSoutH8Bt/X4lgO/n\nba8al3oWW6Uyk4Fn2mCwtG06LdEVrXmzuCIZqvBvmqHFanHPjSpIUuSgp+fNApgCxSxUnFznK1mM\n7ZFommG/v17P2bJNPzrFHWlb7h+vDUiS3f9Uba8JCeaENVr3RaVDljNyEq0dpoRef//A2XctT5Q2\nfU5d/LG1NA+KluWEEELI7FFpsfVTAKsc6y8D8NO87VXjQrEVZaYDz+i8mYdEOdR5ogwdXOKgQbq6\nNkQGkWpu1TkSH6250CGYWiQw4XA5FGpBlSQ+djvXF4tFWbpUn1ObPOjBfZzBhxkJS6oj1ijuos4F\nqw1do+txKRRaHZGpJQKc8vvTat07U4hMSbTO2ZAocaejaOF+JkWHSnnPbJt6df2NkXctS5Q2Xei7\n0yFrLVpEy3JCCCFk9qi02DoK4D861n8YwHN526vGhWIrTDkGnuHBoJ1y50l0LpUSA1/60peMArx6\nGfAXc11ctEanEmqTiQfE7VBoCoy4KEiTuETTunUbjHPa90qn6gXn6uraIJOTk/790GKqFDEGAR4W\n2wJfzfdaIqqWl3mNy0QJrmUSb7yhCxHbESx9f0bFvi9moeXZfM/yihyd8trd3ecQ7NGaZmbbtToP\nipblhBBCSPmZidgqID87Afy253n7Pc+7zV/+GMBv+ttInXHy5En/r15rSx8A4MUXX0xto62tDYcO\nfQ3r1l0NYAmAAwBe8j+bAVwAoAhg1P/8CgDgvPPOw4c+9IDfyn5/27i/TECVeTsHwO/6bY37+14F\n4C0AVgEYArAdQMHf/0oAzwPY5e87DuDXjd5+0+r9hP/5EwDbAFzif64G8FkcO/ZX/vbTfvtDAO7x\n+/NPALaiUGhBV9d6FItFPPfcUTQ3N+Od77wNixefC+BVq92rAHwWwCK/Xfd99+8sgDvOtj82NoYf\n/vAfASwH8L+s+/wzAP8XgO8D+DSA+6y+TkDd/z8AcDOAi/3PTwL4hn++FZH7cuedd2LLlrfglVde\nwUxIe8+AbO/a9PQ0tmx5C1atWoWhoSE8/fQEWlsXInyPN0Jdc9C+2fby5cv9v9zvwooVK1CNdHR0\n4Prrr0dHR8dcd4UQQgghQH6xJSJfBfDLUKOuzwD4fQCvA7BZRP5zebsXj+d5d3ue99ee5/3U87xn\nPM/bkLDvbZ7nnfE877T/ecbzvH+uVF9rnXINPLXQUAN9czD/hwgG89cD6ADwJwCABQsWGOdv8LcV\nATwF4M+gfmT4LShxsQ3Ax/19bwXwDMJiY4l/niKAYahScYASW/dACQ9TfLzsf74HShD+ewBNANb6\nx60A0A6Ri/3vtwFYCeDfAKyDObjv7FyJD33og3j++efR2/tmrFq1Cu94xzvwk5+8CuCN/vG3+337\nJoC7oILFQLz40+fchoGB63D48DhOnz7tr/8ugE8Z93kDgDuhAtNAIGgOQAmPbQDebG3TaLGzAMCz\nxn25B0A/gAM4fPgZbN16C2ZC2nsGZHvXbrppGw4fDj/7H/7w33yhDwC7AXwNSqgG7Zttr1y5EoOD\nQ2hoCL8LDQ33YnBwiGKGEEIIIdnIGwqrhgXAO6B+pr8Vaq7YXgDTAM6P2f82AK9AhU8u9JcLEtpn\nGqFF3AT8rq4NmVOWdu7cmZiapYoFR1P8dAHcQsGeawSJFkNeKMB5GdLvavUeIwAAIABJREFUTJc9\nT9QcsD3++W37d/McfX4Knm2+sddKu+sUoEkKhQXGecz5YlkNOFzzy1oEWCD2PZqenjZS8fR9dtXx\nKvj9Nc+1y9ie5b6Zc+TypZSmvWd2Tay4OVsu0lIRXSmFcXMPOQ+KEEIIISJzUGcLQCuAOwD8HoCl\nEgiU15bSXgnnfwbAJ43vHoC/A3B/zP63AZjO0T7Flo+e9xLMMbIH7cmD0OPHj8sjjzxi1I1KG8xr\nU4iwmHn96y/1i/w2iTKhMF3yOiVsHpE29+q9YpsvBOLFnk+2UJQQu88/py58bIulIYleT0FUEWVd\nSyutOPFucdvV231q8Nt1m5Vs3HitcR63tbu6ZtccJj1nK66w8y/6nxPOe5s0lympbID5nqW5ESaR\nNtdqZGRkxsWZCSGEEDK/qLRBxlUA/gHACah8qUv99R8F8Md52yvh/Av98/6Stf5zAL4cc8xtAP4V\nwN9A5RX9ZwBrEs4x78VWnNX75OSkdHWtl0IhLDjs6MCzzz7rF/u1ozn9jsG8HuiPSWDpbosZl+hw\nueS5LONtk4w+h9CJizq1iHI3hKNduw3dB9O63RRYaTbkC6zzLBCgw78/q0W5N3Yk9qFYLPpiZYEo\nYQqJr89lLtrNcI+jH9pKvuCfN/78LmfCpLIBSe/Z8PCwDA8P5zbFSLs/IhRQhBBCCMlOpcXWYQAP\n+X//2BBb1wL4m7ztlXD+10BNtrnGWv8ggO/EHLMRwC2+UOyBcl/4IYCfi9m/rsRWKYWI46zeu7t7\nEwezX/ziF32RpYXL/RJEbURc7nzApQJ8VYB3JrTtEkIulzwtXBpEOfJdZp1LR81cwihJSD0mgeiK\nE0u6Dw8Z7T1oHJPmttfkix3bel2nTpqpfu4+BAWkPy7RdEed9qf7+1EBbrD20ctuCSJsZh93SXyN\nM3fNraSyAaWWFEh6p1lzihBCCCHlpNJi61UAyyUqtl4P4Gd52yvh/LnFlqONBVCRuR0x2+tCbJVa\niDhbEeI4weFJEFGJq4N1XIDfkWikqhDT9pEM/TFFQUGU0HJFaJpFCbFRRxtp15UmyL4qUXGn54Tp\nOVJJdbfMtL9dAuwXJXr0nK4PGO26a3795m/+pr+9X6LpgzqC6Ipsvcb/vD3xPnhek0RrfEGAlQJE\n52zNpKC12ZYWV0ePHk19pznXihBCCCHlZCZiawHy8y9Q1mw2KwH8oIT28vKPUB7by6z1y6C8rlMR\nkf/ted7zCPtYR3j/+9+PlpaW0LqtW7di69at2Xs7h4Rd2XoBfBOHD9+DrVtvwaFDX4s9LosFt3KM\nu9n4rh3jBMC7oEwqtQtgr7//PVB26d+HMsJcAuVMqLffDaXf7bb/LKU/FwH4eyinvHdBafELoV4V\ns/17AFwK5dT3VwCm/HWNAK7x94m7rkaoTNlRAO/227jBv8b3QE0b/GWoV1OzBsrF7qf+PosA/L9Q\n/i7bjP0Kfp9f57ffCWVVr+n0z/ewse4+qHv7xwD+AsC9AAr48pf/1N/+DX+7vpaboZ7NNgDf8q/n\nEYTvfcFvD7H3oaWlET/8oQfl/vhdY/uLaG5uw/nnnw+TbO9SfEmB9vZ23HTTNoyNjRrbGwG8F8Db\nALwcead1mYETJ07gxRdfxIoVKyAieOaZZ7BixQo6CRJCCCEkloMHD+LgwYOhda+++mrpDeZVZwD+\nCMCXoeZO/RjKt/oSAMcAPJy3vVIWuA0yXgawPePxBajR9u6Y7TUf2ZpJIeK0Y3t6XEViWwRY7h/3\neOLxQWphUjrdQ35kZ5cEkTJz/ymJRs4KEp5fldWNcEBUxO1SUfOidvnX9VmJmnXY87c8URG0leKe\n79Vgncuzjl8oOiqk5lYVxB2RKvh9s81AdIQpuBcrVuh5XUlROtuNUN+bxeJOE2yzzuUy3ShIc3Ob\nTE5Onn2PgrTG0iJbrjTD8HUPCbBDgPzzxcjsUEraMiGEEFLNVDqNsAXA16Gs1P831E/7/wr10/fi\nvO2VsgD4NQD/jLD1+xR8O3eon+d/z9j/twEM+MLwTQAOQlWovSym/ZoXW2mubEmucSLJ815caVpq\n8PtVQzQkDfbfm7J9idX2BQJsknD6nTZt0IPwPRIVRnHtm328XYBviXIZdKX/medQdu7q2K9KVOzF\nCYqNhkhotIRDowSOii5R6RKp9jyq+/170yPh9L5S0kC1k6E9b8z+niSUPWlvXxa6l1E7dy2Y4oXd\n2NhYyrl2RJ65LaRKnRNGspMnxZMQQgipRSpu/S5KkFwHlU91P1RB41kXWdb53w3lLvhTAN8BsN7Y\n9g0AjxrfPw7gr/19vwfgqwCuSmi75sVWnsiW65foLPNetKNbMCjeLsrpb0niuQFdb+tGAcat7XFu\nhNcKsCHUn3D7Q8ZxR1LO324JjwYJi6pdEtTq0nOjnpXoPCVdX2u/pIs708pdiyXzGR2VwGkwrh19\nHtsMBP59v1KSXR/NiFBalLBTgEmJ2tCnibX7JGpmsleiQrhTgM9IkrDbsWNHyrnWi/2umEJqJtFd\nkk40aqhFNYUtIYSQ+mJOxFY9L/UgtkTSXdlcKVZdXevPpoGJpFtku9pQ4kUPuO3B/hoBzrf2v0CA\n3xdlXpEWkdFRJ3MQ7hpUu+pF6VS4D0pgFvGIceyUqJRC+1wDfr/j6mtlMYEYNUSCFks6+uhyH5x2\ntGNGtuLSKHV6oMv1sV8C4WM+GztKaNcvC96bNDfKNGv4sIA171tRbGGXHtlKFlIzje6SZMJRw+Qf\nOChsCSGE1DIVEVsAfh7Av7PW3epHjP4BwD4AjXk7UI1LvYittOhUMFiK2o1nTf8J2jDd81pERR3s\nSFSDRKNI5twm7R5oDo6PSxDR2e/vb6fbuQbV05FrCn/v8/cxoydDotIJbUF1Tsqgvyhul0E91wqi\nbNZ3Gfvrays4zhkWOuGIVKuoelhxAqnf6uOEf9ywcc73SHTuWLLAXbPmSpmcnJTp6Wk/RdAW0lq8\n3ud4FoHAiW6L3jfzBwHXDwZq/w2J5xkdHWVkaxaJ3lsKW0IIIfVLpcTWUwAeML5fCVVceBjAB6Ds\n4D6ctwPVuNSL2NK4olPhwZIe8OZL/zl06JDfxmpr4K4jLkVR6XHrcw3sgyiTHZnRAsmsu/W4pKcN\nDos7FW6DAL/l/70rpY0kATHi98uONLnmgTWIErcvSGATn3Y/dMFh2xwirbiyue6o436aaZH2tb0g\n0bTJgvT3D8ipU6dk/fprrG39/nXp2l5Zo1HR+2YKfff8wE4J5gcmCynW3JodolFDCltCCCH1S6XE\n1t8jPC/qdwE8bXx/O4D/nrcD1bjUm9hyEQyWsqX/mPO6nn32WenqsgWUKQh0ut59EswdOiDAr8cM\n7M2ohxZRLsc7HT15ydjXPM5lwmBHiJb7bW0XFSHTAmhRhr4lGWDo9leLisCtlsBMY8K4L40SGENk\nOae7nlbycfc57kFUUKv7tTHm2lyRM9V/XYzY85YIcLH1HmwU4A3+tYcFTn//gDMqpopl9yWmqxaL\nRRkZGZHu7r7QM7eNN2whxZpbM8c1p9MdNYym7lLYEkIIqQcqJbZ+BuBi4/vTAH7T+P4GAD/O24Fq\nXOaD2AoGS8nOgSMjI5FJ8NGIxl4JhJAtCPSA7LgA98YM7O390ww2dhn77haVwjQp0cjNMuu7Lc70\n57kSpNXFnVMXa75PlHjS6WxmVGZRzLng922PtS7NAr9U8Rcsixe3iLJzT9r/WgkPkh/K2L7ePunf\nB/uag1TF/v4BmZ6elunpaVm3Lpxemlf86Ejt5ORkZiGVNveQREmzzY9GDaOOoBS2hBBC6oFKia2/\nBdDr/30OlPX6LxjbrwQwnbcD1bjMB7ElogZLhUJy2ld3d58xCd6MUtnRJj0QnzwrCDzvXH+daUke\nZ5xh1o/Sc7LihEajLxBaHW01CbBWglS6ov+9ydFvHb15jd/uKrEjMsGcrWhKnTLOeMFYd46Er9E+\nnz2HbL8EdbXsOUnaRCRO8LzOce2B+Hvd6y6xzpV0P5v9Z5R1f9d2HdWIzv8DGqW/f8A5eO/p6Zvx\nYJxCqnSSamKl2ebHRQ0nJyf5PAghhNQVlRJbnwXwbQA9AH4fwD8COMfYfjOAybwdqMZlvoitYLAU\nFUA6tSsY8Kc57mkjhg4JIk/3+21r97sDEpg7mINx230va2RFux7aIsgs2JvW7/dK1CzCbHNIwpbq\nZkpdn9GOvodZ5n7Zka011vqrjfWu4tHniCqG3CDR+7jIsT5tbthi0fPuhoeHM7oAmtuT5//ptNKw\ncM8+N5CUn7SoVR5zEYpdQggh9U6lxNb5AL4J4AyAHwH4FWv7/wfgd/N2oBqX+SK2NJOTk9LVFU3t\nGhkZkSCCkew2BlxqDcSXSWBi4Bq0mamAdptHJKnYbTAHSkfEJkSlQzb7283oVJw7nssAQqdEtoky\nzyhKulhbKIFY2yPAFSn3yRSK5vfl/qdtNuJKhXyP8f311jbX9Sz1zxd3P9U5u7v7zr4Tak6WPQcu\nPGcrSCHTbpFHUu5VtsF7NZIUAapF0qJWtM0nhBBCAipaZwtAC4AGx/qlMCJdtbzMN7GlsX+hDv+6\n/WzMYFlHoRZJNG1OC4g08WG3qQWSnY6mv+8Qd5Fg/f086zjXOVwGEHZKZFGAfSn9X2SInAskaktv\nCw5TKF4mYbt8ly3+UlERtFEJBKqOIpoiKy51sc+4nxvFfT/VMjIycvZdmJ6elv7+aM0xc+5V1CUw\nef5feNtx/5rUtezYsSNRyMyV2EmLANUiWaJWtM0nhBBCAljUmGJr1gh+Ae8UlQ6ooyOudMAhCdIB\nH7K2xYkP1xwus7ixLnZ71HE+LRZ0kWBzTpc2hVgtKvK0yBcrWQwgtJhZm9B/3cb9Er1OV80tXQBZ\nRAlXOxKory3OfXCXf5+uM9bt9u9B1mLQy0XNqQsXD9aLy3myWCzK8PCwDA8PR9zoRkdHZXx8XEZH\nR+Xyy6+SdKGpo3/xhiu2kCmH2JmJUEuLANUiWaNWUQOMh6RQaApFQAkhhJD5AMUWxdascfLkSWlt\nbfdfsL3GQDkukjIgUVfAuBQ2nebWaO2/QVRtLlOExVmRFyQa2SqIim55jrbNwX2SdTokMNXol8BW\n3iUy7TanHfdgrQAnHevNVL+CqEiXq0/6PK+XIH0ym6NkUAzatsJfKno+10UX/Zx86lOfcqaTZhE/\nhw8flnCELZquuGbNlRIIdrfhii1kZiJ2ZirU6jW6k/W6wtHLZFFMCCGE1DMUWxRbZcWMBCjHQtsd\nMM1AwRZFzQIssESGGQXTx+maTUVR85LsOUhJkShtHmEWEnYJwlZjn7j2dP0tvX1aVDpeXJtxhYaL\nEgi33eI2j7BTFx+I6dPbjOvyRLkHfsI4Z9L12GJUL0us7wVR5ht7JU7YbNq0WaICdqH09w/ImjV6\nvtpaa7uKQH7oQx8y+jn7qWwzjUrV87ylPMWee3r6pFBIFsWEEEJIPUOxRbFVFlyRALXYDntpZhnb\nYwb9epmIOa7JH5hrC3edpndLyvlMEZJVoLkiMFo0LY8539qUNleL28a9VbLN5dL3QB//WXFH5u50\nrF/mOLeZumjfL5do1EWXzWNU/wKXQn2cHd1rkM997nPGNer0z2KojeC+pguZmYidckSl6jWyJZK9\n2HM93wNCCCEkKzMRWwWQeUuxWMRTTz2FEydOAABuumkbDh9+BsABAC8B2O7v+Q4AQwDu8bct8td/\n02pxwv/8d9b6Pv/zbf7nyzHH/QTANwBsgyrltgDAQ/45k853O4AmKO+WVn///f623pi+3A3gp/65\nLvE/VwJ4FsCov88IgKcAnPC/96S0+TcAllltbgRwDMCFKcd+1f9cZxx/N4BzETyPAwCWAHgS6hmY\n638GoME69+sR3DsguF8PQJmKfhqqYsPF/uenAPyLf/36mlX/7rzzTgwODvrHvdE4r/5swl13vQtA\nwe/3swCuAPAsPO+96O8fQG+vvvZvAlhu/G2i+tjY2Ijvfe97/rovOPdZsGAB4jh58qT/l/t+v/ji\ni7HHalauXInBwSE0NOj3/mUAB9DQcC8GB4fQ0dGR2ka10tbWhkOHvoZisYjR0VEUi0UcOvQ1tLW1\nhfYrx30khBBC5jV51dl8WFDnkS1XBCtcU0v/em3+qm3PQ4qfmxMfvdklbkv3ZgnXurrD2E/X5nIV\nMDbT9+xaUmmW7X1iXn842jYl4XREiIrkpBlRmNewX4L0PZF0W/QmUVGlx/2+3Byzf5YaZLofccWj\n0yKTEDVnbto4314Bbje2J92DdjHvXXd3n4yMjJxNSw3S1/odfVRFpBcutB0lO/33QEcLC7Me2RLJ\nHgGK60Ot28UzskUIIYQwjZBiKyeuuSyepwWPneJnD4h3SaHQJM3NWliZA2Lt/GeLKT0v6Vf9/UzL\ncbsNs/6UntuzQZT4iaauhcWNKSD0ca6+NItyJ9wtbhE05J/Pnpe1TIICxy6RmZa+mJS6uFDUfClb\nMNmCyL5OWyg1GO3YfSqIEixpQhSi5nM1W8ebAjXu/Fo09glwt9gFo/v7ByxLebugdFwRaVNYq/cg\n+5wt97ykPGIoT+HeerOLzzO/ixBCCKlHKLYotjIT/aV6SqIOeaZ5xZ7IoL27u1dGRkZkfHzcN0U4\nV5T73yKJiin9fY+xfbsAjwqwUmw3OjWo9hzt6P7quUC7rHPc4X/ukCRr8Wgx4QF/0aIsLQK12NFG\nowTCpCBhi3xtNb/Iv1773phmErcLMC5Bba+ZRLa2iBI8LaJMOmwTDS0oo0WLgc0SRBJtM480cxHz\nuayX6NyyRlm/foOsWLHKWr9BlC19mhC8P/NAPy4qdfLkyVkVQ/VmFz+T6B4hhBBSD1BsUWxlJmo6\n4HLI07bswS/YxWJRRkZGpKenzxokF0SlmL1ZVHRGD5Qn/cG2uW+rcUxSBEjv0ybxtuYviDtyo8VP\n4KinojTn+Ist7lpFiS1bBMVFbi71z71blIDyJHBB1ELDtsi3heykBC6FOxz7aPv1fv/ZPCQqoqVr\nbS2RaIRsqXENi0S5P+6RaBRRH/eCqAiSff8GBLg2w7NxnX9IwqmIZiroS35/zpGoWO3079+QZElx\nzDvQt6NSsymG6jntLk90jxBCCKknKLYotjITHgymp5OZA1vXIFUNujdKELG6UlQqmS4grMXSGn9A\n3ul/ukTUsxItJBzXR1fdLd2+7cL3kNWm61q1UNqZsl/RsU7vbwuFPommI9qi5JyY6ygIcJe47dl/\nR9wibo/o6E94G6Sra73vFhgn7O7wr+2Qsc0teFatWi3r1l3tOP+0dU/0fdWuhEMJ16tTRHXf3ff/\nsccei32vswiB2RZD9WwXTwghhMxXKLYotnIRiKb7EgeGw8PDZ4+JH6R+1jGA12mA9nrTQt5sz5XK\nqOcXiQTRNzMtL4tw0sJov8TPgTLnGmmBpoWcPbeqP+ZY3W4eIWum2yWlBOq5Zaa4bfCXJgHeKcCD\nEkS99D0P3/uurg0yOTnpp4MtlLAg7hQl6uw5cckifOPGa0WlVep2zFRE1/P3UtsNR8TsyFlBduzY\nERJEeedHjYyMJL4HMxVD1RjZqgejDkIIIWQuodii2MpFdA5G+sAw/hd7baBhp6zBH3TbDn1mG1pE\n6WiUHTHr9PeznRCTIy/AB/zPUX/9dkm7VmXuod3uWkWZYdgCYG+CSDDNPNokTcgGKYjJKXPxkaMF\nEi0U3SrA08b9W2Pdz4Js3Hid9PS8WaJC6BwJBBgkiEDagnOj6LS7qNmFElRXXfUm6excJ9EIlp6z\nl3S9+prs/oWFoBZUwQ8Hu0S9X7sTUwK7u3sT34NyCJJqMZSoN6MOQgghZK6g2KLYKolisShdXRtS\nB4bHjx+Xffv2OQap5q/4cXO/ChKkBtrFkacFSB78hqMmTb5A2eA4ZioyIFfCYI8EBYUvEHfEaqFE\nB/bToiJjI6KEWMFv5z5Rjo1alBX8PplGEFnmpHkSuDPG7fPAWQERTT/Uc8XsdLxeCRuIFK02m6S9\nfZlMTk7KXXfdJe50xAFxuz8uk8A4RbV3+PDhyDw+dxkBkfSIpD3Ha7d/z1dLIL5fEu2IuW6dfg/s\nfrrdCoOok1tI9vT0leXfVTkNJWYSlao3ow5CCCFkrqDYotgqmaSBYfSX8YJ4nulgp6M3R1IG0TrK\npQfMSx1tZInu9IuKLrWJioCYwsk1h0un1V0m9kDc/X2HqHlWpjPhclEpdi4zCR2ZavCv7w5RQuyA\nKHfCJnGLOzPVz3YufNw/n6vOlylYs4jNgiixeFyCqKK63+vWbfBFkUuwDUg0krhRAqEVPJ9zz9VW\n7+a+OqLpeqYuc40W/7xpkceTjn41iHteXLQOVxCdfcHRTkFGRkbK+m9rJoYSM41KZU1nZIohIYQQ\nkg7FFsVWZuzBlf4+Pj4eGXRFfxnf6xhYQ5RDXxbBpMWAHU1JGmQvEjUvyGVMYbsdxrXRYvVR28eP\nSSBCIMCwAE8KcLnVx+USjdq1iLJyty3MtaOfNqtY4dhu/v1xCSJ1poCwz6dNM5r8/tj32xVZbBHg\nfKvtjoz3rChhYbc7Zj87GmVGNJOOMfvQJ8B7HNdkvkP7E66x03me8fHxyLsfvuZwGYFqEhwzjUql\nGXWMjIwwxZAQQgjJCMUWxVYqrl/K29uXxQ623L+Mu6In50qQppdmfGAKnSclSC+Mi3YUjPNNOgbg\n9/qfN6YM1N9k9MFlyGEP/rX5xHLrONe1NUlUaDRK4NDY6u9znn+vbLOLfgnSMbcL8EjK+XSkzdwn\nzYxjh0Tn02n79bh7drdEhXWnBPPaWgR4XeJ5PW9R6JlqsdDT0+cX0TZTMpsT20pzKQyL8Xizi2qZ\nT5VEOUw20tro7u5jiiEhhBCSEYotiq1Uor+UR9PuzMGW+5dxO7KgB8C7REVnbMHUJuG6SrYNeJsA\nbxO34LlSgIf9wbgWLeH5QoGJxZGUgfjF/mD+jRJOZTwgSoS4zDla/X1ud9wHU5RsjzknJEgzNEUb\nJGp2oe9HukNk0G6Lcb9t4xH7mPXijggVEu6ZFsCuYyAqNfOuxPN2dISjaLqgsDLVsJ93wV/ChZYL\nhTZpaVkqgXFG3DWOin0NLlFSCwV6y2UfHycs4+fU1X4tMEIIIWQ2oNii2Eok+it3+i/nyceYkSHP\n+LQH0Bdag2l7cL3IWgdR6WR9jn0hKlqkRZyOPuk+2fbw9vyoTokWXk6LCK02+hC3z4TfjhaRpiha\nJPFzikyzi/tERYw6M5zPdHe072tSZDEpMmcWTW6TdIt2uw/aUCR63u7uPhkZGXEUFL7Mvz/2PDc7\nmqavb2FKnwIjFZfBy+joqIyNjZ1NlS1ngd5yz3sql318nLCcbft7QgghpN6g2KLYSiT6S3m2X86D\ngfFDEtipvyTAZseguFHUnK4JUdGehRJERuKiR3ogrdPiOiUwjIjb10yBu1OCOUMu63nzGD1Yv8O4\njuT7EKQI6v6bokTPoUoy3EhLfdtlHQtRqZU6AmcLxwZxR5saJD4V85KUa7SFk2nMEXfMIkcfOq3z\n9ogdLQ1ExGrrnOFIn+ctkEKhScIpl22iIplhQV0oLI1Nh3Wlzup3ohzRrNm0Vi9nuqMtLKuxFhgh\nhBBSzVBsUWwlkj2ypay5tbHAyZMnIwNZlRKn073suUpDjvbTokcPSBCpWmwdp6NFZoTGjpC5ImqL\nRAm4AxKNGOk29mbomzZ4OCnRulvLBGiXePGjhVOSaFkgKsJjC54BibofaoGSFL1yRQ/TIlvNCf1P\nuy+uPpjiKTyAV6LfZahhR/qSzt0XOldX1waZnp52RqpcJhO6rls55ifNprX6bKc71sLcNUIIIaRa\noNii2EolOrjSc7YeF5cg6e7uk6uuepNjsJpmhqENMLJGj0whcan/aUeoeiQqSDaKMmjQYkdH1Jol\nPBcpzsGu0e+zjlrZc80KRp+TaojFCcPdkm76sFgCYWNGxlolEGqNooRX2n3U0aa14jb8sKNeOjKX\nNmfLlZaZ9CwnnNtGR0fl0KFDKffDjPQlzc0qip7bFheFSYvezNSBsFLRoXKmO5rUwtw1QgghpFqg\n2KLYSsU1uAqiVklmCPZgMs3AYVTCESM9wI6LhuyWQLgcEXfkQ4sjV6QqbS5VWjTIFRFabhyX1sZG\n61gtFEf9ffT12EJnSIJivdHIS1SIfjGlH7v9vrpSDV1zobTdvPkcpyQsdOMiZUn30zRBCYuPNOOH\noIBx2nONn5elRUn6uZSYLXV+UrlMLOaa2RJzhBBCSD1BsUWxlRlzcHX8+HHZuXNnhsGtuf5Iyv4H\nRAmyBY4BvmkdrgWHeewDKW2brn3bUwbT2yU9GnSev9+EKLGi0w9NAZomLl3W7wUJm2Voi3Qzze6o\nca1xKZO/KIHo7RA178wWbma0aWdKm90SFmb2vdYOkbbQ1RHHpIhXj0RTLS8VoFGuuOJKEckSbVot\nQLO0tp4vhULcdaq2+/sHYudlDQ4OydGjR1POpSKg5Y9shVNxCSGEEFL7UGxRbOXCbRyQJFr0Ol1n\nyzXgXiLhlLikaJnePiBqbo8+lzaxsPuiBZ4tJpIG0y1nB7755x49IMBnBFhjXE/eNo5KdK7YpRLU\nC9NC0E6ZvMD6XpBwCqUtaPT97JTAij2tzUYJDEW0qEkT0fpdiJsDd4UEqZbRfRYsOE+ef/5551wh\n871YuvRCCV+XKdT3CnCjeF7T2ahW0rwp97m0yFfXtXPnThkeHi5JdIXbj14z0/IIIYSQ+oBii2Ir\nF+EBatogu9kYrOp5XnsdA/rFEoirtDY/L2FjBL3+jdZxdvFhPQh/wd+eZPduirq4uUdHJJzy9oJj\nkK8NQexIS6skz19aLG7zCe3a95BEUyajtc+CIslr/e/6vLp/ngDbJIhA6TZ3iXJOXONo0xa95vW6\n51ypxSwcrOdXrZWoVbx7ntzChec501kvv/wqefjhhx2FdrWb40dbTwbDAAAgAElEQVSc78GTTz4p\nSe/Z5OSkdHVtsI7T5h3RZ62jZVkJX0v0x4X5ajhx/Phx2bdvX8kilhBCCKk2KLYotjITmBSYEZkh\nsYvJqoHjlY5Brulo+FFRqW72trT0PV2AVous80RFRu6VwOnwIX8g70prW+4P/Kcd/Wv0B+f7RUWo\nGkQVVjb3OScy0FbtdAhwroRrP+m6U7a41HO14gTlIlHip+jYFnc/0yJLZkqgPVesM+bvpDYvFuCg\nRK3Y10sQgTPP53qW7zGOeyn1Oh577DERyWJHflyAHRJE6qLibfnyDuO80b6Njo4a7W63nkVU2Hpe\na0niaGxsLPGaa1Vw5K0fNjU15ResDovivCKWEEIIqTYotii2UnGnDupf+acdg3czglQ0Br4viEr/\ns/f1jP3ThIMe9L5ktaH/NtMRk+Y09YtKh2sRJdbsQbXZj6LRhj3Q3iPuYroD/jYtTs02tPmFLVJb\nJZrqt0GUeHnJWm8KhSyujaPGd9dcMd3vpaIiTx9IaVMXXW4Rd52yTlHRuQYBPh7zLBdK+FklX8ct\nt9zifD8Dw4kXJFow236Xjkswlw6iImBRU47JyUkRcTlxJqeXpomLvGYctWKWoSm1ftjg4JB4nqsk\nROO8jPARQgipHyi2KLZSia85ZJpUeP4AXJtP2ANciCqSG5fq1mnsr80WbEv1IbEHt4EjobZtbzH6\nYAsA/X27BOlwAwK8ydpvSIARxyA4qzGEviZ9viYJ5jbd5597QAJXP73owruutD0dcZqQIA1Pu/cl\nR0eika2kWle2IYfbxCEo8HxA4i3ymyVwM3xIgAcFuF2ClEjzuSyVNCHz2GOPOSMmQQSqUwJHxqUS\nmHMc8Z9nn0TFufldC+lggO9KXSxFHJVqxlFrka1S6oelm5/U3n0ghBBCNBRbFFuJZKk51NCwVPr7\nBxx28Hry/xUSnZvjGujfL0G0yIx66DavkGRHQjOaZc9pst3+zEK3LiG5XqLCxI5CZLWHv9YxwF/n\nn8e+xr0xbTWJEmMiaj5aq3Vsq0Tnl2nRd4Ek17r6irgLPGvB5DZxUPt/JeUe7HC060lQCwwCfFDC\n0TXXPLmGSJFsM2LS3d0rwTukn9vXJTy/TIs8d7FitX2Z6IhksViMiermF0d5zThKnbOVN32vnJRa\nPyzdar/2InyEEEKIhmKLYiuRLAOhwcEhOXnypD/nQosqc5DbKEFB46RUN/u4Jgns1W1zBp3GaLeR\nPvcnbK1u76ddE80+adt5O/KSJX0PArSLEkNx9b/Mdf0xbenUt6IAmyWauniORIscFwR4vePe2vdl\nud9es3W/WyUQdVETh7C1e9w9WCvxqYYNUijYc+JWSHSe3LkCrIqc3xQkjzzyiERF3TK//2YkMPmH\nA/X5edED/KhI6vTvVTjymjRnK02ETE5OzrhIcKnpe+Wk1JRIRrYIqT7m8ocbQuoNii2KrUTSBkIf\n/ehHpVgsyqZNm/1BsitCkmWgq6MSiwS4KGXfBxLaSJ/7ExQN1scckWDeTlxKnL6mZslneW4KtmnH\nPi4TDNe6Cf9znyRH7T4vwMOiDErM8/+c/+lJOHKkBaTL9GOPtS7pOtO2xd3Xc4x1u0QJ7A0CDPrH\nfkzSxHOxWJTu7j4JizHzfdMpofsl+b3Q29X9CDsW6rl/hyVIWQ3uTW/vppCwMV31hoeHE8+rRchM\nigSXkr5n9rUcg6pSI1u6/2rOVjQyyzlbhFSOavjhhpB6Y16KLQB3A/hrAD8F8AyADSn7vx3AX/n7\n/wWA6xP2rSuxJWKbBBwR4G6Jpvk1iCpGfI4EEZIHJYh0vSRu50Kd6mamiJnHuAbFTRJNM+s0BvRp\nNbJ2+ce8MTJoVuf+SMxxC84OxM1BuTvtzY5amWmPtvCz191ntGVb3OsIl+m8NyqBuHibKLGSJBgX\nW9fsilotlWgkKu55xKf+BWI26XkclcDYwn4eG8Q9fy44/759+4znqkWzKbh7je1ZIlu7xfNaDet3\n+z5o10o1B61QaDkrCNyuevp9dqeI2vPP8gqfUkXObAyqSk2JnJ6ephshIVXATH64IYS4mXdiC8A7\nAPwMwK0ALgOwF8A0gPNj9r8WwL8B+ACAVQA+AuBfAKyJ2b/uxNb09LQfubIHwjq9bpeoVC/XYNmM\nbkyL243wAgnS7PpFibWkQXGf1caF/jFmqlqcACgYx6yQ+BS3uDRFe1nguOakuVfFmO/mOnPpl8A1\n0TyP6bxnnjct0rTbaOuCDPtDlM170j5fkmDOlfluNIkSGtpMI06srZewsYUt+q5LPP+qVasdz0AL\nJS2gbPMMu1ixXm/PA/TEHUWMmrUUi0UjQuN6pxaImsOmUw/bpKenT0RmJnxKTd+bjUGVy1Akj4Ar\nFotno4FMXyKksswkOk0IiWc+iq1nAHzS+O4B+DsA98fs/ycAvmKt+w6Az8TsX5Via6apQm5HwlZR\n85HMAeU5ArxXVNqb6QpoDnBNcaYNDXSqlv67T9yGD/3+303++YuiBJw5h6lgfZrrPWtdXIqba+D9\nWkebjaKE1YQopz0d8UtKVdM27wtFRaomJBCD2up9vUT7Dglqk7mESYsox8ek89splEjZ/xzj/C7x\nulYCN8TdfvtmpCqLADVFUZygWyju4tB6DtUScRt46PfsBQkiXK73IklgZytDEETYktImC2K/n729\nm6S/f2BGaYDufqoI7/j4eI5jyjOomklKJCFkbqi3UhSEVAvzSmwBWAgVpfola/3nAHw55pi/BXCP\nte7DAJ6P2b+qxFbeX8yTrbXtgVm0uGs4AtPnf+6NGXwvlLBxhvkf+pGEQTEkMGY4YHwWJZxCd0RU\nVOVcCeaStUi4jtQR4/jjouZEDUvYVEFfkx7wJ0U50lIY9dLiuDZPgoLAYvThHuvaszg7ZjEH2S/p\nImeJvy3OjdD8vl+U8IpLY2yUqFhbYBybJPreLkE9NPP8el6VFp97JFr3TW83vy832tVCMe5eJgnX\nYN9gbpb5TrnabPL7vlZUOuKixD5kqd3V1bVBCgX9g0b0Wdn/5jmoIoTYMLJFyOww38TWawCcAXCN\ntf5BAN+JOeZfALzDWvcuAH8fs39Via2sqUIuUdbd3ScjIyOOCf5alCQNKHeLijzowrdmnSU92Nwr\nQQTHjmzpv3UKmh5Axwmwl4xjdkg0MqQFij3o1tG3Hke7+hgzahY3KDejHHFRoI2iBuht/gB7t4TF\nSIMo8XNEgnpcnaJE3rn+vZgQFTlMEgE6Lc+OCvbF9FmLZjtqVHA8Xy3OzEWnfWZJY3Q9E0+Cwtf2\ncZ+V5EjlB43ve0TN/bN/AFjgWKfNOfaJEnHh6/e8Vlm3Tqcixj1zFZHU/56CgYp+p+Kej1lU2b6m\naHQte+2uBqPNeOdGEQ6qCCFuylmKghCioNiqY7EVHlAd8ge042L+Eq8HVe40QXue0B6JRqiypKs1\nW8foaNYbJRhsN4kSZGslSBfT87cK/rq41Dkt3FzRsAus79rcYL8AqyUQfnHFll0mGkMCfNG/n583\nrtcciNuFkgsCfFyi88304Ppxa197AG1u023HCZpJx3PSbXaKElJmNO4FxzWaItb1fBeLEo9XSrh+\nVZrQaBDgPAHWWOdbIEr0mP+Tf8hf35Tw7K822tCC3LwvWWuhpd13PT/xcX9OVrCvLk48OjoqPT19\noqKBSefUzpL6XU9OX42bvxT+N3vEb/OBxHOb7XBQRQixmem8S0JIlPkmtiqWRtjb2ys33HBDaHni\niSdKf1IlEKQK2YJDf1eRhY0br00ZHK4WFV3R9YXMwV1aupotZHZIkDpmD3LjIhiQ9HS3gihhZJ7r\nMglHkPZItD5V2sBYi0Pd5u87+t8gwJ9IYLbQJ0H04jwJxEKjROtt6cG1OUeoUVSkzZWm2SyBKYcr\netZpfF8o7ghaq7ht6PV9/7wE87C0w9+YhF0PzQiX+WyyiJsk0Zz0DsS1aQsjU+illQHYHtOH+PTY\nwcEhmZyclNHRUTl69GhkYLJ06YUxz8d2lky6ph1iv6vmgCcamdLXmZyOaUbJOKgihMTBeZeElMYT\nTzwRGf/39uo54/NAbIkSQy6DjJcBbI/Z/08A/Km17r+gBgwy1IBMp/HZA0e9vlMCK/C4AWmTAG8w\nBtZmTSrbtMBMV3vIOGZEolGdfn9Q2SRqDk2TqOjEAVEiyJzXlDanRw/Q94oqTGyfa0iUkYYdSchS\nbHm7sd6VpmbanNtRPD0gPmoNju3BdWA7HtTMMvefkmi0yrZwt4Vk2vn0M1ssUXEzIFGRrtP+zhcV\n5Yl7Ntocwn4vsqYZ2mInLVrmMj3R+2QpcO3qg3t/22wiLk133bqrZfFie15evyjBv1QCt8S4ayr4\nETR3KmB0zpW+zuQfJVwDJw6qCCGEkNljXkW2RImhXwPwzwhbv08BuMDf/scAfs/Y/+ehUgm19fuH\noazjq976/Y/+6I9SBpraCfD2lP30AM41IHfNdYKolLAGxzE6HcuVpqj/vk6UaGkS4HWZBpGBpXm/\nBCJQR+C2G+dyO7YlD/4n/HWHMuyrl7UCbDMGxJ8w/ta1sUyziiaJuh9CgoLLfRK475nnWRJz7luM\n88WJSH3P4yI5jY7ztfrPxhSV9rOZlqgwtE0r0vqVRzA1G+27rN0bxR0F7E/og7t/IyMjZ/99Jc97\nKvgi7CP+87D/DaSJ7/htk5OTMefW7330HjQ0LJXu7r6aFFXlKrpMCCGEzAXzTmyJEkTvBvA3UEWK\nvwNgvbHtGwAetfa/EcD/8Pd/AcBgQttVI7ZuuSVtwK0d+cxogp32tEGU2IgbkJ9nDST7RUWXForb\nmMBlqT7hb2sVNZdnibgt1lfG9NElUlzzywoC/JnjXriuXc/ZMgWaNnEwowmjEszBcQnSgoRd9Gzx\nor/3iRIpx0UZibjaapVoGqI2ejCFZZNxTp3ap8XdQ1abC4z9XIN+1zNfbRy73n9erY57qOtsue5N\n3Pl0NM1+Z/sTnr0+tk+CaK15jc0SjfrpKGg+odPd3Xf231e8o98RCUSovu9FCVJLdVHufolGhpdK\n4LTp/nfb1bVeRFxzrsw02fC7096+LPS9FtIFZ6PoMiGEEFJp5qXYms2lmsRW9sjWblHRpuUSHZC6\nog1T4i5OfIU/sD0/w6DajOrY5hILrbbX+O1q+3ZzW6+oFEWzPS36XPOClsf0Z6njes7xP/XgWO/r\nEnKeAKvELYYaBOj2/3aJl2bHPY0Tt2ti+t8X8+zi5sdpoae3J81pihMkemkQ5ZboMr3Ya/VfP0NX\nYeEkMbbH8ey1uYju6yckGkXSYq9XgIcliOIuELfQMefWubYFqXjxka27Y/qqUi8vuui1Mc9D75ce\n9RobG5ORkRHp7g4/9+7uXnn44YdleHhYxsfHZXR0VNat2yCFQpOYc/dqwQhjNoouE0IIIZWGYquO\nxZaIyMKF54k7hUpHIjolmOPkcmD7qgA3SnhAPiRRUaEjQbrdtHk2plvhuLVtkbgFiY4KmCLM7q8t\nDnVUZ0yCyII5X2mpBMVu7xeV+nermK6N4UFxgwRFdF3i1IyWTDn61Cluc4rVxj3VkZG4wfYGCQvM\ngrhd7bTBRqsEqZu6MLS+r2mieMJar5/Rjf62Xf7z0qKmz++bTi2MmxMVF+HTAsk1F1A/g+1+u/rZ\n7pCoaNHFnc+TIBKnl3MF6HIco8VbnLBTYmnnzp2yY8cOGR8fl8HBISkUWiRcnFqnLbqeR9BmV9d6\nefjhh/3v+617tVbckbyeSN96evrkkUce8Z0Qg/X9/QOG+Y19jdVt8U5rekIIIfUCxVadi63nn3/e\nF1x2JEKLgxf8AZgr5S9scZ3dbS7LfmaxYD0I3OP/vVvCqW+mW94SUS6DcZEfHQGDKFHhiuqY17Ta\nX1xRM12r6w4JomI7JXmekzkPSBtFxKVRmuLFvFdx6Wkui/bVkn6fISrqaArOYQmiPDoKaAty1xw3\nlwA1xa+2lbejlua1XiJBcWldWNkUIx0STftrFOAqCYS+LdYaxY6itbQslebmtpjnZPZ9rQTFpI9L\nYPgBUTb7ettusX+QaGiw+5lWcHq1mFGa7u7emP0/7Li/QxJY7YejPe3ty2JKNzSK+/1Tz6JaixeX\no+gy53oRQgipBii26lxsaR577DF5+9vfLh0dK60BnGnhnRTd0FGS+xIHQ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lha6/pdHnwO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88c4360910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "ax1=fig.add_subplot(211)\n",
    "ax1.scatter(-X_uniformly[:,0], -X_uniformly[:,1])\n",
    "ax1.set_xlabel('First PC')\n",
    "ax1.set_ylabel('Second PC')\n",
    "ax1.set_title('Uniformly sampled data')\n",
    "ax1.set_xlim([-1,5])\n",
    "ax1.set_ylim([-1,2.5])\n",
    "ax2=fig.add_subplot(212)\n",
    "ax2.scatter(-X_new[:,0], -X_new[:,1])\n",
    "ax2.set_xlabel('First PC')\n",
    "ax2.set_ylabel('Second PC')\n",
    "ax2.set_title('Weight-sampled data')\n",
    "ax2.set_xlim([-1,5])\n",
    "ax2.set_ylim([-1,2.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sampledVillagesMetaData = sampledData.drop(feature_columns,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state-code</th>\n",
       "      <th>state-name</th>\n",
       "      <th>district-code</th>\n",
       "      <th>district-name</th>\n",
       "      <th>teh-code</th>\n",
       "      <th>teh-name</th>\n",
       "      <th>vill-code</th>\n",
       "      <th>ward-no</th>\n",
       "      <th>area-name</th>\n",
       "      <th>census_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8438</th>\n",
       "      <td>03</td>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>047</td>\n",
       "      <td>Mansa</td>\n",
       "      <td>00249</td>\n",
       "      <td>Mansa</td>\n",
       "      <td>036085</td>\n",
       "      <td>0000</td>\n",
       "      <td>Khiala Khurd (53)</td>\n",
       "      <td>0304700249036085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12074</th>\n",
       "      <td>03</td>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>053</td>\n",
       "      <td>Sangrur</td>\n",
       "      <td>00272</td>\n",
       "      <td>Sunam</td>\n",
       "      <td>039830</td>\n",
       "      <td>0000</td>\n",
       "      <td>Ladbanjara Kalan (147)</td>\n",
       "      <td>0305300272039830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15472</th>\n",
       "      <td>06</td>\n",
       "      <td>HARYANA</td>\n",
       "      <td>077</td>\n",
       "      <td>Jind</td>\n",
       "      <td>00383</td>\n",
       "      <td>Jind</td>\n",
       "      <td>060156</td>\n",
       "      <td>0000</td>\n",
       "      <td>Shamlo Khurd (63)</td>\n",
       "      <td>0607700383060156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      state-code state-name district-code district-name teh-code teh-name  \\\n",
       "8438          03     PUNJAB           047         Mansa    00249    Mansa   \n",
       "12074         03     PUNJAB           053       Sangrur    00272    Sunam   \n",
       "15472         06    HARYANA           077          Jind    00383     Jind   \n",
       "\n",
       "      vill-code ward-no               area-name         census_id  \n",
       "8438     036085    0000       Khiala Khurd (53)  0304700249036085  \n",
       "12074    039830    0000  Ladbanjara Kalan (147)  0305300272039830  \n",
       "15472    060156    0000       Shamlo Khurd (63)  0607700383060156  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampledVillagesMetaData.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sampledVillagesMetaData.to_csv('/home/armaan/Desktop/selectedVillages.csv')"
   ]
  }
 ],
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