UnemployemntAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.graph_objects as gg\n",
    "import plotly.express as exp\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Unemployment in India - Unemployment in India.csv\")\n",
    "df_11_2020 = pd.read_csv(\"Unemployment_Rate_upto_11_2020 - Unemployment_Rate_upto_11_2020.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Date</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Estimated Unemployment Rate (%)</th>\n",
       "      <th>Estimated Employed</th>\n",
       "      <th>Estimated Labour Participation Rate (%)</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>31-05-2019</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>3.65</td>\n",
       "      <td>11999139.0</td>\n",
       "      <td>43.24</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>30-06-2019</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>3.05</td>\n",
       "      <td>11755881.0</td>\n",
       "      <td>42.05</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>31-07-2019</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>3.75</td>\n",
       "      <td>12086707.0</td>\n",
       "      <td>43.50</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>31-08-2019</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>3.32</td>\n",
       "      <td>12285693.0</td>\n",
       "      <td>43.97</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>30-09-2019</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>5.17</td>\n",
       "      <td>12256762.0</td>\n",
       "      <td>44.68</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Region        Date Frequency  Estimated Unemployment Rate (%)  \\\n",
       "0  Andhra Pradesh  31-05-2019   Monthly                             3.65   \n",
       "1  Andhra Pradesh  30-06-2019   Monthly                             3.05   \n",
       "2  Andhra Pradesh  31-07-2019   Monthly                             3.75   \n",
       "3  Andhra Pradesh  31-08-2019   Monthly                             3.32   \n",
       "4  Andhra Pradesh  30-09-2019   Monthly                             5.17   \n",
       "\n",
       "   Estimated Employed  Estimated Labour Participation Rate (%)   Area  \n",
       "0          11999139.0                                    43.24  Rural  \n",
       "1          11755881.0                                    42.05  Rural  \n",
       "2          12086707.0                                    43.50  Rural  \n",
       "3          12285693.0                                    43.97  Rural  \n",
       "4          12256762.0                                    44.68  Rural  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Date</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Estimated Unemployment Rate (%)</th>\n",
       "      <th>Estimated Employed</th>\n",
       "      <th>Estimated Labour Participation Rate (%)</th>\n",
       "      <th>Region.1</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>31-01-2020</td>\n",
       "      <td>M</td>\n",
       "      <td>5.48</td>\n",
       "      <td>16635535</td>\n",
       "      <td>41.02</td>\n",
       "      <td>South</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>29-02-2020</td>\n",
       "      <td>M</td>\n",
       "      <td>5.83</td>\n",
       "      <td>16545652</td>\n",
       "      <td>40.90</td>\n",
       "      <td>South</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>31-03-2020</td>\n",
       "      <td>M</td>\n",
       "      <td>5.79</td>\n",
       "      <td>15881197</td>\n",
       "      <td>39.18</td>\n",
       "      <td>South</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>30-04-2020</td>\n",
       "      <td>M</td>\n",
       "      <td>20.51</td>\n",
       "      <td>11336911</td>\n",
       "      <td>33.10</td>\n",
       "      <td>South</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>31-05-2020</td>\n",
       "      <td>M</td>\n",
       "      <td>17.43</td>\n",
       "      <td>12988845</td>\n",
       "      <td>36.46</td>\n",
       "      <td>South</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Region        Date Frequency  Estimated Unemployment Rate (%)  \\\n",
       "0  Andhra Pradesh  31-01-2020         M                             5.48   \n",
       "1  Andhra Pradesh  29-02-2020         M                             5.83   \n",
       "2  Andhra Pradesh  31-03-2020         M                             5.79   \n",
       "3  Andhra Pradesh  30-04-2020         M                            20.51   \n",
       "4  Andhra Pradesh  31-05-2020         M                            17.43   \n",
       "\n",
       "   Estimated Employed  Estimated Labour Participation Rate (%) Region.1  \\\n",
       "0            16635535                                    41.02    South   \n",
       "1            16545652                                    40.90    South   \n",
       "2            15881197                                    39.18    South   \n",
       "3            11336911                                    33.10    South   \n",
       "4            12988845                                    36.46    South   \n",
       "\n",
       "   Longitude  Latitude  \n",
       "0    15.9129     79.74  \n",
       "1    15.9129     79.74  \n",
       "2    15.9129     79.74  \n",
       "3    15.9129     79.74  \n",
       "4    15.9129     79.74  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_11_2020.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 754 entries, 0 to 753\n",
      "Data columns (total 7 columns):\n",
      " #   Column                                   Non-Null Count  Dtype  \n",
      "---  ------                                   --------------  -----  \n",
      " 0   Region                                   740 non-null    object \n",
      " 1   Date                                     740 non-null    object \n",
      " 2   Frequency                                740 non-null    object \n",
      " 3   Estimated Unemployment Rate (%)          740 non-null    float64\n",
      " 4   Estimated Employed                       740 non-null    float64\n",
      " 5   Estimated Labour Participation Rate (%)  740 non-null    float64\n",
      " 6   Area                                     740 non-null    object \n",
      "dtypes: float64(3), object(4)\n",
      "memory usage: 41.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 267 entries, 0 to 266\n",
      "Data columns (total 9 columns):\n",
      " #   Column                                   Non-Null Count  Dtype  \n",
      "---  ------                                   --------------  -----  \n",
      " 0   Region                                   267 non-null    object \n",
      " 1   Date                                     267 non-null    object \n",
      " 2   Frequency                                267 non-null    object \n",
      " 3   Estimated Unemployment Rate (%)          267 non-null    float64\n",
      " 4   Estimated Employed                       267 non-null    int64  \n",
      " 5   Estimated Labour Participation Rate (%)  267 non-null    float64\n",
      " 6   Region.1                                 267 non-null    object \n",
      " 7   Longitude                                267 non-null    float64\n",
      " 8   Latitude                                 267 non-null    float64\n",
      "dtypes: float64(4), int64(1), object(4)\n",
      "memory usage: 18.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_11_2020.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(754, 7)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(267, 9)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_11_2020.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Estimated Unemployment Rate (%)</th>\n",
       "      <th>Estimated Employed</th>\n",
       "      <th>Estimated Labour Participation Rate (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>740.000000</td>\n",
       "      <td>7.400000e+02</td>\n",
       "      <td>740.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.787946</td>\n",
       "      <td>7.204460e+06</td>\n",
       "      <td>42.630122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.721298</td>\n",
       "      <td>8.087988e+06</td>\n",
       "      <td>8.111094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.942000e+04</td>\n",
       "      <td>13.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.657500</td>\n",
       "      <td>1.190404e+06</td>\n",
       "      <td>38.062500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.350000</td>\n",
       "      <td>4.744178e+06</td>\n",
       "      <td>41.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.887500</td>\n",
       "      <td>1.127549e+07</td>\n",
       "      <td>45.505000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>76.740000</td>\n",
       "      <td>4.577751e+07</td>\n",
       "      <td>72.570000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Estimated Unemployment Rate (%)  Estimated Employed  \\\n",
       "count                       740.000000        7.400000e+02   \n",
       "mean                         11.787946        7.204460e+06   \n",
       "std                          10.721298        8.087988e+06   \n",
       "min                           0.000000        4.942000e+04   \n",
       "25%                           4.657500        1.190404e+06   \n",
       "50%                           8.350000        4.744178e+06   \n",
       "75%                          15.887500        1.127549e+07   \n",
       "max                          76.740000        4.577751e+07   \n",
       "\n",
       "       Estimated Labour Participation Rate (%)  \n",
       "count                               740.000000  \n",
       "mean                                 42.630122  \n",
       "std                                   8.111094  \n",
       "min                                  13.330000  \n",
       "25%                                  38.062500  \n",
       "50%                                  41.160000  \n",
       "75%                                  45.505000  \n",
       "max                                  72.570000  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Estimated Unemployment Rate (%)</th>\n",
       "      <th>Estimated Employed</th>\n",
       "      <th>Estimated Labour Participation Rate (%)</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>267.000000</td>\n",
       "      <td>2.670000e+02</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>267.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.236929</td>\n",
       "      <td>1.396211e+07</td>\n",
       "      <td>41.681573</td>\n",
       "      <td>22.826048</td>\n",
       "      <td>80.532425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.803283</td>\n",
       "      <td>1.336632e+07</td>\n",
       "      <td>7.845419</td>\n",
       "      <td>6.270731</td>\n",
       "      <td>5.831738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.175420e+05</td>\n",
       "      <td>16.770000</td>\n",
       "      <td>10.850500</td>\n",
       "      <td>71.192400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.845000</td>\n",
       "      <td>2.838930e+06</td>\n",
       "      <td>37.265000</td>\n",
       "      <td>18.112400</td>\n",
       "      <td>76.085600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.650000</td>\n",
       "      <td>9.732417e+06</td>\n",
       "      <td>40.390000</td>\n",
       "      <td>23.610200</td>\n",
       "      <td>79.019300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.755000</td>\n",
       "      <td>2.187869e+07</td>\n",
       "      <td>44.055000</td>\n",
       "      <td>27.278400</td>\n",
       "      <td>85.279900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>75.850000</td>\n",
       "      <td>5.943376e+07</td>\n",
       "      <td>69.690000</td>\n",
       "      <td>33.778200</td>\n",
       "      <td>92.937600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Estimated Unemployment Rate (%)  Estimated Employed  \\\n",
       "count                       267.000000        2.670000e+02   \n",
       "mean                         12.236929        1.396211e+07   \n",
       "std                          10.803283        1.336632e+07   \n",
       "min                           0.500000        1.175420e+05   \n",
       "25%                           4.845000        2.838930e+06   \n",
       "50%                           9.650000        9.732417e+06   \n",
       "75%                          16.755000        2.187869e+07   \n",
       "max                          75.850000        5.943376e+07   \n",
       "\n",
       "       Estimated Labour Participation Rate (%)   Longitude    Latitude  \n",
       "count                               267.000000  267.000000  267.000000  \n",
       "mean                                 41.681573   22.826048   80.532425  \n",
       "std                                   7.845419    6.270731    5.831738  \n",
       "min                                  16.770000   10.850500   71.192400  \n",
       "25%                                  37.265000   18.112400   76.085600  \n",
       "50%                                  40.390000   23.610200   79.019300  \n",
       "75%                                  44.055000   27.278400   85.279900  \n",
       "max                                  69.690000   33.778200   92.937600  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_11_2020.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region                                     14\n",
       "Date                                       14\n",
       "Frequency                                  14\n",
       "Estimated Unemployment Rate (%)            14\n",
       "Estimated Employed                         14\n",
       "Estimated Labour Participation Rate (%)    14\n",
       "Area                                       14\n",
       "dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region                                     0\n",
       "Date                                       0\n",
       "Frequency                                  0\n",
       "Estimated Unemployment Rate (%)            0\n",
       "Estimated Employed                         0\n",
       "Estimated Labour Participation Rate (%)    0\n",
       "Region.1                                   0\n",
       "Longitude                                  0\n",
       "Latitude                                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_11_2020.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region                                     14\n",
       "Date                                       14\n",
       "Frequency                                  14\n",
       "Estimated Unemployment Rate (%)            14\n",
       "Estimated Employed                         14\n",
       "Estimated Labour Participation Rate (%)    14\n",
       "Area                                       14\n",
       "dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region                                     0\n",
       "Date                                       0\n",
       "Frequency                                  0\n",
       "Estimated Unemployment Rate (%)            0\n",
       "Estimated Employed                         0\n",
       "Estimated Labour Participation Rate (%)    0\n",
       "Region.1                                   0\n",
       "Longitude                                  0\n",
       "Latitude                                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_11_2020.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region                                     0\n",
       "Date                                       0\n",
       "Frequency                                  0\n",
       "Estimated Unemployment Rate (%)            0\n",
       "Estimated Employed                         0\n",
       "Estimated Labour Participation Rate (%)    0\n",
       "Area                                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region                                     0\n",
       "Date                                       0\n",
       "Frequency                                  0\n",
       "Estimated Unemployment Rate (%)            0\n",
       "Estimated Employed                         0\n",
       "Estimated Labour Participation Rate (%)    0\n",
       "Area                                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(740, 7)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_11_2020.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['State' , 'Date' , 'Frequency' , 'Estimated Unemployment Rate',\n",
    "              'Estimated Employed', 'Estimated Labour Participation Rate', 'Area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_11_2020.columns = ['State' , 'Date' , 'Frequency' , 'Estimated Unemployment Rate','Estimated Employed', 'Estimated Labour Participation Rate','Region','Longitude','Latitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State', 'Date', 'Frequency', 'Estimated Unemployment Rate',\n",
       "       'Estimated Employed', 'Estimated Labour Participation Rate', 'Area'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State', 'Date', 'Frequency', 'Estimated Unemployment Rate',\n",
       "       'Estimated Employed', 'Estimated Labour Participation Rate', 'Region',\n",
       "       'Longitude', 'Latitude'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_11_2020.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Date</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Estimated Unemployment Rate</th>\n",
       "      <th>Estimated Employed</th>\n",
       "      <th>Estimated Labour Participation Rate</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>31-05-2019</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>3.65</td>\n",
       "      <td>11999139.0</td>\n",
       "      <td>43.24</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>30-06-2019</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>3.05</td>\n",
       "      <td>11755881.0</td>\n",
       "      <td>42.05</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            State        Date Frequency  Estimated Unemployment Rate  \\\n",
       "0  Andhra Pradesh  31-05-2019   Monthly                         3.65   \n",
       "1  Andhra Pradesh  30-06-2019   Monthly                         3.05   \n",
       "\n",
       "   Estimated Employed  Estimated Labour Participation Rate   Area  \n",
       "0          11999139.0                                43.24  Rural  \n",
       "1          11755881.0                                42.05  Rural  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Date</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Estimated Unemployment Rate</th>\n",
       "      <th>Estimated Employed</th>\n",
       "      <th>Estimated Labour Participation Rate</th>\n",
       "      <th>Region</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>31-01-2020</td>\n",
       "      <td>M</td>\n",
       "      <td>5.48</td>\n",
       "      <td>16635535</td>\n",
       "      <td>41.02</td>\n",
       "      <td>South</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>29-02-2020</td>\n",
       "      <td>M</td>\n",
       "      <td>5.83</td>\n",
       "      <td>16545652</td>\n",
       "      <td>40.90</td>\n",
       "      <td>South</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            State        Date Frequency  Estimated Unemployment Rate  \\\n",
       "0  Andhra Pradesh  31-01-2020         M                         5.48   \n",
       "1  Andhra Pradesh  29-02-2020         M                         5.83   \n",
       "\n",
       "   Estimated Employed  Estimated Labour Participation Rate Region  Longitude  \\\n",
       "0            16635535                                41.02  South    15.9129   \n",
       "1            16545652                                40.90  South    15.9129   \n",
       "\n",
       "   Latitude  \n",
       "0     79.74  \n",
       "1     79.74  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_11_2020.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Andhra Pradesh'"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#State with highest unemployment rate\n",
    "\n",
    "df['State'].value_counts().idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State\n",
       "Andhra Pradesh      28\n",
       "Kerala              28\n",
       "West Bengal         28\n",
       "Uttar Pradesh       28\n",
       "Tripura             28\n",
       "Telangana           28\n",
       "Tamil Nadu          28\n",
       "Rajasthan           28\n",
       "Punjab              28\n",
       "Odisha              28\n",
       "Madhya Pradesh      28\n",
       "Maharashtra         28\n",
       "Karnataka           28\n",
       "Jharkhand           28\n",
       "Himachal Pradesh    28\n",
       "Haryana             28\n",
       "Gujarat             28\n",
       "Delhi               28\n",
       "Chhattisgarh        28\n",
       "Bihar               28\n",
       "Meghalaya           27\n",
       "Uttarakhand         27\n",
       "Assam               26\n",
       "Puducherry          26\n",
       "Goa                 24\n",
       "Jammu & Kashmir     21\n",
       "Sikkim              17\n",
       "Chandigarh          12\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['State'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Andhra Pradesh'"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_11_2020['State'].value_counts().idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Chandigarh'"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# State with lowest unemployment rate\n",
    "\n",
    "df['State'].value_counts().idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sikkim'"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_11_2020['State'].value_counts().idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Month of Employment\n",
    "# This code converts the 'Date' coloumn to a datetime type,extracts months as integer, and adds a new coloumn with the corresponding three-letter month abbreviation\n",
    "\n",
    "import datetime as dt\n",
    "import calendar as cal\n",
    "\n",
    "df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)  #This line converts the 'Date' coloumn in dataframe to datetime type.\n",
    "df['month_int'] = df['Date'].dt.month              #This line extracts month component from 'Date' coloumn and assigns it to a new coloumn called 'month_int' in the dataframe df.\n",
    "df['month'] = df['month_int'].apply(lambda x: cal.month_abbr[x])  #This line creates a mew coloumn 'month' in the dataframe df.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_11_2020['Date'] = pd.to_datetime(df_11_2020['Date'], dayfirst=True)\n",
    "df_11_2020['month_int'] = df_11_2020['Date'].dt.month\n",
    "df_11_2020['month'] = df_11_2020['month_int'].apply(lambda x: cal.month_abbr[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'May'"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# month with the highest unemployment \n",
    "\n",
    "df['month'].value_counts().idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mar'"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_11_2020['month'].value_counts().idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Apr'"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Month with the lowest employment\n",
    "\n",
    "df['month'].value_counts().idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Jan'"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_11_2020['month'].value_counts().idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Date</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Estimated Unemployment Rate</th>\n",
       "      <th>Estimated Employed</th>\n",
       "      <th>Estimated Labour Participation Rate</th>\n",
       "      <th>Area</th>\n",
       "      <th>month_int</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>3.65</td>\n",
       "      <td>11999139.0</td>\n",
       "      <td>43.24</td>\n",
       "      <td>Rural</td>\n",
       "      <td>5</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>3.05</td>\n",
       "      <td>11755881.0</td>\n",
       "      <td>42.05</td>\n",
       "      <td>Rural</td>\n",
       "      <td>6</td>\n",
       "      <td>Jun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>3.75</td>\n",
       "      <td>12086707.0</td>\n",
       "      <td>43.50</td>\n",
       "      <td>Rural</td>\n",
       "      <td>7</td>\n",
       "      <td>Jul</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            State       Date Frequency  Estimated Unemployment Rate  \\\n",
       "0  Andhra Pradesh 2019-05-31   Monthly                         3.65   \n",
       "1  Andhra Pradesh 2019-06-30   Monthly                         3.05   \n",
       "2  Andhra Pradesh 2019-07-31   Monthly                         3.75   \n",
       "\n",
       "   Estimated Employed  Estimated Labour Participation Rate   Area  month_int  \\\n",
       "0          11999139.0                                43.24  Rural          5   \n",
       "1          11755881.0                                42.05  Rural          6   \n",
       "2          12086707.0                                43.50  Rural          7   \n",
       "\n",
       "  month  \n",
       "0   May  \n",
       "1   Jun  \n",
       "2   Jul  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Date</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Estimated Unemployment Rate</th>\n",
       "      <th>Estimated Employed</th>\n",
       "      <th>Estimated Labour Participation Rate</th>\n",
       "      <th>Region</th>\n",
       "      <th>Longitude</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>M</td>\n",
       "      <td>5.48</td>\n",
       "      <td>16635535</td>\n",
       "      <td>41.02</td>\n",
       "      <td>South</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.74</td>\n",
       "      <td>1</td>\n",
       "      <td>Jan</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>M</td>\n",
       "      <td>5.83</td>\n",
       "      <td>16545652</td>\n",
       "      <td>40.90</td>\n",
       "      <td>South</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.74</td>\n",
       "      <td>2</td>\n",
       "      <td>Feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>M</td>\n",
       "      <td>5.79</td>\n",
       "      <td>15881197</td>\n",
       "      <td>39.18</td>\n",
       "      <td>South</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.74</td>\n",
       "      <td>3</td>\n",
       "      <td>Mar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            State       Date Frequency  Estimated Unemployment Rate  \\\n",
       "0  Andhra Pradesh 2020-01-31         M                         5.48   \n",
       "1  Andhra Pradesh 2020-02-29         M                         5.83   \n",
       "2  Andhra Pradesh 2020-03-31         M                         5.79   \n",
       "\n",
       "   Estimated Employed  Estimated Labour Participation Rate Region  Longitude  \\\n",
       "0            16635535                                41.02  South    15.9129   \n",
       "1            16545652                                40.90  South    15.9129   \n",
       "2            15881197                                39.18  South    15.9129   \n",
       "\n",
       "   Latitude  month_int month  \n",
       "0     79.74          1   Jan  \n",
       "1     79.74          2   Feb  \n",
       "2     79.74          3   Mar  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_11_2020.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Date</th>\n",
       "      <th>Estimated Unemployment Rate</th>\n",
       "      <th>Estimated Employed</th>\n",
       "      <th>Estimated Labour Participation Rate</th>\n",
       "      <th>Area</th>\n",
       "      <th>month</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>3.65</td>\n",
       "      <td>11999139.0</td>\n",
       "      <td>43.24</td>\n",
       "      <td>Rural</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>3.05</td>\n",
       "      <td>11755881.0</td>\n",
       "      <td>42.05</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Jun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>3.75</td>\n",
       "      <td>12086707.0</td>\n",
       "      <td>43.50</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Jul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>3.32</td>\n",
       "      <td>12285693.0</td>\n",
       "      <td>43.97</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Aug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>5.17</td>\n",
       "      <td>12256762.0</td>\n",
       "      <td>44.68</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Sep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>7.55</td>\n",
       "      <td>10871168.0</td>\n",
       "      <td>44.09</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>6.67</td>\n",
       "      <td>10806105.0</td>\n",
       "      <td>43.34</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>15.63</td>\n",
       "      <td>9299466.0</td>\n",
       "      <td>41.20</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>2020-05-31</td>\n",
       "      <td>15.22</td>\n",
       "      <td>9240903.0</td>\n",
       "      <td>40.67</td>\n",
       "      <td>Urban</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>9.86</td>\n",
       "      <td>9088931.0</td>\n",
       "      <td>37.57</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Jun</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>740 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              State       Date  Estimated Unemployment Rate  \\\n",
       "0    Andhra Pradesh 2019-05-31                         3.65   \n",
       "1    Andhra Pradesh 2019-06-30                         3.05   \n",
       "2    Andhra Pradesh 2019-07-31                         3.75   \n",
       "3    Andhra Pradesh 2019-08-31                         3.32   \n",
       "4    Andhra Pradesh 2019-09-30                         5.17   \n",
       "..              ...        ...                          ...   \n",
       "749     West Bengal 2020-02-29                         7.55   \n",
       "750     West Bengal 2020-03-31                         6.67   \n",
       "751     West Bengal 2020-04-30                        15.63   \n",
       "752     West Bengal 2020-05-31                        15.22   \n",
       "753     West Bengal 2020-06-30                         9.86   \n",
       "\n",
       "     Estimated Employed  Estimated Labour Participation Rate   Area month  \n",
       "0            11999139.0                                43.24  Rural   May  \n",
       "1            11755881.0                                42.05  Rural   Jun  \n",
       "2            12086707.0                                43.50  Rural   Jul  \n",
       "3            12285693.0                                43.97  Rural   Aug  \n",
       "4            12256762.0                                44.68  Rural   Sep  \n",
       "..                  ...                                  ...    ...   ...  \n",
       "749          10871168.0                                44.09  Urban   Feb  \n",
       "750          10806105.0                                43.34  Urban   Mar  \n",
       "751           9299466.0                                41.20  Urban   Apr  \n",
       "752           9240903.0                                40.67  Urban   May  \n",
       "753           9088931.0                                37.57  Urban   Jun  \n",
       "\n",
       "[740 rows x 7 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=['Frequency','month_int'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>5.48</td>\n",
       "      <td>16635535</td>\n",
       "      <td>41.02</td>\n",
       "      <td>South</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.740</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>5.83</td>\n",
       "      <td>16545652</td>\n",
       "      <td>40.90</td>\n",
       "      <td>South</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.740</td>\n",
       "      <td>Feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>5.79</td>\n",
       "      <td>15881197</td>\n",
       "      <td>39.18</td>\n",
       "      <td>South</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.740</td>\n",
       "      <td>Mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>20.51</td>\n",
       "      <td>11336911</td>\n",
       "      <td>33.10</td>\n",
       "      <td>South</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.740</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2020-05-31</td>\n",
       "      <td>17.43</td>\n",
       "      <td>12988845</td>\n",
       "      <td>36.46</td>\n",
       "      <td>South</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.740</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>7.29</td>\n",
       "      <td>30726310</td>\n",
       "      <td>40.39</td>\n",
       "      <td>East</td>\n",
       "      <td>22.9868</td>\n",
       "      <td>87.855</td>\n",
       "      <td>Jun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>2020-07-31</td>\n",
       "      <td>6.83</td>\n",
       "      <td>35372506</td>\n",
       "      <td>46.17</td>\n",
       "      <td>East</td>\n",
       "      <td>22.9868</td>\n",
       "      <td>87.855</td>\n",
       "      <td>Jul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>14.87</td>\n",
       "      <td>33298644</td>\n",
       "      <td>47.48</td>\n",
       "      <td>East</td>\n",
       "      <td>22.9868</td>\n",
       "      <td>87.855</td>\n",
       "      <td>Aug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>9.35</td>\n",
       "      <td>35707239</td>\n",
       "      <td>47.73</td>\n",
       "      <td>East</td>\n",
       "      <td>22.9868</td>\n",
       "      <td>87.855</td>\n",
       "      <td>Sep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>9.98</td>\n",
       "      <td>33962549</td>\n",
       "      <td>45.63</td>\n",
       "      <td>East</td>\n",
       "      <td>22.9868</td>\n",
       "      <td>87.855</td>\n",
       "      <td>Oct</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>267 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              State       Date  Estimated Unemployment Rate  \\\n",
       "0    Andhra Pradesh 2020-01-31                         5.48   \n",
       "1    Andhra Pradesh 2020-02-29                         5.83   \n",
       "2    Andhra Pradesh 2020-03-31                         5.79   \n",
       "3    Andhra Pradesh 2020-04-30                        20.51   \n",
       "4    Andhra Pradesh 2020-05-31                        17.43   \n",
       "..              ...        ...                          ...   \n",
       "262     West Bengal 2020-06-30                         7.29   \n",
       "263     West Bengal 2020-07-31                         6.83   \n",
       "264     West Bengal 2020-08-31                        14.87   \n",
       "265     West Bengal 2020-09-30                         9.35   \n",
       "266     West Bengal 2020-10-31                         9.98   \n",
       "\n",
       "     Estimated Employed  Estimated Labour Participation Rate Region  \\\n",
       "0              16635535                                41.02  South   \n",
       "1              16545652                                40.90  South   \n",
       "2              15881197                                39.18  South   \n",
       "3              11336911                                33.10  South   \n",
       "4              12988845                                36.46  South   \n",
       "..                  ...                                  ...    ...   \n",
       "262            30726310                                40.39   East   \n",
       "263            35372506                                46.17   East   \n",
       "264            33298644                                47.48   East   \n",
       "265            35707239                                47.73   East   \n",
       "266            33962549                                45.63   East   \n",
       "\n",
       "     Longitude  Latitude month  \n",
       "0      15.9129    79.740   Jan  \n",
       "1      15.9129    79.740   Feb  \n",
       "2      15.9129    79.740   Mar  \n",
       "3      15.9129    79.740   Apr  \n",
       "4      15.9129    79.740   May  \n",
       "..         ...       ...   ...  \n",
       "262    22.9868    87.855   Jun  \n",
       "263    22.9868    87.855   Jul  \n",
       "264    22.9868    87.855   Aug  \n",
       "265    22.9868    87.855   Sep  \n",
       "266    22.9868    87.855   Oct  \n",
       "\n",
       "[267 rows x 9 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_11_2020.drop(columns=['Frequency','month_int'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Top 10 states with the highest unemployment\n",
    "df1 = df[['State','Estimated Unemployment Rate']].groupby('State').sum().sort_values('Estimated Unemployment Rate' , ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_11_2020 = df_11_2020[['State','Estimated Unemployment Rate']].groupby('State').sum().sort_values('Estimated Unemployment Rate' , ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Estimated Unemployment Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
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       "      <th>Tripura</th>\n",
       "      <td>793.81</td>\n",
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       "    <tr>\n",
       "      <th>Haryana</th>\n",
       "      <td>735.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jharkhand</th>\n",
       "      <td>576.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>529.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himachal Pradesh</th>\n",
       "      <td>519.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>461.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajasthan</th>\n",
       "      <td>393.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <td>351.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jammu &amp; Kashmir</th>\n",
       "      <td>339.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Punjab</th>\n",
       "      <td>336.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Estimated Unemployment Rate\n",
       "State                                        \n",
       "Tripura                                793.81\n",
       "Haryana                                735.93\n",
       "Jharkhand                              576.38\n",
       "Bihar                                  529.71\n",
       "Himachal Pradesh                       519.13\n",
       "Delhi                                  461.87\n",
       "Rajasthan                              393.63\n",
       "Uttar Pradesh                          351.44\n",
       "Jammu & Kashmir                        339.96\n",
       "Punjab                                 336.87"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>Estimated Unemployment Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Haryana</th>\n",
       "      <td>274.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tripura</th>\n",
       "      <td>250.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jharkhand</th>\n",
       "      <td>195.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>194.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>184.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puducherry</th>\n",
       "      <td>179.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himachal Pradesh</th>\n",
       "      <td>160.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajasthan</th>\n",
       "      <td>158.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jammu &amp; Kashmir</th>\n",
       "      <td>148.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tamil Nadu</th>\n",
       "      <td>121.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Estimated Unemployment Rate\n",
       "State                                        \n",
       "Haryana                                274.77\n",
       "Tripura                                250.55\n",
       "Jharkhand                              195.39\n",
       "Bihar                                  194.71\n",
       "Delhi                                  184.14\n",
       "Puducherry                             179.42\n",
       "Himachal Pradesh                       160.65\n",
       "Rajasthan                              158.68\n",
       "Jammu & Kashmir                        148.30\n",
       "Tamil Nadu                             121.87"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_11_2020.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of people unemployement %')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualisation of this top 10 highest unemployment \n",
    "\n",
    "fig = plt.figure()\n",
    "axb = fig.add_subplot(1,2,1)\n",
    "df1[:10].plot(kind='bar' , color = 'blue' , figsize=(30,5), ax=axb)\n",
    "axb.set_title('Top 10 Highest Unemployment Rate')\n",
    "axb.set_xlabel('State')\n",
    "axb.set_ylabel('Number of people unemployement %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>Estimated Unemployment Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>1747.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr</th>\n",
       "      <td>1205.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jun</th>\n",
       "      <td>1097.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <td>556.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oct</th>\n",
       "      <td>544.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nov</th>\n",
       "      <td>542.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <td>528.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <td>527.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aug</th>\n",
       "      <td>510.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dec</th>\n",
       "      <td>503.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jul</th>\n",
       "      <td>487.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sep</th>\n",
       "      <td>470.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Estimated Unemployment Rate\n",
       "month                             \n",
       "May                        1747.85\n",
       "Apr                        1205.72\n",
       "Jun                        1097.56\n",
       "Mar                         556.43\n",
       "Oct                         544.55\n",
       "Nov                         542.76\n",
       "Feb                         528.13\n",
       "Jan                         527.39\n",
       "Aug                         510.81\n",
       "Dec                         503.36\n",
       "Jul                         487.83\n",
       "Sep                         470.69"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Month with the highest unemployment rate\n",
    "df2 = df[['month','Estimated Unemployment Rate']].groupby('month').sum().sort_values('Estimated Unemployment Rate',ascending = False)\n",
    "df2.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>Estimated Unemployment Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>1747.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr</th>\n",
       "      <td>1205.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jun</th>\n",
       "      <td>1097.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <td>556.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oct</th>\n",
       "      <td>544.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nov</th>\n",
       "      <td>542.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <td>528.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <td>527.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aug</th>\n",
       "      <td>510.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dec</th>\n",
       "      <td>503.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jul</th>\n",
       "      <td>487.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sep</th>\n",
       "      <td>470.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Estimated Unemployment Rate\n",
       "month                             \n",
       "May                        1747.85\n",
       "Apr                        1205.72\n",
       "Jun                        1097.56\n",
       "Mar                         556.43\n",
       "Oct                         544.55\n",
       "Nov                         542.76\n",
       "Feb                         528.13\n",
       "Jan                         527.39\n",
       "Aug                         510.81\n",
       "Dec                         503.36\n",
       "Jul                         487.83\n",
       "Sep                         470.69"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_11_2020 = df_11_2020[['month','Estimated Unemployment Rate']].groupby('month').sum().sort_values('Estimated Unemployment Rate',ascending = False)\n",
    "df2.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of people unemployement %')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax0 = fig.add_subplot(1,2,1)\n",
    "df2[:12].plot(kind='bar' , color = 'red' , figsize = (30,5), ax = ax0)\n",
    "ax0.set_title('Top 10 states with the highest unemployment rate')\n",
    "ax0.set_xlabel('Months')\n",
    "ax0.set_ylabel('Number of people unemployement %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of people unemployement %')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax0 = fig.add_subplot(1,2,1)\n",
    "df2_11_2020[:12].plot(kind='bar' , color = 'green' , figsize = (30,5), ax = ax0)\n",
    "ax0.set_title('Top 10 states with the highest unemployment rate')\n",
    "ax0.set_xlabel('Months')\n",
    "ax0.set_ylabel('Number of people unemployement %')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "name": "Unemployment_Rate",
         "type": "bar",
         "x": [
          "Apr",
          "Aug",
          "Dec",
          "Feb",
          "Jan",
          "Jul",
          "Jun",
          "Mar",
          "May",
          "Nov",
          "Oct",
          "Sep"
         ],
         "y": [
          23.64156862745098,
          9.637924528301888,
          9.497358490566038,
          9.964716981132076,
          9.950754716981132,
          9.033888888888889,
          10.553461538461537,
          10.700576923076921,
          16.646190476190476,
          9.868363636363636,
          9.90090909090909,
          9.051730769230769
         ]
        },
        {
         "name": "Labour_Participation_Rate",
         "type": "bar",
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    "#Visualize labour participation rate & unemployment rate in each month\n",
    "\n",
    "df_EE = df.groupby(['month'])[['Estimated Unemployment Rate','Estimated Employed','Estimated Labour Participation Rate']].mean()\n",
    "df_EE = pd.DataFrame(df_EE).reset_index()\n",
    "month = df_EE.month\n",
    "unemployment_rate = df_EE['Estimated Unemployment Rate']\n",
    "labour_participation_rate = df_EE['Estimated Labour Participation Rate']\n",
    "\n",
    "fig = gg.Figure()\n",
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    "fig.add_trace(gg.Bar(x = month, y = unemployment_rate , name='Unemployment_Rate'))\n",
    "fig.add_trace(gg.Bar(x = month , y = labour_participation_rate , name='Labour_Participation_Rate'))\n",
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    "fig.update_layout(title = 'Unemployment rate and labour participation rate' , xaxis = {'categoryorder':'array', 'categoryarray':['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']})\n",
    "\n",
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        },
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          "Jan",
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    "\n",
    "df_EE_11_2020 = df_11_2020.groupby(['month'])[['Estimated Unemployment Rate','Estimated Employed','Estimated Labour Participation Rate']].mean()\n",
    "df_EE_11_2020 = pd.DataFrame(df_EE_11_2020).reset_index()\n",
    "month = df_EE_11_2020.month\n",
    "unemployment_rate = df_EE_11_2020['Estimated Unemployment Rate']\n",
    "labour_participation_rate = df_EE_11_2020['Estimated Labour Participation Rate']\n",
    "\n",
    "fig = gg.Figure()\n",
    "\n",
    "fig.add_trace(gg.Bar(x = month, y = unemployment_rate , name='Unemployment_Rate'))\n",
    "fig.add_trace(gg.Bar(x = month , y = labour_participation_rate , name='Labour Participation Rate'))\n",
    "\n",
    "fig.update_layout(title = 'Unemployment rate and labour participation rate for upto 11/2020' , xaxis = {'categoryorder':'array', 'categoryarray':['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']})\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Estimated Employed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <td>786655301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maharashtra</th>\n",
       "      <td>559725484.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Bengal</th>\n",
       "      <td>481559064.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>346253296.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tamil Nadu</th>\n",
       "      <td>343547309.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gujarat</th>\n",
       "      <td>319256358.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madhya Pradesh</th>\n",
       "      <td>311233561.0</td>\n",
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       "    <tr>\n",
       "      <th>Karnataka</th>\n",
       "      <td>298679340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajasthan</th>\n",
       "      <td>281149813.0</td>\n",
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       "    <tr>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <td>228314609.0</td>\n",
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       "    <tr>\n",
       "      <th>Telangana</th>\n",
       "      <td>222310557.0</td>\n",
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       "    <tr>\n",
       "      <th>Odisha</th>\n",
       "      <td>183280915.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assam</th>\n",
       "      <td>139224076.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Punjab</th>\n",
       "      <td>127102136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jharkhand</th>\n",
       "      <td>125138732.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kerala</th>\n",
       "      <td>123925186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <td>120497960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haryana</th>\n",
       "      <td>99598029.0</td>\n",
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       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>73570360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jammu &amp; Kashmir</th>\n",
       "      <td>37798565.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttarakhand</th>\n",
       "      <td>37536159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himachal Pradesh</th>\n",
       "      <td>29675064.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tripura</th>\n",
       "      <td>20076074.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meghalaya</th>\n",
       "      <td>18622894.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puducherry</th>\n",
       "      <td>5519230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goa</th>\n",
       "      <td>5431400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chandigarh</th>\n",
       "      <td>3801975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sikkim</th>\n",
       "      <td>1816972.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Estimated Employed\n",
       "State                               \n",
       "Uttar Pradesh            786655301.0\n",
       "Maharashtra              559725484.0\n",
       "West Bengal              481559064.0\n",
       "Bihar                    346253296.0\n",
       "Tamil Nadu               343547309.0\n",
       "Gujarat                  319256358.0\n",
       "Madhya Pradesh           311233561.0\n",
       "Karnataka                298679340.0\n",
       "Rajasthan                281149813.0\n",
       "Andhra Pradesh           228314609.0\n",
       "Telangana                222310557.0\n",
       "Odisha                   183280915.0\n",
       "Assam                    139224076.0\n",
       "Punjab                   127102136.0\n",
       "Jharkhand                125138732.0\n",
       "Kerala                   123925186.0\n",
       "Chhattisgarh             120497960.0\n",
       "Haryana                   99598029.0\n",
       "Delhi                     73570360.0\n",
       "Jammu & Kashmir           37798565.0\n",
       "Uttarakhand               37536159.0\n",
       "Himachal Pradesh          29675064.0\n",
       "Tripura                   20076074.0\n",
       "Meghalaya                 18622894.0\n",
       "Puducherry                 5519230.0\n",
       "Goa                        5431400.0\n",
       "Chandigarh                 3801975.0\n",
       "Sikkim                     1816972.0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# state wise estimated employed\n",
    "\n",
    "df3 = df[['State','Estimated Employed']].groupby('State').sum().sort_values('Estimated Employed', ascending=False)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of Estimated Employed')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#State wise estimated employed visualization\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(1,2,1) # second subplot (ax1) will be positioned in the second coloumn\n",
    "\n",
    "#Employed\n",
    "df3[:10].plot(kind = 'bar', color='green', figsize=(15,6), ax = ax1)\n",
    "ax1.set_title('Estimated Employed people in each state')\n",
    "ax1.set_xlabel('State')\n",
    "ax1.set_ylabel('number of Estimated Employed')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>Estimated Unemployment Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tripura</th>\n",
       "      <td>793.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haryana</th>\n",
       "      <td>735.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jharkhand</th>\n",
       "      <td>576.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>529.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himachal Pradesh</th>\n",
       "      <td>519.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>461.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajasthan</th>\n",
       "      <td>393.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <td>351.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jammu &amp; Kashmir</th>\n",
       "      <td>339.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Punjab</th>\n",
       "      <td>336.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kerala</th>\n",
       "      <td>283.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puducherry</th>\n",
       "      <td>265.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tamil Nadu</th>\n",
       "      <td>259.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <td>258.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Bengal</th>\n",
       "      <td>227.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goa</th>\n",
       "      <td>222.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telangana</th>\n",
       "      <td>216.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maharashtra</th>\n",
       "      <td>211.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <td>209.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madhya Pradesh</th>\n",
       "      <td>207.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chandigarh</th>\n",
       "      <td>191.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karnataka</th>\n",
       "      <td>186.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gujarat</th>\n",
       "      <td>186.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttarakhand</th>\n",
       "      <td>177.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assam</th>\n",
       "      <td>167.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odisha</th>\n",
       "      <td>158.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meghalaya</th>\n",
       "      <td>129.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sikkim</th>\n",
       "      <td>123.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Estimated Unemployment Rate\n",
       "State                                        \n",
       "Tripura                                793.81\n",
       "Haryana                                735.93\n",
       "Jharkhand                              576.38\n",
       "Bihar                                  529.71\n",
       "Himachal Pradesh                       519.13\n",
       "Delhi                                  461.87\n",
       "Rajasthan                              393.63\n",
       "Uttar Pradesh                          351.44\n",
       "Jammu & Kashmir                        339.96\n",
       "Punjab                                 336.87\n",
       "Kerala                                 283.47\n",
       "Puducherry                             265.59\n",
       "Tamil Nadu                             259.96\n",
       "Chhattisgarh                           258.73\n",
       "West Bengal                            227.49\n",
       "Goa                                    222.58\n",
       "Telangana                              216.66\n",
       "Maharashtra                            211.61\n",
       "Andhra Pradesh                         209.36\n",
       "Madhya Pradesh                         207.38\n",
       "Chandigarh                             191.90\n",
       "Karnataka                              186.93\n",
       "Gujarat                                186.59\n",
       "Uttarakhand                            177.74\n",
       "Assam                                  167.13\n",
       "Odisha                                 158.42\n",
       "Meghalaya                              129.57\n",
       "Sikkim                                 123.24"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estimated unemployment rate State wise \n",
    "# Estimated Unemployment rate (%) = (Number of Unemployed / Labour force ) * 100\n",
    "\n",
    "df3_a = df[['State', 'Estimated Unemployment Rate']].groupby('State').sum().sort_values('Estimated Unemployment Rate', ascending=False)\n",
    "df3_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Estimated Unemployment Rate (in %)')"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimated unemployment rate state wise visualization\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(1,2,2)\n",
    "\n",
    "df3_a[:10].plot(kind='bar',color='green', figsize=(15,6), ax = ax1)\n",
    "ax1.set_title('Estimated Unemployment Rate in each state')\n",
    "ax1.set_xlabel('State')\n",
    "ax1.set_ylabel('Estimated Unemployment Rate (in %)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>Estimated Unemployment Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Haryana</th>\n",
       "      <td>274.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tripura</th>\n",
       "      <td>250.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jharkhand</th>\n",
       "      <td>195.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>194.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>184.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puducherry</th>\n",
       "      <td>179.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himachal Pradesh</th>\n",
       "      <td>160.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajasthan</th>\n",
       "      <td>158.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jammu &amp; Kashmir</th>\n",
       "      <td>148.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tamil Nadu</th>\n",
       "      <td>121.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goa</th>\n",
       "      <td>121.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Punjab</th>\n",
       "      <td>119.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttarakhand</th>\n",
       "      <td>111.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Bengal</th>\n",
       "      <td>101.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <td>97.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kerala</th>\n",
       "      <td>94.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <td>86.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maharashtra</th>\n",
       "      <td>79.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sikkim</th>\n",
       "      <td>78.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <td>78.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karnataka</th>\n",
       "      <td>76.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madhya Pradesh</th>\n",
       "      <td>68.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telangana</th>\n",
       "      <td>68.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odisha</th>\n",
       "      <td>64.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gujarat</th>\n",
       "      <td>63.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assam</th>\n",
       "      <td>48.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meghalaya</th>\n",
       "      <td>38.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Estimated Unemployment Rate\n",
       "State                                        \n",
       "Haryana                                274.77\n",
       "Tripura                                250.55\n",
       "Jharkhand                              195.39\n",
       "Bihar                                  194.71\n",
       "Delhi                                  184.14\n",
       "Puducherry                             179.42\n",
       "Himachal Pradesh                       160.65\n",
       "Rajasthan                              158.68\n",
       "Jammu & Kashmir                        148.30\n",
       "Tamil Nadu                             121.87\n",
       "Goa                                    121.67\n",
       "Punjab                                 119.81\n",
       "Uttarakhand                            111.56\n",
       "West Bengal                            101.92\n",
       "Uttar Pradesh                           97.37\n",
       "Kerala                                  94.34\n",
       "Andhra Pradesh                          86.64\n",
       "Maharashtra                             79.79\n",
       "Sikkim                                  78.34\n",
       "Chhattisgarh                            78.19\n",
       "Karnataka                               76.68\n",
       "Madhya Pradesh                          68.54\n",
       "Telangana                               68.33\n",
       "Odisha                                  64.62\n",
       "Gujarat                                 63.76\n",
       "Assam                                   48.56\n",
       "Meghalaya                               38.66"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3_a_11_2020 = df_11_2020[['State', 'Estimated Unemployment Rate']].groupby('State').sum().sort_values('Estimated Unemployment Rate', ascending=False)\n",
    "df3_a_11_2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Estimated Unemployment Rate (in %)')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax0 = fig.add_subplot(1,2,1)\n",
    "\n",
    "df3_a_11_2020[:10].plot(kind='bar',color='blue', figsize=(15,6), ax = ax0)\n",
    "ax0.set_title('Estimated Unemployment Rate in each state')\n",
    "ax0.set_xlabel('State')\n",
    "ax0.set_ylabel('Estimated Unemployment Rate (in %)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>Estimated Employed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <td>555247990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maharashtra</th>\n",
       "      <td>392047582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Bengal</th>\n",
       "      <td>333051643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>236068280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gujarat</th>\n",
       "      <td>227307461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madhya Pradesh</th>\n",
       "      <td>223183353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tamil Nadu</th>\n",
       "      <td>219878981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karnataka</th>\n",
       "      <td>216240176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajasthan</th>\n",
       "      <td>197317522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telangana</th>\n",
       "      <td>162440825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <td>154254800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odisha</th>\n",
       "      <td>127268329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assam</th>\n",
       "      <td>108102755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Punjab</th>\n",
       "      <td>87830342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jharkhand</th>\n",
       "      <td>87706424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kerala</th>\n",
       "      <td>85967949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <td>84213492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haryana</th>\n",
       "      <td>68440590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>46328219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jammu &amp; Kashmir</th>\n",
       "      <td>29790285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttarakhand</th>\n",
       "      <td>27432749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himachal Pradesh</th>\n",
       "      <td>20338849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tripura</th>\n",
       "      <td>13972916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meghalaya</th>\n",
       "      <td>13498153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goa</th>\n",
       "      <td>4423748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puducherry</th>\n",
       "      <td>3652629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sikkim</th>\n",
       "      <td>1876186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Estimated Employed\n",
       "State                               \n",
       "Uttar Pradesh              555247990\n",
       "Maharashtra                392047582\n",
       "West Bengal                333051643\n",
       "Bihar                      236068280\n",
       "Gujarat                    227307461\n",
       "Madhya Pradesh             223183353\n",
       "Tamil Nadu                 219878981\n",
       "Karnataka                  216240176\n",
       "Rajasthan                  197317522\n",
       "Telangana                  162440825\n",
       "Andhra Pradesh             154254800\n",
       "Odisha                     127268329\n",
       "Assam                      108102755\n",
       "Punjab                      87830342\n",
       "Jharkhand                   87706424\n",
       "Kerala                      85967949\n",
       "Chhattisgarh                84213492\n",
       "Haryana                     68440590\n",
       "Delhi                       46328219\n",
       "Jammu & Kashmir             29790285\n",
       "Uttarakhand                 27432749\n",
       "Himachal Pradesh            20338849\n",
       "Tripura                     13972916\n",
       "Meghalaya                   13498153\n",
       "Goa                          4423748\n",
       "Puducherry                   3652629\n",
       "Sikkim                       1876186"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3_11_2020 = df_11_2020[['State', 'Estimated Employed']].groupby('State').sum().sort_values('Estimated Employed', ascending=False)\n",
    "df3_11_2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of Estimated Employed')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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smOtzent7q5/7XV/fzMxMxMXFaRzf68dctGhRKBQKHD16VF3QvSp72dtei+xj0+a5c1O0aFFYWFhg+fLlkuvfp7zm2b59O1JSUrB161a4ubmp178+xlB2I2qps5gFpX79+qhfvz5UKhXCw8Px66+/YtiwYShevDg+//zzNz527dq16NGjB6ZOnaqx/MmTJ7C3t3/rc2vze066w2AKpbfp1asXbt26hQ0bNqBkyZLYtm0bmjdvjgsXLqBs2bKoVq0ajIyMsGLFCgQFBSE5ORlr1qzBxx9/zCLpHZiZmSEgIAD29vbYs2cPzp49i9q1a6s/ZPLyLexdKRQKCCFyfNgtW7YMKpUqR97cctWtWxf29va4fPkyvvrqK8nnMjU1RY0aNbB161b8+OOP6m+9z549w19//VUQh1PgDhw4gIcPH6rPnKhUKmzcuBFlypSR/Hbv7e2NUqVK4ffff8fIkSPVl0pSUlLwxx9/qHvCZXN2dsZnn32GBQsW4MWLF2jTpg1cXV019le2bFmcP38+x4fQ6wIDA7Fjx45cM2tj3bp1GDJkiPr+77//DuC/sXNat26N6dOn4/79++jUqZPkfmrXrg0LCwusXbtW3WsSePmBf/DgwVzPQr7tuXPTunVrTJ06FUWKFIGHh0eejrEw5TVP9u/Gq39/QggsXbpUY7s6derAzs4OixYtwueff17og6saGRmhZs2aKF++PNatW4czZ87g888/f+N7k0KhyPE+8vfff+P+/fvw8vJSL5Pahza/56Q7PohCKSoqCuvXr8e9e/dQsmRJAC/PFu3evRsrVqzA1KlT4e7ujr179+Kzzz5D//79oVKpULt2bezatUvm9Lrn4sWLyMzMzLG8TJkyKFasGMaNG4d79+6hcePGKF26NBISEjB37lyYmJggICBAva2FhQXWrVuHChUqwNraGiVLllT//xQkW1tbNGjQAD/++COKFi0Kd3d3hISE4LfffsvxLbBSpUoAgCVLlsDGxgbm5ubw8PBAkSJF8Ouvv6Jnz56Ij4/Hp59+CicnJzx+/Bjnz5/H48ePsXDhQgDADz/8gObNm6Np06YYMWIEVCoVZsyYASsrqzyfwcrKysKJEydyXefv75/rGY78Klq0KBo1aoSxY8fCysoKCxYswNWrV984RIBSqcTMmTPRrVs3tG7dGv3790d6ejp+/PFHJCQkYPr06TkeM3ToUNSsWRPAy0FLX7d48WK0aNECzZo1Q1BQEEqVKoX4+HhcuXIFZ86cwebNmwEAY8aMwY4dO9CoUSOMGzcOlpaWmD9/fp66yGczNTXF7NmzkZycjOrVq+PYsWOYPHkyWrRogXr16gF4WRz/73//Q69evRAeHo4GDRrAysoKMTExCA0Nha+vL7788kvY29tj7Nix+O6779CjRw906dIFcXFxmDhxIszNzTF+/Hitnzs3w4YNwx9//IEGDRogODgYfn5+yMrKwp07d7B3716MGDFC/fq+D3nN07RpU5iamqJLly4YNWoU0tLSsHDhQjx9+lRjf9bW1pg9ezb69u2LJk2aoF+/fihevDhu3LiB8+fPY968ee+cedGiRTh48CBatWoFV1dXpKWlqc+INWnSBMDL9oRubm74888/0bhxYzg6OqrfN1q3bo2VK1eifPny8PPzw+nTp/Hjjz/m+ELxpve3vP6ekw6RuTF5oQAgtm3bpr6/adMmAUBYWVlp3IyNjUWnTp2EEELExMSIsmXLiq+//lqcOXNGhISEiICAANG4ceO3drv9ULyp1xsAsXTpUiGEEDt37hQtWrQQpUqVEqampsLJyUm0bNlSo8u4EEKsX79elC9fXpiYmGj0MpHq9daqVascmQCIQYMGaSyLjo4WAMSPP/6oXnbv3j3xySefCAcHB2FjYyOaN28uLl68mKNnkRBC/Pzzz8LDw0MYGRnl6LkSEhIiWrVqJRwdHYWJiYkoVaqUaNWqlUZXeiGE2LFjh/Dz81N3BZ8+fXqux5WbN/V6AyCuX78uhPivJ9rrz539//R6b6Ts588eDuHV12/BggWiTJkywsTERJQvX16sW7dO47G5DQ8gxMsehjVr1hTm5ubCyspKNG7cWPz777+Sx+bu7i4qVKgguf78+fOiU6dOwsnJSZiYmIgSJUqIRo0aiUWLFmls9++//4patWoJMzMzUaJECfH111+LJUuW5LnXm5WVlYiIiBANGzYUFhYWwtHRUXz55ZciOTk5x/bLly8XNWvWFFZWVsLCwkKUKVNG9OjRQ4SHh2tst2zZMvX/uZ2dnWjXrp24dOlSvp87t9/N5ORkMWbMGOHt7a1+Hl9fXxEcHKzRC1DquHPr9fb634/Uc+cmr3n++usvUblyZWFubi5KlSolvv76a/HPP//k+ju1a9cuERAQIKysrNTDQMyYMUPjOKysrHJkycvf1/Hjx0WHDh2Em5ubMDMzE0WKFBEBAQE5esju379f+Pv7CzMzMwFA/Vo8ffpU9OnTRzg5OQlLS0tRr149cfToUREQECACAgI09iH1/iZE3n/PSTcohBCi0Kux90yhUGDbtm1o3749AGDjxo3o1q0bLl26BCMjI41tra2tUaJECYwdOxb//POPxkCF9+7dg4uLC44fP85RkskgKRQKDBo0qEC+rb9NREQEKleujPnz52PgwIGF/nxSgoKCsGXLlhy9/wz9uYkofz6IS2/+/v5QqVR49OgR6tevn+s2qampOYqo7PtZWVmFnpHIUEVFReH27dv47rvv4OzsXKATvRIRFTaDGUcpOTkZ586dU/ekiI6Oxrlz53Dnzh2UK1cO3bp1Q48ePbB161ZER0cjLCwMM2bMULdBatWqFcLCwjBp0iRcv34dZ86cQa9eveDm5gZ/f38Zj4xIv/3www9o2rQpkpOTsXnzZo1G3kREus5gLr0dPnwYgYGBOZb37NkTK1euREZGBiZPnozVq1fj/v37KFKkCGrXro2JEyfC19cXALBhwwbMnDkT165dg6WlJWrXro0ZM2agfPny7/twiIiISAcYTKFEREREVNAM5tIbERERUUHT68bcWVlZePDgAWxsbAp9cDIiIiIyHEIIPHv2DCVLltSYs/J1el0oPXjwAC4uLnLHICIiIj119+7dN84xqNeFUvaM13fv3oWtra3MaYiIiEhfJCUlwcXFRV1LSNHrQin7cputrS0LJSIiItLa25rusDE3ERERkQQWSkREREQSWCgRERERSdDrNkpERPR2KpUKGRkZcscgeq9MTExyzOGaHyyUiIgMlBACsbGxSEhIkDsKkSzs7e1RokSJdxprkYUSEZGByi6SnJycYGlpyYF56YMhhEBqaioePXoEAHB2ds73vlgoEREZIJVKpS6SihQpInccovfOwsICAPDo0SM4OTnl+zIcG3MTERmg7DZJlpaWMichkk/27/+7tNFjoUREZMB4uY0+ZAXx+89CiYiIiEgCCyUiIiIiCSyUiIg+IArF+73pgpUrV8Le3l7uGFrR9cwKhQLbt2+XOwbc3d3x888/F+pzsFAiIiKdERQUBIVCkePWvHnzPD0+tw/Ozp0749q1a4WQVtP7Lm5ye50UCgU2bNjw3jJ8CDg8ABER6ZTmzZtjxYoVGsvMzMzyvT8LCwt1V3FDs2LFihxFpC6fidJHPKNEREQ6xczMDCVKlNC4OTg4qNdPmDABrq6uMDMzQ8mSJTFkyBAAQMOGDXH79m0EBwerz64AOc/0TJgwAVWqVMHy5cvh6uoKa2trfPnll1CpVJg5cyZKlCgBJycnTJkyRSPXTz/9BF9fX1hZWcHFxQUDBw5EcnIyAODw4cPo1asXEhMT1c89YcIEAMCLFy8watQolCpVClZWVqhZsyYOHz6sse+VK1fC1dUVlpaW6NChA+Li4vL0WmWPPP3qzdzcXOO4d+7cCW9vb1haWuLTTz9FSkoKVq1aBXd3dzg4OGDw4MFQqVTqfbq7u+OHH35A165dYW1tjZIlS+LXX399Y44LFy6gUaNGsLCwQJEiRfC///1P/docOXIEJiYmiI2N1XjMiBEj0KBBA/X9Y8eOoUGDBrCwsICLiwuGDBmClJQU9fpHjx6hTZs2sLCwgIeHB9atW5en1+hdsVAiIiK9sWXLFsyZMweLFy/G9evXsX37dvj6+gIAtm7ditKlS2PSpEmIiYlBTEyM5H6ioqLwzz//YPfu3Vi/fj2WL1+OVq1a4d69ewgJCcGMGTMwZswYnDhxQv0YpVKJX375BRcvXsSqVatw8OBBjBo1CgBQp04d/Pzzz7C1tVU/98iRIwEAvXr1wr///osNGzYgIiICn332GZo3b47r168DAE6ePInevXtj4MCBOHfuHAIDAzF58uQCeb1SU1Pxyy+/YMOGDdi9ezcOHz6Mjh07YteuXdi1axfWrFmDJUuWYMuWLRqP+/HHH+Hn54czZ85g9OjRCA4Oxr59+ySfo3nz5nBwcEBYWBg2b96M/fv346uvvgIANGjQAJ6enlizZo36MZmZmVi7di169eoF4GWh1axZM3Ts2BERERHYuHEjQkND1fsAXl6WvXXrFg4ePIgtW7ZgwYIF6pG3C5XQY4mJiQKASExMlDsKEZFOef78ubh8+bJ4/vy5xnLg/d601bNnT2FkZCSsrKw0bpMmTRJCCDF79mxRrlw58eLFi1wf7+bmJubMmaOxbMWKFcLOzk59f/z48cLS0lIkJSWplzVr1ky4u7sLlUqlXubt7S2mTZsmmXXTpk2iSJEiks8jhBA3btwQCoVC3L9/X2N548aNxejRo4UQQnTp0kU0b95cY33nzp1z7Ot1AIS5uXmO1yoqKkqdB4C4ceOG+jH9+/cXlpaW4tmzZxrH3r9/f/V9Nze3XPO0aNFC47m3bdsmhBBiyZIlwsHBQSQnJ6vX//3330KpVIrY2FghhBAzZswQFSpUUK/fvn27sLa2Vj+me/fu4n//+5/Gcx49elQolUrx/PlzERkZKQCIEydOqNdfuXJFAMjx//0qqb8DIfJeQ7CNEhER6ZTAwEAsXLhQY5mjoyMA4LPPPsPPP/8MT09PNG/eHC1btkSbNm1gbKzdx5m7uztsbGzU94sXLw4jIyMolUqNZa+esTh06BCmTp2Ky5cvIykpCZmZmUhLS0NKSgqsrKxyfZ4zZ85ACIFy5cppLE9PT1dPLXPlyhV06NBBY33t2rWxe/futx7HnDlz0KRJE41lLi4u6p8tLS1RpkwZjWNyd3eHtbW15HFmP//r96V6l125cgWVK1fWeA3q1q2LrKwsREZGonjx4ggKClKfoatVqxaWL1+OTp06qR9z+vRp3LhxQ+NymhACWVlZiI6OxrVr12BsbIxq1aqp15cvX/69tMdioURERDrFysoKXl5eua5zcXFBZGQk9u3bh/3792PgwIH48ccfERISAhMTkzw/x+vbKhSKXJdlZWUBAG7fvo2WLVtiwIAB+OGHH+Do6IjQ0FD06dPnjdNjZGVlwcjICKdPn84x11h2sfLyBE3+lChRQvK1ArQ/zjeRGuVaCCG5Lnu5k5MT2rRpgxUrVsDT0xO7du3SaKeVlZWF/v37q9ubvcrV1RWRkZFvzFCYPshC6X2+zu/w+09ERLmwsLBA27Zt0bZtWwwaNAjly5fHhQsXULVqVZiammo0TC4o4eHhyMzMxOzZs9VnnTZt2qSxTW7P7e/vD5VKhUePHqF+/fq57tvHx0ejLRSAHPfft9zylC9fPtdtfXx8sGrVKo0za//++y+USqXGmbS+ffvi888/R+nSpVGmTBnUrVtXva5q1aq4dOmSZNFXoUIFZGZmIjw8HDVq1AAAREZGIiEh4V0OM0/YmJuIiHRKeno6YmNjNW5PnjwB8LIn12+//YaLFy/i5s2bWLNmDSwsLODm5gbg5SW1I0eO4P79++rHFIQyZcogMzMTv/76q/p5Fy1apLGNu7s7kpOTceDAATx58gSpqakoV64cunXrhh49emDr1q2Ijo5GWFgYZsyYgV27dgEAhgwZgt27d2PmzJm4du0a5s2bl6fLbgCQkJCQ47V6tadYfv3777/qPPPnz8fmzZsxdOjQXLft1q0bzM3N0bNnT1y8eBGHDh3C4MGD0b17dxQvXly9XbNmzWBnZ4fJkyerG3Fn++abb3D8+HEMGjQI586dw/Xr17Fjxw4MHjwYAODt7Y3mzZujX79+OHnyJE6fPo2+ffu+l2EfWCgREX1A3ndz7vzYvXs3nJ2dNW716tUD8LI7/NKlS1G3bl34+fnhwIED+Ouvv9TtfSZNmoRbt26hTJkyKFasWEG9bKhSpQp++uknzJgxA5UqVcK6deswbdo0jW3q1KmDAQMGoHPnzihWrBhmzpwJ4OVYRz169MCIESPg7e2Ntm3b4uTJk+q2RLVq1cKyZcvw66+/okqVKti7dy/GjBmTp1y9evXK8Vq9rSt/XowYMQKnT5+Gv78/fvjhB8yePRvNmjXLdVtLS0vs2bMH8fHxqF69Oj799FM0btwY8+bN09hOqVQiKCgIKpUKPXr00Fjn5+eHkJAQXL9+HfXr14e/vz/Gjh0LZ2dn9TYrVqyAi4sLAgIC0LFjR/zvf/+Dk5PTOx/r2yjEu1wclVlSUhLs7OyQmJgIW1vbPD+Ol96IyNClpaUhOjoaHh4e6nF1iPLC3d0dw4YNw7Bhwwp83/369cPDhw+xY8eOAt93bt70d5DXGuKDbKNERERE709iYiLCwsKwbt06/Pnnn3LH0QoLJSIiIipU7dq1w6lTp9C/f380bdpU7jhaYaFEREREardu3Srwfb4+ZYs+YWNuIiIiIgkslIiIDFheBhIkMlQF8fvPS29ERAbI1NQUSqUSDx48QLFixWBqairLqMZEchBC4MWLF3j8+DGUSiVMTU3zvS8WSkREBkipVMLDwwMxMTF48OCB3HGIZGFpaQlXV1eNOfy0xUKJiMhAmZqawtXVFZmZmYUyrQeRLjMyMoKxsfE7n0lloUREZMCyJ0HVZsJYIvoPG3MTERERSWChRERERCSBhRIRERGRBBZKRERERBJYKBERERFJYKFEREREJIGFEhEREZEEFkpEREREElgoEREREUlgoUREREQkgYUSERERkQQWSkREREQSWCgRERERSWChRERERCSBhRIRERGRBBZKRERERBJYKBERERFJYKFEREREJIGFEhEREZEEFkpEREREElgoEREREUlgoUREREQkgYUSERERkQQWSkREREQSWCgRERERSWChRERERCSBhRIRERGRBBZKRERERBJkLZQmTJgAhUKhcStRooSckYiIiIjUjOUOULFiRezfv19938jISMY0RERERP+RvVAyNjbO81mk9PR0pKenq+8nJSUVViwiIiIi+dsoXb9+HSVLloSHhwc+//xz3Lx5U3LbadOmwc7OTn1zcXF5j0mJiIjoQ6MQQgi5nvyff/5BamoqypUrh4cPH2Ly5Mm4evUqLl26hCJFiuTYPrczSi4uLkhMTIStrW2en1ehKJD4eSLfq0tERERSkpKSYGdn99YaQtZC6XUpKSkoU6YMRo0aheHDh791+7we5OtYKBEREX3Y8lpDyH7p7VVWVlbw9fXF9evX5Y5CREREpFuFUnp6Oq5cuQJnZ2e5oxARERHJWyiNHDkSISEhiI6OxsmTJ/Hpp58iKSkJPXv2lDMWEREREQCZhwe4d+8eunTpgidPnqBYsWKoVasWTpw4ATc3NzljEREREQGQuVDasGGDnE9PRERE9EY61UaJiIiISJewUCIiIiKSwEKJiIiISAILJSIiIiIJLJSIiIiIJLBQIiIiIpLAQomIiIhIAgslIiIiIgkslIiIiIgksFAiIiIiksBCiYiIiEgCCyUiIiIiCSyUiIiIiCSwUCIiIiKSwEKJiIiISAILJSIiIiIJLJSIiIiIJLBQIiIiIpLAQomIiIhIAgslIiIiIgkslIiIiIgksFAiIiIiksBCiYiIiEgCCyUiIiIiCSyUiIiIiCSwUCIiIiKSwEKJiIiISAILJSIiIiIJLJSIiIiIJLBQIiIiIpLAQomIiIhIAgslIiIiIgkslIiIiIgksFAiIiIiksBCiYiIiEgCCyUiIiIiCSyUiIiIiCSwUCIiIiKSwEKJiIiISAILJSIiIiIJLJSIiIiIJLBQIiIiIpLAQomIiIhIAgslIiIiIgkslIiIiIgksFAiIiIiksBCiYiIiEgCCyUiIiIiCSyUiIiIiCSwUCIiIiKSYJyXjfz9/aFQKPK0wzNnzrxTICIiIiJdkadCqX379uqf09LSsGDBAvj4+KB27doAgBMnTuDSpUsYOHBgoYQkIiIikkOeCqXx48erf+7bty+GDBmCH374Icc2d+/eLdh0RERERDJSCCGENg+ws7NDeHg4ypYtq7H8+vXrqFatGhITEws04JskJSXBzs4OiYmJsLW1zfPj8ngVsUBo9+oSERHR+5DXGkLrxtwWFhYIDQ3NsTw0NBTm5uba7o6IiIhIZ+Xp0turhg0bhi+//BKnT59GrVq1ALxso7R8+XKMGzeuwAMSERERyUXrQunbb7+Fp6cn5s6di99//x0AUKFCBaxcuRKdOnUq8IBEREREctG6jZIuYRslIiIiyo9Ca6MEAAkJCVi2bBm+++47xMfHA3g5ftL9+/fzl5aIiIhIB2l96S0iIgJNmjSBnZ0dbt26hb59+8LR0RHbtm3D7du3sXr16sLISURERPTeaX1Gafjw4QgKCsL169c1erm1aNECR44cKdBwRERERHLSulAKCwtD//79cywvVaoUYmNjCyQUERERkS7QulAyNzdHUlJSjuWRkZEoVqxYgYQiIiIi0gVaF0rt2rXDpEmTkJGRAQBQKBS4c+cOvv32W3zyyScFHpCIiIhILloXSrNmzcLjx4/h5OSE58+fIyAgAF5eXrCxscGUKVMKIyMRERGRLLTu9WZra4vQ0FAcPHgQZ86cQVZWFqpWrYomTZoURj4iIiIi2Wg94GR0dDQ8PDwKK49WOOAkERER5UehDTjp5eWFwMBArF27Fmlpae8UkoiIiEiXaV0onT9/Hv7+/hgxYgRKlCiB/v374+TJk+8cZNq0aVAoFBg2bNg774uIiIioIGhdKFWqVAk//fQT7t+/jxUrViA2Nhb169dHxYoV8dNPP+Hx48dahwgLC8OSJUvg5+en9WOJiIiICku+5noDAGNjY3To0AGbNm3CjBkzEBUVhZEjR6J06dLo0aMHYmJi8rSf5ORkdOvWDUuXLoWDg8Mbt01PT0dSUpLGjYiIiKiw5LtQCg8Px8CBA+Hs7IyffvoJI0eORFRUFA4ePIj79++jXbt2edrPoEGD0KpVqzz1mps2bRrs7OzUNxcXl/zGJyIiInorrYcH+Omnn7BixQpERkaiZcuWWL16NVq2bAml8mXN5eHhgcWLF6N8+fJv3deGDRtw5swZhIWF5em5R48ejeHDh6vvJyUlsVgiIiKiQqN1obRw4UL07t0bvXr1QokSJXLdxtXVFb/99tsb93P37l0MHToUe/fu1Zhc903MzMxgZmambWQiIiKifNF6HKWCsn37dnTo0AFGRkbqZSqVCgqFAkqlEunp6RrrcsNxlIiIiCg/8lpDaH1GCQASEhLw22+/4cqVK1AoFKhQoQL69OkDOzu7PO+jcePGuHDhgsayXr16oXz58vjmm2/eWiQRERERFTatC6Xw8HA0a9YMFhYWqFGjBoQQmDNnDqZOnYq9e/eiatWqedqPjY0NKlWqpLHMysoKRYoUybGciIiISA5aF0rBwcFo27Ytli5dCmPjlw/PzMxE3759MWzYMBw5cqTAQxIRERHJQes2ShYWFjh79myOXm2XL19GtWrVkJqaWqAB34RtlP5jiMdERERUWAptrjdbW1vcuXMnx/K7d+/CxsZG290RERER6SytC6XOnTujT58+2LhxI+7evYt79+5hw4YN6Nu3L7p06VIYGYmIiIhkoXUbpVmzZkGhUKBHjx7IzMwEAJiYmODLL7/E9OnTCzwgERERkVzyPY5SamoqoqKiIISAl5cXLC0tCzrbW7GN0n8M8ZiIiIgKS6GOowQAlpaW8PX1ze/DiYiIiHRengqljh075nmHW7duzXcYIiIiIl2Sp0JJmxG3iYiIiAxFngqlFStWFHYOIiIiIp2T7zZKjx49QmRkJBQKBcqVKwcnJ6eCzEVEREQkO63HUUpKSkL37t1RqlQpBAQEoEGDBihVqhS++OILJCYmFkZGIiIiIlloXSj17dsXJ0+exM6dO5GQkIDExETs3LkT4eHh6NevX2FkJCIiIpKF1uMoWVlZYc+ePahXr57G8qNHj6J58+ZISUkp0IBvwnGU/mOIx0RERFRYCm2utyJFiuTaC87Ozg4ODg7a7o6IiIhIZ2ldKI0ZMwbDhw9HTEyMellsbCy+/vprjB07tkDDEREREclJ60tv/v7+uHHjBtLT0+Hq6goAuHPnDszMzFC2bFmNbc+cOVNwSXPBS2//McRjIiIiKiyFNoVJ+/bt3yUXERERkd7I96S4uoBnlP5jiMdERERUWAp9UlwASE5ORlZWlsYybQoWIiIiIl2mdWPu6OhotGrVClZWVuqebg4ODrC3t2evNyIiIjIoWp9R6tatGwBg+fLlKF68OBTv85oPERER0XukdaEUERGB06dPw9vbuzDyEBEREekMrS+9Va9eHXfv3i2MLEREREQ6ReszSsuWLcOAAQNw//59VKpUCSYmJhrr/fz8CiwcERERkZy0LpQeP36MqKgo9OrVS71MoVBACAGFQgGVSlWgAYmIiIjkonWh1Lt3b/j7+2P9+vVszE1EREQGTetC6fbt29ixYwe8vLwKIw8RERGRztC6MXejRo1w/vz5wshCREREpFO0PqPUpk0bBAcH48KFC/D19c3RmLtt27YFFo6IiIhITlrP9aZUSp+Eet+NuTnX238M8ZiIiIgKS6HN9fb63G5EREREhkrrNkpEREREH4o8F0otW7ZEYmKi+v6UKVOQkJCgvh8XFwcfH58CDUdEREQkpzwXSnv27EF6err6/owZMxAfH6++n5mZicjIyIJNR0RERCSjPBdKr7f51rINOBEREZHeYRslIiIiIgl5LpQUCkWO6Uo4fQkREREZsjwPDyCEQFBQEMzMzAAAaWlpGDBgAKysrABAo/0SERERkSHIc6HUs2dPjftffPFFjm169Ojx7omIiIiIdESeC6UVK1YUZg4iIiIincPG3EREREQSWCgRERERSWChRERERCSBhRIRERGRBBZKRERERBLy1Ottx44ded5h27Zt8x2GiIiISJfkqVBq3769xn2FQqEx19urI3SrVKqCSUYfvPc18DunLSQiIil5uvSWlZWlvu3duxdVqlTBP//8g4SEBCQmJmLXrl2oWrUqdu/eXdh5iYiIiN6bPA84mW3YsGFYtGgR6tWrp17WrFkzWFpa4n//+x+uXLlSoAGJiIiI5KJ1Y+6oqCjY2dnlWG5nZ4dbt24VRCYiIiIinaB1oVS9enUMGzYMMTEx6mWxsbEYMWIEatSoUaDhiIiIiOSkdaG0fPlyPHr0CG5ubvDy8oKXlxdcXV0RExOD3377rTAyEhEREclC6zZKXl5eiIiIwL59+3D16lUIIeDj44MmTZpo9H4jIiIi0ncKIfLfOTotLQ1mZmayFUhJSUmws7NDYmIibG1t8/y49xn3fXU95zHlH4cHICL68OS1htD60ltWVhZ++OEHlCpVCtbW1oiOjgYAjB07lpfeiIiIyKBoXShNnjwZK1euxMyZM2Fqaqpe7uvri2XLlhVoOCIiIiI5aV0orV69GkuWLEG3bt1gZGSkXu7n54erV68WaDgiIiIiOWldKN2/fx9eXl45lmdlZSEjI6NAQhERERHpAq0LpYoVK+Lo0aM5lm/evBn+/v4FEoqIiIhIF2g9PMD48ePRvXt33L9/H1lZWdi6dSsiIyOxevVq7Ny5szAyEhEREclC6zNKbdq0wcaNG7Fr1y4oFAqMGzcOV65cwV9//YWmTZsWRkYiIiIiWbzTOEpy4zhK/+Ex5Z/+/gUQEVF+Fdo4Sp6enoiLi8uxPCEhAZ6entrujoiIiEhnaV0o3bp1CyqVKsfy9PR03L9/v0BCEREREemCPDfm3rFjh/rnPXv2wM7OTn1fpVLhwIEDcHd3L9BwRERERHLKc6HUvn17AIBCoUDPnj011pmYmMDd3R2zZ88u0HBEREREcspzoZSVlQUA8PDwQFhYGIoWLVpooYiIiIh0gdbjKGVPgktERERk6LQulAAgJSUFISEhuHPnDl68eKGxbsiQIQUSjIiIiEhuWhdKZ8+eRcuWLZGamoqUlBQ4OjriyZMnsLS0hJOTk1aF0sKFC7Fw4ULcunULwMvpUcaNG4cWLVpoG4uIiIiowGk9PEBwcDDatGmD+Ph4WFhY4MSJE7h9+zY++ugjzJo1S6t9lS5dGtOnT0d4eDjCw8PRqFEjtGvXDpcuXdI2FhEREVGB03pkbnt7e5w8eRLe3t6wt7fH8ePHUaFCBZw8eRI9e/bE1atX3ymQo6MjfvzxR/Tp0+et23Jk7v/wmPKPI3MTEX14Cm1kbhMTEyj+/xOsePHiuHPnDgDAzs5O/XN+qFQqbNiwASkpKahdu3au26SnpyMpKUnjRkRERFRYtG6j5O/vj/DwcJQrVw6BgYEYN24cnjx5gjVr1sDX11frABcuXEDt2rWRlpYGa2trbNu2DT4+PrluO23aNEycOFHr5yAiIiLKD60vvYWHh+PZs2cIDAzE48eP0bNnT4SGhsLLywsrVqxA5cqVtQrw4sUL3LlzBwkJCfjjjz+wbNkyhISE5FospaenIz09XX0/KSkJLi4uvPQGHtO74KU3IqIPT14vvWldKBW2Jk2aoEyZMli8ePFbt2Ubpf/wmPJPt/4CiIjofSi0NkqFTQihcdaIiIiISC5at1GKi4vDuHHjcOjQITx69Eg9tUm2+Pj4PO/ru+++Q4sWLeDi4oJnz55hw4YNOHz4MHbv3q1tLCIiIqICp3Wh9MUXXyAqKgp9+vRB8eLF1T3g8uPhw4fo3r07YmJiYGdnBz8/P+zevRtNmzbN9z6JiIiICorWbZRsbGwQGhqqdaPtwsA2Sv/hMeUf2ygREX14Cq2NUvny5fH8+fN3CkdERESkD7QulBYsWIDvv/8eISEhiIuL4wCQREREZLC0bqNkb2+PxMRENGrUSGO5EAIKhQIqlarAwhERERHJSetCqVu3bjA1NcXvv//+zo25iYiIiHSZ1oXSxYsXcfbsWXh7exdGHiIiIiKdoXUbpWrVquHu3buFkYWIiIhIp2h9Rmnw4MEYOnQovv76a/j6+sLExERjvZ+fX4GFIyIiIpKT1uMoKZU5T0IpFApZGnNzHKX/8Jjyj+MoERF9ePJaQ2h9Rik6OvqdghERERHpC60LJTc3t8LIQURERKRz8lQo7dixAy1atICJiQl27Njxxm3btm1bIMGIiIiI5JanNkpKpRKxsbFwcnLKtY2Semdso5SDobXnAQzvmNhGiYjow1OgbZSysrJy/ZmIiIjIkGk9jtLq1auRnp6eY/mLFy+wevXqAglFREREpAu0Hh7AyMgIMTExcHJy0lgeFxcHJycnXnp7jaFdpgIM75h46Y2I6MOT1xpC6zNK2eMlve7evXuws7PTdndEREREOivPwwP4+/tDoVBAoVCgcePGMDb+76EqlQrR0dFo3rx5oYQkIiIikkOeC6X27dsDAM6dO4dmzZrB2tpavc7U1BTu7u745JNPCjwgERERkVzyXCiNHz8eAODu7o7PP/8cZmZmhRaKiIiISBdo3UapUaNGePz4sfr+qVOnMGzYMCxZsqRAgxERERHJTetCqWvXrjh06BAAIDY2Fk2aNMGpU6fw3XffYdKkSQUekIiIiEguWhdKFy9eRI0aNQAAmzZtgq+vL44dO4bff/8dK1euLOh8RERERLLRulDKyMhQt0/av3+/em638uXLIyYmpmDTEREREclI60KpYsWKWLRoEY4ePYp9+/aphwR48OABihQpUuABiYiIiOSidaE0Y8YMLF68GA0bNkSXLl1QuXJlAMCOHTvUl+SIiIiIDIHWU5gALweYTEpKgoODg3rZrVu3YGlpmWNqk8LEKUz+w2PKP05hQkT04SnwKUwePXqk/tnIyEijSAKA0qVL49atW9onJSIiItJReS6UnJ2dNYqlChUq4M6dO+r7cXFxqF27dsGmIyIiIpJRngul16/Q3bt3D5mZmW/choiIiEifad2Y+00U77OhDBEREVEhK9BCiYiIiMiQ5HlSXIVCgWfPnsHc3BxCCCgUCiQnJyMpKQkA1P8SERERGYo8F0pCCJQrV07jvr+/v8Z9XnojIiIiQ5LnQil7IlwiIiKiD0WeC6WAgIDCzEFERESkc9iYm4iIiEgCCyUiIiIiCSyUiIiIiCTkqVCKiIhAVlZWYWchIiIi0il5KpT8/f3x5MkTAICnpyfi4uIKNRQRERGRLshToWRvb4/o6GgAwK1bt3h2iYiIiD4IeRoe4JNPPkFAQACcnZ2hUChQrVo1GBkZ5brtzZs3CzQgERERkVzyVCgtWbIEHTt2xI0bNzBkyBD069cPNjY2hZ2NiIiISFZ5HnCyefPmAIDTp09j6NChLJSI8uF9zfIjxPt5HiIiQ5fnQinbihUr1D/fu3cPCoUCpUqVKtBQRERERLpA63GUsrKyMGnSJNjZ2cHNzQ2urq6wt7fHDz/8wEbeREREZFC0PqP0/fff47fffsP06dNRt25dCCHw77//YsKECUhLS8OUKVMKIycRERHRe6cQQrvWDCVLlsSiRYvQtm1bjeV//vknBg4ciPv37xdowDdJSkqCnZ0dEhMTYWtrm+fHva92IsD7ayvCY8q/99mexxCPiYhIH+W1htD60lt8fDzKly+fY3n58uURHx+v7e6IiIiIdJbWhVLlypUxb968HMvnzZuHypUrF0goIiIiIl2gdRulmTNnolWrVti/fz9q164NhUKBY8eO4e7du9i1a1dhZCQiHcbLiURkyLQ+oxQQEIBr166hQ4cOSEhIQHx8PDp27IjIyEjUr1+/MDISERERyULrxty6hI25/8Njyj825n43hnhMRGT48lpDaH3pjYjI0LH4I6JsWl96IyIiIvpQsFAiIiIikqBVoSSEwO3bt/H8+fPCykNERAVMoXh/NyJDo3WhVLZsWdy7d6+w8hARERHpDK0KJaVSibJlyyIuLq6w8hARERHpDK3bKM2cORNff/01Ll68WBh5iIiI3oqXE+l90Xp4gC+++AKpqamoXLkyTE1NYWFhobGe870RERGRodC6UPr5558LIQYRERGR7tG6UOrZs2dh5CAiIiLSOfkaRykqKgpjxoxBly5d8OjRIwDA7t27cenSpQINR0RERCQnrQulkJAQ+Pr64uTJk9i6dSuSk5MBABERERg/fnyBByQiIiKSi9aF0rfffovJkydj3759MDU1VS8PDAzE8ePHCzQcERERkZy0LpQuXLiADh065FherFgxjq9EREREBkXrQsne3h4xMTE5lp89exalSpUqkFBEREREukDrQqlr16745ptvEBsbC4VCgaysLPz7778YOXIkevToodW+pk2bhurVq8PGxgZOTk5o3749IiMjtY1EREREVCi0LpSmTJkCV1dXlCpVCsnJyfDx8UGDBg1Qp04djBkzRqt9hYSEYNCgQThx4gT27duHzMxMfPzxx0hJSdE2FhEREVGBUwghRH4eGBUVhbNnzyIrKwv+/v4oW7bsO4d5/PgxnJycEBISggYNGrx1+6SkJNjZ2SExMRG2trZ5fp73OSR9/l5d7fGY8u99HQ/AY3oXPKb84/vDu3mfv3v0/uS1htB6wMlsZcqUgaenJwBAUUC/sYmJiQAAR0fHXNenp6cjPT1dfT8pKalAnpeIiIgoN/kacPK3335DpUqVYG5uDnNzc1SqVAnLli17pyBCCAwfPhz16tVDpUqVct1m2rRpsLOzU99cXFze6TmJiIiI3kTrM0pjx47FnDlzMHjwYNSuXRsAcPz4cQQHB+PWrVuYPHlyvoJ89dVXiIiIQGhoqOQ2o0ePxvDhw9X3k5KSWCwRERFRodG6jVLRokXx66+/okuXLhrL169fj8GDB+PJkydahxg8eDC2b9+OI0eOwMPDI8+PYxul//CY8o9tX94Njyn/DO1vCeAxvQtDPCZdVmhtlFQqFapVq5Zj+UcffYTMzEyt9iWEwODBg7Ft2zYcPnxYqyKJiIiIqLBp3Ubpiy++wMKFC3MsX7JkCbp166bVvgYNGoS1a9fi999/h42NDWJjYxEbG4vnz59rG4uIiIiowOXpjNKr7YIUCgWWLVuGvXv3olatWgCAEydO4O7du1oPOJldcDVs2FBj+YoVKxAUFKTVvoiIiIgKWp4KpbNnz2rc/+ijjwC8HEsJeDnPW7FixXDp0iWtnjyfQzgRERERvRd5KpQOHTpU2DmIiIiIdE6+xlEiIiIi+hBo3estLS0Nv/76Kw4dOoRHjx4hKytLY/2ZM2cKLBwRERGRnLQulHr37o19+/bh008/RY0aNQps+hIiIiIiXaN1ofT3339j165dqFu3bmHkISIiItIZWrdRKlWqFGxsbAojCxEREZFO0bpQmj17Nr755hvcvn27MPIQERER6QytL71Vq1YNaWlp8PT0hKWlJUxMTDTWx8fHF1g4IiIiIjlpXSh16dIF9+/fx9SpU1G8eHE25iYiIiKDpXWhdOzYMRw/fhyVK1cujDxEREREOkPrNkrly5fnpLVERET0QdC6UJo+fTpGjBiBw4cPIy4uDklJSRo3IiIiIkOhEFrOTKtUvqytXm+bJISAQqGASqUquHRvkZSUBDs7OyQmJsLW1jbPj3ufzare17y/PKb8e59zM/OY8o/HlH98f3g3PCbDlNcaQus2Spwgl4iIiD4UWhdKAQEBhZGDiIiISOdoXSgdOXLkjesbNGiQ7zBEREREukTrQqlhw4Y5lr3aXul9tlEiIiIiKkxa93p7+vSpxu3Ro0fYvXs3qlevjr179xZGRiIiIiJZaH1Gyc7OLseypk2bwszMDMHBwTh9+nSBBCMiIiKSm9ZnlKQUK1YMkZGRBbU7IiIiItlpfUYpIiJC474QAjExMZg+fTqnNSEiIiKDonWhVKVKFSgUCrw+TmWtWrWwfPnyAgtGREREJDetC6Xo6GiN+0qlEsWKFYO5uXmBhSIiIiLSBVoXSm5uboWRg4iIiAyMIUwHpHWhBAAHDhzAgQMH8OjRI2RlZWms4+U3IiIiMhRaF0oTJ07EpEmTUK1aNTg7O+eYHJeIiIjIUGhdKC1atAgrV65E9+7dCyMPERERkc7QehylFy9eoE6dOoWRhYiIiEinaF0o9e3bF7///nthZCEiIiLSKVpfektLS8OSJUuwf/9++Pn5wcTERGP9Tz/9VGDhiIiIiOSUr5G5q1SpAgC4ePGixjo27CYiIiJDonWhdOjQocLIQURERKRzCmxSXCIiIiJDw0KJiIiISAILJSIiIiIJLJSIiIiIJLBQIiIiIpLAQomIiIhIAgslIiIiIgkslIiIiIgksFAiIiIiksBCiYiIiEgCCyUiIiIiCSyUiIiIiCSwUCIiIiKSwEKJiIiISAILJSIiIiIJLJSIiIiIJLBQIiIiIpLAQomIiIhIAgslIiIiIgkslIiIiIgksFAiIiIiksBCiYiIiEgCCyUiIiIiCSyUiIiIiCSwUCIiIiKSwEKJiIiISAILJSIiIiIJLJSIiIiIJLBQIiIiIpLAQomIiIhIAgslIiIiIgkslIiIiIgksFAiIiIiksBCiYiIiEgCCyUiIiIiCSyUiIiIiCSwUCIiIiKSIGuhdOTIEbRp0wYlS5aEQqHA9u3b5YxDREREpEHWQiklJQWVK1fGvHnz5IxBRERElCtjOZ+8RYsWaNGiRZ63T09PR3p6uvp+UlJSYcQiIiIiAqBnbZSmTZsGOzs79c3FxUXuSERERGTA9KpQGj16NBITE9W3u3fvyh2JiIiIDJisl960ZWZmBjMzM7ljEBER0QdCr84oEREREb1PLJSIiIiIJMh66S05ORk3btxQ34+Ojsa5c+fg6OgIV1dXGZMRERERyVwohYeHIzAwUH1/+PDhAICePXti5cqVMqUiIiIieknWQqlhw4YQQsgZgYiIiEgS2ygRERERSWChRERERCSBhRIRERGRBBZKRERERBJYKBERERFJYKFEREREJIGFEhEREZEEFkpEREREElgoEREREUlgoUREREQkgYUSERERkQQWSkREREQSWCgRERERSWChRERERCSBhRIRERGRBBZKRERERBJYKBERERFJYKFEREREJIGFEhEREZEEFkpEREREElgoEREREUlgoUREREQkgYUSERERkQQWSkREREQSWCgRERERSWChRERERCSBhRIRERGRBBZKRERERBJYKBERERFJYKFEREREJIGFEhEREZEEFkpEREREElgoEREREUlgoUREREQkgYUSERERkQQWSkREREQSWCgRERERSWChRERERCSBhRIRERGRBBZKRERERBJYKBERERFJYKFEREREJIGFEhEREZEEFkpEREREElgoEREREUlgoUREREQkgYUSERERkQQWSkREREQSWCgRERERSWChRERERCSBhRIRERGRBBZKRERERBJYKBERERFJYKFEREREJIGFEhEREZEEFkpEREREElgoEREREUlgoUREREQkgYUSERERkQQWSkREREQSWCgRERERSWChRERERCSBhRIRERGRBBZKRERERBJYKBERERFJYKFEREREJIGFEhEREZEEFkpEREREEmQvlBYsWAAPDw+Ym5vjo48+wtGjR+WORERERARA5kJp48aNGDZsGL7//nucPXsW9evXR4sWLXDnzh05YxEREREBABRCCCHXk9esWRNVq1bFwoUL1csqVKiA9u3bY9q0aTm2T09PR3p6uvp+YmIiXF1dcffuXdja2ub5ee3s3i23NhIT38/z8Jjy730dD8Bjehc8pvzj+8O74THlny7/LSUlJcHFxQUJCQmwe1NQIZP09HRhZGQktm7dqrF8yJAhokGDBrk+Zvz48QIAb7zxxhtvvPHGW4Hc7t69+8Z6xRgyefLkCVQqFYoXL66xvHjx4oiNjc31MaNHj8bw4cPV97OyshAfH48iRYpAoVAUat7sylPbs1e6ytCOB+Ax6Qsek34wtGMytOMBeEzvSgiBZ8+eoWTJkm/cTrZCKdvrBY4QQrLoMTMzg5mZmcYye3v7woqWK1tbW4P5hQQM73gAHpO+4DHpB0M7JkM7HoDH9C7eeMnt/8nWmLto0aIwMjLKcfbo0aNHOc4yEREREclBtkLJ1NQUH330Efbt26exfN++fahTp45MqYiIiIj+I+ult+HDh6N79+6oVq0aateujSVLluDOnTsYMGCAnLFyZWZmhvHjx+e49KevDO14AB6TvuAx6QdDOyZDOx6Ax/S+yDo8APBywMmZM2ciJiYGlSpVwpw5c9CgQQM5IxEREREB0IFCiYiIiEhXyT6FCREREZGuYqFEREREJIGFEhEREZEEFkpEREREEmQfmZuISB9lZWXhxo0bePToEbKysjTWsecukeFgoSRBpVJh5cqVOHDgQK5vhAcPHpQp2Ydtx44ded62bdu2hZiEtNG7d2/MnTsXNjY2GstTUlIwePBgLF++XKZk+XPixAl07doVt2/fxusdhxUKBVQqlUzJ8k+pVL5xzkx9PCbSD/fu3cOOHTtw584dvHjxQmPdTz/9JFOq/3B4AAlfffUVVq5ciVatWsHZ2TnHG8icOXNkSvZhUyrzdrVYXz+sMjIy8L///Q9jx46Fp6en3HEKjJGREWJiYuDk5KSx/MmTJyhRogQyMzNlSpY/VapUQbly5TBx4sRc3x/yMn+Urvnzzz817mdkZODs2bNYtWoVJk6ciD59+siUjAzZgQMH0LZtW3h4eCAyMhKVKlXCrVu3IIRA1apVdeKkBAslCUWLFsXq1avRsmVLuaMUuC1btmDTpk25Vu9nzpyRKRVls7e3x5kzZwyiUEpKSoIQAg4ODrh+/TqKFSumXqdSqfDXX3/h22+/xYMHD2RMqT0rKyucP38eXl5eckcpdL///js2btyYo5DSJ2FhYdi8eXOu73lbt26VKdW7SUhIwKlTp3K94tGjRw+ZUmmvRo0aaN68OSZNmgQbGxucP38eTk5O6NatG5o3b44vv/xS7oiAoFw5OzuLyMhIuWMUuLlz5wpra2sxaNAgYWpqKvr37y+aNGki7OzsxHfffSd3PBJCBAUFidmzZ8sdo0AoFAqhVColb0ZGRmLy5Mlyx9RaYGCg+Oeff+SO8V7cuHFDWFpayh0j39avXy9MTExEq1athKmpqWjdurXw9vYWdnZ2IigoSO54+bJjxw5hY2MjlEqlsLOzE/b29uqbg4OD3PG0Ym1tLW7cuCGEEMLe3l5cvHhRCCHEuXPnhJubm4zJ/sM2ShJGjBiBuXPnYt68eW+8bq9vFixYgCVLlqBLly5YtWoVRo0aBU9PT4wbNw7x8fFyx9NaSkoKQkJCcv2mOGTIEJlSvRsvLy/88MMPOHbsGD766CNYWVlprNen4zp06BCEEGjUqBH++OMPODo6qteZmprCzc0NJUuWlDFh3kVERKh/Hjx4MEaMGIHY2Fj4+vrCxMREY1s/P7/3Ha9QPH/+HL/++itKly4td5R8mzp1KubMmYNBgwbBxsYGc+fOhYeHB/r37w9nZ2e54+XLiBEj0Lt3b0ydOhWWlpZyx3knVlZWSE9PBwCULFkSUVFRqFixIoCXl+Z1AS+9vaJjx44a9w8ePAhHR0dUrFgxxxuhvp6utbS0xJUrV+Dm5gYnJyfs27cPlStXxvXr11GrVi3ExcXJHTHPzp49i5YtWyI1NRUpKSlwdHTEkydPYGlpCScnJ9y8eVPuiPni4eEhuU6hUOjlcd2+fRsuLi55bmOmi7IbO0u9ZWav09f2cQ4ODhpfCoUQePbsGSwtLbF27Vq97RxhZWWFS5cuwd3dHUWLFsWhQ4fg6+uLK1euoFGjRoiJiZE7otasrKxw4cIFg7g83759e7Rq1Qr9+vXDqFGjsG3bNgQFBWHr1q1wcHDA/v375Y7IXm+ver0BZocOHWRKUnhKlCiBuLg4uLm5wc3NDSdOnEDlypURHR0t+QGgq4KDg9GmTRssXLgQ9vb2OHHiBExMTPDFF19g6NChcsfLt+joaLkjFDg3NzcAQGpqaq5n//ThDIwh/r+8as6cORqFklKpRLFixVCzZk04ODjImOzdODo64tmzZwCAUqVK4eLFi/D19UVCQgJSU1NlTpc/zZo1Q3h4uEEUSj/99BOSk5MBABMmTEBycjI2btwILy8vnek0xTNKH5i+ffvCxcUF48ePx6JFizB8+HDUrVsX4eHh6NixI3777Te5I+aZvb09Tp48CW9vb9jb2+P48eOoUKECTp48iZ49e+Lq1atyR6T/9/jxY/Tq1Qv//PNPruv18QwM6YeuXbuiWrVqGD58OKZMmYK5c+eiXbt22LdvH6pWraqXVwd+++03TJo0Cb169cr10q++nv3TVSyUJDx//hxCCPX139u3b2Pbtm3w8fHBxx9/LHO6/MvKykJWVhaMjV+eTNy0aRNCQ0Ph5eWFAQMGwNTUVOaEeVesWDH8+++/KFeuHLy9vfHLL7+gWbNmuHr1KqpWraq33xYB3R9XRFvdunXDrVu38PPPPyMwMBDbtm3Dw4cPMXnyZMyePRutWrWSO6JWVq1ahaJFi6pzjxo1CkuWLIGPjw/Wr1+vPoOm615td/U2+nDWLzfx8fFIS0tDyZIlkZWVhVmzZqnf88aOHauXZ8vedAlbXy/9vnjxItcefK6urjIleoU8bch1X9OmTcXChQuFEEI8ffpUODk5idKlSwtzc3OxYMECmdPlT0ZGhpgwYYK4c+eO3FEKRNOmTcW6deuEEEL0799f1KhRQ6xdu1Y0a9ZM1KhRQ+Z0+bd//35haWkpKlasKIyNjUWVKlWEvb29sLOzE4GBgXLHy5cSJUqIkydPCiGEsLGxUfco/fPPP0XdunXljJYv5cqVEwcOHBBCCHHs2DFhYWEhFi9eLNq0aSM6dOggc7q8y+6VqFAo3tpDUV89ffpUct3169ffXxDKVWRkpKhXr16O37fs30ddwEJJQpEiRdTdFJcuXSr8/PyESqUSmzZtEuXLl5c5Xf5ZWVmJ6OhouWMUiLCwMHHw4EEhhBCPHj0SLVq0EDY2NsLf31+cO3dO5nT5V716dTF27FghxMuus1FRUeLZs2eibdu2eluk29jYqH/v3NzcRGhoqBBCiJs3bwoLCwsZk+WPhYWFuH37thBCiFGjRonu3bsLIYS4ePGiKFq0qJzRtHLr1i31bdu2baJMmTJi0aJF4vz58+L8+fNi0aJFomzZsmLbtm1yR8232rVri+fPn+dYfvXqVVGqVCkZEtGr6tSpIxo0aCB27dolzp49K86dO6dx0wVszC0hNTVVPd3C3r170bFjRyiVStSqVQu3b9+WOV3+NWnSBIcPH0ZQUJDcUd5ZtWrV1D8XK1YMu3btkjFNwbly5QrWr18PADA2Nsbz589hbW2NSZMmoV27droxAJuWvL29ERkZCXd3d1SpUgWLFy+Gu7s7Fi1apJddtK2trREXFwdXV1fs3bsXwcHBAABzc3M8f/5c5nR59+olws8++wy//PKLxiC7fn5+cHFxwdixY9G+fXsZEr47BwcHtG/fHjt37lQ3Ocju8dapUyeZ0+WfoQyNcu7cOZw+fRrly5eXO4okFkoSvLy8sH37dnTo0AF79uxRvxE+evQItra2MqfLvxYtWmD06NG4ePFirmP0sBGg/PRhXBFtDRs2TN0Ne/z48WjWrBnWrVsHU1NTrFy5Ut5w+dC0aVP07dsX/v7+uHbtmrqtUnY3dH104cKFXIem8PDwwOXLl2VIVDD++OMPNG3aFF27dsXGjRtx6dIlNG7cGN26ddPL9n7A24dG0adCycfHR+ff19iYW8KWLVvQtWtXqFQqNGrUCPv27QMATJs2DUeOHJHsvaPrDKkRoL+/f66DgSoUCpibm8PLywtBQUEIDAyUIV3+6cO4Iu8qNTUVV69ehaurK4oWLSp3HK0lJCRgzJgxuHv3Lr788ks0b94cwMsi0NTUFN9//73MCbVXtWpVVKhQAb/99hvMzc0BAOnp6ejduzeuXLmi19MbJSYmomHDhihTpgyOHj2KHj164Mcff5Q7Vr41bNgQ5cqVUw+Ncv78eY2hUV4fE1CXHTx4EGPGjMHUqVNz7cGnCycmWCi9QWxsLGJiYlC5cmV1gXHq1CnY2trq9GnCD8Xo0aOxcOFC+Pr6okaNGhBCIDw8HBEREQgKCsLly5dx4MABbN26Fe3atZM7bp7dvHkTycnJ8PPzQ2pqKkaOHKnupTNnzhy96VGVLSMjA97e3ti5cyd8fHzkjkMSTp06hTZt2iArKwuVK1cGAJw/fx4KhQI7d+5EjRo1ZE6Yd0lJSTmWxcbGokmTJmjdujWmT5+uXq4LH8TaMqShUbI/W1//0it0aPBWFkpvcePGDURFRaFBgwawsLBQ/+fpq9WrV6Nz584wMzPTWP7ixQts2LBBryZT7NevH1xdXTF27FiN5ZMnT8bt27exdOlSjB8/Hn///TfCw8NlSknAy4H+9u/fjwoVKsgdpcAcPXoUixcvxs2bN7F582aUKlUKa9asgYeHB+rVqyd3vHxJTU3F2rVrcfXqVQgh4OPjg65du+a4RK/rskdRf132x52+j6JuSEOjhISEvHF9QEDAe0oijYWShLi4OHTq1AmHDh2CQqHA9evX4enpiT59+sDe3h6zZ8+WO2K+GBkZISYmBk5OThrL4+Li4OTkpFdvGnZ2djh9+nSOGdxv3LiBjz76CImJibh69SqqV6+uHplXn+j0uCJamj59Oq5evYply5apG9Tqsz/++APdu3dHt27dsGbNGly+fBmenp5YsGABdu7caTAdC/TV2z58X6ULH8Ta+vjjjxEUFISuXbtiwIABOHv2LIYMGYI1a9bg6dOnOHnypNwRDYr+v2MVkuDgYJiYmODOnTsa34I7d+6M4OBgvS2UpM6I3bt3L8cULrrO3Nwcx44dy1EoHTt2TN3GIisrK8fZM1137do19OnTB8eOHdNYrs/fgE+ePIkDBw5g79698PX1zXGGQt9GR548eTIWLVqEHj16YMOGDerlderUwaRJk2RM9u4uX76ca08qferooY/FjzamTp2q/vL3ww8/oGfPnvjyyy/h5eWFFStWyJwuf3R5eiMWShL27t2LPXv25Jg1u2zZsno5PEB2w2eFQoHGjRtrfKtXqVSIjo5WN0jVF4MHD8aAAQNw+vRpVK9eHQqFAqdOncKyZcvw3XffAQD27NkDf39/mZNqp1evXjA2NsbOnTvh7Oys15d6s9nb2+OTTz6RO0aBiYyMRIMGDXIst7W1RUJCwvsPVABu3ryJDh064MKFCxqT/2b//uljgf4qXf4g1pYhDY2iD9MbsVCSkJKSop6+5FVPnjzRuzMUANRjoJw7dw7NmjWDtbW1ep2pqSnc3d317oNszJgx8PDwwLx587BmzRoAL8frWbp0Kbp27QoAGDBggN6NO6QP44poS1+/5UpxdnbGjRs3cgwFEBoaqrcTlQ4dOhQeHh7Yv38/PD09cerUKcTFxWHEiBGYNWuW3PHyTR8+iD9kw4YNw9OnT3HixIlcpzfSCe99iEs90bJlSzFmzBghxMvRkW/evClUKpX47LPPxCeffCJzuvxbuXJlrqPUku6oVq2aOHr0qNwx6A1mzJghfHx8xIkTJ4SNjY04evSoWLt2rShWrJj49ddf5Y6XL0WKFBHnz58XQghha2srrl69KoQQ4sCBA6JKlSpyRnsnXbt2FXXq1BGnTp0SVlZWYu/evWLNmjXC29tb7Ny5U+54+RIbGyu++OIL4ezsLIyMjPR6uhl9mN6IZ5Qk/Pjjj2jYsCHCw8Px4sULjBo1CpcuXUJ8fDz+/fdfuePlW8+ePQEYVkNhQziWV7szz5gxA6NGjdLpcUXyY8uWLdi0aVOulz/0bYyeUaNGITExEYGBgUhLS0ODBg1gZmaGkSNH4quvvpI7Xr6oVCr1meaiRYviwYMH8Pb2hpubGyIjI2VOl38HDx7En3/+ierVq0OpVMLNzQ1NmzaFra0tpk2bpncTMgNAUFAQ7ty5g7Fjx+r95fmUlBR15yJHR0c8fvwY5cqVg6+vr868L7BQkuDj44OIiAgsXLgQRkZGSElJQceOHTFo0CC9nHIh2/Xr19G7d2+DaChsSMdib2+v8WYnhEDjxo01ttHH48r2yy+/4Pvvv0fPnj3x559/olevXoiKikJYWBgGDRokd7x8mTJlCr7//ntcvnwZWVlZ8PHx0bikrW8qVaqEiIgIeHp6ombNmpg5cyZMTU2xZMkSvb2cCOjHB7G2QkNDcfToUVSpUkXuKO9MH6Y3YqH0BiVKlMDEiRPljlGggoKCDKahsCEdy6FDh+SOUKgWLFiAJUuWoEuXLli1ahVGjRoFT09PjBs3DvHx8XLHyzdLS0uNhrX6bMyYMUhJSQHwsldf69atUb9+fRQpUgQbN26UOV3+6cMHsbZcXFzUje31nT5Mb8RxlF4RERGR5231sacE8HIeMUNpKGxIx2LoLC0tceXKFbi5ucHJyQn79u1D5cqVcf36ddSqVQtxcXFyR3wrbaaF0LfhDqTEx8fDwcFBr7+ErFu3DhkZGQgKCsLZs2fRrFkzxMXFqT+IO3fuLHdEre3duxezZ89WF32GRBenN+IZpVdUqVJFY8TWbOK1brKA/vaU0IcJCPPKkI4lIiIClSpVglKpfGvBro9FeokSJRAXFwc3Nze4ubnhxIkTqFy5MqKjo/Xmm/Gr44wJIbBt2zbY2dmpzyidPn0aCQkJejXP1ts4OjrKHeGddevWTf2zv78/bt26pXMfxHnxesGakpKCMmXKwNLSMkc7Rn0/S1u1alW5Y2jgGaVXvDo+0tmzZzFy5Eh8/fXXqF27NgDg+PHjmD17NmbOnKnubq8PXm0oHB4ervMTEOaVPkymmFdKpRKxsbFwcnJST7+Q25+mvrZR6tu3L1xcXDB+/HgsWrQIw4cPR926dREeHo6OHTvit99+kzuiVr755hvEx8dj0aJFMDIyAvDyy9PAgQNha2urVxOu9u7d+63bKBQKvfs/yjZp0iSMHDkyx3Avz58/x48//ohx48bJlEw7q1atyvO22Z129MHw4cNzXf7q5Obt2rWTtWhnoSShRo0amDBhAlq2bKmxfNeuXRg7dixOnz4tUzLtvT7v0etnzF5dpk8fwvowmWJe3b59G66urlAoFG8d0FTfJsUFXo6QnpWVpR7odNOmTeqJfgcMGABTU1OZE2qnWLFiCA0Nhbe3t8byyMhI1KlTRy8uJWbr0KGD5DqVSoX9+/cjPT1dr/6eXmVI0zYZosDAQJw5cwYqlQre3t4QQuD69eswMjJC+fLlERkZCYVCgdDQUNkm1ealNwkXLlyAh4dHjuUeHh64fPmyDInyz1AbChvScb1a/FhbW6NIkSIAgLt372Lp0qV4/vw52rZti/r168sV8Z0olUp1YQsAnTp1QqdOnWRM9G4yMzNx5cqVHIXSlStXcgxToeu2bduW6/I///wT3333HczMzPTmrEtucvtiCADnz5/X20uLhlT8ZZ8tWrFihfoqQFJSEvr06YN69eqhX79+6Nq1K4KDg7Fnzx55Qr7XUZv0iL+/v+jatavG4IxpaWmia9euwt/fX8ZkZKgiIiKEm5ubUCqVwtvbW5w9e1YUL15cWFtbC1tbW2FkZCS2bdsmd8w8O3/+vFCpVOqf33SLiooSL168kDlx3gUHBwsHBwfx448/iqNHj4qjR4+KH3/8URQpUkQEBwfLHe+dhIaGirp16wpLS0sxatQoER8fL3ekfLG3txcODg5CqVSqf86+2draCqVSKQYOHCh3zHxRKBTi4cOHOZbfv39fmJuby5Ao/0qWLCkuXbqUY/nFixdFyZIlhRBCnD59WhQpUuR9R1PjGSUJixYtQps2beDi4oLKlSsDePkNRKFQYOfOnTKny7/du3fD2toa9erVAwDMnz8fS5cuhY+PD+bPnw8HBweZE2rn6NGjWLx4MW7evInNmzejVKlSWLNmDTw8PNTHqC9GjRoFX19frF27FmvXrkXr1q3RsmVLLFu2DMDLue2mT5+uN+3jqlSpom539WpHCSl2dnZYtGiRXvRCmjVrFkqUKIE5c+aouzY7Oztj1KhRGDFihMzp8ufSpUv49ttvsXv3bvVkv6/PdalPfv75Zwgh0Lt3b0ycOFGjMX72tE3Z7U/1xS+//ALgZXODZcuWaYzbpVKpcOTIEb3rBZyYmIhHjx7luKz2+PFjdftae3v7HIPUvleylWh6ICUlRSxevFgEBweLYcOGiSVLlojk5GS5Y72TSpUqib///lsI8fIMhqmpqRg9erSoWbOmCAoKkjmddrZs2SIsLCxE3759hZmZmYiKihJCCDF//nzRokULmdNp79UpJJ49eyYUCoUICwtTr79y5Yqws7OTKZ32bt26JbKystQ/v+kWGRkpJk+eLNzc3OQNnQ+JiYkiMTFR7hj5dufOHREUFCSMjY1F+/btxeXLl+WOVKAOHz6sV2cr38Td3V24u7sLhUIhXFxc1Pfd3d1FuXLlxMcffyxOnDghd0ytdO3aVXh4eIitW7eKu3fvinv37omtW7cKT09P8cUXXwghhFi/fr346KOPZMvIxtwfGGtra1y8eBHu7u6YMGECLl68iC1btuDMmTNo2bIlYmNj5Y6YZ/7+/ggODkaPHj1gY2OD8+fPw9PTE+fOnUPz5s316lgAzZ5vADSOCQAePnyIkiVL6lX7A208ffoUffr0MZgxiPSFpaUlFAoFBg8ejDp16khu17Zt2/eYqnA8f/4cGRkZGsv0qXdstsDAQGzdulXvrgDkJjk5GcHBwVi9ejUyMzMBAMbGxujZsyfmzJkDKysrnDt3DgBkG4mcl97e4vLly7nOTaWvbxqmpqZITU0FAOzfvx89evQA8HK8lFeHEdAHkZGRaNCgQY7ltra2SEhIeP+BCsDrjU71eaC/Vx05cuSN6xs0aAAHBwe9KpIMZe66tLQ0AMDMmTMlt9G3XqSvSk1NxahRo7Bp06ZceyPq43G93pFFpVLhwoULcHNz07viydraGkuXLsWcOXNw8+ZNCCFQpkwZjcuKck/VwkJJws2bN9GhQwdcuHBBo21F9geXPv5xAUC9evXUY9icOnVKPTXBtWvX9K49grOzM27cuJFjZNrQ0FC9nZsqKCgIZmZmAF5+gA0YMABWVlYAgPT0dDmjvZOGDRvmWKbPA7ga0tx1+tZLT1tff/01Dh06hAULFqBHjx6YP38+7t+/j8WLF2P69Olyx8uXYcOGwdfXF3369IFKpUKDBg1w/PhxWFpaYufOnbn+vek6a2trnR1MV/n2TT5MQ4cOhYeHBx4+fAhLS0tcunQJR44cQbVq1XD48GG54+XbvHnzYGxsjC1btmDhwoUoVaoUAOCff/5B8+bNZU6nnf79+2Po0KE4efIkFAoFHjx4gHXr1mHkyJEYOHCg3PG01rNnTzg5OcHOzg52dnb44osvULJkSfV9Jycn9RlAffP06VON26NHj7B7925Ur14de/fulTue1rLnrps3bx5MTU0xatQo7Nu3D0OGDEFiYqLc8egVf/31FxYsWIBPP/0UxsbGqF+/vnqg2nXr1skdL182b96s7mT0119/qUcbHzZsGL7//nuZ02knJSUFY8eORZ06deDl5QVPT0+Nmy5gGyUJRYsWxcGDB+Hn5wc7OzucOnUK3t7eOHjwIEaMGIGzZ8/KHZEAfP/995gzZ4768oGZmRlGjhyJH374QeZklBdHjhxBcHCwXg3gChjG3HUfCmtra1y6dAlubm4oXbo0tm7diho1aiA6Ohq+vr5ITk6WO6LWzM3NcePGDZQuXRr/+9//YGlpiZ9//hnR0dGoXLmyXjWj6NKlC0JCQtC9e/dcJzcfOnSoTMn+w0tvElQqlfoaadGiRfHgwQN4e3vDzc0NkZGRMqcrGIbQsHHKlCn4/vvvcfnyZWRlZcHHx0fj2jbptmLFiunl35MhzF33ofD09MStW7fg5uYGHx8fbNq0CTVq1MBff/0Fe3t7uePlS/HixXH58mU4Oztj9+7dWLBgAYCX7bGyp9TRF//88w/+/vtv1K1bV+4oklgoSahUqRIiIiLg6emJmjVrYubMmTA1NcWSJUt05nRgfqSkpOCbb74xqIaNlpaW6olJSTe9PtGvEAIxMTGYPn26+hKCPmnUqBH++usvVK1aFX369EFwcDC2bNminruOdEevXr1w/vx5BAQEYPTo0WjVqhV+/fVXZGZm4qeffpI7Xr706tULnTp1Up+Badq0KQDg5MmTejeOkoODg+6PkC7bwAQ6bvfu3eKPP/4QQggRFRUlKlSoIBQKhShatKg4cOCAzOnyb+DAgaJChQpi8+bNwsLCQixfvlz88MMPonTp0mLt2rVyx8uzgwcPilmzZonQ0FAhhBCLFi0SLi4uomjRoqJv374iNTVV5oT0KoVCIZRKpVAoFBq32rVriytXrsgdT2sqlUpkZGSo72/cuFEMHjxYzJ07V6Snp8uYjN7m9u3b4o8//hDnzp2TO8o72bx5s/jpp5/E3bt31ctWrlwptm/fLmMq7a1Zs0Z8+umnIiUlRe4okthGSQvx8fFwcHDQ6y7brq6uWL16NRo2bAhbW1ucOXMGXl5eWLNmDdavX49du3bJHfGtli5dii+//BLu7u64d+8exo8fjylTpqB79+5QKpVYu3YtvvzyS73t0WKIXp/oV6lUolixYjA3N5cpUf5lZmZiypQp6N27N1xcXOSOQ6TX/P39ERUVBSEE3N3dYWJiorFeF4baYKGUi8zMTJibm+PcuXOoVKmS3HEKlCE0bKxUqRL69++PwYMHY/fu3WjTpg2WLVuGnj17AnjZI2T06NG4ceOGzEnp+fPnOHDgAFq3bg0AGD16tMYwB8bGxpg0aZLeFUyvDtyqz7T54hcfH1/IaQrPgQMHcODAATx69CjHcAjLly+XKdW7SUlJQUhISK7jeA0ZMkSmVNqbOHHiG9ePHz/+PSWRxjZKuTA2Noabm5tettd5G0No2Hjz5k31gJ/NmzeHQqFAjRo11Otr1qyJu3fvyhWPXrF69Wrs3LlTXSjNmzcPFStWhIWFBQDg6tWrcHZ2RnBwsJwxtdakSRMcPnwYQUFBckd5Jz///LPcEQrdxIkTMWnSJFSrVi3XXlX66OzZs2jZsiVSU1ORkpICR0dHPHnyBJaWlnByctKrQkkXCqG3kvXCnw5bvny5aNGihYiLi5M7SoH66aefxNy5c4UQL9v5WFhYCFNTU6FUKsXPP/8sc7q8eX3mbGtra/U8b0IIERsbK5RKpRzR6DX169cXW7duVd9//f9qzZo1olatWnJEeyeLFi0SJUqUECNGjBC///67+PPPPzVupDtKlCghVq9eLXeMAhUQECD69esnMjMz1X9Td+7cEQ0aNFC3raWCw0tvEvz9/XHjxg1kZGTAzc1NPTpyNl24bloQ7ty5g/DwcJQpU0Zveh8ZGRnh2rVrKFasGIQQcHFxQWhoqPoyyMOHD1G+fHmDPCOob0qUKIEDBw6gYsWKAF4OBxAWFqb+v7p27RqqV6+ud4M0KpXSY/Xq03QfSUlJ6iFB3jb2jr4NHZKtSJEiOHXqFMqUKSN3lAJjb2+PkydPwtvbG/b29jh+/DgqVKiAkydPomfPnrh69arcEfNMpVJhzpw5ktMB6cIlX156k9CuXTuDOEX7qoyMDHz88cdYvHgxypUrB+Bl425XV1eZk2lHCKHOn33f399f476h/d/pq8TERBgb//c28/jxY431WVlZejk1i6FM++Hg4ICYmBg4OTnB3t4+17+b7L8nfSn+Xte3b1/8/vvvGDt2rNxRCoyJiYn6/6p48eK4c+cOKlSoADs7O9y5c0fmdNqZOHEili1bhuHDh2Ps2LH4/vvvcevWLWzfvh3jxo2TOx4AFkqSJkyYIHeEAmdiYoKLFy/qfRHx+oSQpLtKly6NixcvwtvbO9f1ERERejfH4O3bt7F3715kZmYiICAAPj4+ckfKt4MHD6rHsDHUv6u0tDQsWbIE+/fvh5+fX45eVfo4lpK/vz/Cw8NRrlw5BAYGYty4cXjy5AnWrFkDX19fueNpZd26dVi6dClatWqFiRMnokuXLihTpgz8/Pxw4sQJnWhvxUtvr0lNTcXXX3+N7du3IyMjA02aNMEvv/yCokWLyh2tQIwYMQImJibsOk/vxdChQ7F//36cPn06R8+258+fo1q1amjSpAnmzp0rU0LtHDlyRN2IFnjZ8WPVqlXo0qWLzMlISmBgoOQ6hUKBgwcPvsc0BSM8PBzPnj1DYGAgHj9+jJ49eyI0NBReXl5YsWKF3jSjAAArKytcuXIFrq6ucHZ2xt9//42qVavi5s2b8Pf314nL8iyUXvP1119jwYIF6NatG8zNzbF+/Xo0bNgQmzdvljtagRg8eDBWr14NLy8vVKtWLUfbK338dkW66+HDh6hSpQpMTU3x1VdfoVy5clAoFLh69SrmzZuHzMxMnD17FsWLF5c7ap4EBATA1tYWixcvhoWFBUaPHo2///7bYHpZpqWlISIiItdu9Nk9TfWJSqVCaGgofH19dX/05w+Ut7c3Vq9ejZo1a6J+/fpo1aoVvv32W2zcuBGDBw/Go0eP5I7IQul1ZcqUwZQpU/D5558DAE6dOoW6desiLS1N7+bQyY0hfrsi3RYdHY0vv/wS+/btU8+Dlj3twoIFC/RqSiBHR0ccOXJEPb5aSkoKbG1t8eTJEzg4OMic7t3s3r0bPXr0wJMnT3Ks0+c2Subm5rhy5Qo8PDzkjkK5+Pbbb2Fra4vvvvsOW7ZsQZcuXeDu7o47d+4gODhYJ65+sFB6jampKaKjo1GqVCn1MgsLC1y7do2j8BK9g/j4ePUgoF5eXnr5DV+pVCI2NhZOTk7qZTY2NoiIiND7D2IvLy80a9YM48aN05szfHlRvXp1TJ8+HY0bN5Y7yjupWrUqDhw4AAcHB/j7+7+xram1tTUqVqyI7777Tu8+t06cOIFjx47By8tLZ85isjH3a1QqFUxNTTWWGRsbIzMzU6ZERIbB0dFRY2BQfXX58mXExsaq7wshcOXKFTx79ky9zM/PT45o7+TRo0cYPny4QRVJADBlyhSMHDkSP/zwAz766KMczQ30ZdiDdu3awczMDADQvn37N26bnp6OAwcO4IsvvkBISMh7SFdwatWqhVq1askdQwPPKL1GqVSiRYsW6l9IAPjrr7/QqFEjjT+wrVu3yhGvQISFhWHz5s25jlmhT8fVu3dvzJ07FzY2NhrLU1JSMHjwYL2dmoB0l1KphEKhQG5vm9nL9fUyVe/evVG3bl306dNH7igF6tUxr149C6PP/1d5ERUVhYoVKyItLU3uKDns2LEjz9vqwlklFkqv6dWrV562W7FiRSEnKRwbNmxAjx498PHHH2Pfvn34+OOPcf36dcTGxqJDhw56dVxGRkbqMWBe9eTJE5QoUYJnAanAvT65rxQ3N7dCTlLwUlNT8dlnn6FYsWLw9fXN0Y1eF7pp58fbzqgEBAS8pyTvX2JiIuzs7OSOkcObBmx9la4UsiyUPjB+fn7o378/Bg0aBBsbG5w/fx4eHh7o378/nJ2d3zpBoS5ISkqCEAIODg64fv06ihUrpl6nUqnw119/4dtvv8WDBw9kTEmkX5YtW4YBAwbAwsICRYoU0Tj7olAocPPmTRnT0eu2bNkiOZq1ocwcoSvYRukDExUVhVatWgEAzMzMkJKSAoVCgeDgYDRq1EgvCqXsEYQVCoXGCN3ZFAqFXhwHkS4ZM2YMJk2ahG+//TbP3/j1SWpqaq5FhT62J/vll1/w/fffo2fPnvjzzz/Rq1cvREVFISwsDIMGDZI7Xr6lpaXlGG9NF7BQ+sA4OjqqG52WKlUKFy9ehK+vLxISEtSD6Om6Q4cOQQiBRo0a4Y8//tDoPWVqago3NzeULFlSxoRE+ufFixfo3LmzwRVJjx8/Rq9evfDPP//kul4XLu1oa8GCBViyZAm6dOmCVatWYdSoUfD09MS4ceN0Ym40bahUKkydOhWLFi3Cw4cPce3aNXh6emLs2LFwd3fXiTZzhvUXQW9Vv3597Nu3DwDQqVMnDB06FP369UOXLl30pvtsQEAAGjZsiOjoaLRv3x4BAQHqW+3atVkkEeVDz549sXHjRrljFLhhw4bh6dOnOHHiBCwsLLB7926sWrUKZcuW1apRsS65c+cO6tSpA+Dl8DXZX367d++O9evXyxlNa1OmTMHKlSsxc+ZMjR7nvr6+WLZsmYzJ/sMzSh+YefPmqXtBjB49GiYmJggNDUXHjh31btLIK1eu4O7du6hXrx4AYP78+Vi6dCl8fHwwf/58vR8AkOh9UqlUmDlzJvbs2WMwc6IBL+ez+/PPP1G9enUolUq4ubmhadOmsLW1xbRp09RNEfRJiRIlEBcXBzc3N7i5ueHEiROoXLkyoqOjc+2RqctWr16NJUuWoHHjxhgwYIB6uZ+fH65evSpjsv/wjFIuMjIy0KtXL4NsvOjo6Kg+46JUKjFq1Cjs2LEDP/30k94VFl9//TWSkpIAABcuXMDw4cPRsmVL3Lx5E8OHD5c5HZF+uXDhAvz9/aFUKnHx4kWcPXtWfTt37pzc8fItJSVF3TPW0dERjx8/BvDyjIW+Nnpu1KgR/vrrLwBAnz59EBwcjKZNm6Jz587o0KGDzOm0c//+fXh5eeVYnpWVhYyMDBkS5cQzSrkwMTHBtm3b9O4MS15lZWXhxo0buc7n1KBBA5lSaS86Olo9c/sff/yBNm3aYOrUqThz5gxatmwpczoydIbW6+jQoUNyRygU3t7eiIyMhLu7O6pUqYLFixfD3d0dixYtQokSJeSOly9LlixRv3cPGDAAjo6OCA0NRZs2bfSuUKpYsSKOHj2aY0iNzZs3w9/fX6ZUmlgoSejQoQO2b99ucGcmTpw4ga5du+L27ds5TtHqypgVeWVqaqpugL5//3706NEDwMtvjdlnmogKg6H2OjJEw4YNQ0xMDABg/PjxaNasGdatWwcTExOsWrVK5nT5o1QqNRrdd+rUCQ0aNMCUKVNQrlw5PH/+XMZ0eZM9YPD48ePRvXt33L9/H1lZWdi6dSsiIyOxevVq7Ny5U+6YADiOkqQpU6Zg1qxZaNy4ca7D3uvr4GtVqlRBuXLlMHHiRDg7O+eYL0gXByeT0rZtW7x48QJ169bFDz/8oJ6jb+/evfjqq69w7do1uSOSgSpfvjzGjx+PLl26qMcje7XX0bx58+SOmC+GMmo/AMyaNQsjR47MsTw1NRVXr15F0aJF0alTJ5w4cUKGdPmTkJCAQYMGYe/evTAxMcG3336Lr776ChMmTMDs2bPh4+OD4cOHo0uXLnJHfatXBwzes2cPpk6ditOnTyMrKwtVq1bFuHHj8PHHH8sdEwALJUlvmuBSnwdfs7Kywvnz53O9Jqxv7ty5g4EDB+Lu3bsYMmSIuhtpcHAwVCoVfvnlF5kTkqGytLTElStX4ObmBicnJ+zbtw+VK1fG9evXUatWLcTFxckdUWuGNGo/8LI32IIFC3KdbSE5ORkff/wxEhIScPnyZRnS5c/AgQPx119/oXPnzti9ezeuXLmCZs2aIS0tDePHj9erUcZzm2BaZwn6oAQGBop//vlH7hhEes3Dw0OcPn1aCCFEtWrVxKJFi4QQQuzZs0c4ODjIGS3ffH19xbx584QQQlhbW4uoqCiRlZUl+vXrJ8aNGydzOu1t3rxZmJubi23btmksf/bsmahdu7YoV66ciImJkSdcPrm6uop9+/YJIYSIiooSCoVCDB06VN5Q+aRQKMSjR4/kjpEnbKP0AYiIiFD/PHjwYIwYMQKxsbG5zuekb6PURkVFYcWKFYiKisLcuXPh5OSE3bt3w8XFBRUrVpQ7Hhmo7F5HVatWVfc62rJlC8LDw9GxY0e54+WLIYza/6pPP/0UCQkJ6Nq1K/7++28EBgYiOTkZzZs3x5MnTxASEqJ3jbkfPHig7sDi6ekJc3Nz9O3bV+ZU+VeuXLkczT9epwsDaLJQeoN79+5hx44duV6v16cxRapUqZJjxvPevXurf9bXWc9DQkLQokUL1K1bF0eOHMGUKVPg5OSEiIgILFu2DFu2bJE7IhmoN/U6enUsGH1iCKP2v65v376Ij49H+/bt8eeff2Ls2LGIjY1FSEgInJ2d5Y6ntaysLI0vt0ZGRjnaz+qTiRMn6kW7WBZKEg4cOIC2bdvCw8MDkZGRqFSpEm7dugUhBKpWrSp3PK1ER0fLHaFQfPvtt5g8eTKGDx8OGxsb9fLAwEDMnTtXxmRk6HLrddSpUycZE+Vfdu+j7FH7fX191aP2Hzx4EPv27dObUftzM2rUKDx9+hSNGzeGu7s7QkJCUKpUKblj5YsQAkFBQTAzMwPwcm60AQMG5CiW9KXh/eeff64XbZTYmFtCjRo10Lx5c0yaNEndq8XJyQndunVD8+bN8eWXX8od8YNnbW2NCxcuwMPDQ6Pn0a1bt1C+fHn1COREBc3d3R29e/dGUFAQXF1d5Y7zTrJ7HxkbGyMtLQ0lS5ZEVlYWZs2ahdDQUHh5eWHs2LF6NyDt65dAd+3ahcqVK+cokvSlqACQa8P03OhDw/tXe73pOp5RknDlyhX1nDnGxsZ4/vw5rK2tMWnSJLRr107vC6XLly/nekmxbdu2MiXSnr29PWJiYnL0UDx79qzefmMk/TBixAisXLkSkyZNQmBgIPr06YMOHTqov+nrk+zvyq9OLp09av+oUaPkivXOXr+kow9d5t9GHwqgvNKnczQslCRYWVkhPT0dAFCyZElERUWpGwc/efJEzmjv5ObNm+jQoQMuXLig0W4pu0GdPrVR6tq1K7755hts3rwZCoUCWVlZ+PfffzFy5Ej14JNEhWHw4MEYPHgwzp8/j+XLl2PIkCEYOHAgunbtit69e+vd5fm3NajVR4ZUVBii12eF0GW89Cahffv2aNWqFfr164dRo0Zh27ZtCAoKwtatW+Hg4ID9+/fLHTFf2rRpAyMjIyxduhSenp44deoU4uLiMGLECMyaNQv169eXO2KeZWRkICgoCBs2bIAQAsbGxlCpVOjatStWrlwJIyMjuSPSByIjIwMLFizAN998g4yMDFSqVAlDhw5Fr169dL4IUSqVsLOz04veR0RyYKEk4ebNm0hOToafnx9SU1MxcuRI9fX6OXPm5JiXRl8ULVoUBw8ehJ+fH+zs7HDq1Cl4e3vj4MGDGDFiBM6ePSt3xLe6ceOGxoCZUVFROHv2LLKysuDv74+yZcvKmI4+JBkZGdi2bRtWrFiBffv2oVatWujTpw8ePHiAefPmITAwEL///rvcMd9IqVTi559/fmvvo549e76nRES6hYXSB8bBwQGnT5+Gp6cnypQpg2XLliEwMBBRUVHw9fXVi27ASqUSpUqVQmBgIBo1aoTAwEC9LVxJP505cwYrVqzA+vXrYWRkhO7du6Nv374oX768epuwsDA0aNBA5+fd0qsRkolkwDZKEjw9PREWFoYiRYpoLE9ISEDVqlX1dgqTSpUqISIiAp6enqhZsyZmzpwJU1NTLFmyBJ6ennLHy5OQkBCEhITg8OHDGDRoENLS0uDq6qoumgIDA9mYmwpV9erV0bRpUyxcuBDt27fPMXArAPj4+ODzzz+XIZ12dP3SIJHceEZJgtS3rIcPH8LV1VXd0Fvf7NmzBykpKejYsSNu3ryJ1q1b4+rVqyhSpAg2bNigd+OlZGRk4Pjx4zh8+DAOHz6MEydOID09HV5eXoiMjJQ7Hhmo27dvG8xZTJ5RInozFkqv2bFjB4CXjblXrVqlcd1epVLhwIED2Ldvn0F9CMfHx8PBwUGvv1k+f/4coaGh2LNnD5YuXYrk5GS96sFH+unFixd49OhRjh48+j62EhH9h4XSa14dbfd1JiYmcHd3x+zZs9G6dev3mOrdvTplyZssX768kJMUjLS0NBw7dgyHDh3C4cOHERYWBg8PDwQEBKBBgwYICAjg5TcqNNeuXUOfPn1w7NgxjeX6OBUQEb0ZCyUJHh4eCAsLQ9GiReWOUiCUSiXc3Nzg7+//xoG+tm3b9h5T5U9AQADCwsJQpkwZdVEUEBCA4sWLyx2NPhB169aFsbExvv32Wzg7O+c4G1u5cmWZkhFRQWOhJGH16tXo3LlzjpF2X7x4gQ0bNujdgIYDBw7Ehg0b4Orqit69e+OLL77QGIlXn5iYmMDZ2Rnt27dHw4YN0aBBA4MpaEk/WFlZ4fTp0xq93IjIMLFQkiA1D01cXBycnJz08tR6eno6tm7diuXLl+PYsWNo1aoV+vTpg48//liv2ielpKTg6NGjOHz4MA4dOoRz586hXLlyCAgIQMOGDREQEIBixYrJHZMMWPXq1TFnzhzUq1dP7ihEVMhYKElQKpV4+PBhjg/c8+fPIzAwUO9Hqb19+zZWrlyJ1atXIyMjA5cvX4a1tbXcsfLl2bNnCA0NVbdXOn/+PMqWLYuLFy/KHY0MSFJSkvrn8PBwjBkzBlOnToWvr2+O4QFsbW3fdzwiKiQcR+k1/v7+UCgUUCgUaNy4MYyN/3uJVCoVoqOj0bx5cxkTFozsYxRC6NWcO7mxsrKCo6MjHB0d4eDgAGNjY1y5ckXuWGRg7O3tNc68CiFyDKfBxtxEhoeF0mvat28PADh37hyaNWumcZbF1NQU7u7u+OSTT2RK925evfQWGhqK1q1bY968eWjevPkbe/vpmqysLISHh6svvf37779ISUlRj9Y9f/58BAYGyh2TDMyhQ4fkjkBEMuClNwmrVq1C586dYW5uLneUAvFqY+5evXrhiy++yDHquL6wtbVFSkoKnJ2d0bBhQzRs2BCBgYEoU6aM3NGIiMjAsFD6QCiVSri6uqovLUrZunXre0yVP4sXL0ZgYCDKlSsndxT6gEREROR5Wz8/v0JMQkTvEwul1yiVylwLCVtbW3h7e2PUqFHo2LGjDMneTVBQUJ56tq1YseI9pCHSP9nvDdntkN6EbZSIDAcLpdf8+eefuS5PSEjAqVOnsGLFCqxatQqfffbZe05GRHK6ffu2+uezZ89i5MiR+Prrr1G7dm0AwPHjxzF79mzMnDlT3daRiPQfCyUtzZ8/H6tXr8bJkyfljkJEMqlRowYmTJiAli1baizftWsXxo4di9OnT8uUjIgKmv50ddIRH3/8Ma5duyZ3DCKS0YULF+Dh4ZFjuYeHBy5fvixDIiIqLCyUtPT8+XOD6QlHRPlToUIFTJ48GWlpaepl6enpmDx5MipUqCBjMiIqaBxHSUtLly6Fv7+/3DGISEaLFi1CmzZt4OLiop4A9/z581AoFNi5c6fM6YioILGN0muGDx+e6/LExESEh4cjKioKR48eZbFE9IFLTU3F2rVrcfXqVQgh4OPjg65du8LKykruaERUgFgovUZqRGdbW1uUL18eAwcOhJub23tORURERHJgoURElE+XL1/GnTt38OLFC43lbdu2lSkRERU0tlEiItLSzZs30aFDB1y4cEE9CCUA9UCUHHCSyHCw1xsRkZaGDh0KDw8PPHz4EJaWlrh06RKOHDmCatWq4fDhw3LHI6ICxEtvRERaKlq0KA4ePAg/Pz/Y2dnh1KlT8Pb2xsGDBzFixAicPXtW7ohEVEB4RomISEsqlQrW1tYAXhZNDx48AAC4ubkhMjJSzmhEVMDYRomISEuVKlVCREQEPD09UbNmTcycOROmpqZYsmQJPD095Y5HRAWIl96IiLS0Z88epKSkoGPHjrh58yZat26Nq1evokiRIti4cSMaNWokd0QiKiAslIiICkB8fDwcHBzUPd+IyDCwUCIiIiKSwDZKRER51Lt37zxtt3z58kJOQkTvC88oERHlkVKphJubG/z9/fGmt85t27a9x1REVJhYKBER5dHAgQOxYcMGuLq6onfv3vjiiy/g6OgodywiKkQslIiItJCeno6tW7di+fLlOHbsGFq1aoU+ffrg448/ZkNuIgPEQomIKJ9u376NlStXYvXq1cjIyMDly5fVA1ESkWHgyNxERPmkUCjUk+JmZWXJHYeICgELJSIiLaSnp2P9+vVo2rQpvL29ceHCBcybNw937tzh2SQiA8ThAYiI8ujVxty9evXChg0bUKRIEbljEVEhYhslIqI8UiqVcHV1hb+//xsbbm/duvU9piKiwsQzSkREedSjRw/2bCP6wPCMEhEREZEENuYmIiIiksBCiYiIiEgCCyUiIiIiCSyUiIiIiCSwUCIiIiKSwEKJiIiISAILJSLSWY8ePUL//v3h6uoKMzMzlChRAs2aNcPx48cBvJxrbfv27Vrv193dHT///HPBhiUig8QBJ4lIZ33yySfIyMjAqlWr4OnpiYcPH+LAgQOIj4+XOxoRfSA44CQR6aSEhAQ4ODjg8OHDCAgIyLHe3d0dt2/fVt93c3PDrVu3EBUVheHDh+PEiRNISUlBhQoVMG3aNDRp0gQA0LBhQ4SEhGjsK/tt8NixY/j2228RFhaGokWLokOHDpg2bRqsrKwK8UiJSJfx0hsR6SRra2tYW1tj+/btSE9Pz7E+LCwMALBixQrExMSo7ycnJ6Nly5bYv38/zp49i2bNmqFNmza4c+cOgJfzsJUuXRqTJk1CTEwMYmJiAAAXLlxAs2bN0LFjR0RERGDjxo0IDQ3FV1999Z6OmIh0Ec8oEZHO+uOPP9CvXz88f/4cVatWRUBAAD7//HP4+fkBeNlGadu2bWjfvv0b91OxYkV8+eWX6qLH3d0dw4YNw7Bhw9Tb9OjRAxYWFli8eLF6WWhoKAICApCSkgJzc/MCPz4i0n08o0REOuuTTz7BgwcPsGPHDjRr1gyHDx9G1apVsXLlSsnHpKSkYNSoUfDx8YG9vT2sra1x9epV9RklKadPn8bKlSvVZ7Ksra3RrFkzZGVlITo6uoCPjIj0BRtzE5FOMzc3R9OmTdG0aVOMGzcOffv2xfjx4xEUFJTr9l9//TX27NmDWbNmwcvLCxYWFvj000/x4sWLNz5PVlYW+vfvjyFDhuRY5+rqWhCHQkR6iIUSEekVHx8f9ZAAJiYmUKlUGuuPHj2KoKAgdOjQAcDLNku3bt3S2MbU1DTH46pWrYpLly7By8ur0LITkf7hpTci0klxcXFo1KgR1q5di4iICERHR2Pz5s2YOXMm2rVrB+BlW6MDBw4gNjYWT58+BQB4eXlh69atOHfuHM6fP4+uXbsiKytLY9/u7u44cuQI7t+/jydPngAAvvnmGxw/fhyDBg3CuXPncP36dezYsQODBw9+vwdORDqFhRIR6SRra2vUrFkTc+bMQYMGDVCpUiWMHTsW/fr1w7x58wAAs2fPxr59++Di4gJ/f38AwJw5c+Dg4IA6deqgTZs2aNasGapWraqx70mTJuHWrVsoU6YMihUrBgDw8/NDSEgIrl+/jvr168Pf3x9jx46Fs7Pz+z1wItIp7PVGREREJIFnlIiIiIgksFAiIiIiksBCiYiIiEgCCyUiIiIiCSyUiIiIiCSwUCIiIiKSwEKJiIiISAILJSIiIiIJLJSIiIiIJLBQIiIiIpLAQomIiIhIwv8Bo4ddDU3yKIYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(1,2,1) # second subplot (ax1) will be positioned in the second coloumn\n",
    "\n",
    "#Employed\n",
    "df3_11_2020[:10].plot(kind = 'bar', color='blue', figsize=(15,6), ax = ax1)\n",
    "ax1.set_title('Estimated Employed people in each state')\n",
    "ax1.set_xlabel('State')\n",
    "ax1.set_ylabel('number of Estimated Employed')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>Estimated Employed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jun</th>\n",
       "      <td>766717078.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>699995530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <td>406899254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <td>403011803.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sep</th>\n",
       "      <td>402452126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oct</th>\n",
       "      <td>401411032.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nov</th>\n",
       "      <td>400051335.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jul</th>\n",
       "      <td>399838967.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aug</th>\n",
       "      <td>399610205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dec</th>\n",
       "      <td>391001555.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Estimated Employed\n",
       "month                    \n",
       "Jun           766717078.0\n",
       "May           699995530.0\n",
       "Jan           406899254.0\n",
       "Feb           403011803.0\n",
       "Sep           402452126.0\n",
       "Oct           401411032.0\n",
       "Nov           400051335.0\n",
       "Jul           399838967.0\n",
       "Aug           399610205.0\n",
       "Dec           391001555.0"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Month Wise Estimated Employed\n",
    "\n",
    "df4 = df[['month','Estimated Employed']].groupby('month').sum().sort_values(by='Estimated Employed', ascending=False)\n",
    "df4.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Estimated Employed people')"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Month wise Estimated Employed \n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(1,2,2) # Second subplot (ax1) will be positioned in the second coloumn\n",
    "\n",
    "#Employed\n",
    "df4[:10].plot(kind = 'bar', color='red',figsize=(15,6), ax=ax1)\n",
    "ax1.set_title('Estimated Employed people in each Month')\n",
    "ax1.set_xlabel('Month')\n",
    "ax1.set_ylabel('Number of Estimated Employed people')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Estimated Employed people')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df4_11_2020= df_11_2020[['month','Estimated Employed']].groupby('month').sum().sort_values(by='Estimated Employed', ascending=False)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax0 = fig.add_subplot(1,2,2)\n",
    "\n",
    "\n",
    "df4_11_2020[:10].plot(kind = 'bar', color='red',figsize=(15,6), ax=ax0)\n",
    "ax0.set_title('Estimated employed people in each Month')\n",
    "ax0.set_xlabel('Month')\n",
    "ax0.set_ylabel('Number of Estimated Employed people')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "State=%{x}<br>month=May<br>Estimated Unemployment Rate=%{y}<extra></extra>",
         "legendgroup": "Andhra Pradesh",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Andhra Pradesh",
         "offsetgroup": "Andhra Pradesh",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Andhra Pradesh",
          "Andhra Pradesh",
          "Andhra Pradesh",
          "Andhra Pradesh"
         ],
         "xaxis": "x",
         "y": [
          3.65,
          14.46,
          6.09,
          24.91
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "State=%{x}<br>month=May<br>Estimated Unemployment Rate=%{y}<extra></extra>",
         "legendgroup": "Assam",
         "marker": {
          "color": "#EF553B",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Assam",
         "offsetgroup": "Assam",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Assam",
          "Assam",
          "Assam",
          "Assam"
         ],
         "xaxis": "x",
         "y": [
          4.29,
          9.38,
          7.87,
          10.77
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "State=%{x}<br>month=May<br>Estimated Unemployment Rate=%{y}<extra></extra>",
         "legendgroup": "Bihar",
         "marker": {
          "color": "#00cc96",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Bihar",
         "offsetgroup": "Bihar",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Bihar",
          "Bihar",
          "Bihar",
          "Bihar"
         ],
         "xaxis": "x",
         "y": [
          9.27,
          47.26,
          19.9,
          37.87
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "State=%{x}<br>month=May<br>Estimated Unemployment Rate=%{y}<extra></extra>",
         "legendgroup": "Chhattisgarh",
         "marker": {
          "color": "#ab63fa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Chhattisgarh",
         "offsetgroup": "Chhattisgarh",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Chhattisgarh",
          "Chhattisgarh",
          "Chhattisgarh",
          "Chhattisgarh"
         ],
         "xaxis": "x",
         "y": [
          9.82,
          7.64,
          9.77,
          24.1
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "State=%{x}<br>month=May<br>Estimated Unemployment Rate=%{y}<extra></extra>",
         "legendgroup": "Delhi",
         "marker": {
          "color": "#FFA15A",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Delhi",
         "offsetgroup": "Delhi",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Delhi",
          "Delhi",
          "Delhi",
          "Delhi"
         ],
         "xaxis": "x",
         "y": [
          12.56,
          22.76,
          12.31,
          45.78
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "State=%{x}<br>month=May<br>Estimated Unemployment Rate=%{y}<extra></extra>",
         "legendgroup": "Goa",
         "marker": {
          "color": "#19d3f3",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Goa",
         "offsetgroup": "Goa",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Goa",
          "Goa",
          "Goa"
         ],
         "xaxis": "x",
         "y": [
          2.91,
          20,
          2.75
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "State=%{x}<br>month=May<br>Estimated Unemployment Rate=%{y}<extra></extra>",
         "legendgroup": "Gujarat",
         "marker": {
          "color": "#FF6692",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Gujarat",
         "offsetgroup": "Gujarat",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Gujarat",
          "Gujarat",
          "Gujarat",
          "Gujarat"
         ],
         "xaxis": "x",
         "y": [
          2.88,
          14.58,
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          },
          "title": {
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          "xaxis": {
           "automargin": true,
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           "zerolinewidth": 2
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        },
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        },
        "updatemenus": [
         {
          "buttons": [
           {
            "args": [
             null,
             {
              "frame": {
               "duration": 2000,
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              "fromcurrent": true,
              "mode": "immediate",
              "transition": {
               "duration": 500,
               "easing": "linear"
              }
             }
            ],
            "label": "&#9654;",
            "method": "animate"
           },
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              null
             ],
             {
              "frame": {
               "duration": 0,
               "redraw": true
              },
              "fromcurrent": true,
              "mode": "immediate",
              "transition": {
               "duration": 0,
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             }
            ],
            "label": "&#9724;",
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           "r": 10,
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        ],
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         "anchor": "y",
         "categoryarray": [
          "Andhra Pradesh",
          "Assam",
          "Bihar",
          "Chhattisgarh",
          "Delhi",
          "Goa",
          "Gujarat",
          "Haryana",
          "Himachal Pradesh",
          "Jammu & Kashmir",
          "Jharkhand",
          "Karnataka",
          "Kerala",
          "Madhya Pradesh",
          "Maharashtra",
          "Meghalaya",
          "Odisha",
          "Puducherry",
          "Punjab",
          "Rajasthan",
          "Sikkim",
          "Tamil Nadu",
          "Telangana",
          "Tripura",
          "Uttar Pradesh",
          "Uttarakhand",
          "West Bengal",
          "Chandigarh"
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         "categoryorder": "total descending",
         "domain": [
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        },
        "yaxis": {
         "anchor": "x",
         "domain": [
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          "text": "Estimated Unemployment Rate"
         }
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       }
      }
     },
     "metadata": {},
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   ],
   "source": [
    "# Barplot Unemployemnet Rate (Monthly)\n",
    "\n",
    "fig = exp.bar(data_frame=df,x='State',y='Estimated Unemployment Rate', animation_frame='month',color='State',title='Unemployment Rate of each month')\n",
    "\n",
    "fig.update_layout(xaxis = {'categoryorder' : 'total descending'})\n",
    "\n",
    "fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 2000\n",
    "\n",
    "fig.show()\n"
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