MainGLSS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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    "collapsed": true
   },
   "source": [
    "\n",
    "# New Combined GLSS Data Set\n",
    "\n",
    "We now have a full data set that combines Income, Lighting and Deforestation Data linked by latitutude and longitude information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import sklearn\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hhid</th>\n",
       "      <th>glss</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "      <th>clust</th>\n",
       "      <th>nh</th>\n",
       "      <th>pid</th>\n",
       "      <th>sex</th>\n",
       "      <th>mining_region</th>\n",
       "      <th>age</th>\n",
       "      <th>...</th>\n",
       "      <th>nl_2011_sum_1km</th>\n",
       "      <th>nl_2011_mean_1km</th>\n",
       "      <th>nl_2011_media_1km</th>\n",
       "      <th>nl_2012_sum_1km</th>\n",
       "      <th>nl_2012_mean_1km</th>\n",
       "      <th>nl_2012_media_1km</th>\n",
       "      <th>nl_2013_sum_1km</th>\n",
       "      <th>nl_2013_mean_1km</th>\n",
       "      <th>nl_2013_media_1km</th>\n",
       "      <th>_merge_glss_mal_nl1</th>\n",
       "    </tr>\n",
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       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>4002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
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       "      <td>4002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>matched (3)</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4002</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>matched (3)</td>\n",
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       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>matched (3)</td>\n",
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       "<p>5 rows × 700 columns</p>\n",
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      "text/plain": [
       "   hhid  glss  year  region  clust   nh  pid  sex  mining_region   age  \\\n",
       "0   1.0     4  98.0     1.0   4002  1.0  2.0  2.0            1.0  51.0   \n",
       "1   1.0     4  98.0     1.0   4002  1.0  6.0  1.0            1.0  17.0   \n",
       "2   1.0     4  98.0     1.0   4002  1.0  1.0  1.0            1.0  57.0   \n",
       "3   1.0     4  98.0     1.0   4002  1.0  4.0  1.0            1.0  22.0   \n",
       "4   1.0     4  98.0     1.0   4002  1.0  3.0  2.0            1.0  26.0   \n",
       "\n",
       "          ...           nl_2011_sum_1km  nl_2011_mean_1km  nl_2011_media_1km  \\\n",
       "0         ...                      12.0               4.0                4.0   \n",
       "1         ...                      12.0               4.0                4.0   \n",
       "2         ...                      12.0               4.0                4.0   \n",
       "3         ...                      12.0               4.0                4.0   \n",
       "4         ...                      12.0               4.0                4.0   \n",
       "\n",
       "  nl_2012_sum_1km  nl_2012_mean_1km  nl_2012_media_1km  nl_2013_sum_1km  \\\n",
       "0            10.0          3.333333                5.0             10.0   \n",
       "1            10.0          3.333333                5.0             10.0   \n",
       "2            10.0          3.333333                5.0             10.0   \n",
       "3            10.0          3.333333                5.0             10.0   \n",
       "4            10.0          3.333333                5.0             10.0   \n",
       "\n",
       "   nl_2013_mean_1km  nl_2013_media_1km  _merge_glss_mal_nl1  \n",
       "0          3.333333                5.0          matched (3)  \n",
       "1          3.333333                5.0          matched (3)  \n",
       "2          3.333333                5.0          matched (3)  \n",
       "3          3.333333                5.0          matched (3)  \n",
       "4          3.333333                5.0          matched (3)  \n",
       "\n",
       "[5 rows x 700 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataset location\n",
    "maindb = \"glss4_5_6_alignedfclmal_nl.csv\"\n",
    "\n",
    "# Read in a CSV file and store the contents in a dataframe (df)\n",
    "df2 = pd.read_csv(maindb, low_memory=False, sep=',')\n",
    "df2.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(136285, 700)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corrdf = df2.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a Correlation Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>glss</th>\n",
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       "      <td>0.906768</td>\n",
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       "      <td>-0.132813</td>\n",
       "      <td>-0.152962</td>\n",
       "      <td>-0.130932</td>\n",
       "      <td>-0.131161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0.579349</td>\n",
       "      <td>0.841910</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.136539</td>\n",
       "      <td>0.535980</td>\n",
       "      <td>-0.222512</td>\n",
       "      <td>0.013153</td>\n",
       "      <td>-0.005466</td>\n",
       "      <td>-0.090166</td>\n",
       "      <td>0.014678</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.076963</td>\n",
       "      <td>-0.099606</td>\n",
       "      <td>-0.079454</td>\n",
       "      <td>-0.081191</td>\n",
       "      <td>-0.095241</td>\n",
       "      <td>-0.075262</td>\n",
       "      <td>-0.076108</td>\n",
       "      <td>-0.095169</td>\n",
       "      <td>-0.075008</td>\n",
       "      <td>-0.075059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <td>0.093887</td>\n",
       "      <td>0.119150</td>\n",
       "      <td>0.136539</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089694</td>\n",
       "      <td>-0.046583</td>\n",
       "      <td>0.133078</td>\n",
       "      <td>-0.011839</td>\n",
       "      <td>-0.516582</td>\n",
       "      <td>-0.012522</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.292804</td>\n",
       "      <td>-0.288051</td>\n",
       "      <td>-0.288791</td>\n",
       "      <td>-0.289620</td>\n",
       "      <td>-0.301516</td>\n",
       "      <td>-0.303993</td>\n",
       "      <td>-0.304807</td>\n",
       "      <td>-0.299895</td>\n",
       "      <td>-0.301415</td>\n",
       "      <td>-0.302136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clust</th>\n",
       "      <td>0.998626</td>\n",
       "      <td>0.906768</td>\n",
       "      <td>0.535980</td>\n",
       "      <td>0.089694</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.155307</td>\n",
       "      <td>0.005050</td>\n",
       "      <td>-0.001745</td>\n",
       "      <td>-0.086895</td>\n",
       "      <td>0.013926</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.157059</td>\n",
       "      <td>-0.171089</td>\n",
       "      <td>-0.153095</td>\n",
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       "      <td>-0.168615</td>\n",
       "      <td>-0.150248</td>\n",
       "      <td>-0.150941</td>\n",
       "      <td>-0.167017</td>\n",
       "      <td>-0.148830</td>\n",
       "      <td>-0.149155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nh</th>\n",
       "      <td>-0.163708</td>\n",
       "      <td>-0.210315</td>\n",
       "      <td>-0.222512</td>\n",
       "      <td>-0.046583</td>\n",
       "      <td>-0.155307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007699</td>\n",
       "      <td>-0.002716</td>\n",
       "      <td>0.023667</td>\n",
       "      <td>-0.005303</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045109</td>\n",
       "      <td>0.053004</td>\n",
       "      <td>0.044641</td>\n",
       "      <td>0.045152</td>\n",
       "      <td>0.051970</td>\n",
       "      <td>0.043624</td>\n",
       "      <td>0.044180</td>\n",
       "      <td>0.051399</td>\n",
       "      <td>0.042836</td>\n",
       "      <td>0.043161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pid</th>\n",
       "      <td>0.005572</td>\n",
       "      <td>0.008909</td>\n",
       "      <td>0.013153</td>\n",
       "      <td>0.133078</td>\n",
       "      <td>0.005050</td>\n",
       "      <td>-0.007699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.036562</td>\n",
       "      <td>-0.068954</td>\n",
       "      <td>-0.503714</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.112103</td>\n",
       "      <td>-0.106421</td>\n",
       "      <td>-0.110967</td>\n",
       "      <td>-0.110916</td>\n",
       "      <td>-0.108326</td>\n",
       "      <td>-0.113005</td>\n",
       "      <td>-0.113017</td>\n",
       "      <td>-0.109543</td>\n",
       "      <td>-0.114113</td>\n",
       "      <td>-0.114087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.001990</td>\n",
       "      <td>-0.003766</td>\n",
       "      <td>-0.005466</td>\n",
       "      <td>-0.011839</td>\n",
       "      <td>-0.001745</td>\n",
       "      <td>-0.002716</td>\n",
       "      <td>0.036562</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.044231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011680</td>\n",
       "      <td>0.010254</td>\n",
       "      <td>0.011505</td>\n",
       "      <td>0.011547</td>\n",
       "      <td>0.010257</td>\n",
       "      <td>0.011496</td>\n",
       "      <td>0.011545</td>\n",
       "      <td>0.010926</td>\n",
       "      <td>0.012256</td>\n",
       "      <td>0.012280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mining_region</th>\n",
       "      <td>-0.088914</td>\n",
       "      <td>-0.097291</td>\n",
       "      <td>-0.090166</td>\n",
       "      <td>-0.516582</td>\n",
       "      <td>-0.086895</td>\n",
       "      <td>0.023667</td>\n",
       "      <td>-0.068954</td>\n",
       "      <td>0.008150</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.111202</td>\n",
       "      <td>0.091140</td>\n",
       "      <td>0.108289</td>\n",
       "      <td>0.107256</td>\n",
       "      <td>0.109329</td>\n",
       "      <td>0.127353</td>\n",
       "      <td>0.125682</td>\n",
       "      <td>0.100682</td>\n",
       "      <td>0.118156</td>\n",
       "      <td>0.115947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.014387</td>\n",
       "      <td>0.016360</td>\n",
       "      <td>0.014678</td>\n",
       "      <td>-0.012522</td>\n",
       "      <td>0.013926</td>\n",
       "      <td>-0.005303</td>\n",
       "      <td>-0.503714</td>\n",
       "      <td>0.044231</td>\n",
       "      <td>-0.009448</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025976</td>\n",
       "      <td>0.024399</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.025642</td>\n",
       "      <td>0.024184</td>\n",
       "      <td>0.025483</td>\n",
       "      <td>0.025572</td>\n",
       "      <td>0.024348</td>\n",
       "      <td>0.025526</td>\n",
       "      <td>0.025589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>working_age</th>\n",
       "      <td>0.008129</td>\n",
       "      <td>0.016176</td>\n",
       "      <td>0.023084</td>\n",
       "      <td>-0.028719</td>\n",
       "      <td>0.006941</td>\n",
       "      <td>-0.007712</td>\n",
       "      <td>-0.419941</td>\n",
       "      <td>0.037085</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.418429</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084143</td>\n",
       "      <td>0.082165</td>\n",
       "      <td>0.084617</td>\n",
       "      <td>0.084518</td>\n",
       "      <td>0.082485</td>\n",
       "      <td>0.084582</td>\n",
       "      <td>0.084716</td>\n",
       "      <td>0.083225</td>\n",
       "      <td>0.085245</td>\n",
       "      <td>0.085356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prime_age</th>\n",
       "      <td>0.008036</td>\n",
       "      <td>0.012077</td>\n",
       "      <td>0.014403</td>\n",
       "      <td>-0.020988</td>\n",
       "      <td>0.007413</td>\n",
       "      <td>-0.001670</td>\n",
       "      <td>-0.285547</td>\n",
       "      <td>0.038347</td>\n",
       "      <td>-0.001558</td>\n",
       "      <td>0.156001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075118</td>\n",
       "      <td>0.071836</td>\n",
       "      <td>0.075408</td>\n",
       "      <td>0.075323</td>\n",
       "      <td>0.072610</td>\n",
       "      <td>0.076072</td>\n",
       "      <td>0.076088</td>\n",
       "      <td>0.073241</td>\n",
       "      <td>0.076588</td>\n",
       "      <td>0.076612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.001990</td>\n",
       "      <td>0.003766</td>\n",
       "      <td>0.005466</td>\n",
       "      <td>0.011839</td>\n",
       "      <td>0.001745</td>\n",
       "      <td>0.002716</td>\n",
       "      <td>-0.036562</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.008150</td>\n",
       "      <td>-0.044231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011680</td>\n",
       "      <td>-0.010254</td>\n",
       "      <td>-0.011505</td>\n",
       "      <td>-0.011547</td>\n",
       "      <td>-0.010257</td>\n",
       "      <td>-0.011496</td>\n",
       "      <td>-0.011545</td>\n",
       "      <td>-0.010926</td>\n",
       "      <td>-0.012256</td>\n",
       "      <td>-0.012280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>school_attendance</th>\n",
       "      <td>0.055196</td>\n",
       "      <td>0.047389</td>\n",
       "      <td>0.022735</td>\n",
       "      <td>-0.027779</td>\n",
       "      <td>0.055590</td>\n",
       "      <td>-0.006069</td>\n",
       "      <td>0.405568</td>\n",
       "      <td>-0.073169</td>\n",
       "      <td>0.036554</td>\n",
       "      <td>-0.633182</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006610</td>\n",
       "      <td>0.004652</td>\n",
       "      <td>0.007222</td>\n",
       "      <td>0.007080</td>\n",
       "      <td>0.005806</td>\n",
       "      <td>0.008114</td>\n",
       "      <td>0.007761</td>\n",
       "      <td>0.005954</td>\n",
       "      <td>0.008330</td>\n",
       "      <td>0.008112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>expenditure</th>\n",
       "      <td>0.518439</td>\n",
       "      <td>0.496195</td>\n",
       "      <td>0.331768</td>\n",
       "      <td>-0.071631</td>\n",
       "      <td>0.515700</td>\n",
       "      <td>-0.091113</td>\n",
       "      <td>0.119901</td>\n",
       "      <td>0.001125</td>\n",
       "      <td>-0.001389</td>\n",
       "      <td>-0.008781</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087484</td>\n",
       "      <td>0.059295</td>\n",
       "      <td>0.088438</td>\n",
       "      <td>0.087315</td>\n",
       "      <td>0.066500</td>\n",
       "      <td>0.095597</td>\n",
       "      <td>0.094740</td>\n",
       "      <td>0.067285</td>\n",
       "      <td>0.096208</td>\n",
       "      <td>0.095252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>expenditurepc</th>\n",
       "      <td>0.460320</td>\n",
       "      <td>0.441948</td>\n",
       "      <td>0.296346</td>\n",
       "      <td>-0.137308</td>\n",
       "      <td>0.457769</td>\n",
       "      <td>-0.075129</td>\n",
       "      <td>-0.139586</td>\n",
       "      <td>-0.017665</td>\n",
       "      <td>0.039298</td>\n",
       "      <td>0.083118</td>\n",
       "      <td>...</td>\n",
       "      <td>0.158925</td>\n",
       "      <td>0.124435</td>\n",
       "      <td>0.159632</td>\n",
       "      <td>0.158225</td>\n",
       "      <td>0.133236</td>\n",
       "      <td>0.168422</td>\n",
       "      <td>0.167459</td>\n",
       "      <td>0.134912</td>\n",
       "      <td>0.169785</td>\n",
       "      <td>0.168646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>food_expend</th>\n",
       "      <td>0.539700</td>\n",
       "      <td>0.530884</td>\n",
       "      <td>0.375507</td>\n",
       "      <td>-0.138290</td>\n",
       "      <td>0.534918</td>\n",
       "      <td>-0.099859</td>\n",
       "      <td>0.062195</td>\n",
       "      <td>0.009051</td>\n",
       "      <td>0.049067</td>\n",
       "      <td>-0.011410</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105007</td>\n",
       "      <td>0.069847</td>\n",
       "      <td>0.104200</td>\n",
       "      <td>0.102851</td>\n",
       "      <td>0.080726</td>\n",
       "      <td>0.115296</td>\n",
       "      <td>0.114466</td>\n",
       "      <td>0.083342</td>\n",
       "      <td>0.117867</td>\n",
       "      <td>0.116729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>food_expendpc</th>\n",
       "      <td>0.446177</td>\n",
       "      <td>0.437784</td>\n",
       "      <td>0.306993</td>\n",
       "      <td>-0.178808</td>\n",
       "      <td>0.442437</td>\n",
       "      <td>-0.077443</td>\n",
       "      <td>-0.189553</td>\n",
       "      <td>-0.011591</td>\n",
       "      <td>0.070796</td>\n",
       "      <td>0.096464</td>\n",
       "      <td>...</td>\n",
       "      <td>0.158590</td>\n",
       "      <td>0.120975</td>\n",
       "      <td>0.157040</td>\n",
       "      <td>0.155568</td>\n",
       "      <td>0.132098</td>\n",
       "      <td>0.168497</td>\n",
       "      <td>0.167754</td>\n",
       "      <td>0.135048</td>\n",
       "      <td>0.171269</td>\n",
       "      <td>0.170196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inc</th>\n",
       "      <td>0.213062</td>\n",
       "      <td>0.196845</td>\n",
       "      <td>0.121116</td>\n",
       "      <td>-0.016850</td>\n",
       "      <td>0.212916</td>\n",
       "      <td>-0.041308</td>\n",
       "      <td>0.052420</td>\n",
       "      <td>-0.003082</td>\n",
       "      <td>0.018683</td>\n",
       "      <td>-0.007018</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019040</td>\n",
       "      <td>0.007727</td>\n",
       "      <td>0.020804</td>\n",
       "      <td>0.019929</td>\n",
       "      <td>0.009902</td>\n",
       "      <td>0.023003</td>\n",
       "      <td>0.022247</td>\n",
       "      <td>0.011643</td>\n",
       "      <td>0.024843</td>\n",
       "      <td>0.023740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incpc</th>\n",
       "      <td>0.174981</td>\n",
       "      <td>0.161902</td>\n",
       "      <td>0.099608</td>\n",
       "      <td>-0.036732</td>\n",
       "      <td>0.174852</td>\n",
       "      <td>-0.030812</td>\n",
       "      <td>-0.038953</td>\n",
       "      <td>-0.008791</td>\n",
       "      <td>0.038953</td>\n",
       "      <td>0.023282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044137</td>\n",
       "      <td>0.029947</td>\n",
       "      <td>0.044414</td>\n",
       "      <td>0.043738</td>\n",
       "      <td>0.032819</td>\n",
       "      <td>0.047300</td>\n",
       "      <td>0.046713</td>\n",
       "      <td>0.033914</td>\n",
       "      <td>0.048301</td>\n",
       "      <td>0.047528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rexpenditure</th>\n",
       "      <td>0.334452</td>\n",
       "      <td>0.316303</td>\n",
       "      <td>0.205083</td>\n",
       "      <td>-0.105162</td>\n",
       "      <td>0.333185</td>\n",
       "      <td>-0.053351</td>\n",
       "      <td>0.150026</td>\n",
       "      <td>0.003600</td>\n",
       "      <td>0.019663</td>\n",
       "      <td>-0.018825</td>\n",
       "      <td>...</td>\n",
       "      <td>0.188596</td>\n",
       "      <td>0.160948</td>\n",
       "      <td>0.189483</td>\n",
       "      <td>0.188495</td>\n",
       "      <td>0.167659</td>\n",
       "      <td>0.195648</td>\n",
       "      <td>0.194773</td>\n",
       "      <td>0.168486</td>\n",
       "      <td>0.196371</td>\n",
       "      <td>0.195402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rexpenditurepc</th>\n",
       "      <td>0.294051</td>\n",
       "      <td>0.280315</td>\n",
       "      <td>0.183790</td>\n",
       "      <td>-0.177972</td>\n",
       "      <td>0.292672</td>\n",
       "      <td>-0.043312</td>\n",
       "      <td>-0.170993</td>\n",
       "      <td>-0.019696</td>\n",
       "      <td>0.064246</td>\n",
       "      <td>0.097448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.274779</td>\n",
       "      <td>0.241869</td>\n",
       "      <td>0.275528</td>\n",
       "      <td>0.274315</td>\n",
       "      <td>0.249667</td>\n",
       "      <td>0.282883</td>\n",
       "      <td>0.281982</td>\n",
       "      <td>0.251555</td>\n",
       "      <td>0.284516</td>\n",
       "      <td>0.283393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rfood_expend</th>\n",
       "      <td>0.215583</td>\n",
       "      <td>0.207395</td>\n",
       "      <td>0.137928</td>\n",
       "      <td>-0.189334</td>\n",
       "      <td>0.214384</td>\n",
       "      <td>-0.034139</td>\n",
       "      <td>0.110466</td>\n",
       "      <td>0.011541</td>\n",
       "      <td>0.082295</td>\n",
       "      <td>-0.029376</td>\n",
       "      <td>...</td>\n",
       "      <td>0.202366</td>\n",
       "      <td>0.169960</td>\n",
       "      <td>0.200990</td>\n",
       "      <td>0.199988</td>\n",
       "      <td>0.179711</td>\n",
       "      <td>0.210539</td>\n",
       "      <td>0.209766</td>\n",
       "      <td>0.182567</td>\n",
       "      <td>0.213551</td>\n",
       "      <td>0.212351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rfood_expendpc</th>\n",
       "      <td>0.193053</td>\n",
       "      <td>0.186185</td>\n",
       "      <td>0.123621</td>\n",
       "      <td>-0.231246</td>\n",
       "      <td>0.191909</td>\n",
       "      <td>-0.028106</td>\n",
       "      <td>-0.234746</td>\n",
       "      <td>-0.014652</td>\n",
       "      <td>0.105381</td>\n",
       "      <td>0.118524</td>\n",
       "      <td>...</td>\n",
       "      <td>0.266337</td>\n",
       "      <td>0.232231</td>\n",
       "      <td>0.264350</td>\n",
       "      <td>0.263258</td>\n",
       "      <td>0.241670</td>\n",
       "      <td>0.273850</td>\n",
       "      <td>0.273240</td>\n",
       "      <td>0.245145</td>\n",
       "      <td>0.277293</td>\n",
       "      <td>0.276208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rincpc</th>\n",
       "      <td>0.155735</td>\n",
       "      <td>0.144031</td>\n",
       "      <td>0.088446</td>\n",
       "      <td>-0.038303</td>\n",
       "      <td>0.155627</td>\n",
       "      <td>-0.027323</td>\n",
       "      <td>-0.041272</td>\n",
       "      <td>-0.009462</td>\n",
       "      <td>0.039620</td>\n",
       "      <td>0.024754</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054379</td>\n",
       "      <td>0.040205</td>\n",
       "      <td>0.054700</td>\n",
       "      <td>0.054042</td>\n",
       "      <td>0.042817</td>\n",
       "      <td>0.057280</td>\n",
       "      <td>0.056680</td>\n",
       "      <td>0.043887</td>\n",
       "      <td>0.058256</td>\n",
       "      <td>0.057495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>-0.039345</td>\n",
       "      <td>-0.056438</td>\n",
       "      <td>-0.062092</td>\n",
       "      <td>0.334406</td>\n",
       "      <td>-0.036400</td>\n",
       "      <td>-0.004138</td>\n",
       "      <td>0.203500</td>\n",
       "      <td>-0.021481</td>\n",
       "      <td>-0.178371</td>\n",
       "      <td>-0.059160</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.313797</td>\n",
       "      <td>-0.289884</td>\n",
       "      <td>-0.309338</td>\n",
       "      <td>-0.308852</td>\n",
       "      <td>-0.298036</td>\n",
       "      <td>-0.317558</td>\n",
       "      <td>-0.317124</td>\n",
       "      <td>-0.299916</td>\n",
       "      <td>-0.319590</td>\n",
       "      <td>-0.319031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor_ext</th>\n",
       "      <td>-0.139709</td>\n",
       "      <td>-0.141638</td>\n",
       "      <td>-0.103445</td>\n",
       "      <td>0.323523</td>\n",
       "      <td>-0.137645</td>\n",
       "      <td>0.013916</td>\n",
       "      <td>0.164001</td>\n",
       "      <td>-0.019988</td>\n",
       "      <td>-0.163314</td>\n",
       "      <td>-0.039874</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.232171</td>\n",
       "      <td>-0.213262</td>\n",
       "      <td>-0.228524</td>\n",
       "      <td>-0.228086</td>\n",
       "      <td>-0.219900</td>\n",
       "      <td>-0.235221</td>\n",
       "      <td>-0.234577</td>\n",
       "      <td>-0.220709</td>\n",
       "      <td>-0.236078</td>\n",
       "      <td>-0.235443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ln_rincpc</th>\n",
       "      <td>0.480784</td>\n",
       "      <td>0.457807</td>\n",
       "      <td>0.301746</td>\n",
       "      <td>-0.159695</td>\n",
       "      <td>0.478552</td>\n",
       "      <td>-0.079356</td>\n",
       "      <td>-0.087858</td>\n",
       "      <td>-0.011684</td>\n",
       "      <td>0.073080</td>\n",
       "      <td>0.057046</td>\n",
       "      <td>...</td>\n",
       "      <td>0.130450</td>\n",
       "      <td>0.108305</td>\n",
       "      <td>0.129040</td>\n",
       "      <td>0.128270</td>\n",
       "      <td>0.114451</td>\n",
       "      <td>0.135150</td>\n",
       "      <td>0.134446</td>\n",
       "      <td>0.113862</td>\n",
       "      <td>0.134117</td>\n",
       "      <td>0.133712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ln_rincequiv</th>\n",
       "      <td>0.487836</td>\n",
       "      <td>0.466681</td>\n",
       "      <td>0.310951</td>\n",
       "      <td>-0.157340</td>\n",
       "      <td>0.485264</td>\n",
       "      <td>-0.081521</td>\n",
       "      <td>-0.084462</td>\n",
       "      <td>0.001282</td>\n",
       "      <td>0.071405</td>\n",
       "      <td>0.046305</td>\n",
       "      <td>...</td>\n",
       "      <td>0.121327</td>\n",
       "      <td>0.098902</td>\n",
       "      <td>0.119871</td>\n",
       "      <td>0.119110</td>\n",
       "      <td>0.105056</td>\n",
       "      <td>0.126021</td>\n",
       "      <td>0.125291</td>\n",
       "      <td>0.104482</td>\n",
       "      <td>0.125018</td>\n",
       "      <td>0.124595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ln_rexpenditurepc</th>\n",
       "      <td>0.426521</td>\n",
       "      <td>0.404020</td>\n",
       "      <td>0.261121</td>\n",
       "      <td>-0.301991</td>\n",
       "      <td>0.424692</td>\n",
       "      <td>-0.063501</td>\n",
       "      <td>-0.224172</td>\n",
       "      <td>-0.004375</td>\n",
       "      <td>0.131948</td>\n",
       "      <td>0.107203</td>\n",
       "      <td>...</td>\n",
       "      <td>0.332668</td>\n",
       "      <td>0.299347</td>\n",
       "      <td>0.331835</td>\n",
       "      <td>0.330769</td>\n",
       "      <td>0.308711</td>\n",
       "      <td>0.340855</td>\n",
       "      <td>0.340069</td>\n",
       "      <td>0.311134</td>\n",
       "      <td>0.343160</td>\n",
       "      <td>0.342233</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>nl_2004_sum_1km</th>\n",
       "      <td>-0.186332</td>\n",
       "      <td>-0.172213</td>\n",
       "      <td>-0.108464</td>\n",
       "      <td>-0.273134</td>\n",
       "      <td>-0.186234</td>\n",
       "      <td>0.055195</td>\n",
       "      <td>-0.100883</td>\n",
       "      <td>0.008855</td>\n",
       "      <td>0.081227</td>\n",
       "      <td>0.022317</td>\n",
       "      <td>...</td>\n",
       "      <td>0.936145</td>\n",
       "      <td>0.982496</td>\n",
       "      <td>0.943691</td>\n",
       "      <td>0.944724</td>\n",
       "      <td>0.974040</td>\n",
       "      <td>0.931340</td>\n",
       "      <td>0.932317</td>\n",
       "      <td>0.970987</td>\n",
       "      <td>0.926800</td>\n",
       "      <td>0.928226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2004_mean_1km</th>\n",
       "      <td>-0.169177</td>\n",
       "      <td>-0.152441</td>\n",
       "      <td>-0.090730</td>\n",
       "      <td>-0.277447</td>\n",
       "      <td>-0.169639</td>\n",
       "      <td>0.047627</td>\n",
       "      <td>-0.106179</td>\n",
       "      <td>0.009823</td>\n",
       "      <td>0.101784</td>\n",
       "      <td>0.023553</td>\n",
       "      <td>...</td>\n",
       "      <td>0.970486</td>\n",
       "      <td>0.968418</td>\n",
       "      <td>0.976971</td>\n",
       "      <td>0.976968</td>\n",
       "      <td>0.963051</td>\n",
       "      <td>0.967519</td>\n",
       "      <td>0.967549</td>\n",
       "      <td>0.960826</td>\n",
       "      <td>0.963944</td>\n",
       "      <td>0.964411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2004_media_1km</th>\n",
       "      <td>-0.169428</td>\n",
       "      <td>-0.152317</td>\n",
       "      <td>-0.090134</td>\n",
       "      <td>-0.277343</td>\n",
       "      <td>-0.169932</td>\n",
       "      <td>0.047495</td>\n",
       "      <td>-0.106171</td>\n",
       "      <td>0.009927</td>\n",
       "      <td>0.103376</td>\n",
       "      <td>0.023520</td>\n",
       "      <td>...</td>\n",
       "      <td>0.970013</td>\n",
       "      <td>0.967580</td>\n",
       "      <td>0.976396</td>\n",
       "      <td>0.976510</td>\n",
       "      <td>0.962168</td>\n",
       "      <td>0.966893</td>\n",
       "      <td>0.967014</td>\n",
       "      <td>0.959956</td>\n",
       "      <td>0.963342</td>\n",
       "      <td>0.963920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2005_sum_1km</th>\n",
       "      <td>-0.180280</td>\n",
       "      <td>-0.165259</td>\n",
       "      <td>-0.102166</td>\n",
       "      <td>-0.267463</td>\n",
       "      <td>-0.180380</td>\n",
       "      <td>0.054111</td>\n",
       "      <td>-0.096355</td>\n",
       "      <td>0.007549</td>\n",
       "      <td>0.072238</td>\n",
       "      <td>0.022305</td>\n",
       "      <td>...</td>\n",
       "      <td>0.928105</td>\n",
       "      <td>0.968835</td>\n",
       "      <td>0.932660</td>\n",
       "      <td>0.933065</td>\n",
       "      <td>0.964997</td>\n",
       "      <td>0.925070</td>\n",
       "      <td>0.925590</td>\n",
       "      <td>0.957052</td>\n",
       "      <td>0.915213</td>\n",
       "      <td>0.916240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2005_mean_1km</th>\n",
       "      <td>-0.161961</td>\n",
       "      <td>-0.145283</td>\n",
       "      <td>-0.085586</td>\n",
       "      <td>-0.272421</td>\n",
       "      <td>-0.162513</td>\n",
       "      <td>0.046990</td>\n",
       "      <td>-0.100727</td>\n",
       "      <td>0.007981</td>\n",
       "      <td>0.087668</td>\n",
       "      <td>0.023586</td>\n",
       "      <td>...</td>\n",
       "      <td>0.956935</td>\n",
       "      <td>0.950253</td>\n",
       "      <td>0.960316</td>\n",
       "      <td>0.959732</td>\n",
       "      <td>0.949672</td>\n",
       "      <td>0.956064</td>\n",
       "      <td>0.955670</td>\n",
       "      <td>0.941995</td>\n",
       "      <td>0.946326</td>\n",
       "      <td>0.946337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2005_media_1km</th>\n",
       "      <td>-0.163176</td>\n",
       "      <td>-0.145377</td>\n",
       "      <td>-0.084116</td>\n",
       "      <td>-0.272095</td>\n",
       "      <td>-0.163872</td>\n",
       "      <td>0.047414</td>\n",
       "      <td>-0.100396</td>\n",
       "      <td>0.007900</td>\n",
       "      <td>0.082110</td>\n",
       "      <td>0.023719</td>\n",
       "      <td>...</td>\n",
       "      <td>0.955075</td>\n",
       "      <td>0.952597</td>\n",
       "      <td>0.958206</td>\n",
       "      <td>0.957844</td>\n",
       "      <td>0.951567</td>\n",
       "      <td>0.953506</td>\n",
       "      <td>0.953442</td>\n",
       "      <td>0.943785</td>\n",
       "      <td>0.943629</td>\n",
       "      <td>0.944020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2006_sum_1km</th>\n",
       "      <td>-0.180597</td>\n",
       "      <td>-0.165800</td>\n",
       "      <td>-0.102953</td>\n",
       "      <td>-0.274091</td>\n",
       "      <td>-0.180657</td>\n",
       "      <td>0.054206</td>\n",
       "      <td>-0.098482</td>\n",
       "      <td>0.008054</td>\n",
       "      <td>0.099379</td>\n",
       "      <td>0.021932</td>\n",
       "      <td>...</td>\n",
       "      <td>0.941106</td>\n",
       "      <td>0.977334</td>\n",
       "      <td>0.944576</td>\n",
       "      <td>0.944880</td>\n",
       "      <td>0.975091</td>\n",
       "      <td>0.938648</td>\n",
       "      <td>0.938995</td>\n",
       "      <td>0.967825</td>\n",
       "      <td>0.929567</td>\n",
       "      <td>0.930274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2006_mean_1km</th>\n",
       "      <td>-0.162315</td>\n",
       "      <td>-0.145089</td>\n",
       "      <td>-0.084788</td>\n",
       "      <td>-0.278593</td>\n",
       "      <td>-0.162933</td>\n",
       "      <td>0.046630</td>\n",
       "      <td>-0.102787</td>\n",
       "      <td>0.008627</td>\n",
       "      <td>0.117427</td>\n",
       "      <td>0.023144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.968616</td>\n",
       "      <td>0.956789</td>\n",
       "      <td>0.970739</td>\n",
       "      <td>0.970046</td>\n",
       "      <td>0.957974</td>\n",
       "      <td>0.968311</td>\n",
       "      <td>0.967719</td>\n",
       "      <td>0.951160</td>\n",
       "      <td>0.959608</td>\n",
       "      <td>0.959294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2006_media_1km</th>\n",
       "      <td>-0.161545</td>\n",
       "      <td>-0.144219</td>\n",
       "      <td>-0.084019</td>\n",
       "      <td>-0.277799</td>\n",
       "      <td>-0.162181</td>\n",
       "      <td>0.046378</td>\n",
       "      <td>-0.102564</td>\n",
       "      <td>0.008589</td>\n",
       "      <td>0.118237</td>\n",
       "      <td>0.022988</td>\n",
       "      <td>...</td>\n",
       "      <td>0.968001</td>\n",
       "      <td>0.955998</td>\n",
       "      <td>0.969798</td>\n",
       "      <td>0.969199</td>\n",
       "      <td>0.957309</td>\n",
       "      <td>0.967519</td>\n",
       "      <td>0.967092</td>\n",
       "      <td>0.950385</td>\n",
       "      <td>0.958698</td>\n",
       "      <td>0.958448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2007_sum_1km</th>\n",
       "      <td>-0.180851</td>\n",
       "      <td>-0.166124</td>\n",
       "      <td>-0.103350</td>\n",
       "      <td>-0.283572</td>\n",
       "      <td>-0.180935</td>\n",
       "      <td>0.055393</td>\n",
       "      <td>-0.099662</td>\n",
       "      <td>0.008240</td>\n",
       "      <td>0.070951</td>\n",
       "      <td>0.022929</td>\n",
       "      <td>...</td>\n",
       "      <td>0.942373</td>\n",
       "      <td>0.983703</td>\n",
       "      <td>0.946946</td>\n",
       "      <td>0.947613</td>\n",
       "      <td>0.979005</td>\n",
       "      <td>0.938569</td>\n",
       "      <td>0.939282</td>\n",
       "      <td>0.974337</td>\n",
       "      <td>0.932168</td>\n",
       "      <td>0.933394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2007_mean_1km</th>\n",
       "      <td>-0.163155</td>\n",
       "      <td>-0.146243</td>\n",
       "      <td>-0.086141</td>\n",
       "      <td>-0.289333</td>\n",
       "      <td>-0.163749</td>\n",
       "      <td>0.048214</td>\n",
       "      <td>-0.104682</td>\n",
       "      <td>0.008989</td>\n",
       "      <td>0.088908</td>\n",
       "      <td>0.024304</td>\n",
       "      <td>...</td>\n",
       "      <td>0.975680</td>\n",
       "      <td>0.968203</td>\n",
       "      <td>0.978847</td>\n",
       "      <td>0.978476</td>\n",
       "      <td>0.966927</td>\n",
       "      <td>0.973860</td>\n",
       "      <td>0.973628</td>\n",
       "      <td>0.962810</td>\n",
       "      <td>0.968002</td>\n",
       "      <td>0.968202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2007_media_1km</th>\n",
       "      <td>-0.163730</td>\n",
       "      <td>-0.146710</td>\n",
       "      <td>-0.086328</td>\n",
       "      <td>-0.289177</td>\n",
       "      <td>-0.164331</td>\n",
       "      <td>0.048650</td>\n",
       "      <td>-0.104495</td>\n",
       "      <td>0.008977</td>\n",
       "      <td>0.087752</td>\n",
       "      <td>0.024376</td>\n",
       "      <td>...</td>\n",
       "      <td>0.975140</td>\n",
       "      <td>0.968591</td>\n",
       "      <td>0.978158</td>\n",
       "      <td>0.977915</td>\n",
       "      <td>0.967283</td>\n",
       "      <td>0.973152</td>\n",
       "      <td>0.973148</td>\n",
       "      <td>0.962996</td>\n",
       "      <td>0.967085</td>\n",
       "      <td>0.967535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2008_sum_1km</th>\n",
       "      <td>-0.180336</td>\n",
       "      <td>-0.167562</td>\n",
       "      <td>-0.107026</td>\n",
       "      <td>-0.278735</td>\n",
       "      <td>-0.180138</td>\n",
       "      <td>0.055490</td>\n",
       "      <td>-0.101799</td>\n",
       "      <td>0.009250</td>\n",
       "      <td>0.076384</td>\n",
       "      <td>0.024601</td>\n",
       "      <td>...</td>\n",
       "      <td>0.954233</td>\n",
       "      <td>0.990259</td>\n",
       "      <td>0.957819</td>\n",
       "      <td>0.958712</td>\n",
       "      <td>0.983447</td>\n",
       "      <td>0.947113</td>\n",
       "      <td>0.947753</td>\n",
       "      <td>0.981037</td>\n",
       "      <td>0.943101</td>\n",
       "      <td>0.944431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2008_mean_1km</th>\n",
       "      <td>-0.162157</td>\n",
       "      <td>-0.146771</td>\n",
       "      <td>-0.088561</td>\n",
       "      <td>-0.282748</td>\n",
       "      <td>-0.162537</td>\n",
       "      <td>0.047883</td>\n",
       "      <td>-0.106575</td>\n",
       "      <td>0.010219</td>\n",
       "      <td>0.093961</td>\n",
       "      <td>0.026010</td>\n",
       "      <td>...</td>\n",
       "      <td>0.984469</td>\n",
       "      <td>0.970833</td>\n",
       "      <td>0.986322</td>\n",
       "      <td>0.986232</td>\n",
       "      <td>0.967268</td>\n",
       "      <td>0.978749</td>\n",
       "      <td>0.978482</td>\n",
       "      <td>0.965642</td>\n",
       "      <td>0.975644</td>\n",
       "      <td>0.976054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2008_media_1km</th>\n",
       "      <td>-0.163711</td>\n",
       "      <td>-0.147719</td>\n",
       "      <td>-0.088420</td>\n",
       "      <td>-0.282236</td>\n",
       "      <td>-0.164153</td>\n",
       "      <td>0.048153</td>\n",
       "      <td>-0.106342</td>\n",
       "      <td>0.010062</td>\n",
       "      <td>0.091386</td>\n",
       "      <td>0.026061</td>\n",
       "      <td>...</td>\n",
       "      <td>0.984246</td>\n",
       "      <td>0.971478</td>\n",
       "      <td>0.985568</td>\n",
       "      <td>0.985707</td>\n",
       "      <td>0.967670</td>\n",
       "      <td>0.977738</td>\n",
       "      <td>0.977675</td>\n",
       "      <td>0.966150</td>\n",
       "      <td>0.974754</td>\n",
       "      <td>0.975418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2009_sum_1km</th>\n",
       "      <td>-0.179363</td>\n",
       "      <td>-0.165549</td>\n",
       "      <td>-0.104084</td>\n",
       "      <td>-0.272119</td>\n",
       "      <td>-0.179309</td>\n",
       "      <td>0.054870</td>\n",
       "      <td>-0.099892</td>\n",
       "      <td>0.009084</td>\n",
       "      <td>0.063117</td>\n",
       "      <td>0.024308</td>\n",
       "      <td>...</td>\n",
       "      <td>0.954886</td>\n",
       "      <td>0.987327</td>\n",
       "      <td>0.955208</td>\n",
       "      <td>0.956031</td>\n",
       "      <td>0.981911</td>\n",
       "      <td>0.946061</td>\n",
       "      <td>0.946811</td>\n",
       "      <td>0.978863</td>\n",
       "      <td>0.941297</td>\n",
       "      <td>0.942787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2009_mean_1km</th>\n",
       "      <td>-0.161089</td>\n",
       "      <td>-0.145140</td>\n",
       "      <td>-0.086537</td>\n",
       "      <td>-0.275774</td>\n",
       "      <td>-0.161547</td>\n",
       "      <td>0.047494</td>\n",
       "      <td>-0.104406</td>\n",
       "      <td>0.009995</td>\n",
       "      <td>0.078540</td>\n",
       "      <td>0.025642</td>\n",
       "      <td>...</td>\n",
       "      <td>0.985080</td>\n",
       "      <td>0.967375</td>\n",
       "      <td>0.983096</td>\n",
       "      <td>0.982971</td>\n",
       "      <td>0.965349</td>\n",
       "      <td>0.977303</td>\n",
       "      <td>0.977227</td>\n",
       "      <td>0.962942</td>\n",
       "      <td>0.973222</td>\n",
       "      <td>0.973834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2009_media_1km</th>\n",
       "      <td>-0.161468</td>\n",
       "      <td>-0.145097</td>\n",
       "      <td>-0.085928</td>\n",
       "      <td>-0.275248</td>\n",
       "      <td>-0.161973</td>\n",
       "      <td>0.047701</td>\n",
       "      <td>-0.103962</td>\n",
       "      <td>0.009856</td>\n",
       "      <td>0.075717</td>\n",
       "      <td>0.025656</td>\n",
       "      <td>...</td>\n",
       "      <td>0.984464</td>\n",
       "      <td>0.967856</td>\n",
       "      <td>0.981950</td>\n",
       "      <td>0.982023</td>\n",
       "      <td>0.965584</td>\n",
       "      <td>0.975905</td>\n",
       "      <td>0.976134</td>\n",
       "      <td>0.963095</td>\n",
       "      <td>0.971734</td>\n",
       "      <td>0.972638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2010_sum_1km</th>\n",
       "      <td>-0.174556</td>\n",
       "      <td>-0.159317</td>\n",
       "      <td>-0.097876</td>\n",
       "      <td>-0.290720</td>\n",
       "      <td>-0.174773</td>\n",
       "      <td>0.053391</td>\n",
       "      <td>-0.107424</td>\n",
       "      <td>0.010509</td>\n",
       "      <td>0.095274</td>\n",
       "      <td>0.024594</td>\n",
       "      <td>...</td>\n",
       "      <td>0.974847</td>\n",
       "      <td>0.994200</td>\n",
       "      <td>0.970387</td>\n",
       "      <td>0.970993</td>\n",
       "      <td>0.993777</td>\n",
       "      <td>0.966437</td>\n",
       "      <td>0.967157</td>\n",
       "      <td>0.992026</td>\n",
       "      <td>0.963115</td>\n",
       "      <td>0.964042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2010_mean_1km</th>\n",
       "      <td>-0.156021</td>\n",
       "      <td>-0.137714</td>\n",
       "      <td>-0.078193</td>\n",
       "      <td>-0.292655</td>\n",
       "      <td>-0.156873</td>\n",
       "      <td>0.045214</td>\n",
       "      <td>-0.112003</td>\n",
       "      <td>0.011723</td>\n",
       "      <td>0.112170</td>\n",
       "      <td>0.025834</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999571</td>\n",
       "      <td>0.966276</td>\n",
       "      <td>0.992494</td>\n",
       "      <td>0.992103</td>\n",
       "      <td>0.969576</td>\n",
       "      <td>0.992257</td>\n",
       "      <td>0.992190</td>\n",
       "      <td>0.968717</td>\n",
       "      <td>0.989981</td>\n",
       "      <td>0.989987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2010_media_1km</th>\n",
       "      <td>-0.156124</td>\n",
       "      <td>-0.137187</td>\n",
       "      <td>-0.076963</td>\n",
       "      <td>-0.292804</td>\n",
       "      <td>-0.157059</td>\n",
       "      <td>0.045109</td>\n",
       "      <td>-0.112103</td>\n",
       "      <td>0.011680</td>\n",
       "      <td>0.111202</td>\n",
       "      <td>0.025976</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.965935</td>\n",
       "      <td>0.991644</td>\n",
       "      <td>0.991417</td>\n",
       "      <td>0.968968</td>\n",
       "      <td>0.991079</td>\n",
       "      <td>0.991224</td>\n",
       "      <td>0.968236</td>\n",
       "      <td>0.988972</td>\n",
       "      <td>0.989229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2011_sum_1km</th>\n",
       "      <td>-0.171103</td>\n",
       "      <td>-0.157913</td>\n",
       "      <td>-0.099606</td>\n",
       "      <td>-0.288051</td>\n",
       "      <td>-0.171089</td>\n",
       "      <td>0.053004</td>\n",
       "      <td>-0.106421</td>\n",
       "      <td>0.010254</td>\n",
       "      <td>0.091140</td>\n",
       "      <td>0.024399</td>\n",
       "      <td>...</td>\n",
       "      <td>0.965935</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974792</td>\n",
       "      <td>0.975640</td>\n",
       "      <td>0.994160</td>\n",
       "      <td>0.964774</td>\n",
       "      <td>0.965524</td>\n",
       "      <td>0.992497</td>\n",
       "      <td>0.961460</td>\n",
       "      <td>0.962393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2011_mean_1km</th>\n",
       "      <td>-0.152445</td>\n",
       "      <td>-0.136011</td>\n",
       "      <td>-0.079454</td>\n",
       "      <td>-0.288791</td>\n",
       "      <td>-0.153095</td>\n",
       "      <td>0.044641</td>\n",
       "      <td>-0.110967</td>\n",
       "      <td>0.011505</td>\n",
       "      <td>0.108289</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>...</td>\n",
       "      <td>0.991644</td>\n",
       "      <td>0.974792</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999782</td>\n",
       "      <td>0.972018</td>\n",
       "      <td>0.992666</td>\n",
       "      <td>0.992537</td>\n",
       "      <td>0.971154</td>\n",
       "      <td>0.990291</td>\n",
       "      <td>0.990252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2011_media_1km</th>\n",
       "      <td>-0.153455</td>\n",
       "      <td>-0.137501</td>\n",
       "      <td>-0.081191</td>\n",
       "      <td>-0.289620</td>\n",
       "      <td>-0.154027</td>\n",
       "      <td>0.045152</td>\n",
       "      <td>-0.110916</td>\n",
       "      <td>0.011547</td>\n",
       "      <td>0.107256</td>\n",
       "      <td>0.025642</td>\n",
       "      <td>...</td>\n",
       "      <td>0.991417</td>\n",
       "      <td>0.975640</td>\n",
       "      <td>0.999782</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.972405</td>\n",
       "      <td>0.991995</td>\n",
       "      <td>0.992076</td>\n",
       "      <td>0.971552</td>\n",
       "      <td>0.989611</td>\n",
       "      <td>0.989792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2012_sum_1km</th>\n",
       "      <td>-0.168451</td>\n",
       "      <td>-0.153992</td>\n",
       "      <td>-0.095241</td>\n",
       "      <td>-0.301516</td>\n",
       "      <td>-0.168615</td>\n",
       "      <td>0.051970</td>\n",
       "      <td>-0.108326</td>\n",
       "      <td>0.010257</td>\n",
       "      <td>0.109329</td>\n",
       "      <td>0.024184</td>\n",
       "      <td>...</td>\n",
       "      <td>0.968968</td>\n",
       "      <td>0.994160</td>\n",
       "      <td>0.972018</td>\n",
       "      <td>0.972405</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974876</td>\n",
       "      <td>0.975387</td>\n",
       "      <td>0.996424</td>\n",
       "      <td>0.969545</td>\n",
       "      <td>0.970229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2012_mean_1km</th>\n",
       "      <td>-0.149447</td>\n",
       "      <td>-0.131939</td>\n",
       "      <td>-0.075262</td>\n",
       "      <td>-0.303993</td>\n",
       "      <td>-0.150248</td>\n",
       "      <td>0.043624</td>\n",
       "      <td>-0.113005</td>\n",
       "      <td>0.011496</td>\n",
       "      <td>0.127353</td>\n",
       "      <td>0.025483</td>\n",
       "      <td>...</td>\n",
       "      <td>0.991079</td>\n",
       "      <td>0.964774</td>\n",
       "      <td>0.992666</td>\n",
       "      <td>0.991995</td>\n",
       "      <td>0.974876</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999620</td>\n",
       "      <td>0.972026</td>\n",
       "      <td>0.995469</td>\n",
       "      <td>0.995180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2012_media_1km</th>\n",
       "      <td>-0.150168</td>\n",
       "      <td>-0.132813</td>\n",
       "      <td>-0.076108</td>\n",
       "      <td>-0.304807</td>\n",
       "      <td>-0.150941</td>\n",
       "      <td>0.044180</td>\n",
       "      <td>-0.113017</td>\n",
       "      <td>0.011545</td>\n",
       "      <td>0.125682</td>\n",
       "      <td>0.025572</td>\n",
       "      <td>...</td>\n",
       "      <td>0.991224</td>\n",
       "      <td>0.965524</td>\n",
       "      <td>0.992537</td>\n",
       "      <td>0.992076</td>\n",
       "      <td>0.975387</td>\n",
       "      <td>0.999620</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.972418</td>\n",
       "      <td>0.994915</td>\n",
       "      <td>0.994969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2013_sum_1km</th>\n",
       "      <td>-0.166918</td>\n",
       "      <td>-0.152962</td>\n",
       "      <td>-0.095169</td>\n",
       "      <td>-0.299895</td>\n",
       "      <td>-0.167017</td>\n",
       "      <td>0.051399</td>\n",
       "      <td>-0.109543</td>\n",
       "      <td>0.010926</td>\n",
       "      <td>0.100682</td>\n",
       "      <td>0.024348</td>\n",
       "      <td>...</td>\n",
       "      <td>0.968236</td>\n",
       "      <td>0.992497</td>\n",
       "      <td>0.971154</td>\n",
       "      <td>0.971552</td>\n",
       "      <td>0.996424</td>\n",
       "      <td>0.972026</td>\n",
       "      <td>0.972418</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974643</td>\n",
       "      <td>0.975304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2013_mean_1km</th>\n",
       "      <td>-0.148078</td>\n",
       "      <td>-0.130932</td>\n",
       "      <td>-0.075008</td>\n",
       "      <td>-0.301415</td>\n",
       "      <td>-0.148830</td>\n",
       "      <td>0.042836</td>\n",
       "      <td>-0.114113</td>\n",
       "      <td>0.012256</td>\n",
       "      <td>0.118156</td>\n",
       "      <td>0.025526</td>\n",
       "      <td>...</td>\n",
       "      <td>0.988972</td>\n",
       "      <td>0.961460</td>\n",
       "      <td>0.990291</td>\n",
       "      <td>0.989611</td>\n",
       "      <td>0.969545</td>\n",
       "      <td>0.995469</td>\n",
       "      <td>0.994915</td>\n",
       "      <td>0.974643</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl_2013_media_1km</th>\n",
       "      <td>-0.148397</td>\n",
       "      <td>-0.131161</td>\n",
       "      <td>-0.075059</td>\n",
       "      <td>-0.302136</td>\n",
       "      <td>-0.149155</td>\n",
       "      <td>0.043161</td>\n",
       "      <td>-0.114087</td>\n",
       "      <td>0.012280</td>\n",
       "      <td>0.115947</td>\n",
       "      <td>0.025589</td>\n",
       "      <td>...</td>\n",
       "      <td>0.989229</td>\n",
       "      <td>0.962393</td>\n",
       "      <td>0.990252</td>\n",
       "      <td>0.989792</td>\n",
       "      <td>0.970229</td>\n",
       "      <td>0.995180</td>\n",
       "      <td>0.994969</td>\n",
       "      <td>0.975304</td>\n",
       "      <td>0.999636</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>686 rows × 686 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       hhid      glss      year    region     clust        nh  \\\n",
       "hhid               1.000000  0.927561  0.579349  0.093887  0.998626 -0.163708   \n",
       "glss               0.927561  1.000000  0.841910  0.119150  0.906768 -0.210315   \n",
       "year               0.579349  0.841910  1.000000  0.136539  0.535980 -0.222512   \n",
       "region             0.093887  0.119150  0.136539  1.000000  0.089694 -0.046583   \n",
       "clust              0.998626  0.906768  0.535980  0.089694  1.000000 -0.155307   \n",
       "nh                -0.163708 -0.210315 -0.222512 -0.046583 -0.155307  1.000000   \n",
       "pid                0.005572  0.008909  0.013153  0.133078  0.005050 -0.007699   \n",
       "sex               -0.001990 -0.003766 -0.005466 -0.011839 -0.001745 -0.002716   \n",
       "mining_region     -0.088914 -0.097291 -0.090166 -0.516582 -0.086895  0.023667   \n",
       "age                0.014387  0.016360  0.014678 -0.012522  0.013926 -0.005303   \n",
       "working_age        0.008129  0.016176  0.023084 -0.028719  0.006941 -0.007712   \n",
       "prime_age          0.008036  0.012077  0.014403 -0.020988  0.007413 -0.001670   \n",
       "male               0.001990  0.003766  0.005466  0.011839  0.001745  0.002716   \n",
       "school_attendance  0.055196  0.047389  0.022735 -0.027779  0.055590 -0.006069   \n",
       "expenditure        0.518439  0.496195  0.331768 -0.071631  0.515700 -0.091113   \n",
       "expenditurepc      0.460320  0.441948  0.296346 -0.137308  0.457769 -0.075129   \n",
       "food_expend        0.539700  0.530884  0.375507 -0.138290  0.534918 -0.099859   \n",
       "food_expendpc      0.446177  0.437784  0.306993 -0.178808  0.442437 -0.077443   \n",
       "inc                0.213062  0.196845  0.121116 -0.016850  0.212916 -0.041308   \n",
       "incpc              0.174981  0.161902  0.099608 -0.036732  0.174852 -0.030812   \n",
       "rexpenditure       0.334452  0.316303  0.205083 -0.105162  0.333185 -0.053351   \n",
       "rexpenditurepc     0.294051  0.280315  0.183790 -0.177972  0.292672 -0.043312   \n",
       "rfood_expend       0.215583  0.207395  0.137928 -0.189334  0.214384 -0.034139   \n",
       "rfood_expendpc     0.193053  0.186185  0.123621 -0.231246  0.191909 -0.028106   \n",
       "rincpc             0.155735  0.144031  0.088446 -0.038303  0.155627 -0.027323   \n",
       "poor              -0.039345 -0.056438 -0.062092  0.334406 -0.036400 -0.004138   \n",
       "poor_ext          -0.139709 -0.141638 -0.103445  0.323523 -0.137645  0.013916   \n",
       "ln_rincpc          0.480784  0.457807  0.301746 -0.159695  0.478552 -0.079356   \n",
       "ln_rincequiv       0.487836  0.466681  0.310951 -0.157340  0.485264 -0.081521   \n",
       "ln_rexpenditurepc  0.426521  0.404020  0.261121 -0.301991  0.424692 -0.063501   \n",
       "...                     ...       ...       ...       ...       ...       ...   \n",
       "nl_2004_sum_1km   -0.186332 -0.172213 -0.108464 -0.273134 -0.186234  0.055195   \n",
       "nl_2004_mean_1km  -0.169177 -0.152441 -0.090730 -0.277447 -0.169639  0.047627   \n",
       "nl_2004_media_1km -0.169428 -0.152317 -0.090134 -0.277343 -0.169932  0.047495   \n",
       "nl_2005_sum_1km   -0.180280 -0.165259 -0.102166 -0.267463 -0.180380  0.054111   \n",
       "nl_2005_mean_1km  -0.161961 -0.145283 -0.085586 -0.272421 -0.162513  0.046990   \n",
       "nl_2005_media_1km -0.163176 -0.145377 -0.084116 -0.272095 -0.163872  0.047414   \n",
       "nl_2006_sum_1km   -0.180597 -0.165800 -0.102953 -0.274091 -0.180657  0.054206   \n",
       "nl_2006_mean_1km  -0.162315 -0.145089 -0.084788 -0.278593 -0.162933  0.046630   \n",
       "nl_2006_media_1km -0.161545 -0.144219 -0.084019 -0.277799 -0.162181  0.046378   \n",
       "nl_2007_sum_1km   -0.180851 -0.166124 -0.103350 -0.283572 -0.180935  0.055393   \n",
       "nl_2007_mean_1km  -0.163155 -0.146243 -0.086141 -0.289333 -0.163749  0.048214   \n",
       "nl_2007_media_1km -0.163730 -0.146710 -0.086328 -0.289177 -0.164331  0.048650   \n",
       "nl_2008_sum_1km   -0.180336 -0.167562 -0.107026 -0.278735 -0.180138  0.055490   \n",
       "nl_2008_mean_1km  -0.162157 -0.146771 -0.088561 -0.282748 -0.162537  0.047883   \n",
       "nl_2008_media_1km -0.163711 -0.147719 -0.088420 -0.282236 -0.164153  0.048153   \n",
       "nl_2009_sum_1km   -0.179363 -0.165549 -0.104084 -0.272119 -0.179309  0.054870   \n",
       "nl_2009_mean_1km  -0.161089 -0.145140 -0.086537 -0.275774 -0.161547  0.047494   \n",
       "nl_2009_media_1km -0.161468 -0.145097 -0.085928 -0.275248 -0.161973  0.047701   \n",
       "nl_2010_sum_1km   -0.174556 -0.159317 -0.097876 -0.290720 -0.174773  0.053391   \n",
       "nl_2010_mean_1km  -0.156021 -0.137714 -0.078193 -0.292655 -0.156873  0.045214   \n",
       "nl_2010_media_1km -0.156124 -0.137187 -0.076963 -0.292804 -0.157059  0.045109   \n",
       "nl_2011_sum_1km   -0.171103 -0.157913 -0.099606 -0.288051 -0.171089  0.053004   \n",
       "nl_2011_mean_1km  -0.152445 -0.136011 -0.079454 -0.288791 -0.153095  0.044641   \n",
       "nl_2011_media_1km -0.153455 -0.137501 -0.081191 -0.289620 -0.154027  0.045152   \n",
       "nl_2012_sum_1km   -0.168451 -0.153992 -0.095241 -0.301516 -0.168615  0.051970   \n",
       "nl_2012_mean_1km  -0.149447 -0.131939 -0.075262 -0.303993 -0.150248  0.043624   \n",
       "nl_2012_media_1km -0.150168 -0.132813 -0.076108 -0.304807 -0.150941  0.044180   \n",
       "nl_2013_sum_1km   -0.166918 -0.152962 -0.095169 -0.299895 -0.167017  0.051399   \n",
       "nl_2013_mean_1km  -0.148078 -0.130932 -0.075008 -0.301415 -0.148830  0.042836   \n",
       "nl_2013_media_1km -0.148397 -0.131161 -0.075059 -0.302136 -0.149155  0.043161   \n",
       "\n",
       "                        pid       sex  mining_region       age  \\\n",
       "hhid               0.005572 -0.001990      -0.088914  0.014387   \n",
       "glss               0.008909 -0.003766      -0.097291  0.016360   \n",
       "year               0.013153 -0.005466      -0.090166  0.014678   \n",
       "region             0.133078 -0.011839      -0.516582 -0.012522   \n",
       "clust              0.005050 -0.001745      -0.086895  0.013926   \n",
       "nh                -0.007699 -0.002716       0.023667 -0.005303   \n",
       "pid                1.000000  0.036562      -0.068954 -0.503714   \n",
       "sex                0.036562  1.000000       0.008150  0.044231   \n",
       "mining_region     -0.068954  0.008150       1.000000 -0.009448   \n",
       "age               -0.503714  0.044231      -0.009448  1.000000   \n",
       "working_age       -0.419941  0.037085       0.000329  0.418429   \n",
       "prime_age         -0.285547  0.038347      -0.001558  0.156001   \n",
       "male              -0.036562 -1.000000      -0.008150 -0.044231   \n",
       "school_attendance  0.405568 -0.073169       0.036554 -0.633182   \n",
       "expenditure        0.119901  0.001125      -0.001389 -0.008781   \n",
       "expenditurepc     -0.139586 -0.017665       0.039298  0.083118   \n",
       "food_expend        0.062195  0.009051       0.049067 -0.011410   \n",
       "food_expendpc     -0.189553 -0.011591       0.070796  0.096464   \n",
       "inc                0.052420 -0.003082       0.018683 -0.007018   \n",
       "incpc             -0.038953 -0.008791       0.038953  0.023282   \n",
       "rexpenditure       0.150026  0.003600       0.019663 -0.018825   \n",
       "rexpenditurepc    -0.170993 -0.019696       0.064246  0.097448   \n",
       "rfood_expend       0.110466  0.011541       0.082295 -0.029376   \n",
       "rfood_expendpc    -0.234746 -0.014652       0.105381  0.118524   \n",
       "rincpc            -0.041272 -0.009462       0.039620  0.024754   \n",
       "poor               0.203500 -0.021481      -0.178371 -0.059160   \n",
       "poor_ext           0.164001 -0.019988      -0.163314 -0.039874   \n",
       "ln_rincpc         -0.087858 -0.011684       0.073080  0.057046   \n",
       "ln_rincequiv      -0.084462  0.001282       0.071405  0.046305   \n",
       "ln_rexpenditurepc -0.224172 -0.004375       0.131948  0.107203   \n",
       "...                     ...       ...            ...       ...   \n",
       "nl_2004_sum_1km   -0.100883  0.008855       0.081227  0.022317   \n",
       "nl_2004_mean_1km  -0.106179  0.009823       0.101784  0.023553   \n",
       "nl_2004_media_1km -0.106171  0.009927       0.103376  0.023520   \n",
       "nl_2005_sum_1km   -0.096355  0.007549       0.072238  0.022305   \n",
       "nl_2005_mean_1km  -0.100727  0.007981       0.087668  0.023586   \n",
       "nl_2005_media_1km -0.100396  0.007900       0.082110  0.023719   \n",
       "nl_2006_sum_1km   -0.098482  0.008054       0.099379  0.021932   \n",
       "nl_2006_mean_1km  -0.102787  0.008627       0.117427  0.023144   \n",
       "nl_2006_media_1km -0.102564  0.008589       0.118237  0.022988   \n",
       "nl_2007_sum_1km   -0.099662  0.008240       0.070951  0.022929   \n",
       "nl_2007_mean_1km  -0.104682  0.008989       0.088908  0.024304   \n",
       "nl_2007_media_1km -0.104495  0.008977       0.087752  0.024376   \n",
       "nl_2008_sum_1km   -0.101799  0.009250       0.076384  0.024601   \n",
       "nl_2008_mean_1km  -0.106575  0.010219       0.093961  0.026010   \n",
       "nl_2008_media_1km -0.106342  0.010062       0.091386  0.026061   \n",
       "nl_2009_sum_1km   -0.099892  0.009084       0.063117  0.024308   \n",
       "nl_2009_mean_1km  -0.104406  0.009995       0.078540  0.025642   \n",
       "nl_2009_media_1km -0.103962  0.009856       0.075717  0.025656   \n",
       "nl_2010_sum_1km   -0.107424  0.010509       0.095274  0.024594   \n",
       "nl_2010_mean_1km  -0.112003  0.011723       0.112170  0.025834   \n",
       "nl_2010_media_1km -0.112103  0.011680       0.111202  0.025976   \n",
       "nl_2011_sum_1km   -0.106421  0.010254       0.091140  0.024399   \n",
       "nl_2011_mean_1km  -0.110967  0.011505       0.108289  0.025665   \n",
       "nl_2011_media_1km -0.110916  0.011547       0.107256  0.025642   \n",
       "nl_2012_sum_1km   -0.108326  0.010257       0.109329  0.024184   \n",
       "nl_2012_mean_1km  -0.113005  0.011496       0.127353  0.025483   \n",
       "nl_2012_media_1km -0.113017  0.011545       0.125682  0.025572   \n",
       "nl_2013_sum_1km   -0.109543  0.010926       0.100682  0.024348   \n",
       "nl_2013_mean_1km  -0.114113  0.012256       0.118156  0.025526   \n",
       "nl_2013_media_1km -0.114087  0.012280       0.115947  0.025589   \n",
       "\n",
       "                         ...          nl_2010_media_1km  nl_2011_sum_1km  \\\n",
       "hhid                     ...                  -0.156124        -0.171103   \n",
       "glss                     ...                  -0.137187        -0.157913   \n",
       "year                     ...                  -0.076963        -0.099606   \n",
       "region                   ...                  -0.292804        -0.288051   \n",
       "clust                    ...                  -0.157059        -0.171089   \n",
       "nh                       ...                   0.045109         0.053004   \n",
       "pid                      ...                  -0.112103        -0.106421   \n",
       "sex                      ...                   0.011680         0.010254   \n",
       "mining_region            ...                   0.111202         0.091140   \n",
       "age                      ...                   0.025976         0.024399   \n",
       "working_age              ...                   0.084143         0.082165   \n",
       "prime_age                ...                   0.075118         0.071836   \n",
       "male                     ...                  -0.011680        -0.010254   \n",
       "school_attendance        ...                   0.006610         0.004652   \n",
       "expenditure              ...                   0.087484         0.059295   \n",
       "expenditurepc            ...                   0.158925         0.124435   \n",
       "food_expend              ...                   0.105007         0.069847   \n",
       "food_expendpc            ...                   0.158590         0.120975   \n",
       "inc                      ...                   0.019040         0.007727   \n",
       "incpc                    ...                   0.044137         0.029947   \n",
       "rexpenditure             ...                   0.188596         0.160948   \n",
       "rexpenditurepc           ...                   0.274779         0.241869   \n",
       "rfood_expend             ...                   0.202366         0.169960   \n",
       "rfood_expendpc           ...                   0.266337         0.232231   \n",
       "rincpc                   ...                   0.054379         0.040205   \n",
       "poor                     ...                  -0.313797        -0.289884   \n",
       "poor_ext                 ...                  -0.232171        -0.213262   \n",
       "ln_rincpc                ...                   0.130450         0.108305   \n",
       "ln_rincequiv             ...                   0.121327         0.098902   \n",
       "ln_rexpenditurepc        ...                   0.332668         0.299347   \n",
       "...                      ...                        ...              ...   \n",
       "nl_2004_sum_1km          ...                   0.936145         0.982496   \n",
       "nl_2004_mean_1km         ...                   0.970486         0.968418   \n",
       "nl_2004_media_1km        ...                   0.970013         0.967580   \n",
       "nl_2005_sum_1km          ...                   0.928105         0.968835   \n",
       "nl_2005_mean_1km         ...                   0.956935         0.950253   \n",
       "nl_2005_media_1km        ...                   0.955075         0.952597   \n",
       "nl_2006_sum_1km          ...                   0.941106         0.977334   \n",
       "nl_2006_mean_1km         ...                   0.968616         0.956789   \n",
       "nl_2006_media_1km        ...                   0.968001         0.955998   \n",
       "nl_2007_sum_1km          ...                   0.942373         0.983703   \n",
       "nl_2007_mean_1km         ...                   0.975680         0.968203   \n",
       "nl_2007_media_1km        ...                   0.975140         0.968591   \n",
       "nl_2008_sum_1km          ...                   0.954233         0.990259   \n",
       "nl_2008_mean_1km         ...                   0.984469         0.970833   \n",
       "nl_2008_media_1km        ...                   0.984246         0.971478   \n",
       "nl_2009_sum_1km          ...                   0.954886         0.987327   \n",
       "nl_2009_mean_1km         ...                   0.985080         0.967375   \n",
       "nl_2009_media_1km        ...                   0.984464         0.967856   \n",
       "nl_2010_sum_1km          ...                   0.974847         0.994200   \n",
       "nl_2010_mean_1km         ...                   0.999571         0.966276   \n",
       "nl_2010_media_1km        ...                   1.000000         0.965935   \n",
       "nl_2011_sum_1km          ...                   0.965935         1.000000   \n",
       "nl_2011_mean_1km         ...                   0.991644         0.974792   \n",
       "nl_2011_media_1km        ...                   0.991417         0.975640   \n",
       "nl_2012_sum_1km          ...                   0.968968         0.994160   \n",
       "nl_2012_mean_1km         ...                   0.991079         0.964774   \n",
       "nl_2012_media_1km        ...                   0.991224         0.965524   \n",
       "nl_2013_sum_1km          ...                   0.968236         0.992497   \n",
       "nl_2013_mean_1km         ...                   0.988972         0.961460   \n",
       "nl_2013_media_1km        ...                   0.989229         0.962393   \n",
       "\n",
       "                   nl_2011_mean_1km  nl_2011_media_1km  nl_2012_sum_1km  \\\n",
       "hhid                      -0.152445          -0.153455        -0.168451   \n",
       "glss                      -0.136011          -0.137501        -0.153992   \n",
       "year                      -0.079454          -0.081191        -0.095241   \n",
       "region                    -0.288791          -0.289620        -0.301516   \n",
       "clust                     -0.153095          -0.154027        -0.168615   \n",
       "nh                         0.044641           0.045152         0.051970   \n",
       "pid                       -0.110967          -0.110916        -0.108326   \n",
       "sex                        0.011505           0.011547         0.010257   \n",
       "mining_region              0.108289           0.107256         0.109329   \n",
       "age                        0.025665           0.025642         0.024184   \n",
       "working_age                0.084617           0.084518         0.082485   \n",
       "prime_age                  0.075408           0.075323         0.072610   \n",
       "male                      -0.011505          -0.011547        -0.010257   \n",
       "school_attendance          0.007222           0.007080         0.005806   \n",
       "expenditure                0.088438           0.087315         0.066500   \n",
       "expenditurepc              0.159632           0.158225         0.133236   \n",
       "food_expend                0.104200           0.102851         0.080726   \n",
       "food_expendpc              0.157040           0.155568         0.132098   \n",
       "inc                        0.020804           0.019929         0.009902   \n",
       "incpc                      0.044414           0.043738         0.032819   \n",
       "rexpenditure               0.189483           0.188495         0.167659   \n",
       "rexpenditurepc             0.275528           0.274315         0.249667   \n",
       "rfood_expend               0.200990           0.199988         0.179711   \n",
       "rfood_expendpc             0.264350           0.263258         0.241670   \n",
       "rincpc                     0.054700           0.054042         0.042817   \n",
       "poor                      -0.309338          -0.308852        -0.298036   \n",
       "poor_ext                  -0.228524          -0.228086        -0.219900   \n",
       "ln_rincpc                  0.129040           0.128270         0.114451   \n",
       "ln_rincequiv               0.119871           0.119110         0.105056   \n",
       "ln_rexpenditurepc          0.331835           0.330769         0.308711   \n",
       "...                             ...                ...              ...   \n",
       "nl_2004_sum_1km            0.943691           0.944724         0.974040   \n",
       "nl_2004_mean_1km           0.976971           0.976968         0.963051   \n",
       "nl_2004_media_1km          0.976396           0.976510         0.962168   \n",
       "nl_2005_sum_1km            0.932660           0.933065         0.964997   \n",
       "nl_2005_mean_1km           0.960316           0.959732         0.949672   \n",
       "nl_2005_media_1km          0.958206           0.957844         0.951567   \n",
       "nl_2006_sum_1km            0.944576           0.944880         0.975091   \n",
       "nl_2006_mean_1km           0.970739           0.970046         0.957974   \n",
       "nl_2006_media_1km          0.969798           0.969199         0.957309   \n",
       "nl_2007_sum_1km            0.946946           0.947613         0.979005   \n",
       "nl_2007_mean_1km           0.978847           0.978476         0.966927   \n",
       "nl_2007_media_1km          0.978158           0.977915         0.967283   \n",
       "nl_2008_sum_1km            0.957819           0.958712         0.983447   \n",
       "nl_2008_mean_1km           0.986322           0.986232         0.967268   \n",
       "nl_2008_media_1km          0.985568           0.985707         0.967670   \n",
       "nl_2009_sum_1km            0.955208           0.956031         0.981911   \n",
       "nl_2009_mean_1km           0.983096           0.982971         0.965349   \n",
       "nl_2009_media_1km          0.981950           0.982023         0.965584   \n",
       "nl_2010_sum_1km            0.970387           0.970993         0.993777   \n",
       "nl_2010_mean_1km           0.992494           0.992103         0.969576   \n",
       "nl_2010_media_1km          0.991644           0.991417         0.968968   \n",
       "nl_2011_sum_1km            0.974792           0.975640         0.994160   \n",
       "nl_2011_mean_1km           1.000000           0.999782         0.972018   \n",
       "nl_2011_media_1km          0.999782           1.000000         0.972405   \n",
       "nl_2012_sum_1km            0.972018           0.972405         1.000000   \n",
       "nl_2012_mean_1km           0.992666           0.991995         0.974876   \n",
       "nl_2012_media_1km          0.992537           0.992076         0.975387   \n",
       "nl_2013_sum_1km            0.971154           0.971552         0.996424   \n",
       "nl_2013_mean_1km           0.990291           0.989611         0.969545   \n",
       "nl_2013_media_1km          0.990252           0.989792         0.970229   \n",
       "\n",
       "                   nl_2012_mean_1km  nl_2012_media_1km  nl_2013_sum_1km  \\\n",
       "hhid                      -0.149447          -0.150168        -0.166918   \n",
       "glss                      -0.131939          -0.132813        -0.152962   \n",
       "year                      -0.075262          -0.076108        -0.095169   \n",
       "region                    -0.303993          -0.304807        -0.299895   \n",
       "clust                     -0.150248          -0.150941        -0.167017   \n",
       "nh                         0.043624           0.044180         0.051399   \n",
       "pid                       -0.113005          -0.113017        -0.109543   \n",
       "sex                        0.011496           0.011545         0.010926   \n",
       "mining_region              0.127353           0.125682         0.100682   \n",
       "age                        0.025483           0.025572         0.024348   \n",
       "working_age                0.084582           0.084716         0.083225   \n",
       "prime_age                  0.076072           0.076088         0.073241   \n",
       "male                      -0.011496          -0.011545        -0.010926   \n",
       "school_attendance          0.008114           0.007761         0.005954   \n",
       "expenditure                0.095597           0.094740         0.067285   \n",
       "expenditurepc              0.168422           0.167459         0.134912   \n",
       "food_expend                0.115296           0.114466         0.083342   \n",
       "food_expendpc              0.168497           0.167754         0.135048   \n",
       "inc                        0.023003           0.022247         0.011643   \n",
       "incpc                      0.047300           0.046713         0.033914   \n",
       "rexpenditure               0.195648           0.194773         0.168486   \n",
       "rexpenditurepc             0.282883           0.281982         0.251555   \n",
       "rfood_expend               0.210539           0.209766         0.182567   \n",
       "rfood_expendpc             0.273850           0.273240         0.245145   \n",
       "rincpc                     0.057280           0.056680         0.043887   \n",
       "poor                      -0.317558          -0.317124        -0.299916   \n",
       "poor_ext                  -0.235221          -0.234577        -0.220709   \n",
       "ln_rincpc                  0.135150           0.134446         0.113862   \n",
       "ln_rincequiv               0.126021           0.125291         0.104482   \n",
       "ln_rexpenditurepc          0.340855           0.340069         0.311134   \n",
       "...                             ...                ...              ...   \n",
       "nl_2004_sum_1km            0.931340           0.932317         0.970987   \n",
       "nl_2004_mean_1km           0.967519           0.967549         0.960826   \n",
       "nl_2004_media_1km          0.966893           0.967014         0.959956   \n",
       "nl_2005_sum_1km            0.925070           0.925590         0.957052   \n",
       "nl_2005_mean_1km           0.956064           0.955670         0.941995   \n",
       "nl_2005_media_1km          0.953506           0.953442         0.943785   \n",
       "nl_2006_sum_1km            0.938648           0.938995         0.967825   \n",
       "nl_2006_mean_1km           0.968311           0.967719         0.951160   \n",
       "nl_2006_media_1km          0.967519           0.967092         0.950385   \n",
       "nl_2007_sum_1km            0.938569           0.939282         0.974337   \n",
       "nl_2007_mean_1km           0.973860           0.973628         0.962810   \n",
       "nl_2007_media_1km          0.973152           0.973148         0.962996   \n",
       "nl_2008_sum_1km            0.947113           0.947753         0.981037   \n",
       "nl_2008_mean_1km           0.978749           0.978482         0.965642   \n",
       "nl_2008_media_1km          0.977738           0.977675         0.966150   \n",
       "nl_2009_sum_1km            0.946061           0.946811         0.978863   \n",
       "nl_2009_mean_1km           0.977303           0.977227         0.962942   \n",
       "nl_2009_media_1km          0.975905           0.976134         0.963095   \n",
       "nl_2010_sum_1km            0.966437           0.967157         0.992026   \n",
       "nl_2010_mean_1km           0.992257           0.992190         0.968717   \n",
       "nl_2010_media_1km          0.991079           0.991224         0.968236   \n",
       "nl_2011_sum_1km            0.964774           0.965524         0.992497   \n",
       "nl_2011_mean_1km           0.992666           0.992537         0.971154   \n",
       "nl_2011_media_1km          0.991995           0.992076         0.971552   \n",
       "nl_2012_sum_1km            0.974876           0.975387         0.996424   \n",
       "nl_2012_mean_1km           1.000000           0.999620         0.972026   \n",
       "nl_2012_media_1km          0.999620           1.000000         0.972418   \n",
       "nl_2013_sum_1km            0.972026           0.972418         1.000000   \n",
       "nl_2013_mean_1km           0.995469           0.994915         0.974643   \n",
       "nl_2013_media_1km          0.995180           0.994969         0.975304   \n",
       "\n",
       "                   nl_2013_mean_1km  nl_2013_media_1km  \n",
       "hhid                      -0.148078          -0.148397  \n",
       "glss                      -0.130932          -0.131161  \n",
       "year                      -0.075008          -0.075059  \n",
       "region                    -0.301415          -0.302136  \n",
       "clust                     -0.148830          -0.149155  \n",
       "nh                         0.042836           0.043161  \n",
       "pid                       -0.114113          -0.114087  \n",
       "sex                        0.012256           0.012280  \n",
       "mining_region              0.118156           0.115947  \n",
       "age                        0.025526           0.025589  \n",
       "working_age                0.085245           0.085356  \n",
       "prime_age                  0.076588           0.076612  \n",
       "male                      -0.012256          -0.012280  \n",
       "school_attendance          0.008330           0.008112  \n",
       "expenditure                0.096208           0.095252  \n",
       "expenditurepc              0.169785           0.168646  \n",
       "food_expend                0.117867           0.116729  \n",
       "food_expendpc              0.171269           0.170196  \n",
       "inc                        0.024843           0.023740  \n",
       "incpc                      0.048301           0.047528  \n",
       "rexpenditure               0.196371           0.195402  \n",
       "rexpenditurepc             0.284516           0.283393  \n",
       "rfood_expend               0.213551           0.212351  \n",
       "rfood_expendpc             0.277293           0.276208  \n",
       "rincpc                     0.058256           0.057495  \n",
       "poor                      -0.319590          -0.319031  \n",
       "poor_ext                  -0.236078          -0.235443  \n",
       "ln_rincpc                  0.134117           0.133712  \n",
       "ln_rincequiv               0.125018           0.124595  \n",
       "ln_rexpenditurepc          0.343160           0.342233  \n",
       "...                             ...                ...  \n",
       "nl_2004_sum_1km            0.926800           0.928226  \n",
       "nl_2004_mean_1km           0.963944           0.964411  \n",
       "nl_2004_media_1km          0.963342           0.963920  \n",
       "nl_2005_sum_1km            0.915213           0.916240  \n",
       "nl_2005_mean_1km           0.946326           0.946337  \n",
       "nl_2005_media_1km          0.943629           0.944020  \n",
       "nl_2006_sum_1km            0.929567           0.930274  \n",
       "nl_2006_mean_1km           0.959608           0.959294  \n",
       "nl_2006_media_1km          0.958698           0.958448  \n",
       "nl_2007_sum_1km            0.932168           0.933394  \n",
       "nl_2007_mean_1km           0.968002           0.968202  \n",
       "nl_2007_media_1km          0.967085           0.967535  \n",
       "nl_2008_sum_1km            0.943101           0.944431  \n",
       "nl_2008_mean_1km           0.975644           0.976054  \n",
       "nl_2008_media_1km          0.974754           0.975418  \n",
       "nl_2009_sum_1km            0.941297           0.942787  \n",
       "nl_2009_mean_1km           0.973222           0.973834  \n",
       "nl_2009_media_1km          0.971734           0.972638  \n",
       "nl_2010_sum_1km            0.963115           0.964042  \n",
       "nl_2010_mean_1km           0.989981           0.989987  \n",
       "nl_2010_media_1km          0.988972           0.989229  \n",
       "nl_2011_sum_1km            0.961460           0.962393  \n",
       "nl_2011_mean_1km           0.990291           0.990252  \n",
       "nl_2011_media_1km          0.989611           0.989792  \n",
       "nl_2012_sum_1km            0.969545           0.970229  \n",
       "nl_2012_mean_1km           0.995469           0.995180  \n",
       "nl_2012_media_1km          0.994915           0.994969  \n",
       "nl_2013_sum_1km            0.974643           0.975304  \n",
       "nl_2013_mean_1km           1.000000           0.999636  \n",
       "nl_2013_media_1km          0.999636           1.000000  \n",
       "\n",
       "[686 rows x 686 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PgYi8iSBhnibYGq8H/pCQgP0HCUkSIKTvO0oAyg0aKTI9xRmNNNpWT7ZDVHzs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OE1ANxYeAuHq/4ty8/waAP63T/zOAfwzgP5yuRETfBnnv/gMAH7tKxzcRTJ81Zvpur6rdt5eB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AZplT06F5YNC7Zg+cDMrkeam4UaoLkP6T3oe8xtmGeRYPbjzOOcE6n2IvXul0MI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cd02b4cf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation Matrix\n",
    "\n",
    "corr = (corrdf)\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)\n",
    "# sns.title('Heatmap of Correlation Matrix')\n",
    "corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split Data\n",
    "Split data column-wise to better visualize the uncorrelated feature, taking approximately the first 30 % of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfA = pd.DataFrame(df2[df2.columns[0:190]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hhid</th>\n",
       "      <th>glss</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "      <th>clust</th>\n",
       "      <th>nh</th>\n",
       "      <th>pid</th>\n",
       "      <th>sex</th>\n",
       "      <th>mining_region</th>\n",
       "      <th>age</th>\n",
       "      <th>...</th>\n",
       "      <th>fcl15_sum_1km</th>\n",
       "      <th>fcl15_mean_1km</th>\n",
       "      <th>fcl16_sum_1km</th>\n",
       "      <th>fcl16_mean_1km</th>\n",
       "      <th>mp00_mean_10km</th>\n",
       "      <th>mp00_media_10km</th>\n",
       "      <th>mp00_min_10km</th>\n",
       "      <th>mp00_max_10km</th>\n",
       "      <th>mp01_mean_10km</th>\n",
       "      <th>mp01_media_10km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hhid</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.927561</td>\n",
       "      <td>0.579349</td>\n",
       "      <td>0.093887</td>\n",
       "      <td>0.998626</td>\n",
       "      <td>-0.163708</td>\n",
       "      <td>0.005572</td>\n",
       "      <td>-0.001990</td>\n",
       "      <td>-0.088914</td>\n",
       "      <td>0.014387</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>-0.066415</td>\n",
       "      <td>-0.066452</td>\n",
       "      <td>0.214458</td>\n",
       "      <td>0.214576</td>\n",
       "      <td>0.213148</td>\n",
       "      <td>0.201670</td>\n",
       "      <td>0.212986</td>\n",
       "      <td>0.213083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glss</th>\n",
       "      <td>0.927561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.841910</td>\n",
       "      <td>0.119150</td>\n",
       "      <td>0.906768</td>\n",
       "      <td>-0.210315</td>\n",
       "      <td>0.008909</td>\n",
       "      <td>-0.003766</td>\n",
       "      <td>-0.097291</td>\n",
       "      <td>0.016360</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009138</td>\n",
       "      <td>-0.009199</td>\n",
       "      <td>-0.076762</td>\n",
       "      <td>-0.076767</td>\n",
       "      <td>0.206937</td>\n",
       "      <td>0.207640</td>\n",
       "      <td>0.208894</td>\n",
       "      <td>0.190024</td>\n",
       "      <td>0.207268</td>\n",
       "      <td>0.207928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0.579349</td>\n",
       "      <td>0.841910</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.136539</td>\n",
       "      <td>0.535980</td>\n",
       "      <td>-0.222512</td>\n",
       "      <td>0.013153</td>\n",
       "      <td>-0.005466</td>\n",
       "      <td>-0.090166</td>\n",
       "      <td>0.014678</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.022683</td>\n",
       "      <td>-0.022707</td>\n",
       "      <td>-0.072823</td>\n",
       "      <td>-0.072786</td>\n",
       "      <td>0.141315</td>\n",
       "      <td>0.142603</td>\n",
       "      <td>0.147903</td>\n",
       "      <td>0.122539</td>\n",
       "      <td>0.143857</td>\n",
       "      <td>0.145090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <td>0.093887</td>\n",
       "      <td>0.119150</td>\n",
       "      <td>0.136539</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089694</td>\n",
       "      <td>-0.046583</td>\n",
       "      <td>0.133078</td>\n",
       "      <td>-0.011839</td>\n",
       "      <td>-0.516582</td>\n",
       "      <td>-0.012522</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.259233</td>\n",
       "      <td>-0.259580</td>\n",
       "      <td>-0.258168</td>\n",
       "      <td>-0.258435</td>\n",
       "      <td>0.157069</td>\n",
       "      <td>0.155735</td>\n",
       "      <td>0.211125</td>\n",
       "      <td>0.083610</td>\n",
       "      <td>0.144672</td>\n",
       "      <td>0.143737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clust</th>\n",
       "      <td>0.998626</td>\n",
       "      <td>0.906768</td>\n",
       "      <td>0.535980</td>\n",
       "      <td>0.089694</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.155307</td>\n",
       "      <td>0.005050</td>\n",
       "      <td>-0.001745</td>\n",
       "      <td>-0.086895</td>\n",
       "      <td>0.013926</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001674</td>\n",
       "      <td>0.001593</td>\n",
       "      <td>-0.064065</td>\n",
       "      <td>-0.064107</td>\n",
       "      <td>0.213190</td>\n",
       "      <td>0.213219</td>\n",
       "      <td>0.211362</td>\n",
       "      <td>0.201211</td>\n",
       "      <td>0.211451</td>\n",
       "      <td>0.211459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nh</th>\n",
       "      <td>-0.163708</td>\n",
       "      <td>-0.210315</td>\n",
       "      <td>-0.222512</td>\n",
       "      <td>-0.046583</td>\n",
       "      <td>-0.155307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007699</td>\n",
       "      <td>-0.002716</td>\n",
       "      <td>0.023667</td>\n",
       "      <td>-0.005303</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009690</td>\n",
       "      <td>0.009707</td>\n",
       "      <td>0.019688</td>\n",
       "      <td>0.019689</td>\n",
       "      <td>-0.058276</td>\n",
       "      <td>-0.058596</td>\n",
       "      <td>-0.061513</td>\n",
       "      <td>-0.051012</td>\n",
       "      <td>-0.059671</td>\n",
       "      <td>-0.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pid</th>\n",
       "      <td>0.005572</td>\n",
       "      <td>0.008909</td>\n",
       "      <td>0.013153</td>\n",
       "      <td>0.133078</td>\n",
       "      <td>0.005050</td>\n",
       "      <td>-0.007699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.036562</td>\n",
       "      <td>-0.068954</td>\n",
       "      <td>-0.503714</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.027732</td>\n",
       "      <td>-0.027778</td>\n",
       "      <td>-0.039895</td>\n",
       "      <td>-0.039920</td>\n",
       "      <td>0.101773</td>\n",
       "      <td>0.099760</td>\n",
       "      <td>0.118349</td>\n",
       "      <td>0.084675</td>\n",
       "      <td>0.101816</td>\n",
       "      <td>0.099882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.001990</td>\n",
       "      <td>-0.003766</td>\n",
       "      <td>-0.005466</td>\n",
       "      <td>-0.011839</td>\n",
       "      <td>-0.001745</td>\n",
       "      <td>-0.002716</td>\n",
       "      <td>0.036562</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.008150</td>\n",
       "      <td>0.044231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002699</td>\n",
       "      <td>-0.002696</td>\n",
       "      <td>-0.001962</td>\n",
       "      <td>-0.001957</td>\n",
       "      <td>-0.011951</td>\n",
       "      <td>-0.011582</td>\n",
       "      <td>-0.013828</td>\n",
       "      <td>-0.010646</td>\n",
       "      <td>-0.011605</td>\n",
       "      <td>-0.011289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mining_region</th>\n",
       "      <td>-0.088914</td>\n",
       "      <td>-0.097291</td>\n",
       "      <td>-0.090166</td>\n",
       "      <td>-0.516582</td>\n",
       "      <td>-0.086895</td>\n",
       "      <td>0.023667</td>\n",
       "      <td>-0.068954</td>\n",
       "      <td>0.008150</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.299519</td>\n",
       "      <td>0.299597</td>\n",
       "      <td>0.296038</td>\n",
       "      <td>0.295902</td>\n",
       "      <td>-0.148683</td>\n",
       "      <td>-0.140054</td>\n",
       "      <td>-0.187142</td>\n",
       "      <td>-0.096159</td>\n",
       "      <td>-0.067555</td>\n",
       "      <td>-0.061316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.014387</td>\n",
       "      <td>0.016360</td>\n",
       "      <td>0.014678</td>\n",
       "      <td>-0.012522</td>\n",
       "      <td>0.013926</td>\n",
       "      <td>-0.005303</td>\n",
       "      <td>-0.503714</td>\n",
       "      <td>0.044231</td>\n",
       "      <td>-0.009448</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004375</td>\n",
       "      <td>-0.004355</td>\n",
       "      <td>0.003870</td>\n",
       "      <td>0.003893</td>\n",
       "      <td>-0.031640</td>\n",
       "      <td>-0.031037</td>\n",
       "      <td>-0.034511</td>\n",
       "      <td>-0.029990</td>\n",
       "      <td>-0.034454</td>\n",
       "      <td>-0.033865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>working_age</th>\n",
       "      <td>0.008129</td>\n",
       "      <td>0.016176</td>\n",
       "      <td>0.023084</td>\n",
       "      <td>-0.028719</td>\n",
       "      <td>0.006941</td>\n",
       "      <td>-0.007712</td>\n",
       "      <td>-0.419941</td>\n",
       "      <td>0.037085</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.418429</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.019852</td>\n",
       "      <td>-0.019841</td>\n",
       "      <td>-0.022193</td>\n",
       "      <td>-0.022180</td>\n",
       "      <td>-0.052073</td>\n",
       "      <td>-0.051591</td>\n",
       "      <td>-0.053514</td>\n",
       "      <td>-0.049650</td>\n",
       "      <td>-0.051199</td>\n",
       "      <td>-0.050764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prime_age</th>\n",
       "      <td>0.008036</td>\n",
       "      <td>0.012077</td>\n",
       "      <td>0.014403</td>\n",
       "      <td>-0.020988</td>\n",
       "      <td>0.007413</td>\n",
       "      <td>-0.001670</td>\n",
       "      <td>-0.285547</td>\n",
       "      <td>0.038347</td>\n",
       "      <td>-0.001558</td>\n",
       "      <td>0.156001</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.020234</td>\n",
       "      <td>-0.020236</td>\n",
       "      <td>-0.022969</td>\n",
       "      <td>-0.022972</td>\n",
       "      <td>-0.038982</td>\n",
       "      <td>-0.038991</td>\n",
       "      <td>-0.039855</td>\n",
       "      <td>-0.035933</td>\n",
       "      <td>-0.036858</td>\n",
       "      <td>-0.036870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.001990</td>\n",
       "      <td>0.003766</td>\n",
       "      <td>0.005466</td>\n",
       "      <td>0.011839</td>\n",
       "      <td>0.001745</td>\n",
       "      <td>0.002716</td>\n",
       "      <td>-0.036562</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.008150</td>\n",
       "      <td>-0.044231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002699</td>\n",
       "      <td>0.002696</td>\n",
       "      <td>0.001962</td>\n",
       "      <td>0.001957</td>\n",
       "      <td>0.011951</td>\n",
       "      <td>0.011582</td>\n",
       "      <td>0.013828</td>\n",
       "      <td>0.010646</td>\n",
       "      <td>0.011605</td>\n",
       "      <td>0.011289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>school_attendance</th>\n",
       "      <td>0.055196</td>\n",
       "      <td>0.047389</td>\n",
       "      <td>0.022735</td>\n",
       "      <td>-0.027779</td>\n",
       "      <td>0.055590</td>\n",
       "      <td>-0.006069</td>\n",
       "      <td>0.405568</td>\n",
       "      <td>-0.073169</td>\n",
       "      <td>0.036554</td>\n",
       "      <td>-0.633182</td>\n",
       "      <td>...</td>\n",
       "      <td>0.022633</td>\n",
       "      <td>0.022637</td>\n",
       "      <td>0.016886</td>\n",
       "      <td>0.016867</td>\n",
       "      <td>-0.000980</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>-0.009369</td>\n",
       "      <td>0.005698</td>\n",
       "      <td>0.001409</td>\n",
       "      <td>0.002378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>expenditure</th>\n",
       "      <td>0.518439</td>\n",
       "      <td>0.496195</td>\n",
       "      <td>0.331768</td>\n",
       "      <td>-0.071631</td>\n",
       "      <td>0.515700</td>\n",
       "      <td>-0.091113</td>\n",
       "      <td>0.119901</td>\n",
       "      <td>0.001125</td>\n",
       "      <td>-0.001389</td>\n",
       "      <td>-0.008781</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013955</td>\n",
       "      <td>-0.013965</td>\n",
       "      <td>-0.059840</td>\n",
       "      <td>-0.059852</td>\n",
       "      <td>0.026764</td>\n",
       "      <td>0.027676</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.043272</td>\n",
       "      <td>0.029925</td>\n",
       "      <td>0.030555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>expenditurepc</th>\n",
       "      <td>0.460320</td>\n",
       "      <td>0.441948</td>\n",
       "      <td>0.296346</td>\n",
       "      <td>-0.137308</td>\n",
       "      <td>0.457769</td>\n",
       "      <td>-0.075129</td>\n",
       "      <td>-0.139586</td>\n",
       "      <td>-0.017665</td>\n",
       "      <td>0.039298</td>\n",
       "      <td>0.083118</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000341</td>\n",
       "      <td>-0.000318</td>\n",
       "      <td>-0.034014</td>\n",
       "      <td>-0.034009</td>\n",
       "      <td>-0.041513</td>\n",
       "      <td>-0.040092</td>\n",
       "      <td>-0.070116</td>\n",
       "      <td>-0.014383</td>\n",
       "      <td>-0.036460</td>\n",
       "      <td>-0.035321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>food_expend</th>\n",
       "      <td>0.539700</td>\n",
       "      <td>0.530884</td>\n",
       "      <td>0.375507</td>\n",
       "      <td>-0.138290</td>\n",
       "      <td>0.534918</td>\n",
       "      <td>-0.099859</td>\n",
       "      <td>0.062195</td>\n",
       "      <td>0.009051</td>\n",
       "      <td>0.049067</td>\n",
       "      <td>-0.011410</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005127</td>\n",
       "      <td>0.005161</td>\n",
       "      <td>-0.043697</td>\n",
       "      <td>-0.043653</td>\n",
       "      <td>0.000810</td>\n",
       "      <td>0.001426</td>\n",
       "      <td>-0.023388</td>\n",
       "      <td>0.023829</td>\n",
       "      <td>0.005382</td>\n",
       "      <td>0.005782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>food_expendpc</th>\n",
       "      <td>0.446177</td>\n",
       "      <td>0.437784</td>\n",
       "      <td>0.306993</td>\n",
       "      <td>-0.178808</td>\n",
       "      <td>0.442437</td>\n",
       "      <td>-0.077443</td>\n",
       "      <td>-0.189553</td>\n",
       "      <td>-0.011591</td>\n",
       "      <td>0.070796</td>\n",
       "      <td>0.096464</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013771</td>\n",
       "      <td>0.013830</td>\n",
       "      <td>-0.018928</td>\n",
       "      <td>-0.018877</td>\n",
       "      <td>-0.063122</td>\n",
       "      <td>-0.062067</td>\n",
       "      <td>-0.092334</td>\n",
       "      <td>-0.033316</td>\n",
       "      <td>-0.057987</td>\n",
       "      <td>-0.057164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inc</th>\n",
       "      <td>0.213062</td>\n",
       "      <td>0.196845</td>\n",
       "      <td>0.121116</td>\n",
       "      <td>-0.016850</td>\n",
       "      <td>0.212916</td>\n",
       "      <td>-0.041308</td>\n",
       "      <td>0.052420</td>\n",
       "      <td>-0.003082</td>\n",
       "      <td>0.018683</td>\n",
       "      <td>-0.007018</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003372</td>\n",
       "      <td>0.003367</td>\n",
       "      <td>-0.018764</td>\n",
       "      <td>-0.018768</td>\n",
       "      <td>0.023032</td>\n",
       "      <td>0.022896</td>\n",
       "      <td>0.015974</td>\n",
       "      <td>0.029061</td>\n",
       "      <td>0.024616</td>\n",
       "      <td>0.024419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incpc</th>\n",
       "      <td>0.174981</td>\n",
       "      <td>0.161902</td>\n",
       "      <td>0.099608</td>\n",
       "      <td>-0.036732</td>\n",
       "      <td>0.174852</td>\n",
       "      <td>-0.030812</td>\n",
       "      <td>-0.038953</td>\n",
       "      <td>-0.008791</td>\n",
       "      <td>0.038953</td>\n",
       "      <td>0.023282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009060</td>\n",
       "      <td>0.009059</td>\n",
       "      <td>-0.007926</td>\n",
       "      <td>-0.007928</td>\n",
       "      <td>-0.007329</td>\n",
       "      <td>-0.007180</td>\n",
       "      <td>-0.016182</td>\n",
       "      <td>0.001971</td>\n",
       "      <td>-0.003889</td>\n",
       "      <td>-0.003853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rexpenditure</th>\n",
       "      <td>0.334452</td>\n",
       "      <td>0.316303</td>\n",
       "      <td>0.205083</td>\n",
       "      <td>-0.105162</td>\n",
       "      <td>0.333185</td>\n",
       "      <td>-0.053351</td>\n",
       "      <td>0.150026</td>\n",
       "      <td>0.003600</td>\n",
       "      <td>0.019663</td>\n",
       "      <td>-0.018825</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.029326</td>\n",
       "      <td>-0.029312</td>\n",
       "      <td>-0.061641</td>\n",
       "      <td>-0.061642</td>\n",
       "      <td>-0.063014</td>\n",
       "      <td>-0.061208</td>\n",
       "      <td>-0.084759</td>\n",
       "      <td>-0.039850</td>\n",
       "      <td>-0.058585</td>\n",
       "      <td>-0.057224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rexpenditurepc</th>\n",
       "      <td>0.294051</td>\n",
       "      <td>0.280315</td>\n",
       "      <td>0.183790</td>\n",
       "      <td>-0.177972</td>\n",
       "      <td>0.292672</td>\n",
       "      <td>-0.043312</td>\n",
       "      <td>-0.170993</td>\n",
       "      <td>-0.019696</td>\n",
       "      <td>0.064246</td>\n",
       "      <td>0.097448</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015795</td>\n",
       "      <td>-0.015749</td>\n",
       "      <td>-0.035445</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>-0.146656</td>\n",
       "      <td>-0.143855</td>\n",
       "      <td>-0.177072</td>\n",
       "      <td>-0.113001</td>\n",
       "      <td>-0.139929</td>\n",
       "      <td>-0.137568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rfood_expend</th>\n",
       "      <td>0.215583</td>\n",
       "      <td>0.207395</td>\n",
       "      <td>0.137928</td>\n",
       "      <td>-0.189334</td>\n",
       "      <td>0.214384</td>\n",
       "      <td>-0.034139</td>\n",
       "      <td>0.110466</td>\n",
       "      <td>0.011541</td>\n",
       "      <td>0.082295</td>\n",
       "      <td>-0.029376</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003828</td>\n",
       "      <td>-0.003757</td>\n",
       "      <td>-0.027074</td>\n",
       "      <td>-0.027012</td>\n",
       "      <td>-0.093376</td>\n",
       "      <td>-0.091824</td>\n",
       "      <td>-0.120967</td>\n",
       "      <td>-0.060840</td>\n",
       "      <td>-0.089380</td>\n",
       "      <td>-0.088238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rfood_expendpc</th>\n",
       "      <td>0.193053</td>\n",
       "      <td>0.186185</td>\n",
       "      <td>0.123621</td>\n",
       "      <td>-0.231246</td>\n",
       "      <td>0.191909</td>\n",
       "      <td>-0.028106</td>\n",
       "      <td>-0.234746</td>\n",
       "      <td>-0.014652</td>\n",
       "      <td>0.105381</td>\n",
       "      <td>0.118524</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004876</td>\n",
       "      <td>0.004966</td>\n",
       "      <td>-0.005207</td>\n",
       "      <td>-0.005143</td>\n",
       "      <td>-0.167509</td>\n",
       "      <td>-0.164859</td>\n",
       "      <td>-0.199519</td>\n",
       "      <td>-0.129995</td>\n",
       "      <td>-0.161696</td>\n",
       "      <td>-0.159448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rincpc</th>\n",
       "      <td>0.155735</td>\n",
       "      <td>0.144031</td>\n",
       "      <td>0.088446</td>\n",
       "      <td>-0.038303</td>\n",
       "      <td>0.155627</td>\n",
       "      <td>-0.027323</td>\n",
       "      <td>-0.041272</td>\n",
       "      <td>-0.009462</td>\n",
       "      <td>0.039620</td>\n",
       "      <td>0.024754</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007242</td>\n",
       "      <td>0.007243</td>\n",
       "      <td>-0.007845</td>\n",
       "      <td>-0.007847</td>\n",
       "      <td>-0.016969</td>\n",
       "      <td>-0.016703</td>\n",
       "      <td>-0.025912</td>\n",
       "      <td>-0.007258</td>\n",
       "      <td>-0.013467</td>\n",
       "      <td>-0.013318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>-0.039345</td>\n",
       "      <td>-0.056438</td>\n",
       "      <td>-0.062092</td>\n",
       "      <td>0.334406</td>\n",
       "      <td>-0.036400</td>\n",
       "      <td>-0.004138</td>\n",
       "      <td>0.203500</td>\n",
       "      <td>-0.021481</td>\n",
       "      <td>-0.178371</td>\n",
       "      <td>-0.059160</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.046205</td>\n",
       "      <td>-0.046292</td>\n",
       "      <td>-0.052135</td>\n",
       "      <td>-0.052168</td>\n",
       "      <td>0.209409</td>\n",
       "      <td>0.203664</td>\n",
       "      <td>0.258036</td>\n",
       "      <td>0.159008</td>\n",
       "      <td>0.204087</td>\n",
       "      <td>0.199150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor_ext</th>\n",
       "      <td>-0.139709</td>\n",
       "      <td>-0.141638</td>\n",
       "      <td>-0.103445</td>\n",
       "      <td>0.323523</td>\n",
       "      <td>-0.137645</td>\n",
       "      <td>0.013916</td>\n",
       "      <td>0.164001</td>\n",
       "      <td>-0.019988</td>\n",
       "      <td>-0.163314</td>\n",
       "      <td>-0.039874</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.076784</td>\n",
       "      <td>-0.076842</td>\n",
       "      <td>-0.078964</td>\n",
       "      <td>-0.078982</td>\n",
       "      <td>0.147145</td>\n",
       "      <td>0.141448</td>\n",
       "      <td>0.195206</td>\n",
       "      <td>0.101279</td>\n",
       "      <td>0.147389</td>\n",
       "      <td>0.142320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ln_rincpc</th>\n",
       "      <td>0.480784</td>\n",
       "      <td>0.457807</td>\n",
       "      <td>0.301746</td>\n",
       "      <td>-0.159695</td>\n",
       "      <td>0.478552</td>\n",
       "      <td>-0.079356</td>\n",
       "      <td>-0.087858</td>\n",
       "      <td>-0.011684</td>\n",
       "      <td>0.073080</td>\n",
       "      <td>0.057046</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054911</td>\n",
       "      <td>0.054923</td>\n",
       "      <td>0.023670</td>\n",
       "      <td>0.023664</td>\n",
       "      <td>-0.015632</td>\n",
       "      <td>-0.012687</td>\n",
       "      <td>-0.047754</td>\n",
       "      <td>0.010496</td>\n",
       "      <td>-0.011882</td>\n",
       "      <td>-0.009303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ln_rincequiv</th>\n",
       "      <td>0.487836</td>\n",
       "      <td>0.466681</td>\n",
       "      <td>0.310951</td>\n",
       "      <td>-0.157340</td>\n",
       "      <td>0.485264</td>\n",
       "      <td>-0.081521</td>\n",
       "      <td>-0.084462</td>\n",
       "      <td>0.001282</td>\n",
       "      <td>0.071405</td>\n",
       "      <td>0.046305</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055849</td>\n",
       "      <td>0.055861</td>\n",
       "      <td>0.023864</td>\n",
       "      <td>0.023860</td>\n",
       "      <td>-0.007930</td>\n",
       "      <td>-0.005064</td>\n",
       "      <td>-0.039843</td>\n",
       "      <td>0.017904</td>\n",
       "      <td>-0.004419</td>\n",
       "      <td>-0.001913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ln_rexpenditurepc</th>\n",
       "      <td>0.426521</td>\n",
       "      <td>0.404020</td>\n",
       "      <td>0.261121</td>\n",
       "      <td>-0.301991</td>\n",
       "      <td>0.424692</td>\n",
       "      <td>-0.063501</td>\n",
       "      <td>-0.224172</td>\n",
       "      <td>-0.004375</td>\n",
       "      <td>0.131948</td>\n",
       "      <td>0.107203</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021218</td>\n",
       "      <td>0.021274</td>\n",
       "      <td>-0.001511</td>\n",
       "      <td>-0.001496</td>\n",
       "      <td>-0.181856</td>\n",
       "      <td>-0.176068</td>\n",
       "      <td>-0.229629</td>\n",
       "      <td>-0.134703</td>\n",
       "      <td>-0.176028</td>\n",
       "      <td>-0.171066</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>fcl05_sum_1km</th>\n",
       "      <td>-0.044635</td>\n",
       "      <td>-0.041437</td>\n",
       "      <td>-0.028607</td>\n",
       "      <td>-0.254229</td>\n",
       "      <td>-0.044622</td>\n",
       "      <td>0.007024</td>\n",
       "      <td>-0.026183</td>\n",
       "      <td>-0.007180</td>\n",
       "      <td>0.264087</td>\n",
       "      <td>-0.004941</td>\n",
       "      <td>...</td>\n",
       "      <td>0.259672</td>\n",
       "      <td>0.259894</td>\n",
       "      <td>0.209285</td>\n",
       "      <td>0.209405</td>\n",
       "      <td>0.101212</td>\n",
       "      <td>0.105799</td>\n",
       "      <td>0.074421</td>\n",
       "      <td>0.113857</td>\n",
       "      <td>0.115598</td>\n",
       "      <td>0.118804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl05_mean_1km</th>\n",
       "      <td>-0.044779</td>\n",
       "      <td>-0.041622</td>\n",
       "      <td>-0.028796</td>\n",
       "      <td>-0.254372</td>\n",
       "      <td>-0.044759</td>\n",
       "      <td>0.007056</td>\n",
       "      <td>-0.026211</td>\n",
       "      <td>-0.007173</td>\n",
       "      <td>0.263882</td>\n",
       "      <td>-0.004917</td>\n",
       "      <td>...</td>\n",
       "      <td>0.259456</td>\n",
       "      <td>0.259680</td>\n",
       "      <td>0.209190</td>\n",
       "      <td>0.209311</td>\n",
       "      <td>0.101076</td>\n",
       "      <td>0.105651</td>\n",
       "      <td>0.074312</td>\n",
       "      <td>0.113719</td>\n",
       "      <td>0.115426</td>\n",
       "      <td>0.118623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl06_sum_1km</th>\n",
       "      <td>0.007430</td>\n",
       "      <td>0.015286</td>\n",
       "      <td>0.018909</td>\n",
       "      <td>-0.232832</td>\n",
       "      <td>0.006136</td>\n",
       "      <td>-0.002884</td>\n",
       "      <td>-0.031023</td>\n",
       "      <td>-0.001218</td>\n",
       "      <td>0.271223</td>\n",
       "      <td>-0.001546</td>\n",
       "      <td>...</td>\n",
       "      <td>0.268900</td>\n",
       "      <td>0.268975</td>\n",
       "      <td>0.218899</td>\n",
       "      <td>0.218985</td>\n",
       "      <td>0.132830</td>\n",
       "      <td>0.140076</td>\n",
       "      <td>0.091180</td>\n",
       "      <td>0.158801</td>\n",
       "      <td>0.158134</td>\n",
       "      <td>0.164165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl06_mean_1km</th>\n",
       "      <td>0.007248</td>\n",
       "      <td>0.015127</td>\n",
       "      <td>0.018820</td>\n",
       "      <td>-0.233451</td>\n",
       "      <td>0.005953</td>\n",
       "      <td>-0.002837</td>\n",
       "      <td>-0.031157</td>\n",
       "      <td>-0.001210</td>\n",
       "      <td>0.271498</td>\n",
       "      <td>-0.001520</td>\n",
       "      <td>...</td>\n",
       "      <td>0.269004</td>\n",
       "      <td>0.269081</td>\n",
       "      <td>0.219188</td>\n",
       "      <td>0.219276</td>\n",
       "      <td>0.132699</td>\n",
       "      <td>0.139933</td>\n",
       "      <td>0.091052</td>\n",
       "      <td>0.158698</td>\n",
       "      <td>0.157965</td>\n",
       "      <td>0.163987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl07_sum_1km</th>\n",
       "      <td>-0.032776</td>\n",
       "      <td>-0.041117</td>\n",
       "      <td>-0.042755</td>\n",
       "      <td>-0.145505</td>\n",
       "      <td>-0.031379</td>\n",
       "      <td>0.006662</td>\n",
       "      <td>-0.026725</td>\n",
       "      <td>-0.004598</td>\n",
       "      <td>0.133198</td>\n",
       "      <td>0.007112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.273872</td>\n",
       "      <td>0.274158</td>\n",
       "      <td>0.226720</td>\n",
       "      <td>0.226893</td>\n",
       "      <td>-0.008699</td>\n",
       "      <td>-0.006467</td>\n",
       "      <td>-0.034228</td>\n",
       "      <td>0.006871</td>\n",
       "      <td>-0.012234</td>\n",
       "      <td>-0.010689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl07_mean_1km</th>\n",
       "      <td>-0.032897</td>\n",
       "      <td>-0.041254</td>\n",
       "      <td>-0.042876</td>\n",
       "      <td>-0.145565</td>\n",
       "      <td>-0.031497</td>\n",
       "      <td>0.006690</td>\n",
       "      <td>-0.026721</td>\n",
       "      <td>-0.004591</td>\n",
       "      <td>0.133010</td>\n",
       "      <td>0.007111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.273489</td>\n",
       "      <td>0.273776</td>\n",
       "      <td>0.226460</td>\n",
       "      <td>0.226634</td>\n",
       "      <td>-0.008689</td>\n",
       "      <td>-0.006463</td>\n",
       "      <td>-0.034182</td>\n",
       "      <td>0.006877</td>\n",
       "      <td>-0.012238</td>\n",
       "      <td>-0.010698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl08_sum_1km</th>\n",
       "      <td>-0.014164</td>\n",
       "      <td>-0.030051</td>\n",
       "      <td>-0.046590</td>\n",
       "      <td>-0.263491</td>\n",
       "      <td>-0.012162</td>\n",
       "      <td>0.012275</td>\n",
       "      <td>-0.036505</td>\n",
       "      <td>0.003158</td>\n",
       "      <td>0.169613</td>\n",
       "      <td>0.007971</td>\n",
       "      <td>...</td>\n",
       "      <td>0.185757</td>\n",
       "      <td>0.185901</td>\n",
       "      <td>0.231414</td>\n",
       "      <td>0.231660</td>\n",
       "      <td>0.152022</td>\n",
       "      <td>0.155348</td>\n",
       "      <td>0.093030</td>\n",
       "      <td>0.200108</td>\n",
       "      <td>0.149030</td>\n",
       "      <td>0.151341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl08_mean_1km</th>\n",
       "      <td>-0.014194</td>\n",
       "      <td>-0.030123</td>\n",
       "      <td>-0.046702</td>\n",
       "      <td>-0.263733</td>\n",
       "      <td>-0.012186</td>\n",
       "      <td>0.012300</td>\n",
       "      <td>-0.036557</td>\n",
       "      <td>0.003173</td>\n",
       "      <td>0.169631</td>\n",
       "      <td>0.007997</td>\n",
       "      <td>...</td>\n",
       "      <td>0.185525</td>\n",
       "      <td>0.185670</td>\n",
       "      <td>0.231390</td>\n",
       "      <td>0.231638</td>\n",
       "      <td>0.151791</td>\n",
       "      <td>0.155111</td>\n",
       "      <td>0.092800</td>\n",
       "      <td>0.199895</td>\n",
       "      <td>0.148760</td>\n",
       "      <td>0.151065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl09_sum_1km</th>\n",
       "      <td>-0.021461</td>\n",
       "      <td>-0.037086</td>\n",
       "      <td>-0.051711</td>\n",
       "      <td>-0.309789</td>\n",
       "      <td>-0.019237</td>\n",
       "      <td>0.009259</td>\n",
       "      <td>-0.027941</td>\n",
       "      <td>0.004043</td>\n",
       "      <td>0.322877</td>\n",
       "      <td>-0.000567</td>\n",
       "      <td>...</td>\n",
       "      <td>0.150736</td>\n",
       "      <td>0.150920</td>\n",
       "      <td>0.140919</td>\n",
       "      <td>0.141046</td>\n",
       "      <td>0.051815</td>\n",
       "      <td>0.053486</td>\n",
       "      <td>0.014598</td>\n",
       "      <td>0.088305</td>\n",
       "      <td>0.080362</td>\n",
       "      <td>0.081274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl09_mean_1km</th>\n",
       "      <td>-0.021442</td>\n",
       "      <td>-0.037088</td>\n",
       "      <td>-0.051742</td>\n",
       "      <td>-0.309790</td>\n",
       "      <td>-0.019215</td>\n",
       "      <td>0.009267</td>\n",
       "      <td>-0.027974</td>\n",
       "      <td>0.004049</td>\n",
       "      <td>0.322577</td>\n",
       "      <td>-0.000548</td>\n",
       "      <td>...</td>\n",
       "      <td>0.150482</td>\n",
       "      <td>0.150667</td>\n",
       "      <td>0.140743</td>\n",
       "      <td>0.140872</td>\n",
       "      <td>0.051734</td>\n",
       "      <td>0.053402</td>\n",
       "      <td>0.014522</td>\n",
       "      <td>0.088229</td>\n",
       "      <td>0.080248</td>\n",
       "      <td>0.081157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl10_sum_1km</th>\n",
       "      <td>-0.009305</td>\n",
       "      <td>-0.009067</td>\n",
       "      <td>-0.008585</td>\n",
       "      <td>-0.182154</td>\n",
       "      <td>-0.009144</td>\n",
       "      <td>0.014471</td>\n",
       "      <td>-0.015514</td>\n",
       "      <td>-0.004752</td>\n",
       "      <td>0.319820</td>\n",
       "      <td>-0.004492</td>\n",
       "      <td>...</td>\n",
       "      <td>0.211694</td>\n",
       "      <td>0.211643</td>\n",
       "      <td>0.197215</td>\n",
       "      <td>0.197126</td>\n",
       "      <td>0.052885</td>\n",
       "      <td>0.058084</td>\n",
       "      <td>0.014614</td>\n",
       "      <td>0.075727</td>\n",
       "      <td>0.073692</td>\n",
       "      <td>0.077646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl10_mean_1km</th>\n",
       "      <td>-0.009342</td>\n",
       "      <td>-0.009138</td>\n",
       "      <td>-0.008690</td>\n",
       "      <td>-0.182750</td>\n",
       "      <td>-0.009177</td>\n",
       "      <td>0.014481</td>\n",
       "      <td>-0.015587</td>\n",
       "      <td>-0.004748</td>\n",
       "      <td>0.320322</td>\n",
       "      <td>-0.004481</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212087</td>\n",
       "      <td>0.212037</td>\n",
       "      <td>0.197717</td>\n",
       "      <td>0.197630</td>\n",
       "      <td>0.053013</td>\n",
       "      <td>0.058207</td>\n",
       "      <td>0.014700</td>\n",
       "      <td>0.075872</td>\n",
       "      <td>0.073826</td>\n",
       "      <td>0.077775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl11_sum_1km</th>\n",
       "      <td>-0.080632</td>\n",
       "      <td>-0.093441</td>\n",
       "      <td>-0.087703</td>\n",
       "      <td>-0.222265</td>\n",
       "      <td>-0.077995</td>\n",
       "      <td>0.027532</td>\n",
       "      <td>-0.047194</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.305687</td>\n",
       "      <td>0.005154</td>\n",
       "      <td>...</td>\n",
       "      <td>0.310783</td>\n",
       "      <td>0.310855</td>\n",
       "      <td>0.298793</td>\n",
       "      <td>0.298776</td>\n",
       "      <td>-0.000052</td>\n",
       "      <td>0.004410</td>\n",
       "      <td>-0.038719</td>\n",
       "      <td>0.036959</td>\n",
       "      <td>0.006762</td>\n",
       "      <td>0.010264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl11_mean_1km</th>\n",
       "      <td>-0.080768</td>\n",
       "      <td>-0.093593</td>\n",
       "      <td>-0.087838</td>\n",
       "      <td>-0.222733</td>\n",
       "      <td>-0.078128</td>\n",
       "      <td>0.027555</td>\n",
       "      <td>-0.047266</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.305729</td>\n",
       "      <td>0.005188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.310762</td>\n",
       "      <td>0.310835</td>\n",
       "      <td>0.298908</td>\n",
       "      <td>0.298893</td>\n",
       "      <td>-0.000052</td>\n",
       "      <td>0.004402</td>\n",
       "      <td>-0.038723</td>\n",
       "      <td>0.036973</td>\n",
       "      <td>0.006712</td>\n",
       "      <td>0.010207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl12_sum_1km</th>\n",
       "      <td>0.030145</td>\n",
       "      <td>0.019423</td>\n",
       "      <td>-0.003804</td>\n",
       "      <td>-0.260169</td>\n",
       "      <td>0.031243</td>\n",
       "      <td>-0.002497</td>\n",
       "      <td>-0.038361</td>\n",
       "      <td>0.000580</td>\n",
       "      <td>0.215914</td>\n",
       "      <td>0.003212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197601</td>\n",
       "      <td>0.197683</td>\n",
       "      <td>0.216808</td>\n",
       "      <td>0.216889</td>\n",
       "      <td>0.138431</td>\n",
       "      <td>0.145918</td>\n",
       "      <td>0.089582</td>\n",
       "      <td>0.172437</td>\n",
       "      <td>0.156683</td>\n",
       "      <td>0.163130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl12_mean_1km</th>\n",
       "      <td>0.030081</td>\n",
       "      <td>0.019366</td>\n",
       "      <td>-0.003840</td>\n",
       "      <td>-0.260437</td>\n",
       "      <td>0.031179</td>\n",
       "      <td>-0.002485</td>\n",
       "      <td>-0.038419</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>0.216025</td>\n",
       "      <td>0.003219</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197427</td>\n",
       "      <td>0.197509</td>\n",
       "      <td>0.216766</td>\n",
       "      <td>0.216848</td>\n",
       "      <td>0.138240</td>\n",
       "      <td>0.145700</td>\n",
       "      <td>0.089421</td>\n",
       "      <td>0.172220</td>\n",
       "      <td>0.156468</td>\n",
       "      <td>0.162893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl13_sum_1km</th>\n",
       "      <td>-0.059221</td>\n",
       "      <td>-0.064010</td>\n",
       "      <td>-0.055415</td>\n",
       "      <td>-0.202945</td>\n",
       "      <td>-0.057775</td>\n",
       "      <td>0.016342</td>\n",
       "      <td>-0.008468</td>\n",
       "      <td>-0.005547</td>\n",
       "      <td>0.227088</td>\n",
       "      <td>-0.009653</td>\n",
       "      <td>...</td>\n",
       "      <td>0.423758</td>\n",
       "      <td>0.423759</td>\n",
       "      <td>0.417295</td>\n",
       "      <td>0.417239</td>\n",
       "      <td>0.042529</td>\n",
       "      <td>0.044429</td>\n",
       "      <td>0.014134</td>\n",
       "      <td>0.066254</td>\n",
       "      <td>0.035388</td>\n",
       "      <td>0.036903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl13_mean_1km</th>\n",
       "      <td>-0.059397</td>\n",
       "      <td>-0.064201</td>\n",
       "      <td>-0.055582</td>\n",
       "      <td>-0.203625</td>\n",
       "      <td>-0.057947</td>\n",
       "      <td>0.016391</td>\n",
       "      <td>-0.008621</td>\n",
       "      <td>-0.005537</td>\n",
       "      <td>0.227238</td>\n",
       "      <td>-0.009607</td>\n",
       "      <td>...</td>\n",
       "      <td>0.423965</td>\n",
       "      <td>0.423968</td>\n",
       "      <td>0.417719</td>\n",
       "      <td>0.417665</td>\n",
       "      <td>0.042286</td>\n",
       "      <td>0.044180</td>\n",
       "      <td>0.013875</td>\n",
       "      <td>0.066028</td>\n",
       "      <td>0.035104</td>\n",
       "      <td>0.036614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl14_sum_1km</th>\n",
       "      <td>-0.078321</td>\n",
       "      <td>-0.089148</td>\n",
       "      <td>-0.082353</td>\n",
       "      <td>-0.257762</td>\n",
       "      <td>-0.075735</td>\n",
       "      <td>0.022152</td>\n",
       "      <td>-0.022382</td>\n",
       "      <td>-0.001561</td>\n",
       "      <td>0.389809</td>\n",
       "      <td>-0.014635</td>\n",
       "      <td>...</td>\n",
       "      <td>0.515463</td>\n",
       "      <td>0.515415</td>\n",
       "      <td>0.518776</td>\n",
       "      <td>0.518630</td>\n",
       "      <td>0.083723</td>\n",
       "      <td>0.088244</td>\n",
       "      <td>0.044678</td>\n",
       "      <td>0.114322</td>\n",
       "      <td>0.093820</td>\n",
       "      <td>0.097775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl14_mean_1km</th>\n",
       "      <td>-0.078367</td>\n",
       "      <td>-0.089200</td>\n",
       "      <td>-0.082404</td>\n",
       "      <td>-0.258209</td>\n",
       "      <td>-0.075780</td>\n",
       "      <td>0.022164</td>\n",
       "      <td>-0.022461</td>\n",
       "      <td>-0.001553</td>\n",
       "      <td>0.389872</td>\n",
       "      <td>-0.014603</td>\n",
       "      <td>...</td>\n",
       "      <td>0.515539</td>\n",
       "      <td>0.515493</td>\n",
       "      <td>0.519081</td>\n",
       "      <td>0.518937</td>\n",
       "      <td>0.083758</td>\n",
       "      <td>0.088276</td>\n",
       "      <td>0.044708</td>\n",
       "      <td>0.114352</td>\n",
       "      <td>0.093825</td>\n",
       "      <td>0.097777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl15_sum_1km</th>\n",
       "      <td>0.000251</td>\n",
       "      <td>-0.009138</td>\n",
       "      <td>-0.022683</td>\n",
       "      <td>-0.259233</td>\n",
       "      <td>0.001674</td>\n",
       "      <td>0.009690</td>\n",
       "      <td>-0.027732</td>\n",
       "      <td>-0.002699</td>\n",
       "      <td>0.299519</td>\n",
       "      <td>-0.004375</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.604743</td>\n",
       "      <td>0.604588</td>\n",
       "      <td>0.129587</td>\n",
       "      <td>0.133730</td>\n",
       "      <td>0.090872</td>\n",
       "      <td>0.149715</td>\n",
       "      <td>0.143319</td>\n",
       "      <td>0.146985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl15_mean_1km</th>\n",
       "      <td>0.000172</td>\n",
       "      <td>-0.009199</td>\n",
       "      <td>-0.022707</td>\n",
       "      <td>-0.259580</td>\n",
       "      <td>0.001593</td>\n",
       "      <td>0.009707</td>\n",
       "      <td>-0.027778</td>\n",
       "      <td>-0.002696</td>\n",
       "      <td>0.299597</td>\n",
       "      <td>-0.004355</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.604926</td>\n",
       "      <td>0.604775</td>\n",
       "      <td>0.129529</td>\n",
       "      <td>0.133667</td>\n",
       "      <td>0.090816</td>\n",
       "      <td>0.149657</td>\n",
       "      <td>0.143230</td>\n",
       "      <td>0.146891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl16_sum_1km</th>\n",
       "      <td>-0.066415</td>\n",
       "      <td>-0.076762</td>\n",
       "      <td>-0.072823</td>\n",
       "      <td>-0.258168</td>\n",
       "      <td>-0.064065</td>\n",
       "      <td>0.019688</td>\n",
       "      <td>-0.039895</td>\n",
       "      <td>-0.001962</td>\n",
       "      <td>0.296038</td>\n",
       "      <td>0.003870</td>\n",
       "      <td>...</td>\n",
       "      <td>0.604743</td>\n",
       "      <td>0.604926</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.078006</td>\n",
       "      <td>0.083304</td>\n",
       "      <td>0.031913</td>\n",
       "      <td>0.106457</td>\n",
       "      <td>0.076234</td>\n",
       "      <td>0.080978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcl16_mean_1km</th>\n",
       "      <td>-0.066452</td>\n",
       "      <td>-0.076767</td>\n",
       "      <td>-0.072786</td>\n",
       "      <td>-0.258435</td>\n",
       "      <td>-0.064107</td>\n",
       "      <td>0.019689</td>\n",
       "      <td>-0.039920</td>\n",
       "      <td>-0.001957</td>\n",
       "      <td>0.295902</td>\n",
       "      <td>0.003893</td>\n",
       "      <td>...</td>\n",
       "      <td>0.604588</td>\n",
       "      <td>0.604775</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.078003</td>\n",
       "      <td>0.083295</td>\n",
       "      <td>0.031920</td>\n",
       "      <td>0.106451</td>\n",
       "      <td>0.076170</td>\n",
       "      <td>0.080909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mp00_mean_10km</th>\n",
       "      <td>0.214458</td>\n",
       "      <td>0.206937</td>\n",
       "      <td>0.141315</td>\n",
       "      <td>0.157069</td>\n",
       "      <td>0.213190</td>\n",
       "      <td>-0.058276</td>\n",
       "      <td>0.101773</td>\n",
       "      <td>-0.011951</td>\n",
       "      <td>-0.148683</td>\n",
       "      <td>-0.031640</td>\n",
       "      <td>...</td>\n",
       "      <td>0.129587</td>\n",
       "      <td>0.129529</td>\n",
       "      <td>0.078006</td>\n",
       "      <td>0.078003</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997809</td>\n",
       "      <td>0.970497</td>\n",
       "      <td>0.975608</td>\n",
       "      <td>0.987966</td>\n",
       "      <td>0.985273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mp00_media_10km</th>\n",
       "      <td>0.214576</td>\n",
       "      <td>0.207640</td>\n",
       "      <td>0.142603</td>\n",
       "      <td>0.155735</td>\n",
       "      <td>0.213219</td>\n",
       "      <td>-0.058596</td>\n",
       "      <td>0.099760</td>\n",
       "      <td>-0.011582</td>\n",
       "      <td>-0.140054</td>\n",
       "      <td>-0.031037</td>\n",
       "      <td>...</td>\n",
       "      <td>0.133730</td>\n",
       "      <td>0.133667</td>\n",
       "      <td>0.083304</td>\n",
       "      <td>0.083295</td>\n",
       "      <td>0.997809</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.961380</td>\n",
       "      <td>0.973335</td>\n",
       "      <td>0.987022</td>\n",
       "      <td>0.988158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mp00_min_10km</th>\n",
       "      <td>0.213148</td>\n",
       "      <td>0.208894</td>\n",
       "      <td>0.147903</td>\n",
       "      <td>0.211125</td>\n",
       "      <td>0.211362</td>\n",
       "      <td>-0.061513</td>\n",
       "      <td>0.118349</td>\n",
       "      <td>-0.013828</td>\n",
       "      <td>-0.187142</td>\n",
       "      <td>-0.034511</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090872</td>\n",
       "      <td>0.090816</td>\n",
       "      <td>0.031913</td>\n",
       "      <td>0.031920</td>\n",
       "      <td>0.970497</td>\n",
       "      <td>0.961380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.913837</td>\n",
       "      <td>0.964367</td>\n",
       "      <td>0.955869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mp00_max_10km</th>\n",
       "      <td>0.201670</td>\n",
       "      <td>0.190024</td>\n",
       "      <td>0.122539</td>\n",
       "      <td>0.083610</td>\n",
       "      <td>0.201211</td>\n",
       "      <td>-0.051012</td>\n",
       "      <td>0.084675</td>\n",
       "      <td>-0.010646</td>\n",
       "      <td>-0.096159</td>\n",
       "      <td>-0.029990</td>\n",
       "      <td>...</td>\n",
       "      <td>0.149715</td>\n",
       "      <td>0.149657</td>\n",
       "      <td>0.106457</td>\n",
       "      <td>0.106451</td>\n",
       "      <td>0.975608</td>\n",
       "      <td>0.973335</td>\n",
       "      <td>0.913837</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.959062</td>\n",
       "      <td>0.955853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mp01_mean_10km</th>\n",
       "      <td>0.212986</td>\n",
       "      <td>0.207268</td>\n",
       "      <td>0.143857</td>\n",
       "      <td>0.144672</td>\n",
       "      <td>0.211451</td>\n",
       "      <td>-0.059671</td>\n",
       "      <td>0.101816</td>\n",
       "      <td>-0.011605</td>\n",
       "      <td>-0.067555</td>\n",
       "      <td>-0.034454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.143319</td>\n",
       "      <td>0.143230</td>\n",
       "      <td>0.076234</td>\n",
       "      <td>0.076170</td>\n",
       "      <td>0.987966</td>\n",
       "      <td>0.987022</td>\n",
       "      <td>0.964367</td>\n",
       "      <td>0.959062</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mp01_media_10km</th>\n",
       "      <td>0.213083</td>\n",
       "      <td>0.207928</td>\n",
       "      <td>0.145090</td>\n",
       "      <td>0.143737</td>\n",
       "      <td>0.211459</td>\n",
       "      <td>-0.060000</td>\n",
       "      <td>0.099882</td>\n",
       "      <td>-0.011289</td>\n",
       "      <td>-0.061316</td>\n",
       "      <td>-0.033865</td>\n",
       "      <td>...</td>\n",
       "      <td>0.146985</td>\n",
       "      <td>0.146891</td>\n",
       "      <td>0.080978</td>\n",
       "      <td>0.080909</td>\n",
       "      <td>0.985273</td>\n",
       "      <td>0.988158</td>\n",
       "      <td>0.955869</td>\n",
       "      <td>0.955853</td>\n",
       "      <td>0.998277</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>182 rows × 182 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       hhid      glss      year    region     clust        nh  \\\n",
       "hhid               1.000000  0.927561  0.579349  0.093887  0.998626 -0.163708   \n",
       "glss               0.927561  1.000000  0.841910  0.119150  0.906768 -0.210315   \n",
       "year               0.579349  0.841910  1.000000  0.136539  0.535980 -0.222512   \n",
       "region             0.093887  0.119150  0.136539  1.000000  0.089694 -0.046583   \n",
       "clust              0.998626  0.906768  0.535980  0.089694  1.000000 -0.155307   \n",
       "nh                -0.163708 -0.210315 -0.222512 -0.046583 -0.155307  1.000000   \n",
       "pid                0.005572  0.008909  0.013153  0.133078  0.005050 -0.007699   \n",
       "sex               -0.001990 -0.003766 -0.005466 -0.011839 -0.001745 -0.002716   \n",
       "mining_region     -0.088914 -0.097291 -0.090166 -0.516582 -0.086895  0.023667   \n",
       "age                0.014387  0.016360  0.014678 -0.012522  0.013926 -0.005303   \n",
       "working_age        0.008129  0.016176  0.023084 -0.028719  0.006941 -0.007712   \n",
       "prime_age          0.008036  0.012077  0.014403 -0.020988  0.007413 -0.001670   \n",
       "male               0.001990  0.003766  0.005466  0.011839  0.001745  0.002716   \n",
       "school_attendance  0.055196  0.047389  0.022735 -0.027779  0.055590 -0.006069   \n",
       "expenditure        0.518439  0.496195  0.331768 -0.071631  0.515700 -0.091113   \n",
       "expenditurepc      0.460320  0.441948  0.296346 -0.137308  0.457769 -0.075129   \n",
       "food_expend        0.539700  0.530884  0.375507 -0.138290  0.534918 -0.099859   \n",
       "food_expendpc      0.446177  0.437784  0.306993 -0.178808  0.442437 -0.077443   \n",
       "inc                0.213062  0.196845  0.121116 -0.016850  0.212916 -0.041308   \n",
       "incpc              0.174981  0.161902  0.099608 -0.036732  0.174852 -0.030812   \n",
       "rexpenditure       0.334452  0.316303  0.205083 -0.105162  0.333185 -0.053351   \n",
       "rexpenditurepc     0.294051  0.280315  0.183790 -0.177972  0.292672 -0.043312   \n",
       "rfood_expend       0.215583  0.207395  0.137928 -0.189334  0.214384 -0.034139   \n",
       "rfood_expendpc     0.193053  0.186185  0.123621 -0.231246  0.191909 -0.028106   \n",
       "rincpc             0.155735  0.144031  0.088446 -0.038303  0.155627 -0.027323   \n",
       "poor              -0.039345 -0.056438 -0.062092  0.334406 -0.036400 -0.004138   \n",
       "poor_ext          -0.139709 -0.141638 -0.103445  0.323523 -0.137645  0.013916   \n",
       "ln_rincpc          0.480784  0.457807  0.301746 -0.159695  0.478552 -0.079356   \n",
       "ln_rincequiv       0.487836  0.466681  0.310951 -0.157340  0.485264 -0.081521   \n",
       "ln_rexpenditurepc  0.426521  0.404020  0.261121 -0.301991  0.424692 -0.063501   \n",
       "...                     ...       ...       ...       ...       ...       ...   \n",
       "fcl05_sum_1km     -0.044635 -0.041437 -0.028607 -0.254229 -0.044622  0.007024   \n",
       "fcl05_mean_1km    -0.044779 -0.041622 -0.028796 -0.254372 -0.044759  0.007056   \n",
       "fcl06_sum_1km      0.007430  0.015286  0.018909 -0.232832  0.006136 -0.002884   \n",
       "fcl06_mean_1km     0.007248  0.015127  0.018820 -0.233451  0.005953 -0.002837   \n",
       "fcl07_sum_1km     -0.032776 -0.041117 -0.042755 -0.145505 -0.031379  0.006662   \n",
       "fcl07_mean_1km    -0.032897 -0.041254 -0.042876 -0.145565 -0.031497  0.006690   \n",
       "fcl08_sum_1km     -0.014164 -0.030051 -0.046590 -0.263491 -0.012162  0.012275   \n",
       "fcl08_mean_1km    -0.014194 -0.030123 -0.046702 -0.263733 -0.012186  0.012300   \n",
       "fcl09_sum_1km     -0.021461 -0.037086 -0.051711 -0.309789 -0.019237  0.009259   \n",
       "fcl09_mean_1km    -0.021442 -0.037088 -0.051742 -0.309790 -0.019215  0.009267   \n",
       "fcl10_sum_1km     -0.009305 -0.009067 -0.008585 -0.182154 -0.009144  0.014471   \n",
       "fcl10_mean_1km    -0.009342 -0.009138 -0.008690 -0.182750 -0.009177  0.014481   \n",
       "fcl11_sum_1km     -0.080632 -0.093441 -0.087703 -0.222265 -0.077995  0.027532   \n",
       "fcl11_mean_1km    -0.080768 -0.093593 -0.087838 -0.222733 -0.078128  0.027555   \n",
       "fcl12_sum_1km      0.030145  0.019423 -0.003804 -0.260169  0.031243 -0.002497   \n",
       "fcl12_mean_1km     0.030081  0.019366 -0.003840 -0.260437  0.031179 -0.002485   \n",
       "fcl13_sum_1km     -0.059221 -0.064010 -0.055415 -0.202945 -0.057775  0.016342   \n",
       "fcl13_mean_1km    -0.059397 -0.064201 -0.055582 -0.203625 -0.057947  0.016391   \n",
       "fcl14_sum_1km     -0.078321 -0.089148 -0.082353 -0.257762 -0.075735  0.022152   \n",
       "fcl14_mean_1km    -0.078367 -0.089200 -0.082404 -0.258209 -0.075780  0.022164   \n",
       "fcl15_sum_1km      0.000251 -0.009138 -0.022683 -0.259233  0.001674  0.009690   \n",
       "fcl15_mean_1km     0.000172 -0.009199 -0.022707 -0.259580  0.001593  0.009707   \n",
       "fcl16_sum_1km     -0.066415 -0.076762 -0.072823 -0.258168 -0.064065  0.019688   \n",
       "fcl16_mean_1km    -0.066452 -0.076767 -0.072786 -0.258435 -0.064107  0.019689   \n",
       "mp00_mean_10km     0.214458  0.206937  0.141315  0.157069  0.213190 -0.058276   \n",
       "mp00_media_10km    0.214576  0.207640  0.142603  0.155735  0.213219 -0.058596   \n",
       "mp00_min_10km      0.213148  0.208894  0.147903  0.211125  0.211362 -0.061513   \n",
       "mp00_max_10km      0.201670  0.190024  0.122539  0.083610  0.201211 -0.051012   \n",
       "mp01_mean_10km     0.212986  0.207268  0.143857  0.144672  0.211451 -0.059671   \n",
       "mp01_media_10km    0.213083  0.207928  0.145090  0.143737  0.211459 -0.060000   \n",
       "\n",
       "                        pid       sex  mining_region       age  \\\n",
       "hhid               0.005572 -0.001990      -0.088914  0.014387   \n",
       "glss               0.008909 -0.003766      -0.097291  0.016360   \n",
       "year               0.013153 -0.005466      -0.090166  0.014678   \n",
       "region             0.133078 -0.011839      -0.516582 -0.012522   \n",
       "clust              0.005050 -0.001745      -0.086895  0.013926   \n",
       "nh                -0.007699 -0.002716       0.023667 -0.005303   \n",
       "pid                1.000000  0.036562      -0.068954 -0.503714   \n",
       "sex                0.036562  1.000000       0.008150  0.044231   \n",
       "mining_region     -0.068954  0.008150       1.000000 -0.009448   \n",
       "age               -0.503714  0.044231      -0.009448  1.000000   \n",
       "working_age       -0.419941  0.037085       0.000329  0.418429   \n",
       "prime_age         -0.285547  0.038347      -0.001558  0.156001   \n",
       "male              -0.036562 -1.000000      -0.008150 -0.044231   \n",
       "school_attendance  0.405568 -0.073169       0.036554 -0.633182   \n",
       "expenditure        0.119901  0.001125      -0.001389 -0.008781   \n",
       "expenditurepc     -0.139586 -0.017665       0.039298  0.083118   \n",
       "food_expend        0.062195  0.009051       0.049067 -0.011410   \n",
       "food_expendpc     -0.189553 -0.011591       0.070796  0.096464   \n",
       "inc                0.052420 -0.003082       0.018683 -0.007018   \n",
       "incpc             -0.038953 -0.008791       0.038953  0.023282   \n",
       "rexpenditure       0.150026  0.003600       0.019663 -0.018825   \n",
       "rexpenditurepc    -0.170993 -0.019696       0.064246  0.097448   \n",
       "rfood_expend       0.110466  0.011541       0.082295 -0.029376   \n",
       "rfood_expendpc    -0.234746 -0.014652       0.105381  0.118524   \n",
       "rincpc            -0.041272 -0.009462       0.039620  0.024754   \n",
       "poor               0.203500 -0.021481      -0.178371 -0.059160   \n",
       "poor_ext           0.164001 -0.019988      -0.163314 -0.039874   \n",
       "ln_rincpc         -0.087858 -0.011684       0.073080  0.057046   \n",
       "ln_rincequiv      -0.084462  0.001282       0.071405  0.046305   \n",
       "ln_rexpenditurepc -0.224172 -0.004375       0.131948  0.107203   \n",
       "...                     ...       ...            ...       ...   \n",
       "fcl05_sum_1km     -0.026183 -0.007180       0.264087 -0.004941   \n",
       "fcl05_mean_1km    -0.026211 -0.007173       0.263882 -0.004917   \n",
       "fcl06_sum_1km     -0.031023 -0.001218       0.271223 -0.001546   \n",
       "fcl06_mean_1km    -0.031157 -0.001210       0.271498 -0.001520   \n",
       "fcl07_sum_1km     -0.026725 -0.004598       0.133198  0.007112   \n",
       "fcl07_mean_1km    -0.026721 -0.004591       0.133010  0.007111   \n",
       "fcl08_sum_1km     -0.036505  0.003158       0.169613  0.007971   \n",
       "fcl08_mean_1km    -0.036557  0.003173       0.169631  0.007997   \n",
       "fcl09_sum_1km     -0.027941  0.004043       0.322877 -0.000567   \n",
       "fcl09_mean_1km    -0.027974  0.004049       0.322577 -0.000548   \n",
       "fcl10_sum_1km     -0.015514 -0.004752       0.319820 -0.004492   \n",
       "fcl10_mean_1km    -0.015587 -0.004748       0.320322 -0.004481   \n",
       "fcl11_sum_1km     -0.047194  0.000032       0.305687  0.005154   \n",
       "fcl11_mean_1km    -0.047266  0.000048       0.305729  0.005188   \n",
       "fcl12_sum_1km     -0.038361  0.000580       0.215914  0.003212   \n",
       "fcl12_mean_1km    -0.038419  0.000574       0.216025  0.003219   \n",
       "fcl13_sum_1km     -0.008468 -0.005547       0.227088 -0.009653   \n",
       "fcl13_mean_1km    -0.008621 -0.005537       0.227238 -0.009607   \n",
       "fcl14_sum_1km     -0.022382 -0.001561       0.389809 -0.014635   \n",
       "fcl14_mean_1km    -0.022461 -0.001553       0.389872 -0.014603   \n",
       "fcl15_sum_1km     -0.027732 -0.002699       0.299519 -0.004375   \n",
       "fcl15_mean_1km    -0.027778 -0.002696       0.299597 -0.004355   \n",
       "fcl16_sum_1km     -0.039895 -0.001962       0.296038  0.003870   \n",
       "fcl16_mean_1km    -0.039920 -0.001957       0.295902  0.003893   \n",
       "mp00_mean_10km     0.101773 -0.011951      -0.148683 -0.031640   \n",
       "mp00_media_10km    0.099760 -0.011582      -0.140054 -0.031037   \n",
       "mp00_min_10km      0.118349 -0.013828      -0.187142 -0.034511   \n",
       "mp00_max_10km      0.084675 -0.010646      -0.096159 -0.029990   \n",
       "mp01_mean_10km     0.101816 -0.011605      -0.067555 -0.034454   \n",
       "mp01_media_10km    0.099882 -0.011289      -0.061316 -0.033865   \n",
       "\n",
       "                        ...         fcl15_sum_1km  fcl15_mean_1km  \\\n",
       "hhid                    ...              0.000251        0.000172   \n",
       "glss                    ...             -0.009138       -0.009199   \n",
       "year                    ...             -0.022683       -0.022707   \n",
       "region                  ...             -0.259233       -0.259580   \n",
       "clust                   ...              0.001674        0.001593   \n",
       "nh                      ...              0.009690        0.009707   \n",
       "pid                     ...             -0.027732       -0.027778   \n",
       "sex                     ...             -0.002699       -0.002696   \n",
       "mining_region           ...              0.299519        0.299597   \n",
       "age                     ...             -0.004375       -0.004355   \n",
       "working_age             ...             -0.019852       -0.019841   \n",
       "prime_age               ...             -0.020234       -0.020236   \n",
       "male                    ...              0.002699        0.002696   \n",
       "school_attendance       ...              0.022633        0.022637   \n",
       "expenditure             ...             -0.013955       -0.013965   \n",
       "expenditurepc           ...             -0.000341       -0.000318   \n",
       "food_expend             ...              0.005127        0.005161   \n",
       "food_expendpc           ...              0.013771        0.013830   \n",
       "inc                     ...              0.003372        0.003367   \n",
       "incpc                   ...              0.009060        0.009059   \n",
       "rexpenditure            ...             -0.029326       -0.029312   \n",
       "rexpenditurepc          ...             -0.015795       -0.015749   \n",
       "rfood_expend            ...             -0.003828       -0.003757   \n",
       "rfood_expendpc          ...              0.004876        0.004966   \n",
       "rincpc                  ...              0.007242        0.007243   \n",
       "poor                    ...             -0.046205       -0.046292   \n",
       "poor_ext                ...             -0.076784       -0.076842   \n",
       "ln_rincpc               ...              0.054911        0.054923   \n",
       "ln_rincequiv            ...              0.055849        0.055861   \n",
       "ln_rexpenditurepc       ...              0.021218        0.021274   \n",
       "...                     ...                   ...             ...   \n",
       "fcl05_sum_1km           ...              0.259672        0.259894   \n",
       "fcl05_mean_1km          ...              0.259456        0.259680   \n",
       "fcl06_sum_1km           ...              0.268900        0.268975   \n",
       "fcl06_mean_1km          ...              0.269004        0.269081   \n",
       "fcl07_sum_1km           ...              0.273872        0.274158   \n",
       "fcl07_mean_1km          ...              0.273489        0.273776   \n",
       "fcl08_sum_1km           ...              0.185757        0.185901   \n",
       "fcl08_mean_1km          ...              0.185525        0.185670   \n",
       "fcl09_sum_1km           ...              0.150736        0.150920   \n",
       "fcl09_mean_1km          ...              0.150482        0.150667   \n",
       "fcl10_sum_1km           ...              0.211694        0.211643   \n",
       "fcl10_mean_1km          ...              0.212087        0.212037   \n",
       "fcl11_sum_1km           ...              0.310783        0.310855   \n",
       "fcl11_mean_1km          ...              0.310762        0.310835   \n",
       "fcl12_sum_1km           ...              0.197601        0.197683   \n",
       "fcl12_mean_1km          ...              0.197427        0.197509   \n",
       "fcl13_sum_1km           ...              0.423758        0.423759   \n",
       "fcl13_mean_1km          ...              0.423965        0.423968   \n",
       "fcl14_sum_1km           ...              0.515463        0.515415   \n",
       "fcl14_mean_1km          ...              0.515539        0.515493   \n",
       "fcl15_sum_1km           ...              1.000000        0.999998   \n",
       "fcl15_mean_1km          ...              0.999998        1.000000   \n",
       "fcl16_sum_1km           ...              0.604743        0.604926   \n",
       "fcl16_mean_1km          ...              0.604588        0.604775   \n",
       "mp00_mean_10km          ...              0.129587        0.129529   \n",
       "mp00_media_10km         ...              0.133730        0.133667   \n",
       "mp00_min_10km           ...              0.090872        0.090816   \n",
       "mp00_max_10km           ...              0.149715        0.149657   \n",
       "mp01_mean_10km          ...              0.143319        0.143230   \n",
       "mp01_media_10km         ...              0.146985        0.146891   \n",
       "\n",
       "                   fcl16_sum_1km  fcl16_mean_1km  mp00_mean_10km  \\\n",
       "hhid                   -0.066415       -0.066452        0.214458   \n",
       "glss                   -0.076762       -0.076767        0.206937   \n",
       "year                   -0.072823       -0.072786        0.141315   \n",
       "region                 -0.258168       -0.258435        0.157069   \n",
       "clust                  -0.064065       -0.064107        0.213190   \n",
       "nh                      0.019688        0.019689       -0.058276   \n",
       "pid                    -0.039895       -0.039920        0.101773   \n",
       "sex                    -0.001962       -0.001957       -0.011951   \n",
       "mining_region           0.296038        0.295902       -0.148683   \n",
       "age                     0.003870        0.003893       -0.031640   \n",
       "working_age            -0.022193       -0.022180       -0.052073   \n",
       "prime_age              -0.022969       -0.022972       -0.038982   \n",
       "male                    0.001962        0.001957        0.011951   \n",
       "school_attendance       0.016886        0.016867       -0.000980   \n",
       "expenditure            -0.059840       -0.059852        0.026764   \n",
       "expenditurepc          -0.034014       -0.034009       -0.041513   \n",
       "food_expend            -0.043697       -0.043653        0.000810   \n",
       "food_expendpc          -0.018928       -0.018877       -0.063122   \n",
       "inc                    -0.018764       -0.018768        0.023032   \n",
       "incpc                  -0.007926       -0.007928       -0.007329   \n",
       "rexpenditure           -0.061641       -0.061642       -0.063014   \n",
       "rexpenditurepc         -0.035445       -0.035429       -0.146656   \n",
       "rfood_expend           -0.027074       -0.027012       -0.093376   \n",
       "rfood_expendpc         -0.005207       -0.005143       -0.167509   \n",
       "rincpc                 -0.007845       -0.007847       -0.016969   \n",
       "poor                   -0.052135       -0.052168        0.209409   \n",
       "poor_ext               -0.078964       -0.078982        0.147145   \n",
       "ln_rincpc               0.023670        0.023664       -0.015632   \n",
       "ln_rincequiv            0.023864        0.023860       -0.007930   \n",
       "ln_rexpenditurepc      -0.001511       -0.001496       -0.181856   \n",
       "...                          ...             ...             ...   \n",
       "fcl05_sum_1km           0.209285        0.209405        0.101212   \n",
       "fcl05_mean_1km          0.209190        0.209311        0.101076   \n",
       "fcl06_sum_1km           0.218899        0.218985        0.132830   \n",
       "fcl06_mean_1km          0.219188        0.219276        0.132699   \n",
       "fcl07_sum_1km           0.226720        0.226893       -0.008699   \n",
       "fcl07_mean_1km          0.226460        0.226634       -0.008689   \n",
       "fcl08_sum_1km           0.231414        0.231660        0.152022   \n",
       "fcl08_mean_1km          0.231390        0.231638        0.151791   \n",
       "fcl09_sum_1km           0.140919        0.141046        0.051815   \n",
       "fcl09_mean_1km          0.140743        0.140872        0.051734   \n",
       "fcl10_sum_1km           0.197215        0.197126        0.052885   \n",
       "fcl10_mean_1km          0.197717        0.197630        0.053013   \n",
       "fcl11_sum_1km           0.298793        0.298776       -0.000052   \n",
       "fcl11_mean_1km          0.298908        0.298893       -0.000052   \n",
       "fcl12_sum_1km           0.216808        0.216889        0.138431   \n",
       "fcl12_mean_1km          0.216766        0.216848        0.138240   \n",
       "fcl13_sum_1km           0.417295        0.417239        0.042529   \n",
       "fcl13_mean_1km          0.417719        0.417665        0.042286   \n",
       "fcl14_sum_1km           0.518776        0.518630        0.083723   \n",
       "fcl14_mean_1km          0.519081        0.518937        0.083758   \n",
       "fcl15_sum_1km           0.604743        0.604588        0.129587   \n",
       "fcl15_mean_1km          0.604926        0.604775        0.129529   \n",
       "fcl16_sum_1km           1.000000        0.999998        0.078006   \n",
       "fcl16_mean_1km          0.999998        1.000000        0.078003   \n",
       "mp00_mean_10km          0.078006        0.078003        1.000000   \n",
       "mp00_media_10km         0.083304        0.083295        0.997809   \n",
       "mp00_min_10km           0.031913        0.031920        0.970497   \n",
       "mp00_max_10km           0.106457        0.106451        0.975608   \n",
       "mp01_mean_10km          0.076234        0.076170        0.987966   \n",
       "mp01_media_10km         0.080978        0.080909        0.985273   \n",
       "\n",
       "                   mp00_media_10km  mp00_min_10km  mp00_max_10km  \\\n",
       "hhid                      0.214576       0.213148       0.201670   \n",
       "glss                      0.207640       0.208894       0.190024   \n",
       "year                      0.142603       0.147903       0.122539   \n",
       "region                    0.155735       0.211125       0.083610   \n",
       "clust                     0.213219       0.211362       0.201211   \n",
       "nh                       -0.058596      -0.061513      -0.051012   \n",
       "pid                       0.099760       0.118349       0.084675   \n",
       "sex                      -0.011582      -0.013828      -0.010646   \n",
       "mining_region            -0.140054      -0.187142      -0.096159   \n",
       "age                      -0.031037      -0.034511      -0.029990   \n",
       "working_age              -0.051591      -0.053514      -0.049650   \n",
       "prime_age                -0.038991      -0.039855      -0.035933   \n",
       "male                      0.011582       0.013828       0.010646   \n",
       "school_attendance         0.000191      -0.009369       0.005698   \n",
       "expenditure               0.027676       0.007000       0.043272   \n",
       "expenditurepc            -0.040092      -0.070116      -0.014383   \n",
       "food_expend               0.001426      -0.023388       0.023829   \n",
       "food_expendpc            -0.062067      -0.092334      -0.033316   \n",
       "inc                       0.022896       0.015974       0.029061   \n",
       "incpc                    -0.007180      -0.016182       0.001971   \n",
       "rexpenditure             -0.061208      -0.084759      -0.039850   \n",
       "rexpenditurepc           -0.143855      -0.177072      -0.113001   \n",
       "rfood_expend             -0.091824      -0.120967      -0.060840   \n",
       "rfood_expendpc           -0.164859      -0.199519      -0.129995   \n",
       "rincpc                   -0.016703      -0.025912      -0.007258   \n",
       "poor                      0.203664       0.258036       0.159008   \n",
       "poor_ext                  0.141448       0.195206       0.101279   \n",
       "ln_rincpc                -0.012687      -0.047754       0.010496   \n",
       "ln_rincequiv             -0.005064      -0.039843       0.017904   \n",
       "ln_rexpenditurepc        -0.176068      -0.229629      -0.134703   \n",
       "...                            ...            ...            ...   \n",
       "fcl05_sum_1km             0.105799       0.074421       0.113857   \n",
       "fcl05_mean_1km            0.105651       0.074312       0.113719   \n",
       "fcl06_sum_1km             0.140076       0.091180       0.158801   \n",
       "fcl06_mean_1km            0.139933       0.091052       0.158698   \n",
       "fcl07_sum_1km            -0.006467      -0.034228       0.006871   \n",
       "fcl07_mean_1km           -0.006463      -0.034182       0.006877   \n",
       "fcl08_sum_1km             0.155348       0.093030       0.200108   \n",
       "fcl08_mean_1km            0.155111       0.092800       0.199895   \n",
       "fcl09_sum_1km             0.053486       0.014598       0.088305   \n",
       "fcl09_mean_1km            0.053402       0.014522       0.088229   \n",
       "fcl10_sum_1km             0.058084       0.014614       0.075727   \n",
       "fcl10_mean_1km            0.058207       0.014700       0.075872   \n",
       "fcl11_sum_1km             0.004410      -0.038719       0.036959   \n",
       "fcl11_mean_1km            0.004402      -0.038723       0.036973   \n",
       "fcl12_sum_1km             0.145918       0.089582       0.172437   \n",
       "fcl12_mean_1km            0.145700       0.089421       0.172220   \n",
       "fcl13_sum_1km             0.044429       0.014134       0.066254   \n",
       "fcl13_mean_1km            0.044180       0.013875       0.066028   \n",
       "fcl14_sum_1km             0.088244       0.044678       0.114322   \n",
       "fcl14_mean_1km            0.088276       0.044708       0.114352   \n",
       "fcl15_sum_1km             0.133730       0.090872       0.149715   \n",
       "fcl15_mean_1km            0.133667       0.090816       0.149657   \n",
       "fcl16_sum_1km             0.083304       0.031913       0.106457   \n",
       "fcl16_mean_1km            0.083295       0.031920       0.106451   \n",
       "mp00_mean_10km            0.997809       0.970497       0.975608   \n",
       "mp00_media_10km           1.000000       0.961380       0.973335   \n",
       "mp00_min_10km             0.961380       1.000000       0.913837   \n",
       "mp00_max_10km             0.973335       0.913837       1.000000   \n",
       "mp01_mean_10km            0.987022       0.964367       0.959062   \n",
       "mp01_media_10km           0.988158       0.955869       0.955853   \n",
       "\n",
       "                   mp01_mean_10km  mp01_media_10km  \n",
       "hhid                     0.212986         0.213083  \n",
       "glss                     0.207268         0.207928  \n",
       "year                     0.143857         0.145090  \n",
       "region                   0.144672         0.143737  \n",
       "clust                    0.211451         0.211459  \n",
       "nh                      -0.059671        -0.060000  \n",
       "pid                      0.101816         0.099882  \n",
       "sex                     -0.011605        -0.011289  \n",
       "mining_region           -0.067555        -0.061316  \n",
       "age                     -0.034454        -0.033865  \n",
       "working_age             -0.051199        -0.050764  \n",
       "prime_age               -0.036858        -0.036870  \n",
       "male                     0.011605         0.011289  \n",
       "school_attendance        0.001409         0.002378  \n",
       "expenditure              0.029925         0.030555  \n",
       "expenditurepc           -0.036460        -0.035321  \n",
       "food_expend              0.005382         0.005782  \n",
       "food_expendpc           -0.057987        -0.057164  \n",
       "inc                      0.024616         0.024419  \n",
       "incpc                   -0.003889        -0.003853  \n",
       "rexpenditure            -0.058585        -0.057224  \n",
       "rexpenditurepc          -0.139929        -0.137568  \n",
       "rfood_expend            -0.089380        -0.088238  \n",
       "rfood_expendpc          -0.161696        -0.159448  \n",
       "rincpc                  -0.013467        -0.013318  \n",
       "poor                     0.204087         0.199150  \n",
       "poor_ext                 0.147389         0.142320  \n",
       "ln_rincpc               -0.011882        -0.009303  \n",
       "ln_rincequiv            -0.004419        -0.001913  \n",
       "ln_rexpenditurepc       -0.176028        -0.171066  \n",
       "...                           ...              ...  \n",
       "fcl05_sum_1km            0.115598         0.118804  \n",
       "fcl05_mean_1km           0.115426         0.118623  \n",
       "fcl06_sum_1km            0.158134         0.164165  \n",
       "fcl06_mean_1km           0.157965         0.163987  \n",
       "fcl07_sum_1km           -0.012234        -0.010689  \n",
       "fcl07_mean_1km          -0.012238        -0.010698  \n",
       "fcl08_sum_1km            0.149030         0.151341  \n",
       "fcl08_mean_1km           0.148760         0.151065  \n",
       "fcl09_sum_1km            0.080362         0.081274  \n",
       "fcl09_mean_1km           0.080248         0.081157  \n",
       "fcl10_sum_1km            0.073692         0.077646  \n",
       "fcl10_mean_1km           0.073826         0.077775  \n",
       "fcl11_sum_1km            0.006762         0.010264  \n",
       "fcl11_mean_1km           0.006712         0.010207  \n",
       "fcl12_sum_1km            0.156683         0.163130  \n",
       "fcl12_mean_1km           0.156468         0.162893  \n",
       "fcl13_sum_1km            0.035388         0.036903  \n",
       "fcl13_mean_1km           0.035104         0.036614  \n",
       "fcl14_sum_1km            0.093820         0.097775  \n",
       "fcl14_mean_1km           0.093825         0.097777  \n",
       "fcl15_sum_1km            0.143319         0.146985  \n",
       "fcl15_mean_1km           0.143230         0.146891  \n",
       "fcl16_sum_1km            0.076234         0.080978  \n",
       "fcl16_mean_1km           0.076170         0.080909  \n",
       "mp00_mean_10km           0.987966         0.985273  \n",
       "mp00_media_10km          0.987022         0.988158  \n",
       "mp00_min_10km            0.964367         0.955869  \n",
       "mp00_max_10km            0.959062         0.955853  \n",
       "mp01_mean_10km           1.000000         0.998277  \n",
       "mp01_media_10km          0.998277         1.000000  \n",
       "\n",
       "[182 rows x 182 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j1Exf8mqlyYR1OfS9OFVjdlRqaShOEMSMjymA3VrvIgmZPGTGQNrXcrWJeIbhgYq9J2J0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/eySs3RRj/0QwKRbFHyHAQsbURxbO55IURD7jDv55jZswg0yJplF7Gm4RuriE+sPV/xprs4cKbM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FyHQCeJ+u0FeRHXbN7daroUldw1zqLnjc2zxjDExMVxjqF3vaeK1a5EnNYyikw0Yjo69frrF1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\nAgAwd8IUACYSpgAwkTAFgImEKQBM9AHlK6HkYCI7hgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cd08aa1128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation Matrix\n",
    "corr2 = dfA.corr()\n",
    "corr = (corr2)\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)\n",
    "# sns.title('Heatmap of Correlation Matrix')\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['hhid', 'glss', 'year', 'region', 'clust', 'nh', 'pid', 'sex',\n",
       "       'mining_region', 'age',\n",
       "       ...\n",
       "       'fcl15_mean_1km', 'fcl16_sum_1km', 'fcl16_mean_1km', '_merge_glss_fcl1',\n",
       "       'mp00_mean_10km', 'mp00_media_10km', 'mp00_min_10km', 'mp00_max_10km',\n",
       "       'mp01_mean_10km', 'mp01_media_10km'],\n",
       "      dtype='object', length=190)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfA.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hhid                  True\n",
       "glss                 False\n",
       "year                  True\n",
       "region                True\n",
       "clust                False\n",
       "nh                    True\n",
       "pid                   True\n",
       "sex                   True\n",
       "mining_region         True\n",
       "age                   True\n",
       "working_age           True\n",
       "prime_age             True\n",
       "male                  True\n",
       "educ_attainment       True\n",
       "school_attendance     True\n",
       "expenditure           True\n",
       "expenditurepc         True\n",
       "food_expend           True\n",
       "food_expendpc         True\n",
       "inc                   True\n",
       "incpc                 True\n",
       "rexpenditure          True\n",
       "rexpenditurepc        True\n",
       "rfood_expend          True\n",
       "rfood_expendpc        True\n",
       "rincpc                True\n",
       "poor                  True\n",
       "poor_ext              True\n",
       "ln_rincpc             True\n",
       "ln_rincequiv          True\n",
       "                     ...  \n",
       "fcl05_mean_1km        True\n",
       "fcl06_sum_1km         True\n",
       "fcl06_mean_1km        True\n",
       "fcl07_sum_1km         True\n",
       "fcl07_mean_1km        True\n",
       "fcl08_sum_1km         True\n",
       "fcl08_mean_1km        True\n",
       "fcl09_sum_1km         True\n",
       "fcl09_mean_1km        True\n",
       "fcl10_sum_1km         True\n",
       "fcl10_mean_1km        True\n",
       "fcl11_sum_1km         True\n",
       "fcl11_mean_1km        True\n",
       "fcl12_sum_1km         True\n",
       "fcl12_mean_1km        True\n",
       "fcl13_sum_1km         True\n",
       "fcl13_mean_1km        True\n",
       "fcl14_sum_1km         True\n",
       "fcl14_mean_1km        True\n",
       "fcl15_sum_1km         True\n",
       "fcl15_mean_1km        True\n",
       "fcl16_sum_1km         True\n",
       "fcl16_mean_1km        True\n",
       "_merge_glss_fcl1     False\n",
       "mp00_mean_10km        True\n",
       "mp00_media_10km       True\n",
       "mp00_min_10km         True\n",
       "mp00_max_10km         True\n",
       "mp01_mean_10km        True\n",
       "mp01_media_10km       True\n",
       "Length: 190, dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check to see if there are any missing values in our data set\n",
    "dfA.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hhid</th>\n",
       "      <th>glss</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "      <th>clust</th>\n",
       "      <th>nh</th>\n",
       "      <th>pid</th>\n",
       "      <th>sex</th>\n",
       "      <th>mining_region</th>\n",
       "      <th>age</th>\n",
       "      <th>...</th>\n",
       "      <th>fcl15_sum_1km</th>\n",
       "      <th>fcl15_mean_1km</th>\n",
       "      <th>fcl16_sum_1km</th>\n",
       "      <th>fcl16_mean_1km</th>\n",
       "      <th>mp00_mean_10km</th>\n",
       "      <th>mp00_media_10km</th>\n",
       "      <th>mp00_min_10km</th>\n",
       "      <th>mp00_max_10km</th>\n",
       "      <th>mp01_mean_10km</th>\n",
       "      <th>mp01_media_10km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.362820e+05</td>\n",
       "      <td>136285.000000</td>\n",
       "      <td>136282.000000</td>\n",
       "      <td>136282.000000</td>\n",
       "      <td>136285.000000</td>\n",
       "      <td>136282.000000</td>\n",
       "      <td>136282.000000</td>\n",
       "      <td>136279.000000</td>\n",
       "      <td>136282.000000</td>\n",
       "      <td>136279.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>116054.000000</td>\n",
       "      <td>116054.000000</td>\n",
       "      <td>116054.000000</td>\n",
       "      <td>116054.000000</td>\n",
       "      <td>116054.000000</td>\n",
       "      <td>116054.000000</td>\n",
       "      <td>116054.000000</td>\n",
       "      <td>116054.000000</td>\n",
       "      <td>116054.000000</td>\n",
       "      <td>116054.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.381618e+06</td>\n",
       "      <td>5.339759</td>\n",
       "      <td>1639.784513</td>\n",
       "      <td>5.497637</td>\n",
       "      <td>34685.457915</td>\n",
       "      <td>8.695550</td>\n",
       "      <td>3.557234</td>\n",
       "      <td>1.517615</td>\n",
       "      <td>0.319030</td>\n",
       "      <td>24.304038</td>\n",
       "      <td>...</td>\n",
       "      <td>9.678538</td>\n",
       "      <td>0.002395</td>\n",
       "      <td>22.233434</td>\n",
       "      <td>0.005504</td>\n",
       "      <td>0.618455</td>\n",
       "      <td>0.620199</td>\n",
       "      <td>0.551027</td>\n",
       "      <td>0.676839</td>\n",
       "      <td>0.580516</td>\n",
       "      <td>0.581931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.875556e+06</td>\n",
       "      <td>0.782248</td>\n",
       "      <td>755.834710</td>\n",
       "      <td>2.769334</td>\n",
       "      <td>27771.615372</td>\n",
       "      <td>4.920157</td>\n",
       "      <td>2.579305</td>\n",
       "      <td>0.499691</td>\n",
       "      <td>0.466102</td>\n",
       "      <td>19.716641</td>\n",
       "      <td>...</td>\n",
       "      <td>21.271825</td>\n",
       "      <td>0.005265</td>\n",
       "      <td>44.461776</td>\n",
       "      <td>0.011009</td>\n",
       "      <td>0.132773</td>\n",
       "      <td>0.133177</td>\n",
       "      <td>0.148277</td>\n",
       "      <td>0.120186</td>\n",
       "      <td>0.150359</td>\n",
       "      <td>0.151142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4002.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.258173</td>\n",
       "      <td>0.274931</td>\n",
       "      <td>0.193264</td>\n",
       "      <td>0.334148</td>\n",
       "      <td>0.213610</td>\n",
       "      <td>0.229065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.140170e+05</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2005.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5140.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.566468</td>\n",
       "      <td>0.564576</td>\n",
       "      <td>0.462486</td>\n",
       "      <td>0.638744</td>\n",
       "      <td>0.505737</td>\n",
       "      <td>0.504594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.008306e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>60083.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.646940</td>\n",
       "      <td>0.646774</td>\n",
       "      <td>0.586854</td>\n",
       "      <td>0.700450</td>\n",
       "      <td>0.609653</td>\n",
       "      <td>0.610530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.072609e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>60726.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.002692</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.006439</td>\n",
       "      <td>0.703224</td>\n",
       "      <td>0.705198</td>\n",
       "      <td>0.652918</td>\n",
       "      <td>0.748308</td>\n",
       "      <td>0.673990</td>\n",
       "      <td>0.676075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.120015e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>61200.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>343.000000</td>\n",
       "      <td>0.084901</td>\n",
       "      <td>570.000000</td>\n",
       "      <td>0.140950</td>\n",
       "      <td>0.898326</td>\n",
       "      <td>0.899879</td>\n",
       "      <td>0.877845</td>\n",
       "      <td>0.932386</td>\n",
       "      <td>0.925346</td>\n",
       "      <td>0.923043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 182 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               hhid           glss           year         region  \\\n",
       "count  1.362820e+05  136285.000000  136282.000000  136282.000000   \n",
       "mean   3.381618e+06       5.339759    1639.784513       5.497637   \n",
       "std    2.875556e+06       0.782248     755.834710       2.769334   \n",
       "min    1.000000e+00       4.000000      98.000000       1.000000   \n",
       "25%    5.140170e+05       5.000000    2005.000000       3.000000   \n",
       "50%    6.008306e+06       6.000000    2012.000000       6.000000   \n",
       "75%    6.072609e+06       6.000000    2013.000000       8.000000   \n",
       "max    6.120015e+06       6.000000    2013.000000      10.000000   \n",
       "\n",
       "               clust             nh            pid            sex  \\\n",
       "count  136285.000000  136282.000000  136282.000000  136279.000000   \n",
       "mean    34685.457915       8.695550       3.557234       1.517615   \n",
       "std     27771.615372       4.920157       2.579305       0.499691   \n",
       "min      4002.000000       1.000000       1.000000       1.000000   \n",
       "25%      5140.000000       5.000000       2.000000       1.000000   \n",
       "50%     60083.000000       9.000000       3.000000       2.000000   \n",
       "75%     60726.000000      13.000000       5.000000       2.000000   \n",
       "max     61200.000000      26.000000      30.000000       2.000000   \n",
       "\n",
       "       mining_region            age       ...         fcl15_sum_1km  \\\n",
       "count  136282.000000  136279.000000       ...         116054.000000   \n",
       "mean        0.319030      24.304038       ...              9.678538   \n",
       "std         0.466102      19.716641       ...             21.271825   \n",
       "min         0.000000       0.000000       ...              0.000000   \n",
       "25%         0.000000       9.000000       ...              0.000000   \n",
       "50%         0.000000      19.000000       ...              0.000000   \n",
       "75%         1.000000      36.000000       ...             11.000000   \n",
       "max         1.000000      99.000000       ...            343.000000   \n",
       "\n",
       "       fcl15_mean_1km  fcl16_sum_1km  fcl16_mean_1km  mp00_mean_10km  \\\n",
       "count   116054.000000  116054.000000   116054.000000   116054.000000   \n",
       "mean         0.002395      22.233434        0.005504        0.618455   \n",
       "std          0.005265      44.461776        0.011009        0.132773   \n",
       "min          0.000000       0.000000        0.000000        0.258173   \n",
       "25%          0.000000       0.000000        0.000000        0.566468   \n",
       "50%          0.000000       0.000000        0.000000        0.646940   \n",
       "75%          0.002692      26.000000        0.006439        0.703224   \n",
       "max          0.084901     570.000000        0.140950        0.898326   \n",
       "\n",
       "       mp00_media_10km  mp00_min_10km  mp00_max_10km  mp01_mean_10km  \\\n",
       "count    116054.000000  116054.000000  116054.000000   116054.000000   \n",
       "mean          0.620199       0.551027       0.676839        0.580516   \n",
       "std           0.133177       0.148277       0.120186        0.150359   \n",
       "min           0.274931       0.193264       0.334148        0.213610   \n",
       "25%           0.564576       0.462486       0.638744        0.505737   \n",
       "50%           0.646774       0.586854       0.700450        0.609653   \n",
       "75%           0.705198       0.652918       0.748308        0.673990   \n",
       "max           0.899879       0.877845       0.932386        0.925346   \n",
       "\n",
       "       mp01_media_10km  \n",
       "count    116054.000000  \n",
       "mean          0.581931  \n",
       "std           0.151142  \n",
       "min           0.229065  \n",
       "25%           0.504594  \n",
       "50%           0.610530  \n",
       "75%           0.676075  \n",
       "max           0.923043  \n",
       "\n",
       "[8 rows x 182 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfA.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hhid                 float64\n",
       "glss                   int64\n",
       "year                 float64\n",
       "region               float64\n",
       "clust                  int64\n",
       "nh                   float64\n",
       "pid                  float64\n",
       "sex                  float64\n",
       "mining_region        float64\n",
       "age                  float64\n",
       "working_age          float64\n",
       "prime_age            float64\n",
       "male                 float64\n",
       "educ_attainment       object\n",
       "school_attendance    float64\n",
       "expenditure          float64\n",
       "expenditurepc        float64\n",
       "food_expend          float64\n",
       "food_expendpc        float64\n",
       "inc                  float64\n",
       "incpc                float64\n",
       "rexpenditure         float64\n",
       "rexpenditurepc       float64\n",
       "rfood_expend         float64\n",
       "rfood_expendpc       float64\n",
       "rincpc               float64\n",
       "poor                 float64\n",
       "poor_ext             float64\n",
       "ln_rincpc            float64\n",
       "ln_rincequiv         float64\n",
       "                      ...   \n",
       "fcl05_mean_1km       float64\n",
       "fcl06_sum_1km        float64\n",
       "fcl06_mean_1km       float64\n",
       "fcl07_sum_1km        float64\n",
       "fcl07_mean_1km       float64\n",
       "fcl08_sum_1km        float64\n",
       "fcl08_mean_1km       float64\n",
       "fcl09_sum_1km        float64\n",
       "fcl09_mean_1km       float64\n",
       "fcl10_sum_1km        float64\n",
       "fcl10_mean_1km       float64\n",
       "fcl11_sum_1km        float64\n",
       "fcl11_mean_1km       float64\n",
       "fcl12_sum_1km        float64\n",
       "fcl12_mean_1km       float64\n",
       "fcl13_sum_1km        float64\n",
       "fcl13_mean_1km       float64\n",
       "fcl14_sum_1km        float64\n",
       "fcl14_mean_1km       float64\n",
       "fcl15_sum_1km        float64\n",
       "fcl15_mean_1km       float64\n",
       "fcl16_sum_1km        float64\n",
       "fcl16_mean_1km       float64\n",
       "_merge_glss_fcl1      object\n",
       "mp00_mean_10km       float64\n",
       "mp00_media_10km      float64\n",
       "mp00_min_10km        float64\n",
       "mp00_max_10km        float64\n",
       "mp01_mean_10km       float64\n",
       "mp01_media_10km      float64\n",
       "Length: 190, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfA.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfA.to_csv('200FeatureRedMainData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(136285, 190)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfA.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping Geodata\n",
    "\n",
    "We are dropping all geographical data, so we do consider it in the unsupervised clustering that we are going to perform later on the data. The clustering should be based on other features than location. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfB = dfA.drop(['hhid', 'region', 'clust', 'nh', 'pid', 'x', 'y', 'eanum', 'reg_code', 'dist_name', 'reg_name'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(136285, 179)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfB.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA\n",
    "\n",
    "We want to further reduce these 179 columns with PCA so we have a smaller number of features to work with. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this, we will use a dataset that already mapped categorical columns to numerical ones from the dataset above but still have the 700 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hhid</th>\n",
       "      <th>glss</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "      <th>clust</th>\n",
       "      <th>nh</th>\n",
       "      <th>pid</th>\n",
       "      <th>sex</th>\n",
       "      <th>mining_region</th>\n",
       "      <th>age</th>\n",
       "      <th>...</th>\n",
       "      <th>nl_2011_sum_1km</th>\n",
       "      <th>nl_2011_mean_1km</th>\n",
       "      <th>nl_2011_media_1km</th>\n",
       "      <th>nl_2012_sum_1km</th>\n",
       "      <th>nl_2012_mean_1km</th>\n",
       "      <th>nl_2012_media_1km</th>\n",
       "      <th>nl_2013_sum_1km</th>\n",
       "      <th>nl_2013_mean_1km</th>\n",
       "      <th>nl_2013_media_1km</th>\n",
       "      <th>_merge_glss_mal_nl1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 698 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   hhid  glss  year  region  clust   nh  pid  sex  mining_region   age  \\\n",
       "0   1.0     4  98.0     1.0   4002  1.0  4.0  1.0            1.0  22.0   \n",
       "1   1.0     4  98.0     1.0   4002  1.0  6.0  1.0            1.0  17.0   \n",
       "2   1.0     4  98.0     1.0   4002  1.0  1.0  1.0            1.0  57.0   \n",
       "3   1.0     4  98.0     1.0   4002  1.0  2.0  2.0            1.0  51.0   \n",
       "4   1.0     4  98.0     1.0   4002  1.0  7.0  1.0            1.0  16.0   \n",
       "\n",
       "          ...           nl_2011_sum_1km  nl_2011_mean_1km  nl_2011_media_1km  \\\n",
       "0         ...                      12.0               4.0                4.0   \n",
       "1         ...                      12.0               4.0                4.0   \n",
       "2         ...                      12.0               4.0                4.0   \n",
       "3         ...                      12.0               4.0                4.0   \n",
       "4         ...                      12.0               4.0                4.0   \n",
       "\n",
       "   nl_2012_sum_1km  nl_2012_mean_1km  nl_2012_media_1km  nl_2013_sum_1km  \\\n",
       "0             10.0          3.333333                5.0             10.0   \n",
       "1             10.0          3.333333                5.0             10.0   \n",
       "2             10.0          3.333333                5.0             10.0   \n",
       "3             10.0          3.333333                5.0             10.0   \n",
       "4             10.0          3.333333                5.0             10.0   \n",
       "\n",
       "   nl_2013_mean_1km  nl_2013_media_1km  _merge_glss_mal_nl1  \n",
       "0          3.333333                5.0                    3  \n",
       "1          3.333333                5.0                    3  \n",
       "2          3.333333                5.0                    3  \n",
       "3          3.333333                5.0                    3  \n",
       "4          3.333333                5.0                    3  \n",
       "\n",
       "[5 rows x 698 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataset location\n",
    "maindb = \"glss4_5_6_alignedfclmal_nl_nolabels.csv\"\n",
    "\n",
    "# Read in a CSV file and store the contents in a dataframe (df)\n",
    "dfnstr = pd.read_csv(maindb, low_memory=False, sep=',')\n",
    "dfnstr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfnstrB = dfnstr.drop(['hhid', 'region', 'clust', 'nh', 'pid', 'x', 'y', 'eanum', 'reg_code'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>glss</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>mining_region</th>\n",
       "      <th>age</th>\n",
       "      <th>working_age</th>\n",
       "      <th>prime_age</th>\n",
       "      <th>male</th>\n",
       "      <th>educ_attainment</th>\n",
       "      <th>school_attendance</th>\n",
       "      <th>...</th>\n",
       "      <th>nl_2011_sum_1km</th>\n",
       "      <th>nl_2011_mean_1km</th>\n",
       "      <th>nl_2011_media_1km</th>\n",
       "      <th>nl_2012_sum_1km</th>\n",
       "      <th>nl_2012_mean_1km</th>\n",
       "      <th>nl_2012_media_1km</th>\n",
       "      <th>nl_2013_sum_1km</th>\n",
       "      <th>nl_2013_mean_1km</th>\n",
       "      <th>nl_2013_media_1km</th>\n",
       "      <th>_merge_glss_mal_nl1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
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       "      <td>22.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 689 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   glss  year  sex  mining_region   age  working_age  prime_age  male  \\\n",
       "0     4  98.0  1.0            1.0  22.0          1.0        1.0   1.0   \n",
       "1     4  98.0  1.0            1.0  17.0          1.0        0.0   1.0   \n",
       "2     4  98.0  1.0            1.0  57.0          1.0        0.0   1.0   \n",
       "3     4  98.0  2.0            1.0  51.0          1.0        0.0   0.0   \n",
       "4     4  98.0  1.0            1.0  16.0          1.0        0.0   1.0   \n",
       "\n",
       "   educ_attainment  school_attendance         ...           nl_2011_sum_1km  \\\n",
       "0              4.0                0.0         ...                      12.0   \n",
       "1              1.0                1.0         ...                      12.0   \n",
       "2              1.0                0.0         ...                      12.0   \n",
       "3              0.0                0.0         ...                      12.0   \n",
       "4              1.0                1.0         ...                      12.0   \n",
       "\n",
       "   nl_2011_mean_1km  nl_2011_media_1km  nl_2012_sum_1km  nl_2012_mean_1km  \\\n",
       "0               4.0                4.0             10.0          3.333333   \n",
       "1               4.0                4.0             10.0          3.333333   \n",
       "2               4.0                4.0             10.0          3.333333   \n",
       "3               4.0                4.0             10.0          3.333333   \n",
       "4               4.0                4.0             10.0          3.333333   \n",
       "\n",
       "   nl_2012_media_1km  nl_2013_sum_1km  nl_2013_mean_1km  nl_2013_media_1km  \\\n",
       "0                5.0             10.0          3.333333                5.0   \n",
       "1                5.0             10.0          3.333333                5.0   \n",
       "2                5.0             10.0          3.333333                5.0   \n",
       "3                5.0             10.0          3.333333                5.0   \n",
       "4                5.0             10.0          3.333333                5.0   \n",
       "\n",
       "   _merge_glss_mal_nl1  \n",
       "0                    3  \n",
       "1                    3  \n",
       "2                    3  \n",
       "3                    3  \n",
       "4                    3  \n",
       "\n",
       "[5 rows x 689 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfnstrC = dfnstrB\n",
    "dfnstrC[np.isnan(dfnstrC)] = -1\n",
    "dfnstrC.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca = PCA()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(dfnstrC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  2.60494242e+09   3.40960642e+08   2.52244990e+08   1.26751399e+08\n",
      "   8.64040920e+07   5.09174655e+07   4.10121904e+07   6.86686849e+06\n",
      "   5.55193694e+06   4.44835377e+06   3.83595912e+06   3.62698988e+06\n",
      "   3.26923470e+06   2.88524033e+06   2.44530594e+06   2.36152933e+06\n",
      "   2.07585314e+06   1.58884025e+06   1.42685241e+06   1.19522759e+06\n",
      "   9.07583453e+05   8.23777499e+05   7.07195713e+05   6.24843343e+05\n",
      "   5.59324917e+05   4.14921879e+05   4.01747909e+05   3.73775138e+05\n",
      "   3.00941596e+05   2.28964288e+05   1.70609260e+05   1.36585912e+05\n",
      "   1.34156023e+05   1.27650717e+05   1.10343623e+05   9.98323447e+04\n",
      "   9.92518009e+04   9.64462838e+04   8.50971541e+04   8.02167135e+04\n",
      "   7.16132659e+04   6.14821305e+04   5.69131542e+04   4.52665435e+04\n",
      "   3.84312280e+04   3.28908666e+04   3.14213505e+04   2.74856666e+04\n",
      "   2.40613492e+04   2.21638360e+04   1.92726195e+04   1.65085314e+04\n",
      "   1.55314139e+04   1.32109475e+04   1.15137324e+04   1.03902748e+04\n",
      "   1.01441915e+04   9.01742921e+03   8.02711535e+03   6.74672197e+03\n",
      "   6.02853685e+03   5.67076891e+03   4.98001412e+03   4.37596711e+03\n",
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      "   2.30931006e+03   1.64309516e+03   1.39681137e+03   1.25257808e+03\n",
      "   1.04922400e+03   6.95335895e+02   6.67339556e+02   6.47858829e+02\n",
      "   5.57477029e+02   5.22491097e+02   4.95192852e+02   3.91291285e+02\n",
      "   3.66517633e+02   3.57892050e+02   3.31007882e+02   3.15955201e+02\n",
      "   2.87371240e+02   2.46202398e+02   2.43502080e+02   2.39817733e+02\n",
      "   2.38249926e+02   2.02969858e+02   1.78861079e+02   1.38581959e+02\n",
      "   1.27036220e+02   9.55892670e+01   8.96076937e+01   6.85045702e+01\n",
      "   6.55984474e+01   6.07908682e+01   5.43323071e+01   5.01250286e+01\n",
      "   3.21525312e+01   3.00696203e+01   1.85715669e+01   1.71450291e+01\n",
      "   1.41539153e+01   1.31500195e+01   1.14372411e+01   9.11843405e+00\n",
      "   8.93786014e+00   7.39290688e+00   6.93805592e+00   6.35409787e+00\n",
      "   6.25003736e+00   5.85309678e+00   5.67851934e+00   5.27268311e+00\n",
      "   4.91331239e+00   4.76768146e+00   4.22405543e+00   3.71063269e+00\n",
      "   3.16371602e+00   2.95897979e+00   2.13108277e+00   2.03088980e+00\n",
      "   1.49849987e+00   1.46216792e+00   1.31875447e+00   1.15819611e+00\n",
      "   9.80980958e-01   7.79939072e-01   7.37845440e-01   6.97748076e-01\n",
      "   6.44464764e-01   6.17538425e-01   5.89387777e-01   5.43249586e-01\n",
      "   5.06217282e-01   4.87251376e-01   4.72898750e-01   4.66184646e-01\n",
      "   4.42362389e-01   4.23393892e-01   3.91564734e-01   3.81518252e-01\n",
      "   3.49745172e-01   3.31946596e-01   3.30333376e-01   3.24318285e-01\n",
      "   3.23513992e-01   2.99208290e-01   2.93064207e-01   2.79161153e-01\n",
      "   2.65743553e-01   2.50752494e-01   2.43252405e-01   2.38663833e-01\n",
      "   2.35783522e-01   2.21829895e-01   2.15006644e-01   2.09553869e-01\n",
      "   1.98380032e-01   1.90365466e-01   1.86671765e-01   1.73464460e-01\n",
      "   1.66897428e-01   1.55378939e-01   1.44110882e-01   1.40628134e-01\n",
      "   1.32545635e-01   1.30908517e-01   1.28717759e-01   1.27249274e-01\n",
      "   1.23135525e-01   1.18194850e-01   1.15352985e-01   1.10021793e-01\n",
      "   1.06597533e-01   1.01984485e-01   9.77532876e-02   9.35836209e-02\n",
      "   8.96087975e-02   8.77097849e-02   8.47324458e-02   8.31232679e-02\n",
      "   8.00562760e-02   7.89315652e-02   7.54553394e-02   7.49292150e-02\n",
      "   7.08281884e-02   6.67048744e-02   6.55587081e-02   6.22168565e-02\n",
      "   5.43839063e-02   5.23538372e-02   5.07564823e-02   4.73344399e-02\n",
      "   4.24037021e-02   4.14664206e-02   3.98908767e-02   3.36819337e-02\n",
      "   3.22222056e-02   2.94272096e-02   2.91525488e-02   2.68704738e-02\n",
      "   2.57162508e-02   2.51313408e-02   1.97038520e-02   1.92459694e-02\n",
      "   1.86643535e-02   1.76100783e-02   1.58738093e-02   1.34899384e-02\n",
      "   1.28669289e-02   1.26020513e-02   1.16972202e-02   1.11821418e-02\n",
      "   1.08164169e-02   1.05301850e-02   1.00268195e-02   9.55782787e-03\n",
      "   9.16926252e-03   8.59003632e-03   7.87698134e-03   6.74753779e-03\n",
      "   6.02663775e-03   4.88250334e-03   4.76980683e-03   4.06736486e-03\n",
      "   3.91566014e-03   3.44509799e-03   3.30623270e-03   3.06406473e-03\n",
      "   2.61486972e-03   2.37399489e-03   2.26752019e-03   1.78205910e-03\n",
      "   1.42586984e-03   1.34628044e-03   1.12078168e-03   8.96015196e-04\n",
      "   8.13968944e-04   7.85929427e-04   6.77303182e-04   6.48876944e-04\n",
      "   6.08884359e-04   5.46889439e-04   4.34108657e-04   3.98275119e-04\n",
      "   3.79516296e-04   3.26385892e-04   3.07591031e-04   2.98895399e-04\n",
      "   2.72408621e-04   2.58193624e-04   2.38690998e-04   2.37157801e-04\n",
      "   2.15934261e-04   2.08290660e-04   1.97825982e-04   1.84386168e-04\n",
      "   1.78160692e-04   1.60476465e-04   1.52553508e-04   1.42193600e-04\n",
      "   1.41064371e-04   1.31532472e-04   1.25670857e-04   1.20541813e-04\n",
      "   1.16337773e-04   1.11057115e-04   1.04384130e-04   9.93083153e-05\n",
      "   9.25246483e-05   8.97387594e-05   8.44639477e-05   8.28494670e-05\n",
      "   7.95082638e-05   7.55463410e-05   7.23301935e-05   6.87060811e-05\n",
      "   6.55327443e-05   5.85387252e-05   5.71150625e-05   5.52912798e-05\n",
      "   5.38005317e-05   5.21311028e-05   5.02971347e-05   4.92036120e-05\n",
      "   4.62658816e-05   4.54662932e-05   4.46830173e-05   4.37920231e-05\n",
      "   4.14136655e-05   4.03082810e-05   3.77248833e-05   3.67423760e-05\n",
      "   3.52965611e-05   3.36289121e-05   3.28274381e-05   3.27411627e-05\n",
      "   3.10983202e-05   3.09683412e-05   3.00662968e-05   2.82622188e-05\n",
      "   2.74498192e-05   2.68573925e-05   2.56563668e-05   2.48878502e-05\n",
      "   2.36909300e-05   2.26233219e-05   2.16172667e-05   2.07605013e-05\n",
      "   2.02736932e-05   1.97300627e-05   1.90499871e-05   1.85025446e-05\n",
      "   1.81610853e-05   1.75304812e-05   1.68363307e-05   1.63933437e-05\n",
      "   1.63156091e-05   1.56361341e-05   1.50735280e-05   1.50049387e-05\n",
      "   1.41025982e-05   1.36090127e-05   1.35179167e-05   1.32658534e-05\n",
      "   1.26472179e-05   1.22284339e-05   1.15159362e-05   1.12161924e-05\n",
      "   1.08476191e-05   1.04351343e-05   1.03214206e-05   9.92538391e-06\n",
      "   9.63493178e-06   9.30680521e-06   9.22302500e-06   9.01999625e-06\n",
      "   8.47241628e-06   8.40169081e-06   7.87852943e-06   7.65883769e-06\n",
      "   7.50446809e-06   7.32746414e-06   7.23900181e-06   6.97568122e-06\n",
      "   6.86049160e-06   6.50029036e-06   6.44230451e-06   6.26136738e-06\n",
      "   6.20007294e-06   5.98278740e-06   5.90566866e-06   5.72618513e-06\n",
      "   5.59221729e-06   5.51502882e-06   5.50234138e-06   5.32390482e-06\n",
      "   5.17367716e-06   4.94498381e-06   4.82475338e-06   4.76716901e-06\n",
      "   4.72584943e-06   4.60685451e-06   4.54433853e-06   4.50926261e-06\n",
      "   4.34979477e-06   4.31478396e-06   4.22361959e-06   4.12299994e-06\n",
      "   3.95726596e-06   3.91895456e-06   3.84197419e-06   3.73626630e-06\n",
      "   3.65244631e-06   3.57054638e-06   3.46302225e-06   3.42543089e-06\n",
      "   3.41156695e-06   3.30669984e-06   3.22839043e-06   3.14204434e-06\n",
      "   3.11099947e-06   3.06388093e-06   2.96711877e-06   2.94016567e-06\n",
      "   2.89076955e-06   2.77113037e-06   2.72466353e-06   2.70993106e-06\n",
      "   2.69227735e-06   2.57936902e-06   2.49548819e-06   2.48649565e-06\n",
      "   2.45764730e-06   2.41040557e-06   2.25888183e-06   2.22533162e-06\n",
      "   2.21000126e-06   2.13642579e-06   2.09414027e-06   2.08398746e-06\n",
      "   2.04177198e-06   2.02167516e-06   1.97085112e-06   1.94547711e-06\n",
      "   1.92288582e-06   1.89158674e-06   1.85266116e-06   1.80800793e-06\n",
      "   1.76454108e-06   1.71291980e-06   1.66972251e-06   1.66464981e-06\n",
      "   1.65021871e-06   1.62193762e-06   1.60178916e-06   1.56431895e-06\n",
      "   1.53372133e-06   1.48810557e-06   1.47194143e-06   1.44271972e-06\n",
      "   1.42351244e-06   1.38382918e-06   1.35276567e-06   1.33415247e-06\n",
      "   1.30058226e-06   1.28610025e-06   1.25363526e-06   1.21902862e-06\n",
      "   1.20905000e-06   1.19694040e-06   1.15834972e-06   1.12092125e-06\n",
      "   1.10992431e-06   1.10202324e-06   1.05438556e-06   1.03572738e-06\n",
      "   1.01739833e-06   9.93300381e-07   9.75015646e-07   9.63546948e-07\n",
      "   9.40934981e-07   9.18124696e-07   8.94292537e-07   8.75394275e-07\n",
      "   8.49917132e-07   8.23649135e-07   7.95941753e-07   7.81744970e-07\n",
      "   7.66737168e-07   7.35414660e-07   7.27021607e-07   7.12647198e-07\n",
      "   6.84944709e-07   6.79815597e-07   6.48630027e-07   6.43928581e-07\n",
      "   6.15664954e-07   6.07228898e-07   5.89665403e-07   5.82942788e-07\n",
      "   5.45436336e-07   5.37658068e-07   5.29146592e-07   5.24713970e-07\n",
      "   5.11032006e-07   5.05796761e-07   4.72781888e-07   4.65062886e-07\n",
      "   4.49231566e-07   4.36938531e-07   4.28229851e-07   4.17952403e-07\n",
      "   4.08771718e-07   4.00790033e-07   3.96269375e-07   3.84152033e-07\n",
      "   3.77931582e-07   3.70381296e-07   3.55380100e-07   3.50055006e-07\n",
      "   3.40708601e-07   3.36601571e-07   3.30131672e-07   3.22141684e-07\n",
      "   3.16615994e-07   3.12012412e-07   3.05712921e-07   3.00583824e-07\n",
      "   2.90204747e-07   2.85839548e-07   2.78991793e-07   2.72726273e-07\n",
      "   2.70688104e-07   2.67066897e-07   2.57169193e-07   2.54160278e-07\n",
      "   2.47361578e-07   2.40151658e-07   2.37719461e-07   2.31159796e-07\n",
      "   2.25944776e-07   2.20325015e-07   2.16827929e-07   2.13638082e-07\n",
      "   2.12262455e-07   2.06994025e-07   2.05833159e-07   2.01379193e-07\n",
      "   1.94268713e-07   1.91909933e-07   1.87764508e-07   1.84457605e-07\n",
      "   1.82389004e-07   1.74918761e-07   1.74476528e-07   1.67562214e-07\n",
      "   1.63124996e-07   1.60684173e-07   1.54731191e-07   1.52467702e-07\n",
      "   1.50025505e-07   1.46275742e-07   1.44687114e-07   1.37467032e-07\n",
      "   1.37127250e-07   1.28251241e-07   1.26770491e-07   1.20976075e-07\n",
      "   1.16645190e-07   1.15185821e-07   1.13905534e-07   1.12100622e-07\n",
      "   1.08874866e-07   1.06364837e-07   1.04533444e-07   1.02363511e-07\n",
      "   9.33503918e-08   9.20693686e-08   8.79507251e-08   8.71580789e-08\n",
      "   8.32372810e-08   8.11741190e-08   7.94626090e-08   7.89145242e-08\n",
      "   7.57172909e-08   7.45824100e-08   7.11038821e-08   6.98394215e-08\n",
      "   6.81579184e-08   6.28896303e-08   6.24266513e-08   5.98223790e-08\n",
      "   5.71368306e-08   5.47380508e-08   5.29600312e-08   4.91593571e-08\n",
      "   4.87438736e-08   4.60935615e-08   4.42910226e-08   4.36792678e-08\n",
      "   4.14658738e-08   3.98341021e-08   3.80911072e-08   3.69114336e-08\n",
      "   3.55060548e-08   3.44572249e-08   3.25754736e-08   3.15905463e-08\n",
      "   2.99595803e-08   2.95598627e-08   2.89370503e-08   2.67012507e-08\n",
      "   2.56519146e-08   2.38968615e-08   2.36398105e-08   2.19162099e-08\n",
      "   2.13085850e-08   2.06291343e-08   2.01330319e-08   1.98246480e-08\n",
      "   1.84542140e-08   1.73878731e-08   1.67502733e-08   1.62953848e-08\n",
      "   1.55653917e-08   1.50664375e-08   1.43440704e-08   1.37514082e-08\n",
      "   1.35054833e-08   1.32721333e-08   1.22719945e-08   1.18888182e-08\n",
      "   1.12176494e-08   1.02499937e-08   9.08778372e-09   8.13632798e-09\n",
      "   7.65305025e-09   7.16036792e-09   6.87739880e-09   6.17840412e-09\n",
      "   5.74627296e-09   5.40344056e-09   5.03168722e-09   4.28057466e-09\n",
      "   4.16277475e-09   3.70296615e-09   3.42841935e-09   3.28025739e-09\n",
      "   3.19791681e-09   3.07403175e-09   2.75978389e-09   2.62112364e-09\n",
      "   2.22454762e-09   2.16460623e-09   2.01874759e-09   1.97556463e-09\n",
      "   1.76619256e-09   1.47267604e-09   1.32171805e-09   1.17239534e-09\n",
      "   1.13191773e-09   9.83119762e-10   8.30345041e-10   6.59811882e-10\n",
      "   5.88584618e-10   5.32921821e-10   4.63781879e-10   3.62481594e-10\n",
      "   3.03838355e-10   2.36270452e-10   1.83260483e-10   1.57618046e-10\n",
      "   1.28017347e-10   9.85218147e-11   8.79989098e-11   8.57127715e-11\n",
      "   6.45765247e-11   6.17164703e-11   4.79412100e-11   4.67128874e-11\n",
      "   3.72447068e-11   3.42770950e-11   2.60127766e-11   2.35010251e-11\n",
      "   2.09646795e-11   2.00373192e-11   1.77968453e-11   1.77489535e-11\n",
      "   1.59896624e-11   1.43272319e-11   1.32309704e-11   1.26343635e-11\n",
      "   1.00490402e-11   9.64142432e-12   9.14940776e-12   8.25779927e-12\n",
      "   6.48373114e-12   5.43164589e-12   4.89357871e-12   4.22769567e-12\n",
      "   3.55161856e-12   3.04106302e-12   2.89524546e-12   2.38697758e-12\n",
      "   2.16357999e-12   2.05027941e-12   1.58825036e-12   1.36892916e-12\n",
      "   1.29565793e-12   1.07368096e-12   8.00080772e-13   7.39195962e-13\n",
      "   6.68327328e-13   3.97361174e-13   3.06710945e-13   1.87054442e-13\n",
      "   5.13734308e-22   1.79100795e-23   1.79100795e-23   1.79100795e-23\n",
      "   1.79100795e-23   1.79100795e-23   1.79100795e-23   1.79100795e-23\n",
      "   1.79100795e-23]\n"
     ]
    }
   ],
   "source": [
    "print(pca.explained_variance_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "107"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We want to figure out how many columns according to PCA provide relevant information by thresholding on the variance. Here, the threshold is put at 10, but is subject to variation.\n",
    "sum(pca.explained_variance_ > 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(136285, 107)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reduce the number of columns of above data frame to 107\n",
    "pca.n_components = 107\n",
    "X_reduced = pca.fit_transform(dfnstrC)\n",
    "X_reduced.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  4.18396275  98.00156722   1.5155357  ...,   2.68044243   2.70463621\n",
      "    3.14120329]\n",
      " [  4.18115862  98.00094827   1.50518693 ...,   2.67806941   2.70143721\n",
      "    3.14392034]\n",
      " [  4.18507327  97.99910831   1.53027573 ...,   2.67794608   2.69997958\n",
      "    3.14897331]\n",
      " ..., \n",
      " [  4.25359572  -1.00499604   1.47006808 ...,   0.52501747   0.48457417\n",
      "    2.67827202]\n",
      " [  4.18921793  -1.00471633   1.4727807  ...,   0.32639574   0.22217786\n",
      "    2.91822035]\n",
      " [  4.19175905  -1.00393837   1.46355337 ...,   5.20646775   5.13727058\n",
      "    2.8061952 ]]\n"
     ]
    }
   ],
   "source": [
    "# To map it back to the original form, i.e. 689 columns\n",
    "X_redinverse = pca.inverse_transform(X_reduced)\n",
    "X_redinverse.shape\n",
    "print(X_redinverse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## K-Means Clustering\n",
    "\n",
    "### The following code is only pasted as placeholder and not tested yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_samples 136285, \t n_features 107\n"
     ]
    },
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-31-1ecf7cb90b44>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     68\u001b[0m \u001b[0mx_min\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreduced_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreduced_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m \u001b[0my_min\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreduced_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreduced_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 70\u001b[1;33m \u001b[0mxx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmeshgrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_min\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_min\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     71\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     72\u001b[0m \u001b[1;31m# Obtain labels for each point in mesh. Use last trained model.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36mmeshgrid\u001b[1;34m(*xi, **kwargs)\u001b[0m\n\u001b[0;32m   4677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4678\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcopy_\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4679\u001b[1;33m         \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4680\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4681\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m   4677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4678\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcopy_\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4679\u001b[1;33m         \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4680\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4681\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#from time import time\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.preprocessing import scale\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "\n",
    "n_samples, n_features = X_reduced.shape\n",
    "#n_digits = len(np.unique(digits.target))\n",
    "#labels = digits.target\n",
    "n_digits = 2\n",
    "\n",
    "\n",
    "sample_size = 300\n",
    "\n",
    "print(\"n_samples %d, \\t n_features %d\"\n",
    "      % (n_samples, n_features))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def bench_k_means(estimator, name, data):\n",
    "    t0 = time()\n",
    "    estimator.fit(data)\n",
    "'''    \n",
    "    print('%-9s\\t%.2fs\\t%i\\t%.3f\\t%.3f\\t%.3f\\t%.3f\\t%.3f\\t%.3f'\n",
    "          % (name, (time() - t0), estimator.inertia_,\n",
    "             metrics.homogeneity_score(labels, estimator.labels_),\n",
    "             metrics.completeness_score(labels, estimator.labels_),\n",
    "             metrics.v_measure_score(labels, estimator.labels_),\n",
    "             metrics.adjusted_rand_score(labels, estimator.labels_),\n",
    "             metrics.adjusted_mutual_info_score(labels,  estimator.labels_),\n",
    "             metrics.silhouette_score(data, estimator.labels_,\n",
    "                                      metric='euclidean',\n",
    "                                      sample_size=sample_size)))\n",
    "\n",
    "\n",
    "\n",
    "bench_k_means(KMeans(init='k-means++', n_clusters=n_digits, n_init=10),\n",
    "              name=\"k-means++\", data=data)\n",
    "\n",
    "bench_k_means(KMeans(init='random', n_clusters=n_digits, n_init=10),\n",
    "              name=\"random\", data=data)\n",
    "\n",
    "# in this case the seeding of the centers is deterministic, hence we run the\n",
    "# kmeans algorithm only once with n_init=1\n",
    "pca = PCA(n_components=n_digits).fit(data)\n",
    "bench_k_means(KMeans(init=pca.components_, n_clusters=n_digits, n_init=1),\n",
    "              name=\"PCA-based\",\n",
    "              data=data)\n",
    "print(82 * '_')\n",
    "'''\n",
    "# #############################################################################\n",
    "# Visualize the results on PCA-reduced data\n",
    "\n",
    "reduced_data = PCA(n_components=2).fit_transform(X_reduced)\n",
    "kmeans = KMeans(init='k-means++', n_clusters=n_digits, n_init=10)\n",
    "kmeans.fit(reduced_data)\n",
    "\n",
    "# Step size of the mesh. Decrease to increase the quality of the VQ.\n",
    "h = .02     # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "\n",
    "\n",
    "# Plot the decision boundary. For that, we will assign a color to each\n",
    "x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1\n",
    "y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "# Obtain labels for each point in mesh. Use last trained model.\n",
    "Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(1)\n",
    "plt.clf()\n",
    "plt.imshow(Z, interpolation='nearest',\n",
    "           extent=(xx.min(), xx.max(), yy.min(), yy.max()),\n",
    "           cmap=plt.cm.Paired,\n",
    "           aspect='auto', origin='lower')\n",
    "\n",
    "plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)\n",
    "# Plot the centroids as a white X\n",
    "centroids = kmeans.cluster_centers_\n",
    "plt.scatter(centroids[:, 0], centroids[:, 1],\n",
    "            marker='x', s=169, linewidths=3,\n",
    "            color='w', zorder=10)\n",
    "plt.title('K-means clustering on the digits dataset (PCA-reduced data)\\n'\n",
    "          'Centroids are marked with white cross')\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.show()\n"
   ]
  },
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