GDPassignment Part 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import glob\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the statesie GDP file\n",
    "state_gdp= pd.read_csv(\"C:/Users/Admin/Downloads/GDP assinment/Data I-A/statewise GDP.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "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>Telangana</th>\n",
       "      <th>Tripura</th>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <th>Uttarakhand</th>\n",
       "      <th>West Bengal</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>359433.00</td>\n",
       "      <td>19208.00</td>\n",
       "      <td>724049.00</td>\n",
       "      <td>115523.00</td>\n",
       "      <td>NaN</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>401493.00</td>\n",
       "      <td>21663.00</td>\n",
       "      <td>822903.00</td>\n",
       "      <td>131835.00</td>\n",
       "      <td>NaN</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>452186.00</td>\n",
       "      <td>25593.00</td>\n",
       "      <td>944146.00</td>\n",
       "      <td>149817.00</td>\n",
       "      <td>NaN</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>511178.00</td>\n",
       "      <td>29667.00</td>\n",
       "      <td>1043371.00</td>\n",
       "      <td>161985.00</td>\n",
       "      <td>NaN</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>575631.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1153795.00</td>\n",
       "      <td>184091.00</td>\n",
       "      <td>NaN</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>5</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2016-17</td>\n",
       "      <td>699307.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>290140.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>547396.00</td>\n",
       "      <td>...</td>\n",
       "      <td>654294.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>622385.00</td>\n",
       "      <td>29557.00</td>\n",
       "      <td>15251028.00</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>11.70</td>\n",
       "      <td>12.78</td>\n",
       "      <td>13.65</td>\n",
       "      <td>14.12</td>\n",
       "      <td>NaN</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>12.63</td>\n",
       "      <td>18.14</td>\n",
       "      <td>14.73</td>\n",
       "      <td>13.64</td>\n",
       "      <td>NaN</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>13.05</td>\n",
       "      <td>15.92</td>\n",
       "      <td>10.51</td>\n",
       "      <td>8.12</td>\n",
       "      <td>NaN</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>12.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.58</td>\n",
       "      <td>13.65</td>\n",
       "      <td>NaN</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",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2016-17</td>\n",
       "      <td>14.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.82</td>\n",
       "      <td>...</td>\n",
       "      <td>13.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.76</td>\n",
       "      <td>11.40</td>\n",
       "      <td>11.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 36 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",
       "5   GSDP - CURRENT PRICES (` in Crore)  2016-17       699307.00   \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",
       "10       (% Growth over previous year)  2016-17           14.65   \n",
       "\n",
       "    Arunachal Pradesh      Assam      Bihar  Chhattisgarh       Goa  \\\n",
       "0            11063.00  143175.00  247144.00     158074.00  42367.00   \n",
       "1            12547.00  156864.00  282368.00     177511.00  38120.00   \n",
       "2            14602.00  177745.00  317101.00     206690.00  35921.00   \n",
       "3            16761.00  198098.00  373920.00     234982.00  40633.00   \n",
       "4            18784.00  224234.00  413503.00     260776.00  45002.00   \n",
       "5                 NaN        NaN        NaN     290140.00       NaN   \n",
       "6               13.41       9.56      14.25         12.30    -10.02   \n",
       "7               16.38      13.31      12.30         16.44     -5.77   \n",
       "8               14.79      11.45      17.92         13.69     13.12   \n",
       "9               12.07      13.19      10.59         10.98     10.75   \n",
       "10                NaN        NaN        NaN         11.26       NaN   \n",
       "\n",
       "      Gujarat    Haryana  ...  Telangana   Tripura  Uttar Pradesh  \\\n",
       "0   615606.00  297539.00  ...  359433.00  19208.00      724049.00   \n",
       "1   724495.00  347032.00  ...  401493.00  21663.00      822903.00   \n",
       "2   807623.00  400662.00  ...  452186.00  25593.00      944146.00   \n",
       "3   895027.00  437462.00  ...  511178.00  29667.00     1043371.00   \n",
       "4   994316.00  485184.00  ...  575631.00       NaN     1153795.00   \n",
       "5         NaN  547396.00  ...  654294.00       NaN            NaN   \n",
       "6       17.69      16.63  ...      11.70     12.78          13.65   \n",
       "7       11.47      15.45  ...      12.63     18.14          14.73   \n",
       "8       10.82       9.18  ...      13.05     15.92          10.51   \n",
       "9       11.09      10.91  ...      12.61       NaN          10.58   \n",
       "10        NaN      12.82  ...      13.67       NaN            NaN   \n",
       "\n",
       "    Uttarakhand  West Bengal  Andaman & Nicobar Islands  Chandigarh  \\\n",
       "0     115523.00          NaN                    3979.00    18768.00   \n",
       "1     131835.00          NaN                    4421.00    21609.00   \n",
       "2     149817.00          NaN                    5159.00    24787.00   \n",
       "3     161985.00          NaN                    5721.00    27844.00   \n",
       "4     184091.00          NaN                        NaN    30304.00   \n",
       "5           NaN          NaN                        NaN         NaN   \n",
       "6         14.12          NaN                      11.13       15.14   \n",
       "7         13.64          NaN                      16.68       14.71   \n",
       "8          8.12          NaN                      10.89       12.33   \n",
       "9         13.65          NaN                        NaN        8.84   \n",
       "10          NaN          NaN                        NaN         NaN   \n",
       "\n",
       "        Delhi  Puducherry  All_India GDP  \n",
       "0   343767.00    16818.00     8736039.00  \n",
       "1   391238.00    18875.00     9946636.00  \n",
       "2   443783.00    21870.00    11236635.00  \n",
       "3   492424.00    24089.00    12433749.00  \n",
       "4   551963.00    26533.00    13675331.00  \n",
       "5   622385.00    29557.00    15251028.00  \n",
       "6       13.81       12.23          13.86  \n",
       "7       13.43       15.87          12.97  \n",
       "8       10.96       10.14          10.65  \n",
       "9       12.09       10.15           9.99  \n",
       "10      12.76       11.40          11.52  \n",
       "\n",
       "[11 rows x 36 columns]"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_gdp.head(15)\n",
    "#state_gdp = state_gdp.rename(columns={'Andhra Pradesh\t': 'Andhra Pradesh'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Items  Description', 'Duration', 'Andhra Pradesh', 'Arunachal Pradesh', 'Assam', 'Bihar', 'Chhattisgarh', 'Goa', 'Gujarat', 'Haryana', 'Himachal Pradesh', 'Jammu & Kashmir', 'Jharkhand', 'Karnataka', 'Kerala', 'Madhya Pradesh', 'Maharashtra', 'Manipur', 'Meghalaya', 'Mizoram', 'Nagaland', 'Odisha', 'Punjab', 'Rajasthan', 'Sikkim', 'Tamil Nadu', 'Telangana', 'Tripura', 'Uttar Pradesh', 'Uttarakhand', 'West Bengal', 'Andaman & Nicobar Islands', 'Chandigarh', 'Delhi', 'Puducherry', 'All_India GDP']\n"
     ]
    }
   ],
   "source": [
    "col=list(state_gdp.columns)\n",
    "print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [],
   "source": [
    "union_territories=['Andaman & Nicobar Islands','Chandigarh','Dadar and Nagar Haveli','Daman and Diu','Delhi','Lakshadweep','Puducherry','All_India GDP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [],
   "source": [
    "states=[]\n",
    "for item in col:\n",
    "    if item not in union_territories:\n",
    "        states.append(item)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Items  Description',\n",
       " 'Duration',\n",
       " 'Andhra Pradesh',\n",
       " 'Arunachal Pradesh',\n",
       " 'Assam',\n",
       " 'Bihar',\n",
       " 'Chhattisgarh',\n",
       " 'Goa',\n",
       " 'Gujarat',\n",
       " 'Haryana',\n",
       " 'Himachal Pradesh',\n",
       " 'Jammu & Kashmir',\n",
       " 'Jharkhand',\n",
       " 'Karnataka',\n",
       " 'Kerala',\n",
       " 'Madhya Pradesh',\n",
       " 'Maharashtra',\n",
       " 'Manipur',\n",
       " 'Meghalaya',\n",
       " 'Mizoram',\n",
       " 'Nagaland',\n",
       " 'Odisha',\n",
       " 'Punjab',\n",
       " 'Rajasthan',\n",
       " 'Sikkim',\n",
       " 'Tamil Nadu',\n",
       " 'Telangana',\n",
       " 'Tripura',\n",
       " 'Uttar Pradesh',\n",
       " 'Uttarakhand',\n",
       " 'West Bengal']"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [],
   "source": [
    "states_only_gdp=state_gdp[states]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
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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></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>Odisha</th>\n",
       "      <th>Punjab</th>\n",
       "      <th>Rajasthan</th>\n",
       "      <th>Sikkim</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>West Bengal</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>227872.00</td>\n",
       "      <td>266628.00</td>\n",
       "      <td>436465.00</td>\n",
       "      <td>11165.00</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>NaN</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>258275.00</td>\n",
       "      <td>297734.00</td>\n",
       "      <td>494004.00</td>\n",
       "      <td>12338.00</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>NaN</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>291709.00</td>\n",
       "      <td>334714.00</td>\n",
       "      <td>549701.00</td>\n",
       "      <td>13862.00</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>NaN</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>321971.00</td>\n",
       "      <td>368011.00</td>\n",
       "      <td>612194.00</td>\n",
       "      <td>15209.00</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>NaN</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>341887.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16637.00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2016-17</td>\n",
       "      <td>699307.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>290140.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>547396.00</td>\n",
       "      <td>...</td>\n",
       "      <td>378991.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1338766.00</td>\n",
       "      <td>654294.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>13.34</td>\n",
       "      <td>11.67</td>\n",
       "      <td>13.18</td>\n",
       "      <td>10.51</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>NaN</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>12.95</td>\n",
       "      <td>12.42</td>\n",
       "      <td>11.27</td>\n",
       "      <td>12.35</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>NaN</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>10.37</td>\n",
       "      <td>9.95</td>\n",
       "      <td>11.37</td>\n",
       "      <td>9.72</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>NaN</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>6.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.39</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2016-17</td>\n",
       "      <td>14.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.82</td>\n",
       "      <td>...</td>\n",
       "      <td>10.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.40</td>\n",
       "      <td>13.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 31 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",
       "5   GSDP - CURRENT PRICES (` in Crore)  2016-17       699307.00   \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",
       "10       (% Growth over previous year)  2016-17           14.65   \n",
       "\n",
       "    Arunachal Pradesh      Assam      Bihar  Chhattisgarh       Goa  \\\n",
       "0            11063.00  143175.00  247144.00     158074.00  42367.00   \n",
       "1            12547.00  156864.00  282368.00     177511.00  38120.00   \n",
       "2            14602.00  177745.00  317101.00     206690.00  35921.00   \n",
       "3            16761.00  198098.00  373920.00     234982.00  40633.00   \n",
       "4            18784.00  224234.00  413503.00     260776.00  45002.00   \n",
       "5                 NaN        NaN        NaN     290140.00       NaN   \n",
       "6               13.41       9.56      14.25         12.30    -10.02   \n",
       "7               16.38      13.31      12.30         16.44     -5.77   \n",
       "8               14.79      11.45      17.92         13.69     13.12   \n",
       "9               12.07      13.19      10.59         10.98     10.75   \n",
       "10                NaN        NaN        NaN         11.26       NaN   \n",
       "\n",
       "      Gujarat    Haryana  ...     Odisha     Punjab  Rajasthan    Sikkim  \\\n",
       "0   615606.00  297539.00  ...  227872.00  266628.00  436465.00  11165.00   \n",
       "1   724495.00  347032.00  ...  258275.00  297734.00  494004.00  12338.00   \n",
       "2   807623.00  400662.00  ...  291709.00  334714.00  549701.00  13862.00   \n",
       "3   895027.00  437462.00  ...  321971.00  368011.00  612194.00  15209.00   \n",
       "4   994316.00  485184.00  ...  341887.00        NaN        NaN  16637.00   \n",
       "5         NaN  547396.00  ...  378991.00        NaN        NaN       NaN   \n",
       "6       17.69      16.63  ...      13.34      11.67      13.18     10.51   \n",
       "7       11.47      15.45  ...      12.95      12.42      11.27     12.35   \n",
       "8       10.82       9.18  ...      10.37       9.95      11.37      9.72   \n",
       "9       11.09      10.91  ...       6.19        NaN        NaN      9.39   \n",
       "10        NaN      12.82  ...      10.85        NaN        NaN       NaN   \n",
       "\n",
       "    Tamil Nadu  Telangana   Tripura  Uttar Pradesh  Uttarakhand  West Bengal  \n",
       "0    751485.00  359433.00  19208.00      724049.00    115523.00          NaN  \n",
       "1    855481.00  401493.00  21663.00      822903.00    131835.00          NaN  \n",
       "2    971090.00  452186.00  25593.00      944146.00    149817.00          NaN  \n",
       "3   1092564.00  511178.00  29667.00     1043371.00    161985.00          NaN  \n",
       "4   1212668.00  575631.00       NaN     1153795.00    184091.00          NaN  \n",
       "5   1338766.00  654294.00       NaN            NaN          NaN          NaN  \n",
       "6        13.84      11.70     12.78          13.65        14.12          NaN  \n",
       "7        13.51      12.63     18.14          14.73        13.64          NaN  \n",
       "8        12.51      13.05     15.92          10.51         8.12          NaN  \n",
       "9        10.99      12.61       NaN          10.58        13.65          NaN  \n",
       "10       10.40      13.67       NaN            NaN          NaN          NaN  \n",
       "\n",
       "[11 rows x 31 columns]"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states_only_gdp.head(12)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>Odisha</th>\n",
       "      <th>Punjab</th>\n",
       "      <th>Rajasthan</th>\n",
       "      <th>Sikkim</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>West Bengal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>13.34</td>\n",
       "      <td>11.67</td>\n",
       "      <td>13.18</td>\n",
       "      <td>10.51</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>NaN</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>12.95</td>\n",
       "      <td>12.42</td>\n",
       "      <td>11.27</td>\n",
       "      <td>12.35</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>NaN</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>10.37</td>\n",
       "      <td>9.95</td>\n",
       "      <td>11.37</td>\n",
       "      <td>9.72</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>NaN</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>6.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.39</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2016-17</td>\n",
       "      <td>14.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.82</td>\n",
       "      <td>...</td>\n",
       "      <td>10.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.40</td>\n",
       "      <td>13.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Items  Description Duration  Andhra Pradesh  Arunachal Pradesh  \\\n",
       "6   (% Growth over previous year)  2012-13            8.43              13.41   \n",
       "7   (% Growth over previous year)  2013-14           12.85              16.38   \n",
       "8   (% Growth over previous year)  2014-15           13.40              14.79   \n",
       "9   (% Growth over previous year)  2015-16           15.85              12.07   \n",
       "10  (% Growth over previous year)  2016-17           14.65                NaN   \n",
       "\n",
       "    Assam  Bihar  Chhattisgarh    Goa  Gujarat  Haryana  ...  Odisha  Punjab  \\\n",
       "6    9.56  14.25         12.30 -10.02    17.69    16.63  ...   13.34   11.67   \n",
       "7   13.31  12.30         16.44  -5.77    11.47    15.45  ...   12.95   12.42   \n",
       "8   11.45  17.92         13.69  13.12    10.82     9.18  ...   10.37    9.95   \n",
       "9   13.19  10.59         10.98  10.75    11.09    10.91  ...    6.19     NaN   \n",
       "10    NaN    NaN         11.26    NaN      NaN    12.82  ...   10.85     NaN   \n",
       "\n",
       "    Rajasthan  Sikkim  Tamil Nadu  Telangana  Tripura  Uttar Pradesh  \\\n",
       "6       13.18   10.51       13.84      11.70    12.78          13.65   \n",
       "7       11.27   12.35       13.51      12.63    18.14          14.73   \n",
       "8       11.37    9.72       12.51      13.05    15.92          10.51   \n",
       "9         NaN    9.39       10.99      12.61      NaN          10.58   \n",
       "10        NaN     NaN       10.40      13.67      NaN            NaN   \n",
       "\n",
       "    Uttarakhand  West Bengal  \n",
       "6         14.12          NaN  \n",
       "7         13.64          NaN  \n",
       "8          8.12          NaN  \n",
       "9         13.65          NaN  \n",
       "10          NaN          NaN  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_growth=states_only_gdp[(states_only_gdp['Items  Description']=='(% Growth over previous year)')]\n",
    "per_growth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Odisha</th>\n",
       "      <th>Punjab</th>\n",
       "      <th>Rajasthan</th>\n",
       "      <th>Sikkim</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>West Bengal</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>13.34</td>\n",
       "      <td>11.67</td>\n",
       "      <td>13.18</td>\n",
       "      <td>10.51</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>NaN</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>12.95</td>\n",
       "      <td>12.42</td>\n",
       "      <td>11.27</td>\n",
       "      <td>12.35</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>NaN</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>10.37</td>\n",
       "      <td>9.95</td>\n",
       "      <td>11.37</td>\n",
       "      <td>9.72</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>NaN</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>6.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.39</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016-17</td>\n",
       "      <td>14.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.40</td>\n",
       "      <td>13.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 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",
       "10  2016-17           14.65                NaN    NaN    NaN         11.26   \n",
       "\n",
       "      Goa  Gujarat  Haryana  Himachal Pradesh  ...  Odisha  Punjab  Rajasthan  \\\n",
       "6  -10.02    17.69    16.63             13.89  ...   13.34   11.67      13.18   \n",
       "7   -5.77    11.47    15.45             14.42  ...   12.95   12.42      11.27   \n",
       "8   13.12    10.82     9.18             10.14  ...   10.37    9.95      11.37   \n",
       "9   10.75    11.09    10.91               NaN  ...    6.19     NaN        NaN   \n",
       "10    NaN      NaN    12.82               NaN  ...   10.85     NaN        NaN   \n",
       "\n",
       "    Sikkim  Tamil Nadu  Telangana  Tripura  Uttar Pradesh  Uttarakhand  \\\n",
       "6    10.51       13.84      11.70    12.78          13.65        14.12   \n",
       "7    12.35       13.51      12.63    18.14          14.73        13.64   \n",
       "8     9.72       12.51      13.05    15.92          10.51         8.12   \n",
       "9     9.39       10.99      12.61      NaN          10.58        13.65   \n",
       "10     NaN       10.40      13.67      NaN            NaN          NaN   \n",
       "\n",
       "    West Bengal  \n",
       "6           NaN  \n",
       "7           NaN  \n",
       "8           NaN  \n",
       "9           NaN  \n",
       "10          NaN  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nunique = per_growth.apply(pd.Series.nunique)\n",
    "cols_to_drop = nunique[nunique == 1].index\n",
    "per_growth=per_growth.drop(cols_to_drop, axis=1)\n",
    "per_growth.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>Punjab</th>\n",
       "      <th>Rajasthan</th>\n",
       "      <th>Sikkim</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>West Bengal</th>\n",
       "      <th>Number</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>11.67</td>\n",
       "      <td>13.18</td>\n",
       "      <td>10.51</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>0.0</td>\n",
       "      <td>1.0</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>12.42</td>\n",
       "      <td>11.27</td>\n",
       "      <td>12.35</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>0.0</td>\n",
       "      <td>2.0</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>9.95</td>\n",
       "      <td>11.37</td>\n",
       "      <td>9.72</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>0.0</td>\n",
       "      <td>3.0</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>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.39</td>\n",
       "      <td>10.99</td>\n",
       "      <td>12.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.58</td>\n",
       "      <td>13.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016-17</td>\n",
       "      <td>14.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.40</td>\n",
       "      <td>13.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 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",
       "10  2016-17           14.65               0.00   0.00   0.00         11.26   \n",
       "\n",
       "      Goa  Gujarat  Haryana  Himachal Pradesh  ...  Punjab  Rajasthan  Sikkim  \\\n",
       "6  -10.02    17.69    16.63             13.89  ...   11.67      13.18   10.51   \n",
       "7   -5.77    11.47    15.45             14.42  ...   12.42      11.27   12.35   \n",
       "8   13.12    10.82     9.18             10.14  ...    9.95      11.37    9.72   \n",
       "9   10.75    11.09    10.91              0.00  ...    0.00       0.00    9.39   \n",
       "10   0.00     0.00    12.82              0.00  ...    0.00       0.00    0.00   \n",
       "\n",
       "    Tamil Nadu  Telangana  Tripura  Uttar Pradesh  Uttarakhand  West Bengal  \\\n",
       "6        13.84      11.70    12.78          13.65        14.12          0.0   \n",
       "7        13.51      12.63    18.14          14.73        13.64          0.0   \n",
       "8        12.51      13.05    15.92          10.51         8.12          0.0   \n",
       "9        10.99      12.61     0.00          10.58        13.65          0.0   \n",
       "10       10.40      13.67     0.00           0.00         0.00          0.0   \n",
       "\n",
       "    Number  \n",
       "6      1.0  \n",
       "7      2.0  \n",
       "8      3.0  \n",
       "9      4.0  \n",
       "10     5.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=[1.0,2.0,3.0,4.0,5.0]\n",
    "per_growth=pd.DataFrame(per_growth)\n",
    "per_growth.loc[:,'Number'] = pd.Series(x, index=per_growth.index)\n",
    "per_growth.isnull().sum()\n",
    "per_growth=per_growth.fillna(0)\n",
    "per_growth.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Andhra Pradesh', 'Arunachal Pradesh', 'Assam', 'Bihar', 'Chhattisgarh', 'Goa', 'Gujarat', 'Haryana', 'Himachal Pradesh', 'Jammu & Kashmir', 'Jharkhand', 'Karnataka', 'Kerala', 'Madhya Pradesh', 'Maharashtra', 'Manipur', 'Meghalaya', 'Mizoram', 'Nagaland', 'Odisha', 'Punjab', 'Rajasthan', 'Sikkim', 'Tamil Nadu', 'Telangana', 'Tripura', 'Uttar Pradesh', 'Uttarakhand', 'West Bengal']\n"
     ]
    }
   ],
   "source": [
    "cl=list(per_growth.columns)\n",
    "cl.remove('Duration')\n",
    "cl.remove('Number')\n",
    "print(cl)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 29 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 10))\n",
    "\n",
    "for item,num in zip(cl, range(1,30)):\n",
    "    np.polyfit(per_growth['Number'], per_growth[item],1)\n",
    "    plt.title(item)\n",
    "    plt.subplot(6, 5, num)\n",
    "    plt.scatter(x ='Number', y=item, data = per_growth)\n",
    "    plt.plot(per_growth['Number'], m*per_growth['Number']+c, '--r')\n",
    "plt.savefig(\"best fit line.png\")    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cl=list(per_growth.columns)\n",
    "\n",
    "for item in cl:\n",
    "    if item!='Duration':\n",
    "        bar = per_growth.plot.bar(x='Duration', y=item)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "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>Duration</th>\n",
       "      <th>All_India GDP</th>\n",
       "      <th>Items  Description</th>\n",
       "      <th>Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>13.86</td>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>12.97</td>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>10.65</td>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>9.99</td>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016-17</td>\n",
       "      <td>11.52</td>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration  All_India GDP             Items  Description  Number\n",
       "6   2012-13          13.86  (% Growth over previous year)     1.0\n",
       "7   2013-14          12.97  (% Growth over previous year)     2.0\n",
       "8   2014-15          10.65  (% Growth over previous year)     3.0\n",
       "9   2015-16           9.99  (% Growth over previous year)     4.0\n",
       "10  2016-17          11.52  (% Growth over previous year)     5.0"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_India=state_gdp[['Duration','All_India GDP','Items  Description']]\n",
    "\n",
    "gdp_India=gdp_India[(gdp_India['Items  Description']=='(% Growth over previous year)')]\n",
    "x=[1.0,2.0,3.0,4.0,5.0]\n",
    "gdp_India=pd.DataFrame(gdp_India)\n",
    "gdp_India.loc[:,'Number'] = pd.Series(x, index=gdp_India.index)\n",
    "\n",
    "gdp_India.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bar = gdp_India.plot.bar(x='Duration', y='All_India GDP')\n",
    "fig = plt.figure(figsize=(15, 10))\n",
    "\n",
    "plt.title('All_India GDP')\n",
    "plt.subplot(2,2,1)\n",
    "\n",
    "#gdp India\n",
    "plt.title('All_India GDP')\n",
    "m,c=np.polyfit(gdp_India['Number'], gdp_India['All_India GDP'],1)\n",
    "plt.scatter(x ='Number', y='All_India GDP', data = gdp_India)\n",
    "plt.plot(gdp_India['Number'], m*gdp_India['Number']+c, '--r')\n",
    "\n",
    "#gdp home state - Bihar\n",
    "plt.subplot(2,2,2)\n",
    "plt.title('Bihar')\n",
    "x,y=np.polyfit(per_growth['Number'], per_growth['Bihar'],1)\n",
    "plt.scatter(x ='Number', y='Bihar', data = per_growth)\n",
    "plt.plot(per_growth['Number'], x*per_growth['Number']+y, '--r')\n",
    "plt.savefig(\"Bihar.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "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>index</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>20.71</td>\n",
       "      <td>14.91</td>\n",
       "      <td>10.11</td>\n",
       "      <td>12.86</td>\n",
       "      <td>17.74</td>\n",
       "      <td>15.266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>8.43</td>\n",
       "      <td>12.85</td>\n",
       "      <td>13.4</td>\n",
       "      <td>15.85</td>\n",
       "      <td>14.65</td>\n",
       "      <td>13.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Haryana</td>\n",
       "      <td>16.63</td>\n",
       "      <td>15.45</td>\n",
       "      <td>9.18</td>\n",
       "      <td>10.91</td>\n",
       "      <td>12.82</td>\n",
       "      <td>12.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>12.3</td>\n",
       "      <td>16.44</td>\n",
       "      <td>13.69</td>\n",
       "      <td>10.98</td>\n",
       "      <td>11.26</td>\n",
       "      <td>12.934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Telangana</td>\n",
       "      <td>11.7</td>\n",
       "      <td>12.63</td>\n",
       "      <td>13.05</td>\n",
       "      <td>12.61</td>\n",
       "      <td>13.67</td>\n",
       "      <td>12.732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>13.84</td>\n",
       "      <td>13.51</td>\n",
       "      <td>12.51</td>\n",
       "      <td>10.99</td>\n",
       "      <td>10.4</td>\n",
       "      <td>12.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Karnataka</td>\n",
       "      <td>14.56</td>\n",
       "      <td>18.24</td>\n",
       "      <td>12.7</td>\n",
       "      <td>11.42</td>\n",
       "      <td>0</td>\n",
       "      <td>11.384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>13.41</td>\n",
       "      <td>16.38</td>\n",
       "      <td>14.79</td>\n",
       "      <td>12.07</td>\n",
       "      <td>0</td>\n",
       "      <td>11.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>14.25</td>\n",
       "      <td>12.3</td>\n",
       "      <td>17.92</td>\n",
       "      <td>10.59</td>\n",
       "      <td>0</td>\n",
       "      <td>11.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Odisha</td>\n",
       "      <td>13.34</td>\n",
       "      <td>12.95</td>\n",
       "      <td>10.37</td>\n",
       "      <td>6.19</td>\n",
       "      <td>10.85</td>\n",
       "      <td>10.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gujarat</td>\n",
       "      <td>17.69</td>\n",
       "      <td>11.47</td>\n",
       "      <td>10.82</td>\n",
       "      <td>11.09</td>\n",
       "      <td>0</td>\n",
       "      <td>10.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>13.26</td>\n",
       "      <td>12.79</td>\n",
       "      <td>13.11</td>\n",
       "      <td>11.85</td>\n",
       "      <td>0</td>\n",
       "      <td>10.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mizoram</td>\n",
       "      <td>15.2</td>\n",
       "      <td>23.1</td>\n",
       "      <td>12.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>15.77</td>\n",
       "      <td>7.92</td>\n",
       "      <td>15.14</td>\n",
       "      <td>11.44</td>\n",
       "      <td>0</td>\n",
       "      <td>10.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>14.12</td>\n",
       "      <td>13.64</td>\n",
       "      <td>8.12</td>\n",
       "      <td>13.65</td>\n",
       "      <td>0</td>\n",
       "      <td>9.906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>13.65</td>\n",
       "      <td>14.73</td>\n",
       "      <td>10.51</td>\n",
       "      <td>10.58</td>\n",
       "      <td>0</td>\n",
       "      <td>9.894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Nagaland</td>\n",
       "      <td>15.03</td>\n",
       "      <td>21.98</td>\n",
       "      <td>10.85</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Assam</td>\n",
       "      <td>9.56</td>\n",
       "      <td>13.31</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.19</td>\n",
       "      <td>0</td>\n",
       "      <td>9.502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Tripura</td>\n",
       "      <td>12.78</td>\n",
       "      <td>18.14</td>\n",
       "      <td>15.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jammu &amp; Kashmir</td>\n",
       "      <td>11.31</td>\n",
       "      <td>10.09</td>\n",
       "      <td>4.7</td>\n",
       "      <td>17.91</td>\n",
       "      <td>0</td>\n",
       "      <td>8.802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Sikkim</td>\n",
       "      <td>10.51</td>\n",
       "      <td>12.35</td>\n",
       "      <td>9.72</td>\n",
       "      <td>9.39</td>\n",
       "      <td>0</td>\n",
       "      <td>8.394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>9.81</td>\n",
       "      <td>4.87</td>\n",
       "      <td>6.41</td>\n",
       "      <td>9.58</td>\n",
       "      <td>10.55</td>\n",
       "      <td>8.244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>13.89</td>\n",
       "      <td>14.42</td>\n",
       "      <td>10.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>13.79</td>\n",
       "      <td>13.74</td>\n",
       "      <td>8.78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>13.18</td>\n",
       "      <td>11.27</td>\n",
       "      <td>11.37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Manipur</td>\n",
       "      <td>6.45</td>\n",
       "      <td>17.83</td>\n",
       "      <td>11.39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Punjab</td>\n",
       "      <td>11.67</td>\n",
       "      <td>12.42</td>\n",
       "      <td>9.95</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Goa</td>\n",
       "      <td>-10.02</td>\n",
       "      <td>-5.77</td>\n",
       "      <td>13.12</td>\n",
       "      <td>10.75</td>\n",
       "      <td>0</td>\n",
       "      <td>1.616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                index      6      7      8      9     10    mean\n",
       "13     Madhya Pradesh  20.71  14.91  10.11  12.86  17.74  15.266\n",
       "0      Andhra Pradesh   8.43  12.85   13.4  15.85  14.65  13.036\n",
       "7             Haryana  16.63  15.45   9.18  10.91  12.82  12.998\n",
       "4        Chhattisgarh   12.3  16.44  13.69  10.98  11.26  12.934\n",
       "24          Telangana   11.7  12.63  13.05  12.61  13.67  12.732\n",
       "23         Tamil Nadu  13.84  13.51  12.51  10.99   10.4  12.250\n",
       "11          Karnataka  14.56  18.24   12.7  11.42      0  11.384\n",
       "1   Arunachal Pradesh  13.41  16.38  14.79  12.07      0  11.330\n",
       "3               Bihar  14.25   12.3  17.92  10.59      0  11.012\n",
       "19             Odisha  13.34  12.95  10.37   6.19  10.85  10.740\n",
       "6             Gujarat  17.69  11.47  10.82  11.09      0  10.214\n",
       "12             Kerala  13.26  12.79  13.11  11.85      0  10.202\n",
       "17            Mizoram   15.2   23.1   12.3      0      0  10.120\n",
       "10          Jharkhand  15.77   7.92  15.14  11.44      0  10.054\n",
       "27        Uttarakhand  14.12  13.64   8.12  13.65      0   9.906\n",
       "26      Uttar Pradesh  13.65  14.73  10.51  10.58      0   9.894\n",
       "18           Nagaland  15.03  21.98  10.85      0      0   9.572\n",
       "2               Assam   9.56  13.31  11.45  13.19      0   9.502\n",
       "25            Tripura  12.78  18.14  15.92      0      0   9.368\n",
       "9     Jammu & Kashmir  11.31  10.09    4.7  17.91      0   8.802\n",
       "22             Sikkim  10.51  12.35   9.72   9.39      0   8.394\n",
       "16          Meghalaya   9.81   4.87   6.41   9.58  10.55   8.244\n",
       "8    Himachal Pradesh  13.89  14.42  10.14      0      0   7.690\n",
       "14        Maharashtra  13.79  13.74   8.78      0      0   7.262\n",
       "21          Rajasthan  13.18  11.27  11.37      0      0   7.164\n",
       "15            Manipur   6.45  17.83  11.39      0      0   7.134\n",
       "20             Punjab  11.67  12.42   9.95      0      0   6.808\n",
       "5                 Goa -10.02  -5.77  13.12  10.75      0   1.616"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_growth_for_states = per_growth.append(per_growth.agg(['mean']))\n",
    "#per_growth.set_index('Duration')\n",
    "per_growth_for_states=pd.DataFrame(per_growth_for_states)\n",
    "per_growth_for_states=per_growth_for_states.T\n",
    "per_growth_for_states=per_growth_for_states.fillna(0)\n",
    "per_growth_for_states=per_growth_for_states.drop(['Duration','West Bengal','Number'])\n",
    "per_growth_for_states.reset_index(level=0, inplace=True)\n",
    "#per_growth_for_states=per_growth_for_states.T\n",
    "per_growth_for_states= per_growth_for_states.sort_values(by=['mean'], ascending=False)\n",
    "per_growth_for_states\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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d8qm+7JD0eeB0IlFhomA5LTHNPArYxPabhQXYRiTNB8xHeKT8t+h4BoOk2YnP5zvAS3QlnGYGHgBWtZ2VfJSkd4HVbTfrIM+W9AzyHrBaxrKNEyWSngPGAFvb7tbMkTuSvg7MZvv61PRwGF1NVKOB7Wy/XGSMFRMi6UvADKmZFkm/YcLPbL9GGeNckPRUL4fYdjOLhWFNE0nmbuTUGCapP1N5LsMaOzWxfJVoxnwsNcdVDDOq4ldFn5C0JDC97UvT+xmIZOH8RPJpV9sfFRjigCnzuZUZSVMSMnPr2L6k6HjaiaRVgNUJCZRmEw1LdTyoASKpXw9rZVtEpcXiFLa7+YnkgqS/AW/WigmStic6nj4gCnur2768h//EsCZNbWxKdB+fTXSzvgtsSEzgbFclpIYXkm4jOpJ/R0wxL5RM2OcgOu8Osl0Ko2FJnwUWJxIXt+d8jUxebTvb/lPRsbSL9CyyFM3v17l7xo5H0vzEBEBtuu3OTCdiAZB0IvH5bJ3er0Nc/ychPC9XtH1rD/+JLJC0Et0/tyzvZ6nT/xzbuXmV9YqkJwmPtlKtZ8qOpDeJZ+AsJqAqQNIawHS2T0nv5yKu/bW8z+a2u9ko5EAqpC8CPJV7A8fEhKR96F78mh5YhvAPP8V21modZUfSZFW+ePhTFb8q+kRKNF1u+8D0/mzgx8DFRBfvibZ3LzDEAVPmc6snJeFnozySXrWOu21yTrw3ImkPYH/gcUL+qlln/HqdjmugpO6fPt9ocu7+kbRKT99FSXvZ3r+TMbWDpGG9g+0L0/vngPNs75wMrxewvXihQQ4CSY8T3aynE4WUhW3fnfadAYyznZ38UFoE70DPhfRsOgnrqSWcCO+Jj4Ef2b457VsX2Nf21wsMsaIJKcH7q1yT741I+gFwEUletAml6GgtI5KeAXazfU56/wRwO9EEcQzRFLdsgSFWNCBpAeI+/Xvi2t/M5/e9DofVFiRtC2xEFF2zTLy3IjUI/JDWa9DjOx5Um5B0KvBf23sWHctQkPzMTs01T9AMSfcCZ9o+Mr2/nJDPPpWQGv277W0LDHHASJoEeB9YqSrI5k+aJjofuNf2QUXHUzEhkr4P7AX8AJiamOC+Gdjf9m1FxlbRnMrzq6KvfB3YF0DS1ISO6Wa2z5N0J6F1mmuBqMznhqRZgZNo7rck8jaIPhHYXtLVJeq22Ar4g+2dig6kTdTr/U8DHAg8SRSXXyYkeEYQusl7dDy69nKBpFVtX9u4Q9LvgV8Rhc3c+CLRMY6kbxESbLXJjQuADQqKq118GXjC9ieSxhEm3jXOJnwOsit+AUcScV9O+EeVSYJhHDCJbUt6kZjQuznte4so9mVDms7oMxkXj/YDdpI0xvY7RQfTBo4mfHqWAx4p0XNINyR9jdZF9By/jzMBzwFI+iowLzHBMVbSScBfiwyuXZSs8e3u9PcZPRyT63pmMcJz9FlJtxNSlfWNY7a9SSGRDQJJSxPJ2y+2OMRAtsUvYtL8kCQ116ogm+P1scYahK/l3URx6NwSyNzODTwI42V9lwdG2L5C0rPAIYQCRHbY/lTSv4i1dSlJBaElaO33m/P1ZALSGudkwu8y6+JXkmJu9Zlld41M8vtXEM3qvyfu2TMTEqM3Slq5WT6ooliq4ldFX5mcSDZB3HA+Q/zgAZ4AZikiqDZR5nMDOBn4HrAjLSaJMmY64JvA05Kuo/liMTfj088T/jWlwPbfaq8l/Rm42vYvGg47UtIpwNJAzlJlI4FLJP2kNoUC4+WVNiC89nLkJWBO4B/AisAzdd5lUxFG3znzInEtAXiKkDCrPbDOU0hE7WEtQrb3iKIDGQLuJxpXRhMyNbulCcUPiQLLgwXGNhAuJ+5d6sOxOTesrExo4j8r6S6am5Wv0/mwBszXiYLJ/UUHMlQkucO/EpJQzb6fuX4fX6MrQfhjYGzNI4U4zxzPaTwlbXzbjH4oCWTGvMAz6fW06U89uZ73ccB9wPbAv0vYIHBu+nuz9KeRHH9n47E9Vypg/pxI8P5B0iXENFjOid3a7+mHwCd0PfM/D8xYSETtYw/gUEkP2s7tWbhHJM1MPPPPz4TPzPXXx9IUvxJzE7nKLJE0LdEAsXxtU/q7/jPL8Rp5IOGrupYnlNLbL9lFHETXdaVimFAVvyr6ymNE0vNGIol7W52x66zEIjJXynxuEAW9LWyfX3QgQ8AahO8QwJJN9pvwhMmJC4BliYe7srE20RHTjL8SD0c/71w47cX275O8y+WSlgfuBM4CVgEmKIhlxgXEQuo7xOdzbN2+BYB/FRJV+7iRuH5cBvwZOFzSvMS1ZR26khu5IeCBooMYIo4ipkUhJrMvIzqwIZIXI4oIahDM1fshpWAG4N/p9WTkn2R6gPBSKjMnEomX1SlXA9WVRJJiZkLqsP4Z+ZvA00UE1UZK1/hm+/SiYxgqcpaO7oUvA9vbfrToQIaI0t+7bd8A3JD8R9cBNgGulvQ8IUN6hu0nCwyxv9wPbJAmLH8B3GC7lkv4CqFKkjN7EpOW96WmsMbGYGwvUkRgbeAIwu/9y8Tk9qLE+W0IbEw0WGWHpF822Tw58A0iN3lBZyNqKwcTv6sliSbaEcDrxGe2DJCNlUcD3wL2aih81TgJqPw7hyFV8auir+xHSHptTkymrFa3b0Xg3kKiag9lPjeIh7j3iw5iKLBdxkXHZURn3ReIqYZmEhrXdzyq9vAB8aA6usm+xQi/payxvb+kqYjE2t1EcejHtu8sNrJBsSshJbcwcALxIFtjQfKXh9qD5Nlj+6gkqbEmMdV2DHGPyJE/E4uKZr+3rKmXyLD9gqQFic75qYDHbGeV6LX9TO9H5Y/tpYuOoc1sA5wu6WnbNxUdzBCxALBumbxVE78lpGG3BsYAe9ftGwFcVURQbaTMjW8V+XAt8G1K2gE/sdy7AZJU8SnJu3NfwudmN2APSVcQRc4c/n/UGqY2Ad6hayIFQqHjn0UE1UYeBh7q9ag8+SHhZfxiei/bzwIHJb+z44EVigpuEBzbZNsHRDPf8SR7lkxZiSjI1n5X/005kTGSjgB2Jpqjc+MNWqvDzEuT/F1F8ah5sbKiojuS5iYWwQ/afqJu+5bAA7ZvLyy4QVLyc1uP0K5eyfZbRcdT0TOSepOQs+0cx8ORdBCwE+GTMooozM5EFJy3B35vOyt/veQT2IwjiU75VaiTYMvVjL0iDxq6ByclErxP0byQbtsndCq2dpGmKx8gEi25J6hbIukzRLdkGbx6SomkVwiT6ymJ5o1uz1i2Z+p0XO1E0v2EefeFRcdS0XeS78uOti8rOpZ2ImkdYAtae4dk+XuT1GuTje2RnYilnUiakZievyH9adbQl9X9TNLUtWf5HtYA4ynDc7+kOYli0SbE1M1o4BRCsnlZoinuvVwmGJMU29eA/9h+o277SoQ85xMt/3FFYUh6m8hn3SzpDWDDWmOOpGWAS203SsZWFIikd4EV02f2NrCm7avTvmWBv9mersf/yDBE0tHE9XBb4ELb49L6dE2imHmG7R2KjLGiO1Xxq6Ki5Ei6gJi2mZaQYcvdY2MCymZ8KunrvR1j+/FOxNJu0me1C5GQn4Euve5XCSmDQ1uMjw9bUrGyVcxq3Jdr4RJA0k+AhYiF7wG2n5W0FLFQ/G+x0VVAn4rn9eRcSH+ZWPReU3Qs7UbSZESDwCbAFM2OyfVzg/FJp9Vofc/epeNBDRBJ+9CLF4/tnDt2kfRj4DAiYZGTtFWfSJ5mCxL3tVNtj02yty/VSaBnRxkb3yStD5xKSK1tmV5PAvyUWNucaTvLSW1JLzbZ/HniGvkO8I7tWTsb1eCRNAfwF2Kd1nitFBk+h0j6BFjc9h29rAGA7O/XGxFy50sBzwKnAafZfr7huB8C19qerPNRDpy0Lp0FeNn2x0XH0w4knUo0rDzVZN8cwN62m/nTDXskPUCsP8+XdAvwrO310r4jCQ/WOQoNsmICJD0G7Gb7Ykn3ALfY3i7t251oZMxOPjyp/JwMrJs2vQN8Nr0+F/iF7XFFxFbRmqr4VdFnJH2bkIdaCJidePC7R9KBwD9sX1logIOg5Od2Q2/H5CpF1Bfj05wXHWUlJXfnJrxSxgJP5mqCLWlT+mFEbvuMoYtmaEi/s1FEgvBpwuNg4XSNPA0YZ3ubAkMcFOn7uAMxqTc7JeomLytpkful2qK3TEjaH9iUaBQ4m0hgv0vo488DbFcv+5gTkuYBbiGmpaYBXgGmJ2TYXwfetD13cRFWNCLpTmIC8QvE9b/Z5EZ2/iHJv+ZUokv3I+I7WLuvnU8k1XYqMsbBUMbGN0n3AhcChxCf2ULp85qWmES50PbhRcbYblJR4Whgc9t3FR1Pf5F0M1FYPpTwe+wmSZybZKykTYDLbb/alzVAjs/9NSS9D1wMnGK7pRe1pFmALXNp9kgTXnsD3yVUEhZJ15KTgDG2/1JogIMgFWQXs31Hk30LAnfkmhuRdDAwo+1fpIbMSwkVmY+I55Tf5XoPkDQT8GtgEaIg+yIhFXi07ZeKjG0wSDoGmMT2tqmYfgZwOyHruBRwRE5Nb41Imo+whah9ZnfafqzYqCpaURW/KvpEusGMAm4FriceGGqLjpHETXalImMcKGU+t7Ij6S9EIn5tWhif2v5PcREOnLputGaJ+NJ1X1cMX1Ii8P+IaY2nieRF7Rq5AdFF+LUCQxwUko4FtiLkWx6heXImiwX9xIKk3xASqi8Cf6e7oXeWko4Akh4nJm1OJxb0C9u+O+07gyg2b1VchANH0ihiUmMtoqC3EGE+vw4hm7Sm8/ZHLB2pwaFHbP+8E7G0k5TkXAnYiCjIjqPrvrYpsJPtbxYY4qAoY+ObpHeAVWzfKOkjYDnbN6Z9I4Ajbc9ZYIhDQiq2bJtpkfk9wjNwVNGxFIGkz+Q8USTpC7ZfLzqOdiJpY6Lx4Wwi73MaXdf+nYlp2ayujfWk4teizZ6l0rXk0BwnbZohaWHCp20qYHSuzeqSliDWMh8TjRw1W4jlgMmAn9i+pbgIB06Shp3a9v/S+xF0+WqPBk603R/VkkKRNBfwvu2xddt+2XDY27bP6mxkFX3hM0UHUJENBwOn294i+VDUG0PfRxhG50qZz63slM74NH0Hfw9sRtf4dCNZdmzB+DHxlWkteZWdp8FEwIrAJrb/Lanxu/c8MFsBMbWTtYBdbR9RdCDtRNKSwPS2L03vZyA6yOcnJmZ3zXXikpBJhWgQ+F6T/QayLH4RXfJP2P5E0jhi4qbG2cA5RLE2RxYBfkF0fAJMbvsT4Jz0/fwj8P2ighsIkhYHNqf1PS27hHU9ORa2+sjqwA62b2hyX3sGyFo6KefkbQ+8SZcU7AvAN4Ab03sBXywgpk7wEnHfzpGHiSnfUiLpANt7ttg3FfA3osieJWUrfCX2IDymd0vX/voGj4eJxqqskLQDkQ+BeP69RNIHDYdNCcxMNFaVglTgK0PD1LHA3cCqtt+tbUwT6pcDx9B8rTPscXgevlf3/mJimjQ7JH0fuJmQWr4ibZuU+PzqsaSXyijNnztV8auir8xH18NA47jgW4RsTa6U+dyAcnlsNDAd8IrtTyW9RXTJ1LgV+F0xYQ2K3Yku+F8TZsK/ITqSNyCKDL8tLrTBoTBM/geRoJic+H19jkhavE1MAmRd/FJJzdiBT1psnwF4v5OBDAECHig6iCHgMGLRdGl6/0fCmPxiQlbvA+J6kx22Jyk6hiHkReLeBvAUIQtybXo/TyERtY8pgbfSPfs1oN7D5iHgO8WENTAkLUd0614H/AC4kuhmXYJoDMhKzmsiYyrCb7QZ09L6nldRHHcB3wauJhQ7Rkr6mJjWHklIRGVJathrZHKiwDcSyFVGaVvgREnP2f5H0cEMAdtLet/2gfUbU9L6CjIvokMp1zVzEBMnzRhHrEtz4xGi0CpgR+AGuhqDa3xIXEfO72xo7UfSFEROpNn38ZHORzRo5iOUD96t32j7HUmHAxcUE1ZFA9sBV9q+osm+hWzfAyDpOOKaWRW/hhlV8auir7xMePQ04/8IE9RcKfO59cljg/AWyZGniM5/iG6tDYhkL8CqwGtFBDVI1gf2Ac4kil//SJJXJ0o6hxiBv6S48AbFkUSCc02i8LUs8cC+PrAXMKK40AZPgxn7MjQxYy8suMFxM7CdpPqHvVqjwGaEbEjO/BlYj9aL4Vz5OrAvjJedGAFsZvu85OOzO5kWv0rOjcCSwGXEd/NwSfMSxcp1CCPlXHmCrmTgvcDWkv5OFBo2B/5bVGADZD+iqPw7QqJyrySdNAeRoL+xwNjaRmpc2ZDWyc+1OxxSO7iTkMe+qsm+NYkGqqwpYePbwXRdP0am18cTagh3AlsWFFc7+JjW3lGvENJeOXIFsf68SdKHRKPbBGRYPKnnp8AVkt6zfSSEVCCR9JyOaF7JlpKua54DFqD52mUhwpsuK2yPJq1hJL0NnGz7hWKjaj+SZgVOAn7SbDdxDc1RHecRwge9GbOQWfODpJeBFWzfK+kVevZFNJGvuxPYMyk4DVeWpG+N9dcAxw1xLBUDoCp+VfSV84D9JD0C3Ja2WdLXiIvAKYVFNnjKfG4QBYe76PLYWIkJPTayMrxu4ApgeaKL6QDgUknPU2d8WmBsA+UrwKNJ8uoDuiYAIExC/0J0UubIYkRyojb+PrntccCpkqYjxvqXLCq4NrAzsD9hxr4lcHyDGft7Pf3jYczviIm9h4ipIQNbSPom8E3ic82KBn3uscAGySNlNLGgrydX/6jJiS5WiEmUz5BkGogixCzN/lFOSJqd1ondv3c+orawBzFRie2jkv9jTR//GKLgkivnEQbzZxEND1cTjRCfEt/PTQuLbGDMD+xJxG+SvJftZyTtQxSfc0wOjkfSgsQE23PEb+0B4PPAnMR0W3aJwsSewLWSriW6qg2slPwE1yT/pHXpGt9s3w7cnl6/AayWJgCmsP1WocENnl/SPUE4jviN/cN2o4RZLhxHz4nPrEn+c6sTMnPvE82J1xLnvGS9L0ymlHFdcwqwt6SX6GomlaRliWtizs9YZfcoPpmQ/9uRFh7NmbIdcFbytbzE9gfp3jYC2JVo1MmJ4wi53trr3u4B0xINHqcRjdHDlRkJWezxpHzdzsQzco1X07EVwwzZpX0eqWgj6QL8N6LTYiyRNHue6FK4BhiRq3dImc8NQNJYwmOjZqT5/bSARNL2hBFxVh4brZC0EPGgkK3xqaQnge1tXy7pUeBc2/ulfVsCB9vO0tdA0puEnvUYSa8Cm9u+JO1bBrjMdrbeAGU2Y0+JtH2Ih9IZiC6t64B9bP+rwNAGRDKE7iu2nV0noaS7gGtt7yrpdGAu2z9M+9YBjrA9e5ExDpSUeDmfaH6A6PiEugVWpp/ZZIQv1lO2c5uC6jeSvkx4Ck4FXG/7oYJD6hcpebah7dGp8WZP26enfSsBF+R8TwOQdD2xqN+MaCxaKCU/v09MIW5lu9n01LAnmcwfQjRwTEpcP24HdsnVXL6GpFHEhEat8W0hJmx8WzP5pWRNmrKZg2gay7VAVJE5kn5KPJO8SvjRrVAGv6wyrmtSQ9GxhKf7J0RDwEfEPeBE27k2mI5H0pqEr+XslMiLNOURtrCdvXRjPWk6amq6Pqt36PJ9H0fcw8eT+bRsUyStAFxse+qiY2mFpP8RuatLeznuZ8T05Qydiayir1STXxV9Ii0oVkldMRMkP9OodbaU+dwSpfLY6AnbdxFTbjkzhpjSuJyQlzgwSQ59QMgOXVRYZIPnX8RkG8B9wC8kXU50zW9MFJ9zprRm7Lb/A2xUdBztouSeUTX2Ay6QtDkxqbFa3b4VCdm5XDmYuJYsSUwljiCmGTYkpHnWKy60QfEJIcWzEvlJAPaIpCmJybVTag04tp8jpB1z5X5CXnQ00Qywm6QXiG7k/YAHC4ytXXwXOJS4T0NKzti+VdK+RPEoy+JXKnAtKWkq4AvAG8mcvQwsQjS+1QpCk9v+BDhH0gyEXGdWjW/p+zamdZ/FAAAgAElEQVSF7V3T+2UIT8upgbGSlrf9cJExDhZJcwELEo2YLwJ3236q2Kgq6kmNDY18DJxDSO7/AVg8aixZT6FDCdc1js7/bSX9AfgxcQ6vEQ04TxQaXBtIU+cjieeTMk1HQViV5O4z3YxST8j2kTHA4kUH0Qt3E9f4Hotf6Zi7hz6civ5SFb8q+oXt64gFfuko8bmVzWNjPJK+0sPuT4miX25SKHsCtY6ew4nrdE3y6tS0P1cuABYmpBv3JhJmbxHfxakIc9CcKa0Ze0V+2B4l6RuEt8GDDYv62wj5slxZibgW1n5T/01TDGMkHUFI9WTnQ5SaVP4FzFx0LO3G9jhJ6wJnFx1LGzkKmCu93p3wabs6vX+ezH0sEwY+tO3k4zAHXX5YzwFfLSyyNmH7fcqXUCtj49sGhMR5jSOI5od9gQOJpoifFhDXoJE0DeFftj4xgfIRMBnwiaRzgV/afqfAEAdMCadQLieui2qx/5y617l6ENUo7bomNfX9p+g4hoDNgUNsl9HTdyTwO0k3ZZjfaYntfYqOYSjpyz0gPYfd3+nY+skJwIXp+3dWswMkbUg0dK/Z0cgq+kRV/KpoSS+FhW4Mc4PCCSjzuTWhbB4b9TxNL50ykp4Fjq4ZEQ93bD9PJM1q3WkHpz/ZY/vQutf/kPRtYBXiQeg627l3yfRmxr5VQXH1myR11WdsLzNUsQwFkuYH/pN01efv7Xjbj3QgrLZj+0ngySbbTyognHYyM/Bc0lp/l/CyqfF3Qso4V/YADpX0oO0yTA7Vcz2wNF2d41lT39Fv+4XkjzUv0czxmO0ydFw/AswD3EAUzX+TJFU/JPxRskkeSjq1P8fb3myoYukAZWx8m5V0P0uSqd8hZDfvSFMcpxUZ3CA5jvA82Qq4yPYbyQt3DWKS6FMyXK+VdAplrt4PKQ29rWu2LCiufiGpXx6OtscMVSwdYFpK1MwtqVHi8CvAM5LupLlHc85e9qWjTPcA25dIOgY4Q9K2hD3Oc0QucnZCin9R4I+9SSNWFEPl+VXRkuSJ0ucvSE7+GmU+t97I3WOjntRFfijRxTqKMPSekZD3+iZwEOFzsAnh4ZBFAayiPORqxi7pgoZNixMFh7sJ2YmZCNPhl4DbbGc1ZZPuAYulpFlP9wORkedXkuP5h+23WkjzTECucjySHgN2s32xpHuAW2xvl/btTvgmfqnQIAdIWtDPSRT0XiB+YxN8PzPslAdA0vKEYfn5RJGy2bllWWguK5I2AuawfUCaJL2GrimidwnvqGsKC7AfpN9WPV8hnhlfpuu+NhPxLPlMrr8zAEk7ArPZ/q2kxYjGt6moa3yz/ZciY+wvyWPvF7Yvk7QJ8AcnD9wkXX+p7c/2+B8Zpkh6C9jZ9olN9m0NHGb7c52PbHBIeg44q6RTKEia0va4HvbPZvuFTsY01OS4rql7zu/mD5u2NT6HZPHM3wxJfwLetr1z0bG0A0k39Od420sPVSztJBX1drP9nyYFvm7kts6uUcZ7gKTVgB2I3EhNFvYDokHsKNujioqtomeqya+Knli17vXngMOARwnPodoicQ1gPkJiKCfKfG4TIGl626/V3pfAY6OeHwOjaknPOk5MnRnft71xMuzdGhj2xS9Jj9I6Ef8pMbV3P3CC7WEvWyapX6astl8eqliGmtRVvn+9P0PyFPxA0hzA3rl0ktteq/Y6eUZ9nfg9PVu3/SuE/EuO3ohLEx1oEB5RZekEuhxYDLiD3qV5cpbjGU1c/y8mrutnpKmbD4ClCDmsXHko/SkjNW+oHdOfZgmorL6TkmYlJpibybnY9u86H1X7qJd2sf1oKoB9nzjX23O6Z9teuPZa0qqEbOUI27fWbV8COIMJ5fWyw/Yf6l7fLumbwE+Izy3XxrebgF1TInsnJvTd+BrRgZ0r7wGtVEaeBVoWWIY5pZpCacIoSas0m/KVNC/xrFKqSbHauqboOPrJt+pez0LYCFxF97zPCkAW67R6GprdrgUOS96Oo+k+HZVV41suxawBMCMhbQvx/SvLOrSR0t0D0lTXpZImJTwDBfwv+apWDGOqya+KPiHpdOB929s02fcnYBrbG3U8sDZQ5nMDkPQBsUA8Dbja9qe9/JNskPQmsIbta5vsWw640PbnJa1AdIR20xkebqSi3WpEUfYGuqbZliaMh28CliASbSslr7phSz+nLG0726aM+mmiJvsWBO7IsZtQ0pPAjrYvabJvBNF9XZrFfZIamoeQ1csmsQuQiqwv2v4wve4R2890IKy2I2lqYGrb/0vvR9DljTgaOLFM97qyIOmHvR1j+6ZOxNIO0vfuXKJg9zLd5Vxse+6OB1bRK5IeBg6wfW6TfesDe9n+Rucjq2iFpNkICfeFgfuAtW2/mPbdBjxgOxt56Xok7Ut0ka9s+6O67ZMTjSz/tL1XUfENlLJNoTQi6XHgcWB12x/Xbf8/4lnkAdsrFhXfQJB0WD8Oz67BQ9KlhA9uNw9tSQcA37W9SucjGzhNJtt6Ihs1i0YkjQROtt1NtlfSLMAWtvfrfGQVrSj7PaAiL7JNMlZ0nNWJjphm/A24sIOxtJsynxvExNMmxOJprKQzgDNsP15sWG1hHFEI6lb8SttrnZIi5Hly4D+Ep8EqrjO3ljQtcBkxEbA5cAWwP8O/m2atXvZPQZzP0oQXRe60KvR9kyhk5siX6Brrb2QKomMtO5Js6s+IzruLbJ8taS9gd2DydMwlwMa2s7h+1Bezci1s9QXb7xGd8rX3FxNTYKVBkogmhy8D9+fyHeyJnApbfeQgQgZw0/oJ+7IhaUpiorLVdNsJnY9q0MxN3TWkgfcI6dGs6It/ZT25SYwm+bhW/qIrkO90FEQBfX7gWUlX0TWNsiLwMfBPSbWkrm3vXUyYvVPmKZQmLEM0JZ4jaV3bn0paiJgquhnI0X+ot3VbPQayKn4BywLHtth3E/DrDsbSLkrTgNgLexO/rWaelbOm/VXxa3hxHeFjXNZ7QEVGVJNfFX0i6az/qdnDdnoY38r2zJ2PbPCU+dzqkTQ3YZa8EeFzcDsx9v/X+iJLTkjaH9iNMN+9jAk9v7YGDrS9d/ocf2C71aJ52JC0kbe2fUWTfasQEw2zpY7zv9iepuNBtgFJUxHG3jsRn9lfgINt/7vQwPqJpB0I3WcII+ixdJcDmZLwyzrd9uYdDK8tSPo7kZRZ0/ZdddsXJpoDHrbdq7/UcELSFsCJhGH320Ri9zjiGrk/IYn4LWAP4PhctMqTFGWfqZexrBg+SPolsCdReDawsO17JF0EjLF9VKEBDhJJiwI/IHzNXiN86v5ZbFT9J0kq/6zZ9HlZkPQDQhpqhhaHZNlFLukWYGpigv7Fuu2zEn5079j+QVHxDYR+TNpn5WXZE5LmIyTq72g2DZALkl7s/ajx2PasvR9WDBPLFEoNSXMSRZMxwCmE2srlRONUGZr6SoWkZwnLhF812Xc80Xzar2fpis6Qri2L2m7076z5MJ1iu9WzyrCmFwltbO/S8aDaQPrMeiL7e0BFPlSTXxV95QRgL0lfBEbR1ZG2GpHAPrDA2AZLmc9tPLafBEYCIyUtA+wDnAT8UdKFwNG27ykwxH5jey9JrxG+bL+ia7E1ljCOrnl8/ZUo9OXAFwj94GZ8Efh8ev0G4QGWFWmC7VdEZ920xOdyqMOLLkceISZERXjY3AA0JjE+BB4DejW0HaZsSVwb/5maBWrXyJmBB9L+3NiOMKXdEUDShoTPyw62ax2hV0n6mCikZ1H8Ap6mf7rx2Sw4JL0MrGD7Xkmv0PN5miiq3AnsmVORT9LORAH2UOJ6cn3d7huB9QivouyQNA1wAV3TDK8S97VJ07TDWmmqLxduJfwQS1v8Ao4mJtKXAx6pl2TLnC2Jqb2nJd1N131tQeJ7uWGBsQ2UsnqjACDpRCJRtnV6vw7RODUp8I6kFev923LC9ixFx9BGJpYpFABsP53W1WOA9YE/176jFcOSQ4BjU9GyMe/zE2KNmi3Jl/Pztm9P76cC9iKaGK+zfUyR8fUXSZsQCkYQz/YnSHqr4bApiYbFazoZW7voi4Q2kGXxi4nsflAxvKkmvyr6TJpw2IUwCq0vMhxWgi7k0p5bPcknZW1iumEp4GHgEmB5YCFgV9u/LyzAASJpEkIa6kvE5/Zcrn4vqRD5A8Jw90rbTvJXKxMdhWNsryVpW2BL298pMNw+I+kLwG+IRcVkwJ+Aw22/VGhgbUTS3oQW+QtFxzIUJCmbhen6nd2Zq1SBpHeJ7s4b0vtpCU+9JWzfVnfcksDoHPwCASStXPf2c8BhwKN0N/Wej2gQOK/jQQ6Q9Pv6s+3/StqH3ot80xKyls/YXnao42sXkp4mpg0PU5gpfwQslCa/VgDOsd2qQWJYI+k4Ijm4JfC3JBE1CfGdPBE42/Z2RcbYG+k5qsbcwNnAH2gt55JTMa8b6Vq5uu2ri46l3SQ5x81ouK8Bp9l+v8jYKroj6RlgN9vnpPdPECoWuwDHANPndK2vyJMePLHmI3zbTqvblp0nViNpDboE8DWaT6Qc3/GgBkmaEtodWIAYBviY8BE8yE38jXNC0g3Arbb3SO9rqhY3E7mfvXPK9Uhai8hdQTwr3kA0t9VTazI93varHQyvLUh6FPgXJZfQrqgomqr4VdEvylRkaKTk57YU8HPiocHAecRo+B11x+wC/C7XpFo9kqaz3S0JlQNpAvEcosv6Y+IBb3ri4Xw0sL7tVyWtB7xn+9LCgu0DkmYmpA23Ijy9jiEmbqqHu4rCSDIMi9WugY1FhrrjFiUWkdlMSNWQdDrwvu1tmuz7EzCN7Y06HlgHScWii21P3evBwwRJ44CVbV/XpPi1HHBJxnK3Y4GRtk9qsm9LYD/bX+p8ZH2nibRcTdqr6YIqx2tHPZJuI6TBzyg6loqBkdY3zZLWWRVmJb0PLG/7ZklfBR4Hvm37oXRt/Kvt6YuNsu+kaaE7bL+TXveI7et7O2a4UbYpFABJT/XjcNuee8iCGWLSGu464vOql7Icf7/L+R6Xro0zAq+UKOfzCvBz25dLmgz4H7CT7T9L+jVh5/GNYqMcGJJOA/ZPakalocwS2uqDF6kz8x+tyJdK9rCiX6QHg2fSn1JR1nOT9B/CuPtWYHvg/BYL3usIKYBskLQNMK3tw9L77xI667NIug9YzfbzRcbYX1LH0gqSvkf3KZt76o47t6AQ+8uTRNLlRsJg+E3gu9FI2J0cF/f1SFoc2JzWHZKLdDyoNiHpM4RfYLPzyvHBtVmyukwdQasTDQ/N+Bvh11Z2xhCd2Dnxb+CHxD25kaUIqdVc+TzQSuL2OWJacbizGeW6TvTGNsDpkp62fVPRwQwFZSkO1ZOmNXYBtqC17FBuSevXCLllgB8DY20/lN6L/M7nWmAx4I70uiefLJPf+UF4Mt9KTOgBHE7XFMqhkqbMaQoFwPbEJON1BLFu+zJxj14UeImQhd2YUCXJlpT7KY0CSWIaoCYLuFh6f1F6fw/hT50ltn/euC0py8wBPGq70XM7F8osof0QvT8z53hvq8iQqvhV0WeSLNRqtE7q5qpFW+pzI5KcJ9t+oqeDbN8NTNKZkNrGdoQfRY2jgf8Sk0a/I4p5Ofo2kApdWXmwtWCq9PfSwI/S67It7gFIncd/J5LWPwCuJM5/CeB5whA7O1Ln4NGE5voULQ7L8XO7Onl61XNdw7acn5PeJ76Ho5vsWxIY19lw2oekO+llMWV7kSRddn9nomobRwHHS/qQrgLlTJI2J3wFtygsssFzP7CNpKtcJz2REvXbkMFnZfv0vh6bGgayo4mn3jTA9ZI+oiupNh7bM3UqtnZR0uJQPdsDuxLStwcCBxDT9+sCkwMHFRfagLkS2C9No+zChD6q3yQ8L3PiG3TFnOUkRh/4JlFAqT1Lbgj8un4KBciq+DWR8UNgB7q8jJU8VA9KTQPHAysUFdxASX5fG9I677N247aMeJIoeo0BRgD31kkBzgC8XVRgg0XSvsAUtndN75cBLgWmBsZKWt72w0XGOEB2BM5OE2Blk9Bu5kU6PWG5sjxxfamo6AhZLsoqOo+keYBbiJvLNMArdEmxvU50BWVZICr5uU0JfBeYFeix+JUpXyFkT5A0I1FkWNb2jSlxeGyRwQ0GSV8BZqf5Q3lO01FlXdA3Yz/gj0Th9SNgryRVNgdwNTH9liMjgVWIibazgW2Bd4mF4zxEETo39i06gA5wArBXklJtNPXeikiI5srDdC9+TU9Meb1P86mpLLB9cupkHUnX9/TvwHvAPjW/m0zZnUhgPybpYqLjeiYiQTMnYTafDZIOsL1ni31TEc1HK3U2qrZwHOWfbitjcaieLYC9ic/yQEIu9R5J+wOXAV8tMrgB8lvgSGBrIrG7d92+EWTWNW+7tn6ZgnjGusb2g8VG1XZKN4WS/G//Yfut9LpHcvXGTUxHkgSU9BZxv65xK7HeyQpJCxLNiM8Rxa8HiKn0OYlGxX8XFlx7OBI4IXllLUDYXtT4EXG+ubIBca+ucQTwD+JZ+UDgYOCnBcQ1WGqfyWm0fvbKshmnB8WAiyUdQPi5Xd7BkComYirPr4o+IWkUMRW0FpH0XIjo0F2HuNGsafvO4iIcOGU+NwBJrxPnkG0ysBWSXiU8sK6WtDZwCjCd7U8k/Qj4e05eLwCSvkZ4fi1A8wkp56yvXmYkvUlIzV1P+LX9yPbNad+6wL62v15giANC0uNEgvB0oqi3cJoURdIZwDjbWxUXYUUrJO1ANG/MUrd5LHCY7aOKiWrokPRZotB3ju2Ti46nr0j6caPWf5pIX5zo1H0NuI0ogJ1pe73OR9kekv7/SELWdxaio/yfwAG5yaemZOChtg9s2P5Z4ApgDttzFhFbRc9Iegg4iSgO1fvqTUIUhx6sdZfniKR3gZ/YHiPpg/T6+rRvZUIRYpYe/yMZkCb4lgbWB0bk6lucPq+VyiYtmn5nZ9o+TNIfgCVtL5z2rQ6cYHvmHv8jw4x6z9g6D8iWihY5r9kkPUDcm8+XdAvwbO35Q9KRwOq2sypgSrqeKHxtxoTX/u8D5xKeWFcVGeNgUXi9LwzcV5//kbQPYaNwRVGxDQZJ7wErpvvalwmrktpvcWXgtEwn0TeldzWL0vmuSloWuMj254uOpWLioJr8qugriwC/AGpaupPb/gQ4R9IMxLTD94sKbpCU+dwgEoE/I+NO+B64A9hW0vNEF+9V6bMDmJuQQMyNk4AvEAv5R4APiw2noh+MAyaxbUkvElNRN6d9bxGTfDnyZeCJVFQeR3w/a5xNFGur4tcwxPYfJR1DfIY1/8DnymLs3YjtdyQdQUz9ZlP8AkZJWrO+Q9z228A1tfeSpiEKKj8sIL62kQpc6xYdR5v4KXCFpPdsHwnj/SeuITrmlyoyuHYgaUvbJ7XYJ+Ak2zlKcc5FJAY/SXKO00F4wEg6nrh+ZFv8Al4FPpteP0s0VNVUA75AlyR1lkhaFFiP6BqfmWgQOK/QoAbH3cB3yFQeuwfKOIUyF10ygGX3/7qCkCY7n5i4uTStuT8i1Feym/wiFHEOBWrPwVMC2L41yeodAmRd/LI9hpiObdy+T+ejaStvE1N6AMsAr9u+I70fR6g4ZUd/5LRLxso0kXisqBgqquJXRV+ZEngrLQpfI2T0ajxEPLDnSpnPDUJu7feSZiGkk16iobskY0mG3xLFvQfp6uKqsQ4hZ5kbCwIb2B5VdCAV/eZ+wrB2NFFs3k3SC0QBcz/ie5ojL5ISg8BTREK3NqUyTyERVfSIpO8CvyI+q9nS5heIxNqxZOCtNAimY8ICbQ5cDFwkaV3blzTuTI04VxEysqt3Orh2IWkk8LjtvzbZNxuwue39Oh/ZwEgSy6sDl0h6H7iEuDaamHAYW2iA7eEYSe/bPqt+o6RJieaH5cnTh67UxSHi+Xdh4rn/HGAfSdMTzyPbkmFDnKRvEgWvdQmJsg8JicodgeNsN3p45sQOwHlpsqHVWi27phXbp0j6F/Fd3LVBheQ1wuMyK2w/0+x1GbG9W93rKyUtQTTUTgWMtn1lYcENHAMfpkbFlwnpzVvTvufIUxK2G5Jmp7WnWa55n5uAXdPE5U6E31eNrxGfX8UwQtL5TTZPDsxH/NZ272xEFRMzlexhRZ+QdAdwjO2zJI0mHhx+Sujjnw4sYjvLh4UynxuMl2foiawlGQCSp81rrrugSfoWMNb2K8VF1n+SRMhI2xf1enDFsCJp/89l+7iUyL2M6DCE0JEfUZMLzAlJpwCv2t4lGZQfTnSBfkAUmc+1vXmRMVZ0IWlnQrL3beAGQhYEYoH/I2BaYHfb2ZrMt/DZmJwoDv0GuMX2iM5GNXDSBM3JwEbARvXFoWTMfg3wRWAV27cVEWM7qJOIOg34pe0P6/YtCtya4/OIpJ8S18RXiSLzCrZfLzaq9pAke08nvpcXpG1TEH5miwDL276vuAgHhqRzgcds75u6/XcEjqarOHSz7TWKjHEwSPo6MJvt69PndRiwJilpDWxn++UiY+wLkuYmil3rAfMTktKjCXmym4jC5Y/SpEO21K3VWiZmcrw2TkxImprwxp2PmLI/s+zFsRyRdDNwhsNf9WKiQWwD4tp/MjCz7W8XGeNgSJLZ5xONKdAlyTn+2pLrtSStrc8iSToCa9t+Me27DXggVxl+SesQjUStCpbZyTkCSLqhyeZxRF7k4owLsRUZUk1+VfSV84gk7lnAXsQ00VvEyPhngE0Li2zwlPncoPySDNh+tcm2XKdsdgb2l3SH7eeLDqai7zRIlr2QTJXnJZJNj9UnejNjD8J3CNtHpUR9LYl2DDHVVjEMkLQqIedyGHCQ7bca9k8L7AYcIumRXHX/CXPkZj4bHxGdoL/qeESDIDVubJ5kRf8iaQrbZ0r6DjEF8AkxSZSVJ1YL9iAKDd+WtHpu97kWhdePiemaVYE/AIvHZTLrDmsAbJ8naUrgzOQddR3x+/sqsJTtxwoNcODsQ9dU7EHExOim1BWHComqTdh+HHg8vf6AmCzaodCgBsa/iWv9Pwl55b/VCsuSyuQT8kt68XzJmTJNoSRp5VVtf61u27TAncR18XVCmu23khax/UQxkbaPVECfjeafX27PJScRk6MQUyfXALX72LvE+iZnDiYkKZcE/gGMIL6TGxJSgdl6xtp+gTiHZqxAFFWyQ9L6wKlEo9Ey6fUkRDP+G8CZhQU3QCRNBaxEPC+OBa61/VKxUVVM7FSTXxUDIplMrkgsEq+3/VDBIbWNMp9bGZB0GHC07efT656w7az0yFNH2ryE7M4TNNFCtp29j0hFHkiajOjuf8p2jh56ExWSbiQ+q5/3ctxpwJy2l+5IYG1GUjOD9XHAy878wTYl1nYg5KB+QXhXLp9bkagZabphMWIhfDHhRbeO7Rtymfyqm15rLLw2I/vJ+hqStib8e/5FyAUua/upYqMaGJImAWYB3rT9TtHxVLRG0lPE1PI7hMz5ucDVtj9Oxa/XyXTyS9JSwD1l/g6WcQpF0j3AJfUSvWl6dC/gF7ZPlTQjUUR/0PZGBYU6aCTNShSLftJsNyW4x0n6LLA4kfe5PYeJ2J6Q9CSwJ/BXoiFsUdt3pn1HAF+2vXaBIVY0IOle4ELCb+4jYCHb96Tr52jgQtuHFxljf0gT29fSVWQGeJN43r+m6T+qqOgA1eRXRa+kjs9jgFNs3w5g+zngz4UG1gbKfG6NSPoM0QmUe9fWWoTPxPOE0XVPiU6Tnxnv8+lPKUgPQH3G9pNDFUsnSAvFVYDZ6f5by64YS0ycXE90b1XFr+HPAkTXZ2+cRySkskLSXMD79VJCkn7ZcMzbjf5EOWH7t8k/ajdi2mHlskjo1bD9bPIO+TNwtaTdiA7lHCj9NL2k+ZtsHkN0H69OSERNVTsus2dIiI7qp4lJvauKDWVokHR9D7s/JRQu7gNOS+ueYYntuSQtDqxPTGSsD7wu6SLgSvKelrqBSLrfUXQgQ0gZp1DmBBolzNcAHrF9KoDtV1KhYd8Ox9ZuTga+R0xrP0JIA2ZLyvuMIpQRbgRIxefRRcbVZmYGnrP9iaR3genr9v2dkCzOlnQ/2JzWk6SLdDyowfNVQq79E0mfAJ8DsP22pEOJxqNsil+E+sinxHX/buK5+XjgRCaCZ+iK4UtV/KroFdvjkub/2UXH0m7KfG410uTG0cAmwBQtDsuma8v2XHWv5ywwlCHBdo4LwZ6oSdb0htJx2XwXG5E0guhKnhR4me6LxOyKsbY/TWblMxcdS0WfmJSQYOuNj4kEcDZI+j5wMyEDckXaNilwbMOhlvRSTt2Fkl6h+3VShIfZ4zUJvRq5av/XY3scsJGku4mFchZSxbXCa5KB2gm43Pb9xUbVdh6i+X279kW8su59dvftNDX0DDB10bEMIa8CiwJfIpJPrwAzAgsSk5ePAtsDO0latjYZMBxJPoe3SdoBWJYomKxBJEANbCHpPdt3FRjmQOjL9GjurERMofwzvf9v+q6NScWhnYlGxpz4DHXyapKmJ+7VxzUc9zTx+8uZJYAtbGfXLNWMlPdZmMzuWf3kOZJUPTGpvQph6QFxT8hSGhBA0nJEAe864AfEs8hUxPf0ecIHMkfepCtH9wJxPbkxvRfh+ZsTiwO/tX1Lev+opK3S37PUfNoqKjpNVfyq6CvXA0vTdSEuE2U+N4CRxIPP5kSRb1tC03pDYB4y9jWQtDFwRTPPr7QYWcV2djrJJaOZVEZZOYjQjt/U9mtFB9NG9gAOlfRgxl56EwsPEdf763o5bpV0bE5sB1zZwqdsIdv3AEg6jjCNzqb4RSTNcp5g6CvPAB/Ub0g+gveT2SSi7Q8k7UE+E2v9IUs51H5yKLCHpJttv1J0MEPA5cDcwGL1ksWSZgMuAy4glBSuIaZzflxEkP3B9qfEhMboJMO5ErAuMU20vqQnbH+jyBgrulHGKZQngB/R9bCkbXkAACAASURBVJy1Svr76objZgJyXwu8DLxfdBBtZhTwM3p/Ts6V0cT1/GJiYuiM5EH9AbAUcESBsQ2W/YA/Eo2kHwF7JXnAOYjf340FxjYY7gK+TZzDKGCkpI+JJtqRdDUP5MIsQKOSz3+IQt6XgKr4VVEIVfGroq8cB5wsaRriYfUlGhI1Gcqe1CjzuUF01O1DJJbOBu6wfTdhXn4GsBpx3jlyGtFd0q34RYxVn0aeJqGzEZ2trUb6N+54UAPEduNisMx8GdiuZIUviK7dLwL3SXqB5tfIHGUmysifgJMkPQKc3Mz/StIvgF8SBaKcWJK+TU5eQ/cO7GGN7X2KjqET1E9uN2y/QdI8hNdlTvyTmKTJtdu4KbZLdT4tWJ5I0Dydpg8b72u2vU4hkbWHkcCOjV6dtl+QtB9wpO2TJf0BOKWQCAeB7Q+BS4BL0vrtZ0QhLDdWkjRfXw7MtJmvjFMoxwJ/Tp5zLxETlE/RveFmefJrMmpkJPA7STfZfqvoYNrE1cDvJc1C67xPrnkRiOfkqQFsnyXpHUIydirgV4T0XK7MT6xJa96r00BM5Evah5AZzfE6eTDhbQnxm5uDkAmcFLgT2KqguAbDxNDQV5EZVfGroq/UNPF3TH/qL2hZyp7UUeZzg0jIP5G67sYxYXLpbOAc8rypQs+SIV8kPA2yQtJ3CGmv/xEPP48Rn1mtU+aZ1v+6omBuBb5OmLyWiYfJfwE/UWD7dEmLEovbnSRdRtc1Yw5gZaKofmKGibQZabj+pfvazkSCrcar6diKjEiJtdzu2bsA50j6kNZJtPeKCGwokDQJzRtycjzHGYDHG96XiVloLXU+JV1Sxi+Tufye7XeJ9UyOEvYj+3icyTOpW7oplPScNQuhpDIdcA+wre2PasdImpFoLs3O80tS4xT2V4BnJN0JvNGwL8cmgb+kv1dPfxrJNu+T5JhHED6C/wOwfTHx+ysD44BJbFvSi4SC0c1p31uE33Z22L4duD29fgNYLX2WU2RcdL46Ta81cl3j9jJIuVfkQVX8qugry1DeCn6Zzw2iYDJdev0UsdioJefnKSSiQSBpNWJBUWOv5JdSz5TElMCw9TDogcMJuZqNiXH3jdJI/zLA6cBeBcbWbyQ9C6xq+35Jz9HLb832VzoT2ZCwI3B26rIbTfdFYpZJQtubFh1DRd+xvY2kq4EdiORMLQH6AXAbsKvtS4uKbxC8TRPde9uNybMZ0rEVwwBJdxBSsI+k5Flv94CcpkhrUjRHE1I8zcgyiVZDYTi3CzEp2sqoPLtztF12acebgEMk/SepPQAgaSGiy/zGtOmrwLOdD68isTQheVVWeptC+VOBsQ0Y2wcTv6NW+18hX7+vxuah/6S/J2uyL0da3ceyJ8kxnwysSExalo37iSbT0YRs5W5JkeRDQhIxG2l+SX1qfEiev7a9/9BG1FayK/pXTBxUxa+KPmH7xqJjGCrKfG6JG4lC0GXAn4HDJc1LJELXAc4tLrQBMRPwrbr389B9gfEhIT9xQKeCaiMLEH5sn6b3UwLYvl7S/sDvge8VFNtAOJvUfZZel7nQ/ED6+zRan2c2SUJJUxGeGnMSRfTrbL9UaFAVfcJ2TQ5qUromGv5n+5MCwxosdwOrAr0V7lZNx1YMDx6myzPkYcp1D9iMcp1PM7YHdgUOAw4knqs+ISTmJie8LrMiTZ7MSdzX7rGdo/Rab2xJPPffIWks8AqRuP4S8axSU3yYhPhsK4rh/TS5Vhokfa42rZAavsY3fTVOoUhalfieVgwTyt4YYLvsCioPEgoPZZQvPoqu4uXuxLWjJqP6PDH1lgv7EM/G79L79LWBbIpftqviV8WwRE3sICoqxiNpJWAbYpE4FrgI+FMzH5HcKPO51SPpS8AMth9K739DV9fdaGC/XBdekm4AtrH9WNGxtAtJrwGr275R0kvA9rb/mvb9GLjU9jSFBlnRFEmb9HaM7TM6EctgkTQ3MSE6Z93mt4C1bTf6GlRUDDmSfgZcCPzc9lktjtmQKD6vmel0W0XFsELSQ8BJhI/eR8BCaRp9EiLx9KDtXYuMsa9Imokoni9CV7LpGWAt26WcvpG0MrAQUfQaC9yZuZ9NaZD0KbCY7TuKjqWdSLoLWM72670ctxmx7p68M5FVTKykCeZt6J732TdHRY6ekLQEoRTzG+Aq282k50pB+lznJXJajyUvyCyQ9G9CUvQ64Dzg4owlDisqsqAqflW0RNJawF+BJ4D7iIeFhYEjbO9SYGiDpsznNjGRRsZPbjT0TvtmAbawvV/nIxs4km4BTrF9qqRRxNTGOsQ026nAnLb/r8gYK7ojaTIiofZUs+9jbki6EPgusAkxRTMXYb47p+3SSoZUDG8kHUnIOd5BTPfWpFRnJ8zlFwX+aHvHwoKsmIC+SrskcpN2KT2S3gV+YnuMpA/S6+vTvpWJZ7BZCg2yj0g6lfA93JGu+9phwCe2v1tkbBUTHyUufj1PKD782Pb/WhyzGzFJeq7tDToZX0Xf6eX+/SnRFHe/7WE9ZSRpG6KB4zq6rv2rA2fa3rzI2NpNsoKYmlCOMfA63b1IK4+lYUCSIV4XWJvI91xFKDJdbvv9nv5tRUVF/6mKXxUtSd4MTwAb1qahJO1CaOp+NudOkjKfG4w/vz7/uDPz2BiPpE+AxZstHJOszR22s5GZg/GdkF+xvY+kbxHj/DVj8nHE5M0VhQU4SCT9lJAlmI0k6ViP7aU6HlQbSF3w7wMr2b6u6HgGS9JQ/63t8+q2fQ14FJjd9ouFBVcxUZN8H3cAFqe7n9lRtkcVFVtFd1KCt8/SLrklZSStQ/hhfY3m97SszqeR5Nu5te2/S/oXMalxRNq3IXCs7el6/I8MEyQ9Dexv+5S6bd8lkqEzt0rU54ykzxDd5c2+m490PqKKspOUA64nrvnLNMplSzqKkFM92vavCwixoo+kQsqUQE1x5B3gs+n1u4SFyhREI/FPhqs0uqQHgOvrv2+SNiaaSqex/UFhwbUZSfvQu7dqNrJ0kn4JXGD7lfS6Jwy8RsgZZ+V5JmkpohC2BlG8HAWcaHtMoYFVVJSIqvhV0RJJbxHya9fWbfsC8Crw9dxuKvWU+dwAJJ3OhA8+AjYGLifOcQJs/7wzkbWXlFRb1PadTfatRkxQzdD9X+aDpOmApYjFxy22Xyg4pAEjaT9gT0KP/N/ENNsE2F6v03G1iyQPdZDtc4qOZbA060hO/lEfAQvavrew4CoqGP99/CJxf8vdz6y0lFnaRdL6RPLsdMJj6VTCQ+mnwBtEV3lW0+eNSDqXkBPaV9K+xNTU0cT9e1vgZttrFBljX0kNU0vYvr1u22RE8XwB2/cXFlybSed1NDG9PUWzY3JrDqvIB0m1a/6nRAHshVSIPZNI8O5u+5AiY6zoHUkLE37NewCjbH8gaQpgNcL/8efEM9i5wE22Nyws2B6Q9B7RnHhj3bbPEpNr/2f70aJiq+iZ+vVoet0XPgV2sn3UEIY2JEianJiK/Q3xm1u94JAqKkrDZ4oOoGJYU3soqKf2ftoOx9Juynxu2N60/n1acGwM7GP7nkKCahPJV6nmrWTghFTMrGdK4FuELFbW2H6D6P4pA1sR38Gsk4E9sAdwqKQHbT9YdDBtoOqOqRi2pGLXy0XHUdEztuetk3bZn7hnl0XaZWfinA4hil/HJz+saQlP1TJ4iexDTGoDHARMB2xKl2/sdoVENTAENBbJa8m0STocy1AzElgF2JxIXm9LTGpsCMxDXp9bRWbYflbSD4kC2Jjk2XkEsDSwue3TCg2woq8cCxxi+4LahjQldX66zx1j+3uSDiCKYcOVKel+P649e0zV4Vgq+oHtSZq9bkUqau4O/BbIpviVvNrWBdYkcpEXAicUGlRFRcmoil8VvfF9SfWTM5MQCdElJH2p/sAMDZTLfG6NlCmJ/R5d02sC3iRG3Ov5ELiS8Cga9khapj/H1/w2MuQTQpqsrOxJTKLcl2QDX6K7znpOEqNXS2omAXtd4/bcpb0qKiqGDtt3AXcBO9VJuxwL1Lwtc5V2+Soxkf1Jmir6HIDttyUdChwJHF5kgIMhyfm+Q3js1ZKeO6Q/uXJ68jFr5Kw0HTCezO7XjaxNFC7PJ4pfd9i+GzhT0hnE5Ebua5uKYYzt/6br/bXAvYR0+wjblxcbWUU/+DYwtsW+F4FvpNePMfybh9dIjTg1anmftSQtVrfdtrMuOkhanGh8aCXHnPO9rUdsv5PucfMVHUtvSPoe8Ty8DmFxcRVdE19laJ6qqBhWVLKHFS3px2gxxINCNvIZZT63ZtTJlS2U++RXPZJOA/az/VTRsQyG9H00vfuhQMbfR0l7APOX1dw6fR97JBeJUUl79+f4nPTjKyoqiqcM0i6pyWEz21cnP6lDa0kzSasDZ9ge7gnBliTVgPeBVW1fVXQ8g6Uv9+h6crlfNyMV8lawfXN6/dOa1Luk5YFzcpcFrxieSDqsYdMXCXm8MaRCeh22/buOBFbRbyTdTxS/VrX9Yd32KQgrhRltfzd5Xx5me46CQu2RiSnvI2k5orHhOmB5ohl4KmAJ4HlCnnKz4iIcPJJmp3VhL4umDkmPA3MR/ojnAReVRRK8omK4Uk1+VfTEXEUHMISU+dwmGnJOTDTwjd4PyR/bB0o6IhkP30h4ojQc4n4VXYYTJfo+VsWsioqKIaFk0i53EZ3xVxPyxCPTVOyHhOzcPwuMbdDY/ljSM4T5evaU6R7dB14kJCoBniK8Y2s+x/MUElHFxMJaTbY9A8yR/tRjoCp+DV92AK4Anpc0GngFmBFYDpgGWCkdtwBwUSER9oG+yOWViP2APxK/q4+AvZIc8xzEs8qNBcY2KJLU5vlEUQ+6mobrpzlyKVx+lZiGXRD4HnCY1LoHulJYqagYPNXkV0XFREDd5NeCtu8tOp52ImlOwsOgVQfQ2h0OqaIFktYG/kI8mL5BJAjrse1ZOx5YRUVFRcWQ0ULa5Twyl3ZJUklz2P6rpOmAM4hk4KTAncB6tp8sMsbBImkL4P/bu+8oy8oybePXDUhGFBBMJFEZ+JgZA58Ijo5gGjEjCGYGs+gIiqComAUZzCJiYAAVFBVHQBRREExkPhUDBiToCDgklRye7493txTVVdXVXVVn1zl1/dbq1eecvQvus7q76uz9vO/zvBL4t6r6c995ND1JPgtcVVV7J9mD1n7zGOBm2r/Do6vqJX1mlDT/JbkvbYf2lsC9aTvBzgY+XFX/02c2LS7JdcAOtB1FtwGPrarvd8d2Ad5ZVZv2GHGZJfk4bW7gy4AfAM8CrqHdB9qO9pnr7P4STp8dVqTBs/gljaAkf2bxOV/r0AoOi83wGdbVJEkeDpwGXEYrfv0UWBPYiLa1/7dVtVTztOaDrtXQC2kXGusDe1bV75I8C7igqn7Ta8Bl1K0gPwt4RVWNn9M2EizGStKdFlprl64d1Eqj8h6TfJnWLmlN4FwWn2VZVbVzH9k0uW528TpVdUH3fE/abstVgJNpLcMnmn0mSRpSSa4AXlBVJyf5A/DWqjq8O7Y98OWqWq3PjMsqyUW0+dpfoi3q3mpRsSvJB4D1vc6WNBnbHkqj6WAWL36Nov8EvgrsRvsQ9JJua/82wNHA+L7z816SB9DaEtwb+AmwNW1oObQ2E0+n9c4fRvcEPjnCha8lFmN7CydJ/VhQrV2q6mba7ppRsQ5w4bjnmueq6nLaDo1Fzz8EfKi/RJKGVZLNaT/D1wcOq6rLkzwQuKKq/tpvOo3zE2BT2iKH7wJv7uaT3kJrifizHrPN1HrAZVV1e5LrgbXGHDuRdk9IkiZk8UsaQVX1jr4zDMhDgPcDiwbZrgxQVT9K8k7gAFp7pWHyUeAqWtFrfGvA7wH795Bptnwd+Bfah/FRNHLFWEmaoZFu1ZJkS1qLofszgrt9q2rbvjNoepKcshSnV1U9bs7CSBp6SVYHDqPtGr2Vdu/wW7TC+vuAS4G9eguoiXyYO2fb7wscT1tUC20h5rP6CDVLLuPOBTi/AZ7Kne9tK9pCK0makMUvScOsgFuqqpJcSRuk/KPu2GW0FefD5rHALlX1v92strEuB+4z+Eiz5mvAQUnuRWuBde34E6pqaW7ezDejWIyVpGU2ynMKkrwK+DhtwcpvWHyOpeahJMvRPktdV1V/6zvPLLpqGufcB9iGhdEdQtLMfJD2/eJxwA+5a3HhRFrhy+LXPFJVJ455/MeuK8kDaS1vf1VVw/w55WTg8bT7CR8Cjuje383Av9JmW0rShCx+SRpmvwA2AU4FfgzsmeQc2g2ovYHf9ZhtWd0K3G2SY/cBhnmOyFe631/d/RqvgPEFv2EyisVYSdLE9gL+C3hlVS02T3VUJFkDeAaTz7Lce+ChZmY54GLgaYzQgpSq2mmyY0k2APahrZT/X2yBKGnJdgBeV1WnTrAg8xLadY7msaoq2uKcUbAPsCpAVX0uyd+4c5bl7sAne8wmaZ6z+KWlkuT+TH7xe+LiXzE8Rvm9jbBPcecH732BbwO/6p5fT/tANGy+A7wpycnAjd1rlWQF2ge7Yb5Rs1nfAebYyBRjk5zNUqwMr6pHzGEcSZqP1gWOHvHC1ya0Ff+rAqsBf6bN2VgBuAa4jvbzbWhU1W1JLqG7iTbKurk8bwZeAFzZPT60qm6c8gulAUhyr6r6c985NKlVmHxH6RrA7QPMssySbL805w/7fZ8k96UtdJioHXNV1T6DTzVzVXUDcMOY51+j7QIjyXa03YhP7iedpPnO4pempVv1eQzwxEUvdb+PvTk6lDs2Rvm9jbqq+tyYx79MshmtPcPKwBlVdWVv4ZbdG2m7hX5NK3QV8CZgC2BN4Ln9RZuZqrqw7wxzbEnF2GGa/fJzbIskSVP5Jm3OxKjOsYS2Q+gcYCfaz7HtgZ8AO9NmkO7cX7QZeT/wliTfH8Wb70n+D/AW2p/bZcDrgMOGvOWVhliSs2ifiz9ZVX9I8iDa99AH9ptMUzgbeBETL7zckTu7W8x3J9CuabKkExnyLiRJnkWbM708bcHD+O/5RdtBNTSS3AP4N2B94CLguKq6tTu2E+39PJTR2eEmaQ5Y/NJ07Q9sADwa+AFtWOY1tJWE2zHEN+QZ7fe2oHSzG77dd46ZqKqLk/wzbSX144A/ApvSLjwOrKor+sw3G5KE1sJxol2WFw0+0bJL8viq+g5MWozdmrZy8gzgIwzJzr2q2rXvDJI03yTZfMzTg4FPJbkbbRbFRHMsfzGobHPkEcBLaTM1AFasqtuBo5KsQ/u5tk1f4WbgibTPIRcnORe4grsu+KiqGrrCXjf/5C20NpW/pv3Zfb77M5P6dDawLfCGJJ8AXkgrzGr+eivwnSTfAb5M+x65fZI9acWvx/QZbils3HeAAXof7V7IrlV1dd9hZirJP9Lez3pjXj4vybOBo2jX2T+n3bf70uATShoWaW1gpakluYj2AehLtJlEW1XV2d2xDwDrV9Uw7Wr4u1F+bwtBkpVpH74n29p/yOBTLZ0kjwHOG7HB64vpWjf+J7AbsPpE51TVUK22S3IDsONULTKSrEZry/CvVbXSwMJJkmZVkju4a5Fkom4Bi16vYfuZNl6Sa4GnV9XpSf4XeHlVHdsd2w44vqpW6zXkMkhy6pLOqaptB5FltiT5Jq2o91PgfVX15Z4jSYtJshdwIG1x3wOr6uYlfIl6lORRwAHAI2m7iYq2oG/vqvphn9m0uG4O1jMXLcwcdkmOp40leRFt1/mGwMeAhwArAbtX1ef7SyhpWLjzS9O1HnBZVd2e5Hpav/9FTgS+2k+sWTHK722kJfkX4FhgnUlOKWDeF79oM6K2Bs7qO8gc25fWImkP4LPAnsBNwPOB+wFv6C/aMvsacGySXarqv8cf7FbGfxPYnDY4eigl2Yi2qm6yuYguEJC0EGzHwmoJ+2vubOd7PvDKJCfSZr28BPifvoLNxLAVtqbpSd3v6wMHJzl4qpOrat25j6SFJsnewP+tqp0mOLYj8C7gCFoL1ecAnxt/nuaPrsD16CSrAPcEru1mLw21bkHmBkx8TTPMO7Z/ROsYMxLFL2BL4HVVdWb3/MIkr6K1OHy5hS9J02XxS9N1GXcWGH5DG6J5Uvd8K9oN7GE1yu9t1H0U+B3wBOAXi/o/D6Hp9CAfBc8D3gEcSSt+/aCqzgUOTXIU7c9xsQLSPPcC2veIY5K8sKr+3nKhKxidRPv+8viq+nEvCWeoa6N0Gu175YNpq8rXBDYC/gD8trdwkjRAVfW9vjMM2BdpK6w/B7yN9jPtL8AdtOvIXXtLpvHe2XcAidbdYbGFh0leS5shuH9Vva3bAbY7Fr/mlST7TeOcRQ+rqt49t4lmV9em+KPAi2k7hyYyVDu2k6w65unrgS90O8Ama8c8TMXL9YCLx7226PlPBppE0lCz+KXpOhl4PG2Xw4eAI7obojfTWs59oMdsMzXK723UbQrsUFV++BkOGwC/7HZZ3gzcY8yxI4DP0y6Eh0a13sEvSXIT8PkkK1XVkd3ctkWr4x895KsI/5O2A3Y3WmvYl1TVeUm2oQ1VPrDPcJI0KEn+zPR2fhVt0PyldJ8vh3HuUlV9cMzjM5JsQRs8vwpwSlVd0Fu4GUqyBm021mQ7mvceeKgZqCqLX5oPNqAtkvq7JAcAbwT2rKqPdi+fjwXb+egdwI3A9Sx5cWYBQ1X8AvajLXR+CfAF2nXn9bTFjJsAr+0v2jL7G4u3Y/4vJv+sMlTFPSZ/H7cNNIWkoWbxS9O1D7AqQFV9rltNsiPt4vc1wKE9ZpupUX5vo+6nwL37DjFLtk/yD9M5saqOnOswc+Ry2o4haKu2HgV8t3u+IbBcD5lmRVXt3hXADkvyT7Qh8/8DPLGq/tBvuhl7CPB+2kp/6G4SVtWPkryTNgvgWz1lk6RBOpjptz1chXYz7d20ltr7zlWouTJ+JmlVXQZ8uju2epLHVNXpfWZcFkk2AX5I+/y/GvBn2p/RCsA1wHXAUBW/pHniUlpx4dRu5u2naW2/X1BVR485bz3agirNLxfRCpjn0nb+fq2q/tJvpFn1HFqB7xha8eusrgvJkUmOoC2ImHSO8zy1G6PdjvmkJBMVur47/nXb+UqaTNqidWlhSrISrdB1VlX9pu88WjpJHgIcTusFfVrPcZZZkjuWfNbfVVUN24otAJIcDvypqt6c5I3Ae2m7vW6mrbg7tqpe3GPEGUvyHuDNwJnAU6rqmp4jzViSq4Adq+rUJJfTVu4e3R17AvDfVbVaryElaZ5K8hpgr6raqO8sSyvJ7cDWVbXYTNKuS8JZw/iZJMlxtAU3O9FW/W9Ja6G0M7A/7Wfe2f0llIZTkhfQujn8HliXVly+GHhCVV3UnbM68APazO2n9RRVk0iyJbALrVC0Dm2B29HACVV1Y5/ZZirJDcCTqur73eOnV9V3umNPBI6qqslmiWvAkrx9ac53B7SkybjzS9OS5BzgMODoUbiZu0hV3ZzkM7QWLha/hsAELYdWA05JcittDsVdDNEKoG2Bc/oOMcfeSrsQBjiI9jNo0S7Lw7rjQ2WSFlgBNqMN5b3LgSH6+zjWL2i7F04Ffgzs2f1MuIW2Mv53PWaTpPnuFNrOh2E0Vdur1YFhmh0y1iNoO7Rv7p6v2LWlPCrJOsBHgG36CicNq6r6fJKLgafTWn8fTRsj8JMkxwN/BZ5EK6oM9YK3UVVV59CuSffqdv/uAnyc1t3iOODQYdzx2/kTd7bd/z3t7+Z3uueb9JJIk7KYJWm2WPzSdP2c1vbqA92Hns8CJ9dobB38Ga3f/9DuHFpglqbl0DC5saqu7zvEbBvbMqlr//cH+PusrP27X8NsVP8+jvUpWltKaG27vg38qnt+Pa2AKUmaQDfzcbu+c0xX93P7sWNeemmSfxt32srAU2ifoYfRysBfquqOJFcD9x1z7ALgn/uJJQ2/qvoBbWfXIj9NchHwOuBhtHlf73Jm8/zXFblOT7IHrWPHnrRFi8Na/Poe8GjgeFpLzoOSPJC2EGJnWrF2qCXZGXgZk8+zHMaFmJI0I7Y91LR1fbt3pq3SejTwR+BI4PBhbhmY5FG01nl7At+qKodnaqC6toePnKit0LCbqmWShlPXrmYb2gXVGVV1Zc+RJEmzpGtLvGje1dq0+VfjPxvfQlsE8caqOm+A8WZFkrOAj3Wzfk+mLWJZtFPlcOARVfWgHiNKUu+6+yS70Ba6rQGcABwyrOMGktwbWKeqLuie78mdXUhOphVlh3YxapLn0bqpHA68vHu8HO3n27XAkVX1rt4CSlJPLH5pmXSDol8MvAhYnzY0+jDgi1V1U5/ZllbXtmxV2o3cog26vss/DFfIaC6NePFrZN+bJEmjLMnvgWeO2g6NJK8H7ldVb0jySOAk2s3PO2idUXatqs/3mVGS+pDkYbSC187AerSZX18EjquqYW11uyAkOR/4CnAAcCuwZVWdl2QNWnHvK1V1UJ8ZJakPtj3UsrqDOwtEt9NmAnwCOCDJC6vq5N6SLb2F0LZsZCXZkTZL4/5MvLX/EQMPtZSqarm+M0hTSbIyrS/+RP/OqqoOGXwqSdIceztdu+LxkqwFPLWqjhxspJmrqg+OeXxGki1o839XAU5ZtCtAkhaSJBcCG9NmVb4dOLaqFpuprXnrQcAPq+r2rvvK3QGq6q9J3g98iDZ3W5IWFHd+adqSrArsBOxKa3v4W9puryOq6oruIvjjtF0eD+gtqBaMJO8A9gN+AvyC1obnLqrq3wccS2N0O7/eBVw0nfOH8SbaqEvyL8CxtOHkE6mqWn6AkSRJAzBV6+IkDwfOGsbv/2PnkU5wbHXgYd2sG0laMLrrtptoM32XeKNw2LrjJLkbbfbcVAtnh+o9jZXkj8BuVXVSkouB9y9aoJhkB9p9uzX6zChJfXDnl6YlyWdpha/laFup96uq7489p6quTvIR2jZ5aRBeJ88o8AAAGHNJREFUAhxQVfv2HURT2m+a5xVtjqDml48CvwOeAPyiqm7tOY8kaTAyxbG1gWHdEXAqsDUwUUvmTbvjQ1fUk6QZemffAebYh4BX0GaXncoEC2eH3DnAP9Fa+R4H7JfkNtr73A84s8dsktQbi1+arn8E9gKOrqq/TnHez4FtBxNp9oxC67wFag3gu32H0BJtS/swruG0KbDDqM18kSQtLskzgGeMeelt3XzcsVamdYE4e2DBZtdURb3VAefaSFpwqmrUi187AW+qqg/0HWSO7A9s2D3er3v8CdpijrNphT9JWnAsfmlaplv86dqHnDbHcWbVdFrnad76Im1GgwWw+e3Gqrq+7xBaZj8F7t13CEnSQKxLW/S2yCYs/jPgFuDbwHsGFWqmulaHjx3z0kuT/Nu401YGngL8bFC5pFHVjUyYUlVZaNYghXZdM5Kq6gzgjO7xtcAzkqwErOTsNkkLmTO/tFS6H573Y+LdUb8YfKKZS3IZ8Dlb5w2HJNuPeboqcCCt4HoycO3486vqxAFF0wS63vGPnGheiIZDkocAhwOvq6qhWtwgSVp2SU4FXlVVv+o7y0wleSOwd/d0beA64LZxp90C/Ap4Y1WdN8B40sjprgGmvNk0jDMDNbySvBe4T1Xt1neW2ZJkuuMFoM1pfvechZGkecril6Ylyf2AQ4EnT3SY9oN0KD+8JrkWeHZVuXtoCHQXUtM1tH8vR4XFr+HUtbga+wFhNdqih1uZYMbLMA+HliTdKclhUxy+DbgSOL2qvj2gSLMuye+BZ9rOV5o7SXZl8eLXWsATgc2Bd1fVZwadSwtLklePebo88Abg90y8cLaq6pBBZZsN3bX2jcD1TN3SF9r785pN0oJj8UvTkuRE4GG0PsITtgYc1h0BST4J/LWq3th3Fi1Zkg2XfNadquqSucoijaquHey0PyAsgBkBkrQgJJlqjtfywH2A9YAfANt3Lc+HSpIXAd+oqqsmOLYW8NSqOnLwyaSFIckngJuras++s2i0jfrC2SS/BTagjYH4IvA1WxxK0l1Z/NK0JLkOeFlVHdN3ltlg67zhlWRt4FPAp6rqpEnOeRLwclqrnisHmU+SJGmUJdkKOA44uqr26DvP0kpyO7D1RLvSkzwcOGvYboBKwyTJ44EvVdXafWeRhl2SLYFdgOcA6wDfAo4GTqiqG/vMJknzgcUvTUuS3wCvr6rj+84yG8b0IF/S1nAYwhVAoyzJu4GnAg+rSb6BJQlwLnByVe0zyHzSqEmyPnCvieafJHkY8OequmzwySRJfUmyO7BPVW3Qd5alNVVL5iRPpN2Uv+fgk0kLQ5I3A6+tqvv2nUUaJUkeQyuEPZu2yPs44NCqOr3XYJLUoxX6DqChsR+wT5LTRmQb9cZ9B9Ayew7wwckKX9CqlUkOBfYELH5JM3MI8GtgseIX8DxgU+BpA00kSerbL2jtD4dCkmcAzxjz0tu6+ZZjrQw8Gpiq9aOkaUhy4AQvrwhsBjwO+PBgE2mhS/IfwH2r6k0THNsf+GNVfXzwyWZPV+Q6PckewHtp90NWASx+SVqwLH5pUknGtzjcALikmwUw0XDQnQeTbOacAzXUNqTdcFmSXwIbzW0UaUF4JPDJSY6dCrx4gFkkSfPDhsDVfYdYCusC/zjm+SbAvcedcwvwbeA9gwoljbCdJnjtJuAPwH/Q2thLg/Rq4KBJjv0aeCMw1MWvJI+i7fzaEVgD+AptIaMkLVgWvzSVe417/rvu97tNcGyoJFmqFi1VdelcZdFSuxG4+zTOW707V9LMrEprEzuZ1QYVRJLUvyT3Ad4KfLPvLNNVVZ8GPg2Q5FTg1VX1y35TSaOrquy0ovlmQ+C3kxz7PUO6cLZrQ78LsDNtR/a3aDu+jquqG/rMJknzgcUvTaqqtu07wxy6mKlv5o7nzK/54zzg6cA3lnDeM5i4TZukpfMz4LlM/G/uucDPBxtHkjRXJuj8MNbytN1SDwcuA/YdSKhZNuLXOJKkiV1Da9f+vQmObQoM3XiPJBfSRnqcArwdOHZExpRI0qyx+KWFaux8mrsDB9La5B0LXElrjfJs4B9o2981fxwMHJPkR1V1xEQnJHkR8O+01U+SZuYA4KtJVgIOB/4E3IfW7vDZ3S9J0miYqrvDbcAlwOeBI6vq+sFEmrkkh01x+Dba5//Tq+rbA4okjbwk96Vdd9+PNlNvrKoqZzNrkI4H3tHdR/jZoheTbEErHH29t2TL7kG0dqIPBx4GHJhk0pOrat0B5ZKkeSNVS7P5RQtJkv2W5vyqetdcZZlLSQ4HbqyqV01w7JPAalX1woEH06SSfIC2lf9c2rb+S2k7+TYAngRsCXyoqvbqLaQ0QpK8ENgfuC/t31qAPwJ7V9XRfWaTJGlJupnFk1metqhjPeAHwPZV9beBBJNGVJJdgCNonxn/TJupN1ZV1QMGHkwLVpK1gNOAzYDzuXNB30OBC4Btq+qa/hIuvSRvX5rzq+qdc5VFkuYri1+aVJI/j3tpFdrsF4C/0WYqAdwA3DCsq0iS/AV4dlWdPMGxJwBfqao1B59MU0nyNGAPYBtgpe7lm4EfAh+uqhP6yiaNorRlhJsCawNXAReWHyIkSSMiyVbAccDRVbVH33mkYZbkd8CZwCttw6b5IsnKtO4V23LnNc13abuZb+4zmyRpblj80rQk2Rr4Am249bFVdVP3weHZwLuB51fVj/vMuKySXAF8sqoWWzWT5F3AK6pqvcEn03QkWYH2wRXgqqq6rc88kiRJGk5Jdgf2qaoN+s4iDbMk19IWmH637yySJGnhcuaXpuujwPuq6qhFL1TVTcAXkqxGm8P0sL7CzdAhwNuSrE1b7blo5tczgFcA7+0xm5agK3Zd0XcOaZQlWYP2PfHBLD6zgarae+ChJEmafb+gtT+UNDPHAo+l7aqR5pUkyzHxNc0NPcSRJM0hi1+ari2A/5nk2B9pfZOHUlW9I8k1wN7Aq7lzns3lwF5V9eE+80lSn5JsQmsnuiqwGm1uw1q0zxDXANfRvn9KkjTsNgSu7juENAJeA3w2yWeAU4Brx59QVScOPJUWrK6F+97Ay4CNJzlt+cElkiQNgm0PNS1JfkK74fmUsb2Qu9aH3wDWrqqH9JVvNnSrf9YH7k0rfF1WVXf0m0qS+pXkOGA5YCfgemBL4CfAzsD+wI5VdXZ/CSVJmrkk9wG+D5xeVbv1nUcaZkkeAnyVyYsMVVUWGjQwSV4HvAM4kNbd5z3A7cAuwIq0Tkef7S2gJGlOWPzStCR5DHAicCNwMne2BnwCbTfAk6vq9P4SSpLmQpLLgZfSfgbcBmxTVWd0x/4D2KWqtukxoiRJU0pyzBSHl6ctfns4cBnw6Kq6fCDBpBGV5Pzu4b7Ab4Fbxp9TVZcMNJQWtCQXAJ+ijey4Fdiyqs7rFkEfD/ysqt7UZ0ZJ0uyz7aGmpapOT/IgYE/g/wIPpe2O+i/gw1U1WUvEoZDkvsBTgfuzeO/nqqp9Bp9KkuaFlYG/VNUdSa4G7jvm2AXAP/cTS5KkabvXFMduAy4BPg8cWVXXDyaSNNIeDOxQVSf1HUTqbAz8v6q6PcmtwD0AumucTwCfASx+SdKIsfilaauqPzGCc12SPAs4mrbq80oWX5VWgMUvSQvVr2kzUADOB16Z5ERam5CXMPk8SEmS5oWq2rbvDNICcxawQd8hpDGuAlbvHl9KW9B9Svf8nsAqfYSSJM0ti18SvA/4NrBrVTngWpLu6ovAQ4DPAW8DTgL+AtxB+xyxa2/JJEmSNB+9Hjg8yY20AsO140+oqhsGnkoL2Q9pXYxOBI4C3pFkLdri592B7/aYTZI0R5z5pWlLsjPwMloLg/GtAamqdQceahYk+RvwzKr6Tt9ZJGm+S7I+8GTaz4FTquqCniNJkiRpHklyR/dw0htOVbX8gOJIJNkUuF9VnZJkJeBAYEfajq+TgddW1ZV9ZpQkzT6LX5qWJM8DDgMOB17ePV4OeDptFdeRVfWu3gLOQJJvA1+vqoP7ziJJkiRJ0jBLsitTFL4AquqIwaSRJEkLlcUvTUuS84GvAAcAtwJbVtV5SdagrZL5SlUd1GfGZZVkC+ALwAdp78WWDJI0TpIHA/dn4p2/Jw4+kSRJkiRJkjQxi1+alq414FOr6ntJbgWeUFXf6449C/hQVW3UY8RlNqYlA0yyOs2WDJIWqiSbA18CNgcywSnl90hJkiRJ81mS5wDPAu7HxAv6HjHwUJKkObVC3wE0NK4DVuoe/xHYDPhe9zzA2j1kmi27sYSWDJK0gB0KrAjsAPyCNhRakiRJmlCSFYE9mLrQMJQzwzWckhwA7A2cDfwWr2kkaUGw+KXpOgf4J+Ak4DhgvyS30T4w7Aec2WO2Gamqw6c6nuRuA4oiSfPRQ4FdquqEvoNIkiRpKBwCPB/4OnAKFhrUv92At1TV/n0HkSQNjsUvTdf+wIbd4/26x58AlqetnHl5T7nmRJIA2wLPpe12GOadbZI0E79jgtW6kiRJ0iR2APaoqk/2HUTq3Aqc23cISdJgWfzStFTVGcAZ3eNrgWckWQlYqar+0mu4WZRkK1rB6znAesDVwBd7DSVJ/XoDcGCS86rqor7DSJIkad67Gri07xDSGB8BXprk5Kpy7IUkLRDxe74WuiRb0ApeuwAb0VoyrAi8Hji4qm7rL50k9SvJ2cAGwD2Bi4Frx5/jcGhJkiQtkuTFwIuAp1fV9X3nkQCSHAQ8DTiNxa9pqqr2GXwqSdJccueXJpVkv6U4varq3XMWZpYleQCt2PVcYHPgNuBkWkvH02ir1M638CVJXND9kiRJkpaoqo5IsjlwaZJzmbjQsHMP0bRAJXk+sAdwB7A6i8+hK8DilySNGHd+aVJJ7gBuBK4HsoTTq6rWnftUs6N7bwWcCRwGfLWqrumOrQlcAzy2qk7vL6UkSZIkScMlyRuA/wQuB37P4oUGqmrbQefSwpXkMuB04JVV9de+80iSBsOdX5rKRbRWV+fS5l59bYTme10CbAhsATwW+FOSk9zpJUmSJEnSjLwJ+Ciwp/OVNE/cHTjMwpckLSwWvzSpqnpgki1p7QHfDRyS5FvA0cAJVXVjrwFnoKo2TrI18Dxgx+73a5IcC3yTtitMkha8JMcs6Zyqes4gskiSJGkohHbPwOtqzRdfBbYFvtt3EEnS4Nj2UNOW5DG0QtizgVWB44BDh701YJLlgMfR5n89E7gHrfh1FPCRqjqnx3iS1Kskp07w8lrApsBVwIVVtd1gU0mSJGm+SnIQsFpVvarvLBJAkp2BA2jFr1NYfA4dVXXioHNJkuaWxS8ttSQrAu8F9gSOq6odeo40a7r3tj2tyPdUYBXg11W1Wa/BJGmeSbI+8DXgnVV1fN95JEmSND8k2QN4PfBrJi40VFUdMvBgWrC6ue9TqapafiBhJEkDY/FL05bkUbSi0I7AGsAJwCFVdVqvweZIktVoO8F2qaqn9Z1HkuabJM8G3uMCAUmSJC1ioUHzTZINl3ROVV0yiCySpMGx+KUpJXkYreC1M7Ae8C3gi7QdXzf0mU2S1K8kzwQ+V1Vr9J1FkiRJkiRJWmSFvgNo/kpyIbAxrU3B24Fjq+ov/aaSJA1Sks0neHlFYDPg3cDZg00kSZIkSVNLsuqiRdtJVl3S+S7wlqTR484vTaprVXATcD2wxL8oVbXunIeSJA1U97Ngop8BoRW+nltVFw02lSRJkua7JPcHHgysPP5YVZ04+ERaSJLcDmxdVWdNcU3zd7bilKTR484vTeWdfQeQJPVu2wleuwn4Q1X9cdBhJEmSNL8lWQM4Bnjiope638cWHyw0aK7tBvxuzGNX/0vSAuPOL0mSNKEkKwF7ASdU1U/6ziNJkqT5L8nHaQuoXgb8AHgWcA3wAmA7WucAW2dLkqQ5ZfFLkiRNKsn1wPZVdVrfWSRJkjT/JbkIeCvwJeBWYKtFxa4kHwDWr6rn9BhRkiQtALY9lCRJUzkLeDhg8UuSJEnTsR5wWVXd3i2kWmvMsROBr/YTSwtJkrNZilaHVfWIOYwjSeqBxS9JkjSVvYGjktxCu1lxBeMuIqvqhj6CSZIkaV66DFine/wb4KnASd3zrWjzY6W59nPuet0S4EXACcBVvSSSJA2UbQ8lSdKkktwx5umEHxqqyoHlkiRJAiDJx4Dlqmr3JC8EjgDOAG4GHgN8oKr27jOjFp4kKwC3AFtW1Xl955EkzT13fkmSpKnsxlK0C5EkSdKCtw+wKkBVfS7J34AdgVWA1wCH9phNC5fXNJK0wLjzS5IkLbMkd6uqW/vOIUmSJEmTSbI8cCvu/JKkBcOdX5IkaakkCbAt8FxgB2DtfhNJkiSpT0n+zPR21hSt9dylwNeAD1XV7XOZTZIkLUwWvyRJ0rQk2YpW8HoOsB5wNfDFXkNJkiRpPjiY6beVWwXYBHg3sBaw71yFkiZgCyxJWiBseyhJkiaVZAtawWsXYCPaSt0VgdcDB1fVbf2lkyRJ0rBK8hpgr6raqO8sGj2T7EZcB7gWWOwapqrWHUQuSdLguPNLkiTdRZIH0IpdzwU2p10cngzsB5xGa1NzvoUvSZIkzcAptBba0lxYmt2IkqQR5M4vSZJ0F0nuoF0ongkcBny1qq7pjq0JXAM8tqpO7y+lJEmSJEmSNLHl+g4gSZLmnUuAAFsAjwW2SeJucUmSJEmSJA0Fi1+SJOkuqmpj4FHAEcDjgOOBK5J8unvutnFJkiRJkiTNW7Y9lCRJk0qyHK3g9VzgmcA9aMWvo4CPVNU5PcaTJEmSJEmSFmPxS5IkTUuSFYHtgV2ApwKrAL+uqs16DSZJkiRJkiSNYfFLkiQttSSr0XaC7VJVT+s7jyRJkiRJkrSIxS9JkiRJkiRJkiSNjOX6DiBJkiRJkiRJkiTNFotfkiRJkiRJkiRJGhkWvyRJkiRpDiXZNcm5Sf6a5Jok5yf54Jjj6yZ5R5KNluG/vWL3tQ+ZzcySJEmSNMwsfkmSJEnSHEnyZuAzwEnADsCLgK8DTx9z2rrA24GNluF/sWL3tRa/JEmSJKmzQt8BJEmSJGmEvQY4tKr2HfPa8Une2VcgSZIkSRp17vySJEmSpLlzD+Dy8S9WVQF0rQ5/1r18apJKsujYakk+nuTCJDck+X2Sg5Pcfcx/6q/d7/+16GsXtU9MsnKSA5NcluTmJD9Jsv3YHEme3rVkvL5ryXhmkn+dxfcvSZIkSQNn8UuSJEmS5s55wGuTvDjJ2hMc/xPw/O7x7sDW3S+AVYHlgbcATwbeBmwHfHnM12/X/f6eMV/7p+61rwC7Au8DngacDRy3aD5Ykk26c07pjj8fOAFYa5nfrSRJkiTNA+kWHEqSJEmSZlmSfwL+G9gYKOCXwFeBg6rqL905W9B2f21bVd+b4r+1ArAV8ANgw6q6NMnqtN1f/15Vh48593HAd4DHVtVpY14/HbiiqnZKsiOtJeNERTlJkiRJGlru/JIkSZKkOVJVPwU2A54OfAIIbQfXOV3hakpJXpjk/CR/A26lFb4AHryEL308rd3iD5OssOgX8F1gy+6cnwFrJjkiyROTrLa070+SJEmS5iOLX5IkSZI0h6rq5qo6vqpeU1WbAy8FHgS8ZKqvS/Is4Ejgx8BOwCOBZ3WHV17C/3Yd4N60gtnYX+8A1u9yXQg8A3gAcCLwv0mOSnKvpX2PkiRJkjSfrNB3AEmSJElaSKrqs0kOBP5hCafuBJxZVa9e9EKSf53m/+Zq4I/AM5eQ5RvAN5KsCTwF+DDwMWCXaf5/JEmSJGnesfglSZIkSXMkybpVdeW41+4FrAlc0b10S/f7+N1cqwA3j3vt+eOeT/a13wXeAPytqn61pJxVdR1wVFdc23pJ50uSJEnSfGbxS5IkSZLmzs+SfB34NnAlsCGwF3ADcER3zqXAjcCLk1wH3FpV5wAnAwcneQtwJrA98Lix//GquiXJ74HnJLkAuAn4afe1JwEnJ3k/8HPg7sBDgJWr6s1JXkErdH0L+B9aK8adaK0WJUmSJGlopar6ziBJkiRJIynJ7rS5WlsAawGXAz8C3jV2R1aS5wNvBzYC7lZVSbI88H7gRbSdXScDBwJnAE+rqhO6r30icBDwYGAlYOOqujjJSsC+tN1iG9BaIf4/4GNV9Y0kWwNvBR7aZfsTcAywX1WN33EmSZIkSUPD4pckSZIkSZIkSZJGxnJ9B5AkSZIkSZIkSZJmi8UvSZIkSZIkSZIkjQyLX5IkSZIkSZIkSRoZFr8kSZIkSZIkSZI0Mix+SZIkSZIkSZIkaWRY/JIkSZIkSZIkSdLIsPglSZIkSZIkSZKkkWHxS5IkSZIkSZIkSSPD4pckSZIkSZIkSZJGxv8HJ/yQuGjulnQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "    # this is for plotting purpose\n",
    "#index = np.arange(len(index))\n",
    "fig = plt.figure(figsize=(30, 15))\n",
    "plt.bar(per_growth_for_states['index'], per_growth_for_states['mean'])\n",
    "plt.xlabel('States', fontsize=15)\n",
    "plt.ylabel('Average growth rate', fontsize=15)\n",
    "plt.xticks(per_growth_for_states['index'], fontsize=15, rotation=90)\n",
    "plt.title('Growth rate of each state')\n",
    "plt.savefig(\"Growth rate of each state.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort)\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>All_India GDP</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Items  Description</th>\n",
       "      <th>Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13.860</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12.970</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10.650</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.990</td>\n",
       "      <td>2015-16</td>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.520</td>\n",
       "      <td>2016-17</td>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.798</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      All_India GDP Duration             Items  Description  Number\n",
       "6            13.860  2012-13  (% Growth over previous year)     1.0\n",
       "7            12.970  2013-14  (% Growth over previous year)     2.0\n",
       "8            10.650  2014-15  (% Growth over previous year)     3.0\n",
       "9             9.990  2015-16  (% Growth over previous year)     4.0\n",
       "10           11.520  2016-17  (% Growth over previous year)     5.0\n",
       "mean         11.798      NaN                            NaN     3.0"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#gdp_India.mean(gdp_India['All_India GDP'])\n",
    "gdp_India = gdp_India.append(gdp_India.agg(['mean']))\n",
    "#per_growth.set_index('Duration')\n",
    "gdp_India=pd.DataFrame(gdp_India)\n",
    "#gdp_India=gdp_India.T\n",
    "gdp_India\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#per_growth.set_index('Duration')\n",
    "per_growth_for_states=pd.DataFrame(per_growth_for_states)\n",
    "per_growth_for_states=per_growth_for_states.T\n",
    "per_growth_for_states=per_growth_for_states.fillna(0)\n",
    "per_growth_for_states=per_growth_for_states.drop(['Duration','West Bengal'])\n",
    "per_growth_for_states.reset_index(level=0, inplace=True)\n",
    "#per_growth_for_states=per_growth_for_states.T\n",
    "per_growth_for_states= per_growth_for_states.sort_values(by=['mean'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How will you compare the growth rates of any two states?\n",
    "\n",
    "### A: By copmaring the average for growth rates\n",
    "\n",
    "## Which states have been growing consistently fast, and which ones have been struggling? Rank top 3 fastest and 3 slowest-growing states.\n",
    "\n",
    "### A:  Fastest growing states: \n",
    "###     madhya prodesh, andhra pradesh and haryana\n",
    "###     Slowest growing states:\n",
    "###     Manipurm,punjab,goa\n",
    "## What is the Nation's growth rate?\n",
    "\n",
    "### A: 11.798 per year\n",
    "\n",
    "## What has been the growth rate of your home state, and how does it compare to the national growth rate?\n",
    "### A My home state is bihar and the growt rate is 11.012 per year and the nations growth rate is 11.798 per year\n",
    "### which is almost same.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "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>Duration</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>Andhra Pradesh</th>\n",
       "      <td>379402</td>\n",
       "      <td>411404</td>\n",
       "      <td>464272</td>\n",
       "      <td>526468</td>\n",
       "      <td>609934</td>\n",
       "      <td>699307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <td>11063</td>\n",
       "      <td>12547</td>\n",
       "      <td>14602</td>\n",
       "      <td>16761</td>\n",
       "      <td>18784</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assam</th>\n",
       "      <td>143175</td>\n",
       "      <td>156864</td>\n",
       "      <td>177745</td>\n",
       "      <td>198098</td>\n",
       "      <td>224234</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>247144</td>\n",
       "      <td>282368</td>\n",
       "      <td>317101</td>\n",
       "      <td>373920</td>\n",
       "      <td>413503</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <td>158074</td>\n",
       "      <td>177511</td>\n",
       "      <td>206690</td>\n",
       "      <td>234982</td>\n",
       "      <td>260776</td>\n",
       "      <td>290140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goa</th>\n",
       "      <td>42367</td>\n",
       "      <td>38120</td>\n",
       "      <td>35921</td>\n",
       "      <td>40633</td>\n",
       "      <td>45002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gujarat</th>\n",
       "      <td>615606</td>\n",
       "      <td>724495</td>\n",
       "      <td>807623</td>\n",
       "      <td>895027</td>\n",
       "      <td>994316</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haryana</th>\n",
       "      <td>297539</td>\n",
       "      <td>347032</td>\n",
       "      <td>400662</td>\n",
       "      <td>437462</td>\n",
       "      <td>485184</td>\n",
       "      <td>547396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himachal Pradesh</th>\n",
       "      <td>72720</td>\n",
       "      <td>82820</td>\n",
       "      <td>94764</td>\n",
       "      <td>104369</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jammu &amp; Kashmir</th>\n",
       "      <td>78254</td>\n",
       "      <td>87105</td>\n",
       "      <td>95893</td>\n",
       "      <td>100404</td>\n",
       "      <td>118387</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jharkhand</th>\n",
       "      <td>150918</td>\n",
       "      <td>174724</td>\n",
       "      <td>188567</td>\n",
       "      <td>217107</td>\n",
       "      <td>241955</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karnataka</th>\n",
       "      <td>603778</td>\n",
       "      <td>691700</td>\n",
       "      <td>817886</td>\n",
       "      <td>921788</td>\n",
       "      <td>1.02707e+06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kerala</th>\n",
       "      <td>364048</td>\n",
       "      <td>412313</td>\n",
       "      <td>465041</td>\n",
       "      <td>526002</td>\n",
       "      <td>588337</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madhya Pradesh</th>\n",
       "      <td>315561</td>\n",
       "      <td>380924</td>\n",
       "      <td>437737</td>\n",
       "      <td>481982</td>\n",
       "      <td>543975</td>\n",
       "      <td>640484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maharashtra</th>\n",
       "      <td>1.27297e+06</td>\n",
       "      <td>1.44847e+06</td>\n",
       "      <td>1.64751e+06</td>\n",
       "      <td>1.79212e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manipur</th>\n",
       "      <td>12915</td>\n",
       "      <td>13748</td>\n",
       "      <td>16198</td>\n",
       "      <td>18043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meghalaya</th>\n",
       "      <td>19918</td>\n",
       "      <td>21872</td>\n",
       "      <td>22938</td>\n",
       "      <td>24408</td>\n",
       "      <td>26745</td>\n",
       "      <td>29567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mizoram</th>\n",
       "      <td>7259</td>\n",
       "      <td>8362</td>\n",
       "      <td>10293</td>\n",
       "      <td>11559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nagaland</th>\n",
       "      <td>11839</td>\n",
       "      <td>13619</td>\n",
       "      <td>16612</td>\n",
       "      <td>18414</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odisha</th>\n",
       "      <td>227872</td>\n",
       "      <td>258275</td>\n",
       "      <td>291709</td>\n",
       "      <td>321971</td>\n",
       "      <td>341887</td>\n",
       "      <td>378991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Punjab</th>\n",
       "      <td>266628</td>\n",
       "      <td>297734</td>\n",
       "      <td>334714</td>\n",
       "      <td>368011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajasthan</th>\n",
       "      <td>436465</td>\n",
       "      <td>494004</td>\n",
       "      <td>549701</td>\n",
       "      <td>612194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sikkim</th>\n",
       "      <td>11165</td>\n",
       "      <td>12338</td>\n",
       "      <td>13862</td>\n",
       "      <td>15209</td>\n",
       "      <td>16637</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tamil Nadu</th>\n",
       "      <td>751485</td>\n",
       "      <td>855481</td>\n",
       "      <td>971090</td>\n",
       "      <td>1.09256e+06</td>\n",
       "      <td>1.21267e+06</td>\n",
       "      <td>1.33877e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telangana</th>\n",
       "      <td>359433</td>\n",
       "      <td>401493</td>\n",
       "      <td>452186</td>\n",
       "      <td>511178</td>\n",
       "      <td>575631</td>\n",
       "      <td>654294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tripura</th>\n",
       "      <td>19208</td>\n",
       "      <td>21663</td>\n",
       "      <td>25593</td>\n",
       "      <td>29667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <td>724049</td>\n",
       "      <td>822903</td>\n",
       "      <td>944146</td>\n",
       "      <td>1.04337e+06</td>\n",
       "      <td>1.1538e+06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttarakhand</th>\n",
       "      <td>115523</td>\n",
       "      <td>131835</td>\n",
       "      <td>149817</td>\n",
       "      <td>161985</td>\n",
       "      <td>184091</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Bengal</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andaman &amp; Nicobar Islands</th>\n",
       "      <td>3979</td>\n",
       "      <td>4421</td>\n",
       "      <td>5159</td>\n",
       "      <td>5721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chandigarh</th>\n",
       "      <td>18768</td>\n",
       "      <td>21609</td>\n",
       "      <td>24787</td>\n",
       "      <td>27844</td>\n",
       "      <td>30304</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>343767</td>\n",
       "      <td>391238</td>\n",
       "      <td>443783</td>\n",
       "      <td>492424</td>\n",
       "      <td>551963</td>\n",
       "      <td>622385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puducherry</th>\n",
       "      <td>16818</td>\n",
       "      <td>18875</td>\n",
       "      <td>21870</td>\n",
       "      <td>24089</td>\n",
       "      <td>26533</td>\n",
       "      <td>29557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All_India GDP</th>\n",
       "      <td>8.73604e+06</td>\n",
       "      <td>9.94664e+06</td>\n",
       "      <td>1.12366e+07</td>\n",
       "      <td>1.24337e+07</td>\n",
       "      <td>1.36753e+07</td>\n",
       "      <td>1.5251e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Duration                       2011-12      2012-13      2013-14      2014-15  \\\n",
       "Andhra Pradesh                  379402       411404       464272       526468   \n",
       "Arunachal Pradesh                11063        12547        14602        16761   \n",
       "Assam                           143175       156864       177745       198098   \n",
       "Bihar                           247144       282368       317101       373920   \n",
       "Chhattisgarh                    158074       177511       206690       234982   \n",
       "Goa                              42367        38120        35921        40633   \n",
       "Gujarat                         615606       724495       807623       895027   \n",
       "Haryana                         297539       347032       400662       437462   \n",
       "Himachal Pradesh                 72720        82820        94764       104369   \n",
       "Jammu & Kashmir                  78254        87105        95893       100404   \n",
       "Jharkhand                       150918       174724       188567       217107   \n",
       "Karnataka                       603778       691700       817886       921788   \n",
       "Kerala                          364048       412313       465041       526002   \n",
       "Madhya Pradesh                  315561       380924       437737       481982   \n",
       "Maharashtra                1.27297e+06  1.44847e+06  1.64751e+06  1.79212e+06   \n",
       "Manipur                          12915        13748        16198        18043   \n",
       "Meghalaya                        19918        21872        22938        24408   \n",
       "Mizoram                           7259         8362        10293        11559   \n",
       "Nagaland                         11839        13619        16612        18414   \n",
       "Odisha                          227872       258275       291709       321971   \n",
       "Punjab                          266628       297734       334714       368011   \n",
       "Rajasthan                       436465       494004       549701       612194   \n",
       "Sikkim                           11165        12338        13862        15209   \n",
       "Tamil Nadu                      751485       855481       971090  1.09256e+06   \n",
       "Telangana                       359433       401493       452186       511178   \n",
       "Tripura                          19208        21663        25593        29667   \n",
       "Uttar Pradesh                   724049       822903       944146  1.04337e+06   \n",
       "Uttarakhand                     115523       131835       149817       161985   \n",
       "West Bengal                        NaN          NaN          NaN          NaN   \n",
       "Andaman & Nicobar Islands         3979         4421         5159         5721   \n",
       "Chandigarh                       18768        21609        24787        27844   \n",
       "Delhi                           343767       391238       443783       492424   \n",
       "Puducherry                       16818        18875        21870        24089   \n",
       "All_India GDP              8.73604e+06  9.94664e+06  1.12366e+07  1.24337e+07   \n",
       "\n",
       "Duration                       2015-16      2016-17  \n",
       "Andhra Pradesh                  609934       699307  \n",
       "Arunachal Pradesh                18784          NaN  \n",
       "Assam                           224234          NaN  \n",
       "Bihar                           413503          NaN  \n",
       "Chhattisgarh                    260776       290140  \n",
       "Goa                              45002          NaN  \n",
       "Gujarat                         994316          NaN  \n",
       "Haryana                         485184       547396  \n",
       "Himachal Pradesh                   NaN          NaN  \n",
       "Jammu & Kashmir                 118387          NaN  \n",
       "Jharkhand                       241955          NaN  \n",
       "Karnataka                  1.02707e+06          NaN  \n",
       "Kerala                          588337          NaN  \n",
       "Madhya Pradesh                  543975       640484  \n",
       "Maharashtra                        NaN          NaN  \n",
       "Manipur                            NaN          NaN  \n",
       "Meghalaya                        26745        29567  \n",
       "Mizoram                            NaN          NaN  \n",
       "Nagaland                           NaN          NaN  \n",
       "Odisha                          341887       378991  \n",
       "Punjab                             NaN          NaN  \n",
       "Rajasthan                          NaN          NaN  \n",
       "Sikkim                           16637          NaN  \n",
       "Tamil Nadu                 1.21267e+06  1.33877e+06  \n",
       "Telangana                       575631       654294  \n",
       "Tripura                            NaN          NaN  \n",
       "Uttar Pradesh               1.1538e+06          NaN  \n",
       "Uttarakhand                     184091          NaN  \n",
       "West Bengal                        NaN          NaN  \n",
       "Andaman & Nicobar Islands          NaN          NaN  \n",
       "Chandigarh                       30304          NaN  \n",
       "Delhi                           551963       622385  \n",
       "Puducherry                       26533        29557  \n",
       "All_India GDP              1.36753e+07   1.5251e+07  "
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_gdp\n",
    "total_gdp=state_gdp[(state_gdp['Items  Description']=='GSDP - CURRENT PRICES (` in Crore)')]\n",
    "nunique = total_gdp.apply(pd.Series.nunique)\n",
    "cols_to_drop = nunique[nunique == 1].index\n",
    "total_gdp=total_gdp.drop(cols_to_drop, axis=1)\n",
    "total_gdp=total_gdp.T\n",
    "\n",
    "new_header = total_gdp.iloc[0] \n",
    "total_gdp = total_gdp[1:] \n",
    "total_gdp.columns = new_header \n",
    "total_gdp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_gdp = pd.DataFrame(total_gdp)\n",
    "union_territories=['Andaman & Nicobar Islands','Chandigarh','Delhi','Puducherry','All_India GDP']\n",
    "total_gdp=total_gdp.drop(union_territories) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Duration</th>\n",
       "      <th>level_0</th>\n",
       "      <th>index</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>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>379402</td>\n",
       "      <td>411404</td>\n",
       "      <td>464272</td>\n",
       "      <td>526468</td>\n",
       "      <td>609934</td>\n",
       "      <td>699307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>11063</td>\n",
       "      <td>12547</td>\n",
       "      <td>14602</td>\n",
       "      <td>16761</td>\n",
       "      <td>18784</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Assam</td>\n",
       "      <td>143175</td>\n",
       "      <td>156864</td>\n",
       "      <td>177745</td>\n",
       "      <td>198098</td>\n",
       "      <td>224234</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>247144</td>\n",
       "      <td>282368</td>\n",
       "      <td>317101</td>\n",
       "      <td>373920</td>\n",
       "      <td>413503</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>158074</td>\n",
       "      <td>177511</td>\n",
       "      <td>206690</td>\n",
       "      <td>234982</td>\n",
       "      <td>260776</td>\n",
       "      <td>290140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Duration  level_0              index 2011-12 2012-13 2013-14 2014-15 2015-16  \\\n",
       "0               0     Andhra Pradesh  379402  411404  464272  526468  609934   \n",
       "1               1  Arunachal Pradesh   11063   12547   14602   16761   18784   \n",
       "2               2              Assam  143175  156864  177745  198098  224234   \n",
       "3               3              Bihar  247144  282368  317101  373920  413503   \n",
       "4               4       Chhattisgarh  158074  177511  206690  234982  260776   \n",
       "\n",
       "Duration 2016-17  \n",
       "0         699307  \n",
       "1            NaN  \n",
       "2            NaN  \n",
       "3            NaN  \n",
       "4         290140  "
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "total_gdp= total_gdp.reset_index()\n",
    "total_gdp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "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>Duration</th>\n",
       "      <th>index</th>\n",
       "      <th>2015-16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>1212668.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>1153795.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Karnataka</td>\n",
       "      <td>1027068.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gujarat</td>\n",
       "      <td>994316.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>609934.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>588337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Telangana</td>\n",
       "      <td>575631.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>543975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Haryana</td>\n",
       "      <td>485184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>413503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Odisha</td>\n",
       "      <td>341887.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>260776.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>241955.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Assam</td>\n",
       "      <td>224234.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>184091.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jammu &amp; Kashmir</td>\n",
       "      <td>118387.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Goa</td>\n",
       "      <td>45002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>26745.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>18784.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Sikkim</td>\n",
       "      <td>16637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Tripura</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Punjab</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Nagaland</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mizoram</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Manipur</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Duration              index    2015-16\n",
       "23               Tamil Nadu  1212668.0\n",
       "26            Uttar Pradesh  1153795.0\n",
       "11                Karnataka  1027068.0\n",
       "6                   Gujarat   994316.0\n",
       "0            Andhra Pradesh   609934.0\n",
       "12                   Kerala   588337.0\n",
       "24                Telangana   575631.0\n",
       "13           Madhya Pradesh   543975.0\n",
       "7                   Haryana   485184.0\n",
       "3                     Bihar   413503.0\n",
       "19                   Odisha   341887.0\n",
       "4              Chhattisgarh   260776.0\n",
       "10                Jharkhand   241955.0\n",
       "2                     Assam   224234.0\n",
       "27              Uttarakhand   184091.0\n",
       "9           Jammu & Kashmir   118387.0\n",
       "5                       Goa    45002.0\n",
       "16                Meghalaya    26745.0\n",
       "1         Arunachal Pradesh    18784.0\n",
       "22                   Sikkim    16637.0\n",
       "25                  Tripura        0.0\n",
       "14              Maharashtra        0.0\n",
       "21                Rajasthan        0.0\n",
       "20                   Punjab        0.0\n",
       "18                 Nagaland        0.0\n",
       "17                  Mizoram        0.0\n",
       "15                  Manipur        0.0\n",
       "8          Himachal Pradesh        0.0\n",
       "28              West Bengal        0.0"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for year 2015 and 2016\n",
    "gdp_1516= total_gdp[['index','2015-16']]\n",
    "gdp_1516= gdp_1516.fillna(0)\n",
    "\n",
    "gdp_1516= gdp_1516.sort_values(by=['2015-16'], ascending=False)\n",
    "gdp_1516"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(30, 15))\n",
    "plt.bar(gdp_1516['index'], gdp_1516['2015-16'])\n",
    "plt.xlabel('States', fontsize=15)\n",
    "plt.ylabel('growth rate', fontsize=15)\n",
    "plt.xticks(gdp_1516['index'], fontsize=15, rotation=90)\n",
    "plt.title('Growth of each state in 2015-16')\n",
    "plt.savefig(\"Growth of each state in 2015-16.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the total GDP of the states for the year 2015-16:\n",
    "\n",
    "Which Plot will you use for this? Why? (Remeber to plot the graph in a way such as it is easier to read and compare)\n",
    "\n",
    "A:  bar plot as it is easier to compare the gdp on this plot\n",
    "\n",
    "Identify the top 5 and the bottom 5 states based on total GDP.\n",
    "A:\n",
    "Top 5 : Tamil nadu,Uttar Pradesh, Karnataka, Gujrat, Andhara pradesh.\n",
    "Bottom 5:  Tripura, sikkim, Arunachal pradesh, Meghalaya, Goa\n",
    "\n",
    "What insights can you draw from this graph? What states are performing poorly? (Remember: this will not be solely based on total GDP)\n"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}