trial3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>City</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>country</th>\n",
       "      <th>iso2</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abohar</td>\n",
       "      <td>30.144533</td>\n",
       "      <td>74.195520</td>\n",
       "      <td>India</td>\n",
       "      <td>IN</td>\n",
       "      <td>Punjab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adilabad</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>78.310000</td>\n",
       "      <td>India</td>\n",
       "      <td>IN</td>\n",
       "      <td>Telangana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Agartala</td>\n",
       "      <td>23.836049</td>\n",
       "      <td>91.279386</td>\n",
       "      <td>India</td>\n",
       "      <td>IN</td>\n",
       "      <td>Tripura</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Agra</td>\n",
       "      <td>27.187935</td>\n",
       "      <td>78.003944</td>\n",
       "      <td>India</td>\n",
       "      <td>IN</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ahmadnagar</td>\n",
       "      <td>19.094571</td>\n",
       "      <td>74.738432</td>\n",
       "      <td>India</td>\n",
       "      <td>IN</td>\n",
       "      <td>Maharashtra</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>Vizianagaram</td>\n",
       "      <td>18.113290</td>\n",
       "      <td>83.397743</td>\n",
       "      <td>India</td>\n",
       "      <td>IN</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>Warangal</td>\n",
       "      <td>17.978423</td>\n",
       "      <td>79.600209</td>\n",
       "      <td>India</td>\n",
       "      <td>IN</td>\n",
       "      <td>Telangana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>Jorapokhar</td>\n",
       "      <td>23.700000</td>\n",
       "      <td>86.412670</td>\n",
       "      <td>India</td>\n",
       "      <td>IN</td>\n",
       "      <td>Jharkhand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>Brajrajnagar</td>\n",
       "      <td>21.820000</td>\n",
       "      <td>83.920000</td>\n",
       "      <td>India</td>\n",
       "      <td>IN</td>\n",
       "      <td>Odisha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>Talcher</td>\n",
       "      <td>20.950000</td>\n",
       "      <td>85.230000</td>\n",
       "      <td>India</td>\n",
       "      <td>IN</td>\n",
       "      <td>Odisha</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>213 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             City        Lat       Long country iso2           State\n",
       "0          Abohar  30.144533  74.195520   India   IN          Punjab\n",
       "1        Adilabad  19.400000  78.310000   India   IN       Telangana\n",
       "2        Agartala  23.836049  91.279386   India   IN         Tripura\n",
       "3            Agra  27.187935  78.003944   India   IN   Uttar Pradesh\n",
       "4      Ahmadnagar  19.094571  74.738432   India   IN     Maharashtra\n",
       "..            ...        ...        ...     ...  ...             ...\n",
       "208  Vizianagaram  18.113290  83.397743   India   IN  Andhra Pradesh\n",
       "209      Warangal  17.978423  79.600209   India   IN       Telangana\n",
       "210    Jorapokhar  23.700000  86.412670   India   IN       Jharkhand\n",
       "211  Brajrajnagar  21.820000  83.920000   India   IN          Odisha\n",
       "212       Talcher  20.950000  85.230000   India   IN          Odisha\n",
       "\n",
       "[213 rows x 6 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "c=pd.read_csv('dataset/Indian_Cities_Database.csv')\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc= c[['State','Lat','Long']]\n",
    "cc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Punjab': {'Lat': 31.125536166666663, 'Long': 75.49268766666667}},\n",
       " {'Telangana': {'Lat': 17.930004, 'Long': 78.92139399999999}},\n",
       " {'Tripura': {'Lat': 23.836049, 'Long': 91.279386}},\n",
       " {'Uttar Pradesh': {'Lat': 27.558369, 'Long': 79.53375439285715}},\n",
       " {'Maharashtra': {'Lat': 19.437894555555555, 'Long': 75.37090572222222}},\n",
       " {'Gujarat': {'Lat': 22.172131999999998, 'Long': 71.82683088888889}},\n",
       " {'Mizoram': {'Lat': 23.736701, 'Long': 92.714596}},\n",
       " {'Rajasthan': {'Lat': 26.46041323076923, 'Long': 75.00427223076925}},\n",
       " {'Kerala': {'Lat': 9.611407, 'Long': 76.3798488}},\n",
       " {'West Bengal': {'Lat': 23.729951444444445, 'Long': 88.26926788888889}},\n",
       " {'Haryana': {'Lat': 29.30676490909091, 'Long': 76.58581309090908}},\n",
       " {'Bihar': {'Lat': 25.384672, 'Long': 85.71918683333332}},\n",
       " {'Jammu and Kashmir': {'Lat': 33.837129000000004, 'Long': 74.61865275}},\n",
       " {'Karnataka': {'Lat': 14.8084356, 'Long': 76.3993348}},\n",
       " {'Chhattisgarh': {'Lat': 21.50752233333333, 'Long': 81.739087}},\n",
       " {'Madhya Pradesh': {'Lat': 23.67546114285714, 'Long': 77.27517714285715}},\n",
       " {'Odisha': {'Lat': 20.797267124999998, 'Long': 85.030559}},\n",
       " {'Chandigarh': {'Lat': 30.736292, 'Long': 76.788398}},\n",
       " {'Tamil Nadu ': {'Lat': 10.821000944444442, 'Long': 78.45747294444442}},\n",
       " {'Andhra Pradesh': {'Lat': 16.059975133333335, 'Long': 80.27575453333334}},\n",
       " {'Daman and Diu': {'Lat': 20.564715, 'Long': 71.910156}},\n",
       " {'Uttarakhand': {'Lat': 30.324427, 'Long': 78.033922}},\n",
       " {'Delhi': {'Lat': 28.625976, 'Long': 77.21574749999999}},\n",
       " {'Jharkhand': {'Lat': 23.413133000000002, 'Long': 86.0949815}},\n",
       " {'Assam': {'Lat': 26.334961833333335, 'Long': 93.0454855}},\n",
       " {'Sikkim': {'Lat': 27.325739, 'Long': 88.612155}},\n",
       " {'Manipur': {'Lat': 24.808053, 'Long': 93.944203}},\n",
       " {'Arunachal Pradesh': {'Lat': 27.102349, 'Long': 93.692047}},\n",
       " {'Lakshadweep': {'Lat': 10.566667, 'Long': 72.616667}},\n",
       " {'Nagaland': {'Lat': 25.674673, 'Long': 94.110988}},\n",
       " {'Goa': {'Lat': 15.498289, 'Long': 73.824541}},\n",
       " {'Andaman and Nicobar Islands': {'Lat': 11.666667, 'Long': 92.75}},\n",
       " {'Puducherry': {'Lat': 11.933812, 'Long': 79.829792}},\n",
       " {'Meghalaya': {'Lat': 25.573987, 'Long': 91.896807}},\n",
       " {'Himachal Pradesh': {'Lat': 31.104423, 'Long': 77.166623}},\n",
       " {'Dadra and Nagar Haveli': {'Lat': 20.273855, 'Long': 72.996728}}]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat_long=[]\n",
    "visited = []\n",
    "for i in cc['State']:\n",
    "        if(i not in visited):\n",
    "            b = cc[cc['State']==i]\n",
    "            latc = b['Lat'].mean()\n",
    "            longc = b['Long'].mean()\n",
    "            # print(i,latc,longc)\n",
    "            visited.append(i)\n",
    "            dict = {i:{'Lat':latc,'Long': longc}\n",
    "            }\n",
    "            lat_long.append(dict)\n",
    "lat_long\n",
    "           \n",
    "\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>Arecanut</td>\n",
       "      <td>1254.0</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>Other Kharif pulses</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>Rice</td>\n",
       "      <td>102.0</td>\n",
       "      <td>321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>Banana</td>\n",
       "      <td>176.0</td>\n",
       "      <td>641.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>Cashewnut</td>\n",
       "      <td>720.0</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246086</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>Rice</td>\n",
       "      <td>306.0</td>\n",
       "      <td>801.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246087</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>Sesamum</td>\n",
       "      <td>627.0</td>\n",
       "      <td>463.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246088</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>Sugarcane</td>\n",
       "      <td>324.0</td>\n",
       "      <td>16250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246089</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>Rice</td>\n",
       "      <td>279151.0</td>\n",
       "      <td>597899.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246090</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>Sesamum</td>\n",
       "      <td>175.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246091 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         State_Name                 Crop      Area  Production\n",
       "0       Andaman and Nicobar Islands             Arecanut    1254.0      2000.0\n",
       "1       Andaman and Nicobar Islands  Other Kharif pulses       2.0         1.0\n",
       "2       Andaman and Nicobar Islands                 Rice     102.0       321.0\n",
       "3       Andaman and Nicobar Islands               Banana     176.0       641.0\n",
       "4       Andaman and Nicobar Islands            Cashewnut     720.0       165.0\n",
       "...                             ...                  ...       ...         ...\n",
       "246086                  West Bengal                 Rice     306.0       801.0\n",
       "246087                  West Bengal              Sesamum     627.0       463.0\n",
       "246088                  West Bengal            Sugarcane     324.0     16250.0\n",
       "246089                  West Bengal                 Rice  279151.0    597899.0\n",
       "246090                  West Bengal              Sesamum     175.0        88.0\n",
       "\n",
       "[246091 rows x 4 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "state_data = [{'Punjab': {'Lat': 31.125536166666663, 'Long': 75.49268766666667}},\n",
    " {'Telangana': {'Lat': 17.930004, 'Long': 78.92139399999999}},\n",
    " {'Tripura': {'Lat': 23.836049, 'Long': 91.279386}},\n",
    " {'Uttar Pradesh': {'Lat': 27.558369, 'Long': 79.53375439285715}},\n",
    " {'Maharashtra': {'Lat': 19.437894555555555, 'Long': 75.37090572222222}},\n",
    " {'Gujarat': {'Lat': 22.172131999999998, 'Long': 71.82683088888889}},\n",
    " {'Mizoram': {'Lat': 23.736701, 'Long': 92.714596}},\n",
    " {'Rajasthan': {'Lat': 26.46041323076923, 'Long': 75.00427223076925}},\n",
    " {'Kerala': {'Lat': 9.611407, 'Long': 76.3798488}},\n",
    " {'West Bengal': {'Lat': 23.729951444444445, 'Long': 88.26926788888889}},\n",
    " {'Haryana': {'Lat': 29.30676490909091, 'Long': 76.58581309090908}},\n",
    " {'Bihar': {'Lat': 25.384672, 'Long': 85.71918683333332}},\n",
    " {'Jammu and Kashmir': {'Lat': 33.837129000000004, 'Long': 74.61865275}},\n",
    " {'Karnataka': {'Lat': 14.8084356, 'Long': 76.3993348}},\n",
    " {'Chhattisgarh': {'Lat': 21.50752233333333, 'Long': 81.739087}},\n",
    " {'Madhya Pradesh': {'Lat': 23.67546114285714, 'Long': 77.27517714285715}},\n",
    " {'Odisha': {'Lat': 20.797267124999998, 'Long': 85.030559}},\n",
    " {'Chandigarh': {'Lat': 30.736292, 'Long': 76.788398}},\n",
    " {'Tamil Nadu ': {'Lat': 10.821000944444442, 'Long': 78.45747294444442}},\n",
    " {'Andhra Pradesh': {'Lat': 16.059975133333335, 'Long': 80.27575453333334}},\n",
    " {'Daman and Diu': {'Lat': 20.564715, 'Long': 71.910156}},\n",
    " {'Uttarakhand': {'Lat': 30.324427, 'Long': 78.033922}},\n",
    " {'Delhi': {'Lat': 28.625976, 'Long': 77.21574749999999}},\n",
    " {'Jharkhand': {'Lat': 23.413133000000002, 'Long': 86.0949815}},\n",
    " {'Assam': {'Lat': 26.334961833333335, 'Long': 93.0454855}},\n",
    " {'Sikkim': {'Lat': 27.325739, 'Long': 88.612155}},\n",
    " {'Manipur': {'Lat': 24.808053, 'Long': 93.944203}},\n",
    " {'Arunachal Pradesh': {'Lat': 27.102349, 'Long': 93.692047}},\n",
    " {'Lakshadweep': {'Lat': 10.566667, 'Long': 72.616667}},\n",
    " {'Nagaland': {'Lat': 25.674673, 'Long': 94.110988}},\n",
    " {'Goa': {'Lat': 15.498289, 'Long': 73.824541}},\n",
    " {'Andaman and Nicobar Islands': {'Lat': 11.666667, 'Long': 92.75}},\n",
    " {'Puducherry': {'Lat': 11.933812, 'Long': 79.829792}},\n",
    " {'Meghalaya': {'Lat': 25.573987, 'Long': 91.896807}},\n",
    " {'Himachal Pradesh': {'Lat': 31.104423, 'Long': 77.166623}},\n",
    " {'Dadra and Nagar Haveli': {'Lat': 20.273855, 'Long': 72.996728}}]\n",
    "c=pd.read_csv('dataset/Indian_Cities_Database.csv')\n",
    "\n",
    "\n",
    "a =pd.read_csv('dataset/crop_production.csv')\n",
    "\n",
    "\n",
    "b= a[['State_Name','Crop','Area','Production']]\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>Rice</td>\n",
       "      <td>102.00</td>\n",
       "      <td>321.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>Rice</td>\n",
       "      <td>83.00</td>\n",
       "      <td>300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>Rice</td>\n",
       "      <td>189.20</td>\n",
       "      <td>510.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>Rice</td>\n",
       "      <td>52.00</td>\n",
       "      <td>90.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>Rice</td>\n",
       "      <td>52.94</td>\n",
       "      <td>72.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246049</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>Rice</td>\n",
       "      <td>516.00</td>\n",
       "      <td>1274.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246052</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>Rice</td>\n",
       "      <td>302274.00</td>\n",
       "      <td>730136.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246058</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>Rice</td>\n",
       "      <td>264.00</td>\n",
       "      <td>721.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246086</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>Rice</td>\n",
       "      <td>306.00</td>\n",
       "      <td>801.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246089</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>Rice</td>\n",
       "      <td>279151.00</td>\n",
       "      <td>597899.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15104 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         State_Name  Crop       Area  Production\n",
       "2       Andaman and Nicobar Islands  Rice     102.00      321.00\n",
       "12      Andaman and Nicobar Islands  Rice      83.00      300.00\n",
       "18      Andaman and Nicobar Islands  Rice     189.20      510.84\n",
       "27      Andaman and Nicobar Islands  Rice      52.00       90.17\n",
       "36      Andaman and Nicobar Islands  Rice      52.94       72.57\n",
       "...                             ...   ...        ...         ...\n",
       "246049                  West Bengal  Rice     516.00     1274.00\n",
       "246052                  West Bengal  Rice  302274.00   730136.00\n",
       "246058                  West Bengal  Rice     264.00      721.00\n",
       "246086                  West Bengal  Rice     306.00      801.00\n",
       "246089                  West Bengal  Rice  279151.00   597899.00\n",
       "\n",
       "[15104 rows x 4 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "d =b[b['Crop'] == 'Rice']\n",
    "\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Andaman and Nicobar Islands',\n",
       " 'Andhra Pradesh',\n",
       " 'Arunachal Pradesh',\n",
       " 'Assam',\n",
       " 'Bihar',\n",
       " 'Chandigarh',\n",
       " 'Chhattisgarh',\n",
       " 'Dadra and Nagar Haveli',\n",
       " 'Goa',\n",
       " 'Gujarat',\n",
       " 'Haryana',\n",
       " 'Himachal Pradesh',\n",
       " 'Jammu and Kashmir ',\n",
       " 'Jharkhand',\n",
       " 'Karnataka',\n",
       " 'Kerala',\n",
       " 'Madhya Pradesh',\n",
       " 'Maharashtra',\n",
       " 'Manipur',\n",
       " 'Meghalaya',\n",
       " 'Mizoram',\n",
       " 'Nagaland',\n",
       " 'Odisha',\n",
       " 'Puducherry',\n",
       " 'Punjab',\n",
       " 'Rajasthan',\n",
       " 'Sikkim',\n",
       " 'Tamil Nadu',\n",
       " 'Telangana ',\n",
       " 'Tripura',\n",
       " 'Uttar Pradesh',\n",
       " 'Uttarakhand',\n",
       " 'West Bengal']"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(d['State_Name'].unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State_Name\n",
       "Andaman and Nicobar Islands    7.771493e+04\n",
       "Andhra Pradesh                 4.654831e+07\n",
       "Arunachal Pradesh              2.209673e+06\n",
       "Assam                          4.475147e+07\n",
       "Bihar                          5.773340e+07\n",
       "Chandigarh                     8.500000e+02\n",
       "Chhattisgarh                   5.834253e+07\n",
       "Dadra and Nagar Haveli         2.309820e+05\n",
       "Goa                            5.522800e+05\n",
       "Gujarat                        1.094210e+07\n",
       "Haryana                        1.737199e+07\n",
       "Himachal Pradesh               9.604880e+05\n",
       "Jammu and Kashmir              2.564431e+06\n",
       "Jharkhand                      7.726361e+06\n",
       "Karnataka                      2.486642e+07\n",
       "Kerala                         4.512943e+06\n",
       "Madhya Pradesh                 2.758377e+07\n",
       "Maharashtra                    2.567246e+07\n",
       "Manipur                        1.628585e+06\n",
       "Meghalaya                      1.885907e+06\n",
       "Mizoram                        7.303320e+05\n",
       "Nagaland                       2.963650e+06\n",
       "Odisha                         8.634704e+07\n",
       "Puducherry                     3.851970e+05\n",
       "Punjab                         4.809900e+07\n",
       "Rajasthan                      1.883061e+06\n",
       "Sikkim                         2.256170e+05\n",
       "Tamil Nadu                     3.238690e+07\n",
       "Telangana                      2.384231e+07\n",
       "Tripura                        4.288487e+06\n",
       "Uttar Pradesh                  1.033803e+08\n",
       "Uttarakhand                    4.437218e+06\n",
       "West Bengal                    1.019935e+08\n",
       "Name: Area, dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_mean = d.groupby('State_Name')['Area'].sum()\n",
    "area_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State_Name\n",
       "Andaman and Nicobar Islands     13118.003529\n",
       "Andhra Pradesh                 298840.862786\n",
       "Arunachal Pradesh                9924.822711\n",
       "Assam                           52950.656342\n",
       "Bihar                           57478.402249\n",
       "Chandigarh                        296.384615\n",
       "Chhattisgarh                   279104.856164\n",
       "Dadra and Nagar Haveli          14673.064516\n",
       "Goa                             37690.631579\n",
       "Gujarat                         55639.067055\n",
       "Haryana                        164394.333333\n",
       "Himachal Pradesh                10752.651515\n",
       "Jammu and Kashmir               30493.291852\n",
       "Jharkhand                       33160.405022\n",
       "Karnataka                       55093.784762\n",
       "Kerala                          15264.189597\n",
       "Madhya Pradesh                  36879.411215\n",
       "Maharashtra                     65964.153374\n",
       "Manipur                         35988.766355\n",
       "Meghalaya                       12347.811189\n",
       "Mizoram                         12112.808511\n",
       "Nagaland                        27735.736842\n",
       "Odisha                          89054.990306\n",
       "Puducherry                       8803.196429\n",
       "Punjab                         531662.721893\n",
       "Rajasthan                        9009.154362\n",
       "Sikkim                           5321.333333\n",
       "Tamil Nadu                     201860.213710\n",
       "Telangana                      221072.447284\n",
       "Tripura                        133562.637500\n",
       "Uttar Pradesh                  135812.140906\n",
       "Uttarakhand                     37379.995935\n",
       "West Bengal                    266349.689370\n",
       "Name: Production, dtype: float64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_mean = d.groupby('State_Name')['Production'].mean()\n",
    "prod_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<folium.folium.Map at 0x196dfc5f110>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 'Litchi'\n",
    "\n",
    "import pandas as pd\n",
    "state_data = {'Punjab': {'Lat': 31.125536166666663, 'Long': 75.49268766666667},\n",
    " 'Telangana ': {'Lat': 17.930004, 'Long': 78.92139399999999},\n",
    " 'Tripura': {'Lat': 23.836049, 'Long': 91.279386},\n",
    " 'Uttar Pradesh': {'Lat': 27.558369, 'Long': 79.53375439285715},\n",
    " 'Maharashtra': {'Lat': 19.437894555555555, 'Long': 75.37090572222222},\n",
    " 'Gujarat': {'Lat': 22.172131999999998, 'Long': 71.82683088888889},\n",
    " 'Mizoram': {'Lat': 23.736701, 'Long': 92.714596},\n",
    " 'Rajasthan': {'Lat': 26.46041323076923, 'Long': 75.00427223076925},\n",
    " 'Kerala': {'Lat': 9.611407, 'Long': 76.3798488},\n",
    " 'West Bengal': {'Lat': 23.729951444444445, 'Long': 88.26926788888889},\n",
    " 'Haryana': {'Lat': 29.30676490909091, 'Long': 76.58581309090908},\n",
    " 'Bihar': {'Lat': 25.384672, 'Long': 85.71918683333332},\n",
    " 'Jammu and Kashmir ': {'Lat': 33.837129000000004, 'Long': 74.61865275},\n",
    " 'Karnataka': {'Lat': 14.8084356, 'Long': 76.3993348},\n",
    " 'Chhattisgarh': {'Lat': 21.50752233333333, 'Long': 81.739087},\n",
    " 'Madhya Pradesh': {'Lat': 23.67546114285714, 'Long': 77.27517714285715},\n",
    " 'Odisha': {'Lat': 20.797267124999998, 'Long': 85.030559},\n",
    " 'Chandigarh': {'Lat': 30.736292, 'Long': 76.788398},\n",
    " 'Tamil Nadu': {'Lat': 10.821000944444442, 'Long': 78.45747294444442},\n",
    " 'Andhra Pradesh': {'Lat': 16.059975133333335, 'Long': 80.27575453333334},\n",
    " 'Daman and Diu': {'Lat': 20.564715, 'Long': 71.910156},\n",
    " 'Uttarakhand': {'Lat': 30.324427, 'Long': 78.033922},\n",
    " 'Delhi': {'Lat': 28.625976, 'Long': 77.21574749999999},\n",
    " 'Jharkhand': {'Lat': 23.413133000000002, 'Long': 86.0949815},\n",
    " 'Assam': {'Lat': 26.334961833333335, 'Long': 93.0454855},\n",
    " 'Sikkim': {'Lat': 27.325739, 'Long': 88.612155},\n",
    " 'Manipur': {'Lat': 24.808053, 'Long': 93.944203},\n",
    " 'Arunachal Pradesh': {'Lat': 27.102349, 'Long': 93.692047},\n",
    " 'Lakshadweep': {'Lat': 10.566667, 'Long': 72.616667},\n",
    " 'Nagaland': {'Lat': 25.674673, 'Long': 94.110988},\n",
    " 'Goa': {'Lat': 15.498289, 'Long': 73.824541},\n",
    " 'Andaman and Nicobar Islands': {'Lat': 11.666667, 'Long': 92.75},\n",
    " 'Puducherry': {'Lat': 11.933812, 'Long': 79.829792},\n",
    " 'Meghalaya': {'Lat': 25.573987, 'Long': 91.896807},\n",
    " 'Himachal Pradesh': {'Lat': 31.104423, 'Long': 77.166623},\n",
    " 'Dadra and Nagar Haveli': {'Lat': 20.273855, 'Long': 72.996728}}\n",
    "\n",
    "a =pd.read_csv('dataset/crop_production.csv')\n",
    "a =a.dropna()\n",
    "b= a[['State_Name','Crop','Area','Production']]\n",
    "c= b[b['Crop']==n]\n",
    "d =list(c['State_Name'].unique())\n",
    "area_mean = c.groupby('State_Name')['Area'].sum()\n",
    "prod_mean = c.groupby('State_Name')['Production'].mean()\n",
    "import folium\n",
    "m = folium.Map(location=[20.5937, 78.9629], zoom_start=5)\n",
    "\n",
    "m1 = folium.FeatureGroup(name='crop_map')\n",
    "for i in d:\n",
    "    # print(state_data[i])\n",
    "    if((prod_mean[i])==0):\n",
    "        continue\n",
    "    popup_html = f'<table><tr><th>Crop Name:</th><td>{n}</td></tr><tr><th>State Name:</th><td>{i}</td></tr><tr><th>Area:</th><td><br>{int(area_mean[i])} acre</td></tr><tr><th>Production:</th><td><br>{int(prod_mean[i])} pounds</td></tr></table>'\n",
    "    m1.add_child(folium.Marker(location=[state_data[i]['Lat'],state_data[i]['Long']], popup=popup_html, icon=folium.Icon(color='green')))\n",
    "m.add_child(m1)\n",
    "# m.save('templates/my_map.html')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Rajasthan'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\Naru\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3801\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 3802\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
      "File \u001b[1;32mc:\\Users\\Naru\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\Naru\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Rajasthan'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[141], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m d \u001b[39m=\u001b[39m c\u001b[39m.\u001b[39mgroupby(\u001b[39m'\u001b[39m\u001b[39mState_Name\u001b[39m\u001b[39m'\u001b[39m)[\u001b[39m'\u001b[39m\u001b[39mProduction\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39msum()\n\u001b[1;32m----> 2\u001b[0m d[\u001b[39m'\u001b[39;49m\u001b[39mRajasthan\u001b[39;49m\u001b[39m'\u001b[39;49m]\n",
      "File \u001b[1;32mc:\\Users\\Naru\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\series.py:981\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    978\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_values[key]\n\u001b[0;32m    980\u001b[0m \u001b[39melif\u001b[39;00m key_is_scalar:\n\u001b[1;32m--> 981\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_value(key)\n\u001b[0;32m    983\u001b[0m \u001b[39mif\u001b[39;00m is_hashable(key):\n\u001b[0;32m    984\u001b[0m     \u001b[39m# Otherwise index.get_value will raise InvalidIndexError\u001b[39;00m\n\u001b[0;32m    985\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m    986\u001b[0m         \u001b[39m# For labels that don't resolve as scalars like tuples and frozensets\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Naru\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\series.py:1089\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[1;34m(self, label, takeable)\u001b[0m\n\u001b[0;32m   1086\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_values[label]\n\u001b[0;32m   1088\u001b[0m \u001b[39m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[1;32m-> 1089\u001b[0m loc \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mindex\u001b[39m.\u001b[39;49mget_loc(label)\n\u001b[0;32m   1090\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mindex\u001b[39m.\u001b[39m_get_values_for_loc(\u001b[39mself\u001b[39m, loc, label)\n",
      "File \u001b[1;32mc:\\Users\\Naru\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3802\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m-> 3804\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[0;32m   3805\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[0;32m   3806\u001b[0m     \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3807\u001b[0m     \u001b[39m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3808\u001b[0m     \u001b[39m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3809\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Rajasthan'"
     ]
    }
   ],
   "source": [
    "d = c.groupby('State_Name')['Production'].sum()\n",
    "d['Rajasthan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'State'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\Naru\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3801\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 3802\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
      "File \u001b[1;32mc:\\Users\\Naru\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\Naru\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'State'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[128], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m a \u001b[39m=\u001b[39mpd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdataset/crop_production.csv\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m----> 3\u001b[0m \u001b[39mlist\u001b[39m(a[\u001b[39m'\u001b[39;49m\u001b[39mState\u001b[39;49m\u001b[39m'\u001b[39;49m]\u001b[39m.\u001b[39munique())\n",
      "File \u001b[1;32mc:\\Users\\Naru\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3805\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m   3806\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3807\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[0;32m   3808\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3809\u001b[0m     indexer \u001b[39m=\u001b[39m [indexer]\n",
      "File \u001b[1;32mc:\\Users\\Naru\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3802\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m-> 3804\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[0;32m   3805\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[0;32m   3806\u001b[0m     \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3807\u001b[0m     \u001b[39m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3808\u001b[0m     \u001b[39m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3809\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'State'"
     ]
    }
   ],
   "source": [
    "a =pd.read_csv('dataset/crop_production.csv')\n",
    "\n",
    "list(a['State'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "124"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a =pd.read_csv('dataset/crop_production.csv')\n",
    "\n",
    "a['Crop'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Andhra Pradesh', 'Tamil Nadu', 'Telangana '], dtype=object)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a['Crop']=='Cucumber']['State_Name'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature(in degree celsius)</th>\n",
       "      <th>humidity(in percentage)</th>\n",
       "      <th>ph of soil</th>\n",
       "      <th>rainfall( in mm )</th>\n",
       "      <th>label</th>\n",
       "      <th>water-availability(liters per year)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>20.879744</td>\n",
       "      <td>82.002744</td>\n",
       "      <td>6.502985</td>\n",
       "      <td>202.935536</td>\n",
       "      <td>rice</td>\n",
       "      <td>3000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>21.770462</td>\n",
       "      <td>80.319644</td>\n",
       "      <td>7.038096</td>\n",
       "      <td>226.655537</td>\n",
       "      <td>rice</td>\n",
       "      <td>3020.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>44</td>\n",
       "      <td>23.004459</td>\n",
       "      <td>82.320763</td>\n",
       "      <td>7.840207</td>\n",
       "      <td>263.964248</td>\n",
       "      <td>rice</td>\n",
       "      <td>3040.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>26.491096</td>\n",
       "      <td>80.158363</td>\n",
       "      <td>6.980401</td>\n",
       "      <td>242.864034</td>\n",
       "      <td>rice</td>\n",
       "      <td>3060.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>20.130175</td>\n",
       "      <td>81.604873</td>\n",
       "      <td>7.628473</td>\n",
       "      <td>262.717340</td>\n",
       "      <td>rice</td>\n",
       "      <td>3080.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2216</th>\n",
       "      <td>50</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>32.077828</td>\n",
       "      <td>53.504702</td>\n",
       "      <td>5.186219</td>\n",
       "      <td>838.966732</td>\n",
       "      <td>fingermillet</td>\n",
       "      <td>13.430188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2217</th>\n",
       "      <td>47</td>\n",
       "      <td>27</td>\n",
       "      <td>30</td>\n",
       "      <td>30.485813</td>\n",
       "      <td>58.543432</td>\n",
       "      <td>5.418105</td>\n",
       "      <td>859.584779</td>\n",
       "      <td>fingermillet</td>\n",
       "      <td>24.904736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218</th>\n",
       "      <td>48</td>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>23.913812</td>\n",
       "      <td>51.840114</td>\n",
       "      <td>5.423207</td>\n",
       "      <td>864.427018</td>\n",
       "      <td>fingermillet</td>\n",
       "      <td>24.726521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2219</th>\n",
       "      <td>44</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>21.325485</td>\n",
       "      <td>55.819615</td>\n",
       "      <td>5.242016</td>\n",
       "      <td>871.432052</td>\n",
       "      <td>fingermillet</td>\n",
       "      <td>14.025749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2220</th>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>28.112571</td>\n",
       "      <td>60.167330</td>\n",
       "      <td>6.455560</td>\n",
       "      <td>891.570795</td>\n",
       "      <td>fingermillet</td>\n",
       "      <td>44.588446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2221 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       N   P   K  temperature(in degree celsius)  humidity(in percentage)  \\\n",
       "0     90  42  43                       20.879744                82.002744   \n",
       "1     85  58  41                       21.770462                80.319644   \n",
       "2     60  55  44                       23.004459                82.320763   \n",
       "3     74  35  40                       26.491096                80.158363   \n",
       "4     78  42  42                       20.130175                81.604873   \n",
       "...   ..  ..  ..                             ...                      ...   \n",
       "2216  50  24  23                       32.077828                53.504702   \n",
       "2217  47  27  30                       30.485813                58.543432   \n",
       "2218  48  29  28                       23.913812                51.840114   \n",
       "2219  44  25  20                       21.325485                55.819615   \n",
       "2220  42  30  20                       28.112571                60.167330   \n",
       "\n",
       "      ph of soil  rainfall( in mm )         label  \\\n",
       "0       6.502985         202.935536          rice   \n",
       "1       7.038096         226.655537          rice   \n",
       "2       7.840207         263.964248          rice   \n",
       "3       6.980401         242.864034          rice   \n",
       "4       7.628473         262.717340          rice   \n",
       "...          ...                ...           ...   \n",
       "2216    5.186219         838.966732  fingermillet   \n",
       "2217    5.418105         859.584779  fingermillet   \n",
       "2218    5.423207         864.427018  fingermillet   \n",
       "2219    5.242016         871.432052  fingermillet   \n",
       "2220    6.455560         891.570795  fingermillet   \n",
       "\n",
       "      water-availability(liters per year)  \n",
       "0                             3000.000000  \n",
       "1                             3020.000000  \n",
       "2                             3040.000000  \n",
       "3                             3060.000000  \n",
       "4                             3080.000000  \n",
       "...                                   ...  \n",
       "2216                            13.430188  \n",
       "2217                            24.904736  \n",
       "2218                            24.726521  \n",
       "2219                            14.025749  \n",
       "2220                            44.588446  \n",
       "\n",
       "[2221 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "a = pd.read_csv('dataset/crops1.csv')\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rice\n",
      "maize\n",
      "chickpea\n",
      "kidneybeans\n",
      "pigeonpeas\n",
      "mothbeans\n",
      "mungbean\n",
      "blackgram\n",
      "lentil\n",
      "pomegranate\n",
      "banana\n",
      "mango\n",
      "grapes\n",
      "watermelon\n",
      "muskmelon\n",
      "apple\n",
      "orange\n",
      "papaya\n",
      "coconut\n",
      "cotton\n",
      "Sapota\n",
      "fingermillet\n"
     ]
    }
   ],
   "source": [
    "b = list(a['label'].unique())\n",
    "for i in b:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rice': 3990.2,\n",
       " 'maize': 0.0006487899999999999,\n",
       " 'chickpea': 0.00033079,\n",
       " 'kidneybeans': 0.015198,\n",
       " 'pigeonpeas': 0.03434308511,\n",
       " 'mothbeans': 0.00055734474,\n",
       " 'mungbean': 0.0024994308000000002,\n",
       " 'blackgram': 0.00155628885,\n",
       " 'lentil': 0.0021928924300000006,\n",
       " 'pomegranate': 0.952495,\n",
       " 'banana': 0.00105345,\n",
       " 'mango': 10.534499999999998,\n",
       " 'grapes': 35.345000000000006,\n",
       " 'watermelon': 4.003899999999998,\n",
       " 'muskmelon': 10397.402299999998,\n",
       " 'apple': 1.126225,\n",
       " 'orange': 1.0009603999999999,\n",
       " 'papaya': 17.435,\n",
       " 'coconut': 4555.0,\n",
       " 'cotton': 108274.5,\n",
       " 'Sapota': 2127.9416666666666,\n",
       " 'fingermillet': 35.76485538178218}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict ={}\n",
    "for i in b:\n",
    "    # print(i,a[a['label'] == i]['water-availability(liters per year)'].mean())\n",
    "    dict[i] =a[a['label'] == i]['water-availability(liters per year)'].mean()\n",
    "dict"
   ]
  }
 ],
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