GDP_DataAnalysis_Indian_States.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Start of Part 1A: GSDP (Gross State Domestic Product) data analysis for the states and union territories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Import the python libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Read the CSV data file provided for GSDP analysis\n",
    "gsdp_df=pd.read_csv(\"./Part1A/ab40c054-5031-4376-b52e-9813e776f65e.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Items  Description</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <th>Assam</th>\n",
       "      <th>Bihar</th>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <th>Goa</th>\n",
       "      <th>Gujarat</th>\n",
       "      <th>Haryana</th>\n",
       "      <th>...</th>\n",
       "      <th>Telangana</th>\n",
       "      <th>Tripura</th>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <th>Uttarakhand</th>\n",
       "      <th>West Bengal1</th>\n",
       "      <th>Andaman &amp; Nicobar Islands</th>\n",
       "      <th>Chandigarh</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Puducherry</th>\n",
       "      <th>All_India GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2011-12</td>\n",
       "      <td>379402.0</td>\n",
       "      <td>11063.0</td>\n",
       "      <td>143175.0</td>\n",
       "      <td>247144.0</td>\n",
       "      <td>158074.0</td>\n",
       "      <td>42367.0</td>\n",
       "      <td>615606.0</td>\n",
       "      <td>297539.0</td>\n",
       "      <td>...</td>\n",
       "      <td>359433.0</td>\n",
       "      <td>19208.0</td>\n",
       "      <td>724049.0</td>\n",
       "      <td>115523.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3979.0</td>\n",
       "      <td>18768.0</td>\n",
       "      <td>343767.0</td>\n",
       "      <td>16818.0</td>\n",
       "      <td>8736039.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>411404.0</td>\n",
       "      <td>12547.0</td>\n",
       "      <td>156864.0</td>\n",
       "      <td>282368.0</td>\n",
       "      <td>177511.0</td>\n",
       "      <td>38120.0</td>\n",
       "      <td>724495.0</td>\n",
       "      <td>347032.0</td>\n",
       "      <td>...</td>\n",
       "      <td>401493.0</td>\n",
       "      <td>21663.0</td>\n",
       "      <td>822903.0</td>\n",
       "      <td>131835.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>21609.0</td>\n",
       "      <td>391238.0</td>\n",
       "      <td>18875.0</td>\n",
       "      <td>9946636.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>464272.0</td>\n",
       "      <td>14602.0</td>\n",
       "      <td>177745.0</td>\n",
       "      <td>317101.0</td>\n",
       "      <td>206690.0</td>\n",
       "      <td>35921.0</td>\n",
       "      <td>807623.0</td>\n",
       "      <td>400662.0</td>\n",
       "      <td>...</td>\n",
       "      <td>452186.0</td>\n",
       "      <td>25593.0</td>\n",
       "      <td>944146.0</td>\n",
       "      <td>149817.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5159.0</td>\n",
       "      <td>24787.0</td>\n",
       "      <td>443783.0</td>\n",
       "      <td>21870.0</td>\n",
       "      <td>11236635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>526468.0</td>\n",
       "      <td>16761.0</td>\n",
       "      <td>198098.0</td>\n",
       "      <td>373920.0</td>\n",
       "      <td>234982.0</td>\n",
       "      <td>40633.0</td>\n",
       "      <td>895027.0</td>\n",
       "      <td>437462.0</td>\n",
       "      <td>...</td>\n",
       "      <td>511178.0</td>\n",
       "      <td>29667.0</td>\n",
       "      <td>1043371.0</td>\n",
       "      <td>161985.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>27844.0</td>\n",
       "      <td>492424.0</td>\n",
       "      <td>24089.0</td>\n",
       "      <td>12433749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2015-16</td>\n",
       "      <td>609934.0</td>\n",
       "      <td>18784.0</td>\n",
       "      <td>224234.0</td>\n",
       "      <td>413503.0</td>\n",
       "      <td>260776.0</td>\n",
       "      <td>45002.0</td>\n",
       "      <td>994316.0</td>\n",
       "      <td>485184.0</td>\n",
       "      <td>...</td>\n",
       "      <td>575631.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1153795.0</td>\n",
       "      <td>184091.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30304.0</td>\n",
       "      <td>551963.0</td>\n",
       "      <td>26533.0</td>\n",
       "      <td>13675331.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Items  Description Duration  Andhra Pradesh   \\\n",
       "0  GSDP - CURRENT PRICES (` in Crore)  2011-12         379402.0   \n",
       "1  GSDP - CURRENT PRICES (` in Crore)  2012-13         411404.0   \n",
       "2  GSDP - CURRENT PRICES (` in Crore)  2013-14         464272.0   \n",
       "3  GSDP - CURRENT PRICES (` in Crore)  2014-15         526468.0   \n",
       "4  GSDP - CURRENT PRICES (` in Crore)  2015-16         609934.0   \n",
       "\n",
       "   Arunachal Pradesh     Assam     Bihar  Chhattisgarh      Goa   Gujarat  \\\n",
       "0            11063.0  143175.0  247144.0      158074.0  42367.0  615606.0   \n",
       "1            12547.0  156864.0  282368.0      177511.0  38120.0  724495.0   \n",
       "2            14602.0  177745.0  317101.0      206690.0  35921.0  807623.0   \n",
       "3            16761.0  198098.0  373920.0      234982.0  40633.0  895027.0   \n",
       "4            18784.0  224234.0  413503.0      260776.0  45002.0  994316.0   \n",
       "\n",
       "    Haryana      ...        Telangana  Tripura  Uttar Pradesh  Uttarakhand  \\\n",
       "0  297539.0      ...         359433.0  19208.0       724049.0     115523.0   \n",
       "1  347032.0      ...         401493.0  21663.0       822903.0     131835.0   \n",
       "2  400662.0      ...         452186.0  25593.0       944146.0     149817.0   \n",
       "3  437462.0      ...         511178.0  29667.0      1043371.0     161985.0   \n",
       "4  485184.0      ...         575631.0      NaN      1153795.0     184091.0   \n",
       "\n",
       "   West Bengal1  Andaman & Nicobar Islands  Chandigarh     Delhi  Puducherry  \\\n",
       "0           NaN                     3979.0     18768.0  343767.0     16818.0   \n",
       "1           NaN                     4421.0     21609.0  391238.0     18875.0   \n",
       "2           NaN                     5159.0     24787.0  443783.0     21870.0   \n",
       "3           NaN                     5721.0     27844.0  492424.0     24089.0   \n",
       "4           NaN                        NaN     30304.0  551963.0     26533.0   \n",
       "\n",
       "   All_India GDP  \n",
       "0      8736039.0  \n",
       "1      9946636.0  \n",
       "2     11236635.0  \n",
       "3     12433749.0  \n",
       "4     13675331.0  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 559,
     "metadata": {},
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    }
   ],
   "source": [
    "gsdp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11 entries, 0 to 10\n",
      "Data columns (total 36 columns):\n",
      "Items  Description           11 non-null object\n",
      "Duration                     11 non-null object\n",
      "Andhra Pradesh               11 non-null float64\n",
      "Arunachal Pradesh            9 non-null float64\n",
      "Assam                        9 non-null float64\n",
      "Bihar                        9 non-null float64\n",
      "Chhattisgarh                 11 non-null float64\n",
      "Goa                          9 non-null float64\n",
      "Gujarat                      9 non-null float64\n",
      "Haryana                      11 non-null float64\n",
      "Himachal Pradesh             7 non-null float64\n",
      "Jammu & Kashmir              9 non-null float64\n",
      "Jharkhand                    9 non-null float64\n",
      "Karnataka                    9 non-null float64\n",
      "Kerala                       9 non-null float64\n",
      "Madhya Pradesh               11 non-null float64\n",
      "Maharashtra                  7 non-null float64\n",
      "Manipur                      7 non-null float64\n",
      "Meghalaya                    11 non-null float64\n",
      "Mizoram                      7 non-null float64\n",
      "Nagaland                     7 non-null float64\n",
      "Odisha                       11 non-null float64\n",
      "Punjab                       7 non-null float64\n",
      "Rajasthan                    7 non-null float64\n",
      "Sikkim                       9 non-null float64\n",
      "Tamil Nadu                   11 non-null float64\n",
      "Telangana                    11 non-null float64\n",
      "Tripura                      7 non-null float64\n",
      "Uttar Pradesh                9 non-null float64\n",
      "Uttarakhand                  9 non-null float64\n",
      "West Bengal1                 0 non-null float64\n",
      "Andaman & Nicobar Islands    7 non-null float64\n",
      "Chandigarh                   9 non-null float64\n",
      "Delhi                        11 non-null float64\n",
      "Puducherry                   11 non-null float64\n",
      "All_India GDP                11 non-null float64\n",
      "dtypes: float64(34), object(2)\n",
      "memory usage: 3.2+ KB\n"
     ]
    }
   ],
   "source": [
    "gsdp_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <th>Assam</th>\n",
       "      <th>Bihar</th>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <th>Goa</th>\n",
       "      <th>Gujarat</th>\n",
       "      <th>Haryana</th>\n",
       "      <th>Himachal Pradesh</th>\n",
       "      <th>Jammu &amp; Kashmir</th>\n",
       "      <th>...</th>\n",
       "      <th>Telangana</th>\n",
       "      <th>Tripura</th>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <th>Uttarakhand</th>\n",
       "      <th>West Bengal1</th>\n",
       "      <th>Andaman &amp; Nicobar Islands</th>\n",
       "      <th>Chandigarh</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Puducherry</th>\n",
       "      <th>All_India GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.100000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>280986.561818</td>\n",
       "      <td>8201.516667</td>\n",
       "      <td>100018.167778</td>\n",
       "      <td>181565.673333</td>\n",
       "      <td>120748.879091</td>\n",
       "      <td>22450.120000</td>\n",
       "      <td>448568.674444</td>\n",
       "      <td>228667.271818</td>\n",
       "      <td>50673.064286</td>\n",
       "      <td>53343.001111</td>\n",
       "      <td>...</td>\n",
       "      <td>268570.787273</td>\n",
       "      <td>13739.691429</td>\n",
       "      <td>5.209237e+05</td>\n",
       "      <td>82588.947778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2759.814286</td>\n",
       "      <td>13707.002222</td>\n",
       "      <td>258693.004545</td>\n",
       "      <td>12527.435455</td>\n",
       "      <td>6.479952e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>282573.532407</td>\n",
       "      <td>8073.276061</td>\n",
       "      <td>97589.497456</td>\n",
       "      <td>178700.497677</td>\n",
       "      <td>120928.480448</td>\n",
       "      <td>21443.429799</td>\n",
       "      <td>438025.627165</td>\n",
       "      <td>228195.895576</td>\n",
       "      <td>48384.121107</td>\n",
       "      <td>51712.668467</td>\n",
       "      <td>...</td>\n",
       "      <td>268734.549079</td>\n",
       "      <td>13240.797263</td>\n",
       "      <td>5.087288e+05</td>\n",
       "      <td>80553.858327</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2627.016700</td>\n",
       "      <td>13399.003286</td>\n",
       "      <td>258176.516280</td>\n",
       "      <td>12445.889343</td>\n",
       "      <td>6.433000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.430000</td>\n",
       "      <td>12.070000</td>\n",
       "      <td>9.560000</td>\n",
       "      <td>10.590000</td>\n",
       "      <td>10.980000</td>\n",
       "      <td>-10.020000</td>\n",
       "      <td>10.820000</td>\n",
       "      <td>9.180000</td>\n",
       "      <td>10.140000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>12.780000</td>\n",
       "      <td>1.051000e+01</td>\n",
       "      <td>8.120000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.890000</td>\n",
       "      <td>8.840000</td>\n",
       "      <td>10.960000</td>\n",
       "      <td>10.140000</td>\n",
       "      <td>9.990000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.025000</td>\n",
       "      <td>14.790000</td>\n",
       "      <td>13.190000</td>\n",
       "      <td>14.250000</td>\n",
       "      <td>12.995000</td>\n",
       "      <td>10.750000</td>\n",
       "      <td>11.470000</td>\n",
       "      <td>14.135000</td>\n",
       "      <td>14.155000</td>\n",
       "      <td>11.310000</td>\n",
       "      <td>...</td>\n",
       "      <td>12.840000</td>\n",
       "      <td>17.030000</td>\n",
       "      <td>1.365000e+01</td>\n",
       "      <td>13.650000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.905000</td>\n",
       "      <td>14.710000</td>\n",
       "      <td>13.095000</td>\n",
       "      <td>11.815000</td>\n",
       "      <td>1.224500e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>379402.000000</td>\n",
       "      <td>11063.000000</td>\n",
       "      <td>143175.000000</td>\n",
       "      <td>247144.000000</td>\n",
       "      <td>158074.000000</td>\n",
       "      <td>35921.000000</td>\n",
       "      <td>615606.000000</td>\n",
       "      <td>297539.000000</td>\n",
       "      <td>72720.000000</td>\n",
       "      <td>78254.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>359433.000000</td>\n",
       "      <td>19208.000000</td>\n",
       "      <td>7.240490e+05</td>\n",
       "      <td>115523.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3979.000000</td>\n",
       "      <td>18768.000000</td>\n",
       "      <td>343767.000000</td>\n",
       "      <td>16818.000000</td>\n",
       "      <td>8.736039e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>495370.000000</td>\n",
       "      <td>14602.000000</td>\n",
       "      <td>177745.000000</td>\n",
       "      <td>317101.000000</td>\n",
       "      <td>220836.000000</td>\n",
       "      <td>40633.000000</td>\n",
       "      <td>807623.000000</td>\n",
       "      <td>419062.000000</td>\n",
       "      <td>88792.000000</td>\n",
       "      <td>95893.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>481682.000000</td>\n",
       "      <td>23628.000000</td>\n",
       "      <td>9.441460e+05</td>\n",
       "      <td>149817.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4790.000000</td>\n",
       "      <td>24787.000000</td>\n",
       "      <td>468103.500000</td>\n",
       "      <td>22979.500000</td>\n",
       "      <td>1.183519e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>699307.000000</td>\n",
       "      <td>18784.000000</td>\n",
       "      <td>224234.000000</td>\n",
       "      <td>413503.000000</td>\n",
       "      <td>290140.000000</td>\n",
       "      <td>45002.000000</td>\n",
       "      <td>994316.000000</td>\n",
       "      <td>547396.000000</td>\n",
       "      <td>104369.000000</td>\n",
       "      <td>118387.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>654294.000000</td>\n",
       "      <td>29667.000000</td>\n",
       "      <td>1.153795e+06</td>\n",
       "      <td>184091.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5721.000000</td>\n",
       "      <td>30304.000000</td>\n",
       "      <td>622385.000000</td>\n",
       "      <td>29557.000000</td>\n",
       "      <td>1.525103e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Andhra Pradesh   Arunachal Pradesh          Assam          Bihar  \\\n",
       "count        11.000000           9.000000       9.000000       9.000000   \n",
       "mean     280986.561818        8201.516667  100018.167778  181565.673333   \n",
       "std      282573.532407        8073.276061   97589.497456  178700.497677   \n",
       "min           8.430000          12.070000       9.560000      10.590000   \n",
       "25%          14.025000          14.790000      13.190000      14.250000   \n",
       "50%      379402.000000       11063.000000  143175.000000  247144.000000   \n",
       "75%      495370.000000       14602.000000  177745.000000  317101.000000   \n",
       "max      699307.000000       18784.000000  224234.000000  413503.000000   \n",
       "\n",
       "        Chhattisgarh           Goa        Gujarat        Haryana  \\\n",
       "count      11.000000      9.000000       9.000000      11.000000   \n",
       "mean   120748.879091  22450.120000  448568.674444  228667.271818   \n",
       "std    120928.480448  21443.429799  438025.627165  228195.895576   \n",
       "min        10.980000    -10.020000      10.820000       9.180000   \n",
       "25%        12.995000     10.750000      11.470000      14.135000   \n",
       "50%    158074.000000  35921.000000  615606.000000  297539.000000   \n",
       "75%    220836.000000  40633.000000  807623.000000  419062.000000   \n",
       "max    290140.000000  45002.000000  994316.000000  547396.000000   \n",
       "\n",
       "       Himachal Pradesh  Jammu & Kashmir      ...            Telangana  \\\n",
       "count          7.000000         9.000000      ...            11.000000   \n",
       "mean       50673.064286     53343.001111      ...        268570.787273   \n",
       "std        48384.121107     51712.668467      ...        268734.549079   \n",
       "min           10.140000         4.700000      ...            11.700000   \n",
       "25%           14.155000        11.310000      ...            12.840000   \n",
       "50%        72720.000000     78254.000000      ...        359433.000000   \n",
       "75%        88792.000000     95893.000000      ...        481682.000000   \n",
       "max       104369.000000    118387.000000      ...        654294.000000   \n",
       "\n",
       "            Tripura  Uttar Pradesh    Uttarakhand  West Bengal1  \\\n",
       "count      7.000000   9.000000e+00       9.000000           0.0   \n",
       "mean   13739.691429   5.209237e+05   82588.947778           NaN   \n",
       "std    13240.797263   5.087288e+05   80553.858327           NaN   \n",
       "min       12.780000   1.051000e+01       8.120000           NaN   \n",
       "25%       17.030000   1.365000e+01      13.650000           NaN   \n",
       "50%    19208.000000   7.240490e+05  115523.000000           NaN   \n",
       "75%    23628.000000   9.441460e+05  149817.000000           NaN   \n",
       "max    29667.000000   1.153795e+06  184091.000000           NaN   \n",
       "\n",
       "       Andaman & Nicobar Islands    Chandigarh          Delhi    Puducherry  \\\n",
       "count                   7.000000      9.000000      11.000000     11.000000   \n",
       "mean                 2759.814286  13707.002222  258693.004545  12527.435455   \n",
       "std                  2627.016700  13399.003286  258176.516280  12445.889343   \n",
       "min                    10.890000      8.840000      10.960000     10.140000   \n",
       "25%                    13.905000     14.710000      13.095000     11.815000   \n",
       "50%                  3979.000000  18768.000000  343767.000000  16818.000000   \n",
       "75%                  4790.000000  24787.000000  468103.500000  22979.500000   \n",
       "max                  5721.000000  30304.000000  622385.000000  29557.000000   \n",
       "\n",
       "       All_India GDP  \n",
       "count   1.100000e+01  \n",
       "mean    6.479952e+06  \n",
       "std     6.433000e+06  \n",
       "min     9.990000e+00  \n",
       "25%     1.224500e+01  \n",
       "50%     8.736039e+06  \n",
       "75%     1.183519e+07  \n",
       "max     1.525103e+07  \n",
       "\n",
       "[8 rows x 34 columns]"
      ]
     },
     "execution_count": 561,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsdp_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Drop the column West Bengal1 as this state's data is NOT available\n",
    "gsdp_df.drop('West Bengal1',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11 entries, 0 to 10\n",
      "Data columns (total 35 columns):\n",
      "Items  Description           11 non-null object\n",
      "Duration                     11 non-null object\n",
      "Andhra Pradesh               11 non-null float64\n",
      "Arunachal Pradesh            9 non-null float64\n",
      "Assam                        9 non-null float64\n",
      "Bihar                        9 non-null float64\n",
      "Chhattisgarh                 11 non-null float64\n",
      "Goa                          9 non-null float64\n",
      "Gujarat                      9 non-null float64\n",
      "Haryana                      11 non-null float64\n",
      "Himachal Pradesh             7 non-null float64\n",
      "Jammu & Kashmir              9 non-null float64\n",
      "Jharkhand                    9 non-null float64\n",
      "Karnataka                    9 non-null float64\n",
      "Kerala                       9 non-null float64\n",
      "Madhya Pradesh               11 non-null float64\n",
      "Maharashtra                  7 non-null float64\n",
      "Manipur                      7 non-null float64\n",
      "Meghalaya                    11 non-null float64\n",
      "Mizoram                      7 non-null float64\n",
      "Nagaland                     7 non-null float64\n",
      "Odisha                       11 non-null float64\n",
      "Punjab                       7 non-null float64\n",
      "Rajasthan                    7 non-null float64\n",
      "Sikkim                       9 non-null float64\n",
      "Tamil Nadu                   11 non-null float64\n",
      "Telangana                    11 non-null float64\n",
      "Tripura                      7 non-null float64\n",
      "Uttar Pradesh                9 non-null float64\n",
      "Uttarakhand                  9 non-null float64\n",
      "Andaman & Nicobar Islands    7 non-null float64\n",
      "Chandigarh                   9 non-null float64\n",
      "Delhi                        11 non-null float64\n",
      "Puducherry                   11 non-null float64\n",
      "All_India GDP                11 non-null float64\n",
      "dtypes: float64(33), object(2)\n",
      "memory usage: 3.1+ KB\n"
     ]
    }
   ],
   "source": [
    "gsdp_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Items  Description</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <th>Assam</th>\n",
       "      <th>Bihar</th>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <th>Goa</th>\n",
       "      <th>Gujarat</th>\n",
       "      <th>Haryana</th>\n",
       "      <th>...</th>\n",
       "      <th>Tamil Nadu</th>\n",
       "      <th>Telangana</th>\n",
       "      <th>Tripura</th>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <th>Uttarakhand</th>\n",
       "      <th>Andaman &amp; Nicobar Islands</th>\n",
       "      <th>Chandigarh</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Puducherry</th>\n",
       "      <th>All_India GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2011-12</td>\n",
       "      <td>379402.00</td>\n",
       "      <td>11063.00</td>\n",
       "      <td>143175.00</td>\n",
       "      <td>247144.00</td>\n",
       "      <td>158074.00</td>\n",
       "      <td>42367.00</td>\n",
       "      <td>615606.00</td>\n",
       "      <td>297539.00</td>\n",
       "      <td>...</td>\n",
       "      <td>751485.00</td>\n",
       "      <td>359433.00</td>\n",
       "      <td>19208.00</td>\n",
       "      <td>724049.00</td>\n",
       "      <td>115523.00</td>\n",
       "      <td>3979.00</td>\n",
       "      <td>18768.00</td>\n",
       "      <td>343767.00</td>\n",
       "      <td>16818.00</td>\n",
       "      <td>8736039.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>411404.00</td>\n",
       "      <td>12547.00</td>\n",
       "      <td>156864.00</td>\n",
       "      <td>282368.00</td>\n",
       "      <td>177511.00</td>\n",
       "      <td>38120.00</td>\n",
       "      <td>724495.00</td>\n",
       "      <td>347032.00</td>\n",
       "      <td>...</td>\n",
       "      <td>855481.00</td>\n",
       "      <td>401493.00</td>\n",
       "      <td>21663.00</td>\n",
       "      <td>822903.00</td>\n",
       "      <td>131835.00</td>\n",
       "      <td>4421.00</td>\n",
       "      <td>21609.00</td>\n",
       "      <td>391238.00</td>\n",
       "      <td>18875.00</td>\n",
       "      <td>9946636.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>464272.00</td>\n",
       "      <td>14602.00</td>\n",
       "      <td>177745.00</td>\n",
       "      <td>317101.00</td>\n",
       "      <td>206690.00</td>\n",
       "      <td>35921.00</td>\n",
       "      <td>807623.00</td>\n",
       "      <td>400662.00</td>\n",
       "      <td>...</td>\n",
       "      <td>971090.00</td>\n",
       "      <td>452186.00</td>\n",
       "      <td>25593.00</td>\n",
       "      <td>944146.00</td>\n",
       "      <td>149817.00</td>\n",
       "      <td>5159.00</td>\n",
       "      <td>24787.00</td>\n",
       "      <td>443783.00</td>\n",
       "      <td>21870.00</td>\n",
       "      <td>11236635.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>526468.00</td>\n",
       "      <td>16761.00</td>\n",
       "      <td>198098.00</td>\n",
       "      <td>373920.00</td>\n",
       "      <td>234982.00</td>\n",
       "      <td>40633.00</td>\n",
       "      <td>895027.00</td>\n",
       "      <td>437462.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1092564.00</td>\n",
       "      <td>511178.00</td>\n",
       "      <td>29667.00</td>\n",
       "      <td>1043371.00</td>\n",
       "      <td>161985.00</td>\n",
       "      <td>5721.00</td>\n",
       "      <td>27844.00</td>\n",
       "      <td>492424.00</td>\n",
       "      <td>24089.00</td>\n",
       "      <td>12433749.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2015-16</td>\n",
       "      <td>609934.00</td>\n",
       "      <td>18784.00</td>\n",
       "      <td>224234.00</td>\n",
       "      <td>413503.00</td>\n",
       "      <td>260776.00</td>\n",
       "      <td>45002.00</td>\n",
       "      <td>994316.00</td>\n",
       "      <td>485184.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1212668.00</td>\n",
       "      <td>575631.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1153795.00</td>\n",
       "      <td>184091.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30304.00</td>\n",
       "      <td>551963.00</td>\n",
       "      <td>26533.00</td>\n",
       "      <td>13675331.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>8.43</td>\n",
       "      <td>13.41</td>\n",
       "      <td>9.56</td>\n",
       "      <td>14.25</td>\n",
       "      <td>12.30</td>\n",
       "      <td>-10.02</td>\n",
       "      <td>17.69</td>\n",
       "      <td>16.63</td>\n",
       "      <td>...</td>\n",
       "      <td>13.84</td>\n",
       "      <td>11.70</td>\n",
       "      <td>12.78</td>\n",
       "      <td>13.65</td>\n",
       "      <td>14.12</td>\n",
       "      <td>11.13</td>\n",
       "      <td>15.14</td>\n",
       "      <td>13.81</td>\n",
       "      <td>12.23</td>\n",
       "      <td>13.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>12.85</td>\n",
       "      <td>16.38</td>\n",
       "      <td>13.31</td>\n",
       "      <td>12.30</td>\n",
       "      <td>16.44</td>\n",
       "      <td>-5.77</td>\n",
       "      <td>11.47</td>\n",
       "      <td>15.45</td>\n",
       "      <td>...</td>\n",
       "      <td>13.51</td>\n",
       "      <td>12.63</td>\n",
       "      <td>18.14</td>\n",
       "      <td>14.73</td>\n",
       "      <td>13.64</td>\n",
       "      <td>16.68</td>\n",
       "      <td>14.71</td>\n",
       "      <td>13.43</td>\n",
       "      <td>15.87</td>\n",
       "      <td>12.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>13.40</td>\n",
       "      <td>14.79</td>\n",
       "      <td>11.45</td>\n",
       "      <td>17.92</td>\n",
       "      <td>13.69</td>\n",
       "      <td>13.12</td>\n",
       "      <td>10.82</td>\n",
       "      <td>9.18</td>\n",
       "      <td>...</td>\n",
       "      <td>12.51</td>\n",
       "      <td>13.05</td>\n",
       "      <td>15.92</td>\n",
       "      <td>10.51</td>\n",
       "      <td>8.12</td>\n",
       "      <td>10.89</td>\n",
       "      <td>12.33</td>\n",
       "      <td>10.96</td>\n",
       "      <td>10.14</td>\n",
       "      <td>10.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2015-16</td>\n",
       "      <td>15.85</td>\n",
       "      <td>12.07</td>\n",
       "      <td>13.19</td>\n",
       "      <td>10.59</td>\n",
       "      <td>10.98</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.09</td>\n",
       "      <td>10.91</td>\n",
       "      <td>...</td>\n",
       "      <td>10.99</td>\n",
       "      <td>12.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.58</td>\n",
       "      <td>13.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.84</td>\n",
       "      <td>12.09</td>\n",
       "      <td>10.15</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Items  Description Duration  Andhra Pradesh   \\\n",
       "0  GSDP - CURRENT PRICES (` in Crore)  2011-12        379402.00   \n",
       "1  GSDP - CURRENT PRICES (` in Crore)  2012-13        411404.00   \n",
       "2  GSDP - CURRENT PRICES (` in Crore)  2013-14        464272.00   \n",
       "3  GSDP - CURRENT PRICES (` in Crore)  2014-15        526468.00   \n",
       "4  GSDP - CURRENT PRICES (` in Crore)  2015-16        609934.00   \n",
       "6       (% Growth over previous year)  2012-13             8.43   \n",
       "7       (% Growth over previous year)  2013-14            12.85   \n",
       "8       (% Growth over previous year)  2014-15            13.40   \n",
       "9       (% Growth over previous year)  2015-16            15.85   \n",
       "\n",
       "   Arunachal Pradesh      Assam      Bihar  Chhattisgarh       Goa    Gujarat  \\\n",
       "0           11063.00  143175.00  247144.00     158074.00  42367.00  615606.00   \n",
       "1           12547.00  156864.00  282368.00     177511.00  38120.00  724495.00   \n",
       "2           14602.00  177745.00  317101.00     206690.00  35921.00  807623.00   \n",
       "3           16761.00  198098.00  373920.00     234982.00  40633.00  895027.00   \n",
       "4           18784.00  224234.00  413503.00     260776.00  45002.00  994316.00   \n",
       "6              13.41       9.56      14.25         12.30    -10.02      17.69   \n",
       "7              16.38      13.31      12.30         16.44     -5.77      11.47   \n",
       "8              14.79      11.45      17.92         13.69     13.12      10.82   \n",
       "9              12.07      13.19      10.59         10.98     10.75      11.09   \n",
       "\n",
       "     Haryana      ...        Tamil Nadu  Telangana   Tripura  Uttar Pradesh  \\\n",
       "0  297539.00      ...         751485.00  359433.00  19208.00      724049.00   \n",
       "1  347032.00      ...         855481.00  401493.00  21663.00      822903.00   \n",
       "2  400662.00      ...         971090.00  452186.00  25593.00      944146.00   \n",
       "3  437462.00      ...        1092564.00  511178.00  29667.00     1043371.00   \n",
       "4  485184.00      ...        1212668.00  575631.00       NaN     1153795.00   \n",
       "6      16.63      ...             13.84      11.70     12.78          13.65   \n",
       "7      15.45      ...             13.51      12.63     18.14          14.73   \n",
       "8       9.18      ...             12.51      13.05     15.92          10.51   \n",
       "9      10.91      ...             10.99      12.61       NaN          10.58   \n",
       "\n",
       "   Uttarakhand  Andaman & Nicobar Islands  Chandigarh      Delhi  Puducherry  \\\n",
       "0    115523.00                    3979.00    18768.00  343767.00    16818.00   \n",
       "1    131835.00                    4421.00    21609.00  391238.00    18875.00   \n",
       "2    149817.00                    5159.00    24787.00  443783.00    21870.00   \n",
       "3    161985.00                    5721.00    27844.00  492424.00    24089.00   \n",
       "4    184091.00                        NaN    30304.00  551963.00    26533.00   \n",
       "6        14.12                      11.13       15.14      13.81       12.23   \n",
       "7        13.64                      16.68       14.71      13.43       15.87   \n",
       "8         8.12                      10.89       12.33      10.96       10.14   \n",
       "9        13.65                        NaN        8.84      12.09       10.15   \n",
       "\n",
       "   All_India GDP  \n",
       "0     8736039.00  \n",
       "1     9946636.00  \n",
       "2    11236635.00  \n",
       "3    12433749.00  \n",
       "4    13675331.00  \n",
       "6          13.86  \n",
       "7          12.97  \n",
       "8          10.65  \n",
       "9           9.99  \n",
       "\n",
       "[9 rows x 35 columns]"
      ]
     },
     "execution_count": 564,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Remove the rows: '(% Growth over the previous year)' and 'GSDP - CURRENT PRICES (` in Crore)' for the year 2016-17.\n",
    "gsdp_df=gsdp_df.loc[~(((gsdp_df['Items  Description']==\"GSDP - CURRENT PRICES (` in Crore)\") | (gsdp_df['Items  Description']==\"(% Growth over previous year)\")) & (gsdp_df['Duration']=='2016-17'))]\n",
    "gsdp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis for Average GSDP growth rate for all the states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pick up the rows with Items Description \"(% Growth over previous year)\"\n",
    "gsdp_grow_df=gsdp_df.loc[(gsdp_df['Items  Description']=='(% Growth over previous year)')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the columns Items  Description once the rows are picked up\n",
    "gsdp_grow_df.drop(['Items  Description'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Duration</th>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <th>Assam</th>\n",
       "      <th>Bihar</th>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <th>Goa</th>\n",
       "      <th>Gujarat</th>\n",
       "      <th>Haryana</th>\n",
       "      <th>Himachal Pradesh</th>\n",
       "      <th>...</th>\n",
       "      <th>Tamil Nadu</th>\n",
       "      <th>Telangana</th>\n",
       "      <th>Tripura</th>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <th>Uttarakhand</th>\n",
       "      <th>Andaman &amp; Nicobar Islands</th>\n",
       "      <th>Chandigarh</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Puducherry</th>\n",
       "      <th>All_India GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>8.43</td>\n",
       "      <td>13.41</td>\n",
       "      <td>9.56</td>\n",
       "      <td>14.25</td>\n",
       "      <td>12.30</td>\n",
       "      <td>-10.02</td>\n",
       "      <td>17.69</td>\n",
       "      <td>16.63</td>\n",
       "      <td>13.89</td>\n",
       "      <td>...</td>\n",
       "      <td>13.84</td>\n",
       "      <td>11.70</td>\n",
       "      <td>12.78</td>\n",
       "      <td>13.65</td>\n",
       "      <td>14.12</td>\n",
       "      <td>11.13</td>\n",
       "      <td>15.14</td>\n",
       "      <td>13.81</td>\n",
       "      <td>12.23</td>\n",
       "      <td>13.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>12.85</td>\n",
       "      <td>16.38</td>\n",
       "      <td>13.31</td>\n",
       "      <td>12.30</td>\n",
       "      <td>16.44</td>\n",
       "      <td>-5.77</td>\n",
       "      <td>11.47</td>\n",
       "      <td>15.45</td>\n",
       "      <td>14.42</td>\n",
       "      <td>...</td>\n",
       "      <td>13.51</td>\n",
       "      <td>12.63</td>\n",
       "      <td>18.14</td>\n",
       "      <td>14.73</td>\n",
       "      <td>13.64</td>\n",
       "      <td>16.68</td>\n",
       "      <td>14.71</td>\n",
       "      <td>13.43</td>\n",
       "      <td>15.87</td>\n",
       "      <td>12.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>13.40</td>\n",
       "      <td>14.79</td>\n",
       "      <td>11.45</td>\n",
       "      <td>17.92</td>\n",
       "      <td>13.69</td>\n",
       "      <td>13.12</td>\n",
       "      <td>10.82</td>\n",
       "      <td>9.18</td>\n",
       "      <td>10.14</td>\n",
       "      <td>...</td>\n",
       "      <td>12.51</td>\n",
       "      <td>13.05</td>\n",
       "      <td>15.92</td>\n",
       "      <td>10.51</td>\n",
       "      <td>8.12</td>\n",
       "      <td>10.89</td>\n",
       "      <td>12.33</td>\n",
       "      <td>10.96</td>\n",
       "      <td>10.14</td>\n",
       "      <td>10.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>15.85</td>\n",
       "      <td>12.07</td>\n",
       "      <td>13.19</td>\n",
       "      <td>10.59</td>\n",
       "      <td>10.98</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.09</td>\n",
       "      <td>10.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.99</td>\n",
       "      <td>12.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.58</td>\n",
       "      <td>13.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.84</td>\n",
       "      <td>12.09</td>\n",
       "      <td>10.15</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Duration  Andhra Pradesh   Arunachal Pradesh  Assam  Bihar  Chhattisgarh  \\\n",
       "6  2012-13             8.43              13.41   9.56  14.25         12.30   \n",
       "7  2013-14            12.85              16.38  13.31  12.30         16.44   \n",
       "8  2014-15            13.40              14.79  11.45  17.92         13.69   \n",
       "9  2015-16            15.85              12.07  13.19  10.59         10.98   \n",
       "\n",
       "     Goa  Gujarat  Haryana  Himachal Pradesh      ...        Tamil Nadu  \\\n",
       "6 -10.02    17.69    16.63             13.89      ...             13.84   \n",
       "7  -5.77    11.47    15.45             14.42      ...             13.51   \n",
       "8  13.12    10.82     9.18             10.14      ...             12.51   \n",
       "9  10.75    11.09    10.91               NaN      ...             10.99   \n",
       "\n",
       "   Telangana  Tripura  Uttar Pradesh  Uttarakhand  Andaman & Nicobar Islands  \\\n",
       "6      11.70    12.78          13.65        14.12                      11.13   \n",
       "7      12.63    18.14          14.73        13.64                      16.68   \n",
       "8      13.05    15.92          10.51         8.12                      10.89   \n",
       "9      12.61      NaN          10.58        13.65                        NaN   \n",
       "\n",
       "   Chandigarh  Delhi  Puducherry  All_India GDP  \n",
       "6       15.14  13.81       12.23          13.86  \n",
       "7       14.71  13.43       15.87          12.97  \n",
       "8       12.33  10.96       10.14          10.65  \n",
       "9        8.84  12.09       10.15           9.99  \n",
       "\n",
       "[4 rows x 34 columns]"
      ]
     },
     "execution_count": 567,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsdp_grow_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the record with 2012-13 year as it's not needed for analysis\n",
    "gsdp_grow_df.drop(6,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transpose the data frame\n",
    "gsdp_grow_df=gsdp_grow_df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Duration</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>2015-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <td>12.85</td>\n",
       "      <td>13.4</td>\n",
       "      <td>15.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <td>16.38</td>\n",
       "      <td>14.79</td>\n",
       "      <td>12.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assam</th>\n",
       "      <td>13.31</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>12.3</td>\n",
       "      <td>17.92</td>\n",
       "      <td>10.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         7        8        9\n",
       "Duration           2013-14  2014-15  2015-16\n",
       "Andhra Pradesh       12.85     13.4    15.85\n",
       "Arunachal Pradesh    16.38    14.79    12.07\n",
       "Assam                13.31    11.45    13.19\n",
       "Bihar                 12.3    17.92    10.59"
      ]
     },
     "execution_count": 570,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsdp_grow_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Assign the year range values as column names\n",
    "gsdp_grow_df.columns = gsdp_grow_df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Duration</th>\n",
       "      <th>2013-14</th>\n",
       "      <th>2014-15</th>\n",
       "      <th>2015-16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Duration</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>2015-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <td>12.85</td>\n",
       "      <td>13.4</td>\n",
       "      <td>15.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <td>16.38</td>\n",
       "      <td>14.79</td>\n",
       "      <td>12.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assam</th>\n",
       "      <td>13.31</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>12.3</td>\n",
       "      <td>17.92</td>\n",
       "      <td>10.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Duration           2013-14  2014-15  2015-16\n",
       "Duration           2013-14  2014-15  2015-16\n",
       "Andhra Pradesh       12.85     13.4    15.85\n",
       "Arunachal Pradesh    16.38    14.79    12.07\n",
       "Assam                13.31    11.45    13.19\n",
       "Bihar                 12.3    17.92    10.59"
      ]
     },
     "execution_count": 572,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsdp_grow_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 573,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the record with the Duration & Year range entries\n",
    "gsdp_grow_df=gsdp_grow_df.reindex(gsdp_grow_df.index.drop('Duration'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 574,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Duration</th>\n",
       "      <th>2013-14</th>\n",
       "      <th>2014-15</th>\n",
       "      <th>2015-16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <td>12.85</td>\n",
       "      <td>13.4</td>\n",
       "      <td>15.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <td>16.38</td>\n",
       "      <td>14.79</td>\n",
       "      <td>12.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assam</th>\n",
       "      <td>13.31</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>12.3</td>\n",
       "      <td>17.92</td>\n",
       "      <td>10.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <td>16.44</td>\n",
       "      <td>13.69</td>\n",
       "      <td>10.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Duration          2013-14 2014-15 2015-16\n",
       "Andhra Pradesh      12.85    13.4   15.85\n",
       "Arunachal Pradesh   16.38   14.79   12.07\n",
       "Assam               13.31   11.45   13.19\n",
       "Bihar                12.3   17.92   10.59\n",
       "Chhattisgarh        16.44   13.69   10.98"
      ]
     },
     "execution_count": 574,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsdp_grow_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 575,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reset the index to bring State Name into Column\n",
    "gsdp_grow_df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>Duration</th>\n",
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       "      <th>2014-15</th>\n",
       "      <th>2015-16</th>\n",
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       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
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       "      <td>13.4</td>\n",
       "      <td>15.85</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>16.38</td>\n",
       "      <td>14.79</td>\n",
       "      <td>12.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Assam</td>\n",
       "      <td>13.31</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>12.3</td>\n",
       "      <td>17.92</td>\n",
       "      <td>10.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>16.44</td>\n",
       "      <td>13.69</td>\n",
       "      <td>10.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Duration              index 2013-14 2014-15 2015-16\n",
       "0           Andhra Pradesh    12.85    13.4   15.85\n",
       "1         Arunachal Pradesh   16.38   14.79   12.07\n",
       "2                     Assam   13.31   11.45   13.19\n",
       "3                     Bihar    12.3   17.92   10.59\n",
       "4              Chhattisgarh   16.44   13.69   10.98"
      ]
     },
     "execution_count": 576,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsdp_grow_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 577,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename the column so that it reflects as State Name\n",
    "gsdp_grow_df.rename(columns={'index': 'State'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Duration</th>\n",
       "      <th>State</th>\n",
       "      <th>2013-14</th>\n",
       "      <th>2014-15</th>\n",
       "      <th>2015-16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>12.85</td>\n",
       "      <td>13.4</td>\n",
       "      <td>15.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>16.38</td>\n",
       "      <td>14.79</td>\n",
       "      <td>12.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Assam</td>\n",
       "      <td>13.31</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>12.3</td>\n",
       "      <td>17.92</td>\n",
       "      <td>10.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>16.44</td>\n",
       "      <td>13.69</td>\n",
       "      <td>10.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Duration              State 2013-14 2014-15 2015-16\n",
       "0           Andhra Pradesh    12.85    13.4   15.85\n",
       "1         Arunachal Pradesh   16.38   14.79   12.07\n",
       "2                     Assam   13.31   11.45   13.19\n",
       "3                     Bihar    12.3   17.92   10.59\n",
       "4              Chhattisgarh   16.44   13.69   10.98"
      ]
     },
     "execution_count": 578,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsdp_grow_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reshape (unpivot) the Data frame\n",
    "gsdp_grow_df=pd.melt(gsdp_grow_df, id_vars=\"State\", var_name=\"GDP_Year\", value_name=\"GSDP_Growth_Rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>GDP_Year</th>\n",
       "      <th>GSDP_Growth_Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>12.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>16.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Assam</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>13.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>16.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               State GDP_Year GSDP_Growth_Rate\n",
       "0    Andhra Pradesh   2013-14            12.85\n",
       "1  Arunachal Pradesh  2013-14            16.38\n",
       "2              Assam  2013-14            13.31\n",
       "3              Bihar  2013-14             12.3\n",
       "4       Chhattisgarh  2013-14            16.44"
      ]
     },
     "execution_count": 580,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Few States have the GSDP Growth Rate for only 2 years (the latest year data is missing)\n",
    "#For such states, the mean will be automatically calculated based only the 2 years \n",
    "gsdp_grow_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Assign the data type float for GSDP_Growth_Rate field\n",
    "gsdp_grow_df['GSDP_Growth_Rate'] = gsdp_grow_df['GSDP_Growth_Rate'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate an aggreggate of GSDP_Growth_Rate grouping by State and assign to new Dataframe\n",
    "gsdp_mean_df=pd.DataFrame(gsdp_grow_df.groupby('State')['GSDP_Growth_Rate'].mean().sort_values(ascending = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reset the index and assign column names to the gsdp_mean_df dataframe\n",
    "gsdp_mean_df.reset_index(inplace=True)\n",
    "gsdp_mean_df.columns=['State','Mean_GSDP_Growth_Rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Mean_GSDP_Growth_Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mizoram</td>\n",
       "      <td>17.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tripura</td>\n",
       "      <td>17.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nagaland</td>\n",
       "      <td>16.415000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manipur</td>\n",
       "      <td>14.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>14.413333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Karnataka</td>\n",
       "      <td>14.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>14.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>13.785000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>13.703333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>13.603333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Telangana</td>\n",
       "      <td>12.763333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Assam</td>\n",
       "      <td>12.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>12.626667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>12.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>12.336667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>12.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>12.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Puducherry</td>\n",
       "      <td>12.053333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>11.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>11.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Haryana</td>\n",
       "      <td>11.846667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>11.803333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>11.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>11.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>11.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>All_India GDP</td>\n",
       "      <td>11.203333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Punjab</td>\n",
       "      <td>11.185000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Gujarat</td>\n",
       "      <td>11.126667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Jammu &amp; Kashmir</td>\n",
       "      <td>10.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Sikkim</td>\n",
       "      <td>10.486667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Odisha</td>\n",
       "      <td>9.836667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>6.953333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Goa</td>\n",
       "      <td>6.033333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        State  Mean_GSDP_Growth_Rate\n",
       "0                     Mizoram              17.700000\n",
       "1                     Tripura              17.030000\n",
       "2                    Nagaland              16.415000\n",
       "3                     Manipur              14.610000\n",
       "4           Arunachal Pradesh              14.413333\n",
       "5                   Karnataka              14.120000\n",
       "6             Andhra Pradesh               14.033333\n",
       "7   Andaman & Nicobar Islands              13.785000\n",
       "8                Chhattisgarh              13.703333\n",
       "9                       Bihar              13.603333\n",
       "10                  Telangana              12.763333\n",
       "11                      Assam              12.650000\n",
       "12             Madhya Pradesh              12.626667\n",
       "13                     Kerala              12.583333\n",
       "14                 Tamil Nadu              12.336667\n",
       "15           Himachal Pradesh              12.280000\n",
       "16                      Delhi              12.160000\n",
       "17                 Puducherry              12.053333\n",
       "18                 Chandigarh              11.960000\n",
       "19              Uttar Pradesh              11.940000\n",
       "20                    Haryana              11.846667\n",
       "21                Uttarakhand              11.803333\n",
       "22                  Jharkhand              11.500000\n",
       "23                  Rajasthan              11.320000\n",
       "24                Maharashtra              11.260000\n",
       "25              All_India GDP              11.203333\n",
       "26                     Punjab              11.185000\n",
       "27                    Gujarat              11.126667\n",
       "28            Jammu & Kashmir              10.900000\n",
       "29                     Sikkim              10.486667\n",
       "30                     Odisha               9.836667\n",
       "31                  Meghalaya               6.953333\n",
       "32                        Goa               6.033333"
      ]
     },
     "execution_count": 584,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The data below the Average (mean) GSDP growth rate for all the states over the period of 3 years\n",
    "#(or 2 years wherever available)\n",
    "#All_India GDP growth rate average has also been included as reference.\n",
    "gsdp_mean_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e3oqLi1Pt2rXVp0+fVD+Hn5+fAgMD5e3tLelWKO3Ro0ey2zZo0ECjRo1KdDnnG2+8oYkTJ6pNmzZKSEhQuXLl5Ofnp2zZsunll19W8+bN5eHhodKlS6tUqVI6ffq0PDw8EtVt3bq1fvzxR7Vo0UJZs2ZVwYIFH/j7AQBXsJh7ubYBAABA0s8//6xRo0Zp/fr1yQZkAHjcMEIFAADuyTvvvKMff/xR06ZNI0wBwP9hhAoAAAAAHMSkFAAAAADgIAIVAPyf5cuXq1WrVmrRooVatmypYcOGJZp5LjVVqlRJdgpxZ/Hy8tLevXuTrO/evbs2b97scN3Q0FDNmDFDkrRmzRr179/f4Vq3a1SrVk0+Pj6J/tu2bdsD9+eIQ4cO2SfYeFh8fHx0/fp1p9Ry5HxEREQkmmSiTJkyunr16gP18csvv6h79+7y9vaWl5eX+vTpo2PHjkmS9u7dm2SWS1dJD983gMcL91ABgKSJEyfq999/19y5c1WwYEHZbDZ9+eWX6tSpk1atWqUCBQo87BbTzOHDh5N9DtKDqF69uubOneuUWg/a3/Hjx3XhwgWn9OKo28/WcgZHzkd4eLgOHz7stB5iY2PVv39/LVy4UOXLl5d06zP27dvX4eDsLOnh+wbweCFQAXjs/f3331qxYoVCQ0PtD551c3NT69atFRYWprlz5yowMFCenp6qWLGijhw5osGDB+upp57S2LFjZbFY9MILLyR6ts727ds1e/ZsxcXFKUuWLHrnnXdUpUoVzZo1S7/88osuXryoMmXKaPLkyZo9e7a2bNkim82mQoUKKTAwUPnz59fx48c1YsQI3bhxQyVKlLA/jyg5W7du1bx58xQTEyNvb28NGDBAs2fP1vHjxzVlyhRJ0v79+zVu3Dh98cUX9v0OHjyoFStWKCEhQTlz5lTRokV16dIl9evXT+fPn5e7u7umTJmikiVLKiIiQuPHj9fRo0cVFxenWrVqafjw4fbnXN2L6OhojR49WqdPn9a1a9eUPXt2TZ48WSVKlNCWLVs0e/ZsWSwWubu7a/jw4fLw8EjUn6+vr1atWqXly5fLZrMpV65cCggIUMmSJbV//35NmDDB/j30799fFStW1MyZMxURESF/f3/7M6NuO3TokEaPHq24uDg9++yzOnfunPz8/CRJ48ePV7Zs2RQVFaWQkBCtXbtWS5YskZubm55++mkFBAQoJiZGr7/+unbs2CFJeu211/T0009r4sSJio2NVb169fTNN9+oevXq2rNnj0JDQ7V161a5ubnp9OnTypIliyZOnKiSJUvq9OnTGjFihMLDw5U3b14ZY9SqVSu1bds2xe/L19dXQUFB2rBhg9zd3VW8eHEFBAQob968iT6nv7+/YmJi5OPjozVr1ki69eDkgwcP6tq1a3rttdfsU9SndH7vdHt6+zt/Jlu1aqUcOXIkeX5aRESE3nvvPf3++++yWCyqV6+eBg8erIkTJyp79uwaNGiQLl68qHr16mnx4sWqWbOm1q1bp2+//VbTp0936vcNAGnCAMBjbvPmzaZt27bJvrdt2zbj7e1tjDGmQYMG5qOPPjLGGHPz5k1Tu3Zts3v3bmOMMV999ZUpXbq0OXPmjDl58qTx8vIyV69eNcYYc/ToUVOnTh0TFRVlZs6caZo2bWri4uKMMcasXbvWDBo0yL68YsUK06dPH2OMMT4+PiY4ONgYY8z+/ftNmTJlzA8//JCkx27dupn+/fubuLg4ExERYZo1a2ZCQ0PN5cuXTdWqVc0///xjjDFm2LBhZvny5Un2nzlzpnnvvfeMMcaEhISY6tWrm1OnThljjBk7dqzx9/c3xhjj5+dnPvvsM2OMMfHx8Wbo0KFm3rx5SeqFhISYqlWrmlatWtn/CwgIMMYYs2nTJjN27Fj7tgEBAWbMmDHGGGMaNmxofv75Z2OMMTt37jSzZs1K0t/evXtNly5dTHR0tH27Zs2aGWOM6dGjh1m/fr0xxpjffvvNjB492t5Pv379kvQZFxdnXnrpJRMaGmqMMWbPnj32c/zDDz+YsmXLmrNnzxpjjNm9e7dp1KiRuXLlir1m8+bNjc1mM56enubIkSPmxo0b5uWXXzYvvfSSMcaY0NBQ+3dZunRpc+XKFRMSEmKqVatmzp8/b4wxZsyYMWb48OHGGGM6duxoli1bZowx5vjx46ZSpUomJCTkrt/X6tWrTadOnUxUVJT9vd69eyfZ58yZM6Zy5cr25dKlS5sFCxYYY4z59ddfTYUKFUxsbOxdz++/LVy40FSsWNF4enqaoUOHmlWrVtn3++GHH0zLli2NMcYMHz7cjB071thsNnPz5k3Tu3dvM3fuXPPjjz+aNm3a2D9HnTp1zJQpU4wxxrz11ltmw4YNTv2+ASCtMEIFAJLi4+OTXR8bG5toeujq1atLko4ePSqr1apatWpJunV/0+37Nnbt2qWLFy+qV69e9v0sFov9Ia+VK1e2j+p8++23Onz4sNq1aydJstlsunHjhv755x8dOXJErVu3liRVq1ZNzz33XIr9t2/fXlarVTly5FDTpk21e/du1a9fXy+//LLWrVun1q1b6/vvv7c/8PduKlasqKJFi0qSypUrp61bt0q6de/O4cOH7Q/1jYmJSbFGSpf8NWvWTEWKFNGSJUt0+vRp/fjjj6pSpYokqWXLlho4cKDq16+vOnXqqG/fvkn2Dw0N1enTpxM9VPb69eu6du2amjdvrjFjxmj79u2qXbu2Bg8efNfPefuBxPXr15d068HEd57jggULqlChQpKknTt3qkWLFsqdO7ckqW3btho/frzOnj2rxo0b67vvvtNzzz2nmjVr6siRIzp27Ji2bdumJk2aJDlu+fLl7ZeQPv/889q6davCw8N16NAhLV26VJJUsmRJ+4OK7+a7775T27ZtlS1bNklSjx49NGfOHMXGxiZ5cO6/3b7HqVy5coqNjVVkZORdz++/H1T96quvqkOHDtq3b5/27dun+fPna/78+fafjzt7XL58uSwWizw8PNS5c2ctXrxYffr00YULF3T58mXt3LlTAwYM0Jo1azRw4EDt27dP77//voKCgpz2fQNAWiFQAXjsVa5cWadPn9alS5eSXCq1d+9e+y/8kuy/uEqS+ddTJ26HJJvNplq1amn69On2986fP698+fJp69atiWrYbDb16dNHXbp0kXQrwN15f8ydx7jbpXXu7u6J9rm9bdeuXTV69GhZrVY1adJE2bNnv8uZSHoci8Vi78Fms2nGjBn2y7+uX79+388i+vzzzxUcHKyuXbvK29tbuXLlsk/k4evrq3bt2mnXrl1as2aNFi5cmOSXc5vNJh8fHw0bNsy+fPHiRT355JPq3LmzGjRooF27dmnnzp366KOP7jpZh7u7e5Lv8M7z+O/v6d+MMYqPj1ejRo00Y8YMXbx4UXXq1FGePHn0/fff67vvvpOvr2+S/bJkyWJ/ffv83j7unf3c2UtKbDZbou/AZrOl+MeBf7v9Pd/e3xhz1/N7pwMHDujnn39Wnz591KBBAzVo0ECDBw+Wl5eXdu3apaeeeirVHt3c3PTyyy9rx44dOnTokCZNmqS5c+dq8+bNqlKlirJnz+7U7xsA0gqz/AF47OXPn1/du3fX4MGDE93MHhISoi1btiQ7UlKmTBkZY+z3zmzbts0ehGrVqqVdu3bpxIkTkqQdO3aoVatWyY7o1K1bV6tXr1ZkZKQkacaMGRo+fLieeuoplS9fXqtWrZIk/frrr/YRleR88cUXMsYoPDxcmzZtUr169SRJVatWlZubmxYsWJDor/x3cnd3v6dfwuvWratFixbJGKPY2FgNGDDAPqJyr77//nu1adNGHTp0UPHixbV9+3YlJCQoPj5enp6eunHjhl555RUFBgbqyJEjio2NTdRf3bp1tWHDBl28eFHSrZkZe/bsKUnq3LmzfvvtN7Vt21Zjx47V9evXdenSpRQ/X8mSJeXh4aHvvvtO0q37qY4ePZpsSKxXr542btxonxkvJCREuXLlUtGiRVW1alWdOXNGoaGhql27turUqaPFixerWLFiiYLF3eTIkUNVq1a139905swZ7dmzJ9le7vw89erVU0hIiP1epiVLlujFF19MMjpltVqVkJCQJED+293O751y586t2bNna//+/fZ1ly5dUmRkpEqXLp2k5tKlS+0/N8HBwapdu7YkqUmTJvrkk09UunRpeXh4qGbNmpo6dap9ZM+Z3zcApBVGqABA0pAhQ7Rq1SoNGDBAsbGxio2N1QsvvKAVK1bYL/u6U6ZMmRQUFKTRo0dr6tSpKleunPLkySNJKlWqlMaMGaPBgwfbR4tmz56d7OhQhw4ddOHCBXXs2FEWi0UFCxbUhAkTJElTp06Vv7+/VqxYoWeffVYlSpRIsf+cOXOqbdu2iomJUbdu3RJdLta2bVtt3LhRZcuWTXbfmjVraujQoRo7dqx9xrbkjBw5UuPHj5e3t7fi4uJUu3Zt9enTJ8Xtk9O7d2+9++679pGnypUr2y+fHDFihIYOHSqr1SqLxaL333/f/kv27f4CAgLUt29f9e7dWxaLRTly5NBHH30ki8WioUOH6v3339f06dNlsVg0cOBAFS5cWAkJCQoKCtLAgQP10Ucf2XuxWq2aNWuWAgMDNXXqVBUrVkxPP/20smTJohs3biTqu06dOurVq5d69uwpm82m3Llza+7cuXJzu/V3yZdeekmHDx9W7ty5Va1aNYWHhyd7ud/dTJw4USNHjtTnn3+u/Pnzq3DhwolGs26783yMHDlS58+fV4cOHWSz2VS0aFFNnjw5yT558+ZVxYoV1bJlSy1btizFHurWrZvi+b1T8eLFFRQUpGnTpunvv/9W5syZlTNnTr3//vsqUaKELl26ZN921KhRGjdunP3npl69enr99dcl3frjw8WLF/XKK6/Yj79x40Z5enqm2s/9ft8AkFYsJrU/VwEAMqz4+HgNHDjQ/nwtJDZx4kT7zHznz5+Xj4+PvvnmGz3xxBMu72X27Nlq0qSJfUbFVq1aaf78+SpVqpTLewEA3DtGqADgEXX8+HG98soratSokZo1a/aw20mXChUqpF69eslqtcoYo3Hjxj2UMCVJxYoVk6+vr9zc3JSQkKC+ffsSpgAgA2CECgAAAAAcxKQUAAAAAOCgRypQGWN08+bNVGcxAgAAAABneKQCVWxsrMLCwhQbG/uwWwEAAADwGHikAhUAAAAAuBKBCgAAAAAcRKACAAAAAAcRqAAAAADAQQQqAAAAAHAQgQoAAAAAHESgAgAAAAAHEagAAAAAwEEEKgAAAABwEIEKAAAAABxEoAIAAAAABxGoAAAAAMBBBCoAAAAAcBCBCgAAAAAcRKACAAAAAAcRqAAAAADAQQQqAAAAAHCQNa0PEBkZqc6dO2vOnDk6ceKEpk6dan/vwoULqlSpkubOnZton7Vr12rKlCnKkyePJOnll1+Wr6/vfR/bxCfIYnV/sA+QBrUAAAAAPBrSNFAdPHhQo0aN0qlTpyRJ9evXV/369SVJly5d0iuvvCJ/f/8k+4WFhcnPz09eXl4PdHyL1V2XZi99oBq35R3QzSl1AAAAADw60vSSv+DgYAUGBipfvnxJ3ps0aZI6d+6sYsWKJXnv8OHDWrt2rby9vTV06FCFh4enZZsAAAAA4JA0DVTjx49X9erVk6w/deqUfvzxR/Xo0SPZ/fLmzas33nhDX375pQoWLKgxY8akZZsAAAAA4JA0v4cqOStXrlSXLl3k4eGR7PtBQUH213369FHjxo3vq35YWJgkqVq1ao43mYwDBw44tR4AAACA9MOR/PBQAtW2bdu0YMGCZN+LiIhQSEiIevXqJUkyxsjd/f4mg6hQoYIyZ878oG0m4eyABgAAACBjc/m06VevXlVMTIyKFCmS7PvZsmXTJ598ooMHD0qSli5det8jVAAAAADgCi4foTp79qwKFCiQZP3IkSPl6emphg0bavr06Ro9erRiYmJUrFgxTZo0ydVtAgAAAECqLMYY87CbcJabN28qLCws0SV/TJsOAAAAIK24/JI/AAAAAHhUEKgAAAAAwEEEKgAAAABwEIEKAAAAABxEoHKQiY9P1/UAAAAApL2H8mDfR4FDszzzAAAgAElEQVTFatXFOR85rV6+1wc6rRYAAAAA12CECgAAAAAcRKACAAAAAAcRqAAAAADAQQQqAAAAAHAQgQoAAAAAHESgAgAAAAAHEagAAAAAwEEEKgAAAABwEIEKAAAAABxEoAIAAAAABxGo0ikTH5cuawEAAAD4H+vDbgDJs1gz6fzH/k6pVfCND5xSBwAAAEBijFABAAAAgIMIVAAAAADgIAIVAAAAADiIQAUAAAAADiJQAQAAAICDCFQAAAAA4CACFQAAAAA4iEAFAAAAAA4iUAEAAACAgwhUAAAAAOAgAhUAAAAAOIhABQAAAAAOIlABAAAAgIMIVAAAAADgIAIVAAAAADiIQAUAAAAADiJQAQAAAICDCFQAAAAA4CACFQAAAAA4iEAFAAAAAA4iUAEAAACAgwhUAAAAAOAgAhUAAAAAOCjNA1VkZKS8vLx09uxZSZK/v7+aNGkiHx8f+fj4aOvWrUn2+e2339S2bVs1bdpUI0eOVHx8fFq3CQAAAAD3LU0D1cGDB/XKK6/o1KlT9nVhYWFaunSp1q1bp3Xr1qlx48ZJ9hs2bJjeffddff311zLGKDg4OC3bBAAAAACHpGmgCg4OVmBgoPLlyydJunHjhs6dO6cRI0bI29tbM2fOlM1mS7TPX3/9pZiYGFWuXFmS1LZtW23evDkt2wQAAAAAh1jTsvj48eMTLV++fFk1a9ZUYGCgcubMqf79+2v16tXq2LGjfZuLFy8qb9689uW8efPqwoUL93XcsLAwSVK1atUeoPukDhw4YH/t7NppXf/O2gAAAACScuR38DQNVP9WpEgRBQUF2Ze7d++uL774IlGgstlsslgs9mVjTKLle1GhQgVlzpz5wRv+l7QIUa6qn9a9AwAAAI8jl87yd+TIEX399df2ZWOMrNbEma5AgQK6dOmSffny5cv2SwYBAAAAID1xaaAyxuj9999XeHi44uLitHLlyiSTUhQqVEiZM2e2X6K2bt06vfTSS65sEwAAAADuiUsDVdmyZdWvXz+98soratmypcqVKycvLy9JUt++fXX48GFJ0uTJk/XBBx+oWbNmio6OVo8ePVzZJgAAAADcE5fcQ7V9+3b7665du6pr165Jtpk/f779ddmyZbV69WpXtAYAAAAADnPpCBUAAAAAPEoIVAAAAADgIALVY8oWH5suawEAAAAZiUufQ4X0w83qoROzfJxSq+R/1jmlDgAAAJDRMEIFAAAAAA4iUAEAAACAgwhUSBPcowUAAIDHAfdQIU24WT10YI63U2pVe/0rp9QBAAAAnI0RKgAAAABwEIEKAAAAABxEoAIAAAAABxGokCElOHGiCmfWAgAAwOOFSSmQIblbPRQ6v6VTar3cd4NT6gAAAODxwwgVkAxGwAAAAHAvGKECkuFu9dD6hc2dUsur9yan1AEAAED6wwgVAAAAADiIQAUAAAAADiJQAQAAAICDCFTAQ8CkFwAAAI8GJqUAHgJ3q4dWLGrqlFqde33tlDoAAAC4f4xQAY+Y+ATnjlg5ux4AAMCjhBEq4BFjdffQJ585Z/RLkvr0YAQMAAAgJYxQAbgvzhyxYvQLAABkdIxQAbgvVncPTfvcOSNgvl0Y/QIAABkbI1QAAAAA4CACFQAAAAA4iEAFAAAAAA4iUAEAAACAgwhUAAAAAOAgAhUAAAAAOIhABQAAAAAOIlABAAAAgIMIVAAAAADgIAIVAAAAADiIQAUAAAAADiJQAQAAAICDCFQAAAAA4KB7ClR///23duzYoYSEBJ07dy6tewIAAACADCHVQBUaGqrOnTvrvffe05UrV9SyZUt98803rugNwGMoLiE2XdYCAABIjjW1DYKCghQcHKx+/fopX758+vzzz/XOO++oUaNGrugPwGMmk7uH3g1u5pRaYzpudkodAACAlKQ6QpWQkKB8+fLZl8uVKyeLxXLPB4iMjJSXl5fOnj0rSVq5cqW8vLzk7e0tf39/xcYm/Qvy2rVrVbduXfn4+MjHx0fTpk275+MBwN0wAgYAAJwp1RGqrFmz6ty5c/YQtX//fmXOnPmeih88eFCjRo3SqVOnJEknT57UggULtGbNGmXPnl1+fn76/PPP1atXr0T7hYWFyc/PT15eXvf3aQAgFZncPfTqWueMgH3aJukIWGxCnDzcMzmlvjNrAQCAtJFqoBoyZIh69+6tS5cuqVOnTjp16pRmzZp1T8WDg4MVGBio4cOHS5I8PDwUGBioHDlySJJKly6d7CQXhw8f1qlTpzR37lyVKVNGAQEBevLJJ+/ncwHAQ+HhnknN173ulFqbfOYkWUdgAwAgfUk1UFWtWlXBwcH6+eefZbPZVKlSJeXOnfueio8fPz7RcqFChVSoUCFJ0tWrV7Vs2TJ98MEHSfbLmzevevfurapVq2rq1KkaM2aMpkyZck/HlG6NcElStWrV7nmfe3HgwAH7a2fXTuv6d9am/qNdn5/NR79+i7WjnVJ7Y5vRSeoDAPA4c+Tf8VQDVZ8+ffTJJ5+ofv369nUdO3ZUcHDwfR/stgsXLqhPnz5q166datSokeT9oKCgRMdv3LjxfdWvUKHCPV+WeD/S4hdVV9XPyL1T/9Gun5F7pz4AAEgxUL311ls6efKkzpw5I29vb/v6+Ph4eXh4OHzAEydOqE+fPurevbt69+6d5P2IiAiFhITY76syxsjd3d3h4wEAAABAWkkxUA0fPlx//fWXAgICFBAQYF/v7u6uUqVKOXSwyMhIvfbaaxo0aJBat26d7DbZsmXTJ598oipVqqhSpUpaunTpfY9QAQAcE5sQLw/3VC9ecHktAADSqxT/pStcuLAKFy6szZs3y80t8ezq0dHRDh1s9erVunz5sj799FN9+umnkiRPT0+9/fbbGjlypDw9PdWwYUNNnz5do0ePVkxMjIoVK6ZJkyY5dDwAwP3xcLeq5ZqpTqm1oe1gp9QBACA9S/VPh9u3b9fMmTMVHR0tY4xsNpuuXbumn3/++Z4Psn37dklSr169kkyRftudE1hUr15da9euvef6AID0j9EvAMCjKNV/jSZNmqRBgwZp+fLl6tu3r7755htlz57dFb0BAB4hHu5WtQyZ65RaG9r1d0odAAAelFtqG2TNmlUtWrRQ5cqVlTlzZo0ePVqhoaEuaA0AgHsXm5CQLmsBAB5tqY5QZc6cWbGxsXr22Wf122+/qUaNGrJYLK7oDQCAe+bh7i6vkMVOqbW+XU+n1AEAPPpSDVSenp7q16+fJk6cqE6dOunAgQN66qmnXNEbAADpRmxCgjyc9BgPZ9YCADxcqQaq119/Xa1atVL+/PkVFBSk/fv3J3ouFQAAjwMPd3d5rV7plFrr23dySh0AwMN313uoTp48qYsXL+qZZ56RJJUvX17NmjVLNCMfAAB4cGl9Dxj3mAFA2khxhOqTTz5RUFCQJGnevHl68cUXtWjRIs2aNUvly5d3WYMAADwOPNzd1Wr1OqfU+rK9T7L1W6/e6pT6X7Rv7JQ6APAoSDFQrVy5Uhs3btT58+e1cOFCLV++XD/++KNGjx7NJX8AACCR2ASbPNxTnTzY5bUAIK2lGKiyZs2qggULqmDBgnrjjTdUuXJlbdy4UU888YQr+wMAABmAh7ub2obsdkqtNe1qJ1lHYAOQXqUYqNzvmH0oR44cmj59urJkyeKSpgAAAO7k4e6mDiFhTqm1ql0Fp9QBAOkeHuwrSTlz5iRMAQAAAMC/pDhCdeXKFX366adJXt/26quvpm1nAAAAAJDOpRio6tSpo6NHjyZ5DQAAAAC4JcVA9cEHH7iyDwAAAADIcJjiBgAAPNbiEky6rAUgY0hxhAoAAOBxkMndorfWnnFKrZltijilDoCMgxEqAAAAAHDQPY1QXbhwQVeuXJEx/xvGLl++fJo1BQAAAAAZQaqBatq0afr000/19NNP29dZLBZt27YtTRsDAAB4FMQnGFndLemuFgDnSDVQffXVV9q+fXuiQAUAAIB7Y3W3aObaC06p9Vab/EnWJSQYuTspZDmzFvC4SDVQPfXUU4QpAACAdMrd3aLgkMtOqdWxXdLf+dI6sNnijdyszqnvzFrAvUoxUP3666+SpOeff17jxo2Tt7e3rNb/bc49VAAAAI8+d3eLti6/5JRajV/Jm2Sdm9WivYsuOqV+jV75kqwjsCGtpRio/vOf/yRa3r59u/0191ABAAAgI3CzWvT7x8655LLsG0kvuQRSDFS3A9Tff/+tAgUKJHrv2LFjadsVAAAAAGQAKT6H6tq1a7p27Zr69eun8PBwXbt2TeHh4bp8+XKS0SsAAAAAeBylOEI1ZMgQ7dq1S5JUo0aN/+1gtapp06Zp3xkAAAAApHMpBqoFCxZIkvz9/fXBBx+4rCEAAAAgIzDxRhYnTlLh7HpwjVSnTT9x4oQWLlyoJk2aqHDhwq7oCQAAAEj3LFaLzk8857R6Bd95xmm14Dop3kN12zvvvKMrV66ob9++atu2rebMmaM//vjDFb0BAAAAQLqWaqCqVq2ahg0bpk2bNqlPnz5auXKlWrZs6YreAAAAACBdS/WSv7Vr12rXrl3au3evChUqpDZt2qhu3bqu6A0AAAAA0rVUA9W4ceOULVs29e/fX02bNlXevEmfcA0AAAAAj6NUA9XevXt14MAB7dy5U/3795fNZlOtWrX0zjvvuKI/AAAAAEi3Ur2Hymq1qkaNGmratKk8PT0VExOjHTt2uKI3AAAAAEjXUh2hGj58uL7//nsVLFhQjRs3VlBQkEqWLOmK3gAAAIDHlom3yWJNdfzD5bWQWKqBqnz58vL19VXBggVd0Q8AAAAASRarm/6e+ptTahUYXM4pdZBUqjG1ffv2mjt3rjw9PfXSSy/J399fkZGRrugNAAAAANK1VAPVhAkTFBsbq6CgIH388ceyWCwaO3asK3oDAAAAgHQt1Uv+Dh48qC+//NK+PG7cOB7sCwAAAAC6hxGqhIQE2Ww2+7LNZpO7u3uaNgUAAAAAGUGqgapmzZoaNGiQ9uzZoz179mjw4MGqUaPGPRWPjIyUl5eXzp49K0navXu3vL291aRJE02bNi3Zfc6dO6euXbuqWbNmGjBggKKiou7j4wAAAACA66QaqPz9/fXcc89p6tSp+vDDD1W8eHENHz481cIHDx7UK6+8olOnTkmSYmJiNGLECH388cfauHGjwsLCkn2e1XvvvacuXbpo8+bNqlChgj7++OP7/1QAAAAA4AKpBqoRI0boP//5j1atWqU1a9bI19dXmTNnTrVwcHCwAgMDlS9fPknSoUOHVLRoURUpUkRWq1Xe3t7avHlzon3i4uK0b98+NW3aVJLUtm3bJNsAAAAAQHqR6qQUv/32m4wxslgs91V4/PjxiZYvXryovHnz2pfz5cunCxcuJNrmn3/+UY4cOWS13morb968SbYBAAAAgPQi1UCVL18+tWzZUpUqVVL27Nnt60eNGnVfB7LZbIlCWXIhLbl19xvkJCksLEySVK1atfve924OHDhgf+3s2mld/87a1H+06/OzSf30Wj8j9079R7t+Ru6d+g+3fkb/NxdJOXLOUw1UVapUUZUqVRxq6E4FChTQpUuX7MuXLl2yXw54W+7cuRUREaGEhAS5u7snu829qFChwj1dlni/0uJ/GlfVz8i9U//Rrp+Re6f+w62fkXun/qNdPyP3Tv1Hu35a9/64SjVQDRw40CkHqlSpkk6ePKnTp0+rcOHCWr9+vdq1a5dom0yZMql69erauHGjvL299cUXX+ill15yyvEBAAAAwNnuOinFokWLtGHDBklSkyZNVLVqVb344os6efLkfR8oc+bMmjBhgv7zn/+oRYsWKlGihJo1ayZJGjlypLZt2yZJCgwMVHBwsFq0aKH9+/dr0KBB930sAAAAAHdn4m2pb/QQamU0KY5QLVq0SF9//bXGjh0r6VYg+uqrr7RlyxYtXLjQvj4127dvt7+uVauWvvzyyyTb3DmBRaFChbRkyZJ7/gAAAAAA7p/F6qYL0390Sq38g/6fU+pkRCkGqpCQEC1ZskS5cuWSJLm7u6tQoULq0qWLvL29XdYgAAAAAKRXKV7yZ7PZ7GFKkmrUqCHp1kiVh4dH2ncGAAAAAOlcioEqPj4+0bK/v7/9tSNTmQMAAADAoybFQFWmTBl9++23Sdbv2LFDpUuXTtOmAAAAACAjSPEeqjfffFOvvvqq+vXrpzp16shisWjv3r2aN2+eFi9e7MoeAQAAACBdSjFQlSlTRgsXLtT06dMVFBQkY4yqVq2quXPnqlixYi5sEQAAAADSp7s+2Lds2bKaM2eOq3oBAAAAgAzlrg/2BQAAAACkjEAFAAAAAA4iUAEAAACAg+4rUJ05c0bHjh1Lq14AAAAAIEO566QUd1q0aJG+++47WSwWFSlSRKNHj07DtgAAAAAg/UsxUP3xxx8qUaKEfXnPnj1auHChJKlp06Zp3xkAAAAApHMpBqoJEyaoVKlSGjhwoLJly6aiRYtq1KhRcnd3V5EiRVzZIwAAAACkSykGqnnz5mnTpk3q1auXunXrphEjRmjXrl2KjY1VnTp1XNkjAAAAAKRLd52Uonnz5vrss8904sQJvfrqq8qTJ48aNGggDw8PV/UHAAAAAOlWiiNUf/31l5YtW6ZMmTKpZ8+eioyM1IQJE1SgQAENGjRITzzxhCv7BAAAAIB0J8URqrffflsVKlTQM888o+HDh+vZZ5/Vxx9/rHr16qlv376u7BEAAAAA0qUUR6jCw8PVuHFjRUdHa9myZfb1DRo04B4qAAAAANBdAtVrr72mZs2aKVOmTBoyZEii97iHCgAAAADuEqg6d+6szp07u7IXAAAAAMhQ7jrLHwAAAAAgZQQqAAAAAHAQgQoAAAAAHHTXQHX06FFt2bJFFy5ccFU/AAAAAJBhpBioQkJC1K1bN82fP1+tWrXS999/78q+AAAAACDdS3GWvyVLluirr75S/vz59fPPP2vatGmqW7euK3sDAAAAgHTtrpf85c+fX5JUpUoV/fPPPy5pCAAAAAAyihQDlcViSbTs7u6e5s0AAAAAQEZyz7P8/TtgAQAAAMDjLsV7qI4cOaKqVaval2NiYlS1alUZY2SxWPTTTz+5pEEAAAAASK9SDFRbt251ZR8AAAAAkOGkGKgKFSokSTp58qSOHj2qLFmy6LnnntMzzzzjsuYAAAAAID1LMVBFRUVpyJAh2r9/v4oVKyaLxaITJ07o5Zdf1oQJE+Th4eHKPgEAAAAg3UlxUorZs2erQIEC2rVrl1avXq1Vq1Zp165dypEjh6ZNm+bKHgEAAAAgXUpxhGrHjh364osvEk2XnjVrVo0YMUKdOnVySXMAAAAAkJ6lOELl7u6e7LOnsmTJwjOpAAAAAED38WDfOxlj0qQZAAAAAMhIUrzk7/r169q6dWuy4SkiIiJNmwIAAACAjCDFQPXMM8/os88+S/a9ggULpllDAAAAAJBRpBiolixZ4so+AAAAACDDSTFQSdLu3bv17LPPqnDhwgoNDdWKFStUoUIFvfHGG3JzS/H2q7tatWqVli5dal8+e/asfHx89O6779rXffTRRwoJCdETTzwhSerYsaO6du3q0PEAAAAAIK2kGKhWrVql2bNna9asWYqPj9fbb7+t119/XSdOnNCMGTPk6+vr0AE7dOigDh06SJKOHTumN998UwMHDky0TVhYmKZOnaoqVao4dAwAAAAAcIUUh5mWL1+ulStXqnz58tq4caP+3//7fxowYIAmTJigb775xikHHz16tHx9fZU7d+5E68PCwjR37lx5e3trzJgxunnzplOOBwAAAADOlOIIVUJCgvLmzStJ+umnn1S3bt1bO1itDl/ud6fdu3crJiZGzZs3T7Q+KipK5cqV07Bhw1S0aFH5+fnp448/vq8RsbCwMElStWrVHrjPOx04cMD+2tm107r+nbWp/2jX52eT+um1fkbunfqPdv2M3Dv1H259/s29e/2MyJFzkmKgstlskqS4uDj98ssvGjx4sCQpPj5e0dHRDrb4PytWrNCrr76aZH327Nk1f/58+3Lv3r01YsSI+wpUFSpUUObMmR+4x39Li/9pXFU/I/dO/Ue7fkbunfoPt35G7p36j3b9jNw79R/t+hm59/QsxUBVuXJlTZgwQTdv3lSePHn0/PPP6/r16/r4449Vo0aNBzpobGys9u3bpwkTJiR579y5c9q9e7fat28v6dZDhK3Wu86dAQAAAAAPRYrX7vn5+Sk2NlaXL1/WrFmzJEmTJ0/Wr7/+quHDhz/QQY8cOaJixYopW7ZsSd7LkiWLPvzwQ505c0bGGC1btkyNGzd+oOMBAAAAQFpIcegne/bsiaYyl6TAwEC5u7s/8EHPnDmjAgUKJFrXt29fvfXWW3rhhRc0ZswYDRgwQHFxcapatWqylwYCAAAAwMN212vp/v77b0VHR6tEiRKaNm2aoqKi5O7ursGDBz/QPUotWrRQixYtEq27876ppk2bqmnTpg7XBwAAAABXSPGSv0OHDqlNmzb2GfM2bdqkXLly6dixY1q2bJnLGgQAAACA9CrFQDVjxgxNmzZNrVq1knTrEsCBAwdq7Nix2rBhg8saBAAAAID0KsVAdebMGdWsWdO+bIyRJBUqVEjh4eFp3xkAAAAApHMpBioPD49Ey3de5vfEE0+kXUcAAAAAkEGkGKiyZcumv//+276cPXt2SdL58+eVJUuWtO8MAAAAANK5FANVx44dNWTIEF25csW+Ljw8XP7+/urSpYtLmgMAAACQMZn4hHRZy9lSnDa9ffv2+vPPP9WwYUOVLFlSFotFf/zxh3r06CEvLy9X9ggAAAAgg7FY3XVx1nan1Mr3H0+n1EkLd30O1eDBg9WzZ0/9/PPPkqSKFSsqX758LmkMAAAAANK7uwYqScqTJ48aNWrkil4AAAAAIENJ8R4qAAAAAMDdEagAAAAAwEEEKgAAAABwUKr3UHl6espisdiXLRaLsmbNqueee05+fn5MUgEAAADgsZVqoGrUqJGioqLUtWtXubm5afXq1YqKilKZMmX07rvvas6cOa7oEwAAAADSnVQv+du/f7/Gjx+v559/XmXLltWoUaN07Ngx9erVS3/99ZcregQAAACAdCnVQBUVFaXIyEj7cmRkpGJiYtK0KQAAAADICFK95K9du3bq2LGjmjVrJmOMtmzZog4dOmjJkiUqUaKEK3oEAAAAgHQp1UDVr18/Pf/889qxY4esVqsCAgJUs2ZNhYWFqU2bNq7oEQAAAADSpVQD1eDBg9WxY0eNHDky0foKFSqkWVMAAAAAkBGkeg9V9erVNXXqVDVu3Fhz587VpUuXXNEXAAAAAKR7qQaqLl26KDg4WHPmzFF4eLg6d+6sN9980xW9AQAAAEC6lmqgui0mJkaxsbEyxsjN7Z53AwAAAIBHVqr3UH366adas2aNYmNj1b59ewUHB+vpp592RW8AAAAAkK6lOtQUFhamUaNG6euvv1bv3r21d+9edejQwRW9AQAAAECyTHxCuqiV6gjVlClTFB4ernnz5mnZsmWKiopS9+7dHT4gAAAAADwoi9VdF4O+dEqtfG+2cnjfuwaqP/74Q5999pnWrVunQoUKKSYmRt9++61y5szp8AEBAAAA4FGR4iV//fr1U7du3WS1WvXZZ59p/fr1yp49O2EKAAAAAP5PioHqv//9r8qXL6/nnntORYsWlSRZLBaXNQYAAAAA6V2KgSo0NFRt2rTR+vXrVbduXb311lu6efOmK3sDAAAAgHQtxUBltVrVokULLVmyRGvWrFG+fPl08+ZNNWnSRMuXL3dljwAAAACQLt3TE3pLlSqlUaNG6bvvvtNrr72m4ODgtO4LAAAAANK9ewpUt2XNmlWdOnXS2rVr06ofAAAAAMgw7itQAQAAAAD+h0AFAAAAAA4iUAEAAACAgwhUAAAAAOAgAhUAAAAAOIhABQAAAAAOIlABAAAAgIMIVAAAAADgIOvDOGj37t119epVWa23Dj9mzBhVqlTJ/v7u3bv1wQcf6ObNm2revLl8fX0fRpsAAAAAcFcuD1TGGJ06dUrffvutPVDdKSYmRiNGjNCSJUtUsGBB9e/fXzt27FD9+vVd3SoAAAAA3NX/Z+/O42rK/z+Av66SVNYoWwwlu0QzZM+SbdpE2aqvbFmiFEWJVEOyjT0zjCVLlpF9KZIlpESKwgwilDUhWu75/dHjnF9XMTP3fM41xvv5eHwfX93m8b7Hce855/35fN7vj8qX/P35558AAFdXV1hbWyMiIkLh9ykpKWjUqBEMDAygrq4OKysrHDt2TNWHSQghhBBCCCF/SeUzVK9fv4a5uTnmzJmDwsJCODs7o3HjxujSpQsAICcnB7Vr1xb+ez09PWRnZ/+j90hNTQUAdOjQgd2BA0hKShL+zDq21PFLx6b4/+349Nmk+P/W+F/zsVP8/3b8r/nYKf6XjU/33P9efGViqjyhMjU1hampqfDzkCFDEBcXJyRUcrkcMplM+D3HcQo//x2tW7dGpUqV2BxwKVJ8aVQV/2s+dor/347/NR87xf+y8b/mY6f4/+34X/OxU/z/dvyv+dj/zfFVvuQvMTERFy5cEH7mOE6hlqpOnTp4+vSp8PPTp0+hp6en0mMkhBBCCCGEkL9D5QlVXl4eFi1ahA8fPuDNmzfYt28f+vbtK/zexMQEd+/exf3791FcXIxDhw6he/fuqj5MQgghhBBCCPlLKl/yZ2FhgWvXrsHW1hZyuRwjRoyAqakpbGxssH79eujr62PhwoVwd3fHhw8f0KNHD/Tv31/Vh0kIIYQQQgghf+mL7EPl4eEBDw8Phdf2798v/Nnc3BwHDhxQ9WERQgghhBBCyD+i8iV/hBBCCCGEEPJfQQkVIYQQQgghhCiJEipCCCGEEEIIURIlVIQQQgghhBCiJEqoCCGEEEIIIbrDM5gAACAASURBVERJlFARQgghhBBCiJIooSKEEEIIIYQQJVFCRQghhBBCCCFKooSKEEIIIYQQQpRECRUhhBBCCCGEKIkSKkIIIYQQQghREiVUhBBCCCGEEKIkSqgIIYQQQgghREmUUBFCCCGEEEKIkiihIoQQQgghhBAlUUJFCCGEEEIIIUqihIoQQgghhBBClEQJFSGEEEIIIYQoiRIqQgghhBBCCFESJVSEEEIIIYQQoiRKqAghhBBCCCFESZRQEUIIIYQQQoiSKKEihBBCCCGEECVRQkUIIYQQQgghSqKEihBCCCGEEEKURAkVIYQQQgghhCiJEipCCCGEEEIIURIlVIQQQgghhBCiJEqoCCGEEEIIIURJlFARQgghhBBCiJIooSKEEEIIIYQQJVFCRQghhBBCCCFKooSKEEIIIYQQQpRECRUhhBBCCCGEKIkSKkIIIYQQQghREiVUhBBCCCGEEKIkSqgIIYQQQgghREmUUBFCCCGEEEKIkiihIoQQQgghhBAlUUJFCCGEEEIIIUpS/xJvumrVKhw9ehQA0KNHD8ycObPM7/fu3YuqVasCABwcHDBy5EiVHychhBBCCCGEfI7KE6r4+HicO3cO+/btg0wmw9ixYxEdHY2+ffsK/01qaiqWLl0KU1NTVR8eIYQQQgghhPxtKk+oateuDV9fX2hoaAAADA0N8ejRI4X/JjU1FeHh4cjKysL3338PHx8fVKpUSdWHSgghhBBCCCGfpfKEqmnTpsKf7927h6NHj2LHjh3Ca2/fvkWLFi0wY8YMNGrUCL6+vlizZg08PT3/9nukpqYCADp06MDuwAEkJSUJf2YdW+r4pWNT/P92fPpsUvx/a/yv+dgp/n87/td87BT/y8ane+5/L74yMb9IDRUA3L59GxMmTMDMmTPx3XffCa9ra2vjl19+EX52dXXF7Nmz/1FC1bp1a0lmtKT40qgq/td87BT/vx3/az52iv9l43/Nx07x/9vxv+Zjp/j/7fhf87H/m+N/kS5/SUlJ+N///gcvLy/Y2dkp/O7Ro0fYs2eP8DPHcVBX/2J5HyGEEEIIIYR8ksoTqsePH2Py5MlYvHgxBg0aVOb3mpqaCAsLw4MHD8BxHLZt26bQsIIQQgghhBBC/i1UPvWzYcMGfPjwAQsXLhReGzZsGE6dOoWpU6eiTZs2mD9/PiZOnIjCwkK0b98eo0ePVvVhEkIIIYQQQshfUnlC5e/vD39//zKvDx8+XPhzv3790K9fP1UeFiGEEEIIIYT8Y1+khooQQgghhBBC/gsooSKEEEIIIYQQJVFCRQghhBBCCCFKooSKEEIIIYQQQpRECRUhhBBCCCGEKIkSKkIIIYQQQghREiVUhBBCCCGEEKIkSqgIIYQQQgghREmUUBFCCCGEEEKIkiihIoQQQgghhBAlUUJFCCGEEEIIIUqihIoQQgghhBBClEQJFSGEEEIIIYQoiRIqQgghhBBCCFESJVSEEEIIIYQQoiRKqAghhBBCCCFESZRQEUIIIYQQQoiSKKEihBBCCCGEECVRQkUIIYQQQgghSqKEihBCCCGEEEKURAkVIYQQQgghhCiJEipCCCGEEEIIURIlVIQQQgghhBCiJEqoCCGEEEIIIURJlFARQgghhBBCiJIooSKEEEIIIYQQJVFCRQghhBBCCCFKooSKEEIIIYQQQpRECRUhhBBCCCGEKIkSKkIIIYQQQghREiVUhBBCCCGEEKIkSqgIIYQQQgghREmUUBFCCCGEEEKIkiihIoQQQgghhBAlUUJFCCGEEEIIIUqihIoQQgghhBBClEQJFSGEEEIIIYQoiRIqQgghhBBCCFESJVSEEEIIIYQQoiRKqAghhBBCCCFESV8koTp48CAGDhwIS0tLbNu2rczvb968icGDB6Nfv37w8/NDUVHRFzhKQgghhBBCCPk8lSdU2dnZWLZsGbZv346oqChERkbizp07Cv/NjBkzEBAQgOPHj4PjOOzatUvVh0kIIYQQQgghf0nlCVV8fDw6deqE6tWrQ0tLC/369cOxY8eE32dlZeH9+/do164dAGDw4MEKvyeEEEIIIYSQfwt1Vb9hTk4OateuLfysp6eHlJSUT/6+du3ayM7O/luxOY4DABQUFAivFWlUFHvIAIAPHz6Uea1IoxKT2J+OryVZbAAorlRN0vjQqC5pfJnE8dUqShtfXcL4Goxifyp+JXVpz01lieNXUZM2fnU1HYnjV5Y4PptrW3nxq6trSBa7JL501/yS+Gxum5+KX01dTeL4bMZRPx2fSfjPxOcki6+tVixZbACopMamfOGT9xR1aeNXkDg+KkobX64hXfwiTXalKeXGrySXLHZJfCbhPxNfJnF89tdNDQ0NyGR//7hlHJ+FqMjatWvx4cMHeHh4AAB27dqF1NRUzJ8/HwCQlJSEJUuWYPv27QCAe/fuwc3N7W/NUuXl5eHWrVvSHTwhhBBCCCHkP61169aoVOnvZ5oqn6GqU6cOEhMThZ+fPn0KPT09hd8/ffpU+PnZs2cKv/8cbW1tGBsbo2LFiv8oqySEEEIIIYQQoGSG6p9QeULVuXNnrFy5Ei9evEDlypVx4sQJBAUFCb+vX78+KlWqhKSkJHTo0AH79+9H9+7d/1bsChUqoEqVKlIdOiGEEEIIIYQoUPmSP6CkbXp4eDgKCwsxZMgQjBs3DuPGjcPUqVPRpk0bpKenw9/fH2/evEGrVq2wYMGCf5wpEkIIIYQQQojUvkhCRQghhBBCCCH/BV9kY19CCCGEEEII+S+ghIoQQgghhBBClEQJFSGEEEIIIYQoiRIqQgghhBBCCFESJVSEEEIIIYQQoiRKqAj5l3rz5s2XPgRCCCGEKKG4uPhLHwJRIWqbTsooLi6Gmpralz6Mb05sbCwSExMxadIkDBkyBC9evICPjw8GDx78pQ/tm5CYmIjNmzcjNzdX4fUtW7aIjp2bm4vDhw/j5cuXKH3JnTJliujYqjB69GjIZLIyr2/cuJHZe/zxxx9lzs/333/PLL7UUlJS0LZt2y99GEr52s89Ub3s7GwsWrQIt2/fRrt27eDt7Y2qVat+6cP6x3Jzc1GtWjWF17KyslC/fn3Rse3s7LBv3z7RccjXQf1LH8CXIOWDU0FBAeLi4vD27VsAJcnJw4cPMW3aNNGxV61a9dnfs3o4GzJkiGQXgV27dmHZsmV49eoVAIDjOMhkMty8eZNJ/BcvXuDAgQN4+/YtOI6DXC7Hw4cPsWjRIibxpbRq1SqEhITgyJEjaNu2LQICAuDk5PRVJVRjxozBhg0bJIldUFCAc+fO4d27d+A4Tvhusfrc+/r6YsqUKahXrx6TeKVNnjwZNWvWRNOmTctNTFh59eoV8vPzFc6Pubm56Ljjx48X/lxYWIiTJ0+iRo0aouPy5syZgzNnzqBhw4bCazKZjMk1OTIyEo6Ojp+8frL6/ISFheHVq1ewsbGBjY0NateuzSQuUHLO4+Pj8fLlS4XXbW1tRceW8tx/zvv376GpqSkqRlRU1Gd/L+b8ODk5ffa7KvX5YenGjRtlrptDhgwRFXP27NkwNjaGlZUVjh8/jgULFmDBggWMjvj/PXnyBMHBwUhISEDFihVhbm6O2bNno2bNmqLiPn78GBzHYfz48fjll1+EwYTi4mKMGzcOx44dE33stWrVQmJiItq2bQsNDQ3R8b40juPw8OFDGBgYMIspxWezNKnuieX5JhMqKR+cpk+fjtzcXGRmZsLMzAyXLl1C+/btmb5HSkoKnjx5gv79+0NdXR3R0dFMRlN4Ul4E1q1bhy1btqBp06ZM4/I8PDxQt25dXL16FX369MHp06fRpk0bSd5LCs2bN8fKlSthbW0NbW1tFBYWMo1/+vRprFq1Cq9evQLHcUJCe/LkSSbx8/Pz8fjxY9StW5dJvNK8vb3x7NkzPHz4EKamprh8+TLMzMyYxdfX12fygFqe3NxcRERESBKbt2LFCmzevBlFRUWoXr06cnJy0Lp1a+zevVt07I9vQN27d8fQoUPh4eEhOjYAXLhwAdHR0ZI8dKhqEcbWrVuRlZWF/fv3w9XVFfXq1YOdnR169+6NihUrioo9bdo0PH36FIaGhgoP+Sw+r1Kee96pU6ewbNky4cFGLpcjPz8fFy9eFBX30qVLAIDMzEzcv38fPXr0gJqaGs6dOwcjIyNR58fd3R1AySCgpqYmbG1toa6ujkOHDuHDhw+ijvtjZ8+exbJly/D69Wvm12V/f38kJCQgNzcXTZo0QXp6Otq3by/6oTU7O1sYPOvSpYtk187Zs2ejd+/eWLhwIQBgz549mDVrFsLDw0XFXbFiBS5duoScnByMHDlSeF1dXR09e/YUFZt3/fp1jBo1SuE1lgPIx44dQ3h4OF6/fg0AzO/nO3fuxKJFi5Cfny+8Vr9+fcTExDCJL9VnkyflPbFc3DdoxIgRksXu06cPJ5fLuaCgIO7GjRtcZmYmN3jwYKbv4ejoyL179074+f3795yDgwOz+B07duSaNWum8L/mzZsziT1kyBAmcT6lX79+HMdx3MKFC7mrV69yL1684KysrCR9T1bGjx/PzZ8/n+vevTv39u1bbsGCBdz48eOZvkefPn242NhY7sGDB9zDhw+F/7HSv39/rnnz5lyXLl24Xr16cRYWFlyvXr2YxO7du7fCd+vevXucvb09k9gcx3FHjx7lvLy8uN27d3P79u0T/sfC9OnTuevXrzOJ9SkWFhZcXl4e5+vry92/f5+LjY3lxo0bxyT2kydPFP539uxZrnfv3kxicxzHjRo1inv9+jWzeF9SVlYWFx4ezllYWHAjRozgLC0tuRMnToiKyV/XpKCKc9+nTx/uwoUL3Pjx47krV65wixYt4gIDA5nFHzVqFPf8+XPh51evXnEjR45kEru8+7ednR2T2DxLS0vu1KlTklyXLSwsuIKCAm7OnDnc7du3uZSUFCbPQLa2tgo/29jYiI5ZnvLiWltbM4sfHh7OLJaq9ezZk0tISFD4zLC8n1tYWHCZmZnc9OnTuQcPHnARERHc9OnTmcaX4rNZOr5U98TyfJMzVE5OTvD29kanTp2grv7/p4DFCIuuri5kMhkaN26MjIwM2NraMp9lePnypcIoZWFhobCEjgWxo4bl4Zdm1KtXDxMnTkTv3r2Zn3sAwlroxo0bIz09HSYmJkziAtLP7ixZsgQxMTFwdnaGlpYWDAwMhFFSVqpUqcJs9K08v/76q2Sxy/tuFRQUMIu/d+9efPjwAUlJSQqvi/ls9urVCzKZDO/fv8eRI0egr68PNTU15p8dANDT04OOjg6aNm2K9PR0WFpaYsmSJUxiOzg4CH+WyWSoWbMm/Pz8RMedNWsWgJJlNjY2NjAzM1Oo32S5hGj37t1YunSpZMuNd+/ejf379+Pp06ewtbXF9u3bUadOHWRnZ8POzg59+/ZVOnbDhg3x6NEjpqsqVHnuq1Spgk6dOuHKlSvIy8vDjBkzMHDgQGbxc3JyUL16deHnypUr4+nTp0xif/jwAXfv3kXjxo0BABkZGSgqKmISm1ejRg1YWFgwjcnT09NDxYoVYWhoiIyMDAwaNAh5eXnM30eqpcxt27bF4cOHMWjQIAAltcatW7dmFt/e3h6bNm1iWiagqmXGDRs2RIcOHVChgjT95XR1dWFgYIBmzZrh1q1bGDlyJHbs2MEsvtSfTSnvieX5JhMqKR6ceE2bNkVQUBCGDx8Ob29v5OTkMF9yMnToUNjb26N79+7gOA6xsbFwdnZmFl+KiwC/NENLSwtaWlqSnHsA6NSpE6ZOnQofHx+4uroiLS1N9Dp9XkhICPz8/GBkZMT05vFxHUBycjKSk5Ohra2N6OhoJufm8uXLAAAjIyMEBweXSWhZFaDXrl1bshpCIyMjhISEwMHBATNnzsTz58+ZfreePXvGvHZw69atTON9jo6ODqKiotCqVStERERAT08P79+/ZxI7Li6OSZyP/fDDDwr/L6W1a9dKutz44sWLcHd3R8eOHRVe19fXx9y5c5WKydfxvHjxAlZWVmjevLlC0iOmjkeV515TUxN3796FoaEhEhIS0KlTJ6YDjT179sTo0aNhaWkJjuNw9OhRDBgwgElsX19fODk5QV9fHxzH4fnz58wfyjp06IAFCxagW7duqFSpkvA6i+uyvr4+wsPDYW5ujrCwMABgMhB1+/Zt9O7dG0DJ4EROTg569+7NbLCoefPmkMlk4DgOu3btgr+/P2QyGd69e4dq1aohJCRE9N8BADw9PZmXCbB+5vsUV1dXODs74/vvv1e4LrBK2CpXroyLFy+iWbNmiImJQZs2bZjdUwDpPps8Ke+J5fkmu/xJ2XmlqKgIV69ehZmZGU6ePIkLFy7AwcEBxsbGTN8nNTUVCQkJkMlkMDc3R/PmzZnFLp1QFRYW4uzZszAxMVH6oeBT3rx5g8ePHzN/wMnMzETDhg2RmpqKxMREDBw4EHp6eqLjDh48GL///juDI1TEjxR/qg5g/fr1ot/Dycnpk79jWYA+ZcqUcmsIV6xYITp2UVERkpKS0LFjR8TExOD8+fNwdHRk9tmfO3cuevbsie7duzPvcvnq1SvcuHEDnTt3Rnh4ONLS0uDt7a3QCECs7OxsHD58GK6urli4cCHi4+MxYcIEYWRXGfxDzKcEBQUpHftjOTk50NPTQ2JiIjIyMmBvb89sMAQoGYiSbO08pLmvJCQkACj57JceAOGxSoakPvcJCQnYtm0bwsLCMHz4cGRmZsLe3h6+vr7M3uP48eMK90T+YZ+FgoIC3Lp1CzKZDM2aNSv330KM8q7PrK7Lb968QVxcHAYNGoStW7ciPj4eLi4u6NSpk6i4WVlZn/09y7puKfXv3x/Hjh1DaGgo+vfvj4YNG8LFxQUHDhz40of2l4YNG4bGjRuXOdesEqrbt29j9+7d8PX1xbRp0xAfHw93d3f873//YxJfqs8mr/Q9MTQ0FOfPnxd9T/ycbzKhkvLBSRVtMouKinDu3Lkyy/ykKgotKCiAq6srk6L63bt3IykpCTNnzoStrS20tbVhY2MDNzc3BkcKbN++HSNGjBB+Tk9Px5w5c0Q9SPGzO7t370bVqlUlm91xcnLCzz//LHQvys3NxeTJk5k2M9i5cyeGDRvGLN7H+vbtixMnTiAkJAT29vbQ0dGBh4cH9u7dyyT+n3/+idzcXIURQFZNX7p27Ypnz54pvMZqSdiYMWPQuXNntGjRAmFhYXBxccHevXtVOoOlDP57c/bsWeTl5cHKygpqamo4evQoqlWrhtDQUCbvM3fuXBQWFsLV1RVjxoxBly5dUFBQgMWLF4uOzc8Ax8bGoqCgQLLlxuPGjcOECRMkaeYj5X1FynP/KeW1qhbr9u3bZa4NLK7NWVlZiIiIKBNbio52UpCy8yoA3Lp1C3/++Sc0NTVhaGjItAMcIH3nXkdHR0RGRmLXrl3gOA6Ojo6wtrZmklBt3rwZq1evFpaxsV5mbG9vz+ze+iUEBgbCzs5O0u0mXrx4gWvXrqG4uBjt2rVDrVq1JHuvb3LJ38mTJxEZGanwGqsPuSraZHp5eeHRo0eSdHwqz9u3b/Ho0SMmsXbs2IF169bh0KFD6N27N/z8/ODg4MAsoTp06BCKi4vh4OCAn3/+GQcPHoSXl5eomKVnVx4/foyMjAzhZ5azO1LWAfAiIiIkTaikrCEMDg7G8ePH0aBBA+FzL5PJsG3bNibxz507xyROeXJzczFmzBgEBQXBzs4Otra2zD43/NIYHn/TZnHzHjp0KICSTmeRkZHCWv0ff/wRjo6O4g68lOvXr2Pv3r1YtWoVhgwZAnd3d9jb2zOJrarlxqmpqRg1apTwb8Hy4UnK+4qU555348YNrFu3rkxSwuo7EBgYiNjYWIWHeVbXZg8PD5iZmcHMzEyyOqGrV68iPDxcaB8tl8vx6NEjnDp1SnRsqTqvPn/+HFOnTsXt27fRqFEjyGQy3L17F+3atcPSpUtRpUoVJu8jdedeKcsENm/ejKioKEk6SgMl3RUjIiLQrVs3hU6irN7v9OnTWL16dZk96ljV/rZt2xZLlizBixcvJNlu4uzZs5g9ezbatWsHuVyOgIAAhISESFav+E0mVFI+OEndJhMoKYo9evSoZBd3vpAeKHkoyM3NxdixY5nF19PTQ1xcHJydnaGurs60Be3GjRsxZcoUrF+/Hj179sShQ4dEj4SWN4vAcRzevn0LHR0dUbFLk7IOgFenTh04OzvDxMREYa0+qyUCUtYQnjlzBidOnEDlypWZxPuYlCOhcrkcqampiImJQUREBG7evIni4mIGR10yCyu1vLw8vH79Wkj4X758KdTJsVBcXAy5XI6TJ08iMDAQ+fn5Cq16xVDVTMJvv/3GdOl1aVLeV6Q89zwfHx84OjpKtg/b+fPncezYMabLFHlFRUXw8fFhHre02bNnY8yYMdi3bx+cnJxw4sQJtGzZkknsFy9eoFevXtDV1UWlSpWY1TgtWbIEHTp0wKZNm4SH+YKCAqxcuRIhISFCm3OxcnJysGXLFoSGhsLS0hJjx46Fi4sLk9gA4OLigjdv3qB+/fpYunQpLl++jMmTJzOJ3aRJE0lnRA4dOgRAcYN1ls2OpKob59nZ2cHOzg6PHz/GoUOHMGzYMBgZGWHo0KHo06eP6PjLli3D9u3bhYGWBw8eYMqUKZRQsSTlg5MUHfI+ZmhoiKdPnzKpCypP6QRCJpOhatWqzBIHIyMjTJgwQdhczcPDg8l0b+nGDpaWlrh58ya0tLQQGxsLgM0odGxsLBITEzFp0iQMGTIEL168gI+PD7ONd2fNmqVQB+Dq6sq0DgAA2rVrxzTex+bNm4fk5GQYGRlh6tSpiI+PZ1bAXXpmSgpSjoTOmDEDixYtgqurKwwMDODg4CDUzrEi5XVt/PjxsLa2hpmZGeRyOa5cucK0/sXW1hZdu3ZF+/btYWJigoEDBzKbASs9QFQeVg8fnp6eOHr0KJNYH5PyviLluedpamqWSQhZMjAwkKwRQIcOHXDq1Cl07dpVslUnGhoasLe3R1ZWFqpWrYpFixbBysqKSWyplvslJyeX+bxraGhg+vTpsLGxYfY+UnbuBYCRI0cKf49WrVqhVatWzGI7OzvDysoKJiYmknTQZDGD+TlSdwUGSpKcAwcO4PDhw2jUqBH69u2Lo0eP4sSJE6LvXUVFRQqz1gYGBpDL5WIP+ZO+yRoqZ2fnch+cWIyoSN0mEyhZE52cnAxjY2OFCzyr5ROFhYXYtm0bLl68CHV1dfTo0QNDhgxh8jBbVFQkHHu1atVw6tQpdO/eXXSR7189nLK4gNnb2yMkJATXr19HYmIiAgIC4OTkxLRRhVR1AJ/CSbDz+R9//FFmiQCLv4OXlxeuXbuG9u3bK8yusWqMIHVx8tu3b/HgwQM0a9YM+fn50NLSYhKXJ+V1DQCePHmCK1euAADMzMyYD+jI5XJhSeGLFy+EWkKxVFU87+7ujmbNmsHExERhpoTFZ1/qOhKpzj2Prw3t2rWrwneX1dKk6dOn4+rVqzA1NVW4J7K47ktZW8lzdHREeHg4zp49i6ysLLi5uaFfv344fvy46Nju7u5YuXKlwmsuLi7YvHmzqLi2trZlOtT+nd/9U8uWLcPdu3eFJXkdO3ZEeno6du3axSS+p6cnevTogbZt2yp8b1l8Nm1sbNC3b98y1xg7OztRcVeuXAl3d/dPPveI/dyrqm58+PDhePbsGWxsbDB48GDhnBcVFaF79+6Ij48XFd/NzQ2dOnUSNgres2cPLl68iHXr1ok+9vJ8kzNUUk8h80p3yGNpwoQJTON9zN/fH+/fv4eDgwPkcjn279+PjIwM+Pv7i44tl8uRmJiIPXv2YM6cObhx4wa6du0qOu7nLiAs22Q2b94cK1euhLW1NbS1tZm2/pWyDoAXGRmJ0NBQhSU9DRo0QHR0NJP4c+bMwZkzZxS617H6O3Tq1IlZ95/ySDkSeuHCBQQEBKC4uBiRkZGwsrLC4sWLmXz2eVJe116+fIno6GihxuPu3bt4+PAhs5HWrKws+Pv7Cw0AvL298dNPP6FBgwaiY5d+mDl48CDu3LkDNzc3HD9+nGnd6atXr3Dp0iWhZgtgW8cj1eyplOeet3//fgAlyyJ5LJcmdevWDd26dWMS62NSlgjw/ve//8HT0xMrV67E0KFDcfDgQdF7LU2ZMgU3b94U2pnziouLUadOHbGH/NkBVpYrCTw9PZGZmamwJI/lAPW1a9dw7do1hddYfTY1NDSYHiuPn0Urr8sni3OvqrrxqVOnwtzcvMzr6urqopMpoGTJYlBQEMLDwyGXy9GpUyfMnz9fdNxP+SYTKikfnD7+8kyePBmurq7M4gMlX6IbN24IDzf8Xj+sWuheu3YNx44dE37u1asXfvzxRyax58+fj5o1ayItLQ1qamrIzMzE7NmzmXWUOnXqFJYvX65Q3Pv+/XtcuHBBdOxatWohKCgIqampCAsLw8KFC5kWm0pZB8ALDw/H/v37sXz5cnh6eiIuLk6YdWDhwoULiI6OlmRpzNChQ5GXl4f8/HyFUXpWpCxOXrp0KbZv345x48ahdu3aiIiIwPTp05kmVFJe1zw8PFCrVi2kpqbCwsICcXFxTDszBQQEYMyYMVi8eDFq166NH3/8ET4+PswajgDA4sWL8eTJE6SlpWHcuHHYu3cv0tPTmS1dlLJjo5TJsirOvdRLk+zs7PDq1Svh2sDfE1mQenYQAAYMGID+/ftDJpNh7969uHfvHlq0aCEq5sKFC/Hq1SuEhIQoDIaqq6tDV1dX7CEr7ENVGsdxTJopxcbGwsLCQpjp4u9T1atXR3x8PLS0tPD999+jRo0aot5Hys9mhw4dsHDhQnTv3l2haYTYGZ5evXoBKCn/KH0dzs/Px88//ywqNqC6uvG6desiODhY4Xnt4cOHLjJW8wAAIABJREFUTK4927dvR+3atbF8+XKhROPmzZvM60NL+yYTKikfnD7GskMez9/fHwkJCcjNzUWTJk2Qnp6O9u3bC9OaYjVo0AD3799Ho0aNAJRseKqvr88kdlpaGvbt24czZ86gcuXKCA0NZbZWHCiZqQoKCsJvv/0GNzc3xMTEMPsCLVmyBDExMXBxcYGWlhYMDAyYjj5JWQfAk3rn87p16+LDhw+SJFSrV6/Ghg0bUFRUhKpVq+L58+do0aIFsyWX5Y2EsipOlsvlCt2LjIyMmMQtTcrrWnZ2NjZv3ozQ0FAMGDAA48ePZ7YXCVAyA9a1a1csXrwYMpkMDg4OTB/ogZKZhn379sHOzg46Ojr47bffYG1tzSyhKj3Ts23bNnh5eTGb6ZEyWVbFub937x4iIiIkeXACSpZAbdq0CUVFRahRoways7PRunVrJvuOSd1lDiipI9m5c2eZpdJiZoB1dHSgo6ODtWvXllmGnZmZKfqhnsVyxM+5fv06LCwsFGZ8S8vLy8OKFSuExgzKkvKzmZaWpvD/ANsZnhkzZmDhwoUwNTVFXFwcAgMDma7ikLpufPr06ejZsyeSkpJgZ2eH6OhoJvuShoeH48KFC8LeqQUFBdi6dStiY2MRHh6On376SfR7lOebTKik7OoidYc8AIiPj8fx48cRFBQEZ2dn5OfnM6uTAErWr9rY2MDMzAxqampISkqCnp4enJ2dAYir1ZLJZCgoKBDO0cuXL5kuD6hSpQo6deqEK1euIC8vDzNmzMDAgQOZxNbR0UGFChWwd+9euLm5QVtbm+loTbVq1TBo0CBJ6gB4Uu18zq/lLi4uVvjs8Fj8Hfbu3YvTp09jwYIFmDRpEm7dusVsHT1QsmTr4cOHaNiwIc6dO4e0tDRhJFCsOnXqIDY2FjKZDK9fv8a2bduYt9KV8rpWtWpVAMB3332HjIwM5vuGaGpq4smTJ8K1IDExkXlSztcI8QoKCsq8JkbpmZ5atWoxnemRMllWxbmX6sGJt2/fPsTFxSEkJAQTJ07En3/+ie3btzOJrYoSAXd3d5ibm0vSmj0gIABxcXHMl2HzS2kLCwtRsWJFXL16FYWFhahQoQI6dOggKjZQshwM+Py9Y9q0aaLfR8rPptT7DK5btw7u7u4wMDAQZk3NzMyYxV+1ahVCQkJw5MgRtG3bVqgbZ5VQFRYWYurUqSgqKkLLli3h4ODAZMuGqKgo7NmzB9ra2gBKrv3169fHsGHD0L9/f9HxP+WbTKik7OoiZYc8np6eHipWrAhDQ0NkZGRg0KBBwsZxLEyaNEnh5zFjxjCL7ezsjNGjR+Pp06cICQlBTEwMs4c+oOTh4O7duzA0NERCQgI6derErM5J6iVDUtYB8Pz9/bFnzx74+vpiz5496N+/P9zd3UXH5Zebslp2Wp7atWujatWqMDIyQnp6Ovr27Yvly5czi+/l5YXOnTsDAI4dOwYXFxf4+fkxuSnOnz8fISEhePz4Mfr06SPJWm4pr2s//PADPD09MX36dIwdOxY3b95k+tDt6+uLCRMmIDMzEzY2NsjNzWX6bwuUNB3x8PDA69evsWnTJuzfv5/ZUmZA2pkeKWdPVXHupXpw4unp6UFHRwdNmzZFeno6LC0tmXUXlbrLHFAy+CpVa/b4+HhJlmFnZ2djypQpGDhwIEaPHg1PT080aNAAWVlZ8PX1haWlJZP3OXv2LJYvX16mWdPJkyeZLG+T8rOZkpKCjRs3lpl5FJvM8queKlWqhHnz5sHDwwP+/v6oV68eHj16xHSwTsq68cqVK6OgoADfffcd0tLSmCWDampqQjIFABMnTgRQsty19OusfZMJVfPmzREVFSVJVxc9PT3JOuTx9PX1ER4eDnNzc4SFhQEoGW0VKy0tDa1atfrksbLo7GJra4vWrVvj0qVLKC4uxtq1a5nu3eLh4YHly5cjLCwM69evR2RkJLOlkFIvGZKyDoBnbGyM2bNnA0CZzk9idO3aFbVr12a+vLU0HR0dHDx4EC1btsSOHTtQp04dpg1HpNx8V1dXF0uXLmUS61OkvK55e3vj7t27MDAwQFhYGBISEjB+/HjRcXlt27bFnj17cO/ePRQXF6NJkybMHwB79uwJPT09PHjwAElJSZg2bRrTlsBSzvSUV0eSnJyMJk2awNjYWFRsVZx7qR6ceDo6OoiKikKrVq0QEREBPT09ZtcGVZQImJqaIjo6Gr1792Y6awpItwz7p59+gq2tLUaOHAmgJPHcunUr0tPTERISwiyhCg4Ohq+vr2R7mEn52fTx8cGoUaOY7+PEbyDOJ2kaGhpCTR/LZi9S141bW1vDzc0NixcvhqOjI86ePcukvEQul+PNmzfCZEa/fv0AlCwTZf39Ku2bTKik7OoiZYc8XkhIiFAUbmlpiUOHDmHevHmi4+7cuRNBQUEKHV54YpcIfNxClR8lSE9PR3p6OrNuWzVq1BBGrfbu3Yvc3FzcvXuXSWz+i8hfGFkvGZKyDoBnaWmpsKGsTCaDpqYmmjRpAh8fH6VbSPv7+yM8PFy40H+MxXcrJCQEBw4cgJWVFU6ePAlfX18ms2s8KTbfVdUeSIA017WPG5a8fPkSANC+fXs8evRIdLcwVWx38Pz5c0ydOhV37txBo0aNoK6ujosXL+L9+/fo0KEDqlSpIvo9gJK/i1QzPSdPnsSNGzeEzS5Pnz4NPT09vHv3DlZWVkrVs6ni3POkenDihYSE4PDhw7C1tUVsbCwCAgLg4eHBJLaUXeaaN28uPBjv3LlToVxAbGt2qZdhp6enlztD1Lx5czx58kRU7NJq1Kgh2UasgLSfTU1NTSHhZIlvpFF6uwNednY2s/fh68adnZ2FunGW99xRo0bB1tYWOjo62Lp1K65fv86kUZOVlRV8fHwQGhoqJFVv377F7NmzYW1tLTr+p3yT+1BJid/LhieXy/Hjjz/iyJEjomP/1eg/65oMlviLe2ZmJu7fv4+ePXuiQoUKOHfuHIyMjLB+/XpR8ZOSkiCXy+Hv74+QkBBh5KaoqAjz5s1jUkC7fv16pKWl4fr163B2dsaBAwdgaWkJNzc30bGBkofvAwcOlKkDEHtuSgsODkaDBg2EWbsDBw7g+vXr6NWrF7Zt24ZNmzYpHfuPP/5AlSpVoKenh/Xr1+PKlSto1aoVxo4di8qVKzP6G0jnwoULWLt2LXr37g0XFxc4ODhg+vTpoop8s7KywHEcVq9eDQMDAwwePBhqamo4ePAgHj58iICAAIZ/A/ZGjBhR5jWZTIbr16+D4zhcv35dVPx9+/YBKCl+fvv2LaytraGuro4jR46gSpUqTGpDZ8+ejVq1asHd3V3otFVYWIgVK1bg6dOnTOtPCwsLJZnpGTZsGNavXy/Usr158wZubm7YtGkTBg8erNReaao497yCggIUFBRAR0cHT548wfXr19GlSxfme7FJobCwEPHx8cJgAo9ly/3yFBQUiPr88P++nyJ2L6QBAwYobOzL11IBJUkKq/37wsLCUFRUhG7duinsYcZyf0Z+NoP/bHbt2lXUPYt/Vlu1ahWMjY3Ru3dvhWSW1bOal5eXsMQYALZt24bVq1eLbjn+cYfFj4n97H9qv1ae2AGL4uJizJs3D4cOHYKhoSFkMhnu3LkDGxsbJpMPn/JNzlC9ePEC8+fPx4ULF1BcXIxOnTph3rx5qFWrlujYUnbI+3iatzSW07w3btzAunXryqxZFjNDxY+GOTk54cCBA8LGkbm5uUxqAeLj45GQkICcnByFUTN1dXU4OjqKjg8A48ePx9mzZ1GvXj08fvwY7u7uTEfOpKwD4CUlJSnMlo4YMQKDBw/GggULsGbNGqXjbt26FRs2bICamhp++OEH3L17FwMHDkRCQgLmzp3LpMVwz5498ezZM2F28+3bt9DW1sZ3332HwMBA0UtHzc3N0aFDB2hoaOD+/fuYNGmS6JowfsYvIyNDYUTY1dWVWWEvLzc3F2FhYcjMzMSKFSsQGhqKWbNmCQ/hyvi4sD87OxsBAQEwNDRkMoPBP9Rt374dkZGRwmjrgAED4ODgIDo+ACQnJys8+AFAxYoVMX36dNjY2DB5D0CaTm28ly9fKqz9r1SpEnJzc6Gurq70UiJVnHuepaUlLCwsYGdnh7Zt2zLZB6m0Xbt2YdmyZXj16pXC6yw23502bRqePn0qPJjxWCZUjo6OiIyMFH6Wy+Wwt7fHwYMHlY5ZOmHKycmBnp4eEhMTkZGRwaRGqFatWkhJSREa1PDJVEpKCpNnKV5KSgqAkucSHoumGpGRkXB0dCz34T4jIwPa2tro2bMnGjdu/I9jl35Wu3jxosKxsnxWq1mzJjw9PTF+/HgEBgZCS0uLSTOWv+qwKPVgglhqamoICgrClClThM9P69atUbduXUnf95tMqAICAmBqaorg4GDI5XJERkbCz88P4eHhomNL2SFP6r08eD4+PnB0dJRkzXJOTg6qV68u/Fy5cmUme1bw09BRUVHMv+z8ruFAyRR+6c5vly9fZjZSJmUdAK9ChQo4e/as0Pzi7Nmz0NDQwLNnz1BUVKR03J07d+LIkSPIz89Hnz59cO7cOWhra2PkyJHM/j1++OEH9OrVS+jSc+rUKcTExGDYsGEIDAwU3f599erV+OOPP+Dt7Y2RI0eiadOmOHfuHLPluhcuXBA2MYyLi1MYsWRhzpw56NKlC1JSUqClpQU9PT14e3szm+GMiopCWFgYRowYgQkTJkBdnd3tIy8vD69evRIGWp49e4Z3794xiV16VLs0mUzGdMmulJ3aLC0t4eLiggEDBkAul+PEiRPo3bs3oqKiFNrxK0PKc887evQojh8/jqVLl+L58+ewtbWFtbW16GPnrVu3Dlu2bGHaOZD3559/Kqw6YcnZ2RkJCQkAFJf/qamplbvHkzLmzp2LwsJCuLq6wsvLC126dEFycrLovR8nTZqEyZMnY/LkycJnPikpCWvWrMGyZcuYHDsgXae8v1qc9eTJE4wZM0ap5y5VPav5+flh1apVGDJkCIKCgpg10/g7HRbF+NQMFMdxTOvG9fX10bdvX2bx/so3mVA9ePBAYVRi3LhxzKanpeyQx5N6Tw9NTU2MGjWKSayP9ezZE6NHj4alpSU4jsPRo0cxYMAAZvHbtWvHfKM4vqbs1atXePDgAUxNTVGhQgUkJyfD2NgYO3fuZHLsUtYB8BYsWABfX1/MmDEDHMehYcOGWLhwISIjI0VtQK2urg4tLS1hnTU/mq6mpsbswTsjI0NhpqtXr15YtWoV2rZtyyTxPHnyJLZv344tW7bA2toaM2fOZDaLFBwcDB8fHzx9+hQcx6F+/fpMNwYFgIcPH8LR0RE7duyAhoYGPD09mawXz8nJwZw5c5CTk4MNGzYwbSLDc3Nzg7W1Ndq3bw+O43D16lVmieznkhuWiY+Undq8vLwQGxuL8+fPQ01NDWPHjkWPHj1w9epV0bPYUp57XuXKlWFrawtbW1tER0cjODgYq1atgrm5OXx8fIQVHcrS1dWVJJkCgIYNGzLvnMbjB1iDg4OZn3Pe9evXsXfvXuHB293dncmDt7m5OZYtW4a1a9cKzbHatm2LJUuWwNTUVHR8npOTU7nfU7EzVMOGDQPw+eVlYq8PKSkpSEpKwsiRI+Hm5oYbN25g0aJF6N69u6i4H9c/1qhRA7t27UJiYiIA8YnQ52p/ZTIZYmJiRMXnRUZGIjQ0VGGv0AYNGiA6OppJfFX7JhMqmUyGx48fC9N/jx49Ev3Qp4oOeTyp9/To2rUrtm7diq5duyqM7rK4ocyaNQvHjx9HQkICZDIZXF1dmY3EAdKcG36EbNy4cVi1apVw88/KymJaA6Ovry8kNaw6B37M2NgYv//+O3Jzc4UZEh0dHdHLLkuP9LOeeeFVqVIFu3fvhrW1NTiOw8GDB1G1alXcu3cPcrlcdHy5XA5NTU3ExsbCw8MDxcXFzDaFbtmyJQ4ePCjsu1Z6lpYVNTU15OXlCdege/fuiZ6BKT0r5ebmJtm/ra2tLTp37ozk5GTIZDLMmzcPurq6TGLfvn273GsMx3FMZsd5UnRq4+8rly9fho6OjtCtCmA3Oy7luefdv38fBw4cwKFDh1CvXj14e3vD0tISFy9exLhx43DixAml4vI1HvXq1cPEiRPRu3dvhXu5mNlx/kH+xYsXsLKyQvPmzRU+/6w6gALA5MmTER8fj86dOyM8PBxpaWmYMWMGDAwMRMcuLi6GXC7HyZMnERgYiPz8fGbXNTMzM2zYsIFJrE8p3QShqKgIJ0+eFLWM+WP8zGBpenp6iIuLE30fDg4Ohru7O44fP45KlSrh999/h7u7u+iE6uOl6Ky3K9m6detna39ZCQ8Px/79+7F8+XJ4enoiLi6uTCOkr8k3mVBNmzYNjo6OMDExAcdxuHbtGoKCgkTFlLJD3sek3tNj//79AIDffvtNeI3lul8DAwPo6uoKrcH37NnDrLW5lOfm0aNHCiOp/J4PYpV3QQfYdHr6mFQ7n9+7d09Y1lr6zxzH4f79+6KPGygpTg4KCsJPP/2EChUqoHPnzggNDcWxY8fg6ekpOr65uTl+/PFHaGpq4vvvv4eTkxOaNWvG4MiBq1evIjw8XGHm9NGjR0yXhkydOhVOTk54/PgxJk2ahKtXr4reEd7X1xcymQxr1qzB2rVrhdf5z2ZqaqrYwwZQUtd65MgRvH37FhzHIT09XdioUiwWDWk+R8pObTt27EBwcLCk9xUpzz1v9OjRGDx4MDZu3KjQSbRHjx44f/680nH5Gg9+djwpKUnh92ISKpbdzP6Kt7d3mT3wZs+ezWS5m62tLbp27Yr27dvDxMQEAwcOZF4jJ6WPk4XOnTtj6NChTDb1BUq6FfIKCwsRExODq1evMoktl8vRrVs3eHl5oV+/fqhXr57ozrGA9NuUqKr2V1dXFwYGBmjWrBlu3bqFkSNHil66/yV9kwmVhYUFTExMkJKSArlcjsDAQNGtc/mEbODAgRg+fDiLw/wkqff0kHL9r7+/PxISEpCbm4smTZogPT0d7du3Z5ZQSXluWrVqBR8fHwwYMECYIWERv/QFXWpS7XzOov7wr9StW7fcxhkuLi5M4vv4+MDJyQl6eno4duwYMjIyFAqhxZg9ezbGjBmDffv2wcnJCSdOnEDLli2ZxA4NDYWPjw+6deuGVq1aISUlBcXFxRg3bhzmzZsnaq+ltLQ0Jsf4Vzw8PFC3bl1cvXoVffr0wenTp9GmTRsmsZXdCuDvKu/7yydTYgUHBwOQro4EkPbc88zMzD65tIrfF08Z/MPey5cvUaNGDYXfib2P8Q/yp06dUqibzcnJQVBQENNZASn3wBs9ejRcXFyEWdOIiAihXu5rUDpp4DgOd+7cKdN8hJWKFStiwIABWLduHZN4lStXxsaNG3Hx4kUEBARgy5YtTDaWVcU2JTwpa38rV66MixcvolmzZoiJiUGbNm2Y142r0jeZUFlZWSEoKEjhQcPOzu4v24z+Hdu2bZM8oZJ6Tw8pa7Ti4+Nx/PhxBAUFwdnZGfn5+Uzb80p5boKDgxERESHUTHXu3LncttLKyszMxNWrV2FlZYW5c+ciLS0NgYGBaN26NbP3AKTZ+Zz1koPyxMfH4+effy5zM2U1A/HgwQPs2rVLWBLp5ubG7N9XQ0MD9vb2yMrKQtWqVbFo0SJYWVkxiX3lyhUsW7YMnp6eqFmzJnr27InIyEj4+PgotT9RaVIt8ftYTk4OtmzZgtDQUFhaWmLs2LHMEmVVuXTpEpYtW4adO3fi7t27GDduHMLCwtC+fXulY36qfoTH4qFbFef+jz/+ELpySmH06NHYuHEjatasiadPnyIoKAh37txRSISUtWzZMhQXF6Nv375CW2rWewtJsQceTxWz41Iq3TFPJpOhZs2aTOvNSrcG5zgOt27dYlb3u3jxYuzevRsrV65EtWrVkJ2dzWSD9/DwcMTGxmLTpk1o2LAhoqOjsWfPHrRs2RITJ05kcOQlQkJC4OPjg5ycHElqf+fMmYPdu3fD19cXe/bsQf/+/VU6M8zaN5lQ5ebmws/PT9hrBvjrji9/V506deDs7AwTExOF+iNWGwEC0m2GxpOyRktPTw8VK1aEoaEhMjIyMGjQIOTl5TGJDUh7bjQ0NDB48GBhhqq4uBiXL18WRm/EmjVrFoYOHYqTJ0/i7t27mDVrFoKDg5k1vQCk3/lcSoGBgZgxYwbz7pPR0dHYuXMn0tLS0LdvXyxatAhz5sxh+p2tVKkSXr16hcaNG+PatWswNzdn9sC0YcMGjBs3DitXroSLiwv8/Pxw+/ZtbNy4UWhp/G9XrVo1AEDjxo2Rnp4OExOTL3xE/9zChQsRGhoKAGjSpAnWr1+PmTNnYu/evUrH5B8udu3aBU1NTdja2kJdXR2HDh3Chw8fmBy3Ks59hQoVYGFhgcaNGyvcF1nNwkycOBGurq6wtbXFr7/+iuHDhzPbcmLTpk2YMGEC1qxZg5o1a2LHjh2im2h8bMaMGVi0aBFGjx4NAwMDODg4/OXGy3+XlLPjgHQDsNnZ2Vi0aBG0tbVhamoKb29vprVTvNKtwWUyGWrUqMFsQ259fX306tUL7969w+XLl9GtWzecPXtW9IqcjRs34vDhwwgNDUV6ejpmzJgBPz8/3Lx5E4sXLxY168u7fPkyVq9ejaysLMjlcrRp0wbu7u4wMjISHZvXtGlT4VhXrlzJLO6X8k0mVLq6uti0aROmTZuG1NRUBAQEiC4i3rdvH+zs7NCuXTtGR/lpH++bIJPJkJmZCUNDQ1HLe3hS1iHp6+sjPDwc5ubmQmeggoICJrEB4PXr1zh48CBevXolJMkZGRlMHo5XrFiBzZs3o6ioCDVq1EB2djZat26N3bt3i44NAB8+fICtrS38/PxgZWUFMzMzpucGkH7ncynVqFEDffr0YR7X3d0dAwYMQGRkpPCgxLrt9ejRo+Hp6YmVK1di6NChOHjwILOZRx0dHfz6668YP348IiIiMHDgQISFhUFTU5NJfFXo1KkTpk6dCh8fH7i6uiItLe2rOn6g5PtrbGws/GxoaChqKwLg/2d+Q0NDFRKzdu3aMatlUMW5nzFjBtN4H+vXrx90dHTg7u6OtWvXomPHjqJjlt4uY+LEiZg7dy5sbW2Rk5ODnJwcpo2mzM3NFQbmdu7ciaysLCaxpZwdB6QbgJ09ezaMjY1hZWWF48ePY8GCBczbeG/fvh29evVC3759hZpidXV1ZjVmUpU4REVFITIyEpUrV8bixYvRq1cvDB06FBzHYeDAgaKP+8KFC5g5cyYmTpwIPz8/FBYWIjk5GdOnT8fixYuZfL8A4PTp01i9enWZvftYLllUpW8yoeI4DtWqVcOGDRvw008/wcnJSfRo35YtW2BnZ8d0VPtTMjMzcf/+fQwaNAgAcOLECejo6CApKQkJCQmYOXOmqPhS1iGFhIQgLi4Obdu2haWlJQ4dOsR05+pp06ahSpUqkuyhFRUVhbi4OISEhGDixIn4888/mWyix1NTU8Px48dx+vRpTJs2DTExMcy6hfE7n/PtTpOTk5GcnAxtbW1ER0f/6zfqA0rqMBYtWoRu3bopjHKLWVIFAAcOHMDvv/+OESNGoH79+hg0aBCz2SOepqYmNm7cCJlMhr179+LevXtM249ra2vj119/xYQJE6Cvr8/sgfivNhZl9WDm4uKCN2/eoH79+li6dCkuX77MZMNvVWrSpAnCwsJgY2MDmUyGQ4cO4bvvvmMS+8OHD7h7966wyWhGRoboZI2ninP/ww8/4MaNG8IsRnFxMR4+fCh6qXDp9s4cx4HjOEyePFmYdRPzYMY3AuGXmzVu3BgxMTGIiYlh3mhq586dWLRokUL3vfr16zNpTy3l7Dgg3QBsdna20EGwS5cuzO9R4eHhuHDhAubOnQugZGB369atiI2NRXh4uOiGPoB0JQ4ymQyVK1cGUDLDxi9NZ/XMs3r1aqxfvx4tWrQQXmvZsiVMTEywYMECZlv0hISEwM/PD0ZGRsyf176EbzKh4jt3qampYc6cOdi1axfmz5//hY/q77t79y62bdsGDQ0NACX7KTg5OSEyMlLYP0cMGxubMnVIrHa2nzZtmnCRdHJygpOTE5O4vGfPnil0J2RJT08POjo6aNq0KdLT02FpaclsWQlQsqRt8+bNCAgIgJ6eHg4fPiwUpYv1te98DkBop1q6A5NMJhN9cTc2Noavry+8vb1x+vRp/P7773j27BnGjx+PkSNHokePHqLiAyUdCvnZYy0tLaZLbkrX2eTn52P58uWIi4tDxYoVAYhbVnXmzJlP/k4mkzFLqEaOHImjR48CKGn+0qpVKyZxVSkkJATLly+Hl5cX1NXVYWZmxuz76+vrCycnJ+jr64PjODx//pzZtUcV516qkXopm3XwsXfs2CF5XfT69eslax8t5ew4IN0ALH/94v9c+mcWoqKisGfPHqGur0KFCqhfvz6GDRsmbB4vllQlDmpqanj9+jXevXuHmzdvokuXLgBKtnJhUf/15s0bhWSK17p1a+Tm5oqOz6tSpQqTVVX/Ft9kQsWvc+c5ODiInuL93F4nLFuOAyXL2oqKioSEqrCwUNjZXkwtGF+cqa2tDQsLC5w5cwZ2dnbQ0tJidjHLz89X2AOMtRYtWiA9PV2SzUd1dHQQFRWFVq1aISIiAnp6ekw70jRv3lxhSQPL3eb5nc9//PFH4eLLU3YPGFVjORtYHnV1dfTp0wd9+vTBixcvEBUVhSVLljBJqAwMDDBr1iyYmJgozB6xSGSlXLLJL8uVWvPmzREVFYW2bdsqnJ+vpb4PKKlF4ke7WevatStOnTqFW7duQSaToVmzZswK51Vx7qUaqec7OBYUFCAuLg5v374FAGEGjEVrbVU0mpKyfbTUs+NSN8nisZ7BUFNTU2iSwjdzUFdXZ9atcHouAAAgAElEQVQ8RaoSh/Hjx8PW1hZFRUUYMmQI9PT0cOTIESxbtozJ7PK7d+9QVFRU5hpTVFTEZGacX05rZGSE4ODgMvvHsVxOq0rfVELFd/Irb98fmUwmqkVyo0aNsH79erGH+LeMHDkS9vb26NmzJ+RyOc6cOYNRo0Zh06ZNCmv4/ylfX1/o6urC3Nyc+WgQ78WLF+jVqxd0dXVRqVIl5gnn7du3YWdnJ0n8kJAQHD58GLa2toiNjUVAQAA8PDxEx/3cruQAm/XER44cQUFBAVasWCEkV0DJBTI8PByWlpai30NqKSkp+OWXXxSWDWVlZTHbtb20mjVrwtXVVdhoWSy+pfO1a9cUXmeRUEnZYXHSpElYs2YNLC0ty/2MsuqweO3atTLnhvVAlNTKu6/Url37s7N8f1dWVhYiIiKQm5urMGjGoqZEFede6mZE06dPR25uLjIzM2FmZoZLly6JXgrMU0WjKSnbR0s5Ow5I1wjq40Hq7Oxs9O7dm9k9XS6X482bN9DR0QEAYdPsvLw8ZkvtyytxCAwMFB23f//+MDU1xcuXL4XkWFtbG8HBwUzqm7p27YrFixcrbGxcXFyMBQsWMJlRKr2v3uPHj5GRkSH8zHo5rSrJOFbt7b4C+/btE9ZDl8fOzk7p2La2tgrtN6WWkZGBCxcuoEKFCjA3N0fTpk1x79491KtXT5i5+qdu3ryJI0eO4Pz582jevDkGDhyIzp07M7u4APhkoS2rvWJSUlKgq6srSfxZs2YxL4oFPn1OeCyOfffu3bhy5UqZPVXU1NTQuXNnJoWsUhs0aBBcXFwQFRWFUaNG4cSJE6hduzb8/Py+9KH9pdzcXKGu42vy5MkT1KlTB5mZmeX+vmHDhio+oq9D6Q1CWXRrGzp0KMzMzMrUhoq5Z6nStGnT0LJlS2GkftiwYVi5ciWOHTvGJH7fvn1x4sQJhISEwN7eHjo6OvDw8BDVYZH3cRMoHsuE6tatW9izZw98fX0xbdo0xMfHw93dXfS2BwDg5uaGGjVqMJ8dj4yMhKOjo2TnR+r74tq1a5GamorQ0FAhqXr79i18fX3Rvn17jB49WunYycnJMDU1LfM6x3FYv349JkyYoHRsVXj37h3c3Nzw+PFjtG7dGsXFxUhNTYWRkRFWrVql9DPmf903NUM1a9asz87AiLk5sRoN+zsKCgqQmZmJ6tWrAyhJIo4cOSJ6eUOLFi3QokULeHl54fr16zhy5AiWLl2K1q1bY9CgQaJGPuRyOfbs2YNbt26hffv2kj3A+/j4CPUArN26dUuSvVSk3ngUKHkgGzp0KCIiIjBq1CiF37HaFV5qFStWhIODAx4/foyaNWti8eLFsLa2/tKH9VmXLl2Cl5cXnj9/jkaNGuHnn38Waji/BnztZN26dREfH4/Xr18r/F5sQsW3Rr59+zZMTU3h5eUlSWtkVWO9QWhRURF8fHyYxOKp8tz/9NNPwkh9v379cPjwYSYj9TxdXV3IZDI0btwYGRkZsLW1ZbK/HlA2MeA4Dg8fPmQSm2dsbCxZ+2ipZselHouX+r44fvx4zJs3D926dYOhoSFkMhnu3LkDGxsbUckUULIMe82aNQrbVuTk5MDb2xvPnz//1ydUWlpa2LJlCxISEnD9+nXIZDI4OzszbVAGAM7Ozgo/y2QyaGpqokmTJnBzc/vqBiG/qRkqVczAqMKUKVPKXd5QehqVlcTERCxevBgZGRlITk5WOk5AQADS09PRoUMHnD9/HpaWlpJ0RPT09ESPHj0kqQcYOnQo7t+/L9leKlJKSkqCXC6Hv78/QkJChJthUVER5s2bx2zplpQcHR3xyy+/4PTp03j8+DEmTJiA/v37MxvlloK9vT2mTJmCjh074uDBg4iJicEvv/zypQ/rH3N1dcWHDx8UvkcymUz0Jo9jxoyBsbExOnbsKHwGpZgFVoWPNwi9ffs2Ll26xGSWJDg4GJ07d0bXrl2ZjQ6r4tyXtwySv/bIZDLcvHmTyfvMmTMHGhoaGD58OLy9vTFw4EAcPHjwL7tU/h2RkZEIDQ1V6MDXoEEDREdHi449YcIEhIeHf3LZt5aWFuzt7ZnMVJX2/v37r25bAqlkZ2cjJSUFQEnTBRb13Xy35TVr1qBly5Y4deoU/Pz8MGjQIMyYMUPh+eFbFhgYCHV1daEr5KFDh/DkyRO0bt0aiYmJn5z9/Lf6phKq0vgZmEuXLjGZgVElKZc3cByHy5cv49ixYzhz5gxatGiB/v37w8LCAlpaWkrHHTBgAI4cOQKZTIaXL1/CxcUFBw4cEH28Hyu9nI3Hqh4gISGh3NelrGFhZeXKlUhISEBqaqpChyd1dXV069aNWa2QlA4f/r/27jMsqnPbA/h/BAUEPQq5msjFcmzAxegxRBEroDfSVMSOaC6Ww1UxiDHYfR7RGLBhUBM9117AhpSEaEBCUbE3RAE9ahQlihGwIKHN/eAzE0aKhtmz9wz8f59kw/PuxQjDXm9Z60ccPnwYYWFh8PT0RJMmTdCpUydBi3cIbfjw4YiOjlZ+7Orqih9//FHCiOrG3d1dkIfTt7m5ueGHH34A8Gab3IgRI3Ty9QFQZWtfy5YtMX78eFhYWKg9dr9+/fD06VOVa+omJGK/9prcFl9WVoYrV67A1tYWJ06cQFpaGsaMGaPWmWIFR0dH7Nq1q0oFPiGqLD558gStWrWqcXvbixcvMHXqVJw8ebLO90hMTERoaKhK493i4mKkpaXVeUygarKsr68PPT09/PHHHzAxMVHp49UQXbhwAV999RV69+6NtLQ0rFy5skpBqIZu5MiRiIyMVLnm6emJI0eOiH6MRggNastfZd26dUO3bt2UKzCxsbFqrcCISVPbG5YtW4bU1FRYW1vD2dkZ8+bNU/Y6UJeBgYHyzbdly5Ya6zmQmJiokXFPnTqFW7duoXv37tXujRbClClTlCXlhaaoBBcVFaUTJdKr4+rqCmdnZzRq1AhHjhzBnTt3BC3/qwlvr37r6t7z3r174+zZs4JPOmm6NLKYNLmyps4DdU3Efu012Wdm9OjROHr0KADAycmp2oq7daXJCnytWrUC8KZ4SU1VCtXdebJq1SoEBQVhx44d8PX1RUJCgspqW11lZmYCePPc0LNnTwwbNgwymQzHjx8XpBCLrrO1tcWaNWswffp0bN68WScmXsVWWlqKW7duKRtB37p1S5nwC7VlV0wNLqGqbgXG29sbDg4OUof23jp37oygoCDl9oYnT54Isp/5wIEDaNGiBW7cuIEbN25g3bp1Kp9XZ5Xn7T+mmtpmee/ePezdu1dlNi4nJ0etXkWhoaGIjo5Gt27dsH37dvj6+iob6QlJ0yXlAaBHjx5YsWKFoK+Ppi1evLjGh7FDhw4hKChI5IjeX2lpKXJzc5W/n29/rCtlwdu1a4fJkyejUaNGaNSokbLS1vXr1wW9jy42dxSjSuezZ88QExODV69eqfzeqrvlsjJNv/aa3AzzwQcf4MKFC/j4448Fn7TQZAU+hdqqFKp7PrtZs2aws7PDpUuX8OLFC8ybN0/QM8zXrl1TOQ/32Wef4bvvvhNsfF1UeWXFzc0NX3zxBQICApSTFro6qSm0xYsXY9q0aTAzM0NFRQWeP3+OkJAQhIWFYfjw4VKH95c1qIRKkyswYnj06BGAN4cpHz9+rNxfnZaWViX5qQtNlih+9OiRypaYtz8WanY3ICAAgwYNwsWLF+Hh4YH4+Hjl7EddHT9+HHFxcTAyMsLDhw/h5+enkYQqPz9foyXlAc28PprWvXt3qUOos6KiIkycOFHlYdLLywuAbpUFDw8PR3x8vOAJoKZLI4thz549kMvl2LRpEywsLDBy5Ejo6ekhNjZWsOIF/v7++Oijj3DlyhUMHjwYSUlJ6Natm1pjiv3aazJhS09Pr1JsR6gzWkuWLMGhQ4cwf/58HD58GEOHDhW891tWVpbKNn5/f39BWnIAb/pQ3b17Fx07dsS5c+dgZ2cn6Oy/kZERjhw5AmdnZ1RUVCA6OlrnigkI7ezZsyofDxo0SKVRMxOqN3r37o2EhARkZ2ejUaNG6NixIxo3boyePXvq5ORagzpDZWlpiRYtWijPAr39H6btf7wrz4RWPtj75MkTlJWVCXbAVxMU2zFqIlT5X8VZj3Xr1mHAgAGwsbGBp6enWmcD3t7LO2zYMI2c/9J0SXlAM6+PmHJzc3H79m306dMHeXl5Gl3Noz9NmjQJW7ZsEXwCSoyWAWKp7jxAddfqQlF8JTg4GEOHDkXbtm3VPocqxmtf+W/W48ePlQ1fdSlhFsO4ceMQERGBffv2wdjYGCNGjBDs3OK5c+ewb98+rF69GuPHj8f9+/fh6emp0mNIHQ8fPkRQUBDOnj0LmUyGvn37YvHixRpp7kv1S2FhIVavXo379+/j22+/RXBwMObPn6+zCXmDWqHS9Tfvt88HvXr1CsHBwTh58qRWb3sCxOuXYmRkhJKSErRv3x4ZGRmClPl8O/HW09NTe8zq1LaPXiiaeH3EcuzYMWzcuBHFxcU4cOAAPD09sXDhQri5uUkdWr1nZmaGYcOG4ZNPPlE5a6Pu+44uJUzvIy0tDX369AEAJCcnC/ZeoXjA6NChAzIzMwVZtRXjtd+zZ4/G7wFodktkUlISNm3ahPz8fJWVZiGfJ6rbxi/UDH2vXr2U53eOHDkieE88c3NzwdoDUMOyZMkS9O3bF9euXUPTpk3RqlUrzJs3D1u3bpU6tDppUAlVffrjnZaWhsWLF6Nv376IiYlRNqZr6IYNGwZfX1+sWbMGY8eORWpqqrKXTl3l5eWplO98+2Ohyr/Xto9eKNW9Proyk7h161ZERERg4sSJMDMzw9GjRzFlyhQmVCKwt7eHvb291GFotRUrViAwMBB5eXmQy+UwNzcX7IyTnZ0dZs+ejcDAQPj4+CAjI0Mnyl6L9TdXE1siFVauXIlFixahU6dOGtmGdOfOHcyaNQsPHjxAp06dMHv2bCQlJSn7R6nr2rVr2L59e5WEUKh2H6mpqQgNDUVhYaHGEk6qn3JycjB27FiEh4ejSZMmmDNnjtb3lqxNg0qo6oOioiJ88803ylUpluF8Izg4GIGBgZg4cSJGjBgBExMT7NmzB9HR0Th8+LBaY48bN67Wj4WiyX30Cm+/Punp6ejXr5+g99AUmUymMnHQunVrndxnrYtGjx6NFy9e4PXr1yorAPQna2trxMbGIj8/HzKZTNl4XQhz5szB/fv3YW5ujnXr1uH8+fOCTeSUl5drbNVdLE+ePMHu3bsRHByM//7v/8bUqVMxefJkQcZu1qwZBg0aJMhYbwsLC8P27dsBQDlJd+fOHfzwww+CVZNV/F3UVEK4YsUKzJ8/H507d+b78XvQdOEpXaKnp4cXL14of27u3bunc31hK2NCpUMqr0rFxsbC2NhY6pC0xqVLl7B+/XrMmTNH+dCdkpKCrVu3qt31XBMNiKujqXL4lRUWFuLHH39Uma3MysoS7XtUR6dOnRAeHo6ysjJkZ2dj//79gvSZEYMYVdo0adOmTdi2bRvKysrQvHlz/P7777CyslL7fNC7etV8+umnao0vJm9v72ofKIVYCSgtLcXdu3eVB9tbtGiB06dPC3K4fdSoUe8846rtNLElUvGz2alTJ6xYsQJOTk7Q1//zkUmIn82oqCgcP34cT548wbfffosdO3bg8ePH2LBhA/r376/2+MCbohSKQjia0LJlS52qkiyF7du3o0mTJigoKEBycjIOHTokdUhaYfbs2fD29kZubi5mzJiBK1eu4Ouvv5Y6rDpjQqVD/ud//gf6+vo4efIkTp06pbyuCwd8KzcBfLsOihDVmLZt24Zp06YhLCwMkydPxqJFi3Dr1i3s2LEDH3/8sVpji0VT5fArmzlzJkxNTXVqNvHo0aPw8PDA0qVLsXHjRujr62Pu3Lno06ePSrlebabJLUliOHLkCJKSkrBq1SrMmDED2dnZOHjwoNrj1tZjRyaTCbYtSQyVK7+VlZXhxIkTaN68uSBjf/HFF8jLy0PHjh1Vfm+FSKg0WXJcLJrYEvntt98qX+vc3FxkZ2cD+PPvrRA/m8bGxmjVqhVatWqFa9euYcSIEdiyZYsgK4aKqsBWVlbYuXMnnJycVMYVqmLnJ598glWrVqF///4wMDBQXtelyRChFRQUIDo6Gt27d0ePHj1gbW0NPz8/FBUVqTR6b6gqF/lyd3eHoaEhysvL0aNHDxQUFEgYmXoaVJU/XVefKmJpwqtXrzB9+nTcvn0bLi4uCAwM1IlzBgrl5eW4fPkybG1tkZiYiNOnT2PMmDGCrsIIVTlKTB4eHjo/g66JKm1iGjt2LA4cOIBt27ahbdu2GDJkiE7+LIlt9OjRgsxGK35+NMHOzq7KQ4xQJcfFdP/+fbRt2xbXr1/HhQsX4OLiomycWxeV23oo6OnpoW3bthg/fjyaNWumTrgAVCvIOjs746efflJ7TAVFhcXqHvGEnID19vaudnxdmgwRmpeXF9q3b4/bt2/Dx8cH+/fvh7GxMXr37o1Lly5hw4YNUocoKUtLS5iZmaFPnz7VNhTXZJN0TeIKlQ6pDwmTJrc+GRsb4//+7//wz3/+E61btxYlmSouLhbsPnp6esqzTfr6+pgwYQL+/ve/CzK2QpcuXXD9+nXY2NgIOi7VThNbksRkYmKC2NhYWFtbIzw8HB9++KGgzU2vXLmCLVu2qDScfvToUZXKptpMsSIAvFnFuH37tmCzrW3btsWjR4800gj6zJkzgo8pNj8/P4SFhQEAbGxsYGNjg8mTJ2PXrl11HlNRGa8yuVyOrKws+Pv7Y9u2bXUeW6HyaqPQf6/E+t0Rq5KjLnn+/DlWrlyJjIwMjBo1ClOmTMHcuXMhk8nUPtNdHxw9ehRxcXE4deoULC0t4eLiAnt7e50+PwUwoSKRaWrrU+XzC69fv0ZoaCiSk5OVsx9CzJYlJiZi/fr1KgfzX79+LdgDye7du7Fv3z44ODigoqICO3fuhK+vryAl5xWzlcXFxYiLi0Pr1q2hp6enE9tF325AqqALsSvoapU2hZUrVyImJgbu7u44ceIE5s+fj9mzZws2/sKFCzFlyhQcPXoU3t7e+Pnnn2FtbS3Y+GKYOHGickVAJpPB1NQUixcvVmtMxfvas2fP4O7uDktLS5VtW0K8r+ny+b5Zs2bh5s2byqbECuXl5WpXd63tfdfV1VWtsRUqv7dV/h6EfG+7d+8e9u7dqzJZkZOTg3379qk1buVKt9XRhXO5mtKrVy989tlnePXqFRwdHaGnpweZTIbc3FydLwAjBCsrK1hZWWHu3LlIT09HXFwc1q1bBxsbG7i6uqJ3795Sh1gnTKhIVJqqxiR05/rqrFq1CkFBQdixYwd8fX2RkJCA169fCzb+oUOHcOTIEWVRjZkzZ2LixImCJFS6PIvYrl07ne1LoVC5StvatWtx4cIFzJw5U+qw3klxfu3DDz/E9OnTAUDtJKE6TZo0gaenJx4+fIjmzZsjJCQE7u7ugt9HEx4/foyQkBAYGxvjH//4B7788kvBzk75+fmhsLAQZWVlMDMzA/DmYfv333/HBx98IMg9dPl83zfffIOCggKsXLlS5edSX19f+XoJKS8vDwkJCYIVhDp+/Lgg49QmICAAgwYNwsWLF+Hh4YH4+Hh07txZ4/dtyJYsWYLMzEy0adMGhoaGmDp1KoYPH46nT59i+fLlUoenVbp164Zu3brhwoULWLNmDWJjY3H58mWpw6oTJlQkKk1tfapue4bQmjVrBjs7O1y6dAkvXrzAvHnz4OLiItj4RkZGKvuJjYyMBDskrtguWlpaitOnTyM/P7/az2ujxo0ba3V878PV1RUeHh4YPny4ckuSLti9e7coTbkNDAxQUFCADh064OrVq+jTpw/Ky8s1fl8hLFy4EF26dIG7uzuOHz+OVatWCXYGwMTEBAEBAfj666+V73Hr169HZGQk/vWvfwlyD02WHNc0ExMTmJiYoE2bNlXeIwIDAxEcHCzo/X799Vdcu3ZNsNU7Md7XSktLMXv2bJSVlcHa2hpjxoyBp6en2uO+zwpUWFiYKJOd2sjS0lL57127diEzMxOtW7eGqamphFFpD7lcjvPnz+PYsWNISUmBlZUVvL29dbpiJBMqEpUub30yNDTE3bt30bFjR5w7dw52dnaClDVXbJ1o0aIFxo8fDxcXF+jr6+PYsWNo37692uNXpslqYZoidHNjKWzduhVRUVGYNGkSLCwsMHLkSDg5OVV7ILch+vzzzzFnzhyEhYVh9OjRiI2N1Zmk8/Hjx8rzNH379hX0dyk4OBhr165V2QIzZ84c2Nra4ptvvsHOnTvVvocun+9btGgR7t+/j4yMDNy6dUt5vaysDC9evBD8fra2trC1tRV8XE0yMjJCSUkJ2rdvj4yMDFHjT0xMbLAJVWUymQxWVlZSh6E1li1bhtTUVFhbW8PZ2Rnz5s2DkZGR1GGpjVX+SHSKakwZGRk4f/682tWYxHL+/Hns3bsXq1evxvjx43H//n2MGjUKgYGBao0r5l50TVYLo/cTHx+PFStWoLi4GMOGDcOMGTPQsmVLqcOqlo2NDVq3bl3luibOrynGLCoqwr1792BlZaUTpf3frkJZuXKb0GNXNnz4cEFKMK9fvx53795VTnL17t0bmZmZgpTF17SHDx/i4cOHVbb8NWrUCB07dhS0ubKu2rdvH06cOIE1a9Zg7NixaNeuHeRyuSBFNd5FyN8Fqj8sLS3RokULNG3aFACqvM/rwrno6nCFikSlyQaVmvbvf/9bWe70yJEjKCwsVM7uqqNywvTs2TNcvXpV2ZNBqHMSCpqsFkY1e/XqFY4fP47o6Gg8fvwY48ePh6urK1JSUjBlyhS1G+Rqiljn1x48eICIiAiVhtOAbpbPFTIJLCsrQ0VFRZXqVxUVFYI1/a7ufJ+uFBRwcnJSFgLx9vZGo0aN0Lx5c9jb22Pp0qVShycpRSJjbGwMBwcHpKSkwMPDA02bNhVtZVwXJkRIfLqaML0LEyoSlS5uOVPYu3cvxo0bp/xYiGSqstTUVCxcuBA9evRARUUFli5dipUrVwqyp1iMamFUMycnJzg4OGDWrFkqDS8nTJiA06dPSxhZ7cQ6v+bn54c+ffrA1tZW5x7C3q5CqajWJsQq3qeffoqNGzdWqai4efNmwbZEaqLkuFgyMzOrXPv9999x4MABLF++HOvWrZMgKu0wf/78Wnv9kObVtPtEVyYsNEXXz0TXhAkVierOnTs6u+Xsww8/xKRJk9C9e3eVjvBCvTmuX78e+/fvh4WFBYA3s/azZs0SJKESo1oY1SwhIUFZvbEymUyGTZs2SRDR+xHr/JpcLld766xUNFmpLSAgANOnT0dUVBQsLS1hYGCAGzduwNTUFN99951aY2uy5LiUzMzMMGPGDMFKm+uq+trrR1eVlpYiNTVVp84o0l/DhIpEpctbznr06KHR8cvKypTJFABYWFigoqJCkLHFqBZGNbtw4QI2bdqk3NKmKz20xNo29Y9//APx8fFwcnLSuQc+Tc62mpiYYN++fThz5gxu3ryJRo0awcvLS5DCAmKXHBdbQ1+V0YZePx07dtT4PbTZ25OtM2fOhI+Pj0TRkKaxKAWJovKWs9zcXOWWM8WDJbecAb6+vrCzs8OoUaMAAIcPH8aZM2fw/fffqz325MmTMWPGjCp/RFNTU7Ft2zZBqoVRzYYMGYJFixahU6dOKlva6uvWh/dlaWmpPAMD/HnmQvG+cPPmTSnDaxCCgoKwZMkSlWuaKDkupp9//hn79+/n+9pbFL1+srKy1O71s2DBglo/r4vnHzUtPz8fnp6eSExMlDoU0gCuUJEo6kPp1EOHDmHdunUoKCgAIPxD38qVKxEUFITvv/8ecrkcdnZ2gjUBfP78ebUzkv3798eaNWsEuQfVrFmzZhg0aJDUYWid6s7AKH6vSLMWLVqEBw8e4Pr166KUHNcER0fHKj8rL1++RLt27bB69WqJotIemuz1I0bvR11X+edTLpejsLAQU6ZMkTgq0hSuUJGoHj9+jN27d2PevHl48OABwsLC8NVXX+nEOR5HR0ds2bJFo13mb9y4AWtra7x48QLXr19Hnz59BBnX3d0d0dHR1VYLc3NzQ1xcnCD3IVXnz58H8CYZb968OZycnKCv/+c8VuUCFQ3Z2bNnsX79ekRERODOnTuYNm0aVq9eXS96kGmrnJycakuO6+np6UzJ8YcPH6p8rKjyZ2xsLFFE2uPtXj+Ojo6C9vp59OhRrZ/XxW39Qqv88ymTydC8efNqz9JS/cCEikTl7e0NV1dXjBs3DiUlJYiKisKxY8ewfft2qUN7p9GjR+PQoUMaG3/NmjW4ceMGtm/fjidPnmDu3Lno1auXIKt7y5cvR4sWLapUC9u4cSPu37+PkJAQte9BVXl7e9f4OW51/ZOHhweCg4PRpUsXAG9aFHz11Vc4cuSIxJER6SZN9/pRrL5UfoRU3KOkpASpqalqjV8flJSU4OTJk3j+/LnKdV2oakx/HRMqEtWwYcMQExOjcq225pXaQNHP45dffkFJSUmVVQah3hzd3NwQHR2tLGdeVlYGDw8PxMbGqj32y5cvMX36dPz222/VVgvThdloqr9cXFyqrJIK1biWqqc4v6agmEFX9HDie4Jue3v17m1Cn98sLS1FfHw8wsPDkZ6ejitXrgg6vi6aPHky5HJ5ldea58vqJ56hIlEZGhoiOTkZAwcOBACcPn1a0G0ImnD27FkAQNOmTdG0aVNcvHhR5fNCJVRlZWUoLi5WblcRqnEnoNlqYVQzRTGWmnCF6o2///3vWL16NYYPHw6ZTIYffvgB7du3lzqseq2682tPnz7FwYMHG3wPp/pArII3Dx48wMGDBxEZGYnCwkL4+vpiw4YNotxb2+Xn51eZQKb6iytUJFqSLmoAABPtSURBVKqbN29i3rx5yMvLAwB89NFHCAkJUW71ach27tyJ8PBwODo6AgBSUlLg5eWFCRMmSBwZ1dW5c+cAAAcPHoShoSFGjBgBfX19/PDDD/jjjz8QFBQkcYTaobCwEKGhobhw4QL09fVha2uL2bNno1mzZlKH1iC5urrixx9/lDoM0mLx8fGIiIhARkYGhgwZgqFDh2LJkiWsYFfJ119/jUGDBsHOzk7n2kHQX8eEiiSRn5+Pxo0b68QBzeoqSSnIZDIkJCQIdq/09HScP39e+VBpbW0t2NgkHU9PzyrngUaOHInIyEiJItJucrkcOTk5Kn3ZSDwjRoxQbnUmqo6lpSWcnZ3h7++Pdu3aAQCcnJy0vreemHbu3IlvvvmG7SAaCG75I1FduXIFW7ZsQVFREeRyOSoqKvDo0SOtntXas2cP5HI5Nm3aBAsLC4wcORJ6enqIjY1FTk6O2uP/8ssvcHBwUD7AmJqaAgCys7ORnZ3NA6z1wB9//IG7d++iQ4cOAICsrCyUlZVJHJX2iIiIQEhICF6/fq28Zm5uLuhkBb2fn3/+meen6J1iYmIQGRmJCRMmwNzcHK6urigvL5c6LK1y8OBBJCYmsuJhA8GEikS1cOFCTJkyBUePHoW3tzd+/vlnrV+FUexFz8rKUjlM6uPjg5EjR6o9fnp6OhwcHJRntd7GhEr3zZ8/H97e3mjdujXkcjl+//13rF27VuqwtMbWrVsRHR2N0NBQzJkzB8nJybh06ZLUYdVr7OFE6ujSpQvmz5+PL7/8EklJSYiMjMTTp08xffp0eHl5Kc9JN2T/8R//wcmJBoQJFYmqSZMm8PT0xMOHD9G8eXOEhITA3d1d6rDeW1pamrI3VHJysrIinzoUpcxZ+af+6tevHxITE5GdnQ2ZTIauXbuqVIps6MzMzGBhYYGuXbsiOzsbXl5eCA8Plzqsem3Pnj0qH7OHE9WFvr4+Bg8ejMGDB+PZs2eIiorC2rVrmVABaNGiBdzc3NCzZ080btxYeZ1/6+sn/kUnURkYGKCgoAAdOnTA1atX0adPH53ZJrBixQoEBgYiLy9PWQpViP5NtVWCk8lk2LVrl9r3IGndu3cPe/fuVdnqmpOTg3379kkdmlYwMjLCmTNn0LVrVyQkJKBbt24oLi6WOqx6TawqcNRwmJqawsfHBz4+PlKHohUGDRqEQYMGSR0GiYQJFYnq888/x5w5cxAWFobRo0cjNjYWNjY2Uof1XqytrREbG4v8/HzIZDLBlvKra9x78eJFbN68GUOHDhXkHiStgIAADBo0CBcvXoSHhwfi4+PRuXNnqcPSGkuWLMGhQ4cwf/58HD58GEOHDhWkoTURkVQ8PDzw8uXLKo19qX5ilT8SnaLSTVFREe7duwcrK6tae/Voixs3buD7779HYWGhSnd4IXsJlZSUYO3atfjpp5+wdOlSDB48WLCxSTru7u6IjY3FunXrMGDAANjY2MDT05OlqYmI6qng4GAcPHhQOfmqePZhJcT6iStUJKqHDx9i7969VZISXdhTHBgYiLFjx6Jz584aSQAvXbqEBQsWoFu3boiJieFh1nrEyMgIJSUlaN++PTIyMthQ+S3Hjh3D1q1bUVhYqHKdDx5EpKtOnDiBlJQUnktsIJhQkaj8/f1ha2sLW1tbnViVqszQ0BATJ04UfNySkhKsW7cOcXFxWLZsGZycnAS/B0lr2LBh8PX1xZo1azB27FikpqaidevWUoelNYKDgxESEsLywkRUb3Tt2hUlJSVMqBoIbvkjUXl4eODo0aNSh1EnGzZsgKmpKfr16wcDAwPldXUfAj/77DPk5uZizJgx1a5KzZo1S63xSTu8fPkSJiYm+O2335Ceno6+ffuiadOmUoelFSZPnowdO3agUaNGUodCRCSIhIQELFiwAF26dFGpCCzkMQHSHkyoSFQrVqyAvb09+vXrhyZNmkgdzl/i6Oio/Ldida2kpASpqalqjbtx48ZaP8+ESncpmjXXhD3G3khOTsa//vUvfPrppyoPHvzZJyJd5erqimnTplWZdO3Vq5dEEZEmMaEiUfXr1w9Pnz5VuSaTyXDz5k2JIvrrSktLER8fj/DwcKSnp+PKlStSh0RaytLSEmZmZujTp49KHxIFXTg7KIZx48ahQ4cOVUp5M6EiIl01btw4RERESB0GiYQJFdF7evDgAQ4ePIjIyEgUFhbC19cXEyZMgKmpqdShkZa6efMm4uLicOrUKVhaWsLFxQX29vbc2vYWT09PHDlyROowiIgEs3z5cuTl5WHAgAEqE2rcmVA/MaEiUdW0vU2bZ6Lj4+MRERGBjIwMDBkyBEOHDsWSJUuQmJgodWikQ9LT0xEXF4ezZ8/CxsYGrq6u6N27t9RhaYV169ahVatW6N+/v8qDB4tUEJGuWrBgQbXXuTOhfmJCRaKqnFCVlpYiNTUV3bt3x7JlyySMqnaWlpZwdnaGv78/2rVrBwBwcnISpaRzbm4uPvroI43fh8Rz4cIFrFmzBllZWbh8+bLU4WiFyucTFdivhYjqm+LiYhgaGkodBmkAEyqSVElJCXx8fLB3716pQ6lRdnY2IiMjERsbC3Nzc7i6umLHjh1ISkrSyP22b9+OJk2aoKCgAMnJyTh06JBG7kPikMvlOH/+PI4dO4aUlBRYWVlh6NChcHBwYJU/IqJ6KjExEaGhoSgqKoJcLkdFRQWKi4uRlpYmdWikAUyoSFL5+fnw9PTUie1zZWVlSEpKQmRkJFJSUmBvbw8vLy8MHDhQrXELCgoQHR2N7t27o0ePHjhz5gz8/PxQVFSE6OhodOrUSaDvgMS2bNkypKamwtraGs7OznB0dISRkZHUYWmNsLAw+Pn5cWsMEdU7Q4YMQVBQEHbs2AFfX18kJCTg9evXWLp0qdShkQYwoSJROTo6KkuOy+VyFBYWYurUqfjf//1fiSP7a549e4aoqChERUUhJiZGrbG8vLzQvn173L59Gz4+Pti/fz+MjY3Ru3dvXLp0CRs2bBAoahKbpaUlWrRooVyJeruZdUPf0paYmAhHR8cae9N5eHiIHBERkTBGjhyJyMhIbN68GTY2NhgwYABcXFwQFxcndWikAfpSB0ANS2hoKMzMzAC8ebhs3rw5TExMJI7qrzM1NYWPjw98fHzUHuv58+dYuXIlMjIyMGrUKEyZMgVz586FTCbD4cOHBYiWpNLQE6Z3UZydYuJERPWNoaEh7t69i44dO+LcuXOws7NDaWmp1GGRhjChIlEFBgbip59+kjoMrdKrVy989tlnePXqFRwdHaGnpweZTIbc3FyVJqeke97uq0SqLC0tq6zaAW9Wr3WtPx0RUWX+/v4IDQ3F6tWrsXXrVhw4cACjRo2SOizSEG75I1HNmTMHAwcOxMcff6xS6aahl0fOzMxEmzZtYGhoiKlTp6KwsBBPnz7F8uXL4eTkJHV4RBo3YsQIREVFSR0GEZFGFBYW4m9/+5vUYZCGcIWKRHX16lVcvXoVwJ/nSUpKSpCamiplWJKztLRU/nvXrl3IzMxE69at2TSYGozqVqqIiHRNdUV29PX1YWFhgfHjx6NZs2YSREWaxoSKRKWo5ldaWor4+HiEh4cjPT1d4qi0i0wmg5WVldRhEImKmyWIqD7o1atXlWtyuRxZWVnw9/fHtm3bJIiKNI0JFYnqwYMHOHjwICIjI1FYWAhfX19WsSMirlARUb1QW5EdV1dXESMhMTGhIlHEx8cjIiICGRkZGDJkCEJCQrBkyRLMmjVL6tCISCKV2yg8fvxYeV5QUZSCVRKJqD7Iy8tDQkICjI2NpQ6FNIQJFYnCz88Pzs7OOHDgANq1aweAM9Jve/78OWJjY1FQUKCy/YlJJ9VXe/bskToEIiKN+/XXX3Ht2jWEhIRIHQppCBMqEkVMTAwiIyMxYcIEmJubw9XVFeXl5VKHpVW++OILNGvWDJ07d2aySQ0Cy8oTUUNga2sLW1tbqcMgDWLZdBJVWVkZkpKSEBkZiZSUFNjb28PLywsDBw6UOjTJubu7IzY2VuowiIiIiOgvaCR1ANSw6OvrY/Dgwdi8eTNSUlJgZ2eHtWvXSh2WVrCyskJmZqbUYRARERHRX8AVKiIt4eHhgczMTJiZmcHAwIAH84mIiIh0ABMqIi3x8OHDaq/znAkRERGR9mJCRaQlSkpKkJycjFevXgEAysvLkZOTgy+++ELiyIiIiIioJqzyR6QlAgICUFhYiPv378PW1hZnz55Fz549pQ6LiIiIiGrBohREWiIrKwu7d+/GkCFDMHXqVISHh9e4DZCIiIiItAMTKiItYWZmBplMhg4dOiArKwsWFhYoLS2VOiwiIiIiqgW3/BFpic6dOyMoKAjjx4/Hl19+iSdPnoBHHImIiIi0G4tSEGmJ8vJyXL58Gba2tkhMTMTp06cxZswYdOnSRerQiIiIiKgGTKiItEhWVhaeP3+ucu3TTz+VKBoiIiIiehdu+SPSEnPmzMGNGzfQqlUr5TWZTIbdu3dLGBURERER1YYJFZGWyMzMRFxcHPT09KQOhYiIiIjeE6v8EWmJ7t2749dff5U6DCIiIiL6C7hCRaQl7Ozs4ObmhlatWkFPTw9yuRwymQwnTpyQOjQiIiIiqgETKiItsWXLFuzatQtt2rSROhQiIiIiek9MqIi0RMuWLWFrawuZTCZ1KERERET0nphQEWmJ9u3bY8yYMbC3t0fjxo2V12fNmiVhVERERERUGyZURFqiTZs23O5HREREpGPY2JdIS8nlcuTk5MDCwkLqUIiIiIioBlyhItISBw4cQHBwMF6/fq289p//+Z+Ij4+XMCoiIiIiqg37UBFpiS1btiA6OhouLi6Ij4/H4sWL8fHHH0sdFhERERHVggkVkZYwMzODhYUFunbtiuzsbHh5eSErK0vqsIiIiIioFkyoiLSEkZERzpw5g65du+KXX35BXl4eiouLpQ6LiIiIiGrBhIpISyxevBiJiYno378/CgoKMHToUEycOFHqsIiIiIioFqzyR0REREREVEes8kckMUdHR8hksho/f+LECRGjISIiIqK/ggkVkcT27NkDuVyOTZs2wcLCAiNHjoSenh5iY2ORk5MjdXhEREREVAtu+SPSEiNHjkRkZOQ7rxERERGR9mBRCiItkpaWpvx3cnIy9PT0JIyGiIiIiN6FK1REWuLGjRsIDAxEXl4e5HI5zM3NERISgk6dOkkdGhERERHVgAkVkZbJz8+HTCZDixYtpA6FiIiIiN6BRSmItMSNGzfw/fffo7CwEJXnOXbv3i1hVERERERUGyZURFoiMDAQY8eORefOnWsto05ERERE2oMJFZGWMDQ0xMSJE6UOg4iIiIj+Ap6hItISGzZsgKmpKfr16wcDAwPl9TZt2kgYFRERERHVhgkVkZZwdHRU/lux5a+kpASpqalShURERERE78CEikjLlJaWIj4+HuHh4UhPT8eVK1ekDomIiIiIasCEikhLPHjwAAcPHkRkZCQKCwvh6+uLCRMmwNTUVOrQiIiIiKgGTKiIJBYfH4+IiAhkZGRgyJAhGDp0KJYsWYLExESpQyMiIiKid2CVPyKJ+fn5wdnZGQcOHEC7du0AgGXTiYiIiHQEEyoiicXExCAyMhITJkyAubk5XF1dUV5eLnVYRERERPQeuOWPSEuUlZUhKSkJkZGRSElJgb29Pby8vDBw4ECpQyMiIiKiGjChItJCz549Q1RUFKKiohATEyN1OERERERUAyZUREREREREddRI6gCIiIiIiIh0FRMqIiIiIiKiOmJCRUREREREVEdMqIiISGdduXIF3t7ecHd3h5ubG6ZOnYpbt24BAHx8fPDs2bN3jvG+X0dERFQd9qEiIiKdVFJSgn/+85/Yvn07/uu//gsAEB0djWnTpuHEiRM4derUe43zvl9HRERUHSZURESkk16/fo0XL16gqKhIeW3YsGEwMTHB4sWLAQCTJ0/G1q1bkZmZiS1btqCkpATPnj3DiBEj4O/vjwULFqh8XaNGjbB8+XLk5uaitLQUrq6u8PX1leT7IyIi3cCy6UREpLN27NiB0NBQfPDBB+jZsyd69+4NV1dXGBkZoWvXrkhLS0PLli0xadIkBAUFoX379nj8+DEcHBxw8uRJmJqaKr/O1NQUkyZNwueffw5HR0f88ccfmDZtGsaNGwcXFxepv1UiItJSTKiIiEinvXz5EufPn8f58+dx4sQJAMDhw4dha2urTJRevXqFpKQk3L17F//+979x7NgxJCQkwNzcXJlQGRoa4pNPPkGXLl2UYxcVFcHZ2RkBAQFSfXtERKTluOWPiIh00sWLF3H58mVMnToVDg4OcHBwQEBAANzc3FTORRUVFcHDwwODBw+Gra0tPD09kZCQgLfnEysqKiCXyxEREQEjIyMAwLNnz2BgYCDq90VERLqFVf6IiEgnmZqa4rvvvsOFCxeU1/Ly8vDy5Ut06dIFenp6KCsrw6+//oqXL1/C398fjo6OOHv2LEpKSlBRUQEAyq8zMTFBjx49sGPHDgDA8+fPMX78eOWqFxERUXW45Y+IiHTWmTNnEBYWht9++w0GBgZo1qwZZs6ciQEDBiAgIAAZGRnYsGEDdu/ejbNnz6JJkybo0qULbt++jfnz56N///7KrwsLC0PTpk0RFBSER48eoaSkBG5ubvDz85P62yQiIi3GhIqIiIiIiKiOuOWPiIiIiIiojphQERERERER1RETKiIiIiIiojpiQkVERERERFRHTKiIiIiIiIjqiAkVERERERFRHTGhIiIiIiIiqiMmVERERERERHX0/wbVYMvN0OrkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the Avg % Growth Rate for all the states using seaborn catplot\n",
    "\n",
    "#Set seaborn whitegrid as style\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "#Generate a category style bar plot, with State as x-axis and Mean_GSDP_Growth_Rate as y-axis\n",
    "sns.catplot(x='State', y='Mean_GSDP_Growth_Rate',data=gsdp_mean_df, kind='bar', height=6, aspect=2)\n",
    "\n",
    "#Give Label to the y-axis\n",
    "plt.ylabel('Avg GSDP % Growth Rate')\n",
    "\n",
    "#Provide title to the plot\n",
    "plt.title(\"Mean % State Growth Rate over 3 years\"+\"\\n\"+\"Ordered by the Fastest growing to the Slowest\")\n",
    "\n",
    "#Rotate the x-axis values(State Names) so that they are visible without overlapping\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "#Display the plot visual with Avg GSDP% Growth Rate \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=blue>From the above plot, the top 5 states with best average growth rate are -</font>\n",
    "-  <font color=blue>Mizoram, Tripura, Nagaland, Manipur & Arunachal Pradesh (All 5 North Eastern states)</font>\n",
    "\n",
    "### <font color=blue>The bottom 5 lagging states with least average growth rate are -</font>\n",
    "-  <font color=blue>J & K, Sikkim, Odisha, Meghalaya & Goa</font>\n",
    "\n",
    "### <font color=blue>Curiosity findings </font>\n",
    "-  <font color=blue>All_India GDP growth rate (11.2 %) has also been included for reference.</font>\n",
    "-  <font color=blue>My home state Andhra Pradesh's GSDP has been growing at 14.03%, higher than the Nation's GDP growth rate of 11.2%.</font>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next section in Part 1A: Check the consistency in the growth rate percentage across 3 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Sort the DataFrame such that the data is ordered by state and Year values\n",
    "gsdp_grow_df.sort_values(['State','GDP_Year'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>GDP_Year</th>\n",
       "      <th>GSDP_Growth_Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>16.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>10.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>2015-16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>12.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>13.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        State GDP_Year  GSDP_Growth_Rate\n",
       "28  Andaman & Nicobar Islands  2013-14             16.68\n",
       "61  Andaman & Nicobar Islands  2014-15             10.89\n",
       "94  Andaman & Nicobar Islands  2015-16               NaN\n",
       "0             Andhra Pradesh   2013-14             12.85\n",
       "33            Andhra Pradesh   2014-15             13.40"
      ]
     },
     "execution_count": 587,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Remove the All_India GDP value\n",
    "gsdp_grow_df=gsdp_grow_df[gsdp_grow_df.State != 'All_India GDP']\n",
    "\n",
    "##Few states do not have the GSDP growth rate value for 2015-16 year. Such values will be ignored\n",
    "gsdp_grow_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1160.22x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the 3 years Growth Rate comparison for all the states using seaborn catplot\n",
    "\n",
    "#Set seaborn darkgrid as style\n",
    "sns.axes_style(\"darkgrid\")\n",
    "\n",
    "#Generate a category style bar plot, with State as x-axis and GSDP_Growth_Rate and year as hue\n",
    "sns.catplot(x='State', y='GSDP_Growth_Rate',hue='GDP_Year',data=gsdp_grow_df, kind='bar', height=5, aspect=3)\n",
    "\n",
    "#Give Label to the x-axis\n",
    "plt.xlabel('Annual GSDP Growth Rate')\n",
    "\n",
    "#Provide title to the plot\n",
    "plt.title('GSDP Annual Growth Rate comparison over 3 years')\n",
    "\n",
    "#Rotate the x-axis values(State Names) so that they are visible without overlapping\n",
    "plt.xticks(rotation='vertical')\n",
    "\n",
    "#Display the plot visual with 3 year Growth Rate %\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  <font color=blue> GSDP growth rate of Andhra Pradesh & Meghalaya have been increasing consistently over three years. </font>\n",
    "-  <font color=blue> While other states have inconsistent growth patterns</font>\n",
    "\n",
    "-  <font color=blue>GSDP growth rate values for 2015-16 year are missing for A & N Islands, Himachal Pradesh, Maharashtra, Manipur, Mizoram, Nagaland, Punjab, Rajasthan & Tripura </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next section in Part 1A: GDP of the states for the year 2015-16\n",
    "## Identify top-5 and bottom-5 based on total GDP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 589,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read the rows with Items Description \"GSDP - CURRENT PRICES (` in Crore)\" for the year 2015-16\n",
    "#into new dataframe gsdp_1516_df\n",
    "gsdp_1516_df=gsdp_df.loc[(gsdp_df['Items  Description']==\"GSDP - CURRENT PRICES (` in Crore)\") & (gsdp_df['Duration']=='2015-16')]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Items  Description</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <th>Assam</th>\n",
       "      <th>Bihar</th>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <th>Goa</th>\n",
       "      <th>Gujarat</th>\n",
       "      <th>Haryana</th>\n",
       "      <th>...</th>\n",
       "      <th>Tamil Nadu</th>\n",
       "      <th>Telangana</th>\n",
       "      <th>Tripura</th>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <th>Uttarakhand</th>\n",
       "      <th>Andaman &amp; Nicobar Islands</th>\n",
       "      <th>Chandigarh</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Puducherry</th>\n",
       "      <th>All_India GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2015-16</td>\n",
       "      <td>609934.0</td>\n",
       "      <td>18784.0</td>\n",
       "      <td>224234.0</td>\n",
       "      <td>413503.0</td>\n",
       "      <td>260776.0</td>\n",
       "      <td>45002.0</td>\n",
       "      <td>994316.0</td>\n",
       "      <td>485184.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1212668.0</td>\n",
       "      <td>575631.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1153795.0</td>\n",
       "      <td>184091.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30304.0</td>\n",
       "      <td>551963.0</td>\n",
       "      <td>26533.0</td>\n",
       "      <td>13675331.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Items  Description Duration  Andhra Pradesh   \\\n",
       "4  GSDP - CURRENT PRICES (` in Crore)  2015-16         609934.0   \n",
       "\n",
       "   Arunachal Pradesh     Assam     Bihar  Chhattisgarh      Goa   Gujarat  \\\n",
       "4            18784.0  224234.0  413503.0      260776.0  45002.0  994316.0   \n",
       "\n",
       "    Haryana      ...        Tamil Nadu  Telangana  Tripura  Uttar Pradesh  \\\n",
       "4  485184.0      ...         1212668.0   575631.0      NaN      1153795.0   \n",
       "\n",
       "   Uttarakhand  Andaman & Nicobar Islands  Chandigarh     Delhi  Puducherry  \\\n",
       "4     184091.0                        NaN     30304.0  551963.0     26533.0   \n",
       "\n",
       "   All_India GDP  \n",
       "4     13675331.0  \n",
       "\n",
       "[1 rows x 35 columns]"
      ]
     },
     "execution_count": 590,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsdp_1516_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transpose the Dataframe\n",
    "gsdp_1516_df=gsdp_1516_df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 592,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Items  Description</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration</th>\n",
       "      <td>2015-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <td>609934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <td>18784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assam</th>\n",
       "      <td>224234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     4\n",
       "Items  Description  GSDP - CURRENT PRICES (` in Crore)\n",
       "Duration                                       2015-16\n",
       "Andhra Pradesh                                  609934\n",
       "Arunachal Pradesh                                18784\n",
       "Assam                                           224234"
      ]
     },
     "execution_count": 592,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsdp_1516_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 593,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the columns Items  Description once the rows are picked up\n",
    "gsdp_1516_df.drop(['Items  Description','Duration'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 594,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reset the index to bring State Name into Column\n",
    "gsdp_1516_df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 595,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename the column so that it reflects as State Name\n",
    "gsdp_1516_df.rename(columns={'index': 'State',4:'GSDP_2015-16'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 596,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop rows with GSDP_2015-16 values as null\n",
    "gsdp_1516_df.dropna(subset = ['GSDP_2015-16'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 597,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sort the Data frame values based on descending order of GSDP\n",
    "gsdp_1516_df.sort_values('GSDP_2015-16',ascending=False,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 598,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove the row with All_India GDP as it is whole nation GDP, not state\n",
    "gsdp_1516_df=gsdp_1516_df[gsdp_1516_df.State != 'All_India GDP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 599,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the GSDP for all the states (wherever available) using seaborn catplot\n",
    "\n",
    "#Set seaborn whitegrid as style\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "#Generate a category style bar plot, with State as x-axis and GSDP_2015 as y-axis\n",
    "sns.catplot(x='State', y='GSDP_2015-16',data=gsdp_1516_df, kind='bar', height=6, aspect=2)\n",
    "\n",
    "#Give Label to the y-axis\n",
    "plt.ylabel('GSDP (in crores) during 2015-16')\n",
    "\n",
    "#Provide title to the plot\n",
    "plt.title('Comparison of GSDP of all states during 2015-16'+\"\\n\"+\"Ordered by the Highest GSDP growing to the Lowest\")\n",
    "\n",
    "#Rotate the x-axis values(State Names) so that they are visible without overlapping\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "#Display the plot visual with GSDP for all states\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=blue>From the above plot, the top 5 states with highest GSDP for the year 2015-16 are -</font>\n",
    "-  <font color=blue>Tamil Nadu, Uttar Pradesh, Karnataka, Gujarat & Andhra Pradesh</font>\n",
    "\n",
    "### <font color=blue>The bottom 5 states with lowest GSDP states for the year 2015-16 are -</font>\n",
    "-  <font color=blue>Chandigarh, Meghalaya, Puducherry, Arunachal Pradesh & Sikkim</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Start of Part 1B: GSDP Per capita & Sector / Sub-sector wise contribution to GSDP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 600,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the libraries for reading directories & char encoding detection\n",
    "import os\n",
    "import chardet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 601,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define a function to check and return the file encoding\n",
    "def get_file_encoding(file_name):\n",
    "    csv_content = open(file_name,'rb').read()\n",
    "    char_enc=chardet.detect(csv_content)['encoding']\n",
    "    return char_enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define an empty dataframe, which would be used to hold all state data from CSV files\n",
    "allstate_df=pd.DataFrame()\n",
    "\n",
    "#Set the path variable for reading the csv files\n",
    "path = \"./Part1B\"         \n",
    "\n",
    "#Define a for loop to check the directory and import csv files into dataframe\n",
    "for entry in os.scandir(path):\n",
    "    #Call the function get_fileencoding and assignt he file encoding to a variable\n",
    "    csv_encoding=get_file_encoding(entry.path)\n",
    "    \n",
    "    #Read the csv into itr_df DataFrame\n",
    "    itr_df=pd.read_csv(entry.path,encoding=csv_encoding)\n",
    "\n",
    "    #Assign state name based by extracting it from file name\n",
    "    itr_df['StateName']=entry.path.split('-')[1]\n",
    "    \n",
    "    #Concat the datafram into allstate_df\n",
    "    allstate_df=pd.concat([allstate_df,itr_df],sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2011-12</th>\n",
       "      <th>2012-13</th>\n",
       "      <th>2013-14</th>\n",
       "      <th>2014-15</th>\n",
       "      <th>2015-16</th>\n",
       "      <th>2016-17</th>\n",
       "      <th>Item</th>\n",
       "      <th>S.No.</th>\n",
       "      <th>StateName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9400805.0</td>\n",
       "      <td>11186428.0</td>\n",
       "      <td>12895568.0</td>\n",
       "      <td>14819416.0</td>\n",
       "      <td>17326726.0</td>\n",
       "      <td>20386004.0</td>\n",
       "      <td>Agriculture, forestry and fishing</td>\n",
       "      <td>1</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5204052.0</td>\n",
       "      <td>6123041.0</td>\n",
       "      <td>7114707.0</td>\n",
       "      <td>7893514.0</td>\n",
       "      <td>8644285.0</td>\n",
       "      <td>9717089.0</td>\n",
       "      <td>Crops</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2758776.0</td>\n",
       "      <td>3358438.0</td>\n",
       "      <td>3643026.0</td>\n",
       "      <td>4309078.0</td>\n",
       "      <td>5155487.0</td>\n",
       "      <td>5979648.0</td>\n",
       "      <td>Livestock</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>250314.0</td>\n",
       "      <td>253029.0</td>\n",
       "      <td>280493.0</td>\n",
       "      <td>346160.0</td>\n",
       "      <td>340550.0</td>\n",
       "      <td>335487.0</td>\n",
       "      <td>Forestry and logging</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1187663.0</td>\n",
       "      <td>1451920.0</td>\n",
       "      <td>1857342.0</td>\n",
       "      <td>2270664.0</td>\n",
       "      <td>3186404.0</td>\n",
       "      <td>4353780.0</td>\n",
       "      <td>Fishing and aquaculture</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     2011-12     2012-13     2013-14     2014-15     2015-16     2016-17  \\\n",
       "0  9400805.0  11186428.0  12895568.0  14819416.0  17326726.0  20386004.0   \n",
       "1  5204052.0   6123041.0   7114707.0   7893514.0   8644285.0   9717089.0   \n",
       "2  2758776.0   3358438.0   3643026.0   4309078.0   5155487.0   5979648.0   \n",
       "3   250314.0    253029.0    280493.0    346160.0    340550.0    335487.0   \n",
       "4  1187663.0   1451920.0   1857342.0   2270664.0   3186404.0   4353780.0   \n",
       "\n",
       "                                Item S.No.       StateName  \n",
       "0  Agriculture, forestry and fishing     1  Andhra_Pradesh  \n",
       "1                              Crops   1.1  Andhra_Pradesh  \n",
       "2                          Livestock   1.2  Andhra_Pradesh  \n",
       "3               Forestry and logging   1.3  Andhra_Pradesh  \n",
       "4            Fishing and aquaculture   1.4  Andhra_Pradesh  "
      ]
     },
     "execution_count": 603,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allstate_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 0 to 32\n",
      "Data columns (total 9 columns):\n",
      "2011-12      875 non-null float64\n",
      "2012-13      873 non-null float64\n",
      "2013-14      874 non-null float64\n",
      "2014-15      875 non-null float64\n",
      "2015-16      617 non-null float64\n",
      "2016-17      260 non-null float64\n",
      "Item         891 non-null object\n",
      "S.No.        891 non-null object\n",
      "StateName    891 non-null object\n",
      "dtypes: float64(6), object(3)\n",
      "memory usage: 69.6+ KB\n"
     ]
    }
   ],
   "source": [
    "allstate_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead 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>2011-12</th>\n",
       "      <th>2012-13</th>\n",
       "      <th>2013-14</th>\n",
       "      <th>2014-15</th>\n",
       "      <th>2015-16</th>\n",
       "      <th>2016-17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.750000e+02</td>\n",
       "      <td>8.730000e+02</td>\n",
       "      <td>8.740000e+02</td>\n",
       "      <td>8.750000e+02</td>\n",
       "      <td>6.170000e+02</td>\n",
       "      <td>2.600000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.729506e+06</td>\n",
       "      <td>4.257983e+06</td>\n",
       "      <td>4.808991e+06</td>\n",
       "      <td>5.334342e+06</td>\n",
       "      <td>6.170468e+06</td>\n",
       "      <td>7.507678e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.026948e+07</td>\n",
       "      <td>1.173822e+07</td>\n",
       "      <td>1.332729e+07</td>\n",
       "      <td>1.474436e+07</td>\n",
       "      <td>1.495193e+07</td>\n",
       "      <td>1.649065e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-7.600000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.673950e+04</td>\n",
       "      <td>8.982100e+04</td>\n",
       "      <td>1.002648e+05</td>\n",
       "      <td>1.146455e+05</td>\n",
       "      <td>1.794320e+05</td>\n",
       "      <td>3.444900e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.514180e+05</td>\n",
       "      <td>6.212780e+05</td>\n",
       "      <td>7.074050e+05</td>\n",
       "      <td>7.672680e+05</td>\n",
       "      <td>1.224232e+06</td>\n",
       "      <td>1.934438e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.705124e+06</td>\n",
       "      <td>3.121656e+06</td>\n",
       "      <td>3.579476e+06</td>\n",
       "      <td>4.066346e+06</td>\n",
       "      <td>4.960400e+06</td>\n",
       "      <td>6.372715e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.272967e+08</td>\n",
       "      <td>1.448466e+08</td>\n",
       "      <td>1.647506e+08</td>\n",
       "      <td>1.792122e+08</td>\n",
       "      <td>1.212668e+08</td>\n",
       "      <td>1.338766e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            2011-12       2012-13       2013-14       2014-15       2015-16  \\\n",
       "count  8.750000e+02  8.730000e+02  8.740000e+02  8.750000e+02  6.170000e+02   \n",
       "mean   3.729506e+06  4.257983e+06  4.808991e+06  5.334342e+06  6.170468e+06   \n",
       "std    1.026948e+07  1.173822e+07  1.332729e+07  1.474436e+07  1.495193e+07   \n",
       "min    0.000000e+00  0.000000e+00  0.000000e+00 -7.600000e+01  0.000000e+00   \n",
       "25%    7.673950e+04  8.982100e+04  1.002648e+05  1.146455e+05  1.794320e+05   \n",
       "50%    5.514180e+05  6.212780e+05  7.074050e+05  7.672680e+05  1.224232e+06   \n",
       "75%    2.705124e+06  3.121656e+06  3.579476e+06  4.066346e+06  4.960400e+06   \n",
       "max    1.272967e+08  1.448466e+08  1.647506e+08  1.792122e+08  1.212668e+08   \n",
       "\n",
       "            2016-17  \n",
       "count  2.600000e+02  \n",
       "mean   7.507678e+06  \n",
       "std    1.649065e+07  \n",
       "min    0.000000e+00  \n",
       "25%    3.444900e+05  \n",
       "50%    1.934438e+06  \n",
       "75%    6.372715e+06  \n",
       "max    1.338766e+08  "
      ]
     },
     "execution_count": 605,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allstate_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the data from the years which is NOT to be used for analysis\n",
    "allstate_df.drop(['2011-12','2012-13','2013-14','2015-16','2016-17'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 0 to 32\n",
      "Data columns (total 4 columns):\n",
      "2014-15      875 non-null float64\n",
      "Item         891 non-null object\n",
      "S.No.        891 non-null object\n",
      "StateName    891 non-null object\n",
      "dtypes: float64(1), object(3)\n",
      "memory usage: 34.8+ KB\n"
     ]
    }
   ],
   "source": [
    "allstate_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2014-15</th>\n",
       "      <th>Item</th>\n",
       "      <th>S.No.</th>\n",
       "      <th>StateName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14819416.0</td>\n",
       "      <td>Agriculture, forestry and fishing</td>\n",
       "      <td>1</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7893514.0</td>\n",
       "      <td>Crops</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4309078.0</td>\n",
       "      <td>Livestock</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>346160.0</td>\n",
       "      <td>Forestry and logging</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2270664.0</td>\n",
       "      <td>Fishing and aquaculture</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      2014-15                               Item S.No.       StateName\n",
       "0  14819416.0  Agriculture, forestry and fishing     1  Andhra_Pradesh\n",
       "1   7893514.0                              Crops   1.1  Andhra_Pradesh\n",
       "2   4309078.0                          Livestock   1.2  Andhra_Pradesh\n",
       "3    346160.0               Forestry and logging   1.3  Andhra_Pradesh\n",
       "4   2270664.0            Fishing and aquaculture   1.4  Andhra_Pradesh"
      ]
     },
     "execution_count": 608,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allstate_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create new DataFrame for Per Capita GSDP by slicing data out of allstate_df\n",
    "GSDP_PerCapita_df = allstate_df.loc[allstate_df['Item']=='Per Capita GSDP (Rs.)'].sort_values(by='2014-15',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the columns 'Item' & 'S.No.'\n",
    "GSDP_PerCapita_df.drop(['Item','S.No.'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename the column '2014-15' more appropriately as 'Per_Capita_GSDP_2014-15'\n",
    "GSDP_PerCapita_df.rename(columns={'2014-15': 'Per_Capita_GSDP_2014-15'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Per_Capita_GSDP_2014-15</th>\n",
       "      <th>StateName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>271793.0</td>\n",
       "      <td>Goa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>240274.0</td>\n",
       "      <td>Sikkim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>164077.0</td>\n",
       "      <td>Haryana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>154778.0</td>\n",
       "      <td>Kerala</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>153076.0</td>\n",
       "      <td>Uttarakhand</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Per_Capita_GSDP_2014-15    StateName\n",
       "32                 271793.0          Goa\n",
       "32                 240274.0       Sikkim\n",
       "32                 164077.0      Haryana\n",
       "32                 154778.0       Kerala\n",
       "32                 153076.0  Uttarakhand"
      ]
     },
     "execution_count": 612,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the DataFrame\n",
    "GSDP_PerCapita_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the Per Capita GSDP for all the states using seaborn catplot\n",
    "\n",
    "#Set seaborn whitegrid as style\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "#Generate a category style bar plot, with State as x-axis and Mean_GSDP_Growth_Rate as y-axis\n",
    "sns.catplot(x='StateName',y='Per_Capita_GSDP_2014-15',data=GSDP_PerCapita_df,kind=\"bar\", height=6, aspect=2)\n",
    "\n",
    "#Give Label to the y-axis\n",
    "plt.ylabel('Per Capita GSDP (in Rs.)')\n",
    "\n",
    "#Provide title to the plot\n",
    "plt.title('State wise comparison of Per Capita GSDP (in Rs.) for 2014-15')\n",
    "\n",
    "#Rotate the x-axis values(State Names) so that they are visible without overlapping\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "#Display the plot visual with Per Capita GSDP\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=blue>From the above plot, the top 5 states with highest Per Capita GSDP for the year 2014-15 are -</font>\n",
    "-  <font color=blue>Goa, Sikkim, Haryana, Kerala & Uttarakhand</font>\n",
    "\n",
    "### <font color=blue>From the above plot, the bottom 5 states with lowest Per Capita GSDP for the year 2014-15 are -</font>\n",
    "-  <font color=blue>Jharkhand, Assam, Manipur, Uttar Pradesh & Bihar</font>\n",
    "\n",
    "#### <font color=blue>Goa state has the highest Per Capita GSDP during the year 2014-15: 271793.0</font>\n",
    "#### <font color=blue>Bihar state has the highest Per Capita GSDP during the year 2014-15: 33954.0</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.005\n"
     ]
    }
   ],
   "source": [
    "print(round((GSDP_PerCapita_df.loc[GSDP_PerCapita_df['StateName']=='Goa']['Per_Capita_GSDP_2014-15'])/(GSDP_PerCapita_df.loc[GSDP_PerCapita_df['StateName']=='Bihar']['Per_Capita_GSDP_2014-15']),3).values[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  <font color=blue>Ratio of the Highest (Goa) to the Lowest (Bihar) Per Capita GSDP during the year 2014-15 is: 8.005</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract the 3 sectors' data & GSDP data into a new DataFra,e\n",
    "get_sect_df=allstate_df.loc[allstate_df['Item'].isin(['Primary','Secondary','Tertiary','Gross State Domestic Product'])][['StateName','Item','2014-15']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>StateName</th>\n",
       "      <th>Item</th>\n",
       "      <th>2014-15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Primary</td>\n",
       "      <td>16303716.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Secondary</td>\n",
       "      <td>10488884.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Tertiary</td>\n",
       "      <td>22032942.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Gross State Domestic Product</td>\n",
       "      <td>52646842.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Arunachal_Pradesh</td>\n",
       "      <td>Primary</td>\n",
       "      <td>716959.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            StateName                          Item     2014-15\n",
       "6      Andhra_Pradesh                       Primary  16303716.0\n",
       "10     Andhra_Pradesh                     Secondary  10488884.0\n",
       "26     Andhra_Pradesh                      Tertiary  22032942.0\n",
       "30     Andhra_Pradesh  Gross State Domestic Product  52646842.0\n",
       "6   Arunachal_Pradesh                       Primary    716959.0"
      ]
     },
     "execution_count": 616,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_sect_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 617,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Item</th>\n",
       "      <th>Gross State Domestic Product</th>\n",
       "      <th>Primary</th>\n",
       "      <th>Secondary</th>\n",
       "      <th>Tertiary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StateName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Andhra_Pradesh</th>\n",
       "      <td>52646842.0</td>\n",
       "      <td>16303716.0</td>\n",
       "      <td>10488884.0</td>\n",
       "      <td>22032942.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arunachal_Pradesh</th>\n",
       "      <td>1676119.0</td>\n",
       "      <td>716959.0</td>\n",
       "      <td>287489.0</td>\n",
       "      <td>631844.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assam</th>\n",
       "      <td>19809800.0</td>\n",
       "      <td>5326697.0</td>\n",
       "      <td>4033091.0</td>\n",
       "      <td>9307109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>37391988.0</td>\n",
       "      <td>8019997.0</td>\n",
       "      <td>5984896.0</td>\n",
       "      <td>22179969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <td>23498180.0</td>\n",
       "      <td>6400817.0</td>\n",
       "      <td>8238886.0</td>\n",
       "      <td>7588778.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Item               Gross State Domestic Product     Primary   Secondary  \\\n",
       "StateName                                                                 \n",
       "Andhra_Pradesh                       52646842.0  16303716.0  10488884.0   \n",
       "Arunachal_Pradesh                     1676119.0    716959.0    287489.0   \n",
       "Assam                                19809800.0   5326697.0   4033091.0   \n",
       "Bihar                                37391988.0   8019997.0   5984896.0   \n",
       "Chhattisgarh                         23498180.0   6400817.0   8238886.0   \n",
       "\n",
       "Item                 Tertiary  \n",
       "StateName                      \n",
       "Andhra_Pradesh     22032942.0  \n",
       "Arunachal_Pradesh    631844.0  \n",
       "Assam               9307109.0  \n",
       "Bihar              22179969.0  \n",
       "Chhattisgarh        7588778.0  "
      ]
     },
     "execution_count": 617,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the sector dataframe by pivoting get_sect_df DataFrame\n",
    "sector_df=get_sect_df.pivot(index='StateName', columns='Item', values='2014-15')\n",
    "sector_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sort the data in sector_df dataframe based on GSDP\n",
    "sector_df.sort_values(by='Gross State Domestic Product',ascending = False,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a column and assign the Primary sector % contribution \n",
    "sector_df['Primary%']=round((sector_df['Primary']/sector_df['Gross State Domestic Product'])*100,2)\n",
    "\n",
    "# Create a column and assign the Secondary sector % contribution \n",
    "sector_df['Secondary%']=round((sector_df['Secondary']/sector_df['Gross State Domestic Product'])*100,2)\n",
    "\n",
    "# Create a column and assign the Tertiary sector % contribution \n",
    "sector_df['Tertiary%']=round((sector_df['Tertiary']/sector_df['Gross State Domestic Product'])*100,2)\n",
    "\n",
    "# Create a column and assign the Tax+Subsidy sector % contribution \n",
    "sector_df['Tax&Subsidy%']=round((100-sector_df['Primary%']-sector_df['Secondary%']-sector_df['Tertiary%']),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 620,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the columns 'Gross State Domestic Product','Primary','Secondary','Tertiary'\n",
    "sector_df.drop(['Gross State Domestic Product','Primary','Secondary','Tertiary'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Item</th>\n",
       "      <th>Primary%</th>\n",
       "      <th>Secondary%</th>\n",
       "      <th>Tertiary%</th>\n",
       "      <th>Tax&amp;Subsidy%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StateName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Maharashtra</th>\n",
       "      <td>12.14</td>\n",
       "      <td>26.47</td>\n",
       "      <td>49.46</td>\n",
       "      <td>11.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tamil_Nadu</th>\n",
       "      <td>12.20</td>\n",
       "      <td>30.06</td>\n",
       "      <td>48.82</td>\n",
       "      <td>8.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttar_Pradesh</th>\n",
       "      <td>24.92</td>\n",
       "      <td>24.49</td>\n",
       "      <td>44.06</td>\n",
       "      <td>6.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karnataka</th>\n",
       "      <td>13.09</td>\n",
       "      <td>22.22</td>\n",
       "      <td>54.77</td>\n",
       "      <td>9.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gujarat</th>\n",
       "      <td>17.75</td>\n",
       "      <td>36.90</td>\n",
       "      <td>33.76</td>\n",
       "      <td>11.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Item           Primary%  Secondary%  Tertiary%  Tax&Subsidy%\n",
       "StateName                                                   \n",
       "Maharashtra       12.14       26.47      49.46         11.93\n",
       "Tamil_Nadu        12.20       30.06      48.82          8.92\n",
       "Uttar_Pradesh     24.92       24.49      44.06          6.53\n",
       "Karnataka         13.09       22.22      54.77          9.92\n",
       "Gujarat           17.75       36.90      33.76         11.59"
      ]
     },
     "execution_count": 621,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 622,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2301c80a390>"
      ]
     },
     "execution_count": 622,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J6IvtYmRklKK+VyWpf//9N4MHD6Z79+4pJnoqUKAA0dHRfPHFF0DyZF4v/4jzojt37nDkyBGuXLnCb7/9xuPHj4mJiaFHjx5MnDhRTToBfvnlF2xtbdOs59atW4SHh+Pl5aXue/4efJXXvTfSa0Nvb290Oh3NmjXjm2++ISoq6rXvw+fCwsJ49OgRrVu3pkmTJjRp0oTBgwdTr149Tp8+rT4rDsnt07ZtW7799luqVatG06ZN+fPPP1PV+XzI/ovvqfv372NlZYWpqWma30VvM9GZEEK8C3l4Qgjx2bOxsSEoKEjtyQS4d+8esbGxlC5dGkdHRyIiIrh06RKQ3Bvl7u5OQkICjo6ObN68mcTERPR6PePGjVOf03yeCDg6OrJv3z5u3LgBJPecRUVFpTkpTnBwMBs2bGDDhg00aNCA/Pnzkzt3blasWEHt2rVxcnJix44dPHr0KNUf9MuXL8fX1xcnJyd8fHxwcnLi9OnTr4w/PcuXL6d+/frY2tqSmJiIkZERBgYGJCUlpUraihcvjkajISIiAoBTp07RuXNnKlWqxOXLlzlx4gQAFy5c4PDhw9SoUeOVr4ehoWGaiWFa1q9fj6IoPH78mD/++IM6deqQM2dOtFqtOkP1li1b1PJGRkbodLpUSUKdOnUIDQ1Ve7CWLFnC119/naoXNqNxZ7S+SpUqUbNmTYYNG5ZituuLFy9y5swZDA0Nsba2Zv/+/SxevFiN+3lPbbly5dKM5cmTJ0yYMIEOHTpk6JnOF9WuXZuIiAh1pt5169apx3Lnzs2pU6dQFIXY2Ng0k6A3Ua9ePUJDQ9Hr9cTHx7N58+ZUP5BA8nuqf//+TJkyJdWMxg0aNGDlypVAcqL2119/4ezsnO418+fPz759+9TP2cCBA6levTrBwcHY2tqq+zds2JBuogtgbm7OzJkz1ff3nj17iI+Pf2VPO4CTkxNLly5FURQSExNZtWoVtWrVeuU5APv27aNfv340b94cSH5OXKfTvfY8SO6tnT59eopZ22/cuIGhoSGFCxdO8bk4efIkNjY29O3bFycnJ/U11ul0KcoVK1YMMzMzNdmNiorC1dWVkydPpvtdJIQQH5r07AohPnvFihUjMDCQGTNmcOfOHUxNTbGysmLy5MkUL14cgPHjx+Pt7a323AQFBWFhYUG7du24desWrVq1QlEUatSogZeXF7GxsZiamtKmTRtWr17N2LFj6d+/PzqdDjMzM+bNm4eVlVWG4mvUqBELFy6kXLlyGBgYYGZmRsOGDVOVa9GiBYcOHaJ58+aYm5tToEABddKg9OJPy4MHD9i0aZM6KZWLiwu9e/dmzZo1tGjRItWQYhMTE+bMmcPkyZPx9/fH2NiYOXPmkCdPHmbNmsWECRNISEhAo9Hw448/UqxYMXXyqLQ4ODgwdOhQJkyYoM6OnR4rKytatWpFQkICHTt2VIeP+vj40KNHD2xsbGjatKlaPm/evFSsWBEXF5cUwzfbtGlDVFQUnp6e6PV6ihQpkmKysIx4MW4/P78M1zd16lSWLVtGz549SUpK4vHjx+TPn5/27dvTqlUrjIyMCAkJYerUqSxZsoQcOXKg0Who2bIlbdq0UesZOnQoZmZmGBoaotPpaNy4Mb17936je4DkYeM+Pj507twZCwuLFMmbu7s7f/31F40bN8bW1pYaNWpkuHcxLb169WL8+PG4ublhZWVFnjx5UvSiPzd9+nQURWHatGlMmzYNADs7OwIDAxkwYADjxo3DxcUFnU6Hj48PhQsXfuuYMsrGxoaZM2cyZswYtFotlpaWBAYGvvYHklGjRjFx4kTc3NzQarXUqVMnQ6/T4MGD6devHzly5MDS0pKvv/6a69evZyhWBwcHRo8ezfDhw4mJicHQ0JC8efMSHByMtbU1lpaW6ufi119/xdbWlqZNm6LRaKhRowY2NjZcu3aNIkWKpPj8/Pzzz0yaNIkFCxaQlJTE999/T7Vq1Shbtmya30VCCPGhaZR3+V9KCCGEEOItbdmyBUtLS+rVq4der2fAgAHUrl073fVqhRBCiDchya4QQgghPorz588zZswY4uPj0Wq11KxZk5EjR2JsbPyxQxNCCJENSLIrhBBCCCGEECLbkQmqhBBCCCGEEEJkO5LsCiHEJ+D333/H3d2d5s2b4+Ligo+PD7dv387w+VWqVOHmzZvvLT5XV9cUawu/Cw8PD548eZIpdWm1Wvz9/XFzc8Pd3R03NzfmzZunTqq0e/duZs2a9dp6MlruTc2aNYv169cDMHfuXHbt2gUkLzv04hqu6Vm7di29evVKtd/Ly4tt27YBGWvPF8u/q5iYGDp16pTu8YcPHzJhwgSaNGmCq6srDRs25IcffiA2NlYtc+zYMby8vHBzc8PV1ZXu3btz4cIFAG7evEnZsmXx8PDAw8MDNzc32rVrx9atW9Xz58yZg4ODAx4eHrRo0QI3Nze6dOnClStX3jje9NSvX5/IyMg3Pi+zvW38QgjxOZDZmIUQIoubMmUKZ8+eZf78+RQoUAC9Xs/GjRtp27Ytq1evJn/+/B87xEyV0bVgM2LRokXcvHmTdevWYWRkRExMDJ07dyZ37ty0bduWyMhIHj9+/Np6MlruTX3//ffqvw8ePEjJkiUz/RqZ2Z4Z8fjx43STwNjYWNq1a4ebmxubN2/G2NiYxMREpkyZwtChQ5k3bx6JiYn06tWLhQsXqrNxb9iwgR49ehAWFgaQYskbSF7ztkuXLhgaGtKkSRMAmjdvzpgxY9QyS5YsYciQIaxduzbD8X4KPvX4hRDifZJkVwghsrA7d+6wYsUKdu/ejbW1NQAGBga0aNGCkydPMn/+fMaOHUv9+vWpWLEi586dw9vbm9y5czNhwgQ0Gg1fffUVer1erTM8PJygoCC0Wi1mZmYMHz6cKlWqMGfOHI4dO0Z0dDT29vYEBAQQFBTEjh070Ov1FCxYkLFjx2Jra8vFixcZOXIk8fHxFC9eXF1P9mVeXl506NBBXf7nxe3Zs2ezc+dOjI2NyZ07Nz/++CP58uXD3t6eAwcOsHv3bnbu3ImBgQHXrl3DzMyMKVOmUKJECa5du8bIkSN5/PgxefPmRVEU3N3dadWqVYrr37t3D61Wq64VbGVlhb+/P3q9nuPHj7NixQp0Oh1WVlb06tWLcePGce3aNR49eoSFhQUBAQHExMSkKDd48GBWr17N77//jl6vJ1euXIwePZoSJUqkuLaHhwcjRoxQ12L29fXl8OHDmJmZ4efnR/ny5Tlx4gSlSpXCzMyMkydP4u/vj6GhIQBHjx6lXbt23L9/n1KlSjFt2jRy5Mjxxu+h5+1pbW2Nv78/4eHhWFlZUbFiRS5dusSSJUsACAsLIyQkhPv37+Po6MjEiRMxMDDg33//JSAggPj4eAwMDOjfvz/Ozs7cu3eP4cOH8/DhQyB5zdxBgwbh6+tLQkICHh4erF27Vr0fgFWrVlG0aFH69++v7jMxMWHYsGGEhISo6+3GxMSkeE+5u7tjaWmZ7rqyBQsWZODAgYSEhKjJ7sscHR2ZPn16qv0vx3v06FH8/f2Jj4/H2NiYQYMGUbdu3Tdq88DAQHW97WLFijF69GiOHz/OwoULWb58OQBNmjTBxcWFgQMHcufOHdq0acPevXs5duxYprW3EEJ87iTZFUKILOz48eMUL15cTXRfVKtWLWbOnKlulypVipkzZ5KYmIizszMBAQFqorVq1SoArl69yowZM1i8eDG5c+fmwoULdO3alR07dgDJPWSbN2/GyMiI9evXc/78eVavXo2RkRErV65k1KhRBAcHM3ToUDp06ICnpyf//PMPHTp0eKP7ioqKYtGiRRw4cAATExMWLlzIiRMnUq0ffPjwYTZv3kz+/PmZMGECv/zyC1OmTGHYsGF4eHjQvn17Ll26ROvWrXF3d091na5du9K3b18cHByoVKkSVatWpUmTJpQrVw6Adu3a8fDhQwYPHsy2bdvImTMnK1euBGDMmDEsW7aM0aNHpyh36NAh1q9fz7JlyzA3N2ffvn3079+fP/74I8W1GzVqxN69e3F0dOSvv/7C2tqaI0eOULt2bfbs2cOgQYM4ceIEAB06dGDbtm106NCBRo0aERYWxt27d1m8eDEmJiZ4enqyY8cOWrRokeoejxw5goeHR4p9aa2/unr1ak6dOsXmzZvRaDT06dMnxfG4uDhWrFhBYmIijRo14t9//6VUqVL4+voSEhKCnZ0dd+/e5dtvv8Xe3p5169ZhZ2fHwoULefr0KX5+fsTExPDjjz/i5uaWZo/ykSNHcHJySrXf1NSUvn37AmBtbY2Pjw/du3fniy++oGrVqtSsWRMXF5dXrmFbpkwZzp8/n+axpKQk1qxZQ82aNVMdezHehw8fMnDgQIKCgqhUqRIXLlygY8eOrFmzhkKFCqV77ReFhoby119/sWbNGnLkyMGcOXMYMWIEgYGBDB8+nCdPnvDkyRPi4uLYv38/AwcOJCwsjIYNGxITE5Op7S2EEJ87SXaFECKLS0pKSnN/YmIiGo1G3a5evTqQvJyLkZERjo6OQPLztM+Hc0ZERBAdHU2XLl3U8zQajZocVa5cGSOj5P8a/vzzTyIjI2ndujWA2uv28OFDzp07pyZe1apVo1SpUm90T7a2tpQpU4aWLVtSt25d6tatq8b7ovLly6vDtMuVK8fOnTt5/PgxJ06cYOnSpQCUKFECBweHNK+TP39+1q5dy8WLFzl48CAHDx6kbdu2jBgxIlWC3rRpUwoVKsSSJUu4du0ahw4dokqVKqnq3L17N9euXaNdu3bqvidPnvDo0SNy5cql7mvUqBHe3t4MGzaMI0eO0KVLFyIiIrCwsKBw4cLkzZv3lW3UsGFDzM3NgeQfMh48eJBmuerVqzN//vwU+7y8vFKV27NnDx4eHpiamgLQtm1btVcXkof9GhoaYm5uTtGiRfnvv/+Ii4vj3r179OvXTy2n0Wg4d+4cderUoWfPnkRFRVGrVi2GDBmClZXVK4d7K4qS4j27ceNG9dnkBw8eEBwcTJkyZejatSuenp4cPnyYw4cPExwcTHBwMGvWrEm3bo1Gg5mZmbq9detW/vnnHyD52e3y5cszYcKEdM8HOHHiBIULF6ZSpUpAcrtXrVqVQ4cOZTjZ3bt3L61atVJ74Tt16sS8efMwMDCgVq1aRERE8PDhQ9q2bcvKlSuJiYkhPDyc7t27c+zYsUxtbyGE+NxJsiuEEFlY5cqVuXbtGvfu3UuVHB08eDBFMvbiENeXV5V7nsDq9XocHR1T9AhHRUWRL18+du7cmaIOvV5P9+7dad++PZCcXL/4h/WL13hef1peLKfVaoHkodhLly4lMjKSAwcOMHnyZOrUqcOwYcNSnPti8qLRaFAURR2m+WK96Q3d9Pf3x9PTk5IlS1KyZEk6dOjAhg0bCA4OTpXsLl++nFWrVtGhQwfc3NzIlStXmpN66fV6PDw88PHxUbejo6NT9b7b29uj1WoJCwujaNGiODs7M3jwYIyMjNIdavuiF9v0+b2/i5dfIwMDg3SPP7+eTqejRIkSrF69Wj129+5dbGxsMDY2JiwsjAMHDvD333/j6elJcHBwioT/ZVWqVOHQoUN07NgRSB6e/LxHvn79+mi1Wv755x+OHj1K9+7dcXZ2xtnZGW9vb1xdXYmIiKBChQpp1h0ZGUnp0qXV7Zef2c0InU6XIhmH5PdZej84pUWv16eoQ6/Xq+c3bNiQvXv38uTJE7p3787ly5fZtWsX58+fp0aNGuzZsydT21sIIT53MhuzEEJkYba2tnh5eeHt7c3du3fV/aGhoezYsYMePXqkOsfe3h5FUdizZw+Q/Czm8yTV0dGRiIgILl26BCT39rm7u5OQkJCqHicnJ9asWaPOkjtr1iyGDRtG7ty5KV++vPoH+alTp9IdPmpjY8PJkycBuHjxIufOnQPg7NmzuLq6UqJECXr16kWXLl0yPMmOpaUlVatWVScaunHjBgcOHEiVpEByb+GsWbOIj48HkhOXCxcuqMOYDQ0N1URk3759tGzZEk9PT4oVK0Z4eLj6jOiL5ZycnNiyZQvR0dFA8kzZnTt3TjPWhg0bMm3aNGrXrk2JEiWIjY1l06ZNNG6AdwxBAAAgAElEQVTcOFXZF6/xPtSrV4+NGzeSmJhIUlIS69ate+05z39sOXz4MABnzpyhSZMm3L17l4CAAH7++WcaNmyIn58fJUuW5MKFCxgZGaHT6dJMztu3b8/FixdZsGABiYmJQHIyuG/fPh49eoShoSE2NjYEBQVx5MgR9bx79+4RGxubIpl90ZUrV/j555/p1q3bG7fLi/FWrlyZy5cvq8PLL1y4wOHDh6lRo0aG66tTpw6hoaHqM8dLlizh66+/xsTEhPr163PgwAHOnDlDxYoVqV27NrNmzaJu3boYGhpmensLIcTnTnp2hRAiixsyZAirV6+mT58+JCYmkpiYyFdffcWKFSsoWLBgqvLGxsYEBgYybtw4pk+fTtmyZcmTJw8AJUuWZPz48Xh7e6MoCkZGRgQFBWFhYZGqHk9PT/WZQY1GQ4ECBfjpp58AmD59Or6+vqxYsYLChQtTvHjxNGPv06cPI0aMYM+ePRQvXlwdal2mTBmaNWtG69atyZEjB2ZmZowaNSrDbTJlyhT8/PxYvnw5tra22NnZpegFfm7s2LHMmDEDd3d3TExMSEpKwsHBQe3xc3BwYOjQoUyYMIFu3boxZswYdahs5cqV1ST+xXKjR4+mR48edOvWDY1Gg6WlJXPnzk0z2W7UqBEhISHUqlULSH7O+ty5cxQoUCBV2fr16zN9+nS19zuztWrViitXrtCiRQty5MiBnZ2dOkw6PTY2NsyePRt/f3+ePXuGoij4+/tjZ2dH586dGTFiBK6urpiYmGBvb4+LiwuGhoZUrFgRFxcXli1bRu7cudX6LC0tWbFiBUFBQbRp0wZIHgJetmxZZs2apf4IERgYyIwZM7hz5w6mpqZYWVkxefJkihcvzs2bN9UJmSC5h9rU1BRvb2+++eabN26XvHnzpoh31qxZTJgwgYSEBDQaDT/++CPFihVL89yOHTum6CEfOnQo3333HVFRUXh6eqLX6ylSpAgBAQEAWFlZUaJECczNzTE0NKROnTr4+fmpP35kdnsLIcTnTqPIT4FCCCE+MUFBQTRu3JgSJUoQExODu7s7wcHB72Xpnuxi3759/Pfff2qSOHHiRExNTdXh2EIIIUR2Iz27QgghPjlFixZl8ODBGBgYoNPp6NGjhyS6r1GqVClCQkJYsGABer2eMmXKMG7cuI8dlhBCCPHeSM+uEEIIIYQQQohsRyaoEkIIIYQQQgiR7UiyK4QQQgghhBAi2/nkn9nV6/XExcVhbGyc5kyYQgghhBBCCCE+XYqioNVqsbCwSLVO/Kt88sluXFxcuus7CiGEEEIIIYTIHkqXLo2VlVWGy3/yya6xsTGQfOMmJiZvXc/JkyepUKFCZoUlMUgMEoPEIDFIDNk+DolBYpAYJAaJQWL4EDEkJiZy/vx5NffLqE8+2X0+dNnExARTU9N3qutdz88MEoPEIDFIDBKDxJBRWSEOiUFikBgkBolBYvhQMbzpY6syQZUQQgghhBBCiGxHkl0hhBBCCCGEENnOJz+MWQghhBBCCCG0Wi03b94kISEBACMjI86cOfNRY5IY3iwGMzMz7Ozs3vjZ3HSvmym1CCGEEEIIIcRHdPPmTaysrChatCgajYa4uDgsLCw+akwSQ8ZjUBSF//77j5s3b1KsWLFMua4MYxZCCCGEEEJ88hISEsiTJ88bT2IksgaNRkOePHnUnvnMIMmuEEIIIYQQIluQRPfTltmvnyS7QgghhBBCCCGyHUl2hRBCCCGEEOIDOHbsGF5eXri5ueHq6kr37t25cOHCW9V148YNBgwYkMkRZi8yQZUQQgghhBBCvGeJiYn06tWLhQsXUr58eQA2bNhAjx49CAsLw9DQ8I3qu337NleuXHkfoWYbkuwKIYQQQgghxHsWHx9PTEwMT58+Vfe5u7tjaWmJTqdjz549BAUFodVqMTMzY/jw4VSpUoWkpCSmTp3K7t27MTQ0pEqVKowdO5ZRo0Zx9+5d/ve//xESEsKuXbuYO3cuer0eCwsLfH19qVixIvPmzeP06dNER0djb29PQEDAR2yFD0uSXSGEEEIIIYR4z6ytrfHx8aF79+588cUXVK1alZo1a+Li4sLt27eZMWMGixcvJnfu3Fy4cIGuXbuyY8cO1qxZw6lTp9iwYQMmJiZ4e3uzdetWJk6cyIQJEwgJCeHSpUuMHTuWFStWUKhQIQ4cOEDfvn3Ztm0bALdu3WLz5s0YGX1e6d/ndbdCCCGEEEII8ZF07doVT09PDh8+zOHDhwkODiY4OJj27dsTHR1Nly5d1LIajYbr16+zf/9+PDw8MDMzA2DmzJkAHDx4UC37999/4+DgQKFChQBwdHTExsaGkydPAlC5cuXPLtEFSXaFEEIIIYQQ4r37559/OHr0KN27d8fZ2RlnZ2e8vb1xdXUlNjYWR0dHNZEFiIqKIl++fKmS1Pv376PX61Ps0+v1qZbtURSFpKQkAHLkyPGe7iprk9mYhRBCCCGEEOI9s7GxISgoiCNHjqj77t27R2xsLA0aNCAiIoJLly4BsGfPHtzd3UlISMDR0ZHNmzeTmJiIXq9n3LhxbNmyBUNDQ7RaLZDck7tv3z5u3LgBwIEDB4iKiqJSpUof/kazkPfasxsbG0u7du2YN28ednZ27N+/nx9//JFnz57RrFkzBg8eDMCZM2fw8/MjLi6O6tWr88MPP3yW3exCCCGEEEKI7KlYsWIEBgYyY8YM7ty5g6mpKVZWVkyePJkyZcowfvx4vL29URQFIyMjgoKCsLCwoF27dty6dYtWrVqhKAo1atTAy8uL2NhYTE1NadOmDatXr2bs2LH0798fnU6HmZkZ8+bNw8rK6mPf9kf13jLK48ePM2rUKK5evQpAQkICI0eOZMmSJRQoUIBevXqxZ88e6tWrh4+PDxMnTqRy5cqMHDmSVatW0b59+/cVmhBCCCGEEEJ8cA4ODjg4OKR5rFmzZjRr1izVfkNDQ4YNG8awYcNS7M+VKxcbN2587fm9e/fGwsLiHSP/NL23YcyrVq1i7Nix5MuXD4ATJ05QpEgRChUqhJGREW5ubmzbto1bt26RkJBA5cqVAWjVqpU6a5gQQgghhBBCCPE2NIqiKO/zAvXr12fx4sUcO3aM3bt3q+s67d+/nwULFjBgwAD8/f35/fffAbh27Ro9e/Zk+/btGar/2bNn6ixj6alQpgym7/hrxrO4OE6ePfvW52eFGDIjDolBYpAYsmYMmRGHxCAxSAxZM4bMiENikBg+hxjOnDlD2bJlM3y+XqcjPiHhnWIwNzPDwNDwnep41ziyWwwXL15UJ9Z6WYUKFTA1Nc1wnR8s2f3333/566+/mDp1KgAREREsXLiQvn37Mm3aNJYvXw7A1atX6d27d4Z7d58nu6+78QiP1u90H7U3hL7T+VklhneNQ2KQGCSGrBlDZsUhMUgMEkPWjOFd45AYJIbPIQYL7+8pWfDLDJ9rWbJkpsQQe/HiO52fGXFkpxjS+tEioznfyz7YbMz58+fn3r176va9e/fIly9fqv33799Xhz4LIYQQQgghhBBv44Mlu5UqVeLKlStcu3YNnU7H5s2bqVu3LgULFsTU1JR//vkHgA0bNlC3bt0PFZYQQgghhBBCiGzog63vY2pqyk8//cSAAQN49uwZ9erVo2nTpgAEBAQwatQoYmNjKV++PJ06dfpQYQkhhBBCCCGEyIbee7IbHh6u/tvR0THF9NjPlSlThjVr1rzvUIQQQgghhBCZQJeY+E7P3eoSEzE0McnEiFIztiuMqVnmXyNRq8PE+PWTMd2+e5eWPXtSvFAhNBoN2qQk8trYMHbwYGy/+EItd/fuXUaNGkVwcHCmx5qWmSEhhEdEULFsWSb6+ACwdetWHj16lO2Wf/1gPbtCCCGEEEKI7MHQxIRvV/Z56/NXtQ165xheTrjPnDmTaqIltyEb3vk6L9s0zSPDZfPa2PD73Lnq9vQFC5gVEsLk4cPVfba2th8s0Y2JjWX/P/+wceFCBo4dy4UrVyhaqBDr1q0jKOjdX5OsRpJdIYQQQgghxCfn5YS7f4kOmDy4pm6XsCnyMcJ6peoVKzL3t99w7dqVCvb2nL98mYCZMxk0aBDh4eGMGDECc3NzTp8+zZMnT/D29mbDhg2cPXuWhg0bMmLECGJjYxk5ciR3794lOjoaR0dHfLt25Z/ISGb/+is6nY7ihQtz9NQpAidOpEjBgsQnJNC6Vy/WzJuHTqcj4dkzEp49w8jIiNVbttCmTRuMjLJfapj97kgIIYQQQoj35FMYviuyJm1SEmH79lGxTBn+PnqUWtWq8dOIETwyM0tRLjo6mpUrV7Ju3Tp8fX3Zvn07pqam1K1bl379+rFnzx7Kli3L7NmzSUxMxMXFhbN16gBw7dYtNv/6K1YWFsxbupSt4eH08fIiLCICpxo1yGFujkfjxnT8/nvq1qhB3jx5OHTsGD2HDHmne1P0+ndevkjR69EYZO78yZLsCiGEEEIIkUFZYfiu+HTce/CA7/r3ByBRq6W8vT0Dunbl76NH+crePs1znq9M8+WXX1KqVCny5MkDQK5cuXj8+DGurq6cOHGC3377jcuXL/Po0SOeJiQAUKRgQawsLABwb9SIPn5+9PHyYnNYGP3+/yTAnVq3plPr5PWJ5y5aRKfWrdm+fTurV6+maNGijBw5EoM3TDo1BgZceqFX/W28j554SXaFEEIIIYQQ4i28qkczh5kZtra2bNq2LdUxA2NjcpcujaWdHQ9v3EhxzNjYWP13WkOLlyxZwvbt2/n222+pVasW58+fR1EUAMxMTdVyX9raUiBfPsIjInjw6BFflSmTop7o+/e5decOVStUoHW/fmzcuJFJkyaxf/9+nJycMt4IWZgku0IIIYQQQgjxFl7Vo3n38R20uqQ0j2t1SVx/fItnOXSYal4/s/OLIiIiaNu2LW5ubkRGRnL27Fn0en2avbHujRoxdf582rq5pTo2f/lyurdrlxyPVouBgQEajYZnz569UTxZWeYOihZCCCGEEEII8d507tyZuXPn4ubmxuTJk6lSpQq37t5Ns2z9WrV4HBuLS/36KfZfvHoVDVCiSPLQ4U6dOtG0aVNu3rxJnf///G92ID27QgghhBBCiGwn/lniGy0TlFEZXWfXtkB+flu3JM1jL+63s7MjPDwcgJ9++kndX7NmTWrWrKluPy9jZ2fH9u3bU9QXe/EikDzb83OKonDkxAlqVqpE3v//3O9zJYsWZdTAgep2ly5d6NKly2vv6VMjya4QQogs411nOX1eh8x0KoQQ4nZcFMSlf/xtJ0TKSKKbFUwLDmbvwYPMGT/+Y4fy0UiyK4QQIst411lOQWY6FUII8XlJb5Kscf7+b1RHZi/7kxVIsiuEEEIIIYQQn6isuuxPVpD90nchhBBCCCGEEJ89SXaFEEIIIYQQQmQ7MoxZCCGyAJmYSQghhBAic0myK4QQWYBMzCSEEEIIkbkk2RXiI3nXnjzpxRNCCCGESF9hy3wYm5hner36pEQMjDL2N9i+8L2sWrwCnU6PXq+nQbOGtOn4babH9Co7t+wg8t/jeI/2ead6Ll++zMCBA9HpdEyaNImqVaui1+vp3bs3c+bMyaRoM5cku0J8JO/akye9eEIIIYQQ6TM2MefypNaZXm9xv4x1VtyPvk/wnF+Y89vP5LTOSfzTeIb3HYJdkUI41HHM9LjetxUrVtC3b1/s7Oz45ZdfqFq1KqGhoTRv3hxTU9NXrmn8sUiyK4QQQohU5DlyIYR4N08eP0aXlMSzhASwzol5DnO8R/tgYmLC+dPn+GXWPJ4lPCN/Xlt++OEHChUqxJkzZxgzZgwJCQlYW1sTEBBA/vz5mTdvHhs3bsTQ0JDatWvj4+NDVFQU/fv3p1SpUhw/eZxcuXMzctIorKxzEvbHLlb8tpwcFjnIlz8f5ubJPdx/he1l7e9rSHyWiDYxkUF+QyhXsTxeXl5YW1tz4cIF2rZty6lTp5g2bRoAc+bMwdTUFGNjY+Lj44mLi8PY2JiEhAR27NjB/PnzP2Yzv5Iku0IIIYRIRZ4jTyZJvxDibRUvVQKHOrXo1rozJUqXpGK1SnzT2Jm8+fMxaeQExk4dT778+bh96jqjR4/mt99+Y+jQoQwdOhRnZ2eWL1/OokWLcHBwIDw8nNDQUIyNjRkwYAArVqygXr16nD17lsmTJ2Oa34KJvuP5c0c4tb+pw8LABcxdHETOnDkZO3QU5ubm6PV6tq7bzLiACVjnsmbHpm2sWryCcQETALC3t2fu3LnExcXRqFEjYmNjsbS0ZPPmzSxevBidToePjw9arZaJEyfy66+/4uXlhYFB1l3gR5JdIYQQIouRBCvrkKT//8hcE0K8uf7DBtKuS3v+PfQP/x48gnf37/m2Uzuibt1m/LAxAJgYmhAbG8uDBw+4d+8ezs7OALRv3x6AKVOm4OLiovbOtm7dmvXr11OvXj3y5MlDuXLluPTgGkWLFyXmSQxnIk9R9qty5LbJDYBzkwYcP3IUAwMDRk8Zy8F9f3Pz2k1OHD2OoYGhGmvFihUBsLCwoF69euzcuZNChQpRqFAhbG1tAVi2bBkADx484OTJk3Tq1AkfHx8ePnxIy05tKFOh7Ado1YyTZFcIIYTIYiTBElmRzDWRdcgPD5+GQxEHiY+Pp17Db2js2oTGrk3YtmEru3f8Sf4vCzB38TwAilrbcf/+fYyNjdFoNOr5z549Izo6Gr1en6rupKQkgORnZZ/TgKIooNGAoqi7DQ2TE9r4p/EM6jYA56YNqFD5K4qVLMamNRvVcmZmZuq/W7duTVBQEHZ2drRq1SrV9X/++Wd69+7Nxo0bKVGiBK1ataJnn15MC575lq31fkiy+5mRL0chhBBCiE+b/PDwaTA1M2Xe9EDKlC+DbYH8KIrCpfOXsC9fhvBtYZw8FkmFyl8RGhrKpk2bWLJkCba2tuzbtw8nJyc2bNjAoUOHcHFxISgoiLZt22JkZERoaCgODg7pXrd8pQoETQvkfvR9bL6wYW/YbiwtLbl14yYajYa2nb9DURQCfvBPM5EGqF69Onfu3OHWrVv4+fmlOHb9+nViYmL46quvOHr0KIaGhhgYGJCYmJip7ZcZJNn9zMiXoxBCCCGE+BxoE+MzPHPym8jo0kOVqlWmfbeOjBs6mqQkHQDValajY3cvHOo4Mn9GEImJidhY52bKlCkATJ06lXHjxjF16lRy586Nv78/+fLl48yZM7Ru3ZqkpCScnJzo2LEjd+7cSfO6uW1y08e7L34Dh2NqbkbhokUAKFayOMVLl6Bnu/9hoNFQ1aE6p0+cTDf+Ro0a8ejRI0xe6uiaPXs2AwcOBKB58+b07NmT1atX49Wny2vb5EOTZFcI8dmTEQ9CiKxOvqeEeHPXY6NfebyETZG3qjeja+wCNHRpTEOXxqn2l/2qHDMXzkkVh729Pb///nuq8n379qVv374p9tnZ2REeHq5ud+zeSf23U/26ONWvm6qe4eNHptjuPTi5ziVLlqj7FEVBq9Vy+PBhRo5MWR4gICBA/fcXX3zB2rVrAbj04Fqqsh+bJLtCfMbkj6dkMuJBCJHVyfeUEOJDuXfvHi4uLnh6elK+fPmPHc47kWRXiM+Y/PEkhBBCCCFelC9fPg4fPvyxw8gUWXdRJCGEEEIIIYQQ4i1Jz64QQgjxAlnjVgghhMgeJNkVQgghXiBr3AohhBDZgyS7QoiPSibJEkIIIYQQ74Mku0KIj0omyRJCCCHE+/CllS3mxmaZXm+iTouJofFrywVOncOZyFNotUlE3bxN4WKFAXD/tiWNXZu89vxdu3Zx69YtOnfuzLJlyzAxMcHT0/OdYo/89wSzfpyOmbkZIyeN5stCBXkaF0cfvz4EBWW/v6kk2f1A5BkwIURWJ99TQgghshNzY7N3fiwlLRn9ob2fzwAA7kbdYXhfH+YunvdG14mMjMTU1BSADh06vFmQ6QhdvpoRE/24ee0mf2zYyv/692DV4pV07949U+rPaiTZ/UDkGTAhRFYn31NCCCHE+/c07ik/B8zl+pWr6PUKA/r2p3nz5qxevZpNmzbx8OFDChcuzLFjx9BoNHz55ZdcuXIFU1NT+vbty6JFi9i8eTNPnz7F1NSU6dOnQ04NXu7tKV+pPJfPX6Jitcrkyp2Ljj06ATB13E841quNkbExCfEJxD99ipGxEfej7xN95y7VqlX7yK3yfsjSQ0IIIYQQQgjxgSwPWUqZCmWY/dvPTPk5gMDAQG7dugVAdHQ069evJzAwkDZt2tC+fXtatGihnvvkyRN2797N0qVL2bJlC05OTixfvlw9XqN2TX5ZuZCW37Xmz+3hAMQ/jefksUhqOjnQrst3BM+ez55du3Fr7c6ykCV81zVzeo2zIunZFUIIIUSWJEPrhRDZ0bEj/6I9kMS2DVsBeBb/jIsXLwJQvnx5DA0N0z03Z86cTJ06lU2bNnH16lX++usvvvrqK/W4ffmyABQsVBCbL2w4dfwUt27cxKFOLYyNjSlpX4pZC+cCcOXiFYyMDDHPkYP+/fuTlJSEr68vRYoUeV+3/sFJsis+OJl9VwghREbI0HohRHak0+kZMWEkxUoWB8Bab4G1tTXr16/HzOzVE2rdvHmTLl260LFjR+rVq0eePHnURBnA1PT//kZu5NqE3TvCuXXjJl37/i9VXcsXLqGPdz/WLg+lefPm5MuXj8DAQPz9/TPpTj8+SXbFByez7wohhBBCiM9VpeqV2bJ2M/2HDeR+9H06dm3HmjVrUpUzMjJCp9Ol2HfixAmKFy9Oly5diI+PZ8aMGen2xNZtUJeVv/2OhWUOSpUpneLYsSNHKVSkMDZf5CEpSYuRkREajYZnz55l3o1mAZLsCiGEEEIIIcQH4tWjM3P9Z9OnQ0/0ej0jRoygYMGCqcp9/fXXjBw5EhsbG3Vf3bp1WblyJc2bNwegWrVqXL16Nc3rmJmbU7pcaUqVTZnoKopC6NLV+E7yA6CpR3OmjU3uzf3pp58y4xazDEl2hRBCCCGEENlOvDbhvYwIzOg6u8/ZFsjPb+uWqNsWlhYMH++rbpewSe6Z9fT0TLGOroODA+Hh4anqW7RoUap9lx5cY8nG/5uoSlEUnsY95fKFy/Qe3DdFWY1Gw4SZk9XtoiWKsXXr1gzfz6dEkl0hhBBCCCFEtnM75u4rjz9PMt/UmyS6H8uZyNOMGzoGr56dsc6d62OH89FIsiuEEEIIIYQQ2Ui5iuVZtePdZrPPDmSdXSGEEEIIIYQQ2Y707IrPkix/JIQQQnx65P9vIcSbkGRXfJZk+SMhhBDi0yP/fwsh3sRnkey+66+Az+uQXwKFEEIIIYQQ4tPwWSS77/orIMgvgUIIIYQQQgjxKfkskl0hhBBCCCHE56VQjnyYmJlner36xEQMMjDiM3DqHM5EnkKrTSLq5m0KFysMgPu3LWns2uSNrhkUFMSmTZswMzNj5syZFC6cXJeiKMycOZM/dmxDo9FgYmpCp56dqVqzerp16ZJ0eHzjwuZ92zJ07Z07d3Lu3Dn69++fYv/+/fv55Zdf+O2339I871nCM8YOGUX03What/fEpZUrAHP9Z+P+bQsKFy2coeu/C0l2hRBCCCGEENmOiZk5ER6tM73ejD4e2c9nAAB3o+4wvK8PcxfPe6vrPXz4kJCQEPbv38+8efNYvnw5I0aMAGDTpk2cP3+euYuCMDQy5MbV6/j09mb+ihCsc1m/1fVe1qhRIxo1avTG5x0+cIiiJYoyYcYkerXvgUsrV65fvY5GwwdJdEGWHhJCCCGEEEKID+be3WhGDfJl0P8G0LllR+bOnQvAxIkT8fX1BWD9+vW0b98evV5Pzpw5sbCw4OjRo5w4cYKqVauqdd2/fx+9Xo9WmwhAoaKFGTl5NIaGhty+cYv/temill00/1d+/3WZuj1z8nT6d+qN3/cjuHPnDgALFizAzc2Nli1bMm7cOABWr16Nn58fAHv27MHFxYVWrVoRGpqc9F++fJn69eujKAoARw/9y7ihozE2MiIh4RnPniViYJCcdi4PWUK7Lh0yu0nTJcmuEEIIIYQQQnwgf24Pp37TBswMmcPcRUGEhITw+PFjhgwZwr///suWLVuYNWsWU6ZMUZPEevXq0alTJ5ycnGjcuLFaV6tWrXj06BHfNf+W0YNGsnrpKgoVLYylleUrY9Dr9FSpUZW5i+fxda2aTJ48mcTERBYuXMi6desIDQ0lPj6ee/fuqeckJCQwcuRI5syZw9q1azE2NgagePHi5MuXj5PHIgEI+2MXDV0aU83xaxLi4/HpPZgufboRefQEBey+JE/ePJndpOmSZFcIIYQQQgghPhBPr7bY5MnDmqWrCJ41D61WS0JCAubm5kyaNIkhQ4bQq1cvChUqBMCwYcPQ6XQMHz6c0NBQTpw4QUBAAAC5cuVi5cqVTA+eRZUaVTmy/xB92vfgbtTdV8ZgniMH9Rp+A0CDpg04dOgQJiYmVKhQgTZt2hAYGEiPHj3Imzeves7Zs2f58ssvKV68OAAtWrRQj7Vu3ZrwbWHEP40n8tgJajo5YGRkxIgJfgQtC8bJuQ5rlq7Gs+O3LJr3K6MHjWTH5u2Z2axpkmRXCCGEEEIIIT6Q+TOD2Lx2E/m/zM93XTtgbW2tDgG+cuUKNjY2nPp/7N15nI1l48fx75nNNmQdKuqXdZKQJTNZEkI09q3HVkSyRYjQSMMzljSFEuUp2cq+Z2fCVEL1ZBkqWyTGMhNjFjNzfn94zXlMjGHOzHWOez7vv+as1xfjnPt739d93QcOSJIuXryoXbt2KSQkRD169FC1atXUo0cPpaSkSLo+7fjw4cN6pGxptflXO0386F1VqfGEIrbvlM1mk112x7jJScmOnz09/1cDU+x2eXldX8pp5syZCg4OVnJysnr06KE9e/Y4nmez2Rw5JTleI0nNmjXT3oAabLAAACAASURBVO9+0M5tOxRQJ9Bx1DdV+Obtqlarms79dU6/H/lNY98bp+ULlygxIdHpv8/boewCAAAAgCE/7t6n9l06qE6Dejpx7ITOnz+v5ORknTlzRtOnT9dXX32ln376STt37lT+/Pnl4eGhyMhISVJgYKBiY2N1+fJl2e12Xb58WVOnTlV8XJwkKfZKrP7684xKly8j3/y+irkUo79j/lZiQqL2ff+/4nrl8hX9ELFbkrRpzQY99dRTOnfunJo3by5/f38NGjRIAQEBOnLkiOM1jz76qP766y8dPnxYkrRmzRrHY/ny5VPVmtX0xczP1ah52sWskpKStHH1ejVr/bySkpLk6ekpm82m5KRkJScnKzuxGjMAAAAAy0mMj7vjlZPvxp1eeig9Hbt30sQxocqVy0fFivupYsWKOnXqlGbNmqVevXqpVKlSGjt2rIYMGaJVq1Zp6tSpGj16tK5duyY/Pz+tXLlS77zzjvbv36/+/fvrvffeU98ur8jbx0ceHh5q2aGVqlSvKklq3amNBr7YT8WKF1OFxx51ZChwXwF9s3m7Pp/xHxX1K6apU95X4cKF1bZtW7Vp00Z58+ZV6dKl1bp1a0ep9fHx0bvvvquhQ4fK29tb/v7+af5cTzeqr98if1U5//Jp7l+7bLWebd5E3t7eKlO+rDy9vNSzXXc906Sh8uTN+ktD3YiyCwAAAMBy/rh6Trqa/uNlCj+cqfe926Jb/P4S+nz5XMftBk0bqUHTRjflqFWrluO+atWqadu2bZKkGjVqaNmyZWnec968eY6fhw8frna9Ot1y7C69uqlLr2433f/l+iVpbhcuXFiS1LNnT/Xs2TPNY+3bt1f79u0lSQEBAVq9evVN75ecnKwfd+9TkxZNb3qsZYfWjp9tNptGhwbfMmt2YBozAAAAACDTWrVqpeNHj+m5ls1dHSUNjuwCAAAAADJt9erV+v3iCVfHuAlHdgEAAAAAlkPZBQAAAHDPs8ue5tI4uPdk9b8fZRcAAADAPS8q4aLiL1+l8N6j7Ha7Lly4oNy5c2fZe3LOLgAAAIB73poz2yVJxXIVlk22DJ+fePY2SzXfhajYC069PityWCJDvqvKnTu3SpYs6XSWVJRdAAAAAPe8q8nxWnRq/R0/f1HHGVky7pivXnXq9VmRgwy3xjRmAAAAAIDlUHYBAAAAAJZD2QUAAAAAWA5lFwAAAABgOZRdAAAAAIDlUHYBAAAAAJZD2QUAAAAAWA5lFwAAAABgOZRdAAAAAIDlUHYBAAAAAJZD2QUAAAAAWI5Lyu7KlSvVvHlzNW/eXBMnTpQkHTp0SG3atFGTJk00atQoJSUluSIaAAAAAMACjJfduLg4jR8/XnPnztXKlSu1Z88eRUREaNiwYQoODtaGDRtkt9u1aNEi09EAAAAAABZhvOwmJycrJSVFcXFxSkpKUlJSkry8vBQfH6+qVatKktq0aaP169ebjgYAAAAAsAgv0wP6+vrqtdde03PPPac8efKoZs2a8vb2VrFixRzPKVasmM6ePWs6GgAAAADAIoyX3cjISC1dulTbtm1T/vz5NXToUO3atUs2m83xHLvdnub2ndi/f3+6j1WvXj3TeW+0d+/eTL/WHTJkVQ4ykIEM7pnB2RxkIAMZ3DNDVuUgAxnI4J4ZnM1BhvQZL7s7d+5UYGCgihQpIun6lOXZs2crKirK8Zzz58/Lz8/vrt63UqVKypUrV5Zm/aes+kckAxnIQIbs4g45yEAGMpCBDGQgw91xhxzunCEhIeG2BzfTY/ycXX9/f0VEROjq1auy2+3aunWrnnzySeXKlcvR5FeuXKl69eqZjgYAAAAAsAjjR3br1KmjgwcPqk2bNvL29tbjjz+u3r1769lnn9Xo0aN15coVPfbYY+rWrZvpaAAAAAAAizBediWpd+/e6t27d5r7/P39tWTJElfEAQAAAABYjPFpzAAAAAAAZDfKLgAAAADAcii7AAAAAADLoewCAAAAACyHsgsAAAAAsBzKLgAAAADAcii7AAAAAADLoewCAAAAACyHsgsAAAAAsBzKLgAAAADAcii7AAAAAADLoewCAAAAACyHsgsAAAAAsBzKLgAAAADAcii7AAAAAADLoewCAAAAACyHsgsAAAAAsBzKLgAAAADAcii7AAAAAADLoewCAAAAACyHsgsAAAAAsBzKLgAAAADAcii7AAAAAADLoewCAAAAACyHsgsAAAAAsBzKLgAAAADAcii7AAAAAADLoewCAAAAACyHsgsAAAAAsBzKLgAAAADAcii7AAAAAADLoewCAAAAACyHsgsAAAAAsBzKLgAAAADAcii7AAAAAADLoewCAAAAACyHsgsAAAAAsBzKLgAAAADAcii7AAAAAADLoewCAAAAACyHsgsAAAAAsBzKLgAAAADAcii7AAAAAADLoewCAAAAACyHsgsAAAAAsBzKLgAAAADAcii7AAAAAADLoewCAAAAACyHsgsAAAAAsBzKLgAAAADAcii7AAAAAADLoewCAAAAACyHsgsAAAAAsBzKLgAAAADAcrxu92B4eLiOHj2qmjVrqlKlSqYyAQAAAADglHSP7M6aNUshISH6+eef1adPH61evdpkLgAAAAAAMi3dI7urV6/WihUr5Ovrq6NHj2rkyJEKCgoymQ0AAAAAgExJ98iul5eXfH19JUmlS5dWbGyssVAAAAAAADjjtufspnmi1x0/FQAAAABgQGJSohZ1nOH0e/h4+WRRIveRboNNTk5WTEyM7Hb7LW8XLFjQTEIAAAAAwC35ePno6Pi2Tr1H6VFLsyiNe0m37B45ckQBAQGOcitJtWrVkiTZbDYdOnQo+9MBAAAAAJAJ6ZbdyMhIkzkAAAAAAMgytz0R1263a9euXTpy5Ihy586tChUqqHr16qayAQAAAACQKemW3fPnz+vll19WXFyc/P39ZbPZ9Nlnn6lw4cKaNWuW7rvvPpM5AQAAAAC4Y+mW3enTp+u5557TK6+8ctP9kydP1rhx47I9HAAAAAAAmZHudXb37NlzU9GVpL59++qXX37J1lAAAAAAADgj3bKb3nV1PTw85OGR7ssAAAAAAHC5dFurzWYzmQMAAAAAgCyT7jm7Fy5c0GeffXbLxy5evJhtgQAAgOslJiVqUccZTr+Hj5dPFiUCAODupFt2a9eurSNHjtzysaeeeirbAgEAANfz8fLR0fFtnXqP0qOWZlEaAADuXrplNzQ01GQOAAAAAACyzG1Xmlq8eLF+++03SdLChQsVFBSkN998U1evXjUSDgAAAACAzEi37M6cOVMLFy6UzWbToUOHNGHCBPXq1UtFixbV5MmTTWYEAAAAAOCupDuNed26dZo/f758fX0VFhamp59+Wi1atJDdblezZs1MZgQAAAAA4K7cdhqzr6+vJGnfvn0KCAiQdP2SROldg/dObd26VW3atNFzzz2ncePGSZIiIiIUFBSkxo0bKywszKn3BwAAAADkbOmWXbvdLrvdrri4OP3yyy+qVauWJCk+Pl4JCQmZHvCPP/7QmDFj9NFHH2nVqlU6ePCgwsPDNXLkSH300Udat26d9u/fr/Dw8EyPAQAAAADI2dI9RFu3bl0NGjRISUlJKlOmjMqUKaPjx49r6tSpeuaZZzI94KZNm9SsWTOVKFFCkhQWFqYTJ07o4YcfVqlSpSRJQUFBWr9+vZ5++ulMjwMAAAAAyLnSLbuDBw/WnDlzFBUVpeDgYEnXV2fOlSuXXn/99UwPeOLECXl7e6tPnz46c+aM6tevr3LlyqlYsWKO5/j5+ens2bOZHgMAAAAAkLOlW3a9vLzUs2fPNPcNGzbM6QGTk5O1Z88ezZ07V3nz5tWrr76q3Llzy2azOZ5jt9vT3L4T+/fvT/ex6tWrZzrvjfbu3Zvp17pDhqzKQQYykME9MzibgwxkIIN7ZsiqHGQgAxmyPsPjVR7Xoo4znHqPxKRE/fLzL5l+vTt8TrlDhlu57UpT+/fvV0pKiipXrqzhw4crJiZGnp6e+ve//6377rsvUwMWLVpUgYGBKly4sCSpUaNGWr9+vTw9PR3PiYqKkp+f3129b6VKlZQrV65MZbpTWfWPSAYykIEM2cUdcpCBDGQgAxnIkJMyHB3f1qnXlx611DJ/F9mVISEh4bYHN9OT7gJV33zzjfr06aMLFy5Ikn7++Wc1adJEefLk0Zw5c+56oFTPPPOMdu7cqb///lvJycnasWOHmjZtqmPHjunEiRNKTk7WmjVrVK9evUyPAQAAAADI2dI9sjtjxgx9+umn8vf3lyTlyZNHrVu3Vr169fTiiy9q4MCBmRqwSpUqevnll/Wvf/1L165dU+3atfXCCy+odOnSGjBggBISEvT000+radOmmfsTAQAAAAByvHTL7sWLFx1FV5Ly5s0rSSpSpIgSExOdGrRdu3Zq165dmvsCAwO1atUqp94XAAAAAADpNtOYbzyHVpLmz5/v+Dm1+AIAAAAA4I7SPbJbpEgR/f777ypTpkya+3///XcVKlQo24MBAADgusSkRKdWfE1MSpSPl08WJgIA95du2e3evbtee+01TZo0SRUrVpQk/fbbbxo6dKiGDBliLCAAAEBO5+Pl49SKr6VHLc3CNABwb0i37DZq1EgXLlxQjx49JEk2m00pKSkaOnSo6tataywgAAAAAAB367bX2e3YsaPatGmj33//XXa7XWXKlJGPD1NgACCrOTtFMfU9mKYIAABw3W3LriR5e3unWZUZAJD1nJ2iKDFNEQAA4EbprsYMAAAAAMC9Kt2y6+y1dAEAAAAAcJV0y26XLl0kSZMnTzYWBgAAAACArJDuObvnz5/Xxx9/rDVr1qho0aI3Pf7SSy9lazAAAAAAADIr3bIbEhKitWvXKj4+XkeOHDGZCQAAAAAAp6RbdmvXrq3atWtr9uzZ6tmzp8lMAAAAAAA4JcNLD3Xq1Elvv/22vvnmGyUlJal27doaNWqUfH19TeQDACDH4brLAAA4L8OyO2HCBCUnJ+vDDz9UcnKyFixYoJCQEE2cONFEPgAAchyuuwwAgPMyLLs///yzVq1a5bg9btw4NW/ePFtDAQAAAADgjHQvPZQqOTlZKSkpjtspKSny9PTM1lAAAAAAADgjwyO7gYGBGjRokF544QVJ0sKFC1WrVq1sDwYAAAAAQGZlWHZHjBihGTNm6L333lNycrLq1q2rvn37msgGZBtnF39h4RcAAADAvWVYdr28vDRgwAANGDDARB7ACGcXf2HhFwBATsOOYgD3mgzLLgAAAMCOYgD3mgwXqAIAAAAA4F5D2QUAAAAAWE6GZTc2NlZjx45V9+7dFR0dreDgYMXGxprIBgAAAABApmRYdseNG6cCBQrowoULypUrl65cuaLg4GAT2QAAAAAAyJQMF6g6dOiQQkNDFR4erjx58ujdd9/V888/byIbAADIwZxd/Tf1PVgBGABypgzLrodH2oO/ycnJN90HAACQ1Zxd/VdiBWAAyMkyLLs1a9bU5MmTFR8frx07dmj+/PmqVauWiWyApXG9QgAAACD7ZFh2hw4dqlmzZil//vwKCwtT3bp11bdvXxPZAEvjeoUAAABA9smw7Hp7e6tfv37q16+fiTwAAAAAADgtw7LboEED2Ww2x22bzaY8efKoXLlyGjFihPz8/LI1IAAAAAAAdyvDstuoUSPFxsaqc+fO8vDw0JIlSxQbG6sKFSooODhYH3/8sYmcAAAAAADcsQyXVd6zZ4/Gjx+vihUryt/fX6NHj9avv/6qF198UadPnzaREQAAAACAu5Jh2Y2NjdWVK1cct69cuaL4+PhsDQUAAAAAgDMynMbctm1bdejQQU2bNpXdbtfGjRvVvn17zZ07V6VLlzaREUA24fJHAAAAsKoMy27v3r316KOP6ptvvpGXl5feeustBQQEaP/+/WrdurWJjACyCZc/AgAAgFVlWHYl6fHHH1fZsmVlt9uVnJysXbt2qXbt2tmdDQAAAACATMmw7H7wwQeaNWvW9Sd7eSkxMVFly5bV6tWrsz0cAAAAAACZkeECVStXrtS2bdvUpEkTbdiwQaGhoSpbtqyJbAAAAAAAZEqGZbdw4cLy8/NT6dKlFRkZqVatWunIkSMmsgEAAAAAkCkZTmP28vLSyZMnVbp0ae3Zs0d16tRRQkKCiWzIBqy+CwAAACAnyLDsvvLKK3rrrbc0Y8YMffDBB1qxYoXq169vIBqyA6vvAgAAAMgJMiy7FStW1Jw5cyRJK1as0IkTJ+ThkeHsZwAAAAAAXCbd1hodHa3o6Gj16tVLMTExio6OVkJCgooWLaqBAweazAgAAAAAwF1J98jukCFDtGvXLklSrVq1/vcCLy81adIk+5MBAAAAAJBJ6Zbd2bNnS5LefPNNhYaGGgsEAAAAuCsW+wTuHRmesxsaGqrTp08rJiZGdrvdcf9jjz2WrcEAAAAAd8Nin8C9I8OyO3XqVM2ePVtFihRx3Gez2bRly5ZsDQYAAAAAQGZlWHZXrFihjRs3qnjx4ibyAAAAAADgtAzL7v3330/RBYAcwNnz0FLfg3PRYCX8vwCAe1eGZTcwMFCTJk1Sw4YNlTt3bsf9nLMLANbi7HloEueiwXr4fwEA964My+6yZcskSevXr3fcxzm7AACr4kgeAADWkGHZ3bp1q4kcAAC4BY7kAQBgDR4ZPSE2NlbvvPOOunfvrujoaAUHBys2NtZENgAAAAAAMiXDsjtu3Djlz59fFy5cUK5cuXTlyhUFBwebyAYAAAAAQKZkOI350KFDCg0NVXh4uPLkyaN3331Xzz//vIlsAAAAgIOz59RzPj2Qs2RYdj080h78TU5Ovuk+AAAAILs5e04959MDOUuGZbdmzZqaPHmy4uPjtWPHDs2bN0+1atUykQ0AAAAAgEzJsOwOHTpUs2bNUv78+RUWFqa6deuqb9++JrIBAAAAcENMKce9IMOy6+3trSeffFL9+vVTdHS09uzZo1y5cpnIBgAAAMANMaUc94IMy25YWJj27dunuXPnKj4+XrNmzdKRI0c4ugsAAAC4AEdVgTuTYdndsmWLli9fLkkqUaKE5s2bpzZt2lB2AQAAABfgqCpwZzJcVvnatWvy9vZ23Pb29pbNZsvWUAAAAAAAOCPDI7vVqlXTkCFD1K5dO9lsNq1YsUJVqlQxkQ0AAAAAgEzJsOy+9dZbmjp1qkJDQ+Xl5aXAwED179/fRDYAAAAAADIlw7I7Y8YMjRgxwkQWAAAAAACyRIbn7G7fvt1ADAAAAAAAsk6GR3ZLliypHj16qFq1asqXL5/j/pdeeilbgwEAAACAO0u5luj06tYp1xLl4X1vXwrK2cthpb5HVl8SK8OyW7BgQUnS6dOns3RgAAAAALiXeXj7KGjISqfeY/WUllmUxnWcvRyWlD2XxMqw7IaGhkqS/v77bxUoUCDLA+QU7rq3AwAAAACsKMOye+zYMfXr10+XL1/WkiVL9OKLL2r69OkqU6aMiXyW4a57OwAAAADAijJcoCokJESjRo1SkSJFVLx4cXXp0kXBwcEmsgEAAAAAkCkZlt3o6GjVrl3bcbtz5866cuVKtoYCAAAAAMAZGZZdSUpISJDNZpMkRUVFKSUlJVtDAQAAAADgjAzP2f3Xv/6lnj176sKFC5oyZYrWrl2rl19+2UQ2AAAAAAAyJcOy265dOz388MPavn27kpKSFBISkmZac2ZNnDhRly5d0oQJE3To0CGNGjVKsbGxqlGjhsaOHSsvrwyjAQAAAABwS7edxnzkyBFt3LhRDz30kIYNG6bhw4dnSdH99ttvtXz5csftYcOGKTg4WBs2bJDdbteiRYucHgMAAAAAkHOlW3aXLl2qLl266JNPPlGLFi20c+fOLBkwOjpaYWFh6tOnjyTp9OnTio+PV9WqVSVJbdq00fr167NkLAAAAABAzpTuXOG5c+dq9erVKl68uH788UeFhYWpTp06Tg8YHByswYMH68yZM5Kkc+fOqVixYo7HixUrprNnzzo9DgAAAAAg57rtibHFixeXJD3xxBO6dOmS04MtXrxY999/vwIDA7Vs2TJJUkpKimOlZ0my2+1pbt+p/fv3p/tY9erV7z7sLezduzfTr3WHDFmVgwxkIIN7ZnA2BxnIQAb3zJBVOchABjK4ZwZnc5AhfemW3X8WTk9PT6cHW7dunaKiotSyZUvFxMTo6tWrstlsioqKcjzn/Pnz8vPzu+v3rlSpknLlyuV0xtvJqn9EMpCBDGTILu6QgwxkIAMZyEAGMtwdd8jhzhkSEhJue3AzPXe85HFmjrb+02effeb4edmyZdq9e7dCQ0P1/PPPa+/evapevbpWrlypevXqOT0WAAAAACDnSrfsHj58WNWqVXPcjo+PV7Vq1RzTjPft25dlId59912NHj1aV65c0WOPPaZu3bpl2XsDAAAAAHKedMvupk2bsnXgNm3aqE2bNpIkf39/LVmyJFvHAwAAAADkHOmW3QcffNBkDgAAAAAAsswdn7N7L0tMStSijjOcfg8fL58sSgQAAAAAyE45ouz6ePno6Pi2Tr1H6VFLsygNAAAAACC7ebg6AAAAAAAAWY2yCwAAAACwnBwxjRkAcG9gjQUAAJBVKLsAcjxnCxblKuuwxgIAAMgqlF0AOZ6zBSsrylXKtUSn3yflWqI8vCndAAAAEmUXANyCh7ePgoasdOo9Vk9pmUVpAAAA7n0sUAUAAAAAsBzKLgAAAADAcii7AAAAAADLoewCAAAAACyHsgsAAAAAsBxWY4ZxXNMUN+L3AQAAANmBsgvj3OGapnAf/D4AAAAgOzCNGQAAAABgOZRdAAAAAIDlUHYBAAAAAJZD2QUAAAAAWA5lFwAAAABgOZRdAAAAAIDlcOkhAAAAAHcl5VqiU5f/S7mWKA9vH6cyJCYlalHHGU693sfLuQxwb5RdAAAAAHfFw9tHQUNWZvr1q6e0dDqDj5ePjo5vm+nXO1PWcW9gGjMAAAAAwHIouwAAAAAAy6HsAgAAAAAsh7ILAAAAALAcyi4AAAAAwHJYjRkAAAC4Q+5wyR0Ad4ayCwAAANwhd7jkDoA7Q9kFXIQ9wwAAAED2oewCLuIOe4Yp3AAAALAqyi6Qg7lD4QYAAACyA6sxAwAAAAAsh7ILAAAAALAcyi4AAAAAwHIouwAAAAAAy6HsAgAAAAAsh7ILAAAAALAcyi4AAAAAwHIouwAAAAAAy6HsAgAAAAAsh7ILAAAAALAcyi4AAAAAwHK8XB0AAACklXItUaVHLXX6PTy8fbIoEQAA9x7KLgAAbsbD20dBQ1Y69R6rp7TMojQAANybmMYMAAAAALAcjuwCAICbMJUaAHCvo+wCAICbMJX6Oko/ANy7KLsAAADpoPQDwL2Lc3YBAAAAAJZD2QUAAAAAWA5lFwAAAABgOZRdAAAAAIDlUHYBAAAAAJbDaswAANyAS80AAGANlN0cxtmNODbgAFgdl5oBAMAaKLs5jLMbcWzAAQAAALgXcM4uAAAAAMByOLILAADcEudPAwCcQdkFAABuifOnAQDOYBozAAAAAMByKLsAAAAAAMuh7AIAAAAALIeyCwAAAACwHMouAAAAAMByKLsAAAAAAMuh7AIAAAAALIeyCwAAAACwHMouAAAAAMByKLsAAAAAAMvxcnUAAABSpVxLVOlRS51+Dw9vnyxKBABwV85+Z/B9YX2UXQCA2/Dw9lHQkJVOvcfqKS2zKA0AwJ05+53B94X1uWQa8/Tp09W8eXM1b95ckyZNkiRFREQoKChIjRs3VlhYmCtiAQAAAAAswnjZjYiI0M6dO7V8+XKtWLFCBw4c0Jo1azRy5Eh99NFHWrdunfbv36/w8HDT0QAAAAAAFmG87BYrVkwjRoyQj4+PvL29VaZMGR0/flwPP/ywSpUqJS8vLwUFBWn9+vWmowEAAAAALMJ42S1XrpyqVq0qSTp+/Li+/vpr2Ww2FStWzPEcPz8/nT171nQ0AAAAAIBFuGyBql9//VWvvPKK3njjDXl6eur48eOOx+x2u2w221293/79+9N9rHr16pmNmcbevXsz/Vp3yJBVOchABjK4ZwZnc5CBDGRwzwxZlYMMZCCDe2ZwNgcZ0ueSsrt3714NHDhQI0eOVPPmzbV7925FRUU5Ho+KipKfn99dvWelSpWUK1eurI6aRlb9I5KBDGQgQ3ZxhxxkIAMZyEAGMpDh7rhDDnfOkJCQcNuDm+kxXnbPnDmjfv36KSwsTIGBgZKkKlWq6NixYzpx4oRKliypNWvWqG3btqajAQAAAADukrPXPE59j6y+7rHxsjt79mwlJCRowoQJjvs6deqkCRMmaMCAAUpISNDTTz+tpk2bmo4GAAAAALhLzl7zWMqe6x4bL7ujR4/W6NGjb/nYqlWrDKcBAAAAAFiR8dWYAQAAAADIbi5bjRkA4F7c9XwbAEBazn5e81mNnIKyCwCQ5L7n2wAA0nL285rPauQUlF0AOR57yAEAAKyHsosciXKDG7GHHAAAwHoou8iRKDcAAACAtbEaMwAAAADAcii7AAAAAADLoewCAAAAACyHsgsAAAAAsBzKLgAAAADAcii7AAAAAADLoewCAAAAACyHsgsAAAAAsBzKLgAAAADAcii7AAAAAADL8XJ1AAAAALi/lGuJKj1qqVOv9/D2ycJEAHB7lF0AAABkyMPbR0FDVmb69auntMzCNACQMaYxAwAAAAAsh7ILAAAAALAcyi4AAAAAwHIouwAAAAAAy6HsAgAAAAAsh9WYYRyXLgAAAACQ3Si7MI5LFwAAAADIbkxjBgAAAABYDkd2AbgU09oBAACQHXJE2XV2Yzr1PZzZoHaHDIA7Ylo7AAAAskOOKLvObkxLzm9Qu0MGAAAAAMgpOGcXAAAAAGA5OeLILgAAwL2M9Q0A4O5RdgEAANwc6xsAwN1jGjMAAAAAwHIo5EwDNgAAIABJREFUuwAAAAAAy6HsAgAAAAAsh7ILAAAAALAcyi4AAAAAwHIouwAAAAAAy6HsAgAAAAAsh7ILAAAAALAcL1cHAAAAAABkTsq1RJUetdTp9/Dw9smiRO6DsgsAAAAA9ygPbx8FDVnp1HusntIyi9K4F6YxAwAAAAAsh7ILAAAAALAcyi4AAAAAwHIouwAAAAAAy2GBKgAAANwTnF111qorzgK4NcouAAAA7gnOrjpr1RVnAdwa05gBAAAAAJZD2QUAAAAAWA5lFwAAAABgOZRdAAAAAIDlUHYBAAAAAJZD2QUAAAAAWA5lFwAAAABgOZRdAAAAAIDlUHYBAAAAAJZD2QUAAAAAWA5lFwAAAABgOZRdAAAAAIDlUHYBAAAAAJZD2QUAAAAAWA5lFwAAAABgOZRdAAAAAIDlUHYBAAAAAJZD2QUAAAAAWA5lFwAAAABgOZRdAAAAAIDlUHYBAAAAAJZD2QUAAAAAWA5lFwAAAABgOZRdAAAAAIDlUHYBAAAAAJZD2QUAAAAAWI5bld3Vq1erWbNmaty4sebPn+/qOAAAAACAe5SXqwOkOnv2rMLCwrRs2TL5+PioU6dOqlWrlsqWLevqaAAAAACAe4zblN2IiAgFBASoYMGCkqQmTZpo/fr16t+//21fZ7fbJUmJiYm3fV7BfJ5O5UtISHDq9e6SwdkcZCADGdwzQ1blIAMZyOCeGZzNQQYykME9M2RVDqtnSO16qd3vTtnsd/uKbDJz5kxdvXpVgwcPliQtXrxY//3vfxUSEnLb112+fFlHjhwxEREAAAAA4CLly5dX/vz57/j5bnNkNyUlRTabzXHbbrenuZ2efPnyqXz58vL29r6j5wMAAAAA7h12u13Xrl1Tvnz57up1blN2S5QooT179jhuR0VFyc/PL8PXeXh43FW7BwAAAADcW3Lnzn3Xr3Gb1Zifeuopffvtt7p48aLi4uK0ceNG1atXz9WxAAAAAAD3ILc5slu8eHENHjxY3bp107Vr19SuXTtVrlzZ1bEAAAAAAPcgt1mgCgAAAACArOI205gBAAAAAMgqlF0AAAAAgOVQdgEAAAAAlkPZBQAAAABYDmUXAAAAAGA5bnPpIQAAAABwRnJysjw9PV0dQ1evXlVMTIxuvPDNAw884MJEOVOOv/TQwYMHdfXqVdntdiUnJ+vUqVNq166dq2MZd+3aNUVEROjSpUtp7m/VqpWLEpkXExOj++67L819p0+f1oMPPuiiRK6xaNEihYWFKTo6WpJkt9tls9l06NAhozmio6MVFxeX5v9mYGCgkbETExO1c+fOmz4b+vfvb2R8dxcfH6/cuXMbG69nz56aPXu2sfHuFXa7XadOnVKpUqVcHSXH2bNnj+bMmaOYmJg093/xxRdGxl+xYsVtHzf93e0O2xCu/s4IDw9XbGysJDnGf+2114yMfyO2a6XWrVtr+fLlLs0wffp0zZ49W4UKFXLcZ7PZtGXLFhemyply9JHd0aNHa/fu3YqJiVHp0qUVGRmpatWqGf1QaNy4sWw22033b9iwwVgGSXrttdcUFRWlMmXKpMlj6ovq4sWLWrVqlWJjY2W325WSkqJTp05p0qRJ2T72mTNnZLfb1bt3b33yySeOPXDJycnq1auX1q9fn+0ZUu3YsUNhYWH6+++/ZbfbHUXT5Ifjxx9/rC+++ELlypUzNuY/TZ06VXPmzFFSUpIKFiyoc+fOqVKlSlq8eLGR8YcOHarz58/r1KlTeuKJJ/TDDz+oRo0aRsa+0R9//KEvv/xSly5dSrNnODQ01FiGrVu3KiwszLERmZKSori4OH333XfGMsTFxenMmTO6//77jY35T3/99ZfGjRun3bt3y9vbW4GBgRo5cqQKFy5sLMOXX36pSZMmKS4uznHfgw8+qM2bNxvLsH37dk2fPl3R0dHGP6O6du16y+/LVKaKpiSNGDFC/fv3d9lRmu+//16SdPLkSZ04cUJPP/20PD09tXPnTpUtW9Z42XX1NoSrvzNef/11xcTE6OTJk6pRo4a+//57VatWzcjYN3Lldu306dNv+7jJncVFixbVnj17VLlyZfn4+Bgb90bLli3T1q1b05Rd09avX6+ZM2fq77//liSXbFO6eseglMPLbkREhDZs2KCQkBB169ZNcXFxmjBhgtEMn376qePna9euafPmzUpOTjaaQZKOHj1qtNT906BBg3T//ffrp59+UqNGjbR9+3Y9/vjjRsaeOnWqvv/+e507d06dO3d23O/l5aX69esbyZBq3LhxGjFihMqVK3fbjbrsVKRIEZcWXen6UYvw8HCNHz9er776qo4ePaoFCxYYG//gwYPatGmTxo8fr7Zt2+r111/XkCFDjI2fasCAAQoMDFSNGjVc9vsQGhqqkJAQffbZZ+rTp482b96cpmyZcOnSJTVo0EBFihRRrly5XPKFPXLkSDVs2NDxHbFkyRK9+eabmjlzprEMs2bN0sqVK/X+++9r8ODBCg8P1759+4yNL0njx4/XqFGjVLZsWeO/kwMGDJB0ffZJ7ty51apVK3l5eWnNmjVKSEgwmqV48eIunfmUusOra9euWrVqlWOnS0xMjPr162c8j6u3IVz9nXH48GFt3LjR8Z0xaNAgDRo0yNj4qdxhu/a///2v/vrrLzVt2lReXl7atGmT8Rlyv/zyi7p06ZLmPtMz1Pz8/JQ/f35j493KxIkTNWnSJJdOnXb1jkEph5ddPz8/eXt7q0yZMjp8+LCaN2+uy5cvG83w0EMPpbldpkwZtWnTRn379jWe488//3TZL+O5c+f0xRdfaOLEiWrcuLFefvllde/e3cjYqRsNs2bNUu/evY2MmZ5ChQrpmWeeccnYqdPiHnjgAb366qtq2LChvLz+9xFhcsPOz89Pvr6+KleunCIjI9W4cWNNmTLF2PhFihSRzWbTI488osOHD6tVq1ZKTEw0Nn4qu92u4cOHGx/3Rvnz51dAQID27duny5cva9iwYWrWrJnRDDfuFHSVixcvptkZ9uKLLxqfJlekSBGVKlVKFSpU0JEjR9S5c2ctXLjQaIb8+fMb3wmY6sknn5R0fQNu6dKljvurVq2qNm3aGM3StWtXDR06VAEBAS77nJSuf3cWLFjQcTtPnjyKiooymkFy/TaEO35nXLt2zdj4qVy5XZt65LZTp0766quvlCdPHklS9+7d1a1bNyMZUpmcefRPqUe4CxQooI4dO6pevXppzh82eYT7oYceUvXq1eXh4br1iF29Y1DK4WW3ePHimjlzpgIDAzV58mRJMr5Be+Neebvdrt9++03x8fHGxk+dFnbx4kUFBQXJ399fnp6ejiMnpqYZpJ4r+8gjjygyMlJVqlQxMu6N2rZtq88//9wlU6lTVa9eXaGhoapbt65y5crluL9mzZrZPnbqtLi8efMqb9682rt3b5rHTX5Y+fr6asWKFXrsscc0b948+fn5Gf1/UbZsWY0fP14dOnTQG2+8oQsXLsgVyxs88cQT2rRpkxo2bOiyL6vcuXPr2LFjKlOmjHbv3q2AgADjG3HFihVz+flwlStX1tq1a9W8eXNJ0rZt21SpUiVj40vXi8x3332nChUqaPPmzXr88ceN/b/44YcfJF3/vzFu3LibdoaZ+IxKlZCQoGPHjumRRx6RdP2oWlJSkrHxJWnp0qVKSEhw6eekJNWvX18vvfSSGjduLLvdrq+//lrPPfecsfHdZRvC1d8Z5cqVU0hIiF544QUNHTpU586dc8l3hjts1166dCnNrI9r16451gAxJb0p1SaLZuXKlY2NlZ4ePXqoW7duqlmzpssKtzvsGMzRC1RduXJF4eHhat68uebOnauIiAh1795dAQEBxjL861//cvxss9lUqFAh9erVy1jZ2717920fT92Tnt3CwsJ07NgxDR8+XD169FCtWrUUGRmpRYsWGRlfkrp163bLqdQmpwB17dr1pvtMbjD805UrV3TmzBnj05rPnj2rtWvXqkePHpowYYIiIiL0yiuvOIpGdktKStLevXtVq1Ytbd68Wbt27VLHjh3l7+9vZHx/f3/ZbDbHxtI/fzY5FWv37t2aP3++Jk+erBdeeEEnT55U27ZtNWLECGMZ+vfvf8vz4aZOnZrtY//z3yJPnjyy2Wy6evWq7rvvPsdOIhN+/fVXLV68WCNGjNBrr72miIgIDRgwQC+++GK2j32rz6ZUpj+jdu7cqREjRqh48eKy2+26cOGCpkyZYvS8endYACfVhg0btHv3btlsNgUGBqphw4bGxnaXbYgbvzMmTpyoXbt2Gf/O+Omnn1SjRg1t2bJF3377rTp06KDy5csbGT+VO2zXfvrpp1q+fLnq1asnu92ubdu2qVu3bmlmxmS3G8vutWvXtGPHDlWpUkVjxowxlkG6vqPBx8dHJ06c0LFjx1SvXj2jO647deqkRx555KZp5CbLbq9evZSQkHBTBpNrj+TosssKn/+TmJioo0ePyt/fX6tXr9bBgwfVq1cvo4uvnDx5Ug899JD279+vPXv2qFmzZvLz8zM2ftOmTbV+/XpNnDhRTZs21UMPPaTu3btr1apVxjK4g8WLF2vv3r1644031KpVK+XLl08tW7ZUnz59XB3NqKNHj950yQBXLDjibm61anl2e/bZZ9OcD+fr66tBgwalmcoKM7788kt16tTJ1TGUmJioI0eOyGazqUKFCmmOGJgwZswY1a9f/6Ypiq7w66+/3vRZZfJIu+Qe2xAXL17Uzz//rOTkZFWtWlVFixY1Nra77PwYO3asWrdu7fKjivv370+zA8bUjuL0JCYmqkePHpo3b56xMT/88EP9/vvvGjp0qDp06KBy5cqpbNmyGjVqlLEMbdu2dfn3pDv838jR05hducLnSy+9dNvFPf7zn/8YTCMNGzZMJUuWVGJioqZNm6aWLVsaXXxlwYIFjqPclSpVkpeXl/r162dsJUXJPaZS//TTT5o5c6bjsgEpKSn6888/tXXrVmMZFi5cqI8//lhr1qxRw4YNNWrUKHXo0MFI2U09ipYqdSqc6csfjRs3Ths2bFDJkiUdeWw2m+bPn29k/FQnT57UTz/9pKCgII0ZM0YHDhzQ2LFjjU6fPXjwoD7++OObNqZNHslzh/PhXLlifKrt27frww8/vGl1bpMLdc2bN8/lZff06dOaN2/eTb+TJo8UbNmyRV999VWa+1xxibaxY8dq27ZtaS4/5YrZQK7ehtixY4dGjhypqlWrKiUlRcHBwRo/fryxNTDcYfVf6frU2SlTpujixYtq2bKlWrZsqWLFihnNkJSUpPPnzzt2dERGRioyMtKl523Gxsbqzz//NDrmli1btGDBAn3xxRdq0aKF3njjDeNrC9SuXVvz5s1T3bp15e3t7bjf5Ln1lStX1rZt21y6YzBHl92LFy+6bIXP1IWQlixZIh8fH7Vq1Uqenp5au3atSxY1OHXqlD744ANNnjxZ7dq1U+/evdW2bVtj469Zs0bJycnq0KGDPvjgA61evdr46rcBAQEaOHCgYyr1gQMHjF5LVLq+4mvPnj21fPlyde3aVRs3blTFihWNZpCuL3IRHh6ubt26ycvLy9hKp5GRkUbGycg333yjjRs3OhbYcJU333xT7du315YtW3Ts2DG9+eabGjdunL788ktjGYYPH66OHTu6dIVwdzgfzpUrxqdy5UrIqUqUKKFu3bqpSpUqadYVMDktbtCgQapRo4ZLVynfuXOnS8b9p127dmn9+vXGv6v+ydXbEGFhYVqwYIGj9P/xxx/q37+/sbLrDqv/StePorVu3VpnzpzRmjVr1KlTJ5UtW1bt27dXo0aNjGQYMmSI/vzzT5ddhkqSGjRo4BjbbrcrJiZGL7/8srHxJSklJUW5c+fWtm3bNGjQIMdl+0xas2aNpLQH0ExfycAddgzm6LLryinMqRc6nzJlipYsWeK4v0aNGka/IFIlJyfr4sWL2rx5s6ZNm6aoqCijl3L4z3/+o/79+2vWrFmqX7++1qxZY3yqZPfu3XXlyhU9+OCDeu+99/TDDz8Yv4SDj4+P2rZtq9OnT6tAgQKaNGmSgoKCjGYoW7asXnnlFZ06dUqBgYEaNGiQ8SlRrj6KduMRXVdKSEhQq1atNGrUKAUFBalGjRrGFxvJnTv3TRtxpr399tv68ccfVbZsWQ0cOFARERFGV1qVXLtifCpXroScqmrVqi4dX7p+5MjVq5S7+jMqValSpVyyENI/uXobIikpKc3R7VKlSiklJcXY+K5c/fef/vjjD61atUpr167Vww8/rGeffVZff/21Nm7caOT38/Dhw/r6669d+h06d+5cx882m00FChSQr6+v0QyBgYF6/vnnlTt3btWsWVNdunRRgwYNjGYwOSswPe6wYzBHl90JEyZo2rRpae7r3r275syZYyxDfHy8Tpw4oYcffljS9XNvXHGJk549e6pDhw5q0KCBypcvryZNmhhZ6TT1cjeS1LhxYx06dEh58+bVtm3bJJndE9i5c2d9/fXXkqTHHntMjz32mLGxU+XKlUvR0dF65JFH9PPPPyswMND4dZf//e9/68cff1T58uXl4+OjFi1aqF69ekYzuPooWqFChfT888+rWrVqaY5ehYSEGMsgSZ6entqwYYO2b9+u1157TZs3bza+KnOdOnU0d+5c1alTJ83fhclpUJ6enipUqJD27Nmj/Pnzq0mTJjddoD67ufI0B3daCfmfR3DtdrtOnTplbHzp+qr1W7duVZ06dVw2ZdTVn1Gp7rvvPjVv3lxPPPFEmr8Lk1O6JddtQ6R64IEH9Pnnn6tdu3aSrs+aM3ltV3dY/VeSXnjhBZ0/f14tW7bUp59+6vicbtWqlbHv8TJlyigqKsromiv/5Ofnp/nz5+u7776Tl5eXnn76abVr185oAR8+fLi6du2qEiVKyMPDQ2+99ZYeffRRI2NPmzZNAwYM0JtvvnnLx01+PrjDjsEcWXb79++vQ4cO6dy5c2lWLUxOTlaJEiWMZhk2bJg6d+6sBx98UCkpKTp79qxjuXiTgoKCFBQU5NiAXLt2rZEFP/65kmm9evX0999/O+43WXb9/f21YsUKVa5cOc2UMJMb9S+++KIGDx6sadOmqX379lq9erXxy5ukpKRoz549WrJkid566y0dPHhQderUMZrB1UfRAgICjK5emZ533nlHn3/+ucaMGSM/Pz+tXbtW48aNM5ph5cqVkqTPPvvMcZ/paVBvvfWWvvnmmzTXJTd9XqIrT3O4cdXpM2fO6PDhw47bpv8evvrqK02cODHNdLySJUtq06ZNxjKsX7/+poVmTE+Lc/VnVKq6deuqbt26xsf9J1dtQ6QaP368QkJCNHPmTKWkpCggIEDvvPOOsfFvdOPqv6YNHDjQMXPwRl5eXoqIiDCSIT4+Xk2bNnXsME9l8nNq9OjRio+PV4cOHZSSkqKVK1fq8OHDGj16tLEMMTEx+uijj3Ty5ElNnTpVX3zxhUaMGGFk1mLqwZpbrYZu+oi7O+wYzJGrMV+5ckXR0dEaP358ml98Ly8vFSlSxPiqjgkJCYqMjJTNZpO/v79L9lRHRkZq0KBBio+P11dffaUuXbro/fffd8nRzVTx8fFGz0O61fQS0xv10v8WZbp69aqOHz+uRx991OiH0+jRo1W4cGFt3bpVixcv1pgxY5SSkqJ3333XWIaOHTvqq6++0qJFi2S329WxY0e1aNHC6MrYly9fVlxcXJo9kaZXOJWuT0n7/fffVbduXf35559ppurlFI0aNdK6detcuvCL9L8V4w8cOKAffvjB+IrxN7Lb7YqNjTU+Na9BgwaaM2eO3n//fQ0ePFjh4eHat2+f8WnlruYOn1GpoqOjHZ9VqdegvlXhyU6u3IZYsGCBihUrpmeffVbt2rXTxYsX5eXlpU8++cQxa840V6z+K0nHjx/XvHnz0ixyeerUKaOLK6Z3OSpTl6GS/nd1jVQpKSl6/vnntW7dOmMZBg4cqNq1a2v+/PlasmSJPvzwQx06dEizZs0yluG///1vmtPQ4uLi9MEHHxi9dKA7XOkkRx7Z9fX1la+vr2bMmKHff/89zcqWJ0+eNLpBe/LkSS1YsMDxwZT6RWV6JcWQkBB9+OGHGjJkiIoXL663335bY8aMSXM+cXbaunWr3n///TQf0PHx8fr222+NjJ+awdX++OMPffnllzettmpyysmBAwe0fPlyffPNN8qTJ48mTpxo/LxhVy8W9uGHH2r27NlKSkpSgQIFdOHCBT366KNatmyZsQyStG7dOs2YMUPx8fGOS7688cYbatmypbEM7rDxdP/99yshIcElZXfbtm165plnHKdc7Nu3T5JUsGBBRUREKG/evKpZs6YKFSpkJMuePXvUt29fx0b98OHDja7wWaRIEZUqVUoVKlTQkSNH1LlzZy1cuNDY+JJ7TItz9WdUqmnTpunzzz9XUlKSChUqpLNnz6pSpUpGr2QguW4bYubMmfr2228d109NTEzU3LlztW3bNs2cOVP//ve/s3X89Lhi9V9Jev3111W/fn3t3btXrVu31qZNm1SuXDmjGZ588kkdPHjwpu1ak2W3ZMmSaU4RPH/+vIoXL25sfOn6om0dO3bUwoUL5ePjo8GDB6tFixZGMwwbNkwTJkzQE088ofDwcI0dO9b4rDV3uNJJjiy7qYKDgxUeHu7SqXGDBw9WnTp19N1336lVq1batGmTypQpY2z8VHFxcWnGrV27tiZOnGhs/NDQUIWEhOizzz5Tnz59tHnzZuOr1rnDRv2AAQMUGBjo0lVGbTabEhMTHeNfunTJeBZXLxa2dOlSbd++XaGhoerbt6+OHDmiRYsWGRs/1SeffKKFCxeqS5cuKlKkiJYvX66XXnrJaNl15cZT6vlGycnJatmypWrUqJHm0gUmdgL98ssveuaZZ2465SLV5cuXNXXqVMeql9lp+vTpGj9+vNatW6fKlSsrODhYXbt2NVp28+TJo++++04VKlTQ5s2b9fjjjys+Pt7Y+JJ7TIsbPHiwTp486dIFDSVp+fLlCg8P1/jx4/Xqq6/q6NGjWrBggfEcrtqGWLFihZYsWaJ8+fJJkjw8PPTggw+qU6dOatq0abaPn8odVv+Vrk+hHjhwoJKSklSxYkV16NDB+KKno0eP1u7duxUTE6PSpUsrMjJS1apVc5xPbUJSUlKa74y9e/fKz89P3bp1k2RmSrWnp6cuX77s+L04fvy48TU3Pv74Yw0YMEClSpVy7BCsUaOG0QzusGMwR5fdiIgIbdq0yaVT4xITEzV48GClpKSoUqVK6tixo9q3b288R8GCBR1TqSVp1apVRldDzp8/vwICArRv3z5dvnxZw4YNU7NmzYyNL7nHHlG73e7yVUa7deuml156SVFRURo/frw2b95sfCPO1YuFFStWTAUKFFDZsmUVGRmpZ599Vu+//77RDNL1Dbcbp6n6+fkZ/7J05cZT6pEAk0cE/mngwIGSbl+sTS7E4+/vr2nTpqlFixbKly+f8UvVjR49WkuWLNGIESO0ZMkSNW3aVAMGDDCawR3Ol42OjtapU6f00EMPaefOnTpw4IDxlVal658Jvr6+KleunCIjI9W4cWOXTCl31TaEp6eno+hK0quvvirp+mlpN96f3dxh9V/p+s6oxMRE/d///Z8OHDhgvNhI17etN2zYoJCQEHXr1k1xcXGaMGGC0Qx9+/ZNc7tnz55Gx5euH7zo2rWrzpw5o759++qnn34yNtMgdVZBrly59Pbbb2vQoEEaPXq0HnjgAf35559G16Jx9cELKYeXXVdOjUt14wfTwYMHVaNGDZdcRuDtt9/W8OHD9euvv6pGjRp6+OGHjS6UlTt3bh07dkxlypTR7t27FRAQYHwjzh32iD7xxBPatGmTGjZsaLzUpGrVqpUqVaqk77//XsnJyZoxY4b8/f2NZnD1YmG+vr5avXq1KlasqIULF6pEiRLGj15J168vO2/ePCUlJenQoUNasGCB8X8LV2481alTR8WKFXPJdMB/2rFjh95//33FxMSk+YzesmWLPvjgAyMZihYtqpCQEO3fv1+TJ0/WhAkTjG60SFL58uU1cuRISbrpagamuMO0uCFDhuipp56SdH3BrO7du2vUqFFpSo8Jvr7/396dh0VVt/8Dfw+brKbigxahEahg5BaloCZImoAgSIqmI7mTiUpJUO4CGUsqohmUS4oJioCApIILoOBCLiACaokoKpgogqAwML8/+M6JEbSeX898zqlzv66rK+YMF59bmDnzWe9bH0lJSXjjjTcQExMDIyMjXu5VfPUhmpubUVtbyw0s33//fQAtOy5YfoYKIfsvALi6usLb2xvh4eHw9PREdnY28+27RkZG0NTUhJmZGUpKSuDs7IyamhombRcWFuKNN9547u+d5THFd999F1ZWVsjPz0dTUxNWr16Nrl27Mml76tSpkEgk3GeVlpYWd8yDdS4avhcvAJEmqFJsjbtx4wbu3r3Ly9Y4hR9//BFZWVkIDQ3FpEmTYGZmhidPnmD79u3MYmitrq4Ozc3NzGckz5w5g127diEsLAyTJ09GWVkZPvjgA6arnBMnTkRMTAxSU1NRU1MDLy8vODs748CBAypv28LCQunG1Ho7FKsso63LQLWHdUH4Z7G8Qd+9exfJycmYM2cOgoKCkJubi7lz5zI/b1NXV4fNmzcjJyeHyzD6ySefMH1/xsTE4OjRo1znqWfPnmhublYqUq8qc+fORVRUlNIWwdZYfmC///77CAgIQK9evZRiYVnepLa2FhkZGRg4cCB69uyJXbt2wc3NjekK1ujRo5XKoUkkEmhra+P111+Hv78/k9/HunXrcP36dW5b3ODBg1FcXMz0qMEHH3yA+Ph4BAYGomfPnpg2bRrGjx/P/Fx/RUUFDhw4gBkzZuDrr79GTk4O5s6dC2dnZ6ZxKLDuQ2zevBkh3HSLAAAgAElEQVSXLl1CSEgI1+bjx48REBCAQYMGYfr06Uzi8Pf3x5MnTzBu3Dgu+2+3bt2YZv9VUAz+7969i4KCAgwbNgw6OjrM2l+4cCH69u0LGxsbhIWFYdKkSYiMjFRKGKUqy5YtQ2BgIKRSaZvnWB1TfF4ZKgWW5aiam5vbTPpUVFQwnQDx9fXFiBEjeK10IsrBbmJi4gufd3d3ZxRJi5qaGhgYGKC8vBz5+fl49913mXVepFLpC2ceWZ1fvnr1qtKW4erqaly/fh0DBgxg0j7Ab6f+RRoaGpjsPlBMApWVleHGjRuws7ODmpoaTpw4AXNzc6YZBIlwNDQ0oKGhQanzNHToUOjq6jJp/9dff4WBgQGMjIwQHR2Nc+fO4Y033sCsWbOYduAmTZqE2NhYZu219mySrGexnIgKCgrCq6++yp2/S05ORkFBAUaOHIldu3Yxm6jlOzP2+PHjsXr1anzyySeIiYlBbW0tAgICuFJdYsF3H6KpqQkrV65EamoqzMzMIJFIcO3aNYwbNw4rV65Uadut8Z39V0gDrNraWmRmZsLZ2Rk7d+5ETk4OvLy8BFHOjwXF3yI/Px93797FmDFjoKGhgfT0dBgbGzM9EvXZZ58hPDyce4/u2rULmzZtYlaGCuB/8QIQ6WC3tcrKShgZGSEvLw8lJSXw8PBgcnA6JSXlhc+zyn6rSBG/Z88eaGtrw83NDRoaGkhNTcXTp08RGBio0vZ/+eUXNDc3Y+nSpQgODuZWNmUyGVauXIlDhw6ptP1n8T0jqihnodDc3Ixx48b96evlf0kqlSIiIgJdunQB0DLxoOjQsVJdXY2wsDCuPl1ISAi++OILdOzYkUn7dnZ2+P3337lJp8ePH0NPTw+vvfYaVq1apfKtxIqVfgUNDQ2oq6vj6dOn0NfXx9mzZ1Xafmt2dnawt7eHu7u7UgkDFnbu3IktW7ZAXV0d77zzDq5fvw4nJyecOXMGurq6TLPvhoWFQSaTYfjw4ejQoQN3ncW2uA0bNmDBggXchNSzWO5Gcnd3bzNhrFjRbO85VWhsbEROTg4ePHigdJ3loD83NxebN2+Gg4MDvLy8MHHiRHz66afMO/R79uzBunXr8PDhQ6XrrGoO892HUKioqEB+fj4AwMrKCi+//DKTdhVmzZqFZcuWcdl/Kysr4e/vr1SfXJWEMNj9s+MmLFfyLl++jO+++67NsROWCWgnTZqEbdu2cX3Ip0+fYtq0aUp9PFULDg7GvXv3MGfOHKxatQq6urpYsWIFXnvtNWYxCIGoz+yuWLECjY2NmDFjBj777DMMHToU58+fZ1JPNCsrq821pqYmpKenQ1dXl9lgV5H4JSQkBPv27eOuDxgwgEmGz5ycHJw5cwaVlZVK5940NDTg6emp8vYBIC4uDp6enu1+WJSUlEBPTw92dnYwNTVVWQzTpk3jOg2ttzSrq6vDwcFBZe22p7KyEp06deIe6+jo4N69e0xjWLZsGYYOHYr8/Hzo6urCyMgIixcvZra6/M4772DkyJFcNs+jR48iIyMDkyZNwqpVq1ReaqW4uBhAyz1q0KBBcHV1hUQiwaFDh5Cdna3Stp/1888/49ChQ1i7di3u378PNzc3uLq64j//+Y/K246NjUVaWhrq6+vx3nvv4cSJE9DT08OUKVOYDmwAcB3py5cvc9dYbYv7K0myWFFTU0N2djaGDx8OoOUss5aWFn7//XfIZDImMSxcuBD37t3jVvIUWL4mbGxs8NZbb0FLSws3btzAvHnzeEmk9t1332HHjh3Mkykq8N2HUOjWrRtGjRrFrL1n8Z3993mDWblcjlu3bqm0bYVnz4m2xnolz9/fH56enm2OnbD0bCWLxsbGNpNSqrZkyRJs3LgRH3zwAQIDA5nnoQFaSsWtXr0aubm5aGpqwpAhQ7By5Upm55cBkQ92CwoKsG/fPu6F4OPjw+yF8GzihuLiYgQEBGDo0KFYvXo1kxhae/r0Ka5fv84N6EpKSph0XBRZPJOSkph3XhX+bHPD3bt3MXPmTJXW4VV8EAYFBfFyxqc1Ozs7TJ8+HaNHj4ZcLsfPP/8MR0dHpjHwXZ+upKREadVw5MiR2LhxI/r168c0+Ut+fj5WrVrFPX7//fexefNmZu0DLZMdbm5uXGm0oKAgbNy4ETY2NvD39+dWMlRBQ0MDurq60NXVhYmJCbfSrq6uDg0Nth9frBMPtfa8M8tASycyIyODWSxr1qxBQEAA/Pz8IJfL0aNHD3z99deIi4vDjBkzmMTw22+/MTn/9yKbNm3Cr7/+isWLF2PKlCno1asXTpw4wfz+bWhoyNtAtzW++hBCIYTsv0DL5H1ISIhS6cZXX30V6enpKm9blX2k/5a2tjamTp3KawwTJkyAh4cH3n33Xcjlchw7doyb/FC1Z3cBde7cGXv27EFeXh4AthOny5cvx8CBAxEUFITm5mbExcVhyZIliIqKYhaDqAe7TU1NaG5uxpEjR7Bq1SrU19czr+2qyHa7e/du+Pn58TbgCwgIgFQqRbdu3SCXy3H//n2m5QsGDBiAoKAgXmrcTpo0CcCLt/mwmhn85JNPkJOTA1tbW0RFRaGwsBB+fn4wMTFh0j7QcpM8dOgQzpw5A4lEghkzZjBfXea7Pp2BgQH27t0LV1dXyOVypKSkoGPHjigtLUVzczOzOHR0dLBv3z44OjpySU9YlgQDWhL5JScnIzU1Fa+88goWL16M0aNH49SpU5g9ezYOHz6ssrZb/81bJxHkw/POJrJY2d25cyfkcjk2bdoEExMTjB8/Hurq6khJSWG2aqPQu3dvJCQkoLq6mvub6OvrMy0l0aNHD+blM5515MgR/PTTT9ixYwdcXV3x+eefM13JVJzffuWVV/Dxxx/DwcFBaQKIdV+C7z4EX4SU/RcAoqKisH//fqxfvx6+vr7IzMzEuXPnmMZQWlqKmJgYXvpzCsOGDcPOnTsxbNgwpWMnLO8Zs2bNwpAhQ7i+VEREBLNqCs/uMuGzfN/NmzeVdk7Onj0bycnJTGMQ9WDXzc0Nw4YNw6BBg9C/f384OTlh4sSJzNpXrOZ2794diYmJTJNrPGvYsGE4evQorly5AolEgj59+jBdORFCjdtnz0kCLSn0MzMzERAQwCSGxYsXtyln8eWXXzJfVTIxMYGhoSHkcjmampoQHx/PtCD8ggULeKtPB7TsvAgMDMRXX30FNTU12NraIiQkBAcPHoSvry/zOIKCgiCRSDB06FCm51QBYPr06Rg/fjy2bt2qlGl3xIgROHnypErbLi0t5WbCW38tl8tx48YNlbb9rNa1ZGUyGY4cOcLsDLni915SUqI0Iz9jxgymAyygJVlWXl4e5s2bhw8++ABVVVXw9/dnEodiwqGqqgouLi6wsLBQmgRheR6vubkZ2traOHbsGBYtWoSmpiamk+WnT58GAG7nwy+//KL0POvBLt99CL7ExsYiMDAQGzZsaPMcq2MOrRkaGsLExAR9+vTBlStXMGXKFJUfu3mWEPpzikRxrc9Ms95K3dDQgDt37nD5TwoLC3Ho0CEmddmFVLZPIpHgzp073Dn627dvM783/PvvRC8wffp0eHl5casHMTEx3ItS1TZu3IitW7di7ty5cHNzg1wuR0VFBfc867pofM/ECaHGreKcpCKejIwMXLhwgWkM1dXVmDlzJgIDA+Hu7g43NzfmH5ZLly7FmTNnUF1djddffx3FxcUYNGgQk8FuSEgI/P39MXz4cLzxxhtcfbrZs2dj5cqVsLOzU3kMQEsN7m+//bbNdS8vLybtKxgbG+O7775Tusa6hqa1tfVzdz0o6q2qCsttTn/m2ZlxW1tbTJgwgUnHpbXc3FzY2NgAADIzM5mveG/cuBHBwcFIS0tDv379sHz5ckilUiaD3dYTDnyzsbHB2LFjoa2tjbfffhtSqRR9+vRh1r5i0uPBgwfo3Lmz0nN8bCfluw/BF0UCLicnJ0yePJnnaFp2A506dQp9+vRBRkYG3nzzTeafGULozwlhS/Wnn36K6upqlJWVwdraGqdPn8agQYOYtL106VJERUVx56ifxXLQv3DhQnh6eqJ///6Qy+W4ePEis8R1CqIe7F64cAFRUVFKN+fbt28zeZPs3bsXBgYG+Omnn7B7926lc6MSiQTHjx9XeQyt8T0Tp6Ojg4aGBrz22msoLCyEtbU1s7bbo6mpCUdHxzYDDVVrbm7GpUuXkJGRgZiYGBQVFSnVtGQhJycHhw4dQmBgIKZNm4b6+np8/fXXTNo+d+4c1q1bB19fX3Tp0gV2dnaIi4uDv78/PvroIyYxAC2/g4iIiDbJJFhnBz969CjWr1+vdI+qr6/HqVOnmMXw66+/ctmoWeNz69WzWs+Qy+VyXLt2jXmykeDgYPj7+6OyshJyuRzGxsbMV/qBll0wkZGRcHV1hZ6eHhobG5m0q3g9HD16VKmcRWVlJQIDA5m+Xvz9/SGVSmFkZISDBw+ipKREKXkZK9OnT8fWrVvRpUsX3Lt3D4GBgbh27Vq75T5Uie8+BN927doliMHusmXLsHfvXgQEBCA+Ph5jxoxhPkkkhP6cECZfSkpKcPjwYQQHB8PDwwOLFi3CokWLmLQdFRWFY8eOYfv27ejRowfS09MRHx+Pvn374uOPP2YSg4K9vT369++P/Px8NDc3Y9WqVTAwMGAag6gHu19++SVmzpyJxMRESKVSHD58GH379mXSdmZm5p9+T1ZWFt59910G0fA/E+fq6gpvb2+uxm12djbz1e3WNSzlcjmuXLnCfKuFn58fQkNDMX36dJiYmGDixInPLTeiKkZGRtDU1ISZmRlKSkrg7OyMmpoaJm1v2bIFs2fPRmRkJLy8vLBkyRJcvXoVW7duZVr2ZtWqVfDz8+M1kyPQsnoTGBiIbdu2wdvbGxkZGczzCqipqcHe3h6mpqZKZ59Y7zjgW+tMoxKJBF26dGGajOjs2bPYtGkTysvL0dzcjDfffBM+Pj4wNzdnFgMAdO3aFYGBgbh06RLCwsLw9ddfMz87u27dOjQ1NWHUqFFc3cgpU6YwjeHmzZvYs2cPd37Z29sbH374IdMYAODjjz/GjBkz4Obmhh9++AGTJ0/m5aws330IvnXv3h3Tpk1D//79le6TLOvbAkCvXr24HTeRkZFM21YQQn9OCJMvhoaGkEgkMDU1RUlJCdzc3JhNDG7duhUHDhxASEgIiouL4efnhyVLlqCoqAjh4eEq35XVmouLCwIDA5V25rEqU6cg6sGulpYWPDw8UF5ejo4dOyI0NJRZyZ+/Yt26dcwGu3zPxE2dOhVubm7Q19fHzp07uRq3LCnOQAEtq+udO3dmWvwbaNkap9iiCLScByovL2caQ7du3RAVFQUbGxsua3hDQwOTtvX19fHDDz9gzpw5iImJgZOTE8LCwpjUvm6tc+fOeO+995i22R4DAwMMGTIE586dQ01NDfz8/ODk5MQ0Bj8/P6btCU1FRQVCQ0Ohp6eHgQMHYvHixczO6irk5ubi888/x8cff4wlS5agsbER58+fx6efforw8HAMHjyYWSzffPMNMjIyMG3aNC5LNuuVo+3bt2Pu3Ln49ttv0aVLF+zevVulWcFbS09PR2xsLAoLCzFq1CiEhoZi2bJlzAc1Cu+//z709fXh4+ODzZs3M30ttMZ3H4IviYmJcHd3x4ABA/gOBQBw/PhxbNq0CQ8ePFDaMchy26oQ+nNCmHzp1asXAgMDMXnyZCxevJjbkcNCUlIS4uLioKOjg/DwcIwcORITJkyAXC5n3oeorq7GkiVLuHrkwJ9XQflfE/Vgt0OHDnj48CFMTU1x8eJF2NjYMN8y+iIsXwx8z8Q9evQIKSkpePjwIffvLikpYdaB+OmnnzBy5EiMGjWKS7qioaHBNGEZ0DK4DQ0NVVq9MzY2ZlpaJDg4GJmZmejXrx9Gjx6N1NRUrFy5kln7enp6+OGHHzB37lx069aN+UAXaDmnGhoaiuHDhyvN0rM6b6Ogra2N69evw8zMDGfOnMGQIUOYzQwrvPPOO7h8+TK3HaypqQm3bt0S1BZjVfryyy/Ru3dvuLi44NChQ1izZg3zerebNm1CdHQ0LC0tuWt9+/ZF//79sWbNGiZb844dOwZ7e3vuXnT+/HmcP38eenp6SE9PZ5IQ6ezZs9zXH3/8MVasWAE3NzdUVlaisrKSSeZbHx8fODo6Ii4ujhtg87H7o3U5KrlcDrlcjk8++YTL1s5ycAPw34fgy44dO+Du7s7bZMezgoODsWTJEpibm/O2K6l15l2g5f1RVlYGMzMzZnk3hDD5smLFCly4cAHm5ubw8fFBbm4us10XEokEOjo6AFoWchS7Tvh4TRgaGmL79u1YuHAhLl26hOXLlzOtrgGIfLA7ffp0+Pr6IjIyEhMmTEBKSgqsrKz4DovD8kVpbW3dZiZu6NChzNpfuHAhDAwMeNk2GhUVhdzcXKxYsQJAyyrmzp07cezYMURFRTHNAhwdHc172YCFCxdiy5YtAFqyn0qlUmZtty7vUl9fj/Xr1yMzMxOampoA2G2dVfzOWycok0gkzJOt+Pr6Yv369QgLC0N0dDTi4uKYZsUG+E1YJgQVFRXc+2Ho0KG8lIerra1VGugqWFlZobq6mkkMBQUFsLe3V9oB0xqL34si461iO7mpqSkyMjKQkZHBLPNtcnIyEhIS8OGHH8LY2BjOzs68TJLzWfe5PXz3IUgLAwMDZgPK5ykrK8ONGzfg7OwMADh8+DD09fXxyy+/4MyZM/j8889VHsO4cePaTL50795d5e22NmHCBG6rroODA9MSjurq6nj06BHq6upQVFTEvRfLy8uZH8+Ty+V46aWXsGXLFnz11VeQSqV4+vQp0xhEPdjV1tbG1q1bIZFIsG/fPpSWljKrgSU0vr6++PnnnwG0nD1hfVP4/ffflVLEs5SUlIT4+HguAY+amhqMjY0xadIkjBkzhmksQigbUF9fr5QmniWhZFv96aef+A4BQEtyqIiICADAvn37UF1dzbzOLp8Jy4RAMdGi+Lr1Y1bq6uogk8nadFJkMhlkMhmTGBYsWAAAGDt2bJtBjCprLbemGODt3r2bt2RAvXv3RkBAABYvXozjx48jISEBv//+O+bMmYMpU6ZgxIgRTOJQlKNqaGhAZmYmHj9+DADczgvWWcL57kPw5erVq+0OYhRn+1mtsCt2PZibmyMoKKhN3WWW9X6vX7+OXbt2QUtLCwAwadIkSKVSxMXFcTWpVUWRe0VPTw/29vbIysqCu7s7dHV1md+7u3btiry8PPTr14/7XbAyZ84cuLm5QSaT4YMPPoCRkRHS0tKwbt06pjXRAXBZ6tXV1bFs2TLs2bMHq1evZhqDqAe7YWFh3AyYrq4us+RUQmRubo6NGzeif//+SttGWd0gLS0tUVxczMtkg7q6ulKmWUWmOg0NDeYZaIVQNqCqqgojR46EoaEhOnTowPRDWyhbY/Pz8/H9998rbd0tLy9nup0caCmHNmnSJO4x64EuwG/CMiHiYxvYsGHDEB4erlTvu6mpCWvWrGG2ipOWloaGhgZs2LCBG/gCLQPuqKgojB49mkkcgDAy32poaOC9997De++9h6qqKiQlJeGbb75hNthV4LO8SWt89yH40rNnT0RHR/MdhlKd3zt37qCkpIR7zLre76NHjyCTybgBXmNjI+rq6gCo/nheQEAADA0NYWNjw8vEZGsFBQVKpX8UfamioiKVtz1mzBgMHDgQDx484PrVenp6CAoKYn6uPyQkROnxxIkTmR8RFPVg18TEBF988UWbmzMf29Taw/LM7sOHD3H69Ok2SZpY3SCvXr0Kd3d3XgZYzc3NqK2thb6+PoCWhB8AUFNTw/xcwdKlSxEfH89r2QDFlk0x++KLL+Dl5YWkpCRMnToVhw8fRu/evZnHIYQMn3wmLBOCZ1duKioq4ODgwPQetXjxYnh7e2PUqFGwsrJCU1MTLl26xA0wWHj8+DHOnTuHx48fK31OqKurw9fXl0kMCkJ4X7TWpUsXzJgxAzNmzGDeNp/lTVrjuw/BF01NTW6VnU9C2tY+ZcoUeHh4wM7ODs3NzcjKysLUqVOxfft2lX+OJiYmIi0tDSdPnoSFhQWcnJxga2vLvC8HgGmJwPZ069ZN6dw864k4RcZlCwuLNpPEEomEaak2iZx1SiwBeV5JFxbJR1JSUl74vIuLC2+1LfmQn58PQ0PDNtdZfIhs3rwZly5dQkhICDfgffz4MQICAjBo0CBMnz5d5TEIQXNzM+Lj43HlyhUMGjSIecY+IXFzc0NSUhIiIiIwePBgWFtbw9XVFWlpaUzjeN5AhmWn/vHjx8jMzISTkxNiYmKQk5MDLy8v3rK+svZn2dBZdnTPnDmDgoICSCQS9OvXj5ekKzExMZg6darStQsXLjDNRiuE94VQTJo0CbGxsdi1axf09PTg5uYGV1dXJCcn8x2aKKxevRrLly/nOwzOtGnTlB5LJBJoa2vj9ddfh7e3N7PdQSUlJcjNzYWamhpsbGzQq1cvlJaW4pVXXmG2pbegoABpaWk4ffo0rKys4OzszORzq66uDlFRUbhy5QoGDhyIjz76iPk2ZiFITEzk8iu0x93dnVksoh7stufJkydMsr++qJyHRCJBaGioymMAWlYtli1bhqtXr2LgwIFYvXo185qJAODo6Mid92GtqakJK1euRGpqKszMzCCRSHDt2jWMGzeOWRbiuXPnIioqSinDZmu6urrw8PDARx99pLIYli9fjuLiYrz11ls4efIkRo8eLcrOIwB4enri+++/x/Hjx3Hnzh3MnTsXY8aMwcGDB5nFUFtbi7KyMpiamnJZFVlqbzZW8XHBaisWEY5ffvkFzc3NWLp0KYKDg7nXgkwmw8qVK3Ho0CHeYpPL5bh16xZMTEx4i4Evy5Ytg5aWFlfexMnJCSkpKX86of6/IpQ+BGmxatUqaGhocGV2UlNTcffuXVhZWSEvL4/JThChnCNXyMvLQ3h4OEpKSnD+/HmVt7dw4ULI5XIMHjwYR48ehampKdOa7EJhYWHxwi3lLKsaiHqwe/ToUaxfv547l9fc3IwnT54gNzeX79CY+fDDD+Hk5ITBgwcjNTUV169fVzr7wYqvry9GjBiBfv36KU02sPzQrKioQH5+PoCWLKcsEzRVVlbCyMjouatINTU1mDVrFk6cOKGyGBwdHZGWlgaJRIIHDx7Ay8tLtKsDBw4cQHx8PCIjI+Hh4QEtLS2Ym5tj3bp1TNr/+eef4e/vD11dXUgkEkRERPB6nlmx0k3EKzIyEmfOnMGlS5eUqhZoaGhg+PDhTLfwxsXFISQkRKlE26uvvor09HRmMQiFTCbDhQsXYG1tjSNHjiA3NxcTJ05kduxCKH0I0mL8+PFISEhQuubh4YF9+/Yxu4/Pnz+/3XPkrF4XcrkcZ8+excGDB5GVlQVLS0uMGTMG9vb20NXVVXn7Tk5O3C6wuro6eHp6Mpt8EpKioiLBbCkX9ZndNWvWIDAwENu2bYO3tzcyMjKUPjxVad68efj2228xevTodlfyWM2S19bWclvSfH19uVTxrF28eBEXL15UusYykyHQcr5h1KhRzNprzcjICADwn//857kzoqr+oOjQoQP3WuzcuTNvNfqEwNnZGY6OjlBTU8O+ffvw22+/MS1LtnnzZsTHx6N3797Izs5GZGQkr2eyxPxaIC0UuQOSkpJ4z2sRFRXFe4k2oeCzvAkgnD4EadHY2IirV6+iV69eAFpW3hULOaxqtPN5jnzFihXIzs5G37594ejoCD8/P+Y7o1rnEdDV1YW6ujrT9oXC0tISlpaW+Oyzz7gt5WvXrmW6pVxB1INdAwMDDBkyBOfOnUNNTQ38/PyYnVNUnPH44YcfmLT3PM+WsuAre93Ro0d5aVdoXpRZU9UZNp8d0PAx+8a3pUuXPndgt3fvXgQGBjKJQyKRcCszw4cPZ3as4XlEvAGIPGPAgAEICgpS2hF169YtpjWohVCiTSj4LG8CCKcPQVosXboUs2fPhqGhIZqbm/Ho0SOEhoYiMjIS48aNYxKDoaEhJBIJTE1NUVJSAjc3N2YD7bi4OHTq1AmXL1/G5cuXsXbtWqXnWS6gKNBkMfDmm2/izTff5LaUp6SkMNlSriDqwa62tjauX78OMzMznDlzBkOGDGH2hlTUoHv55ZeRk5ODR48eKT3fo0cPJnE824nl601ZWlqKmJgYXjtQQsDnjOjt27eVkrY9+5jl+Qq+9O/fn+8QALSdaGBdBP5Z9GFNFD799FPY2dnhl19+gbu7O9LT07lVJFaEUKJNKBTlTVpjeaZeKH0I0mLw4MHIyMjAlStXoKamBjMzM2hqamLQoEHM/ja9evVCYGAgd468srKS2YQpH4PZZ5WWliolCnv28b89Q3lr7W0pl0qlsLe3ZxqHqAe7ixYtwvr16xEWFobo6GjExcVxh/pZmTt3Lp4+fap0NlUikcDFxYVJ+0VFRbC0tOQey+VyWFpaMq0HBgijAyUE7c2Isirz0rqGJyCcmrcsTZgwgfv6zp07uHbtGmxsbHDv3j2mZ7gfP36MvLw8roNQV1en9JhF7crWydIU5XYAMC25Q4SnsbERCxYsgEwmQ9++fTFx4kTmn5vLli3D3r17eS3RJhR8lzcRSh+CtKiurkZYWBjKysqwYcMGLF++HAEBAUyyMN++fRsAMGfOHFRUVHCJNXNzc9ussKqKEMpARUVF8R2CIAhhS7mCqBNUPau6uppZWnYFFxcXwR9cLywsxBtvvKHSNhS/h7Vr1+Ldd9+FlZUVPDw8cODAAZW2KzTtZdZMTU0VTKIoRdbof7uDBw9i48aNePLkCeLi4uDi4oIvv/wSY8eOZdK+VCp97nOsalcKqeQOEY6JEyciJiYGqampqKmpgZeXF5ydnUV3rxaKqqoqJCcn4/Hjx0q7ovg++vhgeKoAABymSURBVNAaiz4EabFgwQIMHToUu3btQnx8PDZt2oSioiJER0ervO3WE6StM/dXVlZCJpPRxEcrihq0/2YWFhbo1KkTlxTs2Z0FLCfMRb2ym5+fj61bt+LBgwdKWyxYbjEYPHgwTp8+LeialUuXLlX5m1JHRwcNDQ147bXXUFhYyEv9SL799ttvmD9/Pm7evAlzc3MsWLAAx48fR+fOnfkOjVNRUcF3CExER0cjNjYWU6dOhaGhIRITEzFz5kxmg92/kowqLi4Onp6eKouBBrOkPa6urvD29kZ4eDg8PT2RnZ2Nbt26MY3h+PHj2LRpU5vPbjHuNli0aBFefvllXLhwAe+99x6OHz+ON998k++wlLDoQ5AWt27dgqenJ3bv3g0tLS34+vrC1dWVSdvP5l55/PgxQkJCcOLECWb5Lv4pxLDOKKT7sagHu/7+/pg6dSrMzc15O2fSs2dPeHl5QU1NDWpqatzWn0uXLvEST3tYvCnb60ApzjWLQWRkJLZu3QoAXB283377DampqRg4cCCfoSkRy3ksiUQCfX197nG3bt0E92+PjY1V6WCXkPZMnToVbm5u0NfXx86dO1FQUIBhw4YxjSE4OBhLlizh9bNbKCorK7Fjxw6EhIRg9OjRmDVrFry8vPgOS4kYOvZCoa6ujpqaGu59UVpaykuyydzcXCxduhRDhw5FcnKy0ucpEUdfSkgT5qIe7Gpra2PKlCm8xrB7926kp6cLugi7Kt+UISEh3KRD6w7U/v37ER8fr7J2hSYpKQmHDh1CZWUlNmzYgG3btqGiogIREREYPnw43+GJjrm5OXbv3g2ZTIYrV67gp59+Yla38q+iDiThQ3V1NQ4cOKC0qlpSUoL58+czi8HAwAB2dnbM2hMyxdErU1NTFBcXCybJXmti6NgLxYIFCyCVSnHnzh3MmzcPFy5cwFdffcWs/bq6Onz99dfcau7QoUOZtU3I84hysKs4RG9paYnt27fDwcFBqQ4Wy4Fn165d0bVrV9HW4Tp37hzWrVsHX19fbuYvKysL0dHRmD59Os/RsaOnpwcjIyMYGRkhPz8fbm5uiIqKEu3rgi+JiYlwd3fH8uXLsXHjRmhoaOCzzz6DjY0NVq1axXd4SqgDSfjwySefoEuXLujVqxfz1+DZs2cBtExGBQUFwcHBQSlTOYvEbUIzZMgQLFiwAP7+/pgxYwYKCwuhra3Nd1iEsaSkJO5rFxcXaGtro6mpCQMGDMDDhw+ZxNB6NTclJQV6enpM2iXkz4hysDt16lRIJBLI5XKcOnVK6Ywu6yyjhoaGcHV1xVtvvaVUn04s5xu2bNmC2bNnIzIyEl5eXliyZAmuXr2Kbdu2oV+/fnyHx0zrbUadO3dukxlZKP7tq4k7duyAu7s79PT04O/vz3c4hAhOdXU1YmJieGl7w4YN3AD7zp07uHLlCoA/MoSLqaSHgq+vL8rKymBsbIxvvvkGeXl5TFfZiTAEBATA0NAQNjY2bWodX79+HW5ubiqPYfr06dDQ0MCJEydw8uRJ7jpl8G/r396XEhpRDnafPUTPJ1tbW9ja2vIdBpqamp67iqjKN6W+vj5++OEHzJkzBzExMXByckJYWJjoZqZbr5AI8d/+5MkTaGtrM/nAJIQIV+/evXHp0iVYWVkxb/vVV19tc01dXR09evTA5MmTmccjBD4+PoiMjAQAWFlZwcrKCl5eXvjxxx95juwP1LFXvcTERKSlpeHkyZOwsLCAk5MTbG1tmZ7XpcHsXzdnzhy+QxAVUZceKi0tRUxMDOrq6pRS9u/atYtpHDU1Naivr1eKgfV2rBelQb958yZMTExU2n59fT3mzp0LW1tbeHt7q7QtIbKysuIymlZUVHBf8zEjevToUaxbt07pNVlfX897PUcWWv8dWhPizPS0adNEuZJF+KEoK/LkyRNUVVWhW7duUFdXZ/reaO8zSi6Xo6SkBNeuXcOWLVtUHoNQzJ8/H0VFRUqfF0DLxHX37t0RGxvLJA7F1vLnefvtt5n0IcgfCgoKkJaWhtOnT8PKygrOzs6Crvjxb2NhYdGmBJMC1Z7mh6gHu+PHj4ednR2OHTsGd3d3pKenw8zMDCtXrmQWw6ZNm7BlyxbIZDJ07NgR9+/fh6WlJRISEpjFAACzZ8/G3Llz0a9fP2hpaTFrVyqVcjeF+vp6FBQUYODAgdw2HLF05oVU03TUqFEIDAzEtm3b4O3tjYyMDNTX12P58uXMYuCLs7PzC+sRss4u+E+ooUnEQUj3qPaIrdZvbW0tHj58iODgYCxdupS7rqGhAUNDQ6WzzKokhJrgpH15eXkIDw9HSUkJzp8/z3c4hPBGlNuYFRobG7FgwQLIZDL07dsXEydOhIeHB9MY9u3bh+PHj2PNmjWYN28erly5gj179jCNAWiZCZw6darSNRYzUD4+Pir9+f8UfHcUWzMwMMCQIUNw7tw51NTUwM/PD05OTnyHxYSmpqag/hb/hBqaRBwU74vGxkbk5OTgwYMH7T7P2r1795CRkSG6ZDj6+vrQ19fHK6+80uZ37+/vj5CQECZx/JWa4IQNuVyOs2fP4uDBg8jKyoKlpSWkUins7e35Dk2UaLJaOEQ92NXR0UFDQwNee+01FBYWwtramnkM//nPf9CxY0eYm5ujuLgYo0aNwvr165nHwdcW1XfeeYeXdsnzaWtr4/r16zAzM8OZM2cwZMgQNDY28h0WE4MGDeI7BCX/hBqaRFwWLlyIe/fuwczMTCnXAF/n+W/cuIH8/HzRdSCXLFmCsrIyFBYW4urVq9x1mUyGmpoa5vFcuHABUVFRSsfCbt++LagcKf9mK1asQHZ2Nvr27QtHR0f4+flBR0eH77BEjSarhUPUg91x48bB29sb4eHh8PT0RHZ2Nrp37840Bn19faSkpKBv377YvXs3unfvjidPnjCNAaAZKPIHX19frF+/HmFhYYiOjkZcXBw++OADvsNiQmhbtf8JNTSJuPz22284ePAg32FwrK2teZmo5tu8efNQXl6O4OBgpR1SampqMDMzYx7Pl19+iZkzZyIxMRFSqRSHDx9G3759mcchVnFxcejUqRMuX76My5cvY+3atUrPCynfhFjQZLVwiHKwq6hHpqenB3t7e2RlZcHd3R26urptUrariqKeZ3BwMJKTk+Hi4oIjR44gICAACxYsYBJDazQDRRR+/fVXREREAGjZZl9dXc0NughbVEOTCE2PHj1w+/ZtpvXoSVsODg5cCUWpVAo1NTV07NgRtra2vEzaaWlpwcPDA+Xl5ejYsSNCQ0Ph4uLCPA6xosGs8NBktXCIMkGVhYXFc+uRAcCaNWtUHsOLsh/zYcyYMTh48CBCQkIwZswY9OjRA15eXkhOTuY7NMLY2LFjkZqayncY5P+UlZWhR48euHTpEvLy8uDo6NhuxmhCVEmRTLCqqgp37tyBhYWFUrk6SkTEv/v37yMuLg7Xrl1rs7Knap6enoiKikJ2djbKy8vh7e2N999/H4cOHWIaByFCsW7dOly/fp2brB48eDCKi4t5ycsjdqJc2RVCPTKhoRkootC9e3dMmzYN/fv3R4cOHbjr8+fP5zEqcXJ2doa7uzvGjRvH1dAkhA8+Pj6orq6GTCaDoaEhgJaEOPfv30fXrl15jo4AgKGhIebNmwdnZ2fmbX/00Ufw9fVFZGQkJkyYgJSUFLpfEVHz9fVFWVkZjI2NsXbtWpw9e5b6UTwR5cpua3zVIxNaPU+agSIKGzdubPc63aTZKy8vR1JSElJTU2FiYoLx48fDwcGB2XELQhQuX76MOXPm4KuvvsK7774LoOVzIyEhAd9//z0sLCx4jpAouLm5cce1WFL0X+rq6lBaWgpLS0ulJGaEiMnzMtfzlcxPzEQ/2FVgXY9MaPU8gbbbJZ2cnGBkZMQ8DkJIW+np6QgKCsKTJ0/g6uqKefPmoXPnznyHRUTCy8sL8+bNazMZnJ2djS1btmD79u38BEaUHD58GD/99BPzv8fNmzcRGxuLBw8eoHW3ksWxMEKEaN68ee1mrqf3BHui3MYM8F+PTGj1PH18fBAZGQkA3HZJLy8v/PjjjzxHRljbu3cv1q5di4cPHwL4Y7Ze1TWXSVuPHz/GoUOHsH//flRUVGDy5MlwdnZGVlYWZs6ciYSEBL5DJCLx6NGjdnc9DR8+HOHh4TxEJG4jR45ss2paW1uLnj17IiwsjHk8Pj4+sLGxgbW1Na3mEgLhZa4XM1EOdoVQj0wo9Tznz5+PoqIiVFRUwMHBgbve1NTEvAwTEYbNmzdjx44d6NWrF9+hiJ6DgwPs7e0xf/58vP3229z1Dz/8EDk5OTxGRsRGJpOhubm5TW6L5uZm0dThFpKdO3cqPVZkY9bT0+MlHrlcDn9/f17aJkSIKHO9cIhyG7OFhQU6deoEXV1dAGgzCymmFO61tbV4+PAhgoODsXTpUu66hoYGDA0NoaEhyvkQUZswYQL27t3LdxgELe9PfX19vsMgBKtXr0anTp3alMbbuHEjysrKqCa7yK1cuRJDhw6Fg4ODqJN9EvK8zPWKXXKUuZ49UQ52y8vLX/i8kLYXsxIYGIhly5YpXfP390dISAhPERHWFAlNjh07hoaGBjg4OChNdlBSBfaOHz+OTZs2cefg+EpgR0htbS3mzJmDu3fvwsLCAh06dMDly5fRpUsXbN68GZ06deI7RMIDCwsLrt4v8MfiAR1/IWJ15syZFz7/zjvvMIqEKIhysEv+sGTJEpSVlaGwsFCpTIBMJkNNTQ1SUlJ4jI6w9MUXX7zweUqqwN6oUaOwZMkSmJubK+1AEeOEHOGfXC7HqVOnUFRUBDU1NVhZWcHa2prvsIjAKAa6hIhZRUUFduzYAT8/P9y8eRORkZH4/PPPqVQbD2iwK3Ll5eUoLy9vs41ZTU0NZmZmNFtPCI/Gjx9PSagIIYJ3+vRprFu3DrGxsfjtt98we/ZshIWFCSY/CSGsSaVSODs7Y9KkSWhoaEBSUhIOHjyIrVu38h2a6NBgV+Se3YKkSHJha2uL5cuX02BXRNrL7qkgkUiQkZHBOCLxOnv2LICWzNgdO3Zss6W8dbIqQgjhm7u7O0JCQtC7d28AwK+//orPP/8c+/bt4zkyQvjh6uqK5ORkpWvu7u5ITEzkKSLxouxDIldcXNzm2v379xEXF4fVq1dj7dq1PERF+LBz507I5XJs2rQJJiYmGD9+PNTV1ZGSkoJbt27xHZ6obNiwgfv6zp07KCkp4R5TggtCiNA8ffqUG+gCgJmZGWQyGY8REcIvbW1tZGZmYsSIEQCAnJwc5pVfSAta2SXP5ezsjAMHDvAdBmGsva2ztJ2WEELI88yfPx89e/bEuHHjIJFIkJqaitLSUkRERPAdGiG8KCoqgp+fH+7duwcAePnllxEaGqo0KUTYoJVd8lyampp8h0B4kpubCxsbGwBAZmYm1NXVeY5IXBSlC56HVnYJIUISHByM9evX47PPPoOGhgasra0RFBTEd1iE8MbS0hKpqal48OABNDU1qYwgj2iwS9p1+PBhOq8rUkFBQfD398e9e/cgl8thbGxMNTQZ8/HxAQDs2bMH2tracHNzg4aGBlJTU/H06VOeoyOEEGUvvfQSVqxYwT2Wy+W4desWDAwMeIyKEP5cuHABUVFRqKurg1wuR3NzM27fvo2jR4/yHZro0DZmkWsvKVFtbS169uyJsLAw9OzZk6fICN8ePHgAiURCkx488vDwaJPghbaUE0KEJjY2FqGhoaivr+euGRsbU2JDIlpOTk6YOXMmEhMTIZVKcfjwYRgaGuLLL7/kOzTRoZVdkdu5c6fSY0U2Zj09PZ4iIny7fPkyvvvuO1RXV6P1XBhtnWXv6dOnuH79OkxNTQEAJSUllPSFECI40dHR2L9/P9avXw9fX19kZmbi3LlzfIdFCG+0tLTg4eGB8vJydOzYEaGhoXBxceE7LFGiwa7IGRsb8x0CERh/f394enqiV69eLzw3SlQvICAAUqkU3bp1g1wux/379/HNN9/wHRYhhCgxNDSEiYkJ+vTpgytXrmDKlCnYvXs332ERwpsOHTrg4cOHMDU1xcWLF2FjY4Ompia+wxIlGuwSQpRoa2tj6tSpfIdBAAwbNgxHjx7FlStXIJFI0KdPH6V6u4QQIgQ6Ojo4deoU+vTpg4yMDLz55pt48uQJ32ERwpuPPvoIvr6+iIyMxIQJE5CSkgIrKyu+wxIlOrNLCFESERGBLl26YNiwYejQoQN3/ZVXXuExKnEqLS1FTEyMUoKLW7duYdeuXXyHRgghnKtXr2Lv3r0ICAjAwoULkZOTAx8fH3z00Ud8h0YIb+RyOSQSCerq6lBaWgpLS0vaMccDGuwSQpSMHDmS+1pxU25oaEB2djZfIYnW+PHjYWdnh2PHjsHd3R3p6ekwMzPDypUr+Q6NEEIIIc9RXl6OmJiYNvlP1qxZw2NU4kT74QghShRp8RsbG5Geno7du3ejoKCA56jEqbGxEQsWLIBMJkPfvn0xceJEeHh48B0WIYQoOXjwIKKjo1FdXa10/ciRIzxFRAi/Fi1aBGtra1hbW9NqLs9osEsIUXLz5k3s2bMHCQkJqK6uhre3NyIiIvgOS5R0dHTQ0NCA1157DYWFhbC2tuY7JEIIaSMkJAShoaF03IWQ/yOTyeDv7893GASAGt8BEEKEIT09HTNnzsSECRPw8OFDhIaGwsjICPPnz0eXLl34Dk+Uxo0bB29vb9jZ2SEmJgazZs1C9+7d+Q6LEEKU9OjRA2+99RaMjY2V/iNErN566y0cPXoUDQ0NfIcienRmlxACALCwsICjoyMWLVqEnj17AgAcHBxoGxoPkpKSuK9rampgYGCA27dvQ1dXF5qampgyZQqP0RFCiLLMzEx8//33ePvtt6Gurs5dnz9/Po9REcKfYcOG4ffff1e6JpFIUFRUxFNE4kXbmAkhAIDk5GQkJCTgww8/hLGxMZydnakmHE8CAgJgaGgIGxsbaGpq8h0OIYS80ObNm2Fqaqo00CVEzE6cOMF3COT/0MouIUSJTCbD8ePHkZCQgKysLNja2mLKlCkYMWIE36GJRlFREdLS0nDy5ElYWFjAyckJtra2UFOjkyeEEOHx8PDAvn37+A6DEMHYuHFju9dptwN7NNglhDxXVVUVkpKSkJSUhOTkZL7DEaWCggKkpaXh9OnTsLKygrOzMwYPHsx3WIQQwlm7di2MjIwwfPhwpd0olLCKiFXrwW5jYyOys7PRv39/rFixgseoxIkGu4QQ8g+Ql5eH8PBwlJSU4Pz583yHQwghnNb12RUkEgnlfCDk/zQ0NGDGjBmIiYnhOxTRoTO7hBAiQHK5HGfPnsXBgweRlZUFS0tLSKVS2Nvb8x0aIYQoUdRnJ4S07/Hjx7h9+zbfYYgSDXYJIURgVqxYgezsbPTt2xeOjo7w8/ODjo4O32ERQoiSyMhI+Pj44Isvvmj3+TVr1jCOiBBhGDlyJCQSCYCWyevq6mrMmjWL56jEibYxE0KIwFhYWKBTp07Q1dUFAO4DU4G2BhJChODYsWOwt7dHYmJim/sUALi5ufEQFSH8y8/Ph6GhIYCWz/COHTtCX1+f56jEiQa7hBAiMOXl5S983tjYmFEkhBDyfBYWFpBIJJDL5dz/FaimKBEzR0dH/Pzzz3yHQUCDXUIIIYQQ8je5ubkhKSmJ7zAIEQRfX1+MGDEC/fr1g7a2NnedMpSzR2d2CSGEEELI39LeNmZCxOrixYu4ePEigD/eGw0NDcjOzuYzLFGiwS4hhBBCCPlbaKMgIX9QZChvbGxEeno6du/ejYKCAp6jEica7BJCCCGEkL+FVnYJ+cPNmzexZ88eJCQkoLq6Gt7e3oiIiOA7LFGiM7uEEEIIIeS/1rq8SkVFBbp16wYAXMIqyhxPxCY9PR2xsbEoLCzEqFGjMGbMGCxbtoxqUfOIVnYJIYQQQsh/befOnXyHQIig+Pj4wNHREXFxcejZsycA2vXANxrsEkIIIYSQ/xqVQSNEWXJyMhISEvDhhx/C2NgYzs7OaGpq4jssUaNtzIQQQgghhBDyPyKTyXD8+HEkJCQgKysLtra2mDJlCkaMGMF3aKJDg11CCCGEEEIIUYGqqiokJSUhKSkJycnJfIcjOjTYJYQQQgghhBDyr6PGdwCEEEIIIYQQQsj/Gg12CSGEEEIIIYT869BglxBCCCGEEELIvw4NdgkhhJC/6cKFC5BKpXBxccHYsWMxa9YsXL16FQAwY8YMVFVV/enP+Kvf16dPH6xfv17p2sGDByGVSv//gieEEEL+pajOLiGEEPI3NDQ0YO7cudi6dSveeOMNAMD+/fsxe/ZsHDlyBCdPnvxLP+evfh8AbNu2DUOHDsXbb7/9/xUzIYQQIgY02CWEEEL+hvr6etTU1KCuro675urqCn19fSxduhQA4OXlhejoaBQXFyMqKgoNDQ2oqqqCm5sbFi1ahC+++ELp+9TU1LB69WrcuXMHjY2NcHZ2hre3N/fzfX194efnh/379+Oll15Siuf333/H8uXLcf/+fdy7dw/GxsZYv349DA0NMXLkSIwdOxanTp1CdXU1Zs2ahXPnzqGwsBAaGhrYvHkzunXrhoqKihe2TwghhPwT0DZmQggh5G946aWX4Ofnh1mzZsHBwQF+fn7Yt28fbG1tsWbNGgDAjz/+iO7du2Pr1q34+uuvkZCQgLi4OERHR6Oqqkrp+15++WX4+fnBw8MDCQkJiI+PR05ODtLS0rg2XV1dYW1tjWXLlrWJ58CBAxgwYADi4uJw5MgRaGtrY//+/dzzT58+xZ49e7Bw4UIsX74cXl5eSE5Oxssvv4zExEQA+NP2CSGEkH8CWtklhBBC/qbp06djwoQJOHv2LM6ePYvvv/8e33//PeLj47nvkUgk+O6773D8+HGkpqbi119/hVwuR319vdLPqqurw9mzZ1FdXY2IiAjuWnFxMZycnLjvW7lyJcaNG4e9e/fCwMCAu+7l5YW8vDxs27YNpaWluHr1Kvr37889P3r0aACAiYkJunbtCgsLCwBAjx49UF1d/ZfbJ4QQQoSOBruEEELI3/DLL7/g/PnzmDVrFuzt7WFvb49PP/0UY8eOVTqHW1dXB3d3d7z33nuwtraGh4cHMjIyIJfLlX5ec3Mz5HI5YmNjoaOjAwCoqqpChw4dlL5PX18f33zzDWbNmoWZM2dy18PCwpCfnw8PDw8MHjwYMplMqQ0tLS3ua01NzTb/nr/aPiGEECJ0tI2ZEEII+Ru6dOmCzZs3Iy8vj7t279491NbWonfv3lBXV4dMJsONGzdQW1uLRYsWYeTIkTh9+jQaGhrQ3NwMANz36evrY8CAAdi2bRsA4NGjR5g8eTKOHDnSpu0BAwZg+vTp+Pbbb7lrJ06cgJeXF9zc3GBoaIicnBw0NTX95X/Pf9M+IYQQImS0sksIIYT8Daampti0aRPWrVuHu3fvokOHDjAwMMBXX32F119/HWPGjIFUKkVERATs7Ozg6OgILS0t9O7dG+bm5rhx4wZ69OjBfV9kZCTCw8MRGBgIFxcXNDQ0YOzYsXB1dW23/Y8//hi5ubnc408++QShoaGIiIiApqYmBg0ahLKysv/q3/TftE8IIYQIlUT+7P4pQgghhBBCCCHkH462MRNCCCGEEEII+dehwS4hhBBCCCGEkH8dGuwSQgghhBBCCPnXocEuIYQQQgghhJB/HRrsEkIIIYQQQgj516HBLiGEEEIIIYSQfx0a7BJCCCGEEEII+dehwS4hhBBCCCGEkH+d/weUGOC6HdqQsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot visualisation for the sector wise contribution to State GSDP\n",
    "sector_df.plot(kind='bar', stacked=True, figsize=(16,8))\n",
    "\n",
    "#Give Label to the y-axis\n",
    "plt.ylabel('Percentage of GSDP')\n",
    "\n",
    "#Provide title to the plot\n",
    "plt.title('Sector-wise % contribution to GSDP during 2014-15 for all States'+\"\\n\"+'Ordered using State with Highest GSDP to Lowest')\n",
    "\n",
    "#Provide legend location\n",
    "plt.legend(loc='upper right',title='Sector')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color=blue>Next Section: Categorise the states into four categories based on GDP per capita</font>\n",
    "## <font color=blue>Plot the contribution to sub-sectors to GSDP under each category of state</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a copy of GSDP Per Capita dataframe, to be used for Category plotting\n",
    "GSDP_CTGY_df=GSDP_PerCapita_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C2    9\n",
      "C3    8\n",
      "C4    6\n",
      "C1    4\n",
      "Name: Per_Capita_GSDP_2014-15, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Using qcut function, passing the quantile list as input, fetch the list of Categories \n",
    "state_ctgy=pd.qcut(GSDP_CTGY_df['Per_Capita_GSDP_2014-15'],[0,0.20,0.5, 0.85, 1],labels=['C4','C3','C2','C1'])\n",
    "\n",
    "#Print the state category counts\n",
    "print(state_ctgy.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Per_Capita_GSDP_2014-15</th>\n",
       "      <th>StateName</th>\n",
       "      <th>State_Ctgy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>271793.0</td>\n",
       "      <td>Goa</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>240274.0</td>\n",
       "      <td>Sikkim</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>164077.0</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>154778.0</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>153076.0</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Per_Capita_GSDP_2014-15    StateName State_Ctgy\n",
       "32                 271793.0          Goa         C1\n",
       "32                 240274.0       Sikkim         C1\n",
       "32                 164077.0      Haryana         C1\n",
       "32                 154778.0       Kerala         C1\n",
       "32                 153076.0  Uttarakhand         C2"
      ]
     },
     "execution_count": 625,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add a new column to the GSDP_CTGY_df dataframe\n",
    "GSDP_CTGY_df['State_Ctgy']=state_ctgy.values\n",
    "\n",
    "GSDP_CTGY_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merge the All State dataframe with GSDP Per capita Category Dataframe\n",
    "State_Ctgy_df=pd.merge(allstate_df,GSDP_CTGY_df,how='inner',on='StateName')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 627,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Retain only the 11 Sub-sectors & GSDP info\n",
    "State_Ctgy_df=State_Ctgy_df.loc[ State_Ctgy_df['S.No.'].isin(['1','2','3','4','5','6','7','8','9','10','11','15']) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 314 entries, 0 to 888\n",
      "Data columns (total 6 columns):\n",
      "2014-15                    314 non-null float64\n",
      "Item                       314 non-null object\n",
      "S.No.                      314 non-null object\n",
      "StateName                  314 non-null object\n",
      "Per_Capita_GSDP_2014-15    314 non-null float64\n",
      "State_Ctgy                 314 non-null category\n",
      "dtypes: category(1), float64(2), object(3)\n",
      "memory usage: 15.2+ KB\n"
     ]
    }
   ],
   "source": [
    "State_Ctgy_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the Per Capita, S.No. & StateName columns\n",
    "State_Ctgy_df.drop(['Per_Capita_GSDP_2014-15','S.No.','StateName'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2014-15</th>\n",
       "      <th>Item</th>\n",
       "      <th>State_Ctgy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14819416.0</td>\n",
       "      <td>Agriculture, forestry and fishing</td>\n",
       "      <td>C3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1484300.0</td>\n",
       "      <td>Mining and quarrying</td>\n",
       "      <td>C3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4672266.0</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>C3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1151729.0</td>\n",
       "      <td>Electricity, gas, water supply &amp; other utility...</td>\n",
       "      <td>C3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4664889.0</td>\n",
       "      <td>Construction</td>\n",
       "      <td>C3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      2014-15                                               Item State_Ctgy\n",
       "0  14819416.0                  Agriculture, forestry and fishing         C3\n",
       "5   1484300.0                               Mining and quarrying         C3\n",
       "7   4672266.0                                      Manufacturing         C3\n",
       "8   1151729.0  Electricity, gas, water supply & other utility...         C3\n",
       "9   4664889.0                                       Construction         C3"
      ]
     },
     "execution_count": 630,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "State_Ctgy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename the column to reflect the correct significance\n",
    "State_Ctgy_df.rename(columns={'2014-15': 'Amount','Item':'Sub-sector'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Amount</th>\n",
       "      <th>Sub-sector</th>\n",
       "      <th>State_Ctgy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14819416.0</td>\n",
       "      <td>Agriculture, forestry and fishing</td>\n",
       "      <td>C3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1484300.0</td>\n",
       "      <td>Mining and quarrying</td>\n",
       "      <td>C3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4672266.0</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>C3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1151729.0</td>\n",
       "      <td>Electricity, gas, water supply &amp; other utility...</td>\n",
       "      <td>C3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4664889.0</td>\n",
       "      <td>Construction</td>\n",
       "      <td>C3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Amount                                         Sub-sector State_Ctgy\n",
       "0  14819416.0                  Agriculture, forestry and fishing         C3\n",
       "5   1484300.0                               Mining and quarrying         C3\n",
       "7   4672266.0                                      Manufacturing         C3\n",
       "8   1151729.0  Electricity, gas, water supply & other utility...         C3\n",
       "9   4664889.0                                       Construction         C3"
      ]
     },
     "execution_count": 632,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "State_Ctgy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 314 entries, 0 to 888\n",
      "Data columns (total 3 columns):\n",
      "Amount        314 non-null float64\n",
      "Sub-sector    314 non-null object\n",
      "State_Ctgy    314 non-null category\n",
      "dtypes: category(1), float64(1), object(1)\n",
      "memory usage: 7.9+ KB\n"
     ]
    }
   ],
   "source": [
    "State_Ctgy_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate a groupby Dataframe to get the sum of all Amount values by Category and sub-sector\n",
    "State_Ctgy_df_total=pd.DataFrame(State_Ctgy_df.groupby(['State_Ctgy','Sub-sector'])['Amount'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 635,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sort the dataframe values in descending order\n",
    "State_Ctgy_df_total.sort_values(by=['State_Ctgy','Amount'],ascending=False,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State_Ctgy</th>\n",
       "      <th>Sub-sector</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">C1</th>\n",
       "      <th>Gross State Domestic Product</th>\n",
       "      <td>101930677.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real estate, ownership of dwelling &amp; professional services</th>\n",
       "      <td>14740245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agriculture, forestry and fishing</th>\n",
       "      <td>14391809.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trade, repair, hotels and restaurants</th>\n",
       "      <td>13995159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manufacturing</th>\n",
       "      <td>13758793.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                    Amount\n",
       "State_Ctgy Sub-sector                                                     \n",
       "C1         Gross State Domestic Product                        101930677.0\n",
       "           Real estate, ownership of dwelling & profession...   14740245.0\n",
       "           Agriculture, forestry and fishing                    14391809.0\n",
       "           Trade, repair, hotels and restaurants                13995159.0\n",
       "           Manufacturing                                        13758793.0"
      ]
     },
     "execution_count": 636,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "State_Ctgy_df_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 637,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reset the index to expand the State_Ctgy & Sub-sector into columns\n",
    "State_Ctgy_df_total.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Ctgy</th>\n",
       "      <th>Sub-sector</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C1</td>\n",
       "      <td>Gross State Domestic Product</td>\n",
       "      <td>101930677.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C1</td>\n",
       "      <td>Real estate, ownership of dwelling &amp; professio...</td>\n",
       "      <td>14740245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C1</td>\n",
       "      <td>Agriculture, forestry and fishing</td>\n",
       "      <td>14391809.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C1</td>\n",
       "      <td>Trade, repair, hotels and restaurants</td>\n",
       "      <td>13995159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C1</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>13758793.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State_Ctgy                                         Sub-sector       Amount\n",
       "0         C1                       Gross State Domestic Product  101930677.0\n",
       "1         C1  Real estate, ownership of dwelling & professio...   14740245.0\n",
       "2         C1                  Agriculture, forestry and fishing   14391809.0\n",
       "3         C1              Trade, repair, hotels and restaurants   13995159.0\n",
       "4         C1                                      Manufacturing   13758793.0"
      ]
     },
     "execution_count": 638,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "State_Ctgy_df_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total GSDP for C1, C2, C3 & C4 : 101930677.0 , 586380452.0 , 177966387.0 , 233252066.0\n"
     ]
    }
   ],
   "source": [
    "#Compute the GSDP total for states in each category\n",
    "\n",
    "#GSDP total for Category 1\n",
    "C1_GSDP_total=State_Ctgy_df_total.loc[(State_Ctgy_df_total['State_Ctgy']=='C1') & (State_Ctgy_df_total['Sub-sector']=='Gross State Domestic Product')].Amount.values[0]\n",
    "\n",
    "#GSDP total for Category 2\n",
    "C2_GSDP_total=State_Ctgy_df_total.loc[(State_Ctgy_df_total['State_Ctgy']=='C2') & (State_Ctgy_df_total['Sub-sector']=='Gross State Domestic Product')].Amount.values[0]\n",
    "\n",
    "#GSDP total for Category 3\n",
    "C3_GSDP_total=State_Ctgy_df_total.loc[(State_Ctgy_df_total['State_Ctgy']=='C3') & (State_Ctgy_df_total['Sub-sector']=='Gross State Domestic Product')].Amount.values[0]\n",
    "\n",
    "#GSDP total for Category 4\n",
    "C4_GSDP_total=State_Ctgy_df_total.loc[(State_Ctgy_df_total['State_Ctgy']=='C4') & (State_Ctgy_df_total['Sub-sector']=='Gross State Domestic Product')].Amount.values[0]\n",
    "\n",
    "print(\"Total GSDP for C1, C2, C3 & C4 :\",C1_GSDP_total,\",\",C2_GSDP_total,\",\",C3_GSDP_total,\",\",C4_GSDP_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 640,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a DataFrame for States in Category C1\n",
    "state_C1_df=State_Ctgy_df_total.loc[(State_Ctgy_df_total['State_Ctgy']=='C1') & (State_Ctgy_df_total['Sub-sector'] != 'Gross State Domestic Product')]\n",
    "\n",
    "#Create a DataFrame for States in Category C2\n",
    "state_C2_df=State_Ctgy_df_total.loc[(State_Ctgy_df_total['State_Ctgy']=='C2') & (State_Ctgy_df_total['Sub-sector'] != 'Gross State Domestic Product')]\n",
    "\n",
    "#Create a DataFrame for States in Category C3\n",
    "state_C3_df=State_Ctgy_df_total.loc[(State_Ctgy_df_total['State_Ctgy']=='C3') & (State_Ctgy_df_total['Sub-sector'] != 'Gross State Domestic Product')]\n",
    "\n",
    "#Create a DataFrame for States in Category C4\n",
    "state_C4_df=State_Ctgy_df_total.loc[(State_Ctgy_df_total['State_Ctgy']=='C4') & (State_Ctgy_df_total['Sub-sector'] != 'Gross State Domestic Product')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 641,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bunny\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\Bunny\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Ctgy</th>\n",
       "      <th>Sub-sector</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Sub-Sector%</th>\n",
       "      <th>Cumulative%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C1</td>\n",
       "      <td>Real estate, ownership of dwelling &amp; professio...</td>\n",
       "      <td>14740245.0</td>\n",
       "      <td>14.46</td>\n",
       "      <td>14.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C1</td>\n",
       "      <td>Agriculture, forestry and fishing</td>\n",
       "      <td>14391809.0</td>\n",
       "      <td>14.12</td>\n",
       "      <td>28.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C1</td>\n",
       "      <td>Trade, repair, hotels and restaurants</td>\n",
       "      <td>13995159.0</td>\n",
       "      <td>13.73</td>\n",
       "      <td>42.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C1</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>13758793.0</td>\n",
       "      <td>13.50</td>\n",
       "      <td>55.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C1</td>\n",
       "      <td>Construction</td>\n",
       "      <td>11264451.0</td>\n",
       "      <td>11.05</td>\n",
       "      <td>66.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C1</td>\n",
       "      <td>Other services</td>\n",
       "      <td>8059922.0</td>\n",
       "      <td>7.91</td>\n",
       "      <td>74.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C1</td>\n",
       "      <td>Transport, storage, communication &amp; services r...</td>\n",
       "      <td>6818560.0</td>\n",
       "      <td>6.69</td>\n",
       "      <td>81.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C1</td>\n",
       "      <td>Financial services</td>\n",
       "      <td>3936489.0</td>\n",
       "      <td>3.86</td>\n",
       "      <td>85.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C1</td>\n",
       "      <td>Public administration</td>\n",
       "      <td>3571292.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>88.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C1</td>\n",
       "      <td>Electricity, gas, water supply &amp; other utility...</td>\n",
       "      <td>2000998.0</td>\n",
       "      <td>1.96</td>\n",
       "      <td>90.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>C1</td>\n",
       "      <td>Mining and quarrying</td>\n",
       "      <td>588961.0</td>\n",
       "      <td>0.58</td>\n",
       "      <td>91.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   State_Ctgy                                         Sub-sector      Amount  \\\n",
       "1          C1  Real estate, ownership of dwelling & professio...  14740245.0   \n",
       "2          C1                  Agriculture, forestry and fishing  14391809.0   \n",
       "3          C1              Trade, repair, hotels and restaurants  13995159.0   \n",
       "4          C1                                      Manufacturing  13758793.0   \n",
       "5          C1                                       Construction  11264451.0   \n",
       "6          C1                                     Other services   8059922.0   \n",
       "7          C1  Transport, storage, communication & services r...   6818560.0   \n",
       "8          C1                                 Financial services   3936489.0   \n",
       "9          C1                              Public administration   3571292.0   \n",
       "10         C1  Electricity, gas, water supply & other utility...   2000998.0   \n",
       "11         C1                               Mining and quarrying    588961.0   \n",
       "\n",
       "    Sub-Sector%  Cumulative%  \n",
       "1         14.46        14.46  \n",
       "2         14.12        28.58  \n",
       "3         13.73        42.31  \n",
       "4         13.50        55.81  \n",
       "5         11.05        66.86  \n",
       "6          7.91        74.77  \n",
       "7          6.69        81.46  \n",
       "8          3.86        85.32  \n",
       "9          3.50        88.82  \n",
       "10         1.96        90.78  \n",
       "11         0.58        91.36  "
      ]
     },
     "execution_count": 641,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add the fields % of GSDP contribution and cumulative % to state_C1_df\n",
    "state_C1_df['Sub-Sector%'] = round((state_C1_df['Amount']/(C1_GSDP_total))*100,2)\n",
    "state_C1_df['Cumulative%'] = state_C1_df['Sub-Sector%'].cumsum()\n",
    "state_C1_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 642,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bunny\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\Bunny\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Ctgy</th>\n",
       "      <th>Sub-sector</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Sub-Sector%</th>\n",
       "      <th>Cumulative%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>C2</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>109196530.0</td>\n",
       "      <td>18.62</td>\n",
       "      <td>18.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>C2</td>\n",
       "      <td>Real estate, ownership of dwelling &amp; professio...</td>\n",
       "      <td>92121446.0</td>\n",
       "      <td>15.71</td>\n",
       "      <td>34.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>C2</td>\n",
       "      <td>Agriculture, forestry and fishing</td>\n",
       "      <td>75209022.0</td>\n",
       "      <td>12.83</td>\n",
       "      <td>47.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>C2</td>\n",
       "      <td>Trade, repair, hotels and restaurants</td>\n",
       "      <td>61238862.0</td>\n",
       "      <td>10.44</td>\n",
       "      <td>57.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>C2</td>\n",
       "      <td>Construction</td>\n",
       "      <td>40653562.0</td>\n",
       "      <td>6.93</td>\n",
       "      <td>64.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>C2</td>\n",
       "      <td>Financial services</td>\n",
       "      <td>36296642.0</td>\n",
       "      <td>6.19</td>\n",
       "      <td>70.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>C2</td>\n",
       "      <td>Other services</td>\n",
       "      <td>36253657.0</td>\n",
       "      <td>6.18</td>\n",
       "      <td>76.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>C2</td>\n",
       "      <td>Transport, storage, communication &amp; services r...</td>\n",
       "      <td>33749808.0</td>\n",
       "      <td>5.76</td>\n",
       "      <td>82.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C2</td>\n",
       "      <td>Public administration</td>\n",
       "      <td>18567815.0</td>\n",
       "      <td>3.17</td>\n",
       "      <td>85.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>C2</td>\n",
       "      <td>Electricity, gas, water supply &amp; other utility...</td>\n",
       "      <td>13823378.0</td>\n",
       "      <td>2.36</td>\n",
       "      <td>88.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C2</td>\n",
       "      <td>Mining and quarrying</td>\n",
       "      <td>10373346.0</td>\n",
       "      <td>1.77</td>\n",
       "      <td>89.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   State_Ctgy                                         Sub-sector       Amount  \\\n",
       "13         C2                                      Manufacturing  109196530.0   \n",
       "14         C2  Real estate, ownership of dwelling & professio...   92121446.0   \n",
       "15         C2                  Agriculture, forestry and fishing   75209022.0   \n",
       "16         C2              Trade, repair, hotels and restaurants   61238862.0   \n",
       "17         C2                                       Construction   40653562.0   \n",
       "18         C2                                 Financial services   36296642.0   \n",
       "19         C2                                     Other services   36253657.0   \n",
       "20         C2  Transport, storage, communication & services r...   33749808.0   \n",
       "21         C2                              Public administration   18567815.0   \n",
       "22         C2  Electricity, gas, water supply & other utility...   13823378.0   \n",
       "23         C2                               Mining and quarrying   10373346.0   \n",
       "\n",
       "    Sub-Sector%  Cumulative%  \n",
       "13        18.62        18.62  \n",
       "14        15.71        34.33  \n",
       "15        12.83        47.16  \n",
       "16        10.44        57.60  \n",
       "17         6.93        64.53  \n",
       "18         6.19        70.72  \n",
       "19         6.18        76.90  \n",
       "20         5.76        82.66  \n",
       "21         3.17        85.83  \n",
       "22         2.36        88.19  \n",
       "23         1.77        89.96  "
      ]
     },
     "execution_count": 642,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add the fields % of GSDP contribution and cumulative % to state_C2_df\n",
    "state_C2_df['Sub-Sector%'] = round((state_C2_df['Amount']/(C2_GSDP_total))*100,2)\n",
    "state_C2_df['Cumulative%'] = state_C2_df['Sub-Sector%'].cumsum()\n",
    "state_C2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bunny\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\Bunny\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Ctgy</th>\n",
       "      <th>Sub-sector</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Sub-Sector%</th>\n",
       "      <th>Cumulative%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C3</td>\n",
       "      <td>Agriculture, forestry and fishing</td>\n",
       "      <td>42226888.0</td>\n",
       "      <td>23.73</td>\n",
       "      <td>23.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>C3</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>22038331.0</td>\n",
       "      <td>12.38</td>\n",
       "      <td>36.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>C3</td>\n",
       "      <td>Trade, repair, hotels and restaurants</td>\n",
       "      <td>17245309.0</td>\n",
       "      <td>9.69</td>\n",
       "      <td>45.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>C3</td>\n",
       "      <td>Real estate, ownership of dwelling &amp; professio...</td>\n",
       "      <td>16224118.0</td>\n",
       "      <td>9.12</td>\n",
       "      <td>54.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>C3</td>\n",
       "      <td>Construction</td>\n",
       "      <td>15707921.0</td>\n",
       "      <td>8.83</td>\n",
       "      <td>63.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>C3</td>\n",
       "      <td>Other services</td>\n",
       "      <td>12815417.0</td>\n",
       "      <td>7.20</td>\n",
       "      <td>70.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>C3</td>\n",
       "      <td>Transport, storage, communication &amp; services r...</td>\n",
       "      <td>12231484.0</td>\n",
       "      <td>6.87</td>\n",
       "      <td>77.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>C3</td>\n",
       "      <td>Mining and quarrying</td>\n",
       "      <td>10835771.0</td>\n",
       "      <td>6.09</td>\n",
       "      <td>83.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>C3</td>\n",
       "      <td>Public administration</td>\n",
       "      <td>7927173.0</td>\n",
       "      <td>4.45</td>\n",
       "      <td>88.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>C3</td>\n",
       "      <td>Financial services</td>\n",
       "      <td>5766226.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>91.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>C3</td>\n",
       "      <td>Electricity, gas, water supply &amp; other utility...</td>\n",
       "      <td>4604774.0</td>\n",
       "      <td>2.59</td>\n",
       "      <td>94.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   State_Ctgy                                         Sub-sector      Amount  \\\n",
       "25         C3                  Agriculture, forestry and fishing  42226888.0   \n",
       "26         C3                                      Manufacturing  22038331.0   \n",
       "27         C3              Trade, repair, hotels and restaurants  17245309.0   \n",
       "28         C3  Real estate, ownership of dwelling & professio...  16224118.0   \n",
       "29         C3                                       Construction  15707921.0   \n",
       "30         C3                                     Other services  12815417.0   \n",
       "31         C3  Transport, storage, communication & services r...  12231484.0   \n",
       "32         C3                               Mining and quarrying  10835771.0   \n",
       "33         C3                              Public administration   7927173.0   \n",
       "34         C3                                 Financial services   5766226.0   \n",
       "35         C3  Electricity, gas, water supply & other utility...   4604774.0   \n",
       "\n",
       "    Sub-Sector%  Cumulative%  \n",
       "25        23.73        23.73  \n",
       "26        12.38        36.11  \n",
       "27         9.69        45.80  \n",
       "28         9.12        54.92  \n",
       "29         8.83        63.75  \n",
       "30         7.20        70.95  \n",
       "31         6.87        77.82  \n",
       "32         6.09        83.91  \n",
       "33         4.45        88.36  \n",
       "34         3.24        91.60  \n",
       "35         2.59        94.19  "
      ]
     },
     "execution_count": 643,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add the fields % of GSDP contribution and cumulative % to state_C3_df\n",
    "state_C3_df['Sub-Sector%'] = round((state_C3_df['Amount']/(C3_GSDP_total))*100,2)\n",
    "state_C3_df['Cumulative%'] = state_C3_df['Sub-Sector%'].cumsum()\n",
    "state_C3_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 644,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bunny\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\Bunny\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Ctgy</th>\n",
       "      <th>Sub-sector</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Sub-Sector%</th>\n",
       "      <th>Cumulative%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>C4</td>\n",
       "      <td>Agriculture, forestry and fishing</td>\n",
       "      <td>56735044.0</td>\n",
       "      <td>24.32</td>\n",
       "      <td>24.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>C4</td>\n",
       "      <td>Trade, repair, hotels and restaurants</td>\n",
       "      <td>27221645.0</td>\n",
       "      <td>11.67</td>\n",
       "      <td>35.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>C4</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>24937531.0</td>\n",
       "      <td>10.69</td>\n",
       "      <td>46.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>C4</td>\n",
       "      <td>Real estate, ownership of dwelling &amp; professio...</td>\n",
       "      <td>24018199.0</td>\n",
       "      <td>10.30</td>\n",
       "      <td>56.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>C4</td>\n",
       "      <td>Construction</td>\n",
       "      <td>22632766.0</td>\n",
       "      <td>9.70</td>\n",
       "      <td>66.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>C4</td>\n",
       "      <td>Transport, storage, communication &amp; services r...</td>\n",
       "      <td>16082074.0</td>\n",
       "      <td>6.89</td>\n",
       "      <td>73.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>C4</td>\n",
       "      <td>Other services</td>\n",
       "      <td>15541085.0</td>\n",
       "      <td>6.66</td>\n",
       "      <td>80.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>C4</td>\n",
       "      <td>Public administration</td>\n",
       "      <td>13189552.0</td>\n",
       "      <td>5.65</td>\n",
       "      <td>85.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>C4</td>\n",
       "      <td>Financial services</td>\n",
       "      <td>7654183.0</td>\n",
       "      <td>3.28</td>\n",
       "      <td>89.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>C4</td>\n",
       "      <td>Mining and quarrying</td>\n",
       "      <td>6096419.0</td>\n",
       "      <td>2.61</td>\n",
       "      <td>91.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>C4</td>\n",
       "      <td>Electricity, gas, water supply &amp; other utility...</td>\n",
       "      <td>4282774.0</td>\n",
       "      <td>1.84</td>\n",
       "      <td>93.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   State_Ctgy                                         Sub-sector      Amount  \\\n",
       "37         C4                  Agriculture, forestry and fishing  56735044.0   \n",
       "38         C4              Trade, repair, hotels and restaurants  27221645.0   \n",
       "39         C4                                      Manufacturing  24937531.0   \n",
       "40         C4  Real estate, ownership of dwelling & professio...  24018199.0   \n",
       "41         C4                                       Construction  22632766.0   \n",
       "42         C4  Transport, storage, communication & services r...  16082074.0   \n",
       "43         C4                                     Other services  15541085.0   \n",
       "44         C4                              Public administration  13189552.0   \n",
       "45         C4                                 Financial services   7654183.0   \n",
       "46         C4                               Mining and quarrying   6096419.0   \n",
       "47         C4  Electricity, gas, water supply & other utility...   4282774.0   \n",
       "\n",
       "    Sub-Sector%  Cumulative%  \n",
       "37        24.32        24.32  \n",
       "38        11.67        35.99  \n",
       "39        10.69        46.68  \n",
       "40        10.30        56.98  \n",
       "41         9.70        66.68  \n",
       "42         6.89        73.57  \n",
       "43         6.66        80.23  \n",
       "44         5.65        85.88  \n",
       "45         3.28        89.16  \n",
       "46         2.61        91.77  \n",
       "47         1.84        93.61  "
      ]
     },
     "execution_count": 644,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add the fields % of GSDP contribution and cumulative % to state_C3_df\n",
    "state_C4_df['Sub-Sector%'] = round((state_C4_df['Amount']/(C4_GSDP_total))*100,2)\n",
    "state_C4_df['Cumulative%'] = state_C4_df['Sub-Sector%'].cumsum()\n",
    "state_C4_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 645,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the C1 Category Sub-sector contributions\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "#Generate a category style bar plot, with Sub-Sector% as x-axis and Sub-Sector as y-axis\n",
    "sns.catplot(y='Sub-sector', x='Sub-Sector%',data=state_C1_df[:7] , kind='bar', height=6, aspect=2)\n",
    "\n",
    "#Give Label to the y-axis\n",
    "plt.xlabel('% Contribution to GSDP')\n",
    "\n",
    "#Provide title to the plot\n",
    "plt.title('C1 States:Top Sub-sectors that contribute approx. 80% GSDP')\n",
    "\n",
    "#Display the plot visual with % Sub-sector contribution from C1 states\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 646,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the C2 Category Sub-sector contributions\n",
    "\n",
    "#Generate a category style bar plot, with Sub-Sector% as x-axis and Sub-Sector as y-axis\n",
    "sns.catplot(y='Sub-sector', x='Sub-Sector%',data=state_C2_df[:8] , kind='bar', height=6, aspect=2)\n",
    "\n",
    "#Give Label to the y-axis\n",
    "plt.xlabel('% Contribution to GSDP')\n",
    "\n",
    "#Provide title to the plot\n",
    "plt.title('C2 States:Top Sub-sectors that contribute approx. 80% GSDP')\n",
    "\n",
    "#Display the plot visual with % Sub-sector contribution from C2 states\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 647,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the C3 Category Sub-sector contributions\n",
    "\n",
    "#Generate a category style bar plot, with Sub-Sector% as x-axis and Sub-Sector as y-axis\n",
    "sns.catplot(y='Sub-sector', x='Sub-Sector%',data=state_C3_df[:8] , kind='bar', height=6, aspect=2)\n",
    "\n",
    "#Give Label to the y-axis\n",
    "plt.xlabel('% Contribution to GSDP')\n",
    "\n",
    "#Provide title to the plot\n",
    "plt.title('C3 States:Top Sub-sectors that contribute approx. 80% GSDP')\n",
    "\n",
    "#Display the plot visual with % Sub-sector contribution from C3 states\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 648,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the C4 Category Sub-sector contributions\n",
    "\n",
    "#Generate a category style bar plot, with Sub-Sector% as x-axis and Sub-Sector as y-axis\n",
    "sns.catplot(y='Sub-sector', x='Sub-Sector%',data=state_C4_df[:7] , kind='bar', height=6, aspect=2)\n",
    "\n",
    "#Give Label to the y-axis\n",
    "plt.xlabel('% Contribution to GSDP')\n",
    "\n",
    "#Provide title to the plot\n",
    "plt.title('C4 States:Top Sub-sectors that contribute approx. 80% GSDP')\n",
    "\n",
    "#Display the plot visual with % Sub-sector contribution from C1 states\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color=blue>We have plotted the top 80% contributing sub-sectors for all the four Categories.</font>\n",
    "## <font color=blue>Here are the take aways</font>\n",
    "\n",
    "### The top-four sub-sectors remain the same, although not is same order, for all 4 Categories. </font>\n",
    "-  <font color=blue>Real estate, ownership of dwelling & professio...</font>\n",
    "-  <font color=blue>Agriculture, forestry and fishing</font>\n",
    "-  <font color=blue>Trade, repair, hotels and restaurants</font>\n",
    "-  <font color=blue>Manufacturing</font>\n",
    "\n",
    "### <font color=blue>Category C1: Top-7 sub-sectors contribute > 80%</font>\n",
    "-  <font color=blue>The top-4 sub-sectors have almost same % contribution (ranging between (13%-14%) towards GSDP.</font>\n",
    "-  <font color=blue>This indicates even distribution across multiple sub-sectors and no dependency on single sub-sector for GSDP.</font>\n",
    "\n",
    "### <font color=blue>Category C2: Top-8 sub-sectors contribute > 80%</font>\n",
    "-  <font color=blue>The top-4 sub-sectors have varying % of contribution (ranging between (10%-18%) towards GSDP.</font>\n",
    "-  <font color=blue>This indicates sub-sectors 2,3 & 4 are lagging and need to catchup with top sub-sector in contribution for effective growtg of GSDP per capita.</font>\n",
    "\n",
    "### <font color=blue>Category C3: Top-8 sub-sectors contribute > 80%</font>\n",
    "-  <font color=blue>The top sub-sectors Agriculture, forestry and fishing contributes 23% towards GSDP.</font>\n",
    "-  <font color=blue>This indicates sub-sectors 2,3 & 4,mainly 2nd sub-sector manufacturing, are lagging and need to catchup with top sub-sector in contribution for effective growtg of GSDP per capita.</font>\n",
    "\n",
    "### <font color=blue>Category C4: Top-7 sub-sectors contribute > 80%</font>\n",
    "-  <font color=blue>The top sub-sectors Agriculture, forestry and fishing contributes 24% towards GSDP.</font>\n",
    "-  <font color=blue>This indicates sub-sectors 2,3 & 4 sub-sectors are lagging and need to catchup with top sub-sector in contribution for effective growtg of GSDP per capita.</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## END OF SECTION PART 1B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 2: CORRELATION BETWEEN PER CAPITA GSDP & SCHOOL DROP OUT RATES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 649,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read the Drop out data from CSV and load into DataFrame\n",
    "dropouts_df=pd.read_csv(\"./Part2/rs_session243_au570_1.1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sl. No.</th>\n",
       "      <th>Level of Education - State</th>\n",
       "      <th>Primary - 2012-2013</th>\n",
       "      <th>Primary - 2014-2015</th>\n",
       "      <th>Primary - 2014-2015.1</th>\n",
       "      <th>Upper Primary - 2012-2013</th>\n",
       "      <th>Upper Primary - 2013-2014</th>\n",
       "      <th>Upper Primary - 2014-2015</th>\n",
       "      <th>Secondary - 2012-2013</th>\n",
       "      <th>Secondary - 2013-2014</th>\n",
       "      <th>Secondary - 2014-2015</th>\n",
       "      <th>Senior Secondary - 2012-2013</th>\n",
       "      <th>Senior Secondary - 2013-2014</th>\n",
       "      <th>Senior Secondary - 2014-2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A &amp; N Islands</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.69</td>\n",
       "      <td>5.56</td>\n",
       "      <td>7.20</td>\n",
       "      <td>9.87</td>\n",
       "      <td>14.14</td>\n",
       "      <td>15.87</td>\n",
       "      <td>16.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>3.18</td>\n",
       "      <td>4.35</td>\n",
       "      <td>6.72</td>\n",
       "      <td>3.36</td>\n",
       "      <td>3.78</td>\n",
       "      <td>5.20</td>\n",
       "      <td>12.72</td>\n",
       "      <td>12.65</td>\n",
       "      <td>15.71</td>\n",
       "      <td>0.35</td>\n",
       "      <td>11.79</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>15.16</td>\n",
       "      <td>10.89</td>\n",
       "      <td>10.82</td>\n",
       "      <td>7.47</td>\n",
       "      <td>5.59</td>\n",
       "      <td>6.71</td>\n",
       "      <td>12.93</td>\n",
       "      <td>14.49</td>\n",
       "      <td>17.11</td>\n",
       "      <td>5.11</td>\n",
       "      <td>17.07</td>\n",
       "      <td>18.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Assam</td>\n",
       "      <td>6.24</td>\n",
       "      <td>7.44</td>\n",
       "      <td>15.36</td>\n",
       "      <td>7.20</td>\n",
       "      <td>7.05</td>\n",
       "      <td>10.51</td>\n",
       "      <td>26.77</td>\n",
       "      <td>30.43</td>\n",
       "      <td>27.06</td>\n",
       "      <td>4.69</td>\n",
       "      <td>7.24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.98</td>\n",
       "      <td>4.08</td>\n",
       "      <td>30.14</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sl. No. Level of Education - State  Primary - 2012-2013  \\\n",
       "0       1              A & N Islands                 0.68   \n",
       "1       2             Andhra Pradesh                 3.18   \n",
       "2       3          Arunachal Pradesh                15.16   \n",
       "3       4                      Assam                 6.24   \n",
       "4       5                      Bihar                  NaN   \n",
       "\n",
       "   Primary - 2014-2015  Primary - 2014-2015.1  Upper Primary - 2012-2013  \\\n",
       "0                 1.21                   0.51                       1.23   \n",
       "1                 4.35                   6.72                       3.36   \n",
       "2                10.89                  10.82                       7.47   \n",
       "3                 7.44                  15.36                       7.20   \n",
       "4                 2.09                    NaN                        NaN   \n",
       "\n",
       "   Upper Primary - 2013-2014  Upper Primary - 2014-2015  \\\n",
       "0                       0.51                       1.69   \n",
       "1                       3.78                       5.20   \n",
       "2                       5.59                       6.71   \n",
       "3                       7.05                      10.51   \n",
       "4                       2.98                       4.08   \n",
       "\n",
       "   Secondary - 2012-2013  Secondary - 2013-2014  Secondary - 2014-2015  \\\n",
       "0                   5.56                   7.20                   9.87   \n",
       "1                  12.72                  12.65                  15.71   \n",
       "2                  12.93                  14.49                  17.11   \n",
       "3                  26.77                  30.43                  27.06   \n",
       "4                  30.14                  25.33                  25.90   \n",
       "\n",
       "   Senior Secondary - 2012-2013  Senior Secondary - 2013-2014  \\\n",
       "0                         14.14                         15.87   \n",
       "1                          0.35                         11.79   \n",
       "2                          5.11                         17.07   \n",
       "3                          4.69                          7.24   \n",
       "4                           NaN                           NaN   \n",
       "\n",
       "   Senior Secondary - 2014-2015  \n",
       "0                         16.93  \n",
       "1                           NaN  \n",
       "2                         18.42  \n",
       "3                           NaN  \n",
       "4                           NaN  "
      ]
     },
     "execution_count": 650,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropouts_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 651,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we are to JOIN and find correlation with Per Capita GSDP, we can drop the States / UTs with no info for Per Capita GSDP\n",
    "dropouts_df.drop([0,5,7,8,9,26,18,14,35,36],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 652,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sl. No.</th>\n",
       "      <th>Level of Education - State</th>\n",
       "      <th>Primary - 2012-2013</th>\n",
       "      <th>Primary - 2014-2015</th>\n",
       "      <th>Primary - 2014-2015.1</th>\n",
       "      <th>Upper Primary - 2012-2013</th>\n",
       "      <th>Upper Primary - 2013-2014</th>\n",
       "      <th>Upper Primary - 2014-2015</th>\n",
       "      <th>Secondary - 2012-2013</th>\n",
       "      <th>Secondary - 2013-2014</th>\n",
       "      <th>Secondary - 2014-2015</th>\n",
       "      <th>Senior Secondary - 2012-2013</th>\n",
       "      <th>Senior Secondary - 2013-2014</th>\n",
       "      <th>Senior Secondary - 2014-2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>3.18</td>\n",
       "      <td>4.35</td>\n",
       "      <td>6.72</td>\n",
       "      <td>3.36</td>\n",
       "      <td>3.78</td>\n",
       "      <td>5.20</td>\n",
       "      <td>12.72</td>\n",
       "      <td>12.65</td>\n",
       "      <td>15.71</td>\n",
       "      <td>0.35</td>\n",
       "      <td>11.79</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>15.16</td>\n",
       "      <td>10.89</td>\n",
       "      <td>10.82</td>\n",
       "      <td>7.47</td>\n",
       "      <td>5.59</td>\n",
       "      <td>6.71</td>\n",
       "      <td>12.93</td>\n",
       "      <td>14.49</td>\n",
       "      <td>17.11</td>\n",
       "      <td>5.11</td>\n",
       "      <td>17.07</td>\n",
       "      <td>18.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Assam</td>\n",
       "      <td>6.24</td>\n",
       "      <td>7.44</td>\n",
       "      <td>15.36</td>\n",
       "      <td>7.20</td>\n",
       "      <td>7.05</td>\n",
       "      <td>10.51</td>\n",
       "      <td>26.77</td>\n",
       "      <td>30.43</td>\n",
       "      <td>27.06</td>\n",
       "      <td>4.69</td>\n",
       "      <td>7.24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.98</td>\n",
       "      <td>4.08</td>\n",
       "      <td>30.14</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Chhatisgarh</td>\n",
       "      <td>4.14</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2.91</td>\n",
       "      <td>5.42</td>\n",
       "      <td>3.80</td>\n",
       "      <td>5.85</td>\n",
       "      <td>14.86</td>\n",
       "      <td>23.41</td>\n",
       "      <td>21.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sl. No. Level of Education - State  Primary - 2012-2013  \\\n",
       "1       2             Andhra Pradesh                 3.18   \n",
       "2       3          Arunachal Pradesh                15.16   \n",
       "3       4                      Assam                 6.24   \n",
       "4       5                      Bihar                  NaN   \n",
       "6       7                Chhatisgarh                 4.14   \n",
       "\n",
       "   Primary - 2014-2015  Primary - 2014-2015.1  Upper Primary - 2012-2013  \\\n",
       "1                 4.35                   6.72                       3.36   \n",
       "2                10.89                  10.82                       7.47   \n",
       "3                 7.44                  15.36                       7.20   \n",
       "4                 2.09                    NaN                        NaN   \n",
       "6                 1.42                   2.91                       5.42   \n",
       "\n",
       "   Upper Primary - 2013-2014  Upper Primary - 2014-2015  \\\n",
       "1                       3.78                       5.20   \n",
       "2                       5.59                       6.71   \n",
       "3                       7.05                      10.51   \n",
       "4                       2.98                       4.08   \n",
       "6                       3.80                       5.85   \n",
       "\n",
       "   Secondary - 2012-2013  Secondary - 2013-2014  Secondary - 2014-2015  \\\n",
       "1                  12.72                  12.65                  15.71   \n",
       "2                  12.93                  14.49                  17.11   \n",
       "3                  26.77                  30.43                  27.06   \n",
       "4                  30.14                  25.33                  25.90   \n",
       "6                  14.86                  23.41                  21.26   \n",
       "\n",
       "   Senior Secondary - 2012-2013  Senior Secondary - 2013-2014  \\\n",
       "1                          0.35                         11.79   \n",
       "2                          5.11                         17.07   \n",
       "3                          4.69                          7.24   \n",
       "4                           NaN                           NaN   \n",
       "6                           NaN                           NaN   \n",
       "\n",
       "   Senior Secondary - 2014-2015  \n",
       "1                           NaN  \n",
       "2                         18.42  \n",
       "3                           NaN  \n",
       "4                           NaN  \n",
       "6                          2.76  "
      ]
     },
     "execution_count": 652,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropouts_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "metadata": {},
   "outputs": [],
   "source": [
    "#In the Drop out data, the state names are slighlty different compared to the state name in Per Capita GSDP DataFrame\n",
    "# So rename the states inline with the Per Capita GSDP info\n",
    "\n",
    "dropouts_df.iloc[0,1]='Andhra_Pradesh'\n",
    "dropouts_df.iloc[1,1]='Arunachal_Pradesh'\n",
    "dropouts_df.iloc[4,1]='Chhattisgarh'\n",
    "dropouts_df.iloc[8,1]='Himachal_Pradesh'\n",
    "dropouts_df.iloc[12,1]='Madhya_Pradesh'\n",
    "dropouts_df.iloc[-5,1]='Tamil_Nadu'\n",
    "dropouts_df.iloc[-2,1]='Uttar_Pradesh'\n",
    "dropouts_df.iloc[-1,1]='Uttarakhand'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 654,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Sl. No.</th>\n",
       "      <th>Level of Education - State</th>\n",
       "      <th>Primary - 2012-2013</th>\n",
       "      <th>Primary - 2014-2015</th>\n",
       "      <th>Primary - 2014-2015.1</th>\n",
       "      <th>Upper Primary - 2012-2013</th>\n",
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       "      <th>Upper Primary - 2014-2015</th>\n",
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       "      <th>Secondary - 2013-2014</th>\n",
       "      <th>Secondary - 2014-2015</th>\n",
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       "      <th>Senior Secondary - 2014-2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>3.18</td>\n",
       "      <td>4.35</td>\n",
       "      <td>6.72</td>\n",
       "      <td>3.36</td>\n",
       "      <td>3.78</td>\n",
       "      <td>5.20</td>\n",
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       "      <td>11.79</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Arunachal_Pradesh</td>\n",
       "      <td>15.16</td>\n",
       "      <td>10.89</td>\n",
       "      <td>10.82</td>\n",
       "      <td>7.47</td>\n",
       "      <td>5.59</td>\n",
       "      <td>6.71</td>\n",
       "      <td>12.93</td>\n",
       "      <td>14.49</td>\n",
       "      <td>17.11</td>\n",
       "      <td>5.11</td>\n",
       "      <td>17.07</td>\n",
       "      <td>18.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Assam</td>\n",
       "      <td>6.24</td>\n",
       "      <td>7.44</td>\n",
       "      <td>15.36</td>\n",
       "      <td>7.20</td>\n",
       "      <td>7.05</td>\n",
       "      <td>10.51</td>\n",
       "      <td>26.77</td>\n",
       "      <td>30.43</td>\n",
       "      <td>27.06</td>\n",
       "      <td>4.69</td>\n",
       "      <td>7.24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.98</td>\n",
       "      <td>4.08</td>\n",
       "      <td>30.14</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>4.14</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2.91</td>\n",
       "      <td>5.42</td>\n",
       "      <td>3.80</td>\n",
       "      <td>5.85</td>\n",
       "      <td>14.86</td>\n",
       "      <td>23.41</td>\n",
       "      <td>21.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sl. No. Level of Education - State  Primary - 2012-2013  \\\n",
       "1       2             Andhra_Pradesh                 3.18   \n",
       "2       3          Arunachal_Pradesh                15.16   \n",
       "3       4                      Assam                 6.24   \n",
       "4       5                      Bihar                  NaN   \n",
       "6       7               Chhattisgarh                 4.14   \n",
       "\n",
       "   Primary - 2014-2015  Primary - 2014-2015.1  Upper Primary - 2012-2013  \\\n",
       "1                 4.35                   6.72                       3.36   \n",
       "2                10.89                  10.82                       7.47   \n",
       "3                 7.44                  15.36                       7.20   \n",
       "4                 2.09                    NaN                        NaN   \n",
       "6                 1.42                   2.91                       5.42   \n",
       "\n",
       "   Upper Primary - 2013-2014  Upper Primary - 2014-2015  \\\n",
       "1                       3.78                       5.20   \n",
       "2                       5.59                       6.71   \n",
       "3                       7.05                      10.51   \n",
       "4                       2.98                       4.08   \n",
       "6                       3.80                       5.85   \n",
       "\n",
       "   Secondary - 2012-2013  Secondary - 2013-2014  Secondary - 2014-2015  \\\n",
       "1                  12.72                  12.65                  15.71   \n",
       "2                  12.93                  14.49                  17.11   \n",
       "3                  26.77                  30.43                  27.06   \n",
       "4                  30.14                  25.33                  25.90   \n",
       "6                  14.86                  23.41                  21.26   \n",
       "\n",
       "   Senior Secondary - 2012-2013  Senior Secondary - 2013-2014  \\\n",
       "1                          0.35                         11.79   \n",
       "2                          5.11                         17.07   \n",
       "3                          4.69                          7.24   \n",
       "4                           NaN                           NaN   \n",
       "6                           NaN                           NaN   \n",
       "\n",
       "   Senior Secondary - 2014-2015  \n",
       "1                           NaN  \n",
       "2                         18.42  \n",
       "3                           NaN  \n",
       "4                           NaN  \n",
       "6                          2.76  "
      ]
     },
     "execution_count": 654,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropouts_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename the State columns\n",
    "dropouts_df.rename(columns={'Level of Education - State':'StateName'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 656,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>3.18</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Arunachal_Pradesh</td>\n",
       "      <td>15.16</td>\n",
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       "      <td>7.47</td>\n",
       "      <td>5.59</td>\n",
       "      <td>6.71</td>\n",
       "      <td>12.93</td>\n",
       "      <td>14.49</td>\n",
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       "      <td>5.11</td>\n",
       "      <td>17.07</td>\n",
       "      <td>18.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Assam</td>\n",
       "      <td>6.24</td>\n",
       "      <td>7.44</td>\n",
       "      <td>15.36</td>\n",
       "      <td>7.20</td>\n",
       "      <td>7.05</td>\n",
       "      <td>10.51</td>\n",
       "      <td>26.77</td>\n",
       "      <td>30.43</td>\n",
       "      <td>27.06</td>\n",
       "      <td>4.69</td>\n",
       "      <td>7.24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.98</td>\n",
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       "      <td>30.14</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>4.14</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2.91</td>\n",
       "      <td>5.42</td>\n",
       "      <td>3.80</td>\n",
       "      <td>5.85</td>\n",
       "      <td>14.86</td>\n",
       "      <td>23.41</td>\n",
       "      <td>21.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sl. No.          StateName  Primary - 2012-2013  Primary - 2014-2015  \\\n",
       "1       2     Andhra_Pradesh                 3.18                 4.35   \n",
       "2       3  Arunachal_Pradesh                15.16                10.89   \n",
       "3       4              Assam                 6.24                 7.44   \n",
       "4       5              Bihar                  NaN                 2.09   \n",
       "6       7       Chhattisgarh                 4.14                 1.42   \n",
       "\n",
       "   Primary - 2014-2015.1  Upper Primary - 2012-2013  \\\n",
       "1                   6.72                       3.36   \n",
       "2                  10.82                       7.47   \n",
       "3                  15.36                       7.20   \n",
       "4                    NaN                        NaN   \n",
       "6                   2.91                       5.42   \n",
       "\n",
       "   Upper Primary - 2013-2014  Upper Primary - 2014-2015  \\\n",
       "1                       3.78                       5.20   \n",
       "2                       5.59                       6.71   \n",
       "3                       7.05                      10.51   \n",
       "4                       2.98                       4.08   \n",
       "6                       3.80                       5.85   \n",
       "\n",
       "   Secondary - 2012-2013  Secondary - 2013-2014  Secondary - 2014-2015  \\\n",
       "1                  12.72                  12.65                  15.71   \n",
       "2                  12.93                  14.49                  17.11   \n",
       "3                  26.77                  30.43                  27.06   \n",
       "4                  30.14                  25.33                  25.90   \n",
       "6                  14.86                  23.41                  21.26   \n",
       "\n",
       "   Senior Secondary - 2012-2013  Senior Secondary - 2013-2014  \\\n",
       "1                          0.35                         11.79   \n",
       "2                          5.11                         17.07   \n",
       "3                          4.69                          7.24   \n",
       "4                           NaN                           NaN   \n",
       "6                           NaN                           NaN   \n",
       "\n",
       "   Senior Secondary - 2014-2015  \n",
       "1                           NaN  \n",
       "2                         18.42  \n",
       "3                           NaN  \n",
       "4                           NaN  \n",
       "6                          2.76  "
      ]
     },
     "execution_count": 656,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropouts_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data frame has two columns with 'Primary - 2014-2015' column. As per suggestions for TA on Upgrad Discussion Forum, we will be be using the first occurence of 'Primary - 2014-2015'. The second occurence of 'Primary - 2014-2015' will be dropped along with columns for other years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 657,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>StateName</th>\n",
       "      <th>Primary - 2014-2015</th>\n",
       "      <th>Upper Primary - 2014-2015</th>\n",
       "      <th>Secondary - 2014-2015</th>\n",
       "      <th>Senior Secondary - 2014-2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>4.35</td>\n",
       "      <td>5.20</td>\n",
       "      <td>15.71</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arunachal_Pradesh</td>\n",
       "      <td>10.89</td>\n",
       "      <td>6.71</td>\n",
       "      <td>17.11</td>\n",
       "      <td>18.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Assam</td>\n",
       "      <td>7.44</td>\n",
       "      <td>10.51</td>\n",
       "      <td>27.06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>2.09</td>\n",
       "      <td>4.08</td>\n",
       "      <td>25.90</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>1.42</td>\n",
       "      <td>5.85</td>\n",
       "      <td>21.26</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           StateName  Primary - 2014-2015  Upper Primary - 2014-2015  \\\n",
       "1     Andhra_Pradesh                 4.35                       5.20   \n",
       "2  Arunachal_Pradesh                10.89                       6.71   \n",
       "3              Assam                 7.44                      10.51   \n",
       "4              Bihar                 2.09                       4.08   \n",
       "6       Chhattisgarh                 1.42                       5.85   \n",
       "\n",
       "   Secondary - 2014-2015  Senior Secondary - 2014-2015  \n",
       "1                  15.71                           NaN  \n",
       "2                  17.11                         18.42  \n",
       "3                  27.06                           NaN  \n",
       "4                  25.90                           NaN  \n",
       "6                  21.26                          2.76  "
      ]
     },
     "execution_count": 657,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop extra columns\n",
    "dropouts_df.drop(['Sl. No.','Primary - 2012-2013','Primary - 2014-2015.1','Upper Primary - 2012-2013','Upper Primary - 2013-2014','Secondary - 2012-2013','Secondary - 2013-2014','Senior Secondary - 2012-2013','Senior Secondary - 2013-2014'],axis=1,inplace=True)\n",
    "dropouts_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 658,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Per_Capita_GSDP_2014-15</th>\n",
       "      <th>StateName</th>\n",
       "      <th>State_Ctgy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>271793.0</td>\n",
       "      <td>Goa</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>240274.0</td>\n",
       "      <td>Sikkim</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>164077.0</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>154778.0</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>153076.0</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Per_Capita_GSDP_2014-15    StateName State_Ctgy\n",
       "32                 271793.0          Goa         C1\n",
       "32                 240274.0       Sikkim         C1\n",
       "32                 164077.0      Haryana         C1\n",
       "32                 154778.0       Kerala         C1\n",
       "32                 153076.0  Uttarakhand         C2"
      ]
     },
     "execution_count": 658,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Use the GSDP Per Capita DataFrame created in part 1B\n",
    "GSDP_PerCapita_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 659,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merge the two Dataframes dropouts_df and GSDP_PerCapita_df to form a combined Dataframe with all required details for analysis\n",
    "drops_and_gsdp_df=pd.merge(GSDP_PerCapita_df,dropouts_df,how='inner',on='StateName')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 660,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Per_Capita_GSDP_2014-15</th>\n",
       "      <th>StateName</th>\n",
       "      <th>State_Ctgy</th>\n",
       "      <th>Primary - 2014-2015</th>\n",
       "      <th>Upper Primary - 2014-2015</th>\n",
       "      <th>Secondary - 2014-2015</th>\n",
       "      <th>Senior Secondary - 2014-2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>271793.0</td>\n",
       "      <td>Goa</td>\n",
       "      <td>C1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.07</td>\n",
       "      <td>11.15</td>\n",
       "      <td>13.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>240274.0</td>\n",
       "      <td>Sikkim</td>\n",
       "      <td>C1</td>\n",
       "      <td>4.57</td>\n",
       "      <td>1.57</td>\n",
       "      <td>15.89</td>\n",
       "      <td>11.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>164077.0</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>C1</td>\n",
       "      <td>0.41</td>\n",
       "      <td>5.81</td>\n",
       "      <td>15.89</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>154778.0</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>C1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.32</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>153076.0</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>C2</td>\n",
       "      <td>3.07</td>\n",
       "      <td>1.19</td>\n",
       "      <td>10.40</td>\n",
       "      <td>3.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Per_Capita_GSDP_2014-15    StateName State_Ctgy  Primary - 2014-2015  \\\n",
       "0                 271793.0          Goa         C1                 0.20   \n",
       "1                 240274.0       Sikkim         C1                 4.57   \n",
       "2                 164077.0      Haryana         C1                 0.41   \n",
       "3                 154778.0       Kerala         C1                  NaN   \n",
       "4                 153076.0  Uttarakhand         C2                 3.07   \n",
       "\n",
       "   Upper Primary - 2014-2015  Secondary - 2014-2015  \\\n",
       "0                       0.07                  11.15   \n",
       "1                       1.57                  15.89   \n",
       "2                       5.81                  15.89   \n",
       "3                        NaN                  12.32   \n",
       "4                       1.19                  10.40   \n",
       "\n",
       "   Senior Secondary - 2014-2015  \n",
       "0                         13.91  \n",
       "1                         11.76  \n",
       "2                          5.75  \n",
       "3                          0.47  \n",
       "4                          3.01  "
      ]
     },
     "execution_count": 660,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drops_and_gsdp_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create and visualise a scatter plot for each grade of school drop out % against the Per_Capita_GSDP_2014-15\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 661,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 779.175x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Define the dictionary for keywords to be used in plotting\n",
    "kws = dict(s=100, linewidth=0.5, edgecolor=\"k\")\n",
    "\n",
    "#Create a seaborn FacetGrid with Dataframe and plot State Category as hue\n",
    "g = sns.FacetGrid(drops_and_gsdp_df, hue=\"State_Ctgy\", palette=\"Set1\", height=5, aspect=2)\n",
    "\n",
    "#Label the plot\n",
    "plt.title(\"Per Capita GSDP vs. Primary Drop out %\")\n",
    "\n",
    "#Generate a scatter plot with Per Capita & Primary droput %\n",
    "g = (g.map(plt.scatter, \"Per_Capita_GSDP_2014-15\", \"Primary - 2014-2015\", **kws).add_legend())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  <font color=blue> From the Per Capita vs Primary Drop out % plot we see above, it is evident that for the states in C1 & C2 (which have higher Per Capita than C3 & C4) category, the drop out percentage is low at Primary class compared to the states in C3 & C4 categories. Therefore, with increase in Per Capita GSDP, the drop out percentage can be said to be coming down. </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 662,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 779.175x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a seaborn FacetGrid with Dataframe and plot State Category as hue\n",
    "g = sns.FacetGrid(drops_and_gsdp_df, hue=\"State_Ctgy\", palette=\"Set1\", height=5, aspect=2)\n",
    "\n",
    "#Label the plot\n",
    "plt.title(\"Per Capita GSDP vs. Upper Primary Drop out %\")\n",
    "\n",
    "#Generate a scatter plot with Per Capita & Primary droput %\n",
    "g = (g.map(plt.scatter, \"Per_Capita_GSDP_2014-15\", \"Upper Primary - 2014-2015\", **kws).add_legend())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  <font color=blue> From the Per Capita vs Upper Primary Drop out % plot we see above, it is evident that for the states in C2 category, the drop out percentage is low at Upper Primary class compared to the states in C3 & C4 categories. </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 663,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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22GN8+eWXtG3bFn9/f8qUKcPo0aMBeO2116hfvz5PP/30NT+T4cOHM2jQIM6ePYvVaqVixYr8+9//pkyZMtnezz59+jB27Fjat2+Pw+EgPDyc0NBQHnroIcaMGUO7du3IyMigefPmOV5ndHQ0hw8fpm3btpQtW5bq1asDULp0abp3706HDh3w9/fntttu48EHH+Tnn392ddu5WseOHVmxYkW2A2ljY2NJSEjAMAwcDgf33nsvs2bNyvLY2rVrM3LkSPr06YPD4cDX15f333/fNcA5ICCAbt26cerUKR566KEsr7Vnz55MmDCByMhIMjMzadCggWsAeP/+/Xn99ddZuHAh9erVo169eq7vCwkJYcCAAbz++us8+uij15x306ZNlCpVytWl6YUXXmDIkCGYpsnQoUPdXr+IyPVgmFm17YqI3AB33303mzdvvmLWHSl5MjMz6dOnD3//+99dLUiFaerUqZw6dYoRI0YUelkiIjcbdRMSEZGbxv79+2natCnlypUjLCzsRocjIlLsqWVARERERKSEUsuAiIiIiEgJpWRARERERKSEKjLJgGma2O32LOeyFhERERGRvCsyyUB6ejq7du3K80IuJcHu3btvdAhyA+i+lzy65yWT7nvJo3sunlRkkgFxT4vSlEy67yWP7nnJpPte8uieiycpGRARERERKaGUDIiIiIiIlFBKBkRERERESiglAyIiIiIiJZSSARERERGREkrJgIiIiIhICVWoycC7775LeHg4ERERfPjhhwBs2rSJdu3aERoaypQpUwqzeBERERERyYZXYZ1427ZtbNmyhYSEBDIzMwkPD6dp06YMHTqUjz/+mCpVqtCjRw82bNhAUFBQYYUhIiIiIiJuFFrLQOPGjZk7dy5eXl78/vvvOBwO/vjjD6pXr061atXw8vKiXbt2JCYmFlYIIiIiIiKSjUJrGQDw9vbmvffeY86cOYSFhXHixAkqVqzo2l+pUiV+++23PJ1z165d1zvMYmHHjh03OoRC5XA42Lp9Bxu+2U26acHHcBL0UH2aPNQQi6XkDn0p7vddrqV7XjLpvpc8uueFo2HDhjc6hJtOoSYDAH379qVbt2689NJLHDp0CMMwXPtM07zi69yoX78+NpvteodZpO3YsaNYP9ypqal07z+So/6BUK0thmFgmiaf7NpH8rZYZk0aRUBAwI0O0+OK+32Xa+mel0y67yWP7rl4UqFVqR44cIC9e/cC4OfnR2hoKFu3buXkyZOuY06ePEmlSpUKKwQpBpxOJ937j+TI7a0xKtZxJY+GYWBUrMOR21vTvf9InE7nDY5UREREpOgptGTgyJEjDBs2jPT0dNLT00lKSiI6OpqDBw/y888/43A4+OKLL3jssccKKwQpBlauXstR/0As3lm3Blm8bRz1u5tVa9Z5ODIRERGRoq/QugkFBQXxww8/EBkZidVqJTQ0lIiICMqXL09MTAx2u52goCDCwsIKKwQpBuISN0BA8+wPqliHxSuTadO6lWeCEhERESkmCnXMQExMDDExMVdsa9q0KQkJCYVZrBQjFx3kOK7EMAzsDg8FJCIiIlKMlNxpWKRI8LVeGmieHdM0sVk9FJCIiIhIMaJkQG5qHcOCIHVf9ged/JFObYI9E5CIiIhIMaJkQG5qbUJbcceF/+DMsGe535lh5460FFqHtPBwZCIiIiJFn5IBualZLBZmTRpF1WOrME+kuLoMmaaJeSKFqsdWMWvSqBK98JiIiIhIfhX6omMiBRUQEMDiOVNJXJ3E54nrsTvAZoVOkcG0DumjREBEREQkn5QMSJFgsVgIDwshPCzkRociIiIiUmwoGZBiw+FwkLg6ibhVG7jouDQTUcewINqEtlLrgYiIiEgWlAxIsZCamkr3/iM56h8IAc0xDAPTNNmVkMLsBUuZNWkUAQEBNzpMERERkZuKqkulyHM6nXTvP5Ijt7fGqFjHtUiZYRgYFetw5PbWdO8/EqfTeYMjFREREbm5KBmQIm/l6rUc9Q/E4m3Lcr/F28ZRv7tZtWadhyMTERERubkpGZAiLy5xAwT8LfuDKtZh8cpkzwQkIiIiUkQoGZAi76IDV9cgdwzDwO7wUEAiIiIiRYSSASnyfK24FiNzxzRNbFYPBSQiIiJSRCgZkCKvY1gQpO7L/qCTP9KpTbBnAhIREREpIpQMSJHXJrQVd1z4D84Me5b7nRl27khLoXVICw9HJiIiInJzUzIgRZ7FYmHWpFFUPbYK80SKq8uQaZqYJ1KoemwVsyaN0sJjIiIiIlfRomNSLAQEBLB4zlQSVyfxeeJ67A6wWaFTZDCtQ/ooERARERHJgpIBKTYsFgvhYSGEh4Xc6FBEREREigRVl4qIiIiIlFBKBkRERERESiglAyIiIiIiJZTGDEiOHA4HyStWsPmzz7BcSMPp70ezqCiCw8M1MFdERESkCFMyINlKTU1lQpcuNN+zlxfsdgwMTEx2bvyaQe+/z6DYWAICAm50mCIiIiKSD6rWFbecTicTunShx7ff0dCejoEBgIFBQ3s6Pb79jglduuB0Om9wpCIiIiKSH0oGxK3kFStovmcvfn8mAVfzw+DRPXtJTkz0cGQiIiIicj0oGRC3Ni1axIN2e7bHNLTb2bRggYciEhEREZHrScmAuGW5kObqGuSOgYElLc1DEYmIiIjI9aRkQNxy+vthYmZ7jImJ08/PQxGJiIiIyPWkZEDcahYVxU6bLdtjdthsNIuO9lBEIiIiInI9KRkQt4LDw/mq7j2kuWkdSMNkY917CA4L83BkIiIiInI9aJ0BcctisTAoNpYJXbrw6J69NPzLOgM7bDY21r2HQbGxWnhMJA8cDgeJSYkkJC8lnXR88KF9cCRhrcL0uyQiIh6nZECyFRAQwISEBJJXruTDhQuxpKXh9POjWXQ0E8L08iKSF6mpqfQa0ZPTNU9ha+yDYRiYpsk7P0wmNv5DZrwxU4v4iYiIRykZkBxZLBZaRkTQMiLiRociUmQ5nU56jejJ+aCz+P5lLI5hGPjWtnG+2ll6jejJgmkLlWSLiIjH6BNHRMQDEtcmcrrmKaw2a5b7rTYrp2ucYnXSag9HJiIiJZlaBtxwOBwkr1jB5s8+w3IhDae/H82ioggOD1etnYjk2dLkeGyNfbI9xlbbhyXrlhAWokH5IiLiGUoGspCamsqELl1ovmcvL/xl0OzOjV8z6P33GRQbq369IpIn6aRjGDks4mcYpJP9qt8iIiLXk6q4r+J0OpnQpQs9vv2OhvZ01wq8BgYN7en0+PY7JnTpgtPpvMGRikhR4oMPppnDIn6miQ/Zr+0hIiJyPSkZuEryihU037MXP7KuwfPD4NE9e0lOTPRwZCJSlLUPjsR+ID3bY+z70+nQooOHIhIREVEycI1NixbxoD37ZvqGdjubFizwUEQiUhyEtQqj7E/lcNgdWe532B2UPViO0JahHo5MRERKMiUDV7FcSHN1DXLHwMCSluahiESkOLBYLMx4YyalNtzKxX12V5ch0zS5uM9OqQ23MuONmZqgQEREPEoDiK/i9PfDxMw2ITAxcfr5eTAqESkOAgICWDBtIavWriI+OZ507Phgo0OLDoS+GqpEQEREPE7JwFWaRUWxc+PXNLS779u7w2ajWXS0B6MSkeLCYrHQJrQNbULb3OhQRERE1E3oasHh4XxV9x7SyHrWjzRMNta9h+AwzQMuIiIiIkWbWgauYrFYGBQby4QuXXh0z14a/mWdgR02Gxvr3sOg2Fg154vIFRwOB4lJiSQkLyWddHzwoX1wJGGtwvT3QkREblpKBrIQEBDAhIQEkleu5MOFC7GkpeH086NZdDQTwvTBLiJXSk1NpdeInpyueQpbYx8Mw8A0Td75YTKx8R8y442ZWqhQRERuSoWaDEybNo2VK1cCEBQUxMCBAxkyZAg7duzA788BuH369CEkJKQww8gXi8VCy4gIWkZE3OhQROQm5nQ66TWiJ+eDzuJr+/8FwwzDwLe2jfPVztJrRE8WTFuoigQREbnpFFoysGnTJjZu3MiSJUswDIMXX3yRNWvWsGvXLj755BMqVapUWEWLhzgcDpJXrGDzZ59huZCG09+PZlFRBIeH66VHSozEtYmcrnnqikTgr6w2K6drnGJ10mrCQjTWSEREbi6FlgxUrFiRwYMH4+PjA0CtWrU4duwYx44dY+jQofz222+EhITQp08fvTgWQampqUzo0oXme/bywl/GVezc+DWD3n+fQbGx6hYhJcLS5HhsjX2yPcZW24cl65YoGRARkZuOYV5e+aYQHTp0iKeffppPP/2Ut99+m5EjR3LrrbfSo0cP2rZtS1RUVI7nsNvt7Nq1q7BDlVxwOp3MGTiQV3/ch18W6zGkYfJ2nb/RdeJEJXpS7I2b8ybpIRdzPM5nrS9DXhjqgYhERMSdhg0b3ugQbjqFPoB437599OjRg4EDB1KzZk2mT5/u2vfcc88RHx+fq2Tgsvr162Nz0xxfUu3YscOjD3fSF1/Q5ufDWSYCAH4YhP18mD9OnqRleLjH4ippPH3fJWsVFlfgmHkEw8hmoULTpMKtAQW+X7rnJZPue8mjey6eVKjVtjt27KBLly68+uqrdOjQgZSUFFatWuXab5omXl6a0Kio2bRoEQ/a7dke09BuZ9OCBR6KSOTGaR8cif2A+0UKAez70+nQooOHIhIREcm9QksGjh8/Tu/evZk0aRIRf87IY5omb775JmfOnCEjI4OFCxfelDMJSfYsF9Iw3LQKXGZgYElL81BEIjdOWKswyv5UDofdkeV+h91B2YPlCG0Z6uHIREREclZo1fKzZ8/Gbrczfvx417bo6Gi6d+/O008/TWZmJqGhobRt27awQpBC4vT3w8TMNiEwMXH+OX2sSHFmsViY8cbMS+sM1DiFrfb/rzNg359O2YPlmPHGTI2fERGRm1KhJQPDhg1j2LBhWe7r3LlzYRUrHtAsKoqdG7+mod1914gdNhvNoqM9GJXIjRMQEMCCaQtZtXYV8cnxpGPHBxsdWnQg9NVQJQIiInLTUod9ybPHWrfmxSqV2XToEDYgE7gfgyaABYM0TDbWvYcJYZpGUUoOi8VCm9A2tAltc6NDERERyTUlA5Inl9cX6HjsVxpiuNYX2AG8hUljHx++r1eXQbGxqg0VERERuckpGZBcczqdTOjShR7ffvfntKKXxgwYGDQC6gET77idGfHxmiVKREREpAhQ1a3kWvKKFTTfszfb9QUijh1nw+rVHo5MRERERPJDyYDkmtYXEBERESlelAxIrml9AREREZHiRcmA5Nrl9QWyo/UFRERERIoOJQOSa82iothps2V7jNYXEBERESk6lAxIrgWHh/NV3XtIc9M6cHl9gWCtLyAiIiJSJCgZkFyzWCwMio3lXw/czzc2H1eXIROTb2w+/OuB+7W+gIiIiEgRosngJU8CAgKYkJBA8sqVfLhwIZa0NJx+fjSLjmZCWJgSAREREZEixG0yMGfOHLp27Yrdbuftt99m7dq1eHt7Ex4eTq9evfD29vZknHITsVgstIyIoGVExI0ORUREREQKwG017rJlywCYPHkyv/76K1OnTmXSpEkcPnyY0aNHeyxAEREREREpHDl2E9q0aROff/45Pj4+AEycOJHw8PBCD0xERERERAqX25aBjIwMHA4HFSpUwP6XVWczMzOxWq0eCU5ERERERAqP22TAx8eHpk2bcvDgQcaMGQPAN998w3PPPUdwcLDHAhQRERERkcLhtptQXFwcaWlp/PDDD5w/fx6As2fP0qFDB6K1qJSIiIiISJGX7ZgBPz8/Hn74YdfXahEQERERESk+NCm8iIiIiEgJ5bZl4PI4AXeGDRt23YMRERERERHPcdsycOutt/L5559TpkwZypYte80/EREREREp2ty2DLz88sscOHCA8uXL07lzZ0/GJCIiIiIiHpDtmIHXXnuNU6dOeSoWERERERHxoGxnE7rtttvo06ePp2IRkT85HA4SVycRt2oDFx3ga4WOYUG0CW2FxaJx/yIiInJ9ZJsM/Pbbb6xcuZLffvsNi8VC5cqVefzxx6lWrZqn4hMpcVJTU+nefyRH/QMhoDmGYWCaJrsSUpi9YCmzJo3OXBS1AAAgAElEQVQiICDgRocpIiIixYDbKsa1a9fy5JNPkpKSgs1mw8vLiz179vDMM8/wxRdfeDJGkRLD6XTSvf9IjtzeGqNiHQzDAMAwDIyKdThye2u69x+J0+m8wZGKiIhIceC2ZeDtt99m4cKFVKlS5Yrtv/76K127dqVt27aFHpxISbNy9VqO+gdi8bZlud/ibeOo392sWrOOSgHlPBydiIiIFDduWwYMw7gmEQCoXLmy+iyLFJK4xA0Q8LfsD6pYh8Urkz0TkIiIiBRrblsG7rnnHkaMGEFUVBRVqlTBMAxOnDjBwoULqVu3ridjFCkxLjpwdQ1yxzAM7A4PBSQiIiLFmttkYOzYsbz33nu8+uqrHD9+HNM0uf3222nRogUDBgzwZIwiJYavFUzTzDYhME0Tm9WDQYmIiEix5TYZ8PX1ZeDAgQwcONCT8YjctDwx3WfHsCB2JaRAxTruDzr5I50ig69LeSIiIlKyuX2DOXXqFIMHD+a5555j3rx5V+yLiYkp9MBEbiapqak8+Y++vLEshV2lm3Og/GPsKt2cNxJS6NQ1htTU1OtSTpvQVtxx4T84M+xZ7ndm2LkjLYXWIS2uS3kiIiJSsrlNBkaOHEmFChV45plnmD9/PqNGjXLt++WXXzwSnMjNwJPTfVosFmZNGkXVY6swT6RgmiZwqWuQeSKFqsdWMWvSKA3iFxERkevC7RvFoUOHGDBgAG3atOGTTz7hm2++ITY21oOhidwc8jLd5/UQEBDA4jlTGdn+Hur/8RW1//sl9f/4itcj67J4zlQtOCYiIiLXjdsxA06nk8zMTLy8vChTpgzTp08nOjqa2rVr5zjbiUhxcmm6z+bZH/TndJ9tWre6LmVaLBbCw0IIDwu5LucTERERyYrbloHHHnuMbt26sXfvXgDuvPNOJk+eTL9+/Thy5IjHAhS50TTdp4iIiBRXbpOBAQMGEB4eTnp6umtbkyZNiI2NpUGDBh4JTuRmcHm6z+xouk8REREpirJdgfjJJ5/kvvvuc21LTk6mXr16zJ492yPBidwMOoYFQeq+7A86+SOd2mi6TxERESla3I4ZyMp7771HcLBeeOTm4Ym5/9uEtmL2gqUcyaie5SBiZ4adqmkptA7pc13KExEREfGUPL0t5dRVQsSTPDX3v6b7FBERkeIqTy0DlSpVKqw4RPLkr3P//7W23jAMqFiHIxnV6d5/JIvnTL0uL+mXp/tMXJ3E54nrsTvAZoVOkcG0DumjREBERESKpDwlA2PHji2sOETyJC9z/2u6TxEREZGs5ak6s3v37oUVh0ieXJr7/2/ZH/Tn3P8iIiIikjW3LQMPPvjgNdsuXrzIAw88gGEY7Ny5s1ADE8mO5v4XERERKTi3LQMTJ04kICCAt99+m2XLlpGQkECNGjX44osvWLZsmSdjFLmG5v4XERERKTi3yUCrVq2YNWsWM2fOZNu2bVStWhUfHx/uuOMO7rjjjlydfNq0aURERBAREcHEiRMB2LRpE+3atSM0NJQpU6Zcn6uQEkdz/4uIiIgUXLZjBu666y4++ugjNm/ezNChQ8nMzMz1iTdt2sTGjRtZsmQJ8fHx7N69my+++IKhQ4cyY8YMVqxYwa5du9iwYUOBL0JKnjahrbjjwn9wZtiz3O/MsHNHWgqtQ1p4ODIREREp6uLi4oiMjKRdu3Y888wz7N27lx9++IF//etf2X7flClT2Lcvh8pKNz7//HOeeOIJ2rdvT+fOnfnPf/4DkKtyCyLHAcR+fn5MnDiRevXq4evrm+sTV6xYkcGDB+Pj44O3tze1atXi0KFDVK9enWrVquHl5UW7du1ITEws0AVIyaS5/0VERKQwHD9+nA8//JAFCxawbNkyBgwYQL9+/di/fz+nT5/O9nt37tyZr3W5li9fzpo1a/j4449ZunQp/fr1o2fPnqSnp+eq3IIwTA+sJHbo0CGefvppnn32WQ4ePMikSZOAS60HH3zwAXPmzMnxHHa7nV27dhV2qFLEOJ1OtmzfwYbtu8gwLXgbTh5vfC8PN3pQiYCIiIhcoWHDhjkec+DAAXr37s28efMoX748AElJSYwePRq73c6gQYMIDAxk7NixnD17FofDweTJkzlw4ABDhgzhtttuY9GiRWzdupWZM2eSmZlJq1at6Nu3r9syO3XqxIQJE6hVq5Zr27p163jooYdo166dq9z777+fV155BYfDQWBgIIZhcP/993P48GEGDhwIQMeOHfn3v/9NhQoVcvUzydM6A/mxb98+evTowcCBA7FarRw6dMi1zzTNHGeEuVr9+vWx2bKeW76k2rFjR64e7uLqoYceIuZGB3EDlPT7XhLpnpdMuu8lj+75jVWrVi0eeughHnvsMRo2bEhQUBBRUVH07duXffv2ERkZyahRoxg+fDh16tRh3rx5zJ8/nxEjRvDpp58yfPhwMjIymDt3LgsWLMDLy4uYmBi2bNlCkyZNsizz559/pkaNGldsa9HiUlfnv5bbo0cP+vTpQ4sWLXjllVfw8fEhLCyMp556ioEDB/LTTz9RtmzZXCcCkE0yEBkZicPhfl7G3MwotGPHDvr27cvQoUOJiIhg27ZtnDx50rX/5MmTWtVYRERERG4qo0ePpkuXLiQnJ5OYmMjixYt5/vnnXfuHDBlCUlISiYmJbNq06ZoX+e+//559+/bx5JNPApCWlsaBAwfcJgNeXl656tHw/fffu8YPREREsGbNGsqWLUuNGjX47rvv2LhxI+Hh4Xm6VrfJQP/+/XnllVeYMGECpUqVytNJ4VJ/q969ezNlyhSaNm0KwH333cfBgwf5+eefqVq1Kl988QVPPPFEns8tIiIiIlIYNmzYgMPhoEWLFtSqVYt//OMfPPHEE/zxxx+uY2JiYqhVqxbNmjXjzjvvZMuWLVecw+l0EhwczLhx4wA4ffp0tmNvq1evzsGDB69IKsaMGcNTTz11xXF/TRj+2rumXbt2rFq1iq1btxIbG5un63Wbgjz66KM888wzbN26lcaNG1/zLyezZ8/Gbrczfvx42rdvT/v27YmLi2P8+PHExMQQHh5OzZo1CQsLy1PAIiIiIiKFxWazMWXKFFJTUwH473//y/nz56lUqZJrZs3vvvuOmJgYmjVrxoYNG3A6nQBYrVYcDgf33nsvmzZtIjU1lYyMDHr06MG3337rtszOnTszfvx4Lly4AMBXX33FunXruPPOO7Fara5yGzRo4JqJc82aNa6EoEWLFqxbt47KlStTunTpPF1vtmMGevbsyVdffZWnE142bNgwhg0bluW+hISEfJ1TRERERKQwNWnShOjoaDp37ozVasXHx4chQ4ZQrVo1pk6dSo0aNejatSt///vf8fb2plGjRhw8eBCAZs2a8c9//pOPPvqIV199lRdeeIHMzExCQ0NdPWWy0q5dO37//XdXS0CZMmWYOXMmNpuN+vXru8odOnQoAwYM4N1336Vy5cpUrFgRuDT7Z506dfJVye6R2YSuh8uzCWkA8bU00Khk0n0veXTPSybd95JH91zcmTt3LmFhYVSqVInx48dTuXJlnn/+ec6fP88zzzzDwoUL8fPzy9M5s20ZSExMJCEhgV9//RWr1UrlypVp2bIlkZGRBboQEREREZGSZOfOnYwaNeqa7c2bN6d///65OkeVKlX4n//5H7y9valevToxMTEcPHiQ6OhoXn311TwnApBNMjBt2jS2bt1Kx44dqVy5MqZpcuLECeLi4ti3bx8DBgzIc2EiIiIiIiXRgw8+yNKlSwt0jpCQEEJCQq7YVrNmTbZt25bvc7pNBpYtW8ayZcvw8fG5Ynt4eDjt2rVTMiAiIiIiUsS5nU3IYrFkuZyyw+HA29u7UIMSEREREZHCl+2iY08//TRt27alcuXKGIbBiRMnWLZsGX//+989GaOIiIiIiBQCt8lAjx49CAwMZO3atWzatAnTNKlSpQq9e/cmODjYkzGKiIiIiEghyHY2oaCgIB555BG8vK487MyZM5QpU6ZQAxMRERERuVk5HA6SV6xg82efYbmQhtPfj2ZRUQSHh1+xUvDNzm0ysGvXLmJiYkhNTaVly5aMGTOGW265BYAuXbqwZMkSjwUpcrNzOBwkrk4ibtUGLjrA1wodw4JoE9qqSP1BEBERkZylpqYyoUsXmu/Zywt2OwYGJiY7N37NoPffZ1BsLAEBAQUu59y5c7z99tts374dq9VK6dKlGTx4MPXq1XMdM2HCBE6dOsX48ePzVYbbt5SxY8fy+uuvs379ery8vHjxxRdJT08HyHJgsUhJlZqaypP/6Msby1LYVbo5B8o/xq7SzXkjIYVOXWNcy5mLiIhI0ed0OpnQpQs9vv2OhvZ0DAwADAwa2tPp8e13TOjSBafTWeByunXrRpkyZYiPj2fp0qX07t2bbt26cerUKQA2b95c4Ap6t8nAxYsXCQoKokKFCkyaNIlKlSoxZMiQAhUmUtw4nU669x/JkdtbY1Ssg2H8+QfBMDAq1uHI7a3p3n9kgf8giIiIyM0hecUKmu/Zi9+fScDV/DB4dM9ekhMTC1TO1q1bOX78OH379nV12W/SpAnjxo3D6XRy+vRppkyZwksvvVSgctwmA06nk99//9319YQJE9i/fz/Tp093vfCIlHQrV6/lqH8gFm9blvst3jaO+t3NqjXrPByZiIiIFIZNixbxoN2e7TEN7XY2LVhQoHL27NlDYGDgNd2NL1fWjxgxgn79+lG6dOkCleM2GejatSuRkZFs2LABAD8/P2bOnElcXBw//vhjgQoVKS7iEjdAwN+yP6hiHRavTPZMQCIiIlKoLBfSXF2D3DEwsKSlFawciwWbLevKxs8++4wqVarQtGnTApUB2Qwgbt++PQ0aNLhiBeLbb7+dhIQEPv300wIXLFIcXHSQY0uZYRjYHR4KSERERAqV098PEzPbhMDExOnnV6By6tevz7x58zBN84p3jcmTJzN79mxq1KhB+/btOXPmDBcuXODNN99k6NCheS4n26lFa9Socc22UqVK0b179zwXJFIc+Vq55pf0aqZpYrN6MCgREREpNM2ioti58Wsa2tPdHrPDZqNZdHSBymnUqBEVKlRg2rRp9OrVC6vVyldffUVcXBwbNmxwzVYUFxfHtm3b8pUIQDbdhEQkZx3DgiB1X/YHnfyRTm20UJ+IiEhxEBwezld17yGNrGfXTMNkY917CA4LK1A5hmEwY8YMDh8+TNu2bWnXrh3//ve/mTVr1nWZtvSybFsGRCR7bUJbMXvBUo5kVM9yELEzw07VtBRah/S5AdGJiIjI9WaxWBgUG8uELl14dM9eGv5lnYEdNhsb697DoNjY67LOUPny5XnrrbeyPaZjx4507Ngx32XkKRnYvXv3FYsciJR0FouFWZNG0b3/SI763Q1/Ti9qmiac/JGqaSnMmjRKC4+JiIgUIwEBAUxISCB55Uo+XLgQS1oaTj8/mkVHMyEsrEh97ucpGRg2bJhWHha5SkBAAIvnTCVxdRKfJ67H7gCbFTpFBtM6pE+R+oMgIiIiuWOxWGgZEUHLiIgbHUqBqJuQyHVgsVgIDwshPCzkRociIiIikmt5SgZMM+uBEiJSMjgcDhKTEklIXko66fjgQ/vgSMJaFa0mUREREbkkT8lAfHx8YcUhIje51NRUeo3oyemap7A19nGNjXjnh8nExn/IjDdmXtfZDURERKTwqSpPRHLkdDrpNaIn54PO4lvb5lpXwTAMfGvbOB90ll4jeuJ0Om9wpCIiIpIXGjMgIjlKXJvI6Zqn8HWzLLrVZuV0jVOsTlpNWEjB5lUWEREpChwOB4mrk4hbtYGLjksLkXYMC6JNaKvr1nX23LlzvP3222zfvh2r1Urp0qUZPHgw9erV491332XVqlUYhkGnTp144YUX8lVGjslAZmYmXl7KGURKsqXJ8dga+2R7jK22D0vWLVEyICIixV5qauqlacX9AyGguavr7K6EFGYvWMqsSaMK3HXW6XTSrVs3Hn74YeLj4/Hy8mLLli1069aNd955hy1btpCQkEBmZibh4eEEBQVRs2bNPJeTY9oSHBzMlClTOHr0aL4uRESKvnTSXV2D3DEMg3TsHopIRETkxnA6nXTvP5Ijt7fG+HN9Ibj0OWhUrMOR21vTvf/IAned3bp1K8ePH6dv376uivkmTZowbtw4atWqxdy5c/Hy8uL333/H4XDg7++fr3JyTAYWLVqE1Wrl2WefpUePHqxfv16zComUMD745Ph7b5omPmTdjUhERKS4WLl6LUf9A7F4Z/2ZZ/G2cdTvblatWVegcvbs2UNgYOA1XY6CgoKoUKEC3t7evPfee0RERNC0aVNuu+22fJWTYzJQpUoV+vbtS1JSEk8++SSjR4+mZcuWfPDBB6Snp+erUBEpWtoHR2I/kP3vu31/Oh1adPBQRCIiIjdGXOIGCPhb9gdVrMPilckFKsdisWBzM1bvsr59+7J582aOHz/OokWL8ldObg46cOAAEydO5PXXX+eee+5h2LBhHD16lJdffjlfhYpI0RLWKoyyP5XDYXdkud9hd1D2YDlCW4Z6ODIRERHPuuggV11n3Xxk5lr9+vXZs2fPNS3zkydPZt68eezduxcAPz8/QkNDSUlJyVc5OSYDTz/9NM8//zy+vr4sXryYadOm0aJFC4YPH86OHTvyVaiIFC0Wi4UZb8yk1IZbubjP7vrDZJomF/fZKbXhVma8MVMLj4mISLHna815IV7TNLFZC1ZOo0aNqFChAtOmTcPhuJRZfPXVV8TFxeHn58ewYcNIT08nPT2dpKQkGjZsmK9ycpwm6JFHHqFHjx54e3tfsd1isZCcXLDmDxEpOgICAlgwbSGr1q4iPjmedOz4YKNDiw6EvhqqREBEREqEjmFB7EpIgYp13B908kc6RQYXqBzDMJgxYwbjxo2jbdu2eHl5Ua5cOWbNmkXdunU5cuQIkZGRWK1WQkNDiYiIyFc5OSYDy5cvp0+fPlnuK1WqVL4KFZGiyWKx0Ca0DW1C29zoUERERG6INqGtmL1gKUcyqmc5iNiZYadqWgqtQ7J+f86L8uXL89Zbb2W5LyYmhpiYmAKXkWNV3t13382yZcs4duwYp0+fdv0TERERESlpLBYLsyaNouqxVZgnUq7oOmueSKHqsVXMmjSqyLSY59gykJSURGJi4hXbDMNwDVoQERERESlJAgICWDxnKomrk/g8cT12B9is0CkymNYhfYpMIgC5SAb+93//1xNxiIhIEeBwOFiTuJakhHU4000sPgat2rckJKxVkfrwE8krPftyNYvFQnhYCOFhITc6lALJMRlIT09nw4YNnD9/Hrj0y3D48GH69etX6MGJSME4HA4SVycRt2oDFx2XZkDoGBZEm1B9eEnepaamMqTXa1Q6U41atvoYhoFpmnz57jYWx37OuBljCQgIuNFhilx3evalOMsxGejXrx+//PILJ0+epG7dunz//fc0btzYE7GJSAGkpqbSvf9IjvoHQkBz14fXroQUZi9YyqxJo/ThJbnmdDoZ0us1Glx4GB9fH9d2wzC40/cuKl+4nSG9XuNfCzTFrBQvevaluMvxqd27dy9xcXG0bNmSoUOHMn/+fM6cOeOJ2EQkn5xOJ937j+TI7a0xKtZxLY5iGAZGxTocub013fuPxOl03uBIiyaHw8Hy1cvpMaQ7LwzpQo8h3VmxekWx/nmuSVxLpTPV8LH6ZLnfx+pDpTNVSVqd5OHIRAqXnn0p7nJMBipVqoSXlxd33XUXP/74I3/72984e/asJ2ITkXxauXotR/0Ds5zyDMDibeOo392sWrPOw5EVfampqTwdE827P0zhWOMj/P7wSY41PsI7P0wmus9TpKam3ugQC8XapUlUs1XP9phqtrtYHb/WQxGJeIaefXGnuFQM5ZgM+Pv7s2zZMgIDA1m5ciUpKSlcuHDBE7GJSD7FJW6AgL9lf1DFOixeqYUD88LpdNJrRE/OB53Ft7btihYX39o2zgedpdeInkXugyA3nOmm63rdMQwDpz37VTlFiho9+5IVT1UMnTt3jlGjRtG2bVvat2/Pc889x+7duwGYNm0aERERREREMHHixHyXkWMyMHz4cPbu3csjjzyCxWLh2WefpWvXrvkuUEQK30UHufrwsjs8FFAxkbg2kdM1T2F1s8a81WbldI1TrE5a7eHICp/Fx3DNpe2OaZpYbNk/dyJFjZ59uZqnKoacTifdunWjTJkyxMfHs3TpUnr37k23bt1YsWIFGzduZMmSJcTHx7N7927WrFmTr3JyTAZq1KjBwIEDMQyDd955h+3bt/PMM8/kqzAR8QxfK7n68HLzTituLE2Ox1Yr637Dl9lq+7Bk3RIPReQ5rdq35Bf7z9ke84v9EKGRrTwUkYhn6NmXq3mqYmjr1q0cP36cvn374uV1ac6fJk2aMG7cOAICAhg8eDA+Pj54e3tTq1Ytjh07lq9y3M4m9Nxzz2Vbszh37tx8FSgiha9jWBC7ElKgYh33B538kU6RwZ4LqhhIJz1XLS7p2D0UkeeEhLViceznVL5we5YDKdMd6Zwoc4SWoS1vQHQihUfPvlxtaXI8tsa5qxgKCwnLdzl79uwhMDDwmlmqgoKCrvj60KFDrFy5kvnz5+erHLctA88++yydO3emUqVK+Pv789xzz9GlSxfKlStH9erZD6QRkRurTWgr7rjwH5wZWb+UOjPs3JGWQuuQFh6OrGjzwSdXLS4+ZD1wuyizWCyMmzGWH/y3cvjiQdfPwTRNDl88yA/+Wxk3Y6ymVpRiR8++XM1TFUMWiwWbLfvPk3379tG1a1cGDhzIXXfdla9y3LYMtG7dGoDZs2ezYMEC10P++OOP89RTT+WrMBHxDIvFwqxJoy6tM+B3N/w5vahpmnDyR6qmpTBr0ih9eOVR++BI3vlhMr613f9xtu9Pp0OLDh6MynMCAgL414KZrF21ljVLk3DaL/WTDo1sRcvQlnqepNjSsy9/dbliKLuE4HpUDNWvX5958+ZdU9bkyZNp1qwZ3t7e9O3bl6FDhxIREZHvcnJcdOzUqVPY7Xb8/PwAOH/+fJ7WGTh37hzR0dG8//77VK1alSFDhrBjxw7X+fr06UNISNFexlnkZhQQEMDiOVNJXJ3E54nrsTvAZoVOkcG0DumjD698CGsVRmz8h5yvdjbLvqIOu4OyB8sR+mroDYjOMywWC6FtQgltU3yvUSQrevblMk9VDDVq1IgKFSowbdo0evXqhdVq5auvviIuLo6nnnqKJ554gilTptC0adMClZNjMtC2bVuioqIICQnBNE0SExOJiorK1cm///57hg0bxqFDh1zbdu3axSeffEKlSpXyHbSI5I7FYiE8LITwMCXc14PFYmHGGzPpNaInp2ucwlbbx9XiYt+fTtmD5ZjxhlYhFREpzjxVMWQYBjNmzGDcuHG0bdsWLy8vypUrx6xZs/jwww+x2+2MHz/edXx0dDRPP/10nsvJMRl4+eWXqVevHlu2bAFg8ODB1wxccGfRokWMHDmSgQMHApCWlsaxY8cYOnQov/32GyEhIfTpoxpKESk6AgICWDBtIavWriI+OZ507Phgo0OLDoS+Gqq/ZyIixZwnK4bKly/PW2+9dc32unXrMmzYsAKfH8AwcxoNBxw9epQzZ85cMXCuXr16uS6kRYsWzJ07F9M0GT9+PCNHjuTWW2+lR48erpaHnNjtdnbt2pXrMkVERERE/qphw4bX7VxOpzPriqGWRatiKMdk4N1332XOnDlUqFDh/7/JMEhKSsp1IZeTgapVq16xfc2aNcTHxzN9+vQcz3E5Gahfv36OI6tLmh07dlzXh1uKBt33kkf3vGTSfS95dM/Fk3LsJrR06VJWr17NbbfdVuDCUlJSOHTokGumItM0XYsoyI3ncDhIXrGCzZ99huVCGk5/P5pFRREcHl6kMlwRERERyZ0c38SrVKlyXRIBuPTy/+abb9KkSRP8/f1ZuHAhHToUzyn4iprU1FQmdOlC8z17ecFux8DAxGTnxq8Z9P77DIqNJSAg4EaHKSIiIiLXUY7JQNOmTZk4cSItW7bE19fXtT0vYwYuCwwMpHv37jz99NNkZmYSGhpK27Zt83weub6cTicTunShx7ff4YcBXJrL1sCgoT2dut9+x4QuXZiQkKAWAhEREZFiJMdkIC4uDoDExETXtryOGVi3bp3r/507d6Zz5855iVEKWfKKFTTfs/fPROBafhg8umcvyYmJtAwP93B0IiIiIlJYckwG/voiL8XTpkWLeMFuBzfJAEBDu50PFyxQMiAiIiLCpbGWaxLXkpSwDme6icXHoFX7loSEtbpuPSnOnTvH22+/zfbt27FarZQuXZrBgwe7euhcvbhvfuSYDFy4cIGJEyfy5ZdfkpmZySOPPMJrr73GLbfckq8C5eZjuZCGkU0iAJe6DFnS0jwUkYiIiMjNKzU1lSG9XqPSmWrUstV3rTPw5bvbWBz7OeNmjC3wWEun00m3bt14+OGHiY+Px8vLiy1bttCtWzeWL1/O4cOHr1ncNz9yTFvGjRtHeno606dPZ8aMGRiGwejRowtUqNxcnP5+mGS/3ISJidPPz0MRiYiIiNycnE4nQ3q9RoMLD3On710Yxp9jLQ2DO33vosGFhxnS6zWcTmeBytm6dSvHjx+nb9++rtk3mzRpwrhx43A6na7FfStVqlSgcnJsGfj+++9JSEhwfT1mzBgiIiIKVKjcXJpFRbFz49c0tKe7PWaHzUaz6GgPRiUiIiJy81mTuJZKZ6rh4+uT5X4fqw+VzlQlaXUSIWEh+S5nz549BAYGXtPlKCgoCICxY8fm+9x/lWPLgMPhuCKzcTqdWK3W61K43ByCw8P5qu49pLlpHUjDZGPdewgOC/NwZCIiIiI3l7VLk6hmq57tMdVsd7E6fm2ByrFYLB5ZaDfHZKBp0w/xQAMAACAASURBVKb885//ZPPmzWzevJlXXnmFxo0bF3pg4jkWi4VBsbH864H7+cbm4+oyZGLyjc2Hfz1wP4NiYzWtqIiIiJR4znTT1TXIHcMwcNr/r707j4uq3P8A/jkzMIOmiTaiprjrXaSb5JZZEtsIIYuGuaVxrfSClV4tt1LLJAtNTQ21Vxql3NwV3BBB3M2FTH7e1CxFRVIgURZhZpjz/P7gOomyWMIZhvm8Xy9fMuecec535pkD8z3PVnkX7Kq4ubnhxx9/hBBly5k/fz6+++67hyr7blV2E5oyZQqWLl2K+fPnw2w2o2/fvggPD6+2AKh20Ol0+CQ+Hik7d+KrtWuhKiqCXK8enhkyBJ/4+dldImA2m5GQmIxNu/ah2Aw4qYGBfh7w11ffDAFERERke1Sa0sHClSUEQgiotJUnDFXp3r07HnvsMSxZsgQRERFQq9U4cOAANm3ahJEjRz5U2XerMhkAgDZt2mD9+vXIzs7G9u3b4ejoWG0BUO2hUqngHRAAbzsfE5KTk4PRb8/E1fp/BXTPWWYIOB1/DivWxOGLeR9wNWYiIiI75RPsjf2fHUNrp7YVHnPFkA59iM9DnUeSJERHR2POnDno378/HBwc0LhxY3zxxRfV+j2kymTg/fffx+3btxEUFASVSoXU1FRkZGTgvffeq7YgiGoLWZYx+u2ZyHi8H1SOv/fTkyQJaNoZGaY2GP32TGxYuZgtBERERHbI188HG2I2ovntx6FR3z+I2Gg2IqtRBrz13g99riZNmmDu3LmVHvOwa4JV+W3mhx9+wPz58wEAjz32GD777DMcPXr0oU5KVFvtTEzC1fp/LZMI3E3lqMXVen/Brt1cjI+IiMgeqVQqzImORFr9o7hcfNHSp18IgcvFF5FW/yjmREfazE3DKlsGTCYTjEYjNJrSzKekpKTGgyKylk0J+wDdc5Uf1LQzNuxMgX+/h2v+IyIiItuk0+mwfM1SJO1Kwu64ZMiG0jEC+hAfeOu9bSYRAB4gGXj++efx6quvIjg4GJIkYdu2bZb5TYnqmmIzHmiGAINZoYCIiIioVlKpVND766H311s7lIdSZTIwadIkxMbGIjk5GQ4ODvD19cUQLj5FdZSTGg80Q4CWS21QLWA2m5GQnID4lDgYYYQGGgR7hsDPx/5mACMioj+nymRArVbjpZdeQq9evdCpUycYDAb+kaE6a6CfB07HnwOadq74oOyfEBriqVxQROXIyclBxIxw3GyfC21PjWXWq4Vp8xGz5StEz1rKWa+IiKhKDzSA2MfHB2PGjEFWVhaef/55fP/990rERqQ4f70PWt4+C9lkKHe/bDKgZdE59PP1Ujgyot/JsoyIGeEo9MiHU0etpSVLkiQ4ddSi0CMfETPCy6weT0REVJ4qk4GoqCjExMTA2dkZzZs3R1RUFCIjI5WIjUhxKpUKX8z7AK0yd0FknSszQ4DIOodWmbvwxbwP2DpGVpWQlICb7XOhrqC/mlqrxs12uUhMTlQ4MiIisjVVfqMpLi5Gx44dLY89PDxgNnP0JNVdOp0OG1Yuxszgv8Et7wA63tgPt7wDeD/k79iwcjG7XpDVxaVsgbbD/XNb303bUYPNezYrFBEREdmqKscMODg44NatW5Zm6AsXLtR4UETWplKp8IKfL17w87V2KET3McL4QLNeGVF+dzciIqI7qmwZCA8Px8svv4xr165hwoQJGDp0KMLDw5WIjYiIyqGBxtKF7V5CFij4oRB5X91G9uEbeGfMZOzakcjxA0REVK4qWwY8PT3Rvn17HDp0CLIsY+zYsejQoYMSsRERUTmCPUOwMG0+nDqWXSnblG/C7W+M6CmeQ4f6nSE5SxC/Cuz/7Bg2xGzEnOhIdnMjIqIyKm0ZEEKgpKQEbdq0QVBQEFxcXKBWc4J1IiJr8vPxg/OFxjDftfqdkAVuf2NEqHYkOj7ylzIzDLV2aot/3O6FqRHvsoWAiIjKqDAZ+Pnnn+Ht7Y0DBw6guLgYgwYNwoIFCzBixAgcOnRIyRiJiOguKpUK0bOW4pF9DVF83gAhBAr/7zZ6iuegUZc/sFij1sDlViskJyYrHC0REdVmFSYDUVFRGD9+PDw9PbF9+3YIIbB9+3asW7cOixcvVjJGIiK6h06nw5ola/HvJyfi8WOucNjjhA71K1ksD4Crti0StyQpFCEREdmCCpOBX3/9FUFBQQCAo0ePwsfHByqVCi1atEBBQYFiARIRUflUKhX89f5YPmc5nmj/jweaYUg2lD/wmIiI7FOFycDdiyqdPHkSPXr0sDw2GDhdHRFRbaLSSBXOMHSHEAIqbeUJAxER2ZcKk4FGjRrh7NmzOHHiBLKzsy3JwPfff49mzZopFiAREVXNJ9gbVwyXKj3miiEd+hAfhSIiIiJbUGEyMGHCBISFhSEsLAzjx49H/fr1sWLFCowZMwZvvfWWkjESEVEVfP18kNXoCoxmY7n7jWYjshplwFvvrXBkRERUm1W4zkDXrl2xf/9+FBcX49FHHwUAuLu7Y/369Wjbtq1S8RER0QNQqVSYEx2JqRHvwuVWK7hq20KSSrsOXTGkI6tRBuZER5bpAkpERFTpomMajQYaze/T1D311FM1HhAREf05Op0Oy9csRdKuJOyOS4ZsKB0joA/xgbfem4kAERHdp8oViImIyHaoVCro/fXQ++utHQoREdkA3iYiIiIiIrJTbBkgIiIiohpjNpuxOyEJyfF7IBsFVBoJPsHe8PXzYffFWoDJABERERHViJycnP9NbOCKDlo3y8QG+z87hg0xGzEnOhI6nc7aYdo1pmNEREREVO1kWcbUiHfxj9u90NqprWWVdEmS0NqpLf5xuxemRrwLWZatHKl9YzJARERERNVud0ISXG65QqPWlLtfo9bA5VYrJCcmKxwZ3Y3JABERERFVu6S4ZLhq21R6jKu2LRK3JCkUEZWHyQARERERVTvZKCxdgyoiSRJkg1AoIioPkwEiIiIiqnYqTelg4coIUbo4IlkPkwEiIiIiqnY+wd64YrhU6TFXDOnQh/goFBGVh1OLEhHZMLPZjITkBMSnxMEIIzTQINgzBH4+fpy/m4isytfPBxtiNqL57cfLHURsNBuR1SgD3npvK0RHd/AvBRGRjcrJycHQN4fgs7QFyOyZgd96ZSOzZwYWps3HkDcGIycnx9ohEpEdU6lUmBMdibT6R3G5+KKly5AQApeLLyKt/lHMiY7kjQsrY8uADTKbzUjZsQNH1q+H6nYRco1G3Bz9OjxfeIEXFJGdkGUZETPCUeiRDyet1rJdkiQ4ddSi0DUfETPCsWbJWv5eICKr0el0WL5mKZJ2JWF3XDJkQ+kYAX2ID7z13vz9VAswGbAxOTk5+CQsDM/9eAb/NBggQYKAwPdvncbkZcswOSaGK/kR2YGEpATcbJ9bJhG4m1qrxs12uUhMToSfr5/C0RER/U6lUkHvr4feX2/tUKgcTMdsiCzL+CQsDGNO/oBuBiMk/G8lP0joZjBizMkf8ElYGFfyI7IDcSlboO1Q/kI+d2g7arB5z2aFIiIiIlvEZMCGpOzYged+PIN6KH8KrnqQ8OyPZ5CSkKBwZESkNCOMDzR/txEGhSIiIiJbVKPJQEFBAfr374+MjAwAwOHDhxEYGAi9Xo8FCxbU5KnrpMPr1uEpQ+V/2LsZDDi8Zo1CERGRtWigeaD5uzUovxsRERERUIPJwKlTpzB06FCkp6cDAIqLizFt2jRER0djx44dOH36NPbt21dTp6+TVLeLLF2DKiJBgqqoSKGIiMhagj1DYPjFWOkxhp+NGOA1QKGIiIjIFtVYMrBu3TrMnDkTLi4uAIC0tDS0adMGrq6ucHBwQGBgIBLYneUPkevXg0AVdwIhINerp1BERGQtfj5+cL7QGGaDudz9ZoMZzhcbQ+/NAXtERFSxGptNKDIysszjrKwsNG3a1PLYxcUF169f/8Plnj59+qFjs1XNunfHif0H0cNkqvCY444aNO/RA6mpqQpGRtbCerY/d9f5mBf/hfnffIrbXYpRr5MWkiRBCIGi8wZo/6vFmJH/wsmTJ60YLVUXXuv2h3VeM7p162btEGodxaYWlWW5zGA3IUSVg9/K4+bmBm0FU+nVde7u7picmAi3kz+UO4i4CAKH3f6OT8aO5by9diA1NZW/1OxMeXXu6emJXUm7sCVlC4wwQAMtBngNgP5tPX8P1BG81u0P65yUpFgy0Lx5c2RnZ1seZ2dnW7oQ0YNRqVSYHBODT8LC8OyPZ9DtrnUGUrVaHPz73zA5JoZfAIjsiEqlgr/eH/56f2uHQkRENkixZODJJ5/ExYsXcenSJbRq1Qrbtm3Diy++qNTp6wydTodP4uORsnMnvlq7Fqqi0hWI/ceMwSd+fkwEiIiIiOiBKZYMaLVafPzxx3jzzTdhMBjg4eEBPz+uivlnqFQqeAcEwDsgAACbE4mIiIjoz6nxZGDPnj2Wn3v37o34+PiaPiURERERET0A9ikhIiIiIrJTinUTIiKqTmazGQnJCYhPiYMRRmigQbBnCPx8OHaGiIjoQTEZICKbk5OTg4gZ4bjZPhfanhrL/PoL0+YjZstXiJ61FDqdztphEhER1Xq8fUZENkWWZUTMCEehRz6cOmot65VIkgSnjloUeuQjYkY4ZFm2cqRERES1H5MBIrIpCUkJuNk+F2qtutz9aq0aN9vlIjE5UeHIiIiIbA+TASKyKXEpW6DtoKn0GG1HDTbv2axQRERERLaLyQAR2RQjjJauQRWRJAlGGBSKiIiIyHYxGSAim6KBBkKISo8RQkADrUIRERER2S4mA0RkU4I9Q2D4xVjpMYafjRjgNUChiIiIiGwXkwEisil+Pn5wvtAYZoO53P1mgxnOFxtD761XODIiIiLbw3UGiMimqFQqRM9aWrrOQLtcaDv+vs6A4WcjnC82RvSspTax8BgXTiMiImtjMkBENken02HNkrXYlbQLW1K2wAgDNNBigNcA6CfqbeKLNBdOIyKi2oDJABHZJJVKBX+9P/z1/tYO5Q8rs3Ca9veBzpaF01xLF05bs2StTSQ2RERku/hXhohIYVw4jYiIagsmA0RECuPCaUREVFswGSAiUhgXTiMiotqCyQARkcK4cBoREdUWTAaIiBTGhdOIiKi2YDJARKQwLpxGRES1BZMBIiKF3Vk47ZF9DVF83mDpMiSEQPF5Ax7Z19BmFk4jIiLbxnUGiIisoC4snEZERLaPyQARkZXY8sJpRERUN/DWExERERGRnWIyQERERERkp5gMEBERERHZKSYDRERERER2iskAEREREZGdYjJARERERGSnmAwQEREREdkpJgNERERERHaKyQARERERkZ3iCsRERLWA2WxGQnIC4lPiYIQRGmgQ7BkCPx8/qFS8b0NERDWDyQARkZXl5OQgYkY4brbPhbanBpIkQQiBhWnzEbPlK0TPWgqdTmftMImIqA7i7SYiIiuSZRkRM8JR6JEPp45aSJIEAJAkCU4dtSj0yEfEjHDIsmzlSImIqC5iMkBEZEUJSQm42T4Xaq263P1qrRo32+UiMTlR4ciIiMgeMBkgIrKiuJQt0HbQVHqMtqMGm/dsVigiIiKyJ0wGiIisyAijpWtQRSRJghEGhSIiIiJ7wmSAiMiKNNBACFHpMUIIaKBVKCIiIrInTAaIiKwo2DMEhl+MlR5j+NmIAV4DFIqIiIjsCZMBIiIr8vPxg/OFxjAbzOXuNxvMcL7YGHpvvcKRERGRPWAyQERkRSqVCtGzluKRfQ1RfN5g6TIkhEDxeQMe2dcQ0bOWcuExIiKqEVx0jIjIynQ6HdYsWYtdSbuwJWULjDBAAy0GeA2AfqKeiQAREdUYJgNERLWASqWCv94f/np/a4dCRER2hLebiIiIiIjslFVaBkaMGIEbN27AwaH09LNmzcKTTz5pjVCIiIiIiOyW4smAEALp6elISUmxJANERERERKQ8xbsJXbhwAQAwatQoBAUFYfXq1UqHQEREREREsELLQF5eHnr37o3p06fDZDJh5MiRaNeuHfr06aN0KEREREREdk0Sdya1tpKYmBhkZmZi2rRplR5nMBhw+vRphaIiIiIiorqmW7du1g6h1lG8ZeDEiRMwmUzo3bs3gNIxBH9k7ICbmxu0Wm1NhWeTUlNT+eG2Q6x3+8M6t0+sd/vDOiclKT5mID8/H1FRUTAYDCgoKMDmzZvh6+urdBhERERERHZP8ZYBT09PnDp1CiEhIZBlGcOGDYO7u7vSYRARERER2T2rzO05fvx4jB8//g89587QBqPRWBMh2TyDwWDtEMgKWO/2h3Vun1jv9od1XnM0Gg0kSbJ2GLWG1QcQP6j8/Hz89NNP1g6DiIiIiGwYx5+WZTPJgCzLKCwshKOjI7M5IiIiIvpT2DJQls0kA0REREREVL0Un02IiIiIiIhqByYDRERERER2iskAEREREZGdYjJARERERGSnmAwQEREREdkpJgNERERERHaKyQARERERkZ1iMlCLjBgxAgEBAQgODkZwcDBOnTqFrVu34oUXXoBer0dsbKzl2MOHDyMwMBB6vR4LFiywbD9z5gwGDhyIfv364d1330VJSQkAIDMzE8OHD4efnx/Cw8NRWFio+OujsgoKCtC/f39kZGQAqL46zcvLw+jRo+Hv74/hw4cjOzsbAGA0GvHOO+/A398fAwYMwC+//KLwK6Z763zq1KnQ6/WWa3737t0Aav6zQMpZsmQJAgICEBAQgKioKAC81uu68uqc1zrVaoJqBVmWxbPPPitMJpNl27Vr14Snp6fIzc0VhYWFIjAwUJw/f14UFRUJDw8PcfnyZWEymcSoUaPE3r17hRBCBAQEiJMnTwohhJg6daqIjY0VQggxevRosW3bNiGEEEuWLBFRUVEKv0K62w8//CD69+8vunTpIq5cuVKtdfrBBx+I5cuXCyGE2Lx5sxg3bpwQQogvv/xSTJ8+XQghxLFjx8SgQYOUe8F0X50LIUT//v3F9evXyxynxGeBlHHo0CExePBgYTAYhNFoFCNHjhRbt27ltV6HlVfniYmJvNapVmPLQC1x4cIFAMCoUaMQFBSE1atX4/Dhw3j66afh7OyM+vXro1+/fkhISEBaWhratGkDV1dXODg4IDAwEAkJCbh69SqKi4vRtWtXAMDAgQORkJAAk8mE48ePo1+/fmW2k/WsW7cOM2fOhIuLCwBUa53u3bsXgYGBAID+/ftj//79MJlM2Lt3L4KCggAAPXr0wI0bN5CZman0S7db99Z5UVERMjMzMW3aNAQGBmLRokWQZVmRzwIpo2nTppgyZQo0Gg0cHR3RoUMHpKen81qvw8qr88zMTF7rVKs5WDsAKpWXl4fevXtj+vTpMJlMGDlyJPz9/dG0aVPLMS4uLkhLS0NWVtZ9269fv37f9qZNm+L69evIzc1FgwYN4ODgUGY7WU9kZGSZx9VZp3c/x8HBAQ0aNMCNGzfKLevatWt4/PHHa+x10u/urfOcnBw8/fTTmDlzJho2bIgxY8Zgw4YNqF+/fo1/Fpo1a1bTL5cAdOrUyfJzeno6du7ciZdffpnXeh1WXp3Hxsbi2LFjvNap1mLLQC3h7u6OqKgoNGzYEE2aNEFoaCgWLVoESZIsxwghIEkSZFn+Q9vv/H+3ex+TddVknQohoFKp7nvOne1kHa6urvj888/h4uKCevXqYcSIEdi3b58inwVS1vnz5zFq1ChMmjQJrq6uvNbtwN113r59e17rVKvxk1JLnDhxAkeOHLE8FkKgZcuWZQYBZWdnw8XFBc2bN3+g7Tk5OXBxcUGTJk2Qn58Ps9lc5niqPaqzTl1cXJCTkwMAKCkpQWFhIZydndGsWTNkZWXdVxZZx7lz57Br1y7LYyEEHBwcFPkskHJSU1MRFhaGiRMnYsCAAbzW7cC9dc5rnWo7JgO1RH5+PqKiomAwGFBQUIDNmzdj7ty5OHLkCG7cuIGioiIkJiaib9++ePLJJ3Hx4kVcunQJZrMZ27ZtQ9++fdGyZUtotVqkpqYCAOLi4tC3b184Ojqie/fu2LFjBwBgy5Yt6Nu3rzVfLt2jOuvUw8MDW7ZsAQDs2LED3bt3h6OjIzw8PBAXFwegNPnUarXsNmBFQgh89NFHuHXrFkwmE9auXQtfX19FPgukjF9//RVjx47FvHnzEBAQAIDXel1XXp3zWqfaThJCCGsHQaUWLlyIXbt2QZZlDBs2DK+88gq2bt2K5cuXw2QyITQ0FK+//joA4MiRI5gzZw4MBgM8PDwwdepUSJKEs2fP4r333kNBQQG6dOmCOXPmQKPR4OrVq5gyZQp+++03tGjRAvPnz0ejRo2s/IrJy8sL33zzDVq1alVtdXrz5k1MmTIFV65cQcOGDTFv3jy0atUKBoMBM2bMwOnTp6HRaDB79mx06dLF2m+B3bm7zmNjYxEbG4uSkhLo9Xq8/fbbAKrv+q7os0DKmD17NjZu3IjWrVtbtg0ZMgRt27bltV5HVVTnsizzWqdai8kAEREREZGdYjchIiIiIiI7xWSAiIiIiMhOMRkgIiIiIrJTTAaIiIiIiOwUkwEiIiIiIjvlYO0AiIiqkpGRAV9fX3Tu3NmyTQiBkSNHIjQ0tNrOk5KSgpUrVyIvLw8lJSXo1KkTJk+ejBYtWvyp8q5fv45x48ZhzZo1uHLlCqKiorB48eKHivHcuXP4/PPPcfbsWajVaqhUKoSGhiIsLMyyGummTZuwevVqlJSUwGw2o2vXrpgyZQoaNmyITZs2ITIy0jLtoCzLaNmyJd544w24ubkBAEaMGIGrV6+iYcOGkCQJJpMJbm5ueP/991GvXr1K41u5ciU2btwItVqNJk2aYNasWWjdujXMZjM+/vhjHDhwAGazGaNGjcLQoUPLPHfDhg1ISkrCsmXL7is3JiYGGzZswLZt2yo9/6+//oqXXnoJcXFxaNKkCQBgz549mDJlSpl6jI2NRYMGDap4t4mI7IAgIqrlrly5Irp27Vpm27Vr10T37t3FmTNnquUc8fHxwt/fX6SnpwshhJBlWSxbtkz4+PgIg8Hw0OV/9913IiAg4KHKOHPmjHj66afF7t27Ldt+++03MXjwYLFy5UohhBCnTp0SXl5eIjc3VwghRElJiZg+fbqYMGGCEEKIjRs3itGjR5cp99ChQ6JXr14iIyNDCCHEyy+/LHbu3GnZL8uyePPNN8XHH39caXyHDh0S/v7+Ij8/XwghxOrVq8WwYcMsP7/22mvCZDKJmzdvin79+olTp04JIYTIzc0V06dPF127dr0vNiGEOHHihOjTp0+V79/mzZuFp6en6Ny5s/jtt98s2+fNmyeWLl1a6XOJiOwVuwkRkU1q1qwZ2rRpg/T0dKxfvx4DBw5ESEgIwsLC8MsvvwAApkyZgn/9618ICAjA3LlzKy1vwYIFePfdd9GmTRsAgCRJGD16NN58800YjUbcvn0bkyZNwuDBg9GvXz8MHDgQFy5cAFB6J33OnDkIDQ2Ft7c3Fi1aBKC0RcPd3R1msxnvvfceLl++jFdffRUAsGzZMgwaNAiBgYHw8fHB7t27q3zNCxcuxGuvvQYfHx/Ltjt333U6HQAgOzsbQggUFxcDANRqNcaNG4dBgwZVWO4zzzwDX19ffPvtt+XulyQJvXr1srzeiuh0Orz//vuWO+5PPPEEMjMzAQBJSUkYOHAgHBwc0KhRIwQEBCA+Ph4AsHPnTri4uGDy5Mn3lZmTk4MPP/wQkyZNqvTc169fR1JSElasWHHfvpMnT+K7775DUFAQhg0bhuPHj1daFhGRPWE3ISKySSdPnsTly5fRpEkTrFq1CrGxsahXrx4OHjyIN954Azt37gQAFBcXY/v27ZWWlZubi6tXr+Kpp54qs12SJAQFBQEAEhIS8Oijj2Lt2rUAgBkzZiA2NhbTp08HAFy8eBHffvstioqK8NJLL+GJJ55Ap06dAJR+IZ89ezY+/PBDrFixAlevXsXhw4exatUqODk5Yfv27Vi0aBF8fX0rjfPEiRP497//fd/2zp07W7pQ9e3bFzt27ICXlxf+8pe/wN3dHX379oWHh0elZf/1r3/Fvn37yt1369Yt7Ny5E15eXpWWcXc3LqPRiHnz5sHPzw9Aafedu7vpNG/eHOfOnQMAS3ehTZs2lSnPbDZj4sSJeOedd+DgUPmfq2bNmmHJkiXl7nN2dkb//v3Rr18/pKamYuzYsYiLi0Pz5s0rLZOIyB4wGSAim1BcXIzg4GAApV8SGzdujLlz52Lv3r24dOkShgwZYjk2Ly8PN2/eBAB069atyrJVqtJGUlmWKzzGz88Prq6uWLVqFS5duoRjx47B3d3dsn/w4MFwdHSEo6Mj/Pz8cPDgQUsycK+WLVsiKioKW7duxaVLl3Dq1CkUFhZWGacQwjIuAAA++ugjHD16FLIso6ioCElJSXB0dMSnn36KSZMm4ejRozh+/DgmT56M3r17Y+HChZWW7+TkZPk5KioKS5cuhfjfIvWenp4YOXJklTECwI0bN/DWW2+hQYMGluTl3tiFEJb3vSKffvopevTogT59+uDo0aMPdO7y3J0kdO/eHe7u7jh06BBefPHFP10mEVFdwWSAiGyCk5MT4uLi7tt+5MgRBAcH45133gFQ+oU+KysLjRo1AgDUr1+/yrIbNWqEtm3b4tSpU3jmmWfK7Bs3bhzCw8Px/fffY926dRg+fDgCAwPh7OyMjIwMy3F337mu6ovuf//7X0RERCAsLAx9+vRBjx498MEHH1QZp7u7O44dO2a5Az9t2jQApd2RAgMDAZQOwm3cuDG8vb0RFBSEoKAghIeHw8vLCzdu3Kiw7NOnT5e5sz9p0iTLXf0/4uzZs4iIiICPjw8mT54MtVoNAGjRogWysrIsx2VlZVV5Zz4+Ph5NmjTB7t27cfv2bVy/fh3BwcGIi4uzBzNQlwAAAvZJREFUJIYAMHv2bDzxxBPllpGXl4f//Oc/GDNmjCUZEUJU2dJARGQvOGaAiGzas88+i+3bt1u+aH777bd45ZVX/nA5b7zxBiIjI3Hp0iUApa0P0dHROHv2LNq3b4+DBw9iwIABGDRoENq1a4c9e/bAbDZbnh8fHw9ZlivsUqNWq2EymQAAx48fh5ubG/75z3+iZ8+eSE5OLlNWRSZOnIjly5dj7969ljv2xcXF2L17tyX5UKlUmDdvHq5du2Z53vnz5/H4449bEqR77du3D3v37sXgwYP/wDt2v2vXruGVV15BREQEpk2bZkkEAMDb2xsbN25ESUkJ8vLysH379jJjH8pz8OBBxMfHIy4uDrNnz0br1q0tCWFcXJzlX0WJAAA88sgjiI2NRWJiIgDgxx9/RFpaGp577rmHeq1ERHUFb40QkU179tln8frrr2PUqFGQJAkNGjTAkiVLynRJeRCBgYEQQmDChAkoKSmBwWBAly5d8PXXX0Oj0WDUqFGYMWMGNmzYAADo2rUrfvrpJ8vzi4uLERoaisLCQgwbNgy9e/cu03LQsWNHaLVahIaGYtmyZUhMTIS/vz9kWYanpydu3bqFgoKCSqe7/Nvf/oavv/4an3/+OT799FPIsgyDwYBevXph3bp1AICBAweiqKgIr7/+OoxGIyRJQtu2bbFixQrLl/MTJ05Y7qxLkgQXFxesWLECTZs2/UPv2b2io6NRVFSEVatWYdWqVQAAjUaD9evXY+jQobh8+TKCg4NhMpkwePBg9OzZ86HO9yDUajWio6Mxe/ZsLF68GGq1GgsWLLBMO0pEZO8kcef2EhER/SkjRozA8OHD/1S3GiIiImtiywAR2YUvv/wSW7duLXffq6++apk1yJri4+PLnRoTKG25eO211xSO6H7Dhg2rcLAzF/IiIrI9bBkgIiIiIrJTHEBMRERERGSnmAwQEREREdkpJgNERERERHaKyQARERERkZ1iMkBEREREZKeYDBARERER2an/B/+wQaHpCbFMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 779.175x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a seaborn FacetGrid with Dataframe and plot State Category as hue\n",
    "g = sns.FacetGrid(drops_and_gsdp_df, hue=\"State_Ctgy\", palette=\"Set1\", height=5, aspect=2)\n",
    "\n",
    "#Label the plot\n",
    "plt.title(\"Per Capita GSDP vs. Secondary Drop out %\")\n",
    "\n",
    "#Generate a scatter plot with Per Capita & Primary droput %\n",
    "g = (g.map(plt.scatter, \"Per_Capita_GSDP_2014-15\", \"Secondary - 2014-2015\", **kws).add_legend())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  <font color=blue> From the Per Capita vs Secondary Drop out % plot we see above, the C2 category states have lower drop out percentage than C1 states. But C3 & C4, as expected have higher drop out percentages. </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 779.175x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a seaborn FacetGrid with Dataframe and plot State Category as hue\n",
    "g = sns.FacetGrid(drops_and_gsdp_df, hue=\"State_Ctgy\", palette=\"Set1\", height=5, aspect=2)\n",
    "\n",
    "#Label the plot\n",
    "plt.title(\"Per Capita GSDP vs. Senior Secondary Drop out %\")\n",
    "\n",
    "#Generate a scatter plot with Per Capita & Primary droput %\n",
    "g = (g.map(plt.scatter, \"Per_Capita_GSDP_2014-15\", \"Senior Secondary - 2014-2015\", **kws).add_legend())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=blue>From the Per Capita vs Senior Secondary Drop out % plot we see above, ironically the C1 category states have higher drop out percentage than C2, C3 & C4 states.This can be attributed the fact that most of the students drop out at early stages in C3 & C4 or in some caes C2 states also. But when it comes to C1 states, they continue up until Senior Secondary before dropping out.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 665,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Per_Capita_GSDP_2014-15</th>\n",
       "      <th>Primary - 2014-2015</th>\n",
       "      <th>Upper Primary - 2014-2015</th>\n",
       "      <th>Secondary - 2014-2015</th>\n",
       "      <th>Senior Secondary - 2014-2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Per_Capita_GSDP_2014-15</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.455350</td>\n",
       "      <td>-0.542732</td>\n",
       "      <td>-0.465649</td>\n",
       "      <td>0.326916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Primary - 2014-2015</th>\n",
       "      <td>-0.455350</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.437074</td>\n",
       "      <td>0.126865</td>\n",
       "      <td>0.203475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper Primary - 2014-2015</th>\n",
       "      <td>-0.542732</td>\n",
       "      <td>0.437074</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.549114</td>\n",
       "      <td>-0.004792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Secondary - 2014-2015</th>\n",
       "      <td>-0.465649</td>\n",
       "      <td>0.126865</td>\n",
       "      <td>0.549114</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.057322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior Secondary - 2014-2015</th>\n",
       "      <td>0.326916</td>\n",
       "      <td>0.203475</td>\n",
       "      <td>-0.004792</td>\n",
       "      <td>0.057322</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Per_Capita_GSDP_2014-15  Primary - 2014-2015  \\\n",
       "Per_Capita_GSDP_2014-15                      1.000000            -0.455350   \n",
       "Primary - 2014-2015                         -0.455350             1.000000   \n",
       "Upper Primary - 2014-2015                   -0.542732             0.437074   \n",
       "Secondary - 2014-2015                       -0.465649             0.126865   \n",
       "Senior Secondary - 2014-2015                 0.326916             0.203475   \n",
       "\n",
       "                              Upper Primary - 2014-2015  \\\n",
       "Per_Capita_GSDP_2014-15                       -0.542732   \n",
       "Primary - 2014-2015                            0.437074   \n",
       "Upper Primary - 2014-2015                      1.000000   \n",
       "Secondary - 2014-2015                          0.549114   \n",
       "Senior Secondary - 2014-2015                  -0.004792   \n",
       "\n",
       "                              Secondary - 2014-2015  \\\n",
       "Per_Capita_GSDP_2014-15                   -0.465649   \n",
       "Primary - 2014-2015                        0.126865   \n",
       "Upper Primary - 2014-2015                  0.549114   \n",
       "Secondary - 2014-2015                      1.000000   \n",
       "Senior Secondary - 2014-2015               0.057322   \n",
       "\n",
       "                              Senior Secondary - 2014-2015  \n",
       "Per_Capita_GSDP_2014-15                           0.326916  \n",
       "Primary - 2014-2015                               0.203475  \n",
       "Upper Primary - 2014-2015                        -0.004792  \n",
       "Secondary - 2014-2015                             0.057322  \n",
       "Senior Secondary - 2014-2015                      1.000000  "
      ]
     },
     "execution_count": 665,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a correlation Matrix for the Per Capita GSDP against the school dropt out rate \n",
    "dropouts_corr=drops_and_gsdp_df[['Per_Capita_GSDP_2014-15','Primary - 2014-2015','Upper Primary - 2014-2015','Secondary - 2014-2015','Senior Secondary - 2014-2015']].corr()\n",
    "\n",
    "#Print the Correlation matrix\n",
    "dropouts_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 666,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23019b1c080>"
      ]
     },
     "execution_count": 666,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Generate a heapmap to visualise the correlation betwwen the values\n",
    "\n",
    "cmap = sns.cm.rocket_r\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "sns.heatmap(dropouts_corr,annot=True,cmap = cmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the Heatmap generated using the correlation matrix, we have the following observations:\n",
    "\n",
    "-  <font color=blue> The Per Capita GSDP has negative correlation with Primary, Upper Primary & Secondary drop out percentage i.e. less number of students are dropping out at these 3 levels as the Per Capita GSDP increases. So for states with Low Per Capita GSDP, higher chances of students dropping out at Primary level.</font>\n",
    "\n",
    "-  <font color=blue> The Per Capita GSDP correlation with Primary drop out percentage is -0.46 i.e. with every 1% increase in Per Capita GSDP, the Primary drop out rate can decrease by 0.46 %. For Upper Primary drop out, the number stands at decrease by 0.54% and Secondary drop outs decrease by 0.47 %.</font>\n",
    "\n",
    "-  <font color=blue> The Per Capita GSDP has positive correlation with Senior Secondary drop out percentage i.e. more number of students are dropping out at this levels as the Per Capita GSDP increases. Around 0.33% increase in drop out % at Senior Secondary for every 1% increase in the Per Capita GSDP.</font>\n",
    "    \n",
    "-  <font color=blue> The way this should be looked at is - \"For states with higher Per Capita GSDP, there is a higher chance of students continuing education up until Senior Secondary, but at the same time they are dropping out at that level\"</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}