1.india rainfall.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Analyzing the Indian rainfall 1901 - 2015 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Bad key \"lines.markerfacecolor\" on line 11 in\n",
      "/usr/share/matplotlib/mpl-data/stylelib/classic.mplstyle.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "http://github.com/matplotlib/matplotlib/blob/master/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n",
      "\n",
      "Bad key \"lines.markeredgecolor\" on line 12 in\n",
      "/usr/share/matplotlib/mpl-data/stylelib/classic.mplstyle.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "http://github.com/matplotlib/matplotlib/blob/master/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Loading and Understanding Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/rainfall in india 1901-2015.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SUBDIVISION</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>JAN</th>\n",
       "      <th>FEB</th>\n",
       "      <th>MAR</th>\n",
       "      <th>APR</th>\n",
       "      <th>MAY</th>\n",
       "      <th>JUN</th>\n",
       "      <th>JUL</th>\n",
       "      <th>AUG</th>\n",
       "      <th>SEP</th>\n",
       "      <th>OCT</th>\n",
       "      <th>NOV</th>\n",
       "      <th>DEC</th>\n",
       "      <th>ANNUAL</th>\n",
       "      <th>Jan-Feb</th>\n",
       "      <th>Mar-May</th>\n",
       "      <th>Jun-Sep</th>\n",
       "      <th>Oct-Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1901</td>\n",
       "      <td>49.2</td>\n",
       "      <td>87.1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>528.8</td>\n",
       "      <td>517.5</td>\n",
       "      <td>365.1</td>\n",
       "      <td>481.1</td>\n",
       "      <td>332.6</td>\n",
       "      <td>388.5</td>\n",
       "      <td>558.2</td>\n",
       "      <td>33.6</td>\n",
       "      <td>3373.2</td>\n",
       "      <td>136.3</td>\n",
       "      <td>560.3</td>\n",
       "      <td>1696.3</td>\n",
       "      <td>980.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>446.1</td>\n",
       "      <td>537.1</td>\n",
       "      <td>228.9</td>\n",
       "      <td>753.7</td>\n",
       "      <td>666.2</td>\n",
       "      <td>197.2</td>\n",
       "      <td>359.0</td>\n",
       "      <td>160.5</td>\n",
       "      <td>3520.7</td>\n",
       "      <td>159.8</td>\n",
       "      <td>458.3</td>\n",
       "      <td>2185.9</td>\n",
       "      <td>716.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1903</td>\n",
       "      <td>12.7</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>235.1</td>\n",
       "      <td>479.9</td>\n",
       "      <td>728.4</td>\n",
       "      <td>326.7</td>\n",
       "      <td>339.0</td>\n",
       "      <td>181.2</td>\n",
       "      <td>284.4</td>\n",
       "      <td>225.0</td>\n",
       "      <td>2957.4</td>\n",
       "      <td>156.7</td>\n",
       "      <td>236.1</td>\n",
       "      <td>1874.0</td>\n",
       "      <td>690.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1904</td>\n",
       "      <td>9.4</td>\n",
       "      <td>14.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.4</td>\n",
       "      <td>304.5</td>\n",
       "      <td>495.1</td>\n",
       "      <td>502.0</td>\n",
       "      <td>160.1</td>\n",
       "      <td>820.4</td>\n",
       "      <td>222.2</td>\n",
       "      <td>308.7</td>\n",
       "      <td>40.1</td>\n",
       "      <td>3079.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>506.9</td>\n",
       "      <td>1977.6</td>\n",
       "      <td>571.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1905</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>26.9</td>\n",
       "      <td>279.5</td>\n",
       "      <td>628.7</td>\n",
       "      <td>368.7</td>\n",
       "      <td>330.5</td>\n",
       "      <td>297.0</td>\n",
       "      <td>260.7</td>\n",
       "      <td>25.4</td>\n",
       "      <td>344.7</td>\n",
       "      <td>2566.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>309.7</td>\n",
       "      <td>1624.9</td>\n",
       "      <td>630.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SUBDIVISION  YEAR   JAN    FEB   MAR    APR    MAY    JUN  \\\n",
       "0  ANDAMAN & NICOBAR ISLANDS  1901  49.2   87.1  29.2    2.3  528.8  517.5   \n",
       "1  ANDAMAN & NICOBAR ISLANDS  1902   0.0  159.8  12.2    0.0  446.1  537.1   \n",
       "2  ANDAMAN & NICOBAR ISLANDS  1903  12.7  144.0   0.0    1.0  235.1  479.9   \n",
       "3  ANDAMAN & NICOBAR ISLANDS  1904   9.4   14.7   0.0  202.4  304.5  495.1   \n",
       "4  ANDAMAN & NICOBAR ISLANDS  1905   1.3    0.0   3.3   26.9  279.5  628.7   \n",
       "\n",
       "     JUL    AUG    SEP    OCT    NOV    DEC  ANNUAL  Jan-Feb  Mar-May  \\\n",
       "0  365.1  481.1  332.6  388.5  558.2   33.6  3373.2    136.3    560.3   \n",
       "1  228.9  753.7  666.2  197.2  359.0  160.5  3520.7    159.8    458.3   \n",
       "2  728.4  326.7  339.0  181.2  284.4  225.0  2957.4    156.7    236.1   \n",
       "3  502.0  160.1  820.4  222.2  308.7   40.1  3079.6     24.1    506.9   \n",
       "4  368.7  330.5  297.0  260.7   25.4  344.7  2566.7      1.3    309.7   \n",
       "\n",
       "   Jun-Sep  Oct-Dec  \n",
       "0   1696.3    980.3  \n",
       "1   2185.9    716.7  \n",
       "2   1874.0    690.6  \n",
       "3   1977.6    571.0  \n",
       "4   1624.9    630.8  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4c2d30c1d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4116 entries, 0 to 4115\n",
      "Data columns (total 19 columns):\n",
      "SUBDIVISION    4116 non-null object\n",
      "YEAR           4116 non-null int64\n",
      "JAN            4112 non-null float64\n",
      "FEB            4113 non-null float64\n",
      "MAR            4110 non-null float64\n",
      "APR            4112 non-null float64\n",
      "MAY            4113 non-null float64\n",
      "JUN            4111 non-null float64\n",
      "JUL            4109 non-null float64\n",
      "AUG            4112 non-null float64\n",
      "SEP            4110 non-null float64\n",
      "OCT            4109 non-null float64\n",
      "NOV            4105 non-null float64\n",
      "DEC            4106 non-null float64\n",
      "ANNUAL         4090 non-null float64\n",
      "Jan-Feb        4110 non-null float64\n",
      "Mar-May        4107 non-null float64\n",
      "Jun-Sep        4106 non-null float64\n",
      "Oct-Dec        4103 non-null float64\n",
      "dtypes: float64(17), int64(1), object(1)\n",
      "memory usage: 611.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "Collapsed": "false"
   },
   "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>YEAR</th>\n",
       "      <th>JAN</th>\n",
       "      <th>FEB</th>\n",
       "      <th>MAR</th>\n",
       "      <th>APR</th>\n",
       "      <th>MAY</th>\n",
       "      <th>JUN</th>\n",
       "      <th>JUL</th>\n",
       "      <th>AUG</th>\n",
       "      <th>SEP</th>\n",
       "      <th>OCT</th>\n",
       "      <th>NOV</th>\n",
       "      <th>DEC</th>\n",
       "      <th>ANNUAL</th>\n",
       "      <th>Jan-Feb</th>\n",
       "      <th>Mar-May</th>\n",
       "      <th>Jun-Sep</th>\n",
       "      <th>Oct-Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>4116.000000</td>\n",
       "      <td>4112.000000</td>\n",
       "      <td>4113.000000</td>\n",
       "      <td>4110.000000</td>\n",
       "      <td>4112.000000</td>\n",
       "      <td>4113.000000</td>\n",
       "      <td>4111.000000</td>\n",
       "      <td>4109.000000</td>\n",
       "      <td>4112.000000</td>\n",
       "      <td>4110.000000</td>\n",
       "      <td>4109.000000</td>\n",
       "      <td>4105.000000</td>\n",
       "      <td>4106.000000</td>\n",
       "      <td>4090.000000</td>\n",
       "      <td>4110.000000</td>\n",
       "      <td>4107.000000</td>\n",
       "      <td>4106.000000</td>\n",
       "      <td>4103.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1958.218659</td>\n",
       "      <td>18.957320</td>\n",
       "      <td>21.805325</td>\n",
       "      <td>27.359197</td>\n",
       "      <td>43.127432</td>\n",
       "      <td>85.745417</td>\n",
       "      <td>230.234444</td>\n",
       "      <td>347.214334</td>\n",
       "      <td>290.263497</td>\n",
       "      <td>197.361922</td>\n",
       "      <td>95.507009</td>\n",
       "      <td>39.866163</td>\n",
       "      <td>18.870580</td>\n",
       "      <td>1411.008900</td>\n",
       "      <td>40.747786</td>\n",
       "      <td>155.901753</td>\n",
       "      <td>1064.724769</td>\n",
       "      <td>154.100487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>33.140898</td>\n",
       "      <td>33.585371</td>\n",
       "      <td>35.909488</td>\n",
       "      <td>46.959424</td>\n",
       "      <td>67.831168</td>\n",
       "      <td>123.234904</td>\n",
       "      <td>234.710758</td>\n",
       "      <td>269.539667</td>\n",
       "      <td>188.770477</td>\n",
       "      <td>135.408345</td>\n",
       "      <td>99.519134</td>\n",
       "      <td>68.685410</td>\n",
       "      <td>42.369611</td>\n",
       "      <td>903.846565</td>\n",
       "      <td>59.308277</td>\n",
       "      <td>201.316965</td>\n",
       "      <td>707.741531</td>\n",
       "      <td>166.942660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1901.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>62.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.400000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1930.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>70.350000</td>\n",
       "      <td>175.600000</td>\n",
       "      <td>155.975000</td>\n",
       "      <td>100.525000</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>804.500000</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>24.050000</td>\n",
       "      <td>573.850000</td>\n",
       "      <td>34.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>1958.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>15.700000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>138.700000</td>\n",
       "      <td>284.800000</td>\n",
       "      <td>259.400000</td>\n",
       "      <td>173.900000</td>\n",
       "      <td>65.200000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1121.300000</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>74.800000</td>\n",
       "      <td>881.100000</td>\n",
       "      <td>98.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>26.800000</td>\n",
       "      <td>31.300000</td>\n",
       "      <td>49.950000</td>\n",
       "      <td>97.200000</td>\n",
       "      <td>305.150000</td>\n",
       "      <td>418.400000</td>\n",
       "      <td>377.800000</td>\n",
       "      <td>265.800000</td>\n",
       "      <td>148.400000</td>\n",
       "      <td>46.100000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>1644.775000</td>\n",
       "      <td>50.375000</td>\n",
       "      <td>196.950000</td>\n",
       "      <td>1288.175000</td>\n",
       "      <td>213.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>583.700000</td>\n",
       "      <td>403.500000</td>\n",
       "      <td>605.600000</td>\n",
       "      <td>595.100000</td>\n",
       "      <td>1168.600000</td>\n",
       "      <td>1609.900000</td>\n",
       "      <td>2362.800000</td>\n",
       "      <td>1664.600000</td>\n",
       "      <td>1222.000000</td>\n",
       "      <td>948.300000</td>\n",
       "      <td>648.900000</td>\n",
       "      <td>617.500000</td>\n",
       "      <td>6331.100000</td>\n",
       "      <td>699.500000</td>\n",
       "      <td>1745.800000</td>\n",
       "      <td>4536.900000</td>\n",
       "      <td>1252.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              YEAR          JAN          FEB          MAR          APR  \\\n",
       "count  4116.000000  4112.000000  4113.000000  4110.000000  4112.000000   \n",
       "mean   1958.218659    18.957320    21.805325    27.359197    43.127432   \n",
       "std      33.140898    33.585371    35.909488    46.959424    67.831168   \n",
       "min    1901.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%    1930.000000     0.600000     0.600000     1.000000     3.000000   \n",
       "50%    1958.000000     6.000000     6.700000     7.800000    15.700000   \n",
       "75%    1987.000000    22.200000    26.800000    31.300000    49.950000   \n",
       "max    2015.000000   583.700000   403.500000   605.600000   595.100000   \n",
       "\n",
       "               MAY          JUN          JUL          AUG          SEP  \\\n",
       "count  4113.000000  4111.000000  4109.000000  4112.000000  4110.000000   \n",
       "mean     85.745417   230.234444   347.214334   290.263497   197.361922   \n",
       "std     123.234904   234.710758   269.539667   188.770477   135.408345   \n",
       "min       0.000000     0.400000     0.000000     0.000000     0.100000   \n",
       "25%       8.600000    70.350000   175.600000   155.975000   100.525000   \n",
       "50%      36.600000   138.700000   284.800000   259.400000   173.900000   \n",
       "75%      97.200000   305.150000   418.400000   377.800000   265.800000   \n",
       "max    1168.600000  1609.900000  2362.800000  1664.600000  1222.000000   \n",
       "\n",
       "               OCT          NOV          DEC       ANNUAL      Jan-Feb  \\\n",
       "count  4109.000000  4105.000000  4106.000000  4090.000000  4110.000000   \n",
       "mean     95.507009    39.866163    18.870580  1411.008900    40.747786   \n",
       "std      99.519134    68.685410    42.369611   903.846565    59.308277   \n",
       "min       0.000000     0.000000     0.000000    62.300000     0.000000   \n",
       "25%      14.600000     0.700000     0.100000   804.500000     4.100000   \n",
       "50%      65.200000     9.500000     3.000000  1121.300000    19.200000   \n",
       "75%     148.400000    46.100000    17.500000  1644.775000    50.375000   \n",
       "max     948.300000   648.900000   617.500000  6331.100000   699.500000   \n",
       "\n",
       "           Mar-May      Jun-Sep      Oct-Dec  \n",
       "count  4107.000000  4106.000000  4103.000000  \n",
       "mean    155.901753  1064.724769   154.100487  \n",
       "std     201.316965   707.741531   166.942660  \n",
       "min       0.000000    57.400000     0.000000  \n",
       "25%      24.050000   573.850000    34.200000  \n",
       "50%      74.800000   881.100000    98.200000  \n",
       "75%     196.950000  1288.175000   213.500000  \n",
       "max    1745.800000  4536.900000  1252.500000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Data Pre-Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4116, 19)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ANDAMAN & NICOBAR ISLANDS', 'ARUNACHAL PRADESH',\n",
       "       'ASSAM & MEGHALAYA', 'NAGA MANI MIZO TRIPURA',\n",
       "       'SUB HIMALAYAN WEST BENGAL & SIKKIM', 'GANGETIC WEST BENGAL',\n",
       "       'ORISSA', 'JHARKHAND', 'BIHAR', 'EAST UTTAR PRADESH',\n",
       "       'WEST UTTAR PRADESH', 'UTTARAKHAND', 'HARYANA DELHI & CHANDIGARH',\n",
       "       'PUNJAB', 'HIMACHAL PRADESH', 'JAMMU & KASHMIR', 'WEST RAJASTHAN',\n",
       "       'EAST RAJASTHAN', 'WEST MADHYA PRADESH', 'EAST MADHYA PRADESH',\n",
       "       'GUJARAT REGION', 'SAURASHTRA & KUTCH', 'KONKAN & GOA',\n",
       "       'MADHYA MAHARASHTRA', 'MATATHWADA', 'VIDARBHA', 'CHHATTISGARH',\n",
       "       'COASTAL ANDHRA PRADESH', 'TELANGANA', 'RAYALSEEMA', 'TAMIL NADU',\n",
       "       'COASTAL KARNATAKA', 'NORTH INTERIOR KARNATAKA',\n",
       "       'SOUTH INTERIOR KARNATAKA', 'KERALA', 'LAKSHADWEEP'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"SUBDIVISION\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SUBDIVISION     0\n",
       "YEAR            0\n",
       "JAN             4\n",
       "FEB             3\n",
       "MAR             6\n",
       "APR             4\n",
       "MAY             3\n",
       "JUN             5\n",
       "JUL             7\n",
       "AUG             4\n",
       "SEP             6\n",
       "OCT             7\n",
       "NOV            11\n",
       "DEC            10\n",
       "ANNUAL         26\n",
       "Jan-Feb         6\n",
       "Mar-May         9\n",
       "Jun-Sep        10\n",
       "Oct-Dec        13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Filling Na values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Filling NaN for months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "def fillna_months(li , df):\n",
    "    for i in li:\n",
    "        df[i].fillna(df[i].mean() , inplace=True)\n",
    "    return df\n",
    "li = [\"JAN\" , \"FEB\" , \"MAR\" , \"APR\" , \"MAY\" , \"JUN\" , \"JUL\" , \"AUG\" , \"SEP\" , \"OCT\" , \"NOV\" , \"DEC\" ]\n",
    "df2 = fillna_months(li , df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SUBDIVISION     0\n",
       "YEAR            0\n",
       "JAN             0\n",
       "FEB             0\n",
       "MAR             0\n",
       "APR             0\n",
       "MAY             0\n",
       "JUN             0\n",
       "JUL             0\n",
       "AUG             0\n",
       "SEP             0\n",
       "OCT             0\n",
       "NOV             0\n",
       "DEC             0\n",
       "ANNUAL         26\n",
       "Jan-Feb         6\n",
       "Mar-May         9\n",
       "Jun-Sep        10\n",
       "Oct-Dec        13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "Collapsed": "false"
   },
   "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>SUBDIVISION</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>JAN</th>\n",
       "      <th>FEB</th>\n",
       "      <th>MAR</th>\n",
       "      <th>APR</th>\n",
       "      <th>MAY</th>\n",
       "      <th>JUN</th>\n",
       "      <th>JUL</th>\n",
       "      <th>AUG</th>\n",
       "      <th>SEP</th>\n",
       "      <th>OCT</th>\n",
       "      <th>NOV</th>\n",
       "      <th>DEC</th>\n",
       "      <th>ANNUAL</th>\n",
       "      <th>Jan-Feb</th>\n",
       "      <th>Mar-May</th>\n",
       "      <th>Jun-Sep</th>\n",
       "      <th>Oct-Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1901</td>\n",
       "      <td>49.2</td>\n",
       "      <td>87.1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>528.8</td>\n",
       "      <td>517.5</td>\n",
       "      <td>365.1</td>\n",
       "      <td>481.1</td>\n",
       "      <td>332.6</td>\n",
       "      <td>388.5</td>\n",
       "      <td>558.2</td>\n",
       "      <td>33.6</td>\n",
       "      <td>3373.2</td>\n",
       "      <td>136.3</td>\n",
       "      <td>560.3</td>\n",
       "      <td>1696.3</td>\n",
       "      <td>980.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>446.1</td>\n",
       "      <td>537.1</td>\n",
       "      <td>228.9</td>\n",
       "      <td>753.7</td>\n",
       "      <td>666.2</td>\n",
       "      <td>197.2</td>\n",
       "      <td>359.0</td>\n",
       "      <td>160.5</td>\n",
       "      <td>3520.7</td>\n",
       "      <td>159.8</td>\n",
       "      <td>458.3</td>\n",
       "      <td>2185.9</td>\n",
       "      <td>716.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1903</td>\n",
       "      <td>12.7</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>235.1</td>\n",
       "      <td>479.9</td>\n",
       "      <td>728.4</td>\n",
       "      <td>326.7</td>\n",
       "      <td>339.0</td>\n",
       "      <td>181.2</td>\n",
       "      <td>284.4</td>\n",
       "      <td>225.0</td>\n",
       "      <td>2957.4</td>\n",
       "      <td>156.7</td>\n",
       "      <td>236.1</td>\n",
       "      <td>1874.0</td>\n",
       "      <td>690.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1904</td>\n",
       "      <td>9.4</td>\n",
       "      <td>14.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.4</td>\n",
       "      <td>304.5</td>\n",
       "      <td>495.1</td>\n",
       "      <td>502.0</td>\n",
       "      <td>160.1</td>\n",
       "      <td>820.4</td>\n",
       "      <td>222.2</td>\n",
       "      <td>308.7</td>\n",
       "      <td>40.1</td>\n",
       "      <td>3079.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>506.9</td>\n",
       "      <td>1977.6</td>\n",
       "      <td>571.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1905</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>26.9</td>\n",
       "      <td>279.5</td>\n",
       "      <td>628.7</td>\n",
       "      <td>368.7</td>\n",
       "      <td>330.5</td>\n",
       "      <td>297.0</td>\n",
       "      <td>260.7</td>\n",
       "      <td>25.4</td>\n",
       "      <td>344.7</td>\n",
       "      <td>2566.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>309.7</td>\n",
       "      <td>1624.9</td>\n",
       "      <td>630.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SUBDIVISION  YEAR   JAN    FEB   MAR    APR    MAY    JUN  \\\n",
       "0  ANDAMAN & NICOBAR ISLANDS  1901  49.2   87.1  29.2    2.3  528.8  517.5   \n",
       "1  ANDAMAN & NICOBAR ISLANDS  1902   0.0  159.8  12.2    0.0  446.1  537.1   \n",
       "2  ANDAMAN & NICOBAR ISLANDS  1903  12.7  144.0   0.0    1.0  235.1  479.9   \n",
       "3  ANDAMAN & NICOBAR ISLANDS  1904   9.4   14.7   0.0  202.4  304.5  495.1   \n",
       "4  ANDAMAN & NICOBAR ISLANDS  1905   1.3    0.0   3.3   26.9  279.5  628.7   \n",
       "\n",
       "     JUL    AUG    SEP    OCT    NOV    DEC  ANNUAL  Jan-Feb  Mar-May  \\\n",
       "0  365.1  481.1  332.6  388.5  558.2   33.6  3373.2    136.3    560.3   \n",
       "1  228.9  753.7  666.2  197.2  359.0  160.5  3520.7    159.8    458.3   \n",
       "2  728.4  326.7  339.0  181.2  284.4  225.0  2957.4    156.7    236.1   \n",
       "3  502.0  160.1  820.4  222.2  308.7   40.1  3079.6     24.1    506.9   \n",
       "4  368.7  330.5  297.0  260.7   25.4  344.7  2566.7      1.3    309.7   \n",
       "\n",
       "   Jun-Sep  Oct-Dec  \n",
       "0   1696.3    980.3  \n",
       "1   2185.9    716.7  \n",
       "2   1874.0    690.6  \n",
       "3   1977.6    571.0  \n",
       "4   1624.9    630.8  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df = df2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "del df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Filling in cuts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df[\"Jan-Feb\"].fillna(df[\"JAN\"] + df[\"FEB\"] , inplace=True)\n",
    "df[\"Mar-May\"].fillna(df[\"MAR\"] + df[\"APR\"]+df[\"MAY\"] , inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df[\"Jun-Sep\"].fillna(df[\"JUN\"] + df[\"JUL\"]+df[\"AUG\"]+df[\"SEP\"] , inplace=True)\n",
    "df[\"Oct-Dec\"].fillna(df[\"OCT\"] + df[\"NOV\"]+df[\"DEC\"] , inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## filling NaN for Annual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df[\"ANNUAL\"].fillna(df[\"Jan-Feb\"]+df[\"Mar-May\"]+df[\"Jun-Sep\"]+df[\"Oct-Dec\"] , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4c2d331d10>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.isna().sum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4c2cd53350>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C0j2SrurcbQRBEHSAIayzL7tBez5LetFCit0wNh9XA+QQnuOBLXKbc3Koz14+T3jPBkEwGOlp4ugySgl727eSvLvKsDdwse1XbD9KyjW7HSxMbPIB4NwW5hoEQdCnNBMbp9to187+yBwQbZKkUbmsWljPdfPn7wJfoSufi0EQDHmG+8q+Bj8E3kxKRPIM8O1cXjVkp6QPAnNt39Wo4/CgDYJgIPB8lz66jXbCJfzV9gLbPcCPyaoaaof1fCewVw7ZeTGwq6T/q9F3eNAGQdDvDOF8460Le0nrFE73BXotdaYA43Mi3Q2BjYE7bB9ne3QO2TmeFMrzY62OHwRB0HGGsBqnlJ19DS/anSWNJYU3fgz4T4AcwnMyKRzCfOCzthd0fupBEASdpRtX7GUpJeyb8aLN9b8BfKPO9ZuBm8uMHQRB0G8Md2EfBEEwHBjKK/uWctDm8s9lD9mZkk7PZctIOk/SfZLulbRzLl9B0q9z/tmZkk7rk7sJgiBog5755Y9uo6UctJJ2ITlPbWV7CxblnD0MwPZbgd2Bb0vqHePMnH92a+Cdkt7f/vSDIAg6iFX+6DIaCvsa3rNHAKfZfiXXmZvLNwduKJT9HRhn+yXbN+XyV4G7qREdMwiCYKAI08sl2QTYUdLtkm6RtG0uvxfYW9LIbHb5Nha3uUfSqsCHyA+FIAiCwYJ7VProNloV9iOBUcAOwJeByZIETCI5VU0lhUb4I8n8EgBJI4GLgO/bfqRW5+FBGwTBQDCUV/atWuPMAS5zyrp7h6QeYA3bzwJH91aS9EfgoUK7icBDtr9br3PbE3Ndpo7ep/v8koMg6Ep6FnTfir0sra7srwB2BZC0CbAMMC9b3ayYy3cH5tu+P59/HVgFOKrtWQdBEPQBQ1mN02oO2knApGyO+SpwsG1LWhO4Lq/0nwI+nvsYDRxPSkx+d9L4cLbtCHUcBMGgwUNYj9BqDlqAJeLa2H4M2LRK+RyqR8MMgiAYNHTjir0s4UEbBEGQGcrCvp0ctGMl3Zbzz06V1JuNahVJv8oetDMlHVJoc7Ckh/JxcOdvJwiCoHXs8ke30U4O2tOBk22PBU7I5wCfBe63PYak6/92DqOwGknfvz0p9v2JhexWQRAEA07PgqVKH91GOzloDaycP69CSlDSW75Strt/XW43H3gfcL3t52w/D1xP9STmQRAEA0LY2VfnKJLlzZmkh8Y7cvnZpAQmTwMrAQfa7pFULzftYkg6HDgc4LhVxxDZqoIg6A96ujDmTVnaeRc5Ajja9nokR6re+PbvA6YBbyDlpz1b0srUyE1breNISxgEwUBgq/TRbbQj7A8GLsuff8miHLSHkL1rbc8GHgU2o3Zu2iAIgkHBUHaqakfYPw3slD/vyqKwCE8AuwFIWotkd/8IcB3wXkmj8sbse3NZEATBoGAoW+O0k4P2MOB7ObjZy2QdO3AqcL6k+0iqm2Nsz8v9nArcmeudYrty0zcIgmDAWNCFVjZlaScHLaQQxpV1nyat2qv1M4kUaiEIgmDQ0Y26+LKUSUu4nqSbJM3KTlKfz+WrSbo+O0hdX7SZl7RzdraaKemWQvnRuWyGpIskLdc3txUEQdA8nVbjSNojp2+dLenYKteXlfSLfP12SRsUrh2Xyx+U9L52763MO8t84Iu230KKX/9ZSZsDxwI32N6YlIjk2DzBVYFzgL1yysIDcvm6wH+RMldtCYwAxrd7A0EQBJ2ixyp9NELSCOAHwPtJWfwmZNlZ5FDgedsbAWcB38ptNyfJxy1I/kjn5P5apkxawmds350//wuYRbKP3xu4IFe7ANgnf/4IyRrnidxmbqG7kcDyWc+/AmGNEwTBIKLDppfbAbNtP5LTsV5MkptFinL0EmC37JC6N3Cx7VdsPwrMZpHFY0s0tRuRXzG2Bm4H1rL9DKQHArBmrrYJMErSzZLuknRQrvMUKTH5E8AzwD9s/6adyQdBEHSSBT0qfZSgjCPpwjq25wP/AFYv2bYpSgt7Sa8DLgWOsv3POlVHkjZuP0BysPqapE2yTn9vYEOSw9WKkpYIk5zHirSEQRD0O82s7ItyKh+HV3RXxpG0Vp3STqhlKWt6uTRJ0P/Mdq8j1V8lrWP7GUnrAL3qmjnAPNsvAi9KuhUYk689mlMXIukyUoiF/6scL9ISBkEwEDQTLqEop2pQxpG0t86crN5ehRRPrONOqGWscUQKhTDL9ncKl6aQvGjJ/1+ZP18J7ChppKQVSFEuZ5HUNzvk1IUiOV7NamfyQRAEncRNHCW4E9hY0oaSliFtuE6pqFOUo/sDN+bc3lOA8dlaZ0NgY+COlm+Mciv7d5LSC94naVou+ypwGjBZ0qEkQX4AgO1Zkq4FpgM9wLm2ZwBIugS4m2Thcw/1n4pBEAT9SicDodmeL+lIUqSAEcAk2zMlnQJMtT2FtJC+UNJs0op+fG47U9Jk4H6SvPys7QXtzEce5H6/ocYJgqAs4+Zc0Za0/sPa+5eWN+/8yyVd5YEVaQmDIAgyC4Zwqux2PGjPkPSApOmSLs/OVEjaQNK/swftNEk/KvS1jKSJkv6c2+7Xd7cWBEHQHD0uf3QbZVb2vR60d0taCbhL0vWkTFPHZb3Ut4DjgGNym4dzusJKjgfm2t5E0lLAah24hyAIgo7QM4RX9g2FfXaY6nWe+pekWcC6FQ5Rt5F2khvxSVJse2z3APOannEQBEEf4SEs7NvxoC3ySeCawvmGku6RdIukHXPbVfO1UyXdLemXOd59EATBoKCniaPbaNuDVtLxJFXPz3LRM8AbbW8NfAH4uVJawpEkx4A/2N4G+BMpfEK1scKDNgiCfseo9NFtlBL2NTxokXQw8EHgo9kRgBy452/5813Aw6R4OX8DXgIuz81/CWxTbbzIQRsEwUAwv4mj22jZg1bSHqQN2b1sv1Qof31vKE5JbyJ5fj2SHwa/ImW8guRBe3+H7iMIgqBthvLKvh0P2u8DywLXp+cBt9n+NPBu4BRJ84EFwKcL6QePIXmLfRd4lpScPAiCYFDQhXnES1PGGuf3VI/AdnWN+peSVD7Vrj1OehgEQRAMOoa16WUQBMFwoQt9pUrTsgdt4fqXJFnSGhXl20paIGn/ivKVJT0l6ezO3EIQBEFnmC+VPrqNlj1obd8vaT1gd1LUy4XkDdpvkaK9VXIqcEuV8iAIggFlWK/s6+SghZQg9yss+R19jqS3L+afRdLbgLWASEcYBMGgI5yqMkUPWkl7AU/ZvreizrrAvsCPKsqXAr4NfLmN+QZBEPQZPSp/dBstedCSVDvHAydUqfpd4JgqgfY/A1xt+8kqbSrHCg/aIAj6nR5U+ug2WspBK+mtpMTh92Yb+9HA3ZK2A8YBF+fyNYA9s83920npCj8DvA5YRtILto+tHC9y0AZBMBAMZWHTUNhX86C1fR+wZqHOY8A42/NID4He8vOBq2xfAVxRKP9Err+EoA+CIBgo5nffgr00ZdQ4vR60uxYSkuzZx/MKgiDodzqccHxQ0Y4HbbHOBjXKP1Gj/Hzg/EZjB0EQ9CfduPFalvCgDYIgyHSjSWVZ2vKglfQ5SQ/m8tNz2XYFdc+9kvZt1E8QBMFgYCjb2beTg3YtYG9gK9uvSOrdsJ1B2nydL2kdksXOr2r1YzvCHAdBMCjwcFbj1MpBCxwGnGb7lXxtbv7/pULz5ch7GXX6CWEfBMGgoBuTkpSlnRy0m5Ds5m/PuWa3LdTbXtJM4D5SPPv5dfoJgiAYFAxla5x2ctCOBEYBO5BCIEzONvnYvt32FsC2wHGSlqvTT7WxwoM2CIJ+Z9iHS6iRg3YOcJkTd5D2LBYLc2x7FvAisGWdfpYgctAGQTAQDOUN2pZz0JI8YnfNdTYBlgHmSdpQ0shcvj6wKfBYnX6CIAgGBUNZ2LeTg3YSMEnSDOBV4GDblvQu4FhJr5G+k8/YnpfLl+jHdtX0hkEQBP3Ngi5Uz5SlXQ/aj1WpfyFwYZP9BEEQDDjduGIvS3jQBkEQZLrRyqYsLXvQShor6bbsKTs1hzdG0kclTc/HHyWNKfS1R/a4nS0pIl4GQTCo6MGlj26jHQ/a04GTbV+To2CeDuwMPArsZPt5Se8nxaXfPuel/QEpZ+0c4E5JU8KDNgiCwcKwVuPU8Xw1sHKutgrwdK7zx0Lz20iJTQC2A2bbfgRA0sWkcAsh7IMgGBR033q9PO140B4FnCHpSeBM4LgqTQ4Frsmf1wWKKQnnsChxeRAEwYAzX+WPdpC0mqTrJT2U/x9Vo97Buc5Dkg6ucn1KtohsSDsetEcAR9teDziaZENfrL8LSdgf01tUpduqD9LwoA2CYCDoR539scANtjcGbsjniyFpNeBEYHuSZuTE4kNB0oeBF8oO2I4H7cFA7+df5sn01t8KOBfY2/bfcvEcYL1Ct6PJqp9KwoM2CIKBoB9j4+wNXJA/XwDsU6XO+4DrbT9n+3ngemAPWLj4/gLw9bIDtuNB+zSwU/68K/BQrv9G0kPg47b/XKh/J7Bx9rBdBhgPTCk70SAIgr6mHz1o18r7ob37omtWqVNP9X0q8G3gpcpGtWjHg/Yw4Hs5NMLLwOH52gnA6sA5OS7a/LxKny/pSOA6YAQwyfbMshMNgiDoa5pRz0g6nEVyD2Ci7YmF678F1q7S9PiyQ1Qps6SxwEa2j877qKVo14P2bVXqfwr4VI2+rgYiPEIQBIOSBU3UzYJ9Yp3r76l1TdJfJa1j+5mc5GlulWpzSObsvYwGbgbeDrxN0mMkGb6mpJtt70wdmrLGCYIgGMr04wbtFNK+J/n/K6vUuQ54r6RReWP2vcB1tn9o+w22NwDeBfy5kaCHcjr75STdkfPJzpR0ci7fMCcueUjSL7IeHknL5vPZ+foGuXxpSRdIui9741Yz1QyCIBgw+nGD9jRgd0kPkRxNTwOQNE7SuQC2nyPp5u/Mxym5rCXK6OxfAXa1/UK2yvm9pGtIO8Fn2b5Y0o9IZpY/zP8/b3sjSeOBbwEHAgcAy9p+q6QVgPslXWT7sVYnHwRB0En6y4M2WynuVqV8KgU1uO1JpAjDtfp5jJwvpBENV/Y5OUmvLefS+TDJAueSXF40HSqaFF0C7JYtegysmDd0lyeFRa6aqSoIgmAgcBP/uo2ydvYjsiXOXJKt58PA3wu5ZYsmQQvNhfL1f5Cscy4hZa16BngCOLOdV5IgCIJOM5STl5QS9rYX2B5L2g3eDnhLtWr5/1qestuRNrvfAGwIfFHSm6qNFx60QRAMBAtw6aPbaMoax/bfSaY/OwCr9qYfZHFv2IWesvn6KsBzwEeAa22/Znsu8AdgXI1xwoM2CIJ+ZyiHOC5jjfN6Savmz8sD7wFmATcB++dqRdOhoknR/sCNtk1S3eyqxIqkB8YDnbqRIAiCdhnKapwy1jjrABfkePRLAZNtXyXpfuBiSV8H7mFRILSfABdKmk1a0Y/P5T8AzgNmkFQ959me3rlbCYIgaI9u3HgtSxkP2umksMaV5Y9QCH5WKH+ZZGZZWf5CtfIgCILBQjeu2MsSOWiDIAgyQ3ll344H7ZHZS9aS1ijUHyXp8pyD9g5JWxaurSrpEkkPZC/at/fNbQVBEDTPfLv00W2040H7B+AqknVOka8C02zvK2kzkq6+11PseySLnP1zeIUVOnETQRAEnaD7RHh5yujszaJsKAs9aG3fA5DDGBfZHPjv3PYBSRtIWgv4N/Bu4BP52qskL9ogCIJBQTeaVJalJQ9a27fXqX4v8OHcbjtgfZId/puAZ4HzJN0j6dxsghkEQTAoGPbhEio9aIt6+CqcBozKD4fPkcwy55PeIrYBfmh7a1LohCXyLkJ40AZBMDAMdzv7hdj+u6SbSXkQq2Y0z8nID4GFKQ0fzccKwJzCW8El1BD2xaQAU0fv032P0CAIupIFXSnGy9GqB21Nz9dscbNMPv0UcKvtf9r+C/CkpE3ztd2A+9uafRAEQQcZ7iv7Wh60/wV8hZRjcbqkq3NKwrcAP5W0gCTMDy309TngZ/lh8Aj5DSAIgmAw4C40qSxLOx603we+X6X8T8DGNfqaRo3gZ0EQBAPNULbGCQ/aIAiCTDeqZ8rSjgftT3LZ9OwV+7pc/kZJN2XzyumS9qzo741cZ1onAAAfSklEQVSSXpD0pb65pSAIgtYY7qaXvR60Y4CxwB6SdgCOtj3G9lak8MVH5vr/j6TX35oU8fKciv7OAq7pyOyDIAg6yAL3lD66jXY8aP8JC80rl2eRp7GBlfPnVViU1ARJ+5A2Zl/sxOSDIAg6SfeJ8PK05UEr6TzgL8BmwP/k6icBH5M0B7iaZIFD9pY9Bji5kzcQBEHQKYa7GqemB63tQ0g5ZWcBB+bqE4DzbY8G9iQlMlmKJOTPynHt6xIetEEQDATDOi1hkUIO2j0KZQuAXwD75aJDgcn52p+A5YA1gO2B0yU9BhwFfFVSr56/cpzIQRsEQb9ju/TRbbTqQfugpI1ymYAPscir9glySGNJbyEJ+2dt72h7A9sbAN8Fvmn77A7fTxAEQcsM5ZV9Sx60wK+B30lamZRP9l7giFz/i8CPJR1N2qz9hLvxMRgEwbCjG61sytKyBy3wzhr17691rVDnpDKTC4Ig6E+G8qo0PGiDIAgy3aieKUs7HrTnS3pU0rR8jM3lXy6UzZC0QNJqktbLnrWzcj+f7+ubC4IgaIbhrrOvlYMW4Mu2LylWtn0GcAaApA+RPG2fk7Qs8EXbd0taCbhL0vVZ7RMEQTDgDOXtxZY9aEv2PwG4KPfzDPBM/vwvSbOAdYmY9kEQDBKGdfISqJuD9hs52NlZeeVebLMCyR7/0ir9bUDa9K2XyzYIgqBfGdZ29lDTg/Y4UpiEbYHVSKEQinwI+IPt54qFOTrmpcBRvfF1KgkP2iAIBoKhrLNv2YPW9jNOvAKcB2xXUX08WYXTS9b5Xwr8zPZldcYJD9ogCPqdYb2yr5WDVtI6uUzAPhQSkEtaBdgJuLJQJuAnwCzb3+nkTQRBEHSC4b6yXwe4SdJ04E6Szv4qUi7Z+4D7SLFvvl5osy/wG9vFUMbvBD4O7FowzVwssUkQBMFA0l9RL7M5+vWSHsr/j6pR7+Bc5yFJBxfKJ0i6L++ZXitpjYZjDvbXkamj9xncEwyCYNAwbs4Vaqf9FmttX1rezPzr7S2PJel04Dnbp0k6Fhhl+5iKOqsBU0l5uw3cBbwN+BcpT8jmtuflvl5qFJmgKZ19EATBUKbHLn20yd7ABfnzBSRVeCXvI2lSnrP9PHA9ycJR+Vgxq8dXppAkqhbteND+rqCOeVrSFYU2O+fymZJuKZTvIelBSbPz0ywIgmDQ0I/JS9bKvke9PkhrVqmzLvBk4XwOsK7t10iBJ+8jr/BJ+6F1admD1vaOvRUkXUrejM2bueeQLHaekLRmLh8B/ADYPU/6TklTwoM2CILBQjMrdkmHA4cXiibanli4/ltg7SpNjy87RJUyZzl8BMlX6RFSlsDjWHzfdAna9qDNoQ92BQ7JRR8BLrP9RG4/N5dvB8y2/UhudzHpVSaEfRAEg4JmVuxZsE+sc/09ta5J+qukdWw/ky0b51apNgfYuXA+mmT6Pjb3/3DuazLQUFPSrgctJMubGwoOUpsAoyTdLOkuSQfl8qqvJGXGD4Ig6A/6UWc/Bei1rjmYgpl6geuA90oala113pvLngI2l/T6XG93UmrYurSVgzazMP5NZiRpx/gDpA2Gr0nahBqvJNXGCw/aIAgGgh4vKH20yWnA7pIeIgnr0wAkjZN0LkCOPnAqyeT9TuCUvFn7NCmn963ZJH4s8M1GAzYVz9723yXdTNoRniFpdZJ6Zt9CtTnAvGxj/6KkW4ExuXy9Qr3R1NhBLr4ehellEAT9RX85S9n+Gzl9a0X5VOBThfNJwKQq9X4E/KiZMVv2oM2XDwCusv1yocmVwI6SRuZgaNuTXjHuBDaWtKGkZUjhFKY0M9kgCIK+ZCiHS2gpB232oIUksE8rVrY9S9K1wHSgBzjX9gwASUeSdE4jgEm2Z3bmNoIgCNqnG8MglCU8aIMgGDK060G77qgtSsubp56f2dZY/U3koA2CIMgs8DBOXlLHg3ZXSXcr5Zm9QNLIinbbKuWf3b9QdnruY5ak72dX3yAIgkHBUNbZlzG97PWgHUMy8dlD0jtI8RzG294SeJxFNqO93rLfIunne8veQYp8uRWwJSnpyU4duo8gCIK2GdYhjnOCkkoP2gXAK7b/nMuvB/YrNPscKUlJ0SvMwHLAMsCyuZ+/tjX7IAiCDjLcV/ZLeNACdwBLSxqXq+xPtqGXtC7J7n4xG1DbfwJuIiUdfwa4znZDr68gCIL+oh89aPudljxogS1IZpdnSbqDFF95fq7+XeAYe3EXM0kbAW/JfaxLSmLy7mrjhQdtEAQDwVBe2bfsQWv7TGBHAEnvJcXEgRRo/+K897oGsKek+cDGwG29KiFJ1wA7ALdWGSc8aIMg6HeGuzVOrRy0vaGLlwWOIattbG9oewPbGwCXAJ+xfQXwBLBT9qxdmrQ5G2qcIAgGDUNZjdOyB62kMyR9MJf90PaNDfq5hBQK+T7SZu21tn/VxtyDIAg6SgeSkgxawoM2CIIhQ7setMsvv35pefPvfz/eVX5C4UEbBEGQGeyL33YIYR8EQZDpGcIbtCHsgyAIMrGyD4IgGAYMXVFPc04Eg+0ADo923duum+Ya7bq7XRwu50E7iDk82nV1u4EYM9oNz3bDnm4X9kEQBEEJQtgHQRAMA7pd2E+Mdl3dbiDGjHbDs92wZ9B70AZBEATt0+0r+yAIgqAEIeyDAUXSLwZ6DkH/IGltSXtJ+pCktQd6PsONYanGySGbP2v7G1WuVU2o0ovtJeLvlxhvDeBv7scvW9I7bf+hzvUdbN/WZJ83UdvvxLZ3a6a/3OcTtt/YbLsm+l/D9rwm6n/T9lf7aj41xtwIWKvy5yVpR+Bp2w832d+Ztr9Uot6HgXeRfqa/t315M+M0OadPAScANwIihTg/xfakvhozWJyuEPatCmBJ6wFfA94AXAH8HDgV+Dhwke3PV2lTLeyygTHAaNsjGsx1B+A04Lk81oWkJC5LAQfZvrZO2xHAqF7hJGkZ4BPA0bbfUqP+f5Ayf11re0YOO/1VYHnbW9cZ6x5SesljbP+93j0V2rytSvEOwFeAuba3LdNPRZ9NCfuc9rL3Z/C07fk16n0ImETKoLYA+A/bfyzR/922tyk7n4q29wM/Ay5uRkBLugr4qu3pFeXjgBNtf6jJeTT8TiWdA2wEXJSLDgQetv3ZEv1fSvpur7HLBZOR9CDwDtt/y+erA3+0vWmJtvsCN9r+Rz5fFdjZKU9GUJaB9uoqcwC/qnJMAR4HFtRpdxNwEvA+4CxgOumXe+0mxn4XcA1wG/ChEvWnAu8FDgCeB3bI5ZsB99RpNx74B/A0cAuwCzAHuBzYpkab84EbgP8mrZjOAx4A9ikxz6WAo4A/Ax9v4WeyE/Bb4HfA+xvU3abG8TbgmQZtjwNOKJw/kX+ODwDH1Wk3Hdgsf94euKXkfd0LjAJWq3Y0aDsm/yweBm7P3+8bSow5o861+1r42TxZos5M8mKv8Psws2T/7yE91B4mLWw2K9HmBmCZwvkywG9LjjetSlnNv6U4anyPAz2BliZdUgAD91ac/xVYtuQYuwE3kx4Yuzcxt2mFz7MqrtUT9jOAjfLnbYBXgH0bjDUDWCp/Xg54gSYeZLnd5qSHzL+Af/b+X6f++4DfZ0G/S8kxbqp3NGh7N7Bi5XdIWt3/vl67eud12r0CPAI8WuV4pInvdQfSAuMJ0oP4sDp1Zzd7rfIhVDhWB+aUmN9lwPqF8/VJb7vN/O6sAnwaeBL4I3AIsHSNuj8F7iEtvk7MP9cfAV8AvtBgnOlVypp+CA73o6sCoUnajaSWMfBN29eXaDOKpCME+AuwgqQVAWw/V6X+B4DjSQLweNfRe9eg+Fr774pr9XRmr9qened1t6RH3ViH+qrza7TtlyX92fZfyk5U0qHAsaT7/YHzX1Gd+ncCrwfOAP6UyxaqPGzfXa2d7V3KzqlG+xcLp9/LZQtymsxarCnpC7XObX+nRrv7XUf9VRan/ZDbJF1JEvpnAz+uUf1OSYfZXux6/vncVaPNXaTfp2oJNF6rNa+spjRJUM+SdEc+354ksEuR1TAfI6lE7yGt9N8FHAzsXKXJw/no5cr8/0olhpsq6TvAD/JcP0ft7yWoQbfo7IsC+OtlBbCkx0jCt9ofhG2/qUqbHpL65F6qCGfbezUYcwHwYh5zeeCl3kvAcraXrtFuDlAUQF8onlcTTpJeAmYX+n9zPle+v63qzPOPwGOkVVWpB0RONl9vg3bXGu0+XFkXmEd6C/pXgzH/DGxh+7WK8mVJ6o+Na7Q7sV6/tk+u0e6edoW9pG2BCcB+pO/4YuCXrrFRLGktkrruVRYJsXEkVce+zTzAS8xtp3rXbd9Soo/LSGrJC4HzbT9TuDbV9rg6bVeseHg3JC/OvkZSHwH8BvhGs/0Md7pF2LclgJscq+0/hhbHrSecbPuUKm3Wr9en7cfrjLd7rTcjSdvavrNe380g6bwqxasBWwGHuk7+YknfBNYGjrT9Ui5bkbRS/ovt4zo1z9z3Z0iC+dmK8jVJ6q2XG8z1QNJezcWkjdo5TYy9C7BlPp1Z73up0f7NpL2fCba3LFF/fWBj27/Nb0kjGz18c7tdW5jb24GfAK+z/UZJY4D/tP2ZJvp4ne0Xmhk3KDDQeqQyB2kzsObRZF9vJr0l1NwUq9FuPeDLJertWvi8YcW1D7d4/9vWubYP8CXgfW1+x5sDpwAPAVNr1PlwxbEvsCOwUotjrg/c3qDOCNIm4DzSqvdu4NlcNrJOu8mFz9+quPabOu0mVvs5AR8FfthgricCm7TwPbT8OwOsQ9oIvgN4Oc/hrSXGPAy4k2SBA7AxcEMTc96SZAl2UO/RoP7t+W/onkJZqb9B4B3A/cAT+XwMcE4rv3PD+RjwCTQ12bQJuSWwBUklUrZdq38QawBHALeS9I1nlmhzd7XP1c4b9FNG+J5Dstz573xvX2vy+1yfpLO/NwvSecAGdeqfV+W4krR5uWszYzf7nZBUYm/Nx/Il6t9Tawzqb5TfX+daQ2sVYE3gZOAS4Jf581qd/p3JwvpGkjXV10lvSY828b1PI6mJit9TqU3P/PdzE8ng4TzSXtglDdrcXuXncm/J8Vp+UMSx6OiKDVpJI4FvAp8kmVsuBYzO6oHjXaHPLbQ7jKQ7HQ1MBj4FXOka+trcZiXSivUjwCYkXeqbbI8uO90an6udV469fp7vBJJ9+PrAONuP1WjybmCM02blCiQzyFNLTTLp7FchqRv2t/1Q3hSuNRa2D6kz78mkTb7SSNqUZP1Sr041ff+qkhrp++vpJ+tdq/czqutxLumdJF+O80nWJyJZVt0u6aOuvdfUyu/MD0ib5B+xPTWP34xO9hXbr0rqnftIyidq2p+0ur7H9iF5z+HcBm2elPQOwNl/5L+AWWUna/vJ3rlmFpRtGyS6QtiTrD9WIr3i/gtA0srAmflYwjkq08ofxFzSKvn/kUz7nJ06yuIan6udL6QV4UuyxlkAYPslVfw1NOBZ0kNwLZKFzUP15lcP249LqrrxDItZgBRZjfTG9fEG3VdzKFoN2EpSPX3/CpK2Jgno5QtWQ70b57WYK2k723dU3MO2pO+sHt8m+TjcUyi7UtLlwP9S+2HYyu/MG0i+HN/JwnYyUPNnUIVbJH2V9N3sDnyG5L9Shn/b7pE0P/8dzgWWMHao4NMkS6p1gaeA64CGDlyZth4UQaJbNmgfIulCXVE+AnjAtS0y1iD9QUwgCbXJwCdsr1dnrKNJm1wrklZpvwCudxXLnRrt/05S+4ikz+717hXwLtujarS7Etia5Cz2c9t/lPRIvXEbWOP02B7TYK6rkCxGJpC8KVcl6f7vqNeuSj+bkqwy3l7j+k4sKdD+BjxU662sxJjrk/TyVQWoFoV26H0AFseXa5iDStqO9HtyPotbxhwEjLd9e5053W978xautfQ7U2g/mrwxC6wAXO4GIR8kLQUcSnIAFEn4nlv5N1aj7TkkL+3xwBdJ/h3Tar35tUv+O/4eyRpHJGuczzt74wbl6BZh/2fbmzR7raJeU38QBcuG8aTNqxNzmz83GKdla55mhW8NaxyRVuxftb1nvblU9LUWyZJkPLBetQdioxW6a4QjkPQvlrQJ7+3nFdJ+yPG2byg739xvzdAGWWg/6WwWKOlgFplCnuQqPhaFtmuShNib85xnA9+2PbfBfGaRQgI8X1G+Gik0wGY12nXMAiw/eMfXUlVKeqPtJ8r2V2K8DYCVXRHqoXD9MODm/KYqkkXOfiR17Cdcwzcj6AMGetOgzEGKa7PEbj/JqWNKC/1tSsEFv0T9t5L2DB4uUfeNHbrntUivq3+knPv7WOB0kjC7iWSq2OrY69co34m0T9B77EjaSK7qNVlyrBEk/W+z1lGbAX+qc/1ucniDPNenSULmVOpsJpJUm6eTNqvvJjkMzctlde+TlB/1zvw9rZSPnUkbjP9Z8r5eD7y+RL2WLKNYfDP40ia/81phL7ahdkiPGb3fG2kf7C6Sl+97gN+VGHMXkrfvzHxcQoqL0/bf2HA7ukVn/1ngMkmfZJHn4LYk3WtNfbqkr9g+PX8+wPYvAWw/KGm5Gm16V6FLXAJekXQb9VehV5B++ZF0qe39ytxgJbb/Cnwf+H4te3pJm7DobeVvJJVTTRVFRdspDapU8124ihordEktrdCd9hzulfQ/NeZZ723iY3W6HuFFq/cDgYm2LwUulTStTrtW94ewPVHS06QHyha5eCbJEbCmPjyveE8geYYKWErSfOB/XMW/ItPqXkbxZ1dKNVlgKul+evcuKn8PqjnVzfciVd0HgZ86qV9+K+n0eoMpOVOeTbJKO5lFG96TJB1p++om5z+s6Qo1Ti+SdiX9EYlkBldXqBRf8ytf+eupAOr0N4Jk+vkz13BaUcEDU014YzYSvq7iOKbkbPY7kmPS7FxWV89faPssKabJRaSV52Kbu27SeazMd9MKVVQcJkUUXQ040DWiNEqaAYy1PV/SA8DhztFRJc2o8/NraX+oHfI+0Z55jo/msjcBPyRFMz2rib4a7WXU/JsoOc/9SJ7sF5PUmnWdnCTdDXyA5Gj2OMlEd2a+NstVorkW2t5M0s3fW1G+FelBWFf9FSxOV6zss84Tkm3wtMpy19a/tmwGWY1Gq9DeajU+N+Lt1BG+NdiPtLK/SdK1pD/Asve1NrA76a3gI8CvSYGwZjYx54WU/G5a6XfhQ0fSWNJc/4Nk239pnaYXkSxO5pFiFP0u97ERSVjVGXLJFZCTeWvdn6ekExr0W8ss9iBSsL15hcqPSPoYaTOytLB3A8soYIykf5KtkvJn8rltr1yn77OAsyRtSPq9uUHS46Q4VbXelk4gvRGMIKlcewX9TqSAc/VYu1LQ53lMz3tMQRN0hbBn8aBPvX9wRSuLWivZlswgG2H7f+tcbvWPqWnh6xQo7XKl8AH7AEcDa0n6IWnV9Zs6bRcA1wLXKsWZmQDcLOkU2y0L7AbfTdO0qqqy/Q1JN5DUPb8pCPClSOqSWtwv6SDbP62Yx8dIYZXrUS1Wy4okq5fVqe0DsbSrxM2x/WwDwb0EauC74Ab5GMpg+9FsPbY8yXR2EwqLsIq6V+W3jZW8+Mb1VJJ6rR71Yt9EXJwm6So1TrNkvedL0FxQsoGmIHzPIGXzKS1889vOASQVR9XAZBXjfCCPtQHJ7HOS7adanHrHaUdV1eJ465I2BP9Nlf2hst+NknPe50mCfjJ1rHkaWBVVvdZoL8P2n8rMM/d1uO2JJeq9ifTg3Zv0FnoxcJXrxAuq0c9E24eXqNdrkrrEJUqYpAaL0xXCPq8M/u5FmWp2Ia1kHyOF5n21RrtrgM/06kEHO/0pfCVdQNKxX0MK2DWj02N0AiWHtvGk+Ci9qqpzbW/Yx+M2tT9UaLcaKWLpR4ELgO9VrGirtemNlLrEJWosSmrsZfT6LlT9e6gzfindfX7wTieFyPgnFQ8b1w4b3ep4HTNJDbpH2N9OWlU9nfW2vyXFg9kKeM32p2q0O4AUN+QC4Ay36MDTH/S38M1/uL0CZjGHIxrobgeCgqpqAsnq4wIaqKr6G0lnkMwgJ5IWIV0RobGsIYGkk6ij/nSdMCQV/Vxre4/yM1ys7TYO2/yW6BZhP905NrukM0neoV9R8gKc5vpx21ckbRLtQYq/vTC5SNmVSH/QbcJ3IGlGVdWf5J/hK6S4Rn36M5T0KLUFr22/uYm+RruJUMwDSStWdEGiWzZoixYmu5LykuIUn6NR29dIQnRZkv10qQTJ/Y3tukG2gkVk66v/zcegoZ9/hpUJQpYiWSl9ieQIVpesMtyPpC4c2ft35Np2/bX6KauS2QT4Mim430K508LDumkruiDRLcL+RkmTgWdIyaBvBJC0Dim7T1Uk7UHK9jSF5OH3Uq26QdBNOMeFyW+3HycJ0mnAB2zfX6KLK0kmqHfRIPJoA8oK31+Scs7+mPYiVpZSFQVL0i3C/iiSmdY6pF34Xt372qREJLU4HjigVdvxIBisZJPMT5LMbX8P7G374fqtFmN0q3rzCn5dst582z9sZQBJN9jeDcD2FZVlQTm6QmcfBMHiKOUsng98F1gisJntyxq0n0jyQr2vyXFHANfZfk/Dyou3O4kUCvlyCm8SdRwiUQppsgIp1tPOLHqLWBm4pp73bbAkXSHsVT9eTWxeBsMOSedTf4P2kw3a30+KrPooSfg2TFJfaDuFFOW0nidyZZtq5s+u5y8h6fOkt/o3kGLg9wr7fwI/tn122fGDLhH2QRB0FtUIruc6SeoLbScDOwDXU/APsP1fHZvg4uN9rh2v7iDRFTp7SVeREolcEZusQQCSDqpz2bYvrNe+V6grxe6vGgG2Dr+mvK6ePE7V+VaGpahBj6RVbf899zUKmGD7nGbmMNzpipW9pL1JXpTvIVniXARc3aynYBAMFWoEnBMp9PG6tusu5CTtRUqj+AaSLn19YJbtLeq1a5WK+S4H7EaKrb9/ibbTbI+tKCsdUTZIdIWw70XS8qQ46+NJUSKvJgULu35AJxYEA4iSkfxHgWOA+4FvuEbmqEKbe0k+K7+1vXUOQTKhZMyajUke7JtTeCtoJl6RUla2C10ldHeVutOBMb3B7PIm8fS+ejANVbrKkcf2v23/wva+pNyZW5PipQTBsEPSSEmfIgn495AS1R/YSNBnXsu2+ktJWsr2TaRsZ2U4jxRrfz4pk9RPSd7pzfASKd1nGX4DTJa0W45ZdDHxd980XaGz7yXHsP4P0sp+HZKjRp8kOQ6CwYykz5Kiat4A7FFmY7WCv0t6HSmi6M8kzSUJ7zIsb/sGScrjniTpd6Q8zbXmW4zSOYL0VjC55HhfAw4DPg0LE47/pGTbINMVahylpMUTSLljLyMFCvvDwM4qCAaOHIdnLilFYGUcnh7bYxq0XwF4Odf/GMl2/Wf17N4Lbf9Aynd7CWkP7SngNNub1mmzU2Ge84HHG0VzlTSSlPv5EFJIZQHrkd4svjqYAxsORrpF2J9H2pT9re1BGdsmCPqTGqaTAkaTBOGeNdpV81nptV9/mRJ5hCVtC8wCViUlZFkFON32bXXGqwyrYJJ9f83xJJ1Fimd1tBflA16JtLH8b9s18wEHS9Itwr5uoKUIeRoMZ1QlXWMrDkfqozzCrY6nAcgHPJTpFp39t+tcq5XVPgiGLGoxXWM93CCPcPacrde+oWVNM+PRRj7gYEm6YmUfBMHiqJ/TNeb+nyXpzi8CbqdCNeMOZ46SdAVwWaXjlVI+4P9o9uEy3OkaYS9pddKr6ma5aBbw8zIbSkEw1BiIdI1ZfbI76W1iK5IX7UV9FVVWHcoHHCS6QthLegtp1/86UmIGkWzsdwd2tf3AAE4vCAaMgUrXmJOfTADOAE7py9g1reYDDhanW4T9JcBk25MryvcDPmJ7v4GZWRAMHvojXWMW8h8gCfoNSImBJsUqe/DTLcL+wVo2vPWuBUHQOSRdQLKeuYbk6zJjgKcUNEG3CPuaeS4jAXEQ9A95U7g3pHGfJlQPOk+3mF6uKekLVcoFvL6/JxMEwxH3b0L1oMN0i7D/McmTrhrn9udEgiAIupGuUOMEQRAE7dEVr2WSlpN0sKS9lPiKpKskfU/SGgM9vyAIgsFOV6zsc87L14AVgVHADOBXwLuAsbY/OIDTC4IgGPR0i7CfYXvLHPJ0ju21C9fubRTONQiCYLjTFWoc4FUA2/OBpyuuLej/6QRBEHQX3WKNM1rS98nxuvNn8vm6AzetIAiC7qBb1DgH17tu+4L+mksQBEE30hXCPgiCIGiPrlDjVCQrJn+eB9xk+/8GZlZBEATdQ1es7HOy4kpWIyVKfsj2sf08pSAIgq6iK4R9LXIyhbtsjx3ouQRBEAxmusX0sio5h2UQBEHQgG7R2a9WpXgUcBDQJynRgiAIhhJdIexZlH+yN8FxD/A34GbgiAGaUxAEQdfQLcL+QOBJ28/AQrv7/YDl6J57CIIgGDC6RWf/I+AVAEnvBv6blFj5H8DEAZxXEARBV9Atq+IRtp/Lnw8EJtq+FLhU0rQBnFcQBEFX0C0r+xE54iXAbsCNhWvd8sAKgiAYMLpFUF4E3CJpHvBv4HcAkjYiqXKCIAiCOnSNU5WkHYB1gN/YfjGXbQK8zvbdAzq5IAiCQU7XCPsgCIKgdbpFZx8EQRC0QQj7IAiCYUAI+yAIgmFACPsgCIJhQAj7IAiCYcD/BxjfJFc/pCwTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "Collapsed": "false"
   },
   "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>SUBDIVISION</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>JAN</th>\n",
       "      <th>FEB</th>\n",
       "      <th>MAR</th>\n",
       "      <th>APR</th>\n",
       "      <th>MAY</th>\n",
       "      <th>JUN</th>\n",
       "      <th>JUL</th>\n",
       "      <th>AUG</th>\n",
       "      <th>SEP</th>\n",
       "      <th>OCT</th>\n",
       "      <th>NOV</th>\n",
       "      <th>DEC</th>\n",
       "      <th>ANNUAL</th>\n",
       "      <th>Jan-Feb</th>\n",
       "      <th>Mar-May</th>\n",
       "      <th>Jun-Sep</th>\n",
       "      <th>Oct-Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1901</td>\n",
       "      <td>49.2</td>\n",
       "      <td>87.1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>528.8</td>\n",
       "      <td>517.5</td>\n",
       "      <td>365.1</td>\n",
       "      <td>481.1</td>\n",
       "      <td>332.6</td>\n",
       "      <td>388.5</td>\n",
       "      <td>558.2</td>\n",
       "      <td>33.6</td>\n",
       "      <td>3373.2</td>\n",
       "      <td>136.3</td>\n",
       "      <td>560.3</td>\n",
       "      <td>1696.3</td>\n",
       "      <td>980.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>446.1</td>\n",
       "      <td>537.1</td>\n",
       "      <td>228.9</td>\n",
       "      <td>753.7</td>\n",
       "      <td>666.2</td>\n",
       "      <td>197.2</td>\n",
       "      <td>359.0</td>\n",
       "      <td>160.5</td>\n",
       "      <td>3520.7</td>\n",
       "      <td>159.8</td>\n",
       "      <td>458.3</td>\n",
       "      <td>2185.9</td>\n",
       "      <td>716.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1903</td>\n",
       "      <td>12.7</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>235.1</td>\n",
       "      <td>479.9</td>\n",
       "      <td>728.4</td>\n",
       "      <td>326.7</td>\n",
       "      <td>339.0</td>\n",
       "      <td>181.2</td>\n",
       "      <td>284.4</td>\n",
       "      <td>225.0</td>\n",
       "      <td>2957.4</td>\n",
       "      <td>156.7</td>\n",
       "      <td>236.1</td>\n",
       "      <td>1874.0</td>\n",
       "      <td>690.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1904</td>\n",
       "      <td>9.4</td>\n",
       "      <td>14.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.4</td>\n",
       "      <td>304.5</td>\n",
       "      <td>495.1</td>\n",
       "      <td>502.0</td>\n",
       "      <td>160.1</td>\n",
       "      <td>820.4</td>\n",
       "      <td>222.2</td>\n",
       "      <td>308.7</td>\n",
       "      <td>40.1</td>\n",
       "      <td>3079.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>506.9</td>\n",
       "      <td>1977.6</td>\n",
       "      <td>571.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1905</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>26.9</td>\n",
       "      <td>279.5</td>\n",
       "      <td>628.7</td>\n",
       "      <td>368.7</td>\n",
       "      <td>330.5</td>\n",
       "      <td>297.0</td>\n",
       "      <td>260.7</td>\n",
       "      <td>25.4</td>\n",
       "      <td>344.7</td>\n",
       "      <td>2566.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>309.7</td>\n",
       "      <td>1624.9</td>\n",
       "      <td>630.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SUBDIVISION  YEAR   JAN    FEB   MAR    APR    MAY    JUN  \\\n",
       "0  ANDAMAN & NICOBAR ISLANDS  1901  49.2   87.1  29.2    2.3  528.8  517.5   \n",
       "1  ANDAMAN & NICOBAR ISLANDS  1902   0.0  159.8  12.2    0.0  446.1  537.1   \n",
       "2  ANDAMAN & NICOBAR ISLANDS  1903  12.7  144.0   0.0    1.0  235.1  479.9   \n",
       "3  ANDAMAN & NICOBAR ISLANDS  1904   9.4   14.7   0.0  202.4  304.5  495.1   \n",
       "4  ANDAMAN & NICOBAR ISLANDS  1905   1.3    0.0   3.3   26.9  279.5  628.7   \n",
       "\n",
       "     JUL    AUG    SEP    OCT    NOV    DEC  ANNUAL  Jan-Feb  Mar-May  \\\n",
       "0  365.1  481.1  332.6  388.5  558.2   33.6  3373.2    136.3    560.3   \n",
       "1  228.9  753.7  666.2  197.2  359.0  160.5  3520.7    159.8    458.3   \n",
       "2  728.4  326.7  339.0  181.2  284.4  225.0  2957.4    156.7    236.1   \n",
       "3  502.0  160.1  820.4  222.2  308.7   40.1  3079.6     24.1    506.9   \n",
       "4  368.7  330.5  297.0  260.7   25.4  344.7  2566.7      1.3    309.7   \n",
       "\n",
       "   Jun-Sep  Oct-Dec  \n",
       "0   1696.3    980.3  \n",
       "1   2185.9    716.7  \n",
       "2   1874.0    690.6  \n",
       "3   1977.6    571.0  \n",
       "4   1624.9    630.8  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df2 = df[[\"SUBDIVISION\" ,\"YEAR\", \"Jan-Feb\" , \"Mar-May\" , \"Jun-Sep\" , \"Oct-Dec\" , \"ANNUAL\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ANDAMAN & NICOBAR ISLANDS', 'ARUNACHAL PRADESH',\n",
       "       'ASSAM & MEGHALAYA', 'NAGA MANI MIZO TRIPURA',\n",
       "       'SUB HIMALAYAN WEST BENGAL & SIKKIM', 'GANGETIC WEST BENGAL',\n",
       "       'ORISSA', 'JHARKHAND', 'BIHAR', 'EAST UTTAR PRADESH',\n",
       "       'WEST UTTAR PRADESH', 'UTTARAKHAND', 'HARYANA DELHI & CHANDIGARH',\n",
       "       'PUNJAB', 'HIMACHAL PRADESH', 'JAMMU & KASHMIR', 'WEST RAJASTHAN',\n",
       "       'EAST RAJASTHAN', 'WEST MADHYA PRADESH', 'EAST MADHYA PRADESH',\n",
       "       'GUJARAT REGION', 'SAURASHTRA & KUTCH', 'KONKAN & GOA',\n",
       "       'MADHYA MAHARASHTRA', 'MATATHWADA', 'VIDARBHA', 'CHHATTISGARH',\n",
       "       'COASTAL ANDHRA PRADESH', 'TELANGANA', 'RAYALSEEMA', 'TAMIL NADU',\n",
       "       'COASTAL KARNATAKA', 'NORTH INTERIOR KARNATAKA',\n",
       "       'SOUTH INTERIOR KARNATAKA', 'KERALA', 'LAKSHADWEEP'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.SUBDIVISION.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "Collapsed": "false"
   },
   "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>SUBDIVISION</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>Jan-Feb</th>\n",
       "      <th>Mar-May</th>\n",
       "      <th>Jun-Sep</th>\n",
       "      <th>Oct-Dec</th>\n",
       "      <th>ANNUAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1901</td>\n",
       "      <td>136.3</td>\n",
       "      <td>560.3</td>\n",
       "      <td>1696.3</td>\n",
       "      <td>980.3</td>\n",
       "      <td>3373.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1902</td>\n",
       "      <td>159.8</td>\n",
       "      <td>458.3</td>\n",
       "      <td>2185.9</td>\n",
       "      <td>716.7</td>\n",
       "      <td>3520.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1903</td>\n",
       "      <td>156.7</td>\n",
       "      <td>236.1</td>\n",
       "      <td>1874.0</td>\n",
       "      <td>690.6</td>\n",
       "      <td>2957.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1904</td>\n",
       "      <td>24.1</td>\n",
       "      <td>506.9</td>\n",
       "      <td>1977.6</td>\n",
       "      <td>571.0</td>\n",
       "      <td>3079.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1905</td>\n",
       "      <td>1.3</td>\n",
       "      <td>309.7</td>\n",
       "      <td>1624.9</td>\n",
       "      <td>630.8</td>\n",
       "      <td>2566.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SUBDIVISION  YEAR  Jan-Feb  Mar-May  Jun-Sep  Oct-Dec  ANNUAL\n",
       "0  ANDAMAN & NICOBAR ISLANDS  1901    136.3    560.3   1696.3    980.3  3373.2\n",
       "1  ANDAMAN & NICOBAR ISLANDS  1902    159.8    458.3   2185.9    716.7  3520.7\n",
       "2  ANDAMAN & NICOBAR ISLANDS  1903    156.7    236.1   1874.0    690.6  2957.4\n",
       "3  ANDAMAN & NICOBAR ISLANDS  1904     24.1    506.9   1977.6    571.0  3079.6\n",
       "4  ANDAMAN & NICOBAR ISLANDS  1905      1.3    309.7   1624.9    630.8  2566.7"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### Analyzing In Andaman & Nicobar Islands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.xticks(rotation=90)\n",
    "plt.title(\"Rainfall in Andaman & Nicobar Islands [2000 - *]\")\n",
    "plt.ylabel(\"Annual rainfall in MM\")\n",
    "sns.barplot(x=\"YEAR\" , y=\"ANNUAL\" , data=df2.loc[df2[\"SUBDIVISION\"] == \"ANDAMAN & NICOBAR ISLANDS\"].loc[df2[\"YEAR\"] >2000] )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### Understating rainfall in Jammu kashmir (Top End) VS Tamil Nadu (Bottom End)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Annual rainfall in MM')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,20))\n",
    "fig.add_subplot(2,2,1)\n",
    "sns.barplot(x=\"YEAR\" , y=\"ANNUAL\" , data=df2.loc[df2[\"SUBDIVISION\"] == 'JAMMU & KASHMIR'].loc[df[\"YEAR\"] >2000] )\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel(\"Annual rainfall in MM\")\n",
    "plt.title(\"Rainfall in JM&kashmir[2000 - *]\")\n",
    "\n",
    "\n",
    "fig.add_subplot(2,2,2)\n",
    "plt.xticks(rotation=90)\n",
    "plt.title(\"Rainfall in TamilNadu [2000 - *]\")\n",
    "sns.barplot(x=\"YEAR\" , y=\"ANNUAL\" , data=df2.loc[df2[\"SUBDIVISION\"] == 'TAMIL NADU'].loc[df[\"YEAR\"] >2000])\n",
    "plt.ylabel(\"Annual rainfall in MM\")\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "Collapsed": "false"
   },
   "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>SUBDIVISION</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>Jan-Feb</th>\n",
       "      <th>Mar-May</th>\n",
       "      <th>Jun-Sep</th>\n",
       "      <th>Oct-Dec</th>\n",
       "      <th>ANNUAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1901</td>\n",
       "      <td>136.3</td>\n",
       "      <td>560.3</td>\n",
       "      <td>1696.3</td>\n",
       "      <td>980.3</td>\n",
       "      <td>3373.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1902</td>\n",
       "      <td>159.8</td>\n",
       "      <td>458.3</td>\n",
       "      <td>2185.9</td>\n",
       "      <td>716.7</td>\n",
       "      <td>3520.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SUBDIVISION  YEAR  Jan-Feb  Mar-May  Jun-Sep  Oct-Dec  ANNUAL\n",
       "0  ANDAMAN & NICOBAR ISLANDS  1901    136.3    560.3   1696.3    980.3  3373.2\n",
       "1  ANDAMAN & NICOBAR ISLANDS  1902    159.8    458.3   2185.9    716.7  3520.7"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "jm_df = df.loc[df[\"SUBDIVISION\"] == \"JAMMU & KASHMIR\"][[\"YEAR\",\"SUBDIVISION\" , \"ANNUAL\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>YEAR</th>\n",
       "      <th>SUBDIVISION</th>\n",
       "      <th>ANNUAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1702</td>\n",
       "      <td>1901</td>\n",
       "      <td>JAMMU &amp; KASHMIR</td>\n",
       "      <td>979.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1703</td>\n",
       "      <td>1902</td>\n",
       "      <td>JAMMU &amp; KASHMIR</td>\n",
       "      <td>760.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1704</td>\n",
       "      <td>1903</td>\n",
       "      <td>JAMMU &amp; KASHMIR</td>\n",
       "      <td>1286.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1705</td>\n",
       "      <td>1904</td>\n",
       "      <td>JAMMU &amp; KASHMIR</td>\n",
       "      <td>982.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1706</td>\n",
       "      <td>1905</td>\n",
       "      <td>JAMMU &amp; KASHMIR</td>\n",
       "      <td>1157.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      YEAR      SUBDIVISION  ANNUAL\n",
       "1702  1901  JAMMU & KASHMIR   979.9\n",
       "1703  1902  JAMMU & KASHMIR   760.5\n",
       "1704  1903  JAMMU & KASHMIR  1286.6\n",
       "1705  1904  JAMMU & KASHMIR   982.2\n",
       "1706  1905  JAMMU & KASHMIR  1157.7"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jm_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "tn_df = df.loc[df[\"SUBDIVISION\"] == \"TAMIL NADU\"][[\"YEAR\",\"SUBDIVISION\" , \"ANNUAL\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "jm_tn_df = pd.concat([jm_df , tn_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "del jm_df,tn_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Annual rainfall in MM')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ss87K5MmTs+WWW2bs2LH52Mc+NuCYRx11VObNm7e8v6rVVi1tz+JotsMOO7TFl60BAAAAy3bTTTcNuNWMJ5eB/h5U1ZWttR2Gcr8VTgAAAAB0SuAEAAAAQKcETgAAAAB0SuAEAAAAQKcETgAAAAB0SuAEAAAAQKcETgAAAMBqZ/3111/4/sQTT8zYsWNz3333LWy79NJLU1U55ZRTFrZdffXVqaqccMIJSZJDDjkkT3va0/LAAw8s7HP44YenqnL33Xdn7ty52WqrrRaZ9+ijj154/+K+8IUv5AUveEGmTJmS//zP/1xq7f3HeOSRR/La1742xxxzzMLr5557bqoqN99888K2J554Iu9///uz1VZbZeutt86OO+6YX//610mSiRMn5u67717ku7/hDW9Ikpx22mmpqnzve99bYvyzzz47SbLLLrvkiiuuWDjW1ltvnW222SavfvWr85vf/Gap32NljBmWUQEAAIA1wmFXHrPsTsvh5O1nLPc9M2fOzI477phzzz03hxxyyML2rbfeOrNnz8473/nOJMmsWbOy7bbbLnLv85///Jx//vl5+9vfnieeeCKXXHJJNt100+Wu4bHHHstRRx2VOXPmZIMNNhhSUPPoo49mv/32y/bbb58ZM/7yvWfOnJmddtops2bNytFHH50kmT17dm6//fZcd911WWuttTJv3ryst956Q6pt6623zsyZM7P77rsnGfj30N8ll1yScePGZcaMGTn22GPzhS98YUjzLA+BE8Bqpuv/oA/VivyHHwAAhtuvfvWrPPjggzn++OPzsY99bJHAabPNNsv999+f//mf/8mznvWsfOtb38rrX//6Re6fPn16Zs+enbe//e259NJL88pXvjLf/OY3V6iWxx57LPfcc0823HDDTJw4cZl9p02blsmTJ+df//VfF7Y/+OCD+eEPf5hLLrkkb3zjGxcGTnfccUc22WSTrLVW32a0CRMmDLmunXfeOT/4wQ/y5z//OX/6058yZ86cTJ06dZn3vfzlL89nPvOZIc+zPGypAwAAAFZbM2fOzPTp07PzzjvnlltuyZ133rnI9be85S0566yz8qMf/Sjbbbdd1l133UWuT548OXfddVfuvffezJw5M9OmTVuhOh577LFss8022WeffTJ//vxl9v/kJz+ZMWPG5NOf/vQi7eedd1723HPPbLnlltloo41y1VVXJUn233//XHjhhZk6dWqOOOKIXH311Yvct+uuu2bq1KmZOnVq3vWudy1yrarymte8Jt/+9rdz/vnn541vfOOQvtO3vvWt7LPPPkPqu7wETgAAAMBqa9asWZk2bVrWWmut7LvvvjnrrLMWub7//vvnrLPOWhhMDWTffffNrFmz8tOf/jQ777zzwvaqGrD/QO0f+tCHcuCBB+aII47I3nvvnYcffjhnnnlmPvjBDw44xk477ZQf//jH+eUvf7lIe//Qa9q0aZk5c2aSvhVNt9xySz7+8Y9nrbXWyu67777IuUyXXHJJrrnmmlxzzTX5r//6ryXmmzZtWmbNmpVZs2Yt9fewwK677ppnPetZ+e53v5u3ve1tg/ZdUbbUAQAAAKul6667Lrfeemte+9rXJuk7E+l5z3te3vve9y7s8+xnPztPecpTctFFF+Wkk07Kj370oyXGmTZtWrbbbrscfPDBC7esJcnGG2+ce++9d5G+8+fPz6RJk5YY49vf/nYOP/zwTJw4MXfeeWfe+ta3Zr311ltq4PSqV70qBx98cPbaa6/84Ac/yHOe85zcc889ufjii3P99denqvL444+nqvLJT34yVZV11103e+21V/baa6/81V/9Vc4777yF5zIty0te8pJcf/31eepTn5ott9xy0L6XXHJJ1ltvvRxyyCH5l3/5l/zbv/3bkOZYHlY4AQAAAKulmTNn5uijj87cuXMzd+7c3H777fnd7363xIHdH/nIR/KJT3wia6+99oDjbLbZZjnuuOPynve8Z5H29ddfP5tsssnClUTz58/Pt771rey0005LjPHiF784p59+epLk7//+7/PAAw/khhtuyPbbb7/U+vfbb7988IMfzJ577pk//OEPOfvss3PQQQflN7/5TebOnZvbbrstkyZNyuWXX56rrroqt99+e5K+J9Zdd9112XzzzYf+y0ry8Y9/PB/72MeG1PepT31qPv3pT+f0008f0hbB5SVwAgAAAFYrjz32WNZdd93MmjUrb37zmxe59uY3vzmzZs1apO0Vr3jFMs8iOuyww7LFFlss0X766afn2GOPzdSpU7PbbrtlxowZA/b79Kc/nWuuuSZTpkzJS17ykrzuda/LjjvumA984AODzvvud787++67b974xjfmi1/84hLfZ7/99stXv/rV3Hnnndl7772z1VZbZZtttsmYMWPyvve9b9CxF7fXXntl1113HXL/TTbZJNOnT89//Md/LNc8Q1Gttc4HHWk77LBDu+KKK0a6DIAV4il1AACMpJtuuikvfOELR7SGa6+9Nn/7t3+bn/3sZyNax5PZQH8PqurK1toOQ7nfCicAAABgtfG5z30u06dPz7HHHjvSpbASHBoOAAAArDbe/e53593vfvdIl8FKssIJAAAAgE4JnAAAAADolMAJAAAAgE4JnAAAAADolMAJAAAAWG3cc889mTp1aqZOnZpnP/vZ2XTTTRd+fvTRR3PuueemqnLzzTcvvGfu3Lmpqnz4wx9e2Hb33XfnKU95St73vvclSY4++uiccMIJSZJDDjkkZ5999qB1VFWOOOKIhZ9POOGEHH300Yv02XbbbTN9+vRF2g455JBMmjQp2267bbbccsscdNBB+d3vfrfw+vrrr79I/9NOO21hjWsST6kDAAAAluqMk47sdLwDDz9h0Osbb7xxrrnmmiR9IdH666+fI4/8Sw0zZ87MTjvtlFmzZi0SAD3vec/L17/+9Xz0ox9Nkpx11lmZMmXKCte57rrr5mtf+1o+9KEPZdy4cUtcv+mmm/LEE0/ksssuy0MPPZT11ltv4bXjjz8+b3nLW9Jay6c//ensuuuuuf7667POOuuscD2jjRVOAAAAwKjw4IMP5oc//GFOOeWUzJo1a5FrT33qU/PCF74wV1xxRZJk9uzZ2X///Vd4rjFjxuTQQw/NiSeeOOD1r371qznwwAOzxx575IILLhiwT1XlAx/4QJ797Gfnm9/85grXMhoJnAAAAIBR4bzzzsuee+6ZLbfcMhtttFGuuuqqRa5PmzYts2bNyrx587L22mvnOc95zkrN9973vjdf+cpXct999y1xbfbs2TnggAMyffr0zJw5c9Bxtttuu0W2AD4ZCJwAAACAUWHmzJmZNm1akr5wafGgZ88998xFF12UmTNn5oADDljp+TbccMMcdNBB+cxnPrNI+89//vOMHz8+m2++eXbfffdcddVVuffee5c6Tmtt0HmqaqVrXd0InAAAAIDV3j333JOLL74473rXuzJx4sQcf/zxmT179iJhzjrrrJPtt98+n/rUp7Lffvt1Mu/f/d3f5ZRTTslDDz20sG3mzJm5+eabM3HixGyxxRa5//77c8455yx1jKuvvjovfOELk/Rt/Xv00UcXXps/f/6AZ0SNdgInAAAAYLV39tln56CDDspvfvObzJ07N7fddlsmTZqUyy+/fJF+RxxxRD7xiU9k44037mTejTbaKPvvv39OOeWUJMkTTzyRs846K9ddd13mzp2buXPn5vzzzx9wW11rLZ/5zGdyxx13ZM8990ySvPrVr86Xv/zlJMkf//jHnHnmmdl11107qXV1InACAAAAVnszZ87Mm9/85kXa9ttvv3z1q19dpG3KlCk5+OCDlzneYYcdlgkTJmTChAl5+ctfPmjfI444InfffXeS5LLLLsumm26aTTfddOH1V73qVbnxxhtzxx13JEk++MEPZtttt82WW26Zn//857nkkksWPqHupJNOyte+9rVMnTo1L3vZy/LWt741r3rVq5b9Cxhlaln7CEejHXbYoS04lR5gtDnsymNGZN6Tt58xIvMCALB6uemmmxZu/+LJa6C/B1V1ZWtth6Hcb4UTAAAAAJ0SOAEAAADQqWELnKrq1Kq6s6quX6z9/1bVLVV1Q1V9sl/7h6pqTu/a6/q179lrm1NV/zhc9QIAAADQjTHDOPZpSf49yekLGqpq1yRvSrJNa+1PVfWsXvuLkkxLMiXJc5J8t6q27N32H0lem2Rekp9X1QWttRuHsW4AAAB4UmutpapGugxGSBfnfQ/bCqfW2mVJ5i/W/H+S/Gtr7U+9Pnf22t+UZFZr7U+ttV8nmZPkJb2fOa21/26tPZpkVq8vAAAAMAzGjh2be+65p5PQgdGntZZ77rknY8eOXalxhnOF00C2TLJzVR2X5JEkR7bWfp5k0yQ/6ddvXq8tSW5brP2lq6JQAAAAeDKaMGFC5s2bl7vuumukS2GEjB07NhMmTFipMVZ14DQmyTOTvCzJjknOrKrnJRlonV7LwCuwBoxYq+rQJIcmyWabbdZJsQAAAPBk85SnPCWTJk0a6TIY5Vb1U+rmJfla6/OzJE8kGddrf26/fhOS3D5I+xJaa59vre3QWtth/Pjxw1I8AAAAAMu2qgOn85LsliS9Q8HXSXJ3kguSTKuqdatqUpLJSX6W5OdJJlfVpKpaJ30Hi1+wimsGAAAAYDkM25a6qpqZZJck46pqXpIZSU5NcmpVXZ/k0SQHt75TyG6oqjOT3JjksSTvba093hvnfUm+nWTtJKe21m4YrpoBAAAAWHnDFji11qYv5dLbl9L/uCTHDdD+jSTf6LA0AAAAAIbRqt5SBwAAAMAaTuAEAAAAQKcETgAAAAB0atjOcAIAAAAY7Q678pgRmffk7WeMyLxdscIJAAAAgE4JnAAAAADolMAJAAAAgE4JnAAAAADolMAJAAAAgE4JnAAAAADolMAJAAAAgE4JnAAAAADolMAJAAAAgE4JnAAAAADolMAJAAAAgE4JnAAAAADolMAJAAAAgE4JnAAAAADolMAJAAAAgE4JnAAAAADo1JiRLgAAVtRhVx4zIvOevP2MEZkXAABGCyucAAAAAOiUwAkAAACATgmcAAAAAOiUwAkAAACATgmcAAAAAOiUwAkAAACATgmcAAAAAOiUwAkAAACATgmcAAAAAOiUwAkAAACATgmcAAAAAOiUwAkAAACATgmcAAAAAOiUwAkAAACATgmcAAAAAOiUwAkAAACATgmcAAAAAOiUwAkAAACATgmcAAAAAOiUwAkAAACATgmcAAAAAOiUwAkAAACATgmcAAAAAOiUwAkAAACATgmcAAAAAOiUwAkAAACATgmcAAAAAOiUwAkAAACATgmcAAAAAOiUwAkAAACATgmcAAAAAOiUwAkAAACATg1b4FRVp1bVnVV1/QDXjqyqVlXjep+rqj5TVXOq6rqq2q5f34Or6tbez8HDVS8AAAAA3RjOFU6nJdlz8caqem6S1yb5bb/mvZJM7v0cmuSzvb4bJZmR5KVJXpJkRlU9cxhrBgAAAGAlDVvg1Fq7LMn8AS6dmOT/JWn92t6U5PTW5ydJnlFVmyR5XZKLWmvzW2v3JrkoA4RYAAAAAKw+VukZTlX1xiS/a61du9ilTZPc1u/zvF7b0toHGvvQqrqiqq646667OqwaAAAAgOWxygKnqnpakqOS/MtAlwdoa4O0L9nY2udbazu01nYYP378ihcKAAAAwEpZlSuctkgyKcm1VTU3yYQkV1XVs9O3cum5/fpOSHL7IO0AAAAArKZWWeDUWvtFa+1ZrbWJrbWJ6QuTtmut/T7JBUkO6j2t7mVJ7mut3ZHk20n2qKpn9g4L36PXBgAAAMBqatgCp6qameTHSV5QVfOq6p2DdP9Gkv9OMifJF5K8J0laa/OTfDTJz3s/H+m1AQAAALCaGjNcA7fWpi/j+sR+71uS9y6l36lJTu20OAAAAACGzSp9Sh0AAAAAaz6BEwAAAACdEjgBAAAA0CmBEwAAAACdEjgBAAAA0Klhe0odAKPLGScdOSLzHnj4CSMyLwAAMHyscAIAAACgUwInAAAAADolcAIAAACgUwInAAAAADolcAIAAACgUwInAAAAADolcAIAAACgUwInAAAz6n1zAAAgAElEQVQAADolcAIAAACgUwInAAAAADolcAIAAACgUwInAAAAADolcAIAAACgUwInAAAAADolcAIAAACgU2NGugBYExx25TGrfM6Tt5+xyucEAACAobDCCQAAAIBOCZwAAAAA6JTACQAAAIBOCZwAAAAA6JTACQAAAIBOCZwAAAAA6JTACQAAAIBOCZwAAAAA6JTACQAAAIBOCZwAAAAA6JTACQAAAIBOCZwAAAAA6JTACQAAAIBOCZwAAAAA6JTACQAAAIBOCZwAAAAA6JTACQAAAIBOCZwAAAAA6JTACQAAAIBOCZwAAAAA6JTACQAAAIBOCZwAAAAA6JTACQAAAIBOCZwAAAAA6JTACQAAAIBOCZwAAAAA6NSYkS4AAGB1cdiVx6zyOU/efsYqnxMAYLhZ4QQAAABApwROAAAAAHRK4AQAAABAp4YtcKqqU6vqzqq6vl/b8VV1c1VdV1XnVtUz+l37UFXNqapbqup1/dr37LXNqap/HK56AQAAAOjGcK5wOi3Jnou1XZRkq9baNkl+meRDSVJVL0oyLcmU3j3/WVVrV9XaSf4jyV5JXpRkeq8vAAAAAKupYXtKXWvtsqqauFjbd/p9/EmSt/TevynJrNban5L8uqrmJHlJ79qc1tp/J0lVzer1vXG46gYAWJXOOOnIEZn3wMNPGJF5AYChGe3/H2Ekz3D630m+2Xu/aZLb+l2b12tbWvsSqurQqrqiqq646667hqFcAAAAAIZiRAKnqjoqyWNJvrKgaYBubZD2JRtb+3xrbYfW2g7jx4/vplAAAAAAltuwbalbmqo6OMkbkuzeWlsQHs1L8tx+3SYkub33fmntAAAAAKyGVukKp6raM8k/JHlja+3hfpcuSDKtqtatqklJJif5WZKfJ5lcVZOqap30HSx+waqsGQAAAIDlM2wrnKpqZpJdkoyrqnlJZqTvqXTrJrmoqpLkJ621d7fWbqiqM9N3GPhjSd7bWnu8N877knw7ydpJTm2t3TBcNQMAAACw8obzKXXTB2g+ZZD+xyU5boD2byT5RoelAQAAADCMRvIpdQAAAACsgQROAAAAAHRK4AQAAABApwROAAAAAHRK4AQAAABApwROAAAAAHRK4AQAAABApwROAAAAAHRK4AQAAABApwROAAAAAHRK4AQAAABApwROAAAAAHRK4AQAAABApwROAAAAAHRK4AQAAABApwROAAAAAHRK4AQAAABApwROAAAAAHRK4AQAAABApwROAAAAAHRK4AQAAABApwROAAAAAHRK4AQAAABApwROAAAAAHRqzEgXAADA6HLYlceMyLwnbz9jROYFAJafFU4AAAAAdMoKJwAAWIOccdKRIzLvgYefMCLzArB6EjgBAADAk5At0gwnW+oAAAAA6JTACQAAAIBOCZwAAAAA6JTACQAAAIBOCZwAAAAA6JTACQAAAIBOCZwAAAAA6NSYkS4AAFgznXHSkSMy74GHnzAi8wIA8BdWOAEAAADQKYETAAAAAJ0SOAEAAADQqaWe4VRVDyRp/Zt6nytJa61tOMy1AQAAADAKDXZo+PeSPDvJ15LMaq39dtWUBAAAAMBottQtda21fZK8LsldSb5QVd+vqvdU1UarrDoAAAAARp1Bz3Bqrd3XWvtikr2SfC7JR5IcsgrqAgAAAGCUGmxLXarqFUmmJ9k5yeVJ3txa+8GqKAwAAACA0WmwQ8PnJvlDkllJDk3yWK99uyRprV21CuoDWGlnnHTkiMx74OEnjMi8AAAAI22wFU5z0/dUutf1fvprSXYbppoAAABYzGFXHjMi8568/YwRmRcY3ZYaOLXWdlmFdQAAAACwhhhsS92+g93YWvta9+UAAAAAMNoNtqXu7CTX9H6SpPpda0kETgAwCozUFoydRmRWAABWB4MFTvslOSDJNknOTzKztTZnlVQFAAAAwKi11tIutNbOba1NS/LqJL9K8qmquryqXr3KqgMAAABg1Flq4NTPI0nuS3J/kvWSjB3WigAAAAAY1ZYaOFXVrlX1+SRXJtk1yUmttRe31r49lIGr6tSqurOqru/XtlFVXVRVt/Zen9lrr6r6TFXNqarrqmq7fvcc3Ot/a1UdvMLfFAAAAIBVYrAVTt9L8pIklydZN8lBvVDoM1X1mSGMfVqSPRdr+8ck32utTe6N/4+99r2STO79HJrks0lfQJVkRpKX9mqZsSCkAgAAAGD1NNih4e9YmYFba5dV1cTFmt+UZJfe+y8luTTJP/TaT2+ttSQ/qapnVNUmvb4XtdbmJ0lVXZS+EGvmytQGAAAAwPBZauDUWvvSMMz3V621O3rj31FVz+q1b5rktn795vXaltYOAAAAwGpqsBVOq1IN0NYGaV9ygKpD07cdL5tttll3lQEAwAo47MpjRmTenUZkVgBY1FCeUtel/+ltlUvv9c5e+7wkz+3Xb0KS2wdpX0Jr7fOttR1aazuMHz++88IBAAAAGJpVHThdkGTBk+YOTnJ+v/aDek+re1mS+3pb776dZI+qembvsPA9em0AAAAArKaWuaWuqsYn+dskE/v3b63972XcNzN9h36Pq6p56Xva3L8mObOq3pnkt0ne2uv+jSSvTzInycPpHVjeWptfVR9N8vNev48sOEAcAAAAgNXTUM5wOj/JD5J8N8njQx24tTZ9KZd2H6BvS/LepYxzapJThzovAAAAACNrKIHT01pr/zDslQAAAACwRhjKGU5fr6rXD3slAAAAAKwRhhI4HZ6+0OmPVXV/VT1QVfcPd2EAAAAAjE7L3FLXWttgVRQCAAAAwJphqYFTVf11a+3mqtpuoOuttauGrywAAAAARqvBVjj9fZJDk3xqgGstyW7DUhEAAAAAo9pSA6fW2qG9111XXTkAAAAAjHZDOTQcAAAAAIZM4AQAAABAp5b5lLo10WFXHjMi8568/YwRmRcAAABgVRrsKXUDPp1uAU+pAwAAAGAgg61wGujpdAt4Sh2w3EZqdeFOIzIrAADAk9dgT6nzdDoAAAAAlttgW+r2HezG1trXui8HAAAAgNFusC11ew9yrSUROAEAAACwhMG21L1jVRYCAAAAwJphsBVOC1XV3ySZkmTsgrbW2keGqygAAFjcGScdOSLzHnj4CSMyLwCMZssMnKrqc0melmTXJP+V5C1JfjbMdQHAamsk/tHrH7wA3fO/5wDDZ60h9HlFa+2gJPe21o5J8vIkzx3esgAAAAAYrYYSOD3Se324qp6T5M9JJg1fSQAAAACMZkM5w+nCqnpGkuOTXJW+J9R9YVirAgAAAGDUWmrgVFVvba2dleTLrbU/JDmnqr6eZGxr7b5VViEAAACwxvAQiCeHwbbUfaj3es6Chtban4RNAAAAAAxmsC1191TVJUkmVdUFi19srb1x+MoCAAAAYLQaLHD6myTbJTkjyadWTTmsTg678pgRmffk7WeMyLwAAABAN5YaOLXWHk3yk6p6RWvtrlVYEwAAAACj2FCeUvfMqjouycT+/Vtruw1XUQAAAACMXkMJnM5K8rkk/5Xk8eEtBwAAAIDRbiiB02Ottc8OeyUAAAAArBGGEjhdWFXvSXJukj8taGytzR+2qgAAgCeNkXpYzU4jMivAk8NQAqeDe68f7NfWkjyv+3IAAAAAGO2WGTi11iatikIAAAAAWDMsNXCqqt1aaxdX1b4DXW+tfW34ygIAAABgtBpshdOrk1ycZO8BrrUkAicAAAAAlrDUwKm1NqP3+o5VVw4AAAAAo91QDg1PVf1NkilJxi5oa619ZLiKAgAAAGD0WmtZHarqc0kOSPJ/k1SStybZfJjrAgAAAGCUWmbglOQVrbWDktzbWjsmycuTPHd4ywIAAABgtBrKlrpHeq8PV9VzktyTZNLwlQQAAAAr7rArj1nlc568/YxVPieszoYSOF1YVc9IcnySq9L3hLovDGtVAAAAAIxagwZOVbVWku+11v6Q5Jyq+nqSsa21+1ZJdQAAAACMOoOe4dRaeyLJp/p9/pOwCQAAAIDBDOXQ8O9U1X5VVcNeDQAAAACj3lDOcPr7JOsleayqHklSSVprbcNhrQwAAACAUWmZgVNrbYNVUQgAAAAAa4ahbKkDAAAAgCETOAEAAADQKYETAAAAAJ1a6hlOVbXRYDe21uZ3Xw4AAAAAo91gh4ZfmaSl76l0i2tJnjcsFQEAAAAwqi01cGqtTVqVhQAAAACwZhhshdNCVfXMJJOTjF3Q1lq7bLiKAgAAAGD0Wuah4VX1riSXJfl2kmN6r0evzKRV9YGquqGqrq+qmVU1tqomVdVPq+rWqppdVev0+q7b+zynd33iyswNAAAAwPAaylPqDk+yY5LftNZ2TfLiJHet6IRVtWmS9yfZobW2VZK1k0xL8okkJ7bWJie5N8k7e7e8M8m9rbXnJzmx1w8AAACA1dRQAqdHWmuPJH2rjVprNyd5wUrOOybJU6tqTJKnJbkjyW5Jzu5d/1KSfXrv39T7nN713atqoIPMAQAAAFgNDCVwmldVz0hyXpKLqur8JLev6ISttd8lOSHJb9MXNN2Xvifi/aG19tiCOZNs2nu/aZLbevc+1uu/8YrODwAAAMDwWuah4a21N/feHl1VlyR5epJvreiEvQPI35RkUpI/JDkryV4DTb3glkGu9R/30CSHJslmm222ouUBAAAAsJKGcmj4Zgt+kvw6yTVJnr0Sc74mya9ba3e11v6c5GtJXpHkGb0tdkkyIX9ZRTUvyXN7tYxJX+A1f/FBW2ufb63t0FrbYfz48StRHgAAAAArY5krnJL8f+lbUVRJxqZvZdItSaas4Jy/TfKyqnpakj8m2T3JFUkuSfKWJLOSHJzk/F7/C3qff9y7fnFrbYkVTgAAAACsHoaypW7r/p+rarskh63ohK21n1bV2UmuSvJYkquTfD59wdasqjq213ZK75ZTkpxRVXPSt7Jp2orODQAAAMDwG8oKp0W01q6qqh1XZtLW2owkMxZr/u8kLxmg7yNJ3roy8wEAAACw6iwzcKqqv+/3ca0k2yW5a9gqAgAAAGBUG8oKpw36vX8sfVvfzhmecgAAAAAY7YZyhtMxq6IQAAAAANYMQ9lSt2WSI5NM7N+/tbbb8JUFAAAAwGg1lC11ZyX5XJL/SvL48JYDAAAAwGg3lMDpsdbaZ4e9EgAAAADWCGsNoc+FVfWeqtqkqjZa8DPslQEAAAAwKg1lhdPBvdcP9mtrSZ7XfTkAAAAAjHZDeUrdpFVRCAAAAABrhqGscEpVvSJLPqXu9GGqCQAAAIBRbJmBU1WdkWSLJNfkL0+pa0kETgAAAAAsYSgrnHZI8qLWWhvuYgAAAAAY/YbylLrrkzx7uAsBAAAAYM0wlBVO45LcWFU/S/KnBY2ttTcOW1UAAAAwipxx0pEjMu+Bh58wIvPCsgwlcDp6uIsAAAAAYM2xzMCptfb9/p+r6pVJ3pbk+wPfAQAAAMCT2VBWOKWqpqYvZNo/ya+TnDOcRQEAAAAwei01cKqqLZNMSzI9yT1JZiep1tquq6g2AAAAAEahwVY43ZzkB0n2bq3NSZKq+sAqqQoAAACAUWutQa7tl+T3SS6pqi9U1e5JatWUBQAAAMBotdTAqbV2bmvtgCR/neTSJB9I8ldV9dmq2mMV1QcAAADAKDPYCqckSWvtodbaV1prb0gyIck1Sf5x2CsDAAAAYFRaZuDUX2ttfmvt5NbabsNVEAAAAACj23IFTgAAAACwLAInAAAAADolcAIAAACgUwInAAAAADolcAIAAACgUwInAAAAADolcAIAAACgUwInAAAAADolcAIAAACgUwInAAAAADolcAIAAACgUwInAAAAADolcAIAAACgUwInAAAAADolcAIAAACgUwInAAAAADolcAIAAACgUwInAAAAADolcAIAAACgUwInAAAAADolcAIAAACgUwInAAAAADo1ZqQLAAAAYPV1xklHjsi8Bx5+wojMC3TDCicAAAAAOiVwAgAAAKBTAicAAAAAOiVwAgAAAKBTAicAAAAAOiVwAgAAAKBTIxI4VdUzqursqrq5qm6qqpdX1UZVdVFV3dp7fWavb1XVZ6pqTlVdV1XbjUTNAAAAAAzNSK1wOinJt1prf51k2yQ3JfnHJN9rrU1O8r3e5yTZK8nk3s+hST676ssFAAAAYKhWeeBUVRsmeVWSU5KktfZoa+0PSd6U5Eu9bl9Kss//3969B+tWl/cB/z5y0BhovCCi4aBYJV6iFZUQE9oGtU3AOGI7anQSb1Vxokas0WhtZ2zaqZeOEy+djhmjCWIdFU2ixBit9dbmgoqCF6RWNCgo6LESFaFR5Okf7zqyOWzgB66917uPn8/MnrPe31p77+d85917v/u713rfafukJKf1yplJbl1Vd9zmsQEAAAAYtMQZTv8wyZ4kf1RVZ1fV66rqoCSHdffFSTL9e/vp+MOTXLjh/S+a1gAAAABYQ0sUTruS3D/Ja7r7fkm+m6svn9tMbbLW1zqo6uSqOquqztqzZ888kwIAAABwoy1ROF2U5KLu/sh0++1ZFVBf23up3PTv1zccf8SG99+d5Kv7ftDufm13H9Pdxxx66KFbNjwAAAAA12/bC6fuviTJhVV192npIUk+m+SMJE+Y1p6Q5J3T9hlJHj+9Wt0Dk3xr76V3AAAAAKyfXQt93t9K8qaqunmSLyZ5Ulbl1+lV9eQkX07yqOnYdyd5aJLzk1w+HQsAAADAmlqkcOruc5Ics8muh2xybCd5xpYPBQAAAMAslngOJwAAAAD2YwonAAAAAGalcAIAAABgVgonAAAAAGalcAIAAABgVgonAAAAAGalcAIAAABgVgonAAAAAGalcAIAAABgVgonAAAAAGalcAIAAABgVgonAAAAAGalcAIAAABgVgonAAAAAGalcAIAAABgVgonAAAAAGalcAIAAABgVgonAAAAAGalcAIAAABgVgonAAAAAGalcAIAAABgVgonAAAAAGalcAIAAABgVgonAAAAAGalcAIAAABgVgonAAAAAGalcAIAAABgVgonAAAAAGalcAIAAABgVgonAAAAAGalcAIAAABgVruWHuDHyRtf9dxt/5yPO+Xl2/45AQAAgB9vznACAAAAYFYKJwAAAABmpXACAAAAYFYKJwAAAABmpXACAAAAYFYKJwAAAABmpXACAAAAYFYKJwAAAABmpXACAAAAYFYKJwAAAABmpXACAAAAYFYKJwAAAABmpXACAAAAYFYKJwAAAABmpXACAAAAYFYKJwAAAABmpXACAAAAYFYKJwAAAABmpXACAAAAYFYKJwAAAABmtVjhVFUHVNXZVfWu6fZdquojVfX5qnprVd18Wr/FdPv8af+RS80MAAAAwA1b8gynU5Kct+H2y5K8oruPSnJpkidP609Ocml33y3JK6bjAAAAAFhTixROVbU7ya8med10u5I8OMnbp0PekOQR0/ZJ0+1M+x8yHQ8AAADAGlrqDKdXJvmdJFdNtw9J8nfdfeV0+6Ikh0/bhye5MEmm/d+ajgcAAABgDW174VRVD0vy9e7++MblTQ7tgX0bP+7JVXVWVZ21Z8+eGSYFAAAA4KZY4gyn45I8vKouSPKWrC6le2WSW1fVrumY3Um+Om1flOSIJJn23yrJN/f9oN392u4+pruPOfTQQ7f2fwAAAADAddr2wqm7/0137+7uI5M8JskHuvvXk3wwySOnw56Q5J3T9hnT7Uz7P9Dd1zrDCQAAAID1sOSr1O3r+UmeU1XnZ/UcTa+f1l+f5JBp/TlJXrDQfAAAAAAM2HXDh2yd7v5Qkg9N219Mcuwmx/y/JI/a1sEAAAAAuMnW6QwnAAAAAPYDCicAAAAAZqVwAgAAAGBWCicAAAAAZqVwAgAAAGBWCicAAAAAZqVwAgAAAGBWCicAAAAAZqVwAgAAAGBWCicAAAAAZqVwAgAAAGBWu5YeALhp3viq5y7yeR93yssX+bwAAADsHM5wAgAAAGBWCicAAAAAZqVwAgAAAGBWCicAAAAAZqVwAgAAAGBWCicAAAAAZqVwAgAAAGBWCicAAAAAZrVr6QFgX2981XMX+byPO+Xli3xeAAAA2N84wwkAAACAWSmcAAAAAJiVwgkAAACAWSmcAAAAAJiVwgkAAACAWSmcAAAAAJiVwgkAAACAWSmcAAAAAJiVwgkAAACAWSmcAAAAAJiVwgkAAACAWSmcAAAAAJiVwgkAAACAWSmcAAAAAJiVwgkAAACAWSmcAAAAAJiVwgkAAACAWSmcAAAAAJiVwgkAAACAWSmcAAAAAJiVwgkAAACAWSmcAAAAAJiVwgkAAACAWSmcAAAAAJiVwgkAAACAWSmcAAAAAJiVwgkAAACAWSmcAAAAAJiVwgkAAACAWSmcAAAAAJiVwgkAAACAWSmcAAAAAJjVthdOVXVEVX2wqs6rqnOr6pRp/bZV9b6q+vz0722m9aqqV1fV+VX1qaq6/3bPDAAAAMC4Jc5wujLJb3f3PZM8MMkzqupeSV6Q5P3dfVSS90+3k+TEJEdNbycnec32jwwAAADAqG0vnLr74u7+xLT9nSTnJTk8yUlJ3jAd9oYkj5i2T0pyWq+cmeTWVXXHbR4bAAAAgEGLPodTVR2Z5H5JPpLksO6+OFmVUkluPx12eJILN7zbRdPavh/r5Ko6q6rO2rNnz1aODQAAAMD1WKxwqqqDk/xxkmd397ev79BN1vpaC92v7e5juvuYQw89dK4xAQAAALiRFimcqurArMqmN3X3n0zLX9t7qdz079en9YuSHLHh3Xcn+ep2zQoAAADAjbPEq9RVktcnOa+7f2/DrjOSPGHafkKSd25Yf/z0anUPTPKtvZfeAQAAALB+di3wOY9L8rgkn66qc6a1FyZ5aZLTq+rJSb6c5FHTvncneWiS85NcnuRJ2zsuAAAAADfGthdO3f2X2fx5mZLkIZsc30mesaVDAQAAADCbRV+lDgAAAID9j8IJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFkpnAAAAACYlcIJAAAAgFntmMKpqk6oqs9V1flV9YKl5wEAAABgczuicKqqA5L81yQnJrlXksdW1b2WnQoAAACAzeyIwinJsUnO7+4vdvf3krwlyUkLzwQAAADAJqq7l57hBlXVI5Oc0N1PmW4/LsnPd/czNxxzcpKTp5t3T/K5bR/0ht0uyTeWHmIHkNMYOY2T1Rg5jZPVGDmNkdM4WY2R0zhZjZHTGDmNk9WYdczpzt196MiBu7Z6kpnUJmvXaMq6+7VJXrs949w0VXVWdx+z9BzrTk5j5DROVmPkNE5WY+Q0Rk7jZDVGTuNkNUZOY+Q0TlZjdnpOO+WSuouSHLHh9u4kX11oFgAAAACux04pnD6W5KiquktV3TzJY5KcsfBMAAAAAGxiR1xS191XVtUzk7w3yQFJ/rC7z114rJtirS/5WyNyGiOncbIaI6dxshojpzFyGierMXIaJ6sxchojp3GyGrOjc9oRTxoOAAAAwM6xUy6pAwAAAGCHUDgBAAAAMCuFEwAAAACzUjgBAAAAMCuF0zarqn++9Azrpqp+qqruusn6P1pinnVVVXeoqjtM24dW1b+sqp9deq6doKpevPQM666q7jLdp+6x9CzrpKruVFU/MW1XVT2pqv5LVf1mVe2IV3rdLlX18L1Zcf2q6p9W1d2n7X9cVc+tql9deq51VFUHV9Ujq+pfV9VvVdUJVeXx6z6q6h5V9fyqenVVvWravufSc+0UVfWkpWdYN9N96iFVdfA+6ycsNdM6qqpjq+rnpu17VdVzquqhS8+1E1TVaUvPsO6mxwjPqapfXnqWm8qr1G2zqvpyd99p6TnWRVU9Oskrk3w9yYFJntjdH5v2faK777/kfOuiqp6W5AVJKsnLkjwxyblJjkvyn7v79ctNt16q6tX7LiV5XJLTkqS7n7XtQ62hqnpHdz9i2j4pq6/DDyX5xSQv6e5Tl5tufVTVZ5Ic292XV9XLktw1yTuSPDhJuvtfLTnfOqmqK5J8N8lfJHlzkvd29w+WnWr9VNUrkxybZFeS9yZ5SFaZ/VKSs7v7eQuOt1amxwjPS/LJJA9K8tdZ/bH0Pkl+vbs/veB4a6Oqnp/ksUnekuSiaXl3ksckeUt3v3Sp2XYKj8+vqaqeleQZSc5LcnSSU7r7ndM+j88nVfWiJCdm9f38fUl+PqvHUv8sq5+B/2m56dZLVZ2x71JW39c/kCTd/fBtH2oNVdVHu/vYafupWX0d/mmSX07yZzvx+7nCaQts8gX1w11JHtzdB23nPOusqs5JcmJ3X1xVx2ZVCrywu/+kqs7u7vstPOJaqKpPZ/VD7JZJvpTkbt19SVXdJskHu/voRQdcI1V1UVY/7P97Vl9zSfLyJM9Nku5+wzKTrZeNX19V9ddZ/fL2t1V1uyTv7+77Ljvheqiqz3b3vabtjyf5ue6+arr9STldrarOzqqIe2RWv+jeO6sHSW/u7g8vOds6qapzs8rmljt9IS8AAAoMSURBVEm+kuTwqdA8MKvC6d6LDrhGqupTSR445XO7JG/q7l+ZzoD+/e7+xYVHXAtV9X+S/Gx3f3+f9ZsnObe7j1pmsvUy3Z823ZXkZ7r7Fts5zzqbHnf+QndfVlVHJnl7kjd296s8Pr/alNPRSW6R5JIku7v721V1yyQf6W5Xa0yq6hNJPpvkdUk6q6+7N2f1eCEeJ6zs8/j8Y0ke2t17quqgJGd2932WnfDGcznA1vgnSX4jyWX7rFdWf9Xkagd098VJ0t0fraoHJXlXVe3O6psRK9/v7suTXF5VX+juS5Kkuy+tKjld0z2T/MckJyR5Xnd/papepGi6lo33m13d/bdJ0t3fqKqrFpppHV1YVQ/u7g8kuSDJEUm+VFWHLDvWWuruvjTJHyT5g+kS4EcneWlV7e7uI5Ydb210d/eGr7O9X4tXxVMd7KuSXDFtfzfJ7ZOkuz9VVT+12FTr56okP53VH6Q2uuO0j5XDkvxKkkv3Wa+szp7jagd092VJ0t0XVNXxSd5eVXfO1X/MI7lyOpN37+PzbydJd1/hsdS1HJPklCT/NqvH5+dU1RWKpmu52XRCwc2yOjloT5J093er6splR7tpFE5b48wkl2/2BVRVn1tgnnX2naq6a3d/IUmmM52Oz+qSFc9PdLWrqurA6a+XP3yej+n5UvyCskF3fyfJs6vqAUn+W1X9eWS0mftW1bezeuB4i6q6w3TW3M2THLDwbOvkKUlOq6p/n+RbSc6ZzuS5TZLnLDnYGrrGLyFTMf7qJK+efklh5c+r6n8l+Yms/tJ7elWdmdUldf9z0cnWz7uTvKeqPpzVZStvS5Kqum380rvRs5O8v6o+n+TCae1OSe6W5JmLTbV+3pXk4O4+Z98dVfWh7R9nrV1SVUfvzWo60+lhSf4wq0taWfleVf3k9EfhB+xdrKpbRdl7DdPZ4a+oqrdN/34tuojN3CrJx7P6GdcbHp8fnB36c88ldSyqqu6bVTn3+X3WD0zy6O5+0zKTrZequlOSizc5Xf7wJPfs7v+xzGTrraoqydOzOi38N5aeZyeoqltndZ/6m6VnWSfTk+/+TFYPji5K8rG9l9axUlXHd/eHlp5jJ6iqX8jqTKcza/WiGf8iyZeTvN396pqmJ9+9V5JPdvf7prWbJTmwu/9+0eHWyJTJsUkOz+qXkr3fpzyPGjfadKXBlXvPqN9n33Hd/VcLjLV2quoWm30fmi4BvqPnmbtutXqhjOO6+4VLz7ITVNVPJjls7xUJO4nCaQtV1WFZ/eDvJF/t7q8tPNLaktUYOY2T1Rg5jZHTOFmNkdM4Wd10VXXw3kujuG5yGierMXIaJ6sxOzUnhdMWqKr7JXlNVqfEfWVa3p3k75I8vbs/sdRs66aqjk7y+9k8q9/s7rOXmm2dyGncDWTl629yA9+n3Kcm7k/jZDVGTuNk9aMrr742RE7jZDVGTuNkNWan5uS6ya3xR0me1t0f2bhYVQ+c9nlVo6udmuvO6tTIaq9TI6dRp8bX34jr+z51auS016lxfxp1amQ14tTIadSpkdUNqqrrej65SnLwds6yzuQ0TlZj5DROVmP2x5w8ke7WOGjfB0dJ0t1nJjlogXnWmazGyGmcrMbIaYycxslqjJzGyWrMi7N6IYN/sM/bwfFYfyM5jZPVGDmNk9WY/S4nZzhtjb+YXhnrtFz9aiFHJHl8kvcsNtV6ktUYOY2T1Rg5jZHTOFmNkdM4WY35RJJ3dPfH991RVU9ZYJ51Jadxshojp3GyGrPf5eQ5nLZIVZ2Y5KRc89VCzujudy862BqS1Rg5jZPVGDmNkdM4WY2R0zhZ3bCqunuSb3b3nk32HeZJ1lfkNE5WY+Q0TlZj9secFE4AAAAAzGpHXge47qrqVlX10qo6r6r+7/R23rR266XnWyeyGiOncbIaI6cxchonqzFyGierMRty+t9yum5yGierMXIaJ6sx+2NOCqetcXqSS5M8qLsP6e5Dkjwoq5fxfduik60fWY2R0zhZjZHTGDmNk9UYOY2T1Zi9OR2/T06XRk4byWmcrMbIaZysxux3ObmkbgtU1ee6++43dt+PI1mNkdM4WY2R0xg5jZPVGDmNk9UYOY2R0zhZjZHTOFmN2R9zcobT1vhSVf1OVR22d6GqDquq5+fqV1lhRVZj5DROVmPkNEZO42Q1Rk7jZDVGTmPkNE5WY+Q0TlZj9rucFE5b49eSHJLkw1V1aVV9M8mHktw2yaOXHGwNyWqMnMbJaoycxshpnKzGyGmcrMbIaYycxslqjJzGyWrMfpeTS+q2SFXdI8nuJGd292Ub1k/o7vcsN9n6kdUYOY2T1Rg5jZHTOFmNkdM4WY2R0xg5jZPVGDmNk9WY/S0nZzhtgap6VpJ3Jnlmks9U1Ukbdr94manWk6zGyGmcrMbIaYycxslqjJzGyWqMnMbIaZysxshpnKzG7I857Vp6gP3UU5M8oLsvq6ojk7y9qo7s7lclqUUnWz+yGiOncbIaI6cxchonqzFyGierMXIaI6dxshojp3GyGrPf5aRw2hoH7D39rbsvqKrjs7qz3Dk79I6yhWQ1Rk7jZDVGTmPkNE5WY+Q0TlZj5DRGTuNkNUZO42Q1Zr/LySV1W+OSqjp6743pTvOwJLdLcp/FplpPshojp3GyGiOnMXIaJ6sxchonqzFyGiOncbIaI6dxshqz3+XkScO3QFXtTnJld1+yyb7juvuvFhhrLclqjJzGyWqMnMbIaZysxshpnKzGyGmMnMbJaoycxslqzP6Yk8IJAAAAgFm5pA4AAACAWSmcAAAAAJiVwgkAYCa18pdVdeKGtUdX1Xuq6gdVdc6GtxdsOObQqvp+VT1tn493QVV9uqo+VVUfnl6pBgBg7XkOJwCAGVXVvZO8Lcn9khyQ5JwkJyT5ZHcffB3v8/Qkj03yg+4+fsP6BUmO6e5vVNXvJvnp7n7q1v4PAAB+dM5wAgCYUXd/JsmfJXl+khclOa27v3AD7/bYJL+dZHdVHX4dx/xNkuvaBwCwVnYtPQAAwH7od5N8Isn3khwzrd2yqs7ZcMxLuvutVXVEkjt090er6vQkv5bk9zb5mCckecdWDg0AMBeFEwDAzLr7u1X11iSXdfffT8tXdPfRmxz+mCSnT9tvSfL6XLNw+mBVHZbk60n+3VbNDAAwJ5fUAQBsjaumtxvy2CRPnJ6v6Ywk962qozbsf1CSOyc5N8l/mHtIAICtoHACAFhIVd09yUHdfXh3H9ndRyZ5SVZnPf1Qd1+R5NlJHl9Vt93+SQEAbhyFEwDA9rhlVZ2z4e2lWZ3d9Kf7HPfH0/o1dPfFSd6c5BlbPyoAwI+munvpGQAAAADYjzjDCQAAAIBZKZwAAAAAmJXCCQAAAIBZKZwAAAAAmJXCCQAAAIBZKZwAAAAAmJXCCQAAAIBZKZwAAAAAmNX/B9loka7Z8LaIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.xticks(rotation=90)\n",
    "plt.title(\"Rainfall in Jammu vs TN [2000 - *]\")\n",
    "sns.barplot(x=\"YEAR\" , y=\"ANNUAL\" , data=jm_tn_df.loc[df[\"YEAR\"] > 2000] , hue=\"SUBDIVISION\" , palette=\"terrain\")\n",
    "plt.ylabel(\"Annual rainfall in MM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df3 = df2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "del df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### Average Rainfall all over india in 1990 - 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "grp_rf = df3.groupby(\"SUBDIVISION\")[[\"YEAR\" , \"ANNUAL\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "grp_rf = grp_rf.mean()[\"ANNUAL\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "grp_rf = grp_rf.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "plt.xticks(rotation=90)\n",
    "plt.title(\"Average rainfall from 1990 - 2015 all over india\")\n",
    "sns.barplot(y=grp_rf.index , x=grp_rf.values , palette=\"winter\" , orient=\"h\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### A best place to spend time in summer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "grp_rf = df3.groupby(\"SUBDIVISION\")[\"Mar-May\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "grp_rf = grp_rf.mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SUBDIVISION\n",
       "ARUNACHAL PRADESH                     773.280602\n",
       "ASSAM & MEGHALAYA                     623.687826\n",
       "NAGA MANI MIZO TRIPURA                538.769565\n",
       "ANDAMAN & NICOBAR ISLANDS             458.027442\n",
       "SUB HIMALAYAN WEST BENGAL & SIKKIM    422.968696\n",
       "KERALA                                377.253913\n",
       "JAMMU & KASHMIR                       292.552174\n",
       "LAKSHADWEEP                           222.229510\n",
       "HIMACHAL PRADESH                      221.726957\n",
       "GANGETIC WEST BENGAL                  181.766087\n",
       "COASTAL KARNATAKA                     160.051304\n",
       "UTTARAKHAND                           147.773913\n",
       "SOUTH INTERIOR KARNATAKA              143.861739\n",
       "TAMIL NADU                            134.386087\n",
       "ORISSA                                120.184348\n",
       "COASTAL ANDHRA PRADESH                102.515652\n",
       "JHARKHAND                              86.095652\n",
       "BIHAR                                  80.126957\n",
       "NORTH INTERIOR KARNATAKA               78.460870\n",
       "RAYALSEEMA                             78.358261\n",
       "TELANGANA                              56.173043\n",
       "CHHATTISGARH                           53.092174\n",
       "PUNJAB                                 50.440000\n",
       "KONKAN & GOA                           39.161739\n",
       "MADHYA MAHARASHTRA                     35.692174\n",
       "HARYANA DELHI & CHANDIGARH             35.112174\n",
       "VIDARBHA                               32.851304\n",
       "EAST UTTAR PRADESH                     32.553913\n",
       "MATATHWADA                             30.352174\n",
       "EAST MADHYA PRADESH                    30.096522\n",
       "WEST UTTAR PRADESH                     30.026087\n",
       "EAST RAJASTHAN                         17.487826\n",
       "WEST RAJASTHAN                         17.006087\n",
       "WEST MADHYA PRADESH                    15.217391\n",
       "GUJARAT REGION                          8.161739\n",
       "SAURASHTRA & KUTCH                      7.140870\n",
       "Name: Mar-May, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "plt.xticks(rotation=90)\n",
    "plt.title(\"Best Place to spend Summer in India\")\n",
    "sns.barplot(y=grp_rf.index , x=grp_rf.values , palette=\"summer\" , orient=\"h\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# so as we came to the conclusion that \n",
    "* Arunachal Pradesh , \n",
    "* Assam , \n",
    "* Tirupura , \n",
    "* Andaman Nicobar , \n",
    "* sikkim ,\n",
    "* kerala \n",
    "# are heavy rainfall area and best plcae to spend time in summer ,now we will see what districts are good place to spend time in summer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "del df, df3  ,grp_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/district wise rainfall normal.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATE_UT_NAME</th>\n",
       "      <th>DISTRICT</th>\n",
       "      <th>JAN</th>\n",
       "      <th>FEB</th>\n",
       "      <th>MAR</th>\n",
       "      <th>APR</th>\n",
       "      <th>MAY</th>\n",
       "      <th>JUN</th>\n",
       "      <th>JUL</th>\n",
       "      <th>AUG</th>\n",
       "      <th>SEP</th>\n",
       "      <th>OCT</th>\n",
       "      <th>NOV</th>\n",
       "      <th>DEC</th>\n",
       "      <th>ANNUAL</th>\n",
       "      <th>Jan-Feb</th>\n",
       "      <th>Mar-May</th>\n",
       "      <th>Jun-Sep</th>\n",
       "      <th>Oct-Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ANDAMAN And NICOBAR ISLANDS</td>\n",
       "      <td>NICOBAR</td>\n",
       "      <td>107.3</td>\n",
       "      <td>57.9</td>\n",
       "      <td>65.2</td>\n",
       "      <td>117.0</td>\n",
       "      <td>358.5</td>\n",
       "      <td>295.5</td>\n",
       "      <td>285.0</td>\n",
       "      <td>271.9</td>\n",
       "      <td>354.8</td>\n",
       "      <td>326.0</td>\n",
       "      <td>315.2</td>\n",
       "      <td>250.9</td>\n",
       "      <td>2805.2</td>\n",
       "      <td>165.2</td>\n",
       "      <td>540.7</td>\n",
       "      <td>1207.2</td>\n",
       "      <td>892.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ANDAMAN And NICOBAR ISLANDS</td>\n",
       "      <td>SOUTH ANDAMAN</td>\n",
       "      <td>43.7</td>\n",
       "      <td>26.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>90.5</td>\n",
       "      <td>374.4</td>\n",
       "      <td>457.2</td>\n",
       "      <td>421.3</td>\n",
       "      <td>423.1</td>\n",
       "      <td>455.6</td>\n",
       "      <td>301.2</td>\n",
       "      <td>275.8</td>\n",
       "      <td>128.3</td>\n",
       "      <td>3015.7</td>\n",
       "      <td>69.7</td>\n",
       "      <td>483.5</td>\n",
       "      <td>1757.2</td>\n",
       "      <td>705.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ANDAMAN And NICOBAR ISLANDS</td>\n",
       "      <td>N &amp; M ANDAMAN</td>\n",
       "      <td>32.7</td>\n",
       "      <td>15.9</td>\n",
       "      <td>8.6</td>\n",
       "      <td>53.4</td>\n",
       "      <td>343.6</td>\n",
       "      <td>503.3</td>\n",
       "      <td>465.4</td>\n",
       "      <td>460.9</td>\n",
       "      <td>454.8</td>\n",
       "      <td>276.1</td>\n",
       "      <td>198.6</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2913.3</td>\n",
       "      <td>48.6</td>\n",
       "      <td>405.6</td>\n",
       "      <td>1884.4</td>\n",
       "      <td>574.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>LOHIT</td>\n",
       "      <td>42.2</td>\n",
       "      <td>80.8</td>\n",
       "      <td>176.4</td>\n",
       "      <td>358.5</td>\n",
       "      <td>306.4</td>\n",
       "      <td>447.0</td>\n",
       "      <td>660.1</td>\n",
       "      <td>427.8</td>\n",
       "      <td>313.6</td>\n",
       "      <td>167.1</td>\n",
       "      <td>34.1</td>\n",
       "      <td>29.8</td>\n",
       "      <td>3043.8</td>\n",
       "      <td>123.0</td>\n",
       "      <td>841.3</td>\n",
       "      <td>1848.5</td>\n",
       "      <td>231.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>EAST SIANG</td>\n",
       "      <td>33.3</td>\n",
       "      <td>79.5</td>\n",
       "      <td>105.9</td>\n",
       "      <td>216.5</td>\n",
       "      <td>323.0</td>\n",
       "      <td>738.3</td>\n",
       "      <td>990.9</td>\n",
       "      <td>711.2</td>\n",
       "      <td>568.0</td>\n",
       "      <td>206.9</td>\n",
       "      <td>29.5</td>\n",
       "      <td>31.7</td>\n",
       "      <td>4034.7</td>\n",
       "      <td>112.8</td>\n",
       "      <td>645.4</td>\n",
       "      <td>3008.4</td>\n",
       "      <td>268.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 STATE_UT_NAME       DISTRICT    JAN   FEB    MAR    APR  \\\n",
       "0  ANDAMAN And NICOBAR ISLANDS        NICOBAR  107.3  57.9   65.2  117.0   \n",
       "1  ANDAMAN And NICOBAR ISLANDS  SOUTH ANDAMAN   43.7  26.0   18.6   90.5   \n",
       "2  ANDAMAN And NICOBAR ISLANDS  N & M ANDAMAN   32.7  15.9    8.6   53.4   \n",
       "3            ARUNACHAL PRADESH          LOHIT   42.2  80.8  176.4  358.5   \n",
       "4            ARUNACHAL PRADESH     EAST SIANG   33.3  79.5  105.9  216.5   \n",
       "\n",
       "     MAY    JUN    JUL    AUG    SEP    OCT    NOV    DEC  ANNUAL  Jan-Feb  \\\n",
       "0  358.5  295.5  285.0  271.9  354.8  326.0  315.2  250.9  2805.2    165.2   \n",
       "1  374.4  457.2  421.3  423.1  455.6  301.2  275.8  128.3  3015.7     69.7   \n",
       "2  343.6  503.3  465.4  460.9  454.8  276.1  198.6  100.0  2913.3     48.6   \n",
       "3  306.4  447.0  660.1  427.8  313.6  167.1   34.1   29.8  3043.8    123.0   \n",
       "4  323.0  738.3  990.9  711.2  568.0  206.9   29.5   31.7  4034.7    112.8   \n",
       "\n",
       "   Mar-May  Jun-Sep  Oct-Dec  \n",
       "0    540.7   1207.2    892.1  \n",
       "1    483.5   1757.2    705.3  \n",
       "2    405.6   1884.4    574.7  \n",
       "3    841.3   1848.5    231.0  \n",
       "4    645.4   3008.4    268.1  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATE_UT_NAME    0\n",
       "DISTRICT         0\n",
       "JAN              0\n",
       "FEB              0\n",
       "MAR              0\n",
       "APR              0\n",
       "MAY              0\n",
       "JUN              0\n",
       "JUL              0\n",
       "AUG              0\n",
       "SEP              0\n",
       "OCT              0\n",
       "NOV              0\n",
       "DEC              0\n",
       "ANNUAL           0\n",
       "Jan-Feb          0\n",
       "Mar-May          0\n",
       "Jun-Sep          0\n",
       "Oct-Dec          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ANDAMAN And NICOBAR ISLANDS', 'ARUNACHAL PRADESH', 'ASSAM',\n",
       "       'MEGHALAYA', 'MANIPUR', 'MIZORAM', 'NAGALAND', 'TRIPURA',\n",
       "       'WEST BENGAL', 'SIKKIM', 'ORISSA', 'JHARKHAND', 'BIHAR',\n",
       "       'UTTAR PRADESH', 'UTTARANCHAL', 'HARYANA', 'CHANDIGARH', 'DELHI',\n",
       "       'PUNJAB', 'HIMACHAL', 'JAMMU AND KASHMIR', 'RAJASTHAN',\n",
       "       'MADHYA PRADESH', 'GUJARAT', 'DADAR NAGAR HAVELI', 'DAMAN AND DUI',\n",
       "       'MAHARASHTRA', 'GOA', 'CHATISGARH', 'ANDHRA PRADESH', 'TAMIL NADU',\n",
       "       'PONDICHERRY', 'KARNATAKA', 'KERALA', 'LAKSHADWEEP'], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.STATE_UT_NAME.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## we need to care about only 6 states "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df2 = df.groupby(\"STATE_UT_NAME\")[[ \"ANNUAL\" , \"DISTRICT\" , \"Mar-May\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "* 'ARUNACHAL PRADESH' , \n",
    "* Assam , \n",
    "* Tirupura , \n",
    "* Andaman Nicobar , \n",
    "* sikkim ,\n",
    "* kerala "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df2 = df2.max().sort_values(by=\"Mar-May\" , ascending=False)[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df2.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATE_UT_NAME</th>\n",
       "      <th>ANNUAL</th>\n",
       "      <th>DISTRICT</th>\n",
       "      <th>Mar-May</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>4402.1</td>\n",
       "      <td>WEST SIANG</td>\n",
       "      <td>1256.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MANIPUR</td>\n",
       "      <td>7229.3</td>\n",
       "      <td>UKHRUL</td>\n",
       "      <td>1195.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MEGHALAYA</td>\n",
       "      <td>6379.9</td>\n",
       "      <td>WEST GARO HIL</td>\n",
       "      <td>1175.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASSAM</td>\n",
       "      <td>3772.2</td>\n",
       "      <td>UDALGURI(DARA</td>\n",
       "      <td>1114.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TRIPURA</td>\n",
       "      <td>2641.8</td>\n",
       "      <td>WEST TRIPURA</td>\n",
       "      <td>831.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       STATE_UT_NAME  ANNUAL       DISTRICT  Mar-May\n",
       "0  ARUNACHAL PRADESH  4402.1     WEST SIANG   1256.5\n",
       "1            MANIPUR  7229.3         UKHRUL   1195.9\n",
       "2          MEGHALAYA  6379.9  WEST GARO HIL   1175.9\n",
       "3              ASSAM  3772.2  UDALGURI(DARA   1114.1\n",
       "4            TRIPURA  2641.8   WEST TRIPURA    831.3"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df2[\"location\"] = df2[\"DISTRICT\"]+',' +df2[\"STATE_UT_NAME\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.barplot(y=\"location\" , x=\"Mar-May\" , data=df2 , palette=\"winter\" )\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# so these are the top 15 location to spend summer in India"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------\n",
      "WEST SIANG,ARUNACHAL PRADESH\n",
      "---------------\n",
      "UKHRUL,MANIPUR\n",
      "---------------\n",
      "WEST GARO HIL,MEGHALAYA\n",
      "---------------\n",
      "UDALGURI(DARA,ASSAM\n",
      "---------------\n",
      "WEST TRIPURA,TRIPURA\n",
      "---------------\n",
      "WEST SIKKIM,SIKKIM\n",
      "---------------\n",
      "SERCHHIP,MIZORAM\n",
      "---------------\n",
      "ZUNHEBOTO,NAGALAND\n",
      "---------------\n",
      "WAYANAD,KERALA\n",
      "---------------\n",
      "SOUTH ANDAMAN,ANDAMAN And NICOBAR ISLANDS\n",
      "---------------\n",
      "WEST MIDNAPOR,WEST BENGAL\n",
      "---------------\n",
      "UDHAMPUR,JAMMU AND KASHMIR\n",
      "---------------\n",
      "UNA,HIMACHAL\n",
      "---------------\n",
      "VIRUDHUNAGAR,TAMIL NADU\n",
      "---------------\n",
      "SUNDARGARH,ORISSA\n"
     ]
    }
   ],
   "source": [
    "for i in df2[\"location\"].values:\n",
    "    print(f\"---------------\\n{i}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# OTHER SESISON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/rainfall in india 1901-2015.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SUBDIVISION</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>JAN</th>\n",
       "      <th>FEB</th>\n",
       "      <th>MAR</th>\n",
       "      <th>APR</th>\n",
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       "      <th>DEC</th>\n",
       "      <th>ANNUAL</th>\n",
       "      <th>Jan-Feb</th>\n",
       "      <th>Mar-May</th>\n",
       "      <th>Jun-Sep</th>\n",
       "      <th>Oct-Dec</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1901</td>\n",
       "      <td>49.2</td>\n",
       "      <td>87.1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>528.8</td>\n",
       "      <td>517.5</td>\n",
       "      <td>365.1</td>\n",
       "      <td>481.1</td>\n",
       "      <td>332.6</td>\n",
       "      <td>388.5</td>\n",
       "      <td>558.2</td>\n",
       "      <td>33.6</td>\n",
       "      <td>3373.2</td>\n",
       "      <td>136.3</td>\n",
       "      <td>560.3</td>\n",
       "      <td>1696.3</td>\n",
       "      <td>980.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>446.1</td>\n",
       "      <td>537.1</td>\n",
       "      <td>228.9</td>\n",
       "      <td>753.7</td>\n",
       "      <td>666.2</td>\n",
       "      <td>197.2</td>\n",
       "      <td>359.0</td>\n",
       "      <td>160.5</td>\n",
       "      <td>3520.7</td>\n",
       "      <td>159.8</td>\n",
       "      <td>458.3</td>\n",
       "      <td>2185.9</td>\n",
       "      <td>716.7</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1903</td>\n",
       "      <td>12.7</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>235.1</td>\n",
       "      <td>479.9</td>\n",
       "      <td>728.4</td>\n",
       "      <td>326.7</td>\n",
       "      <td>339.0</td>\n",
       "      <td>181.2</td>\n",
       "      <td>284.4</td>\n",
       "      <td>225.0</td>\n",
       "      <td>2957.4</td>\n",
       "      <td>156.7</td>\n",
       "      <td>236.1</td>\n",
       "      <td>1874.0</td>\n",
       "      <td>690.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1904</td>\n",
       "      <td>9.4</td>\n",
       "      <td>14.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.4</td>\n",
       "      <td>304.5</td>\n",
       "      <td>495.1</td>\n",
       "      <td>502.0</td>\n",
       "      <td>160.1</td>\n",
       "      <td>820.4</td>\n",
       "      <td>222.2</td>\n",
       "      <td>308.7</td>\n",
       "      <td>40.1</td>\n",
       "      <td>3079.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>506.9</td>\n",
       "      <td>1977.6</td>\n",
       "      <td>571.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1905</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>26.9</td>\n",
       "      <td>279.5</td>\n",
       "      <td>628.7</td>\n",
       "      <td>368.7</td>\n",
       "      <td>330.5</td>\n",
       "      <td>297.0</td>\n",
       "      <td>260.7</td>\n",
       "      <td>25.4</td>\n",
       "      <td>344.7</td>\n",
       "      <td>2566.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>309.7</td>\n",
       "      <td>1624.9</td>\n",
       "      <td>630.8</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SUBDIVISION  YEAR   JAN    FEB   MAR    APR    MAY    JUN  \\\n",
       "0  ANDAMAN & NICOBAR ISLANDS  1901  49.2   87.1  29.2    2.3  528.8  517.5   \n",
       "1  ANDAMAN & NICOBAR ISLANDS  1902   0.0  159.8  12.2    0.0  446.1  537.1   \n",
       "2  ANDAMAN & NICOBAR ISLANDS  1903  12.7  144.0   0.0    1.0  235.1  479.9   \n",
       "3  ANDAMAN & NICOBAR ISLANDS  1904   9.4   14.7   0.0  202.4  304.5  495.1   \n",
       "4  ANDAMAN & NICOBAR ISLANDS  1905   1.3    0.0   3.3   26.9  279.5  628.7   \n",
       "\n",
       "     JUL    AUG    SEP    OCT    NOV    DEC  ANNUAL  Jan-Feb  Mar-May  \\\n",
       "0  365.1  481.1  332.6  388.5  558.2   33.6  3373.2    136.3    560.3   \n",
       "1  228.9  753.7  666.2  197.2  359.0  160.5  3520.7    159.8    458.3   \n",
       "2  728.4  326.7  339.0  181.2  284.4  225.0  2957.4    156.7    236.1   \n",
       "3  502.0  160.1  820.4  222.2  308.7   40.1  3079.6     24.1    506.9   \n",
       "4  368.7  330.5  297.0  260.7   25.4  344.7  2566.7      1.3    309.7   \n",
       "\n",
       "   Jun-Sep  Oct-Dec  \n",
       "0   1696.3    980.3  \n",
       "1   2185.9    716.7  \n",
       "2   1874.0    690.6  \n",
       "3   1977.6    571.0  \n",
       "4   1624.9    630.8  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>YEAR</th>\n",
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       "      <th>1904</th>\n",
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       "      <th>2015</th>\n",
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       "      <th>SUBDIVISION</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ANDAMAN &amp; NICOBAR ISLANDS</th>\n",
       "      <td>3373.2</td>\n",
       "      <td>3520.7</td>\n",
       "      <td>2957.4</td>\n",
       "      <td>3079.6</td>\n",
       "      <td>2566.7</td>\n",
       "      <td>2534.4</td>\n",
       "      <td>3347.9</td>\n",
       "      <td>3576.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2899.4</td>\n",
       "      <td>...</td>\n",
       "      <td>534.0</td>\n",
       "      <td>429.6</td>\n",
       "      <td>877.3</td>\n",
       "      <td>493.7</td>\n",
       "      <td>332.2</td>\n",
       "      <td>710.7</td>\n",
       "      <td>620.5</td>\n",
       "      <td>598.6</td>\n",
       "      <td>249.1</td>\n",
       "      <td>473.2</td>\n",
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       "      <th>ARUNACHAL PRADESH</th>\n",
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       "      <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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>587.7</td>\n",
       "      <td>590.9</td>\n",
       "      <td>499.9</td>\n",
       "      <td>394.7</td>\n",
       "      <td>1017.5</td>\n",
       "      <td>634.9</td>\n",
       "      <td>724.9</td>\n",
       "      <td>626.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>623.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASSAM &amp; MEGHALAYA</th>\n",
       "      <td>2498.6</td>\n",
       "      <td>2952.1</td>\n",
       "      <td>2544.7</td>\n",
       "      <td>2664.1</td>\n",
       "      <td>2790.6</td>\n",
       "      <td>2663.4</td>\n",
       "      <td>2521.1</td>\n",
       "      <td>2277.8</td>\n",
       "      <td>2377.0</td>\n",
       "      <td>2887.3</td>\n",
       "      <td>...</td>\n",
       "      <td>515.5</td>\n",
       "      <td>486.1</td>\n",
       "      <td>474.4</td>\n",
       "      <td>426.2</td>\n",
       "      <td>882.3</td>\n",
       "      <td>439.4</td>\n",
       "      <td>493.7</td>\n",
       "      <td>503.5</td>\n",
       "      <td>431.9</td>\n",
       "      <td>620.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIHAR</th>\n",
       "      <td>952.3</td>\n",
       "      <td>1206.2</td>\n",
       "      <td>1016.3</td>\n",
       "      <td>1269.5</td>\n",
       "      <td>1498.5</td>\n",
       "      <td>1265.1</td>\n",
       "      <td>1126.4</td>\n",
       "      <td>737.5</td>\n",
       "      <td>1390.5</td>\n",
       "      <td>1398.5</td>\n",
       "      <td>...</td>\n",
       "      <td>94.3</td>\n",
       "      <td>140.2</td>\n",
       "      <td>62.6</td>\n",
       "      <td>102.5</td>\n",
       "      <td>51.1</td>\n",
       "      <td>107.6</td>\n",
       "      <td>46.5</td>\n",
       "      <td>122.4</td>\n",
       "      <td>112.9</td>\n",
       "      <td>105.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHHATTISGARH</th>\n",
       "      <td>1387.0</td>\n",
       "      <td>923.9</td>\n",
       "      <td>1331.2</td>\n",
       "      <td>1492.4</td>\n",
       "      <td>1251.1</td>\n",
       "      <td>1394.6</td>\n",
       "      <td>1308.8</td>\n",
       "      <td>1488.7</td>\n",
       "      <td>1284.1</td>\n",
       "      <td>1407.2</td>\n",
       "      <td>...</td>\n",
       "      <td>66.8</td>\n",
       "      <td>28.0</td>\n",
       "      <td>42.9</td>\n",
       "      <td>15.4</td>\n",
       "      <td>19.4</td>\n",
       "      <td>54.4</td>\n",
       "      <td>25.4</td>\n",
       "      <td>56.4</td>\n",
       "      <td>63.7</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COASTAL ANDHRA PRADESH</th>\n",
       "      <td>993.8</td>\n",
       "      <td>1063.6</td>\n",
       "      <td>1316.2</td>\n",
       "      <td>860.2</td>\n",
       "      <td>795.2</td>\n",
       "      <td>1021.5</td>\n",
       "      <td>817.9</td>\n",
       "      <td>842.3</td>\n",
       "      <td>867.6</td>\n",
       "      <td>1254.6</td>\n",
       "      <td>...</td>\n",
       "      <td>156.3</td>\n",
       "      <td>21.6</td>\n",
       "      <td>147.1</td>\n",
       "      <td>65.1</td>\n",
       "      <td>181.2</td>\n",
       "      <td>131.2</td>\n",
       "      <td>66.1</td>\n",
       "      <td>76.4</td>\n",
       "      <td>128.0</td>\n",
       "      <td>71.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COASTAL KARNATAKA</th>\n",
       "      <td>3056.9</td>\n",
       "      <td>3699.2</td>\n",
       "      <td>3317.4</td>\n",
       "      <td>3141.1</td>\n",
       "      <td>2652.3</td>\n",
       "      <td>2836.0</td>\n",
       "      <td>3535.2</td>\n",
       "      <td>3655.5</td>\n",
       "      <td>3172.9</td>\n",
       "      <td>3020.8</td>\n",
       "      <td>...</td>\n",
       "      <td>366.3</td>\n",
       "      <td>101.9</td>\n",
       "      <td>259.0</td>\n",
       "      <td>160.4</td>\n",
       "      <td>145.9</td>\n",
       "      <td>124.1</td>\n",
       "      <td>104.9</td>\n",
       "      <td>147.9</td>\n",
       "      <td>224.3</td>\n",
       "      <td>254.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EAST MADHYA PRADESH</th>\n",
       "      <td>1332.7</td>\n",
       "      <td>984.2</td>\n",
       "      <td>1178.1</td>\n",
       "      <td>1144.2</td>\n",
       "      <td>886.0</td>\n",
       "      <td>1369.7</td>\n",
       "      <td>1009.6</td>\n",
       "      <td>1430.6</td>\n",
       "      <td>1122.0</td>\n",
       "      <td>1222.2</td>\n",
       "      <td>...</td>\n",
       "      <td>82.5</td>\n",
       "      <td>17.7</td>\n",
       "      <td>16.7</td>\n",
       "      <td>8.2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>12.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>23.9</td>\n",
       "      <td>25.3</td>\n",
       "      <td>105.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EAST RAJASTHAN</th>\n",
       "      <td>412.5</td>\n",
       "      <td>578.3</td>\n",
       "      <td>686.1</td>\n",
       "      <td>694.5</td>\n",
       "      <td>279.8</td>\n",
       "      <td>647.4</td>\n",
       "      <td>551.8</td>\n",
       "      <td>870.2</td>\n",
       "      <td>781.6</td>\n",
       "      <td>685.3</td>\n",
       "      <td>...</td>\n",
       "      <td>25.4</td>\n",
       "      <td>17.6</td>\n",
       "      <td>31.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>5.9</td>\n",
       "      <td>13.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>22.1</td>\n",
       "      <td>75.3</td>\n",
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       "    <tr>\n",
       "      <th>EAST UTTAR PRADESH</th>\n",
       "      <td>873.2</td>\n",
       "      <td>905.2</td>\n",
       "      <td>1216.4</td>\n",
       "      <td>1071.2</td>\n",
       "      <td>891.6</td>\n",
       "      <td>997.7</td>\n",
       "      <td>666.3</td>\n",
       "      <td>744.7</td>\n",
       "      <td>1106.9</td>\n",
       "      <td>1116.4</td>\n",
       "      <td>...</td>\n",
       "      <td>45.6</td>\n",
       "      <td>57.2</td>\n",
       "      <td>19.3</td>\n",
       "      <td>24.2</td>\n",
       "      <td>14.7</td>\n",
       "      <td>36.7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.6</td>\n",
       "      <td>27.7</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GANGETIC WEST BENGAL</th>\n",
       "      <td>1487.6</td>\n",
       "      <td>1347.7</td>\n",
       "      <td>1354.3</td>\n",
       "      <td>1383.1</td>\n",
       "      <td>1666.9</td>\n",
       "      <td>1395.6</td>\n",
       "      <td>1337.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1697.8</td>\n",
       "      <td>1385.9</td>\n",
       "      <td>...</td>\n",
       "      <td>191.9</td>\n",
       "      <td>176.7</td>\n",
       "      <td>148.8</td>\n",
       "      <td>249.5</td>\n",
       "      <td>151.3</td>\n",
       "      <td>248.1</td>\n",
       "      <td>106.3</td>\n",
       "      <td>246.3</td>\n",
       "      <td>146.2</td>\n",
       "      <td>165.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GUJARAT REGION</th>\n",
       "      <td>554.3</td>\n",
       "      <td>863.9</td>\n",
       "      <td>887.6</td>\n",
       "      <td>492.6</td>\n",
       "      <td>811.5</td>\n",
       "      <td>890.6</td>\n",
       "      <td>997.0</td>\n",
       "      <td>1027.5</td>\n",
       "      <td>977.8</td>\n",
       "      <td>974.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HARYANA DELHI &amp; CHANDIGARH</th>\n",
       "      <td>390.1</td>\n",
       "      <td>419.8</td>\n",
       "      <td>428.9</td>\n",
       "      <td>527.8</td>\n",
       "      <td>322.9</td>\n",
       "      <td>593.6</td>\n",
       "      <td>443.9</td>\n",
       "      <td>689.0</td>\n",
       "      <td>769.3</td>\n",
       "      <td>605.2</td>\n",
       "      <td>...</td>\n",
       "      <td>55.7</td>\n",
       "      <td>54.6</td>\n",
       "      <td>86.9</td>\n",
       "      <td>35.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>44.4</td>\n",
       "      <td>15.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>54.6</td>\n",
       "      <td>114.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HIMACHAL PRADESH</th>\n",
       "      <td>1345.1</td>\n",
       "      <td>951.6</td>\n",
       "      <td>993.3</td>\n",
       "      <td>987.6</td>\n",
       "      <td>930.2</td>\n",
       "      <td>1317.5</td>\n",
       "      <td>875.4</td>\n",
       "      <td>987.6</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>1406.9</td>\n",
       "      <td>...</td>\n",
       "      <td>250.0</td>\n",
       "      <td>225.9</td>\n",
       "      <td>93.6</td>\n",
       "      <td>188.2</td>\n",
       "      <td>186.8</td>\n",
       "      <td>157.3</td>\n",
       "      <td>93.7</td>\n",
       "      <td>138.1</td>\n",
       "      <td>254.6</td>\n",
       "      <td>322.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAMMU &amp; KASHMIR</th>\n",
       "      <td>979.9</td>\n",
       "      <td>760.5</td>\n",
       "      <td>1286.6</td>\n",
       "      <td>982.2</td>\n",
       "      <td>1157.7</td>\n",
       "      <td>1193.1</td>\n",
       "      <td>1069.6</td>\n",
       "      <td>1227.3</td>\n",
       "      <td>1339.9</td>\n",
       "      <td>1109.4</td>\n",
       "      <td>...</td>\n",
       "      <td>174.9</td>\n",
       "      <td>339.8</td>\n",
       "      <td>170.8</td>\n",
       "      <td>241.5</td>\n",
       "      <td>227.1</td>\n",
       "      <td>219.2</td>\n",
       "      <td>180.7</td>\n",
       "      <td>136.7</td>\n",
       "      <td>281.9</td>\n",
       "      <td>579.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JHARKHAND</th>\n",
       "      <td>1274.5</td>\n",
       "      <td>1197.6</td>\n",
       "      <td>1174.3</td>\n",
       "      <td>1480.7</td>\n",
       "      <td>1354.7</td>\n",
       "      <td>1314.3</td>\n",
       "      <td>1455.7</td>\n",
       "      <td>1214.2</td>\n",
       "      <td>1447.9</td>\n",
       "      <td>1297.6</td>\n",
       "      <td>...</td>\n",
       "      <td>145.5</td>\n",
       "      <td>105.8</td>\n",
       "      <td>59.7</td>\n",
       "      <td>80.8</td>\n",
       "      <td>36.4</td>\n",
       "      <td>86.8</td>\n",
       "      <td>17.7</td>\n",
       "      <td>109.0</td>\n",
       "      <td>123.1</td>\n",
       "      <td>102.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KERALA</th>\n",
       "      <td>3248.6</td>\n",
       "      <td>3326.6</td>\n",
       "      <td>3271.2</td>\n",
       "      <td>3129.7</td>\n",
       "      <td>2741.6</td>\n",
       "      <td>2708.0</td>\n",
       "      <td>3671.1</td>\n",
       "      <td>2648.3</td>\n",
       "      <td>3050.2</td>\n",
       "      <td>2848.6</td>\n",
       "      <td>...</td>\n",
       "      <td>677.2</td>\n",
       "      <td>338.4</td>\n",
       "      <td>406.7</td>\n",
       "      <td>323.1</td>\n",
       "      <td>360.9</td>\n",
       "      <td>313.5</td>\n",
       "      <td>287.4</td>\n",
       "      <td>218.5</td>\n",
       "      <td>364.5</td>\n",
       "      <td>465.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KONKAN &amp; GOA</th>\n",
       "      <td>2792.9</td>\n",
       "      <td>2673.9</td>\n",
       "      <td>3084.3</td>\n",
       "      <td>2393.7</td>\n",
       "      <td>1682.8</td>\n",
       "      <td>2418.9</td>\n",
       "      <td>2766.6</td>\n",
       "      <td>2735.5</td>\n",
       "      <td>2860.5</td>\n",
       "      <td>2544.0</td>\n",
       "      <td>...</td>\n",
       "      <td>221.7</td>\n",
       "      <td>36.9</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>9.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>18.7</td>\n",
       "      <td>23.8</td>\n",
       "      <td>51.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAKSHADWEEP</th>\n",
       "      <td>1459.2</td>\n",
       "      <td>2158.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1176.9</td>\n",
       "      <td>1574.9</td>\n",
       "      <td>1811.6</td>\n",
       "      <td>1458.8</td>\n",
       "      <td>1959.1</td>\n",
       "      <td>1463.5</td>\n",
       "      <td>1849.2</td>\n",
       "      <td>...</td>\n",
       "      <td>361.2</td>\n",
       "      <td>188.6</td>\n",
       "      <td>316.9</td>\n",
       "      <td>180.3</td>\n",
       "      <td>115.8</td>\n",
       "      <td>196.2</td>\n",
       "      <td>99.6</td>\n",
       "      <td>131.1</td>\n",
       "      <td>76.7</td>\n",
       "      <td>223.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MADHYA MAHARASHTRA</th>\n",
       "      <td>769.0</td>\n",
       "      <td>784.0</td>\n",
       "      <td>837.9</td>\n",
       "      <td>603.5</td>\n",
       "      <td>537.8</td>\n",
       "      <td>744.3</td>\n",
       "      <td>815.1</td>\n",
       "      <td>722.0</td>\n",
       "      <td>755.2</td>\n",
       "      <td>915.2</td>\n",
       "      <td>...</td>\n",
       "      <td>49.7</td>\n",
       "      <td>7.6</td>\n",
       "      <td>19.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.2</td>\n",
       "      <td>4.4</td>\n",
       "      <td>12.4</td>\n",
       "      <td>61.7</td>\n",
       "      <td>75.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MATATHWADA</th>\n",
       "      <td>908.7</td>\n",
       "      <td>744.1</td>\n",
       "      <td>1032.6</td>\n",
       "      <td>690.5</td>\n",
       "      <td>505.9</td>\n",
       "      <td>770.7</td>\n",
       "      <td>557.9</td>\n",
       "      <td>682.5</td>\n",
       "      <td>892.7</td>\n",
       "      <td>1134.4</td>\n",
       "      <td>...</td>\n",
       "      <td>59.1</td>\n",
       "      <td>10.8</td>\n",
       "      <td>20.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>16.9</td>\n",
       "      <td>97.9</td>\n",
       "      <td>83.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAGA MANI MIZO TRIPURA</th>\n",
       "      <td>2168.4</td>\n",
       "      <td>2175.0</td>\n",
       "      <td>2230.7</td>\n",
       "      <td>2519.8</td>\n",
       "      <td>2527.6</td>\n",
       "      <td>2514.4</td>\n",
       "      <td>2121.7</td>\n",
       "      <td>2319.3</td>\n",
       "      <td>2308.7</td>\n",
       "      <td>2267.9</td>\n",
       "      <td>...</td>\n",
       "      <td>498.8</td>\n",
       "      <td>525.8</td>\n",
       "      <td>305.3</td>\n",
       "      <td>330.1</td>\n",
       "      <td>541.4</td>\n",
       "      <td>467.4</td>\n",
       "      <td>427.3</td>\n",
       "      <td>428.9</td>\n",
       "      <td>267.5</td>\n",
       "      <td>473.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NORTH INTERIOR KARNATAKA</th>\n",
       "      <td>773.0</td>\n",
       "      <td>716.1</td>\n",
       "      <td>928.5</td>\n",
       "      <td>578.6</td>\n",
       "      <td>506.2</td>\n",
       "      <td>783.8</td>\n",
       "      <td>746.7</td>\n",
       "      <td>595.1</td>\n",
       "      <td>675.8</td>\n",
       "      <td>759.4</td>\n",
       "      <td>...</td>\n",
       "      <td>107.9</td>\n",
       "      <td>73.7</td>\n",
       "      <td>161.3</td>\n",
       "      <td>90.4</td>\n",
       "      <td>55.6</td>\n",
       "      <td>95.2</td>\n",
       "      <td>55.3</td>\n",
       "      <td>75.7</td>\n",
       "      <td>148.7</td>\n",
       "      <td>123.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORISSA</th>\n",
       "      <td>1284.2</td>\n",
       "      <td>1349.0</td>\n",
       "      <td>1568.3</td>\n",
       "      <td>1514.8</td>\n",
       "      <td>1357.3</td>\n",
       "      <td>1408.2</td>\n",
       "      <td>1447.1</td>\n",
       "      <td>1430.6</td>\n",
       "      <td>1535.5</td>\n",
       "      <td>1598.5</td>\n",
       "      <td>...</td>\n",
       "      <td>160.5</td>\n",
       "      <td>91.8</td>\n",
       "      <td>83.1</td>\n",
       "      <td>72.0</td>\n",
       "      <td>106.4</td>\n",
       "      <td>138.7</td>\n",
       "      <td>57.0</td>\n",
       "      <td>113.4</td>\n",
       "      <td>148.7</td>\n",
       "      <td>110.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PUNJAB</th>\n",
       "      <td>523.5</td>\n",
       "      <td>398.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>451.5</td>\n",
       "      <td>434.3</td>\n",
       "      <td>677.5</td>\n",
       "      <td>589.2</td>\n",
       "      <td>740.4</td>\n",
       "      <td>846.1</td>\n",
       "      <td>616.3</td>\n",
       "      <td>...</td>\n",
       "      <td>79.5</td>\n",
       "      <td>64.1</td>\n",
       "      <td>60.3</td>\n",
       "      <td>30.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>44.9</td>\n",
       "      <td>34.7</td>\n",
       "      <td>18.6</td>\n",
       "      <td>75.5</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAYALSEEMA</th>\n",
       "      <td>714.0</td>\n",
       "      <td>818.4</td>\n",
       "      <td>1138.2</td>\n",
       "      <td>433.4</td>\n",
       "      <td>703.4</td>\n",
       "      <td>912.6</td>\n",
       "      <td>640.4</td>\n",
       "      <td>617.1</td>\n",
       "      <td>870.6</td>\n",
       "      <td>1001.1</td>\n",
       "      <td>...</td>\n",
       "      <td>105.2</td>\n",
       "      <td>21.2</td>\n",
       "      <td>109.1</td>\n",
       "      <td>61.1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>67.6</td>\n",
       "      <td>74.0</td>\n",
       "      <td>77.3</td>\n",
       "      <td>64.2</td>\n",
       "      <td>126.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAURASHTRA &amp; KUTCH</th>\n",
       "      <td>181.3</td>\n",
       "      <td>401.1</td>\n",
       "      <td>522.8</td>\n",
       "      <td>185.6</td>\n",
       "      <td>290.0</td>\n",
       "      <td>540.3</td>\n",
       "      <td>509.6</td>\n",
       "      <td>527.9</td>\n",
       "      <td>586.4</td>\n",
       "      <td>549.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>8.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOUTH INTERIOR KARNATAKA</th>\n",
       "      <td>1003.0</td>\n",
       "      <td>1020.1</td>\n",
       "      <td>1269.9</td>\n",
       "      <td>845.8</td>\n",
       "      <td>759.4</td>\n",
       "      <td>1004.5</td>\n",
       "      <td>998.5</td>\n",
       "      <td>790.2</td>\n",
       "      <td>1087.4</td>\n",
       "      <td>1157.3</td>\n",
       "      <td>...</td>\n",
       "      <td>187.2</td>\n",
       "      <td>117.5</td>\n",
       "      <td>200.1</td>\n",
       "      <td>153.9</td>\n",
       "      <td>171.6</td>\n",
       "      <td>176.1</td>\n",
       "      <td>152.6</td>\n",
       "      <td>142.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>230.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUB HIMALAYAN WEST BENGAL &amp; SIKKIM</th>\n",
       "      <td>2113.2</td>\n",
       "      <td>3180.4</td>\n",
       "      <td>2404.5</td>\n",
       "      <td>2169.9</td>\n",
       "      <td>3005.0</td>\n",
       "      <td>2618.0</td>\n",
       "      <td>2530.0</td>\n",
       "      <td>1988.2</td>\n",
       "      <td>2670.1</td>\n",
       "      <td>3046.1</td>\n",
       "      <td>...</td>\n",
       "      <td>429.6</td>\n",
       "      <td>421.7</td>\n",
       "      <td>393.0</td>\n",
       "      <td>478.9</td>\n",
       "      <td>590.0</td>\n",
       "      <td>453.9</td>\n",
       "      <td>407.7</td>\n",
       "      <td>443.4</td>\n",
       "      <td>394.2</td>\n",
       "      <td>518.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAMIL NADU</th>\n",
       "      <td>960.3</td>\n",
       "      <td>1138.2</td>\n",
       "      <td>1163.9</td>\n",
       "      <td>663.1</td>\n",
       "      <td>821.1</td>\n",
       "      <td>990.2</td>\n",
       "      <td>885.6</td>\n",
       "      <td>765.0</td>\n",
       "      <td>954.6</td>\n",
       "      <td>943.7</td>\n",
       "      <td>...</td>\n",
       "      <td>150.2</td>\n",
       "      <td>104.7</td>\n",
       "      <td>250.0</td>\n",
       "      <td>157.2</td>\n",
       "      <td>116.7</td>\n",
       "      <td>132.8</td>\n",
       "      <td>79.9</td>\n",
       "      <td>92.2</td>\n",
       "      <td>155.5</td>\n",
       "      <td>242.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TELANGANA</th>\n",
       "      <td>827.7</td>\n",
       "      <td>630.4</td>\n",
       "      <td>1283.4</td>\n",
       "      <td>526.7</td>\n",
       "      <td>724.9</td>\n",
       "      <td>855.2</td>\n",
       "      <td>843.7</td>\n",
       "      <td>982.8</td>\n",
       "      <td>769.5</td>\n",
       "      <td>975.9</td>\n",
       "      <td>...</td>\n",
       "      <td>127.5</td>\n",
       "      <td>9.4</td>\n",
       "      <td>129.3</td>\n",
       "      <td>19.8</td>\n",
       "      <td>32.0</td>\n",
       "      <td>37.4</td>\n",
       "      <td>22.6</td>\n",
       "      <td>33.0</td>\n",
       "      <td>141.9</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTTARAKHAND</th>\n",
       "      <td>1562.5</td>\n",
       "      <td>1324.2</td>\n",
       "      <td>1306.5</td>\n",
       "      <td>1492.0</td>\n",
       "      <td>1302.1</td>\n",
       "      <td>1537.9</td>\n",
       "      <td>1194.4</td>\n",
       "      <td>1256.2</td>\n",
       "      <td>1717.7</td>\n",
       "      <td>1963.9</td>\n",
       "      <td>...</td>\n",
       "      <td>244.4</td>\n",
       "      <td>243.0</td>\n",
       "      <td>178.4</td>\n",
       "      <td>114.8</td>\n",
       "      <td>64.5</td>\n",
       "      <td>133.1</td>\n",
       "      <td>76.4</td>\n",
       "      <td>65.0</td>\n",
       "      <td>158.8</td>\n",
       "      <td>222.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIDARBHA</th>\n",
       "      <td>1093.3</td>\n",
       "      <td>748.9</td>\n",
       "      <td>1229.4</td>\n",
       "      <td>753.2</td>\n",
       "      <td>941.8</td>\n",
       "      <td>1191.2</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1243.4</td>\n",
       "      <td>1061.6</td>\n",
       "      <td>1294.0</td>\n",
       "      <td>...</td>\n",
       "      <td>87.7</td>\n",
       "      <td>5.8</td>\n",
       "      <td>24.9</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>56.3</td>\n",
       "      <td>107.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEST MADHYA PRADESH</th>\n",
       "      <td>718.2</td>\n",
       "      <td>911.7</td>\n",
       "      <td>949.6</td>\n",
       "      <td>774.4</td>\n",
       "      <td>696.5</td>\n",
       "      <td>1010.2</td>\n",
       "      <td>650.4</td>\n",
       "      <td>832.6</td>\n",
       "      <td>830.2</td>\n",
       "      <td>959.5</td>\n",
       "      <td>...</td>\n",
       "      <td>70.4</td>\n",
       "      <td>17.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>68.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEST RAJASTHAN</th>\n",
       "      <td>158.9</td>\n",
       "      <td>195.6</td>\n",
       "      <td>307.0</td>\n",
       "      <td>167.9</td>\n",
       "      <td>114.4</td>\n",
       "      <td>269.5</td>\n",
       "      <td>353.2</td>\n",
       "      <td>581.9</td>\n",
       "      <td>426.2</td>\n",
       "      <td>293.2</td>\n",
       "      <td>...</td>\n",
       "      <td>28.6</td>\n",
       "      <td>28.1</td>\n",
       "      <td>43.6</td>\n",
       "      <td>14.5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>10.7</td>\n",
       "      <td>19.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>36.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEST UTTAR PRADESH</th>\n",
       "      <td>775.9</td>\n",
       "      <td>736.6</td>\n",
       "      <td>808.4</td>\n",
       "      <td>945.2</td>\n",
       "      <td>433.5</td>\n",
       "      <td>978.7</td>\n",
       "      <td>589.5</td>\n",
       "      <td>866.3</td>\n",
       "      <td>903.0</td>\n",
       "      <td>875.9</td>\n",
       "      <td>...</td>\n",
       "      <td>58.3</td>\n",
       "      <td>49.3</td>\n",
       "      <td>28.8</td>\n",
       "      <td>22.7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>36.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>39.0</td>\n",
       "      <td>95.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36 rows × 230 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    ANNUAL                                  \\\n",
       "YEAR                                  1901    1902    1903    1904    1905   \n",
       "SUBDIVISION                                                                  \n",
       "ANDAMAN & NICOBAR ISLANDS           3373.2  3520.7  2957.4  3079.6  2566.7   \n",
       "ARUNACHAL PRADESH                      NaN     NaN     NaN     NaN     NaN   \n",
       "ASSAM & MEGHALAYA                   2498.6  2952.1  2544.7  2664.1  2790.6   \n",
       "BIHAR                                952.3  1206.2  1016.3  1269.5  1498.5   \n",
       "CHHATTISGARH                        1387.0   923.9  1331.2  1492.4  1251.1   \n",
       "COASTAL ANDHRA PRADESH               993.8  1063.6  1316.2   860.2   795.2   \n",
       "COASTAL KARNATAKA                   3056.9  3699.2  3317.4  3141.1  2652.3   \n",
       "EAST MADHYA PRADESH                 1332.7   984.2  1178.1  1144.2   886.0   \n",
       "EAST RAJASTHAN                       412.5   578.3   686.1   694.5   279.8   \n",
       "EAST UTTAR PRADESH                   873.2   905.2  1216.4  1071.2   891.6   \n",
       "GANGETIC WEST BENGAL                1487.6  1347.7  1354.3  1383.1  1666.9   \n",
       "GUJARAT REGION                       554.3   863.9   887.6   492.6   811.5   \n",
       "HARYANA DELHI & CHANDIGARH           390.1   419.8   428.9   527.8   322.9   \n",
       "HIMACHAL PRADESH                    1345.1   951.6   993.3   987.6   930.2   \n",
       "JAMMU & KASHMIR                      979.9   760.5  1286.6   982.2  1157.7   \n",
       "JHARKHAND                           1274.5  1197.6  1174.3  1480.7  1354.7   \n",
       "KERALA                              3248.6  3326.6  3271.2  3129.7  2741.6   \n",
       "KONKAN & GOA                        2792.9  2673.9  3084.3  2393.7  1682.8   \n",
       "LAKSHADWEEP                         1459.2  2158.8     NaN  1176.9  1574.9   \n",
       "MADHYA MAHARASHTRA                   769.0   784.0   837.9   603.5   537.8   \n",
       "MATATHWADA                           908.7   744.1  1032.6   690.5   505.9   \n",
       "NAGA MANI MIZO TRIPURA              2168.4  2175.0  2230.7  2519.8  2527.6   \n",
       "NORTH INTERIOR KARNATAKA             773.0   716.1   928.5   578.6   506.2   \n",
       "ORISSA                              1284.2  1349.0  1568.3  1514.8  1357.3   \n",
       "PUNJAB                               523.5   398.0   571.0   451.5   434.3   \n",
       "RAYALSEEMA                           714.0   818.4  1138.2   433.4   703.4   \n",
       "SAURASHTRA & KUTCH                   181.3   401.1   522.8   185.6   290.0   \n",
       "SOUTH INTERIOR KARNATAKA            1003.0  1020.1  1269.9   845.8   759.4   \n",
       "SUB HIMALAYAN WEST BENGAL & SIKKIM  2113.2  3180.4  2404.5  2169.9  3005.0   \n",
       "TAMIL NADU                           960.3  1138.2  1163.9   663.1   821.1   \n",
       "TELANGANA                            827.7   630.4  1283.4   526.7   724.9   \n",
       "UTTARAKHAND                         1562.5  1324.2  1306.5  1492.0  1302.1   \n",
       "VIDARBHA                            1093.3   748.9  1229.4   753.2   941.8   \n",
       "WEST MADHYA PRADESH                  718.2   911.7   949.6   774.4   696.5   \n",
       "WEST RAJASTHAN                       158.9   195.6   307.0   167.9   114.4   \n",
       "WEST UTTAR PRADESH                   775.9   736.6   808.4   945.2   433.5   \n",
       "\n",
       "                                                                            \\\n",
       "YEAR                                  1906    1907    1908    1909    1910   \n",
       "SUBDIVISION                                                                  \n",
       "ANDAMAN & NICOBAR ISLANDS           2534.4  3347.9  3576.4     NaN  2899.4   \n",
       "ARUNACHAL PRADESH                      NaN     NaN     NaN     NaN     NaN   \n",
       "ASSAM & MEGHALAYA                   2663.4  2521.1  2277.8  2377.0  2887.3   \n",
       "BIHAR                               1265.1  1126.4   737.5  1390.5  1398.5   \n",
       "CHHATTISGARH                        1394.6  1308.8  1488.7  1284.1  1407.2   \n",
       "COASTAL ANDHRA PRADESH              1021.5   817.9   842.3   867.6  1254.6   \n",
       "COASTAL KARNATAKA                   2836.0  3535.2  3655.5  3172.9  3020.8   \n",
       "EAST MADHYA PRADESH                 1369.7  1009.6  1430.6  1122.0  1222.2   \n",
       "EAST RAJASTHAN                       647.4   551.8   870.2   781.6   685.3   \n",
       "EAST UTTAR PRADESH                   997.7   666.3   744.7  1106.9  1116.4   \n",
       "GANGETIC WEST BENGAL                1395.6  1337.0  1400.0  1697.8  1385.9   \n",
       "GUJARAT REGION                       890.6   997.0  1027.5   977.8   974.6   \n",
       "HARYANA DELHI & CHANDIGARH           593.6   443.9   689.0   769.3   605.2   \n",
       "HIMACHAL PRADESH                    1317.5   875.4   987.6  1077.0  1406.9   \n",
       "JAMMU & KASHMIR                     1193.1  1069.6  1227.3  1339.9  1109.4   \n",
       "JHARKHAND                           1314.3  1455.7  1214.2  1447.9  1297.6   \n",
       "KERALA                              2708.0  3671.1  2648.3  3050.2  2848.6   \n",
       "KONKAN & GOA                        2418.9  2766.6  2735.5  2860.5  2544.0   \n",
       "LAKSHADWEEP                         1811.6  1458.8  1959.1  1463.5  1849.2   \n",
       "MADHYA MAHARASHTRA                   744.3   815.1   722.0   755.2   915.2   \n",
       "MATATHWADA                           770.7   557.9   682.5   892.7  1134.4   \n",
       "NAGA MANI MIZO TRIPURA              2514.4  2121.7  2319.3  2308.7  2267.9   \n",
       "NORTH INTERIOR KARNATAKA             783.8   746.7   595.1   675.8   759.4   \n",
       "ORISSA                              1408.2  1447.1  1430.6  1535.5  1598.5   \n",
       "PUNJAB                               677.5   589.2   740.4   846.1   616.3   \n",
       "RAYALSEEMA                           912.6   640.4   617.1   870.6  1001.1   \n",
       "SAURASHTRA & KUTCH                   540.3   509.6   527.9   586.4   549.7   \n",
       "SOUTH INTERIOR KARNATAKA            1004.5   998.5   790.2  1087.4  1157.3   \n",
       "SUB HIMALAYAN WEST BENGAL & SIKKIM  2618.0  2530.0  1988.2  2670.1  3046.1   \n",
       "TAMIL NADU                           990.2   885.6   765.0   954.6   943.7   \n",
       "TELANGANA                            855.2   843.7   982.8   769.5   975.9   \n",
       "UTTARAKHAND                         1537.9  1194.4  1256.2  1717.7  1963.9   \n",
       "VIDARBHA                            1191.2  1010.8  1243.4  1061.6  1294.0   \n",
       "WEST MADHYA PRADESH                 1010.2   650.4   832.6   830.2   959.5   \n",
       "WEST RAJASTHAN                       269.5   353.2   581.9   426.2   293.2   \n",
       "WEST UTTAR PRADESH                   978.7   589.5   866.3   903.0   875.9   \n",
       "\n",
       "                                    ... Mar-May                               \\\n",
       "YEAR                                ...    2006   2007   2008   2009    2010   \n",
       "SUBDIVISION                         ...                                        \n",
       "ANDAMAN & NICOBAR ISLANDS           ...   534.0  429.6  877.3  493.7   332.2   \n",
       "ARUNACHAL PRADESH                   ...   587.7  590.9  499.9  394.7  1017.5   \n",
       "ASSAM & MEGHALAYA                   ...   515.5  486.1  474.4  426.2   882.3   \n",
       "BIHAR                               ...    94.3  140.2   62.6  102.5    51.1   \n",
       "CHHATTISGARH                        ...    66.8   28.0   42.9   15.4    19.4   \n",
       "COASTAL ANDHRA PRADESH              ...   156.3   21.6  147.1   65.1   181.2   \n",
       "COASTAL KARNATAKA                   ...   366.3  101.9  259.0  160.4   145.9   \n",
       "EAST MADHYA PRADESH                 ...    82.5   17.7   16.7    8.2     2.4   \n",
       "EAST RAJASTHAN                      ...    25.4   17.6   31.6    8.4     1.6   \n",
       "EAST UTTAR PRADESH                  ...    45.6   57.2   19.3   24.2    14.7   \n",
       "GANGETIC WEST BENGAL                ...   191.9  176.7  148.8  249.5   151.3   \n",
       "GUJARAT REGION                      ...     1.1    0.3    3.3    0.3     0.0   \n",
       "HARYANA DELHI & CHANDIGARH          ...    55.7   54.6   86.9   35.2     7.2   \n",
       "HIMACHAL PRADESH                    ...   250.0  225.9   93.6  188.2   186.8   \n",
       "JAMMU & KASHMIR                     ...   174.9  339.8  170.8  241.5   227.1   \n",
       "JHARKHAND                           ...   145.5  105.8   59.7   80.8    36.4   \n",
       "KERALA                              ...   677.2  338.4  406.7  323.1   360.9   \n",
       "KONKAN & GOA                        ...   221.7   36.9   43.0    5.9     9.7   \n",
       "LAKSHADWEEP                         ...   361.2  188.6  316.9  180.3   115.8   \n",
       "MADHYA MAHARASHTRA                  ...    49.7    7.6   19.9    8.9     8.6   \n",
       "MATATHWADA                          ...    59.1   10.8   20.2    8.4     9.2   \n",
       "NAGA MANI MIZO TRIPURA              ...   498.8  525.8  305.3  330.1   541.4   \n",
       "NORTH INTERIOR KARNATAKA            ...   107.9   73.7  161.3   90.4    55.6   \n",
       "ORISSA                              ...   160.5   91.8   83.1   72.0   106.4   \n",
       "PUNJAB                              ...    79.5   64.1   60.3   30.1     8.6   \n",
       "RAYALSEEMA                          ...   105.2   21.2  109.1   61.1    79.0   \n",
       "SAURASHTRA & KUTCH                  ...     1.9    0.3    0.6    0.1     0.1   \n",
       "SOUTH INTERIOR KARNATAKA            ...   187.2  117.5  200.1  153.9   171.6   \n",
       "SUB HIMALAYAN WEST BENGAL & SIKKIM  ...   429.6  421.7  393.0  478.9   590.0   \n",
       "TAMIL NADU                          ...   150.2  104.7  250.0  157.2   116.7   \n",
       "TELANGANA                           ...   127.5    9.4  129.3   19.8    32.0   \n",
       "UTTARAKHAND                         ...   244.4  243.0  178.4  114.8    64.5   \n",
       "VIDARBHA                            ...    87.7    5.8   24.9    9.2     9.6   \n",
       "WEST MADHYA PRADESH                 ...    70.4   17.8    8.8    7.9     1.4   \n",
       "WEST RAJASTHAN                      ...    28.6   28.1   43.6   14.5     4.1   \n",
       "WEST UTTAR PRADESH                  ...    58.3   49.3   28.8   22.7    10.0   \n",
       "\n",
       "                                                                       \n",
       "YEAR                                 2011   2012   2013   2014   2015  \n",
       "SUBDIVISION                                                            \n",
       "ANDAMAN & NICOBAR ISLANDS           710.7  620.5  598.6  249.1  473.2  \n",
       "ARUNACHAL PRADESH                   634.9  724.9  626.0  466.0  623.5  \n",
       "ASSAM & MEGHALAYA                   439.4  493.7  503.5  431.9  620.9  \n",
       "BIHAR                               107.6   46.5  122.4  112.9  105.5  \n",
       "CHHATTISGARH                         54.4   25.4   56.4   63.7   71.2  \n",
       "COASTAL ANDHRA PRADESH              131.2   66.1   76.4  128.0   71.9  \n",
       "COASTAL KARNATAKA                   124.1  104.9  147.9  224.3  254.8  \n",
       "EAST MADHYA PRADESH                  12.1    2.9   23.9   25.3  105.5  \n",
       "EAST RAJASTHAN                        5.9   13.1    4.6   22.1   75.3  \n",
       "EAST UTTAR PRADESH                   36.7    6.4   12.6   27.7   80.0  \n",
       "GANGETIC WEST BENGAL                248.1  106.3  246.3  146.2  165.6  \n",
       "GUJARAT REGION                        0.0    0.0    4.7    2.5   12.5  \n",
       "HARYANA DELHI & CHANDIGARH           44.4   15.8   10.0   54.6  114.9  \n",
       "HIMACHAL PRADESH                    157.3   93.7  138.1  254.6  322.3  \n",
       "JAMMU & KASHMIR                     219.2  180.7  136.7  281.9  579.3  \n",
       "JHARKHAND                            86.8   17.7  109.0  123.1  102.6  \n",
       "KERALA                              313.5  287.4  218.5  364.5  465.9  \n",
       "KONKAN & GOA                          4.5    1.7   18.7   23.8   51.7  \n",
       "LAKSHADWEEP                         196.2   99.6  131.1   76.7  223.9  \n",
       "MADHYA MAHARASHTRA                    8.2    4.4   12.4   61.7   75.3  \n",
       "MATATHWADA                            7.3    2.9   16.9   97.9   83.8  \n",
       "NAGA MANI MIZO TRIPURA              467.4  427.3  428.9  267.5  473.4  \n",
       "NORTH INTERIOR KARNATAKA             95.2   55.3   75.7  148.7  123.6  \n",
       "ORISSA                              138.7   57.0  113.4  148.7  110.7  \n",
       "PUNJAB                               44.9   34.7   18.6   75.5  115.0  \n",
       "RAYALSEEMA                           67.6   74.0   77.3   64.2  126.4  \n",
       "SAURASHTRA & KUTCH                    0.0    0.2    8.7    2.7    7.4  \n",
       "SOUTH INTERIOR KARNATAKA            176.1  152.6  142.0  195.0  230.2  \n",
       "SUB HIMALAYAN WEST BENGAL & SIKKIM  453.9  407.7  443.4  394.2  518.5  \n",
       "TAMIL NADU                          132.8   79.9   92.2  155.5  242.8  \n",
       "TELANGANA                            37.4   22.6   33.0  141.9  132.0  \n",
       "UTTARAKHAND                         133.1   76.4   65.0  158.8  222.6  \n",
       "VIDARBHA                              8.4    0.8    7.0   56.3  107.2  \n",
       "WEST MADHYA PRADESH                   5.5    4.0   11.7    7.5   68.9  \n",
       "WEST RAJASTHAN                       10.7   19.9    8.9   36.0   71.0  \n",
       "WEST UTTAR PRADESH                   36.3    6.4    7.2   39.0   95.9  \n",
       "\n",
       "[36 rows x 230 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index=\"SUBDIVISION\" , columns=\"YEAR\" , values=[\"ANNUAL\" , \"Mar-May\"] ,aggfunc=\"mean\")"
   ]
  }
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