Maize_Prescriptive.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import re\n",
    "import numpy as np\n",
    "from itertools import product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "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>District</th>\n",
       "      <th>Maize_Production</th>\n",
       "      <th>TotalNPK</th>\n",
       "      <th>SowingSeedsDistributed_Maize</th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea</th>\n",
       "      <th>Maize_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BALLARI</td>\n",
       "      <td>104045</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>2859.005</td>\n",
       "      <td>23446.847903</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>75198</td>\n",
       "      <td>0.136288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BIDAR</td>\n",
       "      <td>2791</td>\n",
       "      <td>249.381182</td>\n",
       "      <td>24.550</td>\n",
       "      <td>2371.831678</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>19908</td>\n",
       "      <td>0.010114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KALABURAGI</td>\n",
       "      <td>14621</td>\n",
       "      <td>710.101614</td>\n",
       "      <td>165.000</td>\n",
       "      <td>6221.617910</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>48802</td>\n",
       "      <td>0.013150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>YADGIRI</td>\n",
       "      <td>2673</td>\n",
       "      <td>368.617469</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1253.317144</td>\n",
       "      <td>961.0</td>\n",
       "      <td>22035</td>\n",
       "      <td>0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RAICHUR</td>\n",
       "      <td>723</td>\n",
       "      <td>61.547519</td>\n",
       "      <td>14.350</td>\n",
       "      <td>188.537479</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>26571</td>\n",
       "      <td>0.000525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>KOPPAL</td>\n",
       "      <td>190252</td>\n",
       "      <td>10997.550403</td>\n",
       "      <td>5292.000</td>\n",
       "      <td>38936.400172</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>82552</td>\n",
       "      <td>0.149521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>VIJAYANAGARA</td>\n",
       "      <td>104045</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>2859.005</td>\n",
       "      <td>23446.847903</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>75198</td>\n",
       "      <td>0.136288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        District  Maize_Production      TotalNPK  \\\n",
       "24       BALLARI            104045   8404.154608   \n",
       "25         BIDAR              2791    249.381182   \n",
       "26    KALABURAGI             14621    710.101614   \n",
       "27       YADGIRI              2673    368.617469   \n",
       "28       RAICHUR               723     61.547519   \n",
       "29        KOPPAL            190252  10997.550403   \n",
       "30  VIJAYANAGARA            104045   8404.154608   \n",
       "\n",
       "    SowingSeedsDistributed_Maize  LivestockCensus_2012_Cattle  \\\n",
       "24                      2859.005                 23446.847903   \n",
       "25                        24.550                  2371.831678   \n",
       "26                       165.000                  6221.617910   \n",
       "27                         0.000                  1253.317144   \n",
       "28                        14.350                   188.537479   \n",
       "29                      5292.000                 38936.400172   \n",
       "30                      2859.005                 23446.847903   \n",
       "\n",
       "    AreaUnderCereals_Maize  \\\n",
       "24                 49869.5   \n",
       "25                  1382.0   \n",
       "26                  2634.0   \n",
       "27                   961.0   \n",
       "28                  2600.0   \n",
       "29                 48210.0   \n",
       "30                 49869.5   \n",
       "\n",
       "    NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea  \\\n",
       "24                                              75198                  \n",
       "25                                              19908                  \n",
       "26                                              48802                  \n",
       "27                                              22035                  \n",
       "28                                              26571                  \n",
       "29                                              82552                  \n",
       "30                                              75198                  \n",
       "\n",
       "    Maize_Ratio  \n",
       "24     0.136288  \n",
       "25     0.010114  \n",
       "26     0.013150  \n",
       "27     0.004033  \n",
       "28     0.000525  \n",
       "29     0.149521  \n",
       "30     0.136288  "
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_pattern(col_name):\n",
    "    return re.sub(r'_[0-9]{3}$', '', col_name)\n",
    "\n",
    "Zones = pd.read_excel('Zones.xlsx')\n",
    "Dataset = pd.read_csv('KAG 2016-17/Agriculture/Agriculture_KAG_2016_17.csv')\n",
    "Dataset.columns = map(remove_pattern, Dataset.columns)\n",
    "\n",
    "param = pd.read_csv('Params/3_Param_Maize_Multicollinearity.csv')\n",
    "\n",
    "Working_Directory = pd.read_csv('Working_Directory.csv')\n",
    "Working_Directory = Working_Directory[['District','Maize_Production','TotalNPK', 'SowingSeedsDistributed_Maize',\n",
    "'LivestockCensus_2012_Cattle', 'AreaUnderCereals_Maize','NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea','Maize_Ratio']].copy()\n",
    "\n",
    "for x in param.columns:\n",
    "    if(x != 'SowingSeedsDistributed_Maize' and x != 'AreaUnderCereals_Maize'):\n",
    "        Working_Directory[x] = Working_Directory[x]*Working_Directory['Maize_Ratio']\n",
    "        \n",
    "Working_Directory[24:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>TotalNPK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2567.461578</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>438.180210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25037.139270</td>\n",
       "      <td>14867.0</td>\n",
       "      <td>3036.080250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7729.614277</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>222.225258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.0</td>\n",
       "      <td>10902.406570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.0</td>\n",
       "      <td>25702.835480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LivestockCensus_2012_Cattle  AreaUnderCereals_Maize      TotalNPK\n",
       "0                  2567.461578                  1129.0    438.180210\n",
       "1                 25037.139270                 14867.0   3036.080250\n",
       "2                  7729.614277                  1987.0    222.225258\n",
       "3                 83821.593090                 92194.0  10902.406570\n",
       "4                 85657.632090                173400.0  25702.835480"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Case_file = pd.read_csv('Params/3_Param_Maize_Multicollinearity.csv')\n",
    "for x in Case_file.columns:\n",
    "    Working_Directory[x] = Case_file[x]\n",
    "y = Working_Directory['Maize_Production']\n",
    "Case_file[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>District</th>\n",
       "      <th>Maize_Production</th>\n",
       "      <th>TotalNPK</th>\n",
       "      <th>SowingSeedsDistributed_Maize</th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea</th>\n",
       "      <th>Maize_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>2879</td>\n",
       "      <td>438.180210</td>\n",
       "      <td>4.450</td>\n",
       "      <td>2567.461578</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>9348</td>\n",
       "      <td>0.018798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BENGALURU(R)</td>\n",
       "      <td>42583</td>\n",
       "      <td>3036.080250</td>\n",
       "      <td>134.730</td>\n",
       "      <td>25037.139270</td>\n",
       "      <td>14867.0</td>\n",
       "      <td>20281</td>\n",
       "      <td>0.157645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RAMANAGARA</td>\n",
       "      <td>10450</td>\n",
       "      <td>222.225258</td>\n",
       "      <td>260.150</td>\n",
       "      <td>7729.614277</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>23868</td>\n",
       "      <td>0.029741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHITRADURGA</td>\n",
       "      <td>271975</td>\n",
       "      <td>10902.406570</td>\n",
       "      <td>5004.000</td>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.0</td>\n",
       "      <td>92506</td>\n",
       "      <td>0.303824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DAVANAGERE</td>\n",
       "      <td>593544</td>\n",
       "      <td>25702.835480</td>\n",
       "      <td>6124.600</td>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.0</td>\n",
       "      <td>78364</td>\n",
       "      <td>0.257947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KOLAR</td>\n",
       "      <td>1759</td>\n",
       "      <td>220.782906</td>\n",
       "      <td>51.500</td>\n",
       "      <td>2278.829821</td>\n",
       "      <td>640.0</td>\n",
       "      <td>20140</td>\n",
       "      <td>0.009950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CHIKKABALLAPURA</td>\n",
       "      <td>105491</td>\n",
       "      <td>8196.896749</td>\n",
       "      <td>3538.150</td>\n",
       "      <td>71206.945310</td>\n",
       "      <td>59377.0</td>\n",
       "      <td>50370</td>\n",
       "      <td>0.289061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SHIVAMOGGA</td>\n",
       "      <td>226532</td>\n",
       "      <td>10428.246260</td>\n",
       "      <td>3814.020</td>\n",
       "      <td>104722.000400</td>\n",
       "      <td>43827.0</td>\n",
       "      <td>32347</td>\n",
       "      <td>0.184215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TUMAKURU</td>\n",
       "      <td>66868</td>\n",
       "      <td>2746.313635</td>\n",
       "      <td>1639.850</td>\n",
       "      <td>37944.093760</td>\n",
       "      <td>24207.0</td>\n",
       "      <td>144708</td>\n",
       "      <td>0.071991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHIKKAMAGALURU</td>\n",
       "      <td>61353</td>\n",
       "      <td>8156.290370</td>\n",
       "      <td>2923.790</td>\n",
       "      <td>40572.445630</td>\n",
       "      <td>25831.0</td>\n",
       "      <td>26174</td>\n",
       "      <td>0.120188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DAKSHINA KANNADA</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16589</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UDUPI</td>\n",
       "      <td>109</td>\n",
       "      <td>2.304863</td>\n",
       "      <td>0.000</td>\n",
       "      <td>94.499008</td>\n",
       "      <td>8.0</td>\n",
       "      <td>699</td>\n",
       "      <td>0.000375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HASSAN</td>\n",
       "      <td>333937</td>\n",
       "      <td>17039.273890</td>\n",
       "      <td>8925.330</td>\n",
       "      <td>151008.140300</td>\n",
       "      <td>79058.0</td>\n",
       "      <td>23393</td>\n",
       "      <td>0.248999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KODAGU</td>\n",
       "      <td>17921</td>\n",
       "      <td>3519.022414</td>\n",
       "      <td>246.410</td>\n",
       "      <td>7599.582730</td>\n",
       "      <td>3448.0</td>\n",
       "      <td>106</td>\n",
       "      <td>0.085941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MANDYA</td>\n",
       "      <td>19387</td>\n",
       "      <td>1448.830355</td>\n",
       "      <td>794.030</td>\n",
       "      <td>8774.134623</td>\n",
       "      <td>4892.0</td>\n",
       "      <td>16025</td>\n",
       "      <td>0.023925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MYSURU</td>\n",
       "      <td>137154</td>\n",
       "      <td>6945.331415</td>\n",
       "      <td>1573.430</td>\n",
       "      <td>61547.727440</td>\n",
       "      <td>37283.0</td>\n",
       "      <td>19842</td>\n",
       "      <td>0.111996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CHAMARAJANAGAR</td>\n",
       "      <td>147886</td>\n",
       "      <td>7289.684770</td>\n",
       "      <td>2054.600</td>\n",
       "      <td>80396.926680</td>\n",
       "      <td>38569.0</td>\n",
       "      <td>44070</td>\n",
       "      <td>0.306251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BELAGAVI</td>\n",
       "      <td>576758</td>\n",
       "      <td>48288.815130</td>\n",
       "      <td>11883.580</td>\n",
       "      <td>159047.086200</td>\n",
       "      <td>131163.0</td>\n",
       "      <td>139708</td>\n",
       "      <td>0.268283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>VIJAYAPURA</td>\n",
       "      <td>226327</td>\n",
       "      <td>10420.071650</td>\n",
       "      <td>2682.760</td>\n",
       "      <td>48619.507080</td>\n",
       "      <td>40207.0</td>\n",
       "      <td>113515</td>\n",
       "      <td>0.192153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BAGALKOT</td>\n",
       "      <td>251699</td>\n",
       "      <td>22940.521250</td>\n",
       "      <td>5678.150</td>\n",
       "      <td>75578.052000</td>\n",
       "      <td>44300.0</td>\n",
       "      <td>117497</td>\n",
       "      <td>0.270487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DHARAWAD</td>\n",
       "      <td>90634</td>\n",
       "      <td>6447.725408</td>\n",
       "      <td>2471.350</td>\n",
       "      <td>33424.645600</td>\n",
       "      <td>30900.0</td>\n",
       "      <td>20816</td>\n",
       "      <td>0.172487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>GADAG</td>\n",
       "      <td>138476</td>\n",
       "      <td>5591.244159</td>\n",
       "      <td>4111.000</td>\n",
       "      <td>34261.981770</td>\n",
       "      <td>34163.0</td>\n",
       "      <td>41664</td>\n",
       "      <td>0.240174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>HAVERI</td>\n",
       "      <td>413487</td>\n",
       "      <td>21731.170720</td>\n",
       "      <td>11325.600</td>\n",
       "      <td>95150.640270</td>\n",
       "      <td>170696.0</td>\n",
       "      <td>63016</td>\n",
       "      <td>0.334321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>UTTARA KANNADA</td>\n",
       "      <td>24088</td>\n",
       "      <td>1025.616827</td>\n",
       "      <td>1469.200</td>\n",
       "      <td>22563.771080</td>\n",
       "      <td>4576.0</td>\n",
       "      <td>8460</td>\n",
       "      <td>0.066964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BALLARI</td>\n",
       "      <td>104045</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>2859.005</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>75198</td>\n",
       "      <td>0.136288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BIDAR</td>\n",
       "      <td>2791</td>\n",
       "      <td>249.381182</td>\n",
       "      <td>24.550</td>\n",
       "      <td>2371.831678</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>19908</td>\n",
       "      <td>0.010114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KALABURAGI</td>\n",
       "      <td>14621</td>\n",
       "      <td>710.101614</td>\n",
       "      <td>165.000</td>\n",
       "      <td>6221.617910</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>48802</td>\n",
       "      <td>0.013150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>YADGIRI</td>\n",
       "      <td>2673</td>\n",
       "      <td>368.617469</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1253.317144</td>\n",
       "      <td>961.0</td>\n",
       "      <td>22035</td>\n",
       "      <td>0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RAICHUR</td>\n",
       "      <td>723</td>\n",
       "      <td>61.547519</td>\n",
       "      <td>14.350</td>\n",
       "      <td>188.537479</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>26571</td>\n",
       "      <td>0.000525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>KOPPAL</td>\n",
       "      <td>190252</td>\n",
       "      <td>10997.550400</td>\n",
       "      <td>5292.000</td>\n",
       "      <td>38936.400170</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>82552</td>\n",
       "      <td>0.149521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>VIJAYANAGARA</td>\n",
       "      <td>104045</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>2859.005</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>75198</td>\n",
       "      <td>0.136288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            District  Maize_Production      TotalNPK  \\\n",
       "0          BENGALURU              2879    438.180210   \n",
       "1       BENGALURU(R)             42583   3036.080250   \n",
       "2         RAMANAGARA             10450    222.225258   \n",
       "3        CHITRADURGA            271975  10902.406570   \n",
       "4         DAVANAGERE            593544  25702.835480   \n",
       "5              KOLAR              1759    220.782906   \n",
       "6    CHIKKABALLAPURA            105491   8196.896749   \n",
       "7         SHIVAMOGGA            226532  10428.246260   \n",
       "8           TUMAKURU             66868   2746.313635   \n",
       "9     CHIKKAMAGALURU             61353   8156.290370   \n",
       "10  DAKSHINA KANNADA                 0      0.000000   \n",
       "11             UDUPI               109      2.304863   \n",
       "12            HASSAN            333937  17039.273890   \n",
       "13            KODAGU             17921   3519.022414   \n",
       "14            MANDYA             19387   1448.830355   \n",
       "15            MYSURU            137154   6945.331415   \n",
       "16    CHAMARAJANAGAR            147886   7289.684770   \n",
       "17          BELAGAVI            576758  48288.815130   \n",
       "18        VIJAYAPURA            226327  10420.071650   \n",
       "19          BAGALKOT            251699  22940.521250   \n",
       "20          DHARAWAD             90634   6447.725408   \n",
       "21             GADAG            138476   5591.244159   \n",
       "22            HAVERI            413487  21731.170720   \n",
       "23    UTTARA KANNADA             24088   1025.616827   \n",
       "24           BALLARI            104045   8404.154608   \n",
       "25             BIDAR              2791    249.381182   \n",
       "26        KALABURAGI             14621    710.101614   \n",
       "27           YADGIRI              2673    368.617469   \n",
       "28           RAICHUR               723     61.547519   \n",
       "29            KOPPAL            190252  10997.550400   \n",
       "30      VIJAYANAGARA            104045   8404.154608   \n",
       "\n",
       "    SowingSeedsDistributed_Maize  LivestockCensus_2012_Cattle  \\\n",
       "0                          4.450                  2567.461578   \n",
       "1                        134.730                 25037.139270   \n",
       "2                        260.150                  7729.614277   \n",
       "3                       5004.000                 83821.593090   \n",
       "4                       6124.600                 85657.632090   \n",
       "5                         51.500                  2278.829821   \n",
       "6                       3538.150                 71206.945310   \n",
       "7                       3814.020                104722.000400   \n",
       "8                       1639.850                 37944.093760   \n",
       "9                       2923.790                 40572.445630   \n",
       "10                         0.000                     0.000000   \n",
       "11                         0.000                    94.499008   \n",
       "12                      8925.330                151008.140300   \n",
       "13                       246.410                  7599.582730   \n",
       "14                       794.030                  8774.134623   \n",
       "15                      1573.430                 61547.727440   \n",
       "16                      2054.600                 80396.926680   \n",
       "17                     11883.580                159047.086200   \n",
       "18                      2682.760                 48619.507080   \n",
       "19                      5678.150                 75578.052000   \n",
       "20                      2471.350                 33424.645600   \n",
       "21                      4111.000                 34261.981770   \n",
       "22                     11325.600                 95150.640270   \n",
       "23                      1469.200                 22563.771080   \n",
       "24                      2859.005                 23446.847900   \n",
       "25                        24.550                  2371.831678   \n",
       "26                       165.000                  6221.617910   \n",
       "27                         0.000                  1253.317144   \n",
       "28                        14.350                   188.537479   \n",
       "29                      5292.000                 38936.400170   \n",
       "30                      2859.005                 23446.847900   \n",
       "\n",
       "    AreaUnderCereals_Maize  \\\n",
       "0                   1129.0   \n",
       "1                  14867.0   \n",
       "2                   1987.0   \n",
       "3                  92194.0   \n",
       "4                 173400.0   \n",
       "5                    640.0   \n",
       "6                  59377.0   \n",
       "7                  43827.0   \n",
       "8                  24207.0   \n",
       "9                  25831.0   \n",
       "10                     0.0   \n",
       "11                     8.0   \n",
       "12                 79058.0   \n",
       "13                  3448.0   \n",
       "14                  4892.0   \n",
       "15                 37283.0   \n",
       "16                 38569.0   \n",
       "17                131163.0   \n",
       "18                 40207.0   \n",
       "19                 44300.0   \n",
       "20                 30900.0   \n",
       "21                 34163.0   \n",
       "22                170696.0   \n",
       "23                  4576.0   \n",
       "24                 49869.5   \n",
       "25                  1382.0   \n",
       "26                  2634.0   \n",
       "27                   961.0   \n",
       "28                  2600.0   \n",
       "29                 48210.0   \n",
       "30                 49869.5   \n",
       "\n",
       "    NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea  \\\n",
       "0                                                9348                  \n",
       "1                                               20281                  \n",
       "2                                               23868                  \n",
       "3                                               92506                  \n",
       "4                                               78364                  \n",
       "5                                               20140                  \n",
       "6                                               50370                  \n",
       "7                                               32347                  \n",
       "8                                              144708                  \n",
       "9                                               26174                  \n",
       "10                                              16589                  \n",
       "11                                                699                  \n",
       "12                                              23393                  \n",
       "13                                                106                  \n",
       "14                                              16025                  \n",
       "15                                              19842                  \n",
       "16                                              44070                  \n",
       "17                                             139708                  \n",
       "18                                             113515                  \n",
       "19                                             117497                  \n",
       "20                                              20816                  \n",
       "21                                              41664                  \n",
       "22                                              63016                  \n",
       "23                                               8460                  \n",
       "24                                              75198                  \n",
       "25                                              19908                  \n",
       "26                                              48802                  \n",
       "27                                              22035                  \n",
       "28                                              26571                  \n",
       "29                                              82552                  \n",
       "30                                              75198                  \n",
       "\n",
       "    Maize_Ratio  \n",
       "0      0.018798  \n",
       "1      0.157645  \n",
       "2      0.029741  \n",
       "3      0.303824  \n",
       "4      0.257947  \n",
       "5      0.009950  \n",
       "6      0.289061  \n",
       "7      0.184215  \n",
       "8      0.071991  \n",
       "9      0.120188  \n",
       "10     0.000000  \n",
       "11     0.000375  \n",
       "12     0.248999  \n",
       "13     0.085941  \n",
       "14     0.023925  \n",
       "15     0.111996  \n",
       "16     0.306251  \n",
       "17     0.268283  \n",
       "18     0.192153  \n",
       "19     0.270487  \n",
       "20     0.172487  \n",
       "21     0.240174  \n",
       "22     0.334321  \n",
       "23     0.066964  \n",
       "24     0.136288  \n",
       "25     0.010114  \n",
       "26     0.013150  \n",
       "27     0.004033  \n",
       "28     0.000525  \n",
       "29     0.149521  \n",
       "30     0.136288  "
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Working_Directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>TotalNPK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>8404.154608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2371.831678</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>249.381182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6221.617910</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>710.101614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1253.317144</td>\n",
       "      <td>961.0</td>\n",
       "      <td>368.617469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>188.537479</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>61.547519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>38936.400170</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>10997.550400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>8404.154608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    LivestockCensus_2012_Cattle  AreaUnderCereals_Maize      TotalNPK\n",
       "24                 23446.847900                 49869.5   8404.154608\n",
       "25                  2371.831678                  1382.0    249.381182\n",
       "26                  6221.617910                  2634.0    710.101614\n",
       "27                  1253.317144                   961.0    368.617469\n",
       "28                   188.537479                  2600.0     61.547519\n",
       "29                 38936.400170                 48210.0  10997.550400\n",
       "30                 23446.847900                 49869.5   8404.154608"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if(len(Case_file) < 31):\n",
    "    row_24 = Case_file.iloc[24]\n",
    "    halved_row_24 = row_24 / 2\n",
    "    Case_file.iloc[24] = halved_row_24\n",
    "    Case_file.loc[len(Case_file)] = halved_row_24\n",
    "\n",
    "Case_file[24:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>TotalNPK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2567.461578</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>438.180210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25037.139270</td>\n",
       "      <td>14867.0</td>\n",
       "      <td>3036.080250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7729.614277</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>222.225258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.0</td>\n",
       "      <td>10902.406570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.0</td>\n",
       "      <td>25702.835480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2278.829821</td>\n",
       "      <td>640.0</td>\n",
       "      <td>220.782906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>71206.945310</td>\n",
       "      <td>59377.0</td>\n",
       "      <td>8196.896749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>104722.000400</td>\n",
       "      <td>43827.0</td>\n",
       "      <td>10428.246260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37944.093760</td>\n",
       "      <td>24207.0</td>\n",
       "      <td>2746.313635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>40572.445630</td>\n",
       "      <td>25831.0</td>\n",
       "      <td>8156.290370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>94.499008</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.304863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>151008.140300</td>\n",
       "      <td>79058.0</td>\n",
       "      <td>17039.273890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7599.582730</td>\n",
       "      <td>3448.0</td>\n",
       "      <td>3519.022414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8774.134623</td>\n",
       "      <td>4892.0</td>\n",
       "      <td>1448.830355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>61547.727440</td>\n",
       "      <td>37283.0</td>\n",
       "      <td>6945.331415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>80396.926680</td>\n",
       "      <td>38569.0</td>\n",
       "      <td>7289.684770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>159047.086200</td>\n",
       "      <td>131163.0</td>\n",
       "      <td>48288.815130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>48619.507080</td>\n",
       "      <td>40207.0</td>\n",
       "      <td>10420.071650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>75578.052000</td>\n",
       "      <td>44300.0</td>\n",
       "      <td>22940.521250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>33424.645600</td>\n",
       "      <td>30900.0</td>\n",
       "      <td>6447.725408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>34261.981770</td>\n",
       "      <td>34163.0</td>\n",
       "      <td>5591.244159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>95150.640270</td>\n",
       "      <td>170696.0</td>\n",
       "      <td>21731.170720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>22563.771080</td>\n",
       "      <td>4576.0</td>\n",
       "      <td>1025.616827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>8404.154608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2371.831678</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>249.381182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6221.617910</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>710.101614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1253.317144</td>\n",
       "      <td>961.0</td>\n",
       "      <td>368.617469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>188.537479</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>61.547519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>38936.400170</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>10997.550400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>8404.154608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    LivestockCensus_2012_Cattle  AreaUnderCereals_Maize      TotalNPK\n",
       "0                   2567.461578                  1129.0    438.180210\n",
       "1                  25037.139270                 14867.0   3036.080250\n",
       "2                   7729.614277                  1987.0    222.225258\n",
       "3                  83821.593090                 92194.0  10902.406570\n",
       "4                  85657.632090                173400.0  25702.835480\n",
       "5                   2278.829821                   640.0    220.782906\n",
       "6                  71206.945310                 59377.0   8196.896749\n",
       "7                 104722.000400                 43827.0  10428.246260\n",
       "8                  37944.093760                 24207.0   2746.313635\n",
       "9                  40572.445630                 25831.0   8156.290370\n",
       "10                     0.000000                     0.0      0.000000\n",
       "11                    94.499008                     8.0      2.304863\n",
       "12                151008.140300                 79058.0  17039.273890\n",
       "13                  7599.582730                  3448.0   3519.022414\n",
       "14                  8774.134623                  4892.0   1448.830355\n",
       "15                 61547.727440                 37283.0   6945.331415\n",
       "16                 80396.926680                 38569.0   7289.684770\n",
       "17                159047.086200                131163.0  48288.815130\n",
       "18                 48619.507080                 40207.0  10420.071650\n",
       "19                 75578.052000                 44300.0  22940.521250\n",
       "20                 33424.645600                 30900.0   6447.725408\n",
       "21                 34261.981770                 34163.0   5591.244159\n",
       "22                 95150.640270                170696.0  21731.170720\n",
       "23                 22563.771080                  4576.0   1025.616827\n",
       "24                 23446.847900                 49869.5   8404.154608\n",
       "25                  2371.831678                  1382.0    249.381182\n",
       "26                  6221.617910                  2634.0    710.101614\n",
       "27                  1253.317144                   961.0    368.617469\n",
       "28                   188.537479                  2600.0     61.547519\n",
       "29                 38936.400170                 48210.0  10997.550400\n",
       "30                 23446.847900                 49869.5   8404.154608"
      ]
     },
     "execution_count": 322,
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          "title": {
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   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "# Create a histogram for Maize_Production\n",
    "fig = px.histogram(Working_Directory, x='Maize_Production', nbins=10, title='Frequency Distribution of Maize Production')\n",
    "\n",
    "# Show the histogram\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10th percentile: 1759.0\n",
      "15th percentile: 2732.0\n",
      "20th percentile: 2879.0\n",
      "25th percentile: 12535.5\n",
      "75th percentile: 208289.5\n",
      "90th percentile: 333937.0\n",
      "95th percentile: 495122.5\n",
      "99th percentile: 588508.2\n"
     ]
    }
   ],
   "source": [
    "# List of desired percentiles\n",
    "percentiles = [0.1, 0.15, 0.20, 0.25, 0.75, 0.90, 0.95, 0.99]\n",
    "\n",
    "# Calculate quantiles for the given percentiles\n",
    "quantile_values = Working_Directory['Maize_Production'].quantile(percentiles)\n",
    "\n",
    "# Print each quantile with its corresponding percentile\n",
    "for p, q in zip(percentiles, quantile_values):\n",
    "    print(f\"{int(p*100)}th percentile: {q}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "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>District</th>\n",
       "      <th>Maize_Production</th>\n",
       "      <th>TotalNPK</th>\n",
       "      <th>SowingSeedsDistributed_Maize</th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea</th>\n",
       "      <th>Maize_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>2879</td>\n",
       "      <td>438.180210</td>\n",
       "      <td>4.450</td>\n",
       "      <td>2567.461578</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>9348</td>\n",
       "      <td>0.018798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BENGALURU(R)</td>\n",
       "      <td>42583</td>\n",
       "      <td>3036.080250</td>\n",
       "      <td>134.730</td>\n",
       "      <td>25037.139270</td>\n",
       "      <td>14867.0</td>\n",
       "      <td>20281</td>\n",
       "      <td>0.157645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RAMANAGARA</td>\n",
       "      <td>10450</td>\n",
       "      <td>222.225258</td>\n",
       "      <td>260.150</td>\n",
       "      <td>7729.614277</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>23868</td>\n",
       "      <td>0.029741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHITRADURGA</td>\n",
       "      <td>271975</td>\n",
       "      <td>10902.406570</td>\n",
       "      <td>5004.000</td>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.0</td>\n",
       "      <td>92506</td>\n",
       "      <td>0.303824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DAVANAGERE</td>\n",
       "      <td>593544</td>\n",
       "      <td>25702.835480</td>\n",
       "      <td>6124.600</td>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.0</td>\n",
       "      <td>78364</td>\n",
       "      <td>0.257947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KOLAR</td>\n",
       "      <td>1759</td>\n",
       "      <td>220.782906</td>\n",
       "      <td>51.500</td>\n",
       "      <td>2278.829821</td>\n",
       "      <td>640.0</td>\n",
       "      <td>20140</td>\n",
       "      <td>0.009950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CHIKKABALLAPURA</td>\n",
       "      <td>105491</td>\n",
       "      <td>8196.896749</td>\n",
       "      <td>3538.150</td>\n",
       "      <td>71206.945310</td>\n",
       "      <td>59377.0</td>\n",
       "      <td>50370</td>\n",
       "      <td>0.289061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SHIVAMOGGA</td>\n",
       "      <td>226532</td>\n",
       "      <td>10428.246260</td>\n",
       "      <td>3814.020</td>\n",
       "      <td>104722.000400</td>\n",
       "      <td>43827.0</td>\n",
       "      <td>32347</td>\n",
       "      <td>0.184215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TUMAKURU</td>\n",
       "      <td>66868</td>\n",
       "      <td>2746.313635</td>\n",
       "      <td>1639.850</td>\n",
       "      <td>37944.093760</td>\n",
       "      <td>24207.0</td>\n",
       "      <td>144708</td>\n",
       "      <td>0.071991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHIKKAMAGALURU</td>\n",
       "      <td>61353</td>\n",
       "      <td>8156.290370</td>\n",
       "      <td>2923.790</td>\n",
       "      <td>40572.445630</td>\n",
       "      <td>25831.0</td>\n",
       "      <td>26174</td>\n",
       "      <td>0.120188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DAKSHINA KANNADA</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16589</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UDUPI</td>\n",
       "      <td>109</td>\n",
       "      <td>2.304863</td>\n",
       "      <td>0.000</td>\n",
       "      <td>94.499008</td>\n",
       "      <td>8.0</td>\n",
       "      <td>699</td>\n",
       "      <td>0.000375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HASSAN</td>\n",
       "      <td>333937</td>\n",
       "      <td>17039.273890</td>\n",
       "      <td>8925.330</td>\n",
       "      <td>151008.140300</td>\n",
       "      <td>79058.0</td>\n",
       "      <td>23393</td>\n",
       "      <td>0.248999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KODAGU</td>\n",
       "      <td>17921</td>\n",
       "      <td>3519.022414</td>\n",
       "      <td>246.410</td>\n",
       "      <td>7599.582730</td>\n",
       "      <td>3448.0</td>\n",
       "      <td>106</td>\n",
       "      <td>0.085941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MANDYA</td>\n",
       "      <td>19387</td>\n",
       "      <td>1448.830355</td>\n",
       "      <td>794.030</td>\n",
       "      <td>8774.134623</td>\n",
       "      <td>4892.0</td>\n",
       "      <td>16025</td>\n",
       "      <td>0.023925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MYSURU</td>\n",
       "      <td>137154</td>\n",
       "      <td>6945.331415</td>\n",
       "      <td>1573.430</td>\n",
       "      <td>61547.727440</td>\n",
       "      <td>37283.0</td>\n",
       "      <td>19842</td>\n",
       "      <td>0.111996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CHAMARAJANAGAR</td>\n",
       "      <td>147886</td>\n",
       "      <td>7289.684770</td>\n",
       "      <td>2054.600</td>\n",
       "      <td>80396.926680</td>\n",
       "      <td>38569.0</td>\n",
       "      <td>44070</td>\n",
       "      <td>0.306251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BELAGAVI</td>\n",
       "      <td>576758</td>\n",
       "      <td>48288.815130</td>\n",
       "      <td>11883.580</td>\n",
       "      <td>159047.086200</td>\n",
       "      <td>131163.0</td>\n",
       "      <td>139708</td>\n",
       "      <td>0.268283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>VIJAYAPURA</td>\n",
       "      <td>226327</td>\n",
       "      <td>10420.071650</td>\n",
       "      <td>2682.760</td>\n",
       "      <td>48619.507080</td>\n",
       "      <td>40207.0</td>\n",
       "      <td>113515</td>\n",
       "      <td>0.192153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BAGALKOT</td>\n",
       "      <td>251699</td>\n",
       "      <td>22940.521250</td>\n",
       "      <td>5678.150</td>\n",
       "      <td>75578.052000</td>\n",
       "      <td>44300.0</td>\n",
       "      <td>117497</td>\n",
       "      <td>0.270487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DHARAWAD</td>\n",
       "      <td>90634</td>\n",
       "      <td>6447.725408</td>\n",
       "      <td>2471.350</td>\n",
       "      <td>33424.645600</td>\n",
       "      <td>30900.0</td>\n",
       "      <td>20816</td>\n",
       "      <td>0.172487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>GADAG</td>\n",
       "      <td>138476</td>\n",
       "      <td>5591.244159</td>\n",
       "      <td>4111.000</td>\n",
       "      <td>34261.981770</td>\n",
       "      <td>34163.0</td>\n",
       "      <td>41664</td>\n",
       "      <td>0.240174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>HAVERI</td>\n",
       "      <td>413487</td>\n",
       "      <td>21731.170720</td>\n",
       "      <td>11325.600</td>\n",
       "      <td>95150.640270</td>\n",
       "      <td>170696.0</td>\n",
       "      <td>63016</td>\n",
       "      <td>0.334321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>UTTARA KANNADA</td>\n",
       "      <td>24088</td>\n",
       "      <td>1025.616827</td>\n",
       "      <td>1469.200</td>\n",
       "      <td>22563.771080</td>\n",
       "      <td>4576.0</td>\n",
       "      <td>8460</td>\n",
       "      <td>0.066964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BALLARI</td>\n",
       "      <td>104045</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>2859.005</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>75198</td>\n",
       "      <td>0.136288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BIDAR</td>\n",
       "      <td>2791</td>\n",
       "      <td>249.381182</td>\n",
       "      <td>24.550</td>\n",
       "      <td>2371.831678</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>19908</td>\n",
       "      <td>0.010114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KALABURAGI</td>\n",
       "      <td>14621</td>\n",
       "      <td>710.101614</td>\n",
       "      <td>165.000</td>\n",
       "      <td>6221.617910</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>48802</td>\n",
       "      <td>0.013150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>YADGIRI</td>\n",
       "      <td>2673</td>\n",
       "      <td>368.617469</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1253.317144</td>\n",
       "      <td>961.0</td>\n",
       "      <td>22035</td>\n",
       "      <td>0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RAICHUR</td>\n",
       "      <td>723</td>\n",
       "      <td>61.547519</td>\n",
       "      <td>14.350</td>\n",
       "      <td>188.537479</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>26571</td>\n",
       "      <td>0.000525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>KOPPAL</td>\n",
       "      <td>190252</td>\n",
       "      <td>10997.550400</td>\n",
       "      <td>5292.000</td>\n",
       "      <td>38936.400170</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>82552</td>\n",
       "      <td>0.149521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>VIJAYANAGARA</td>\n",
       "      <td>104045</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>2859.005</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>75198</td>\n",
       "      <td>0.136288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            District  Maize_Production      TotalNPK  \\\n",
       "0          BENGALURU              2879    438.180210   \n",
       "1       BENGALURU(R)             42583   3036.080250   \n",
       "2         RAMANAGARA             10450    222.225258   \n",
       "3        CHITRADURGA            271975  10902.406570   \n",
       "4         DAVANAGERE            593544  25702.835480   \n",
       "5              KOLAR              1759    220.782906   \n",
       "6    CHIKKABALLAPURA            105491   8196.896749   \n",
       "7         SHIVAMOGGA            226532  10428.246260   \n",
       "8           TUMAKURU             66868   2746.313635   \n",
       "9     CHIKKAMAGALURU             61353   8156.290370   \n",
       "10  DAKSHINA KANNADA                 0      0.000000   \n",
       "11             UDUPI               109      2.304863   \n",
       "12            HASSAN            333937  17039.273890   \n",
       "13            KODAGU             17921   3519.022414   \n",
       "14            MANDYA             19387   1448.830355   \n",
       "15            MYSURU            137154   6945.331415   \n",
       "16    CHAMARAJANAGAR            147886   7289.684770   \n",
       "17          BELAGAVI            576758  48288.815130   \n",
       "18        VIJAYAPURA            226327  10420.071650   \n",
       "19          BAGALKOT            251699  22940.521250   \n",
       "20          DHARAWAD             90634   6447.725408   \n",
       "21             GADAG            138476   5591.244159   \n",
       "22            HAVERI            413487  21731.170720   \n",
       "23    UTTARA KANNADA             24088   1025.616827   \n",
       "24           BALLARI            104045   8404.154608   \n",
       "25             BIDAR              2791    249.381182   \n",
       "26        KALABURAGI             14621    710.101614   \n",
       "27           YADGIRI              2673    368.617469   \n",
       "28           RAICHUR               723     61.547519   \n",
       "29            KOPPAL            190252  10997.550400   \n",
       "30      VIJAYANAGARA            104045   8404.154608   \n",
       "\n",
       "    SowingSeedsDistributed_Maize  LivestockCensus_2012_Cattle  \\\n",
       "0                          4.450                  2567.461578   \n",
       "1                        134.730                 25037.139270   \n",
       "2                        260.150                  7729.614277   \n",
       "3                       5004.000                 83821.593090   \n",
       "4                       6124.600                 85657.632090   \n",
       "5                         51.500                  2278.829821   \n",
       "6                       3538.150                 71206.945310   \n",
       "7                       3814.020                104722.000400   \n",
       "8                       1639.850                 37944.093760   \n",
       "9                       2923.790                 40572.445630   \n",
       "10                         0.000                     0.000000   \n",
       "11                         0.000                    94.499008   \n",
       "12                      8925.330                151008.140300   \n",
       "13                       246.410                  7599.582730   \n",
       "14                       794.030                  8774.134623   \n",
       "15                      1573.430                 61547.727440   \n",
       "16                      2054.600                 80396.926680   \n",
       "17                     11883.580                159047.086200   \n",
       "18                      2682.760                 48619.507080   \n",
       "19                      5678.150                 75578.052000   \n",
       "20                      2471.350                 33424.645600   \n",
       "21                      4111.000                 34261.981770   \n",
       "22                     11325.600                 95150.640270   \n",
       "23                      1469.200                 22563.771080   \n",
       "24                      2859.005                 23446.847900   \n",
       "25                        24.550                  2371.831678   \n",
       "26                       165.000                  6221.617910   \n",
       "27                         0.000                  1253.317144   \n",
       "28                        14.350                   188.537479   \n",
       "29                      5292.000                 38936.400170   \n",
       "30                      2859.005                 23446.847900   \n",
       "\n",
       "    AreaUnderCereals_Maize  \\\n",
       "0                   1129.0   \n",
       "1                  14867.0   \n",
       "2                   1987.0   \n",
       "3                  92194.0   \n",
       "4                 173400.0   \n",
       "5                    640.0   \n",
       "6                  59377.0   \n",
       "7                  43827.0   \n",
       "8                  24207.0   \n",
       "9                  25831.0   \n",
       "10                     0.0   \n",
       "11                     8.0   \n",
       "12                 79058.0   \n",
       "13                  3448.0   \n",
       "14                  4892.0   \n",
       "15                 37283.0   \n",
       "16                 38569.0   \n",
       "17                131163.0   \n",
       "18                 40207.0   \n",
       "19                 44300.0   \n",
       "20                 30900.0   \n",
       "21                 34163.0   \n",
       "22                170696.0   \n",
       "23                  4576.0   \n",
       "24                 49869.5   \n",
       "25                  1382.0   \n",
       "26                  2634.0   \n",
       "27                   961.0   \n",
       "28                  2600.0   \n",
       "29                 48210.0   \n",
       "30                 49869.5   \n",
       "\n",
       "    NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea  \\\n",
       "0                                                9348                  \n",
       "1                                               20281                  \n",
       "2                                               23868                  \n",
       "3                                               92506                  \n",
       "4                                               78364                  \n",
       "5                                               20140                  \n",
       "6                                               50370                  \n",
       "7                                               32347                  \n",
       "8                                              144708                  \n",
       "9                                               26174                  \n",
       "10                                              16589                  \n",
       "11                                                699                  \n",
       "12                                              23393                  \n",
       "13                                                106                  \n",
       "14                                              16025                  \n",
       "15                                              19842                  \n",
       "16                                              44070                  \n",
       "17                                             139708                  \n",
       "18                                             113515                  \n",
       "19                                             117497                  \n",
       "20                                              20816                  \n",
       "21                                              41664                  \n",
       "22                                              63016                  \n",
       "23                                               8460                  \n",
       "24                                              75198                  \n",
       "25                                              19908                  \n",
       "26                                              48802                  \n",
       "27                                              22035                  \n",
       "28                                              26571                  \n",
       "29                                              82552                  \n",
       "30                                              75198                  \n",
       "\n",
       "    Maize_Ratio  \n",
       "0      0.018798  \n",
       "1      0.157645  \n",
       "2      0.029741  \n",
       "3      0.303824  \n",
       "4      0.257947  \n",
       "5      0.009950  \n",
       "6      0.289061  \n",
       "7      0.184215  \n",
       "8      0.071991  \n",
       "9      0.120188  \n",
       "10     0.000000  \n",
       "11     0.000375  \n",
       "12     0.248999  \n",
       "13     0.085941  \n",
       "14     0.023925  \n",
       "15     0.111996  \n",
       "16     0.306251  \n",
       "17     0.268283  \n",
       "18     0.192153  \n",
       "19     0.270487  \n",
       "20     0.172487  \n",
       "21     0.240174  \n",
       "22     0.334321  \n",
       "23     0.066964  \n",
       "24     0.136288  \n",
       "25     0.010114  \n",
       "26     0.013150  \n",
       "27     0.004033  \n",
       "28     0.000525  \n",
       "29     0.149521  \n",
       "30     0.136288  "
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   "source": [
    "Finding the delta after removing the districts that are outliers, then for rest of the calculation we only remove the lower outliers.\n",
    "We tried replacement using the Q1 and Q3 percentile methods :\n",
    "1) We tried finding outliers using the lower and upper bounds, but that was giving unsatisfactory results with the partitioning.\n",
    "2) We used the Q1 and Q3 values as the bounds and replaced values below and above them with Q1 and Q3 resp., this wasn't as successful as we anticipated (Percentage changes of 500% for 5% delta, 1000% for 10% delta) so we are back to the discarding method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "134853.12903225806\n"
     ]
    }
   ],
   "source": [
    "print((Working_Directory['Maize_Production'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115742.80769230769\n",
      "4259.335323076923\n",
      "5787.140384615384\n",
      "9259.424615384616\n",
      "125002.2323076923\n",
      "28\n"
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    "# Ensure that the index of y and Case_file are aligned\n",
    "y = y.loc[Case_file.index]\n",
    "\n",
    "# Identify rows in Case_file that have any zeros\n",
    "rows_to_remove = (Case_file == 0).any(axis=1)\n",
    "\n",
    "rows_to_remove |= Working_Directory['Maize_Production']  < 1000\n",
    "\n",
    "rows_to_remove |= Working_Directory['Maize_Production'] > 500000\n",
    "\n",
    "delta_O = 0.08*(Working_Directory[~rows_to_remove]['Maize_Production'].mean())\n",
    "\n",
    "print((Working_Directory[~rows_to_remove]['Maize_Production'].mean()))\n",
    "\n",
    "print(0.0368*(Working_Directory[~rows_to_remove]['Maize_Production'].mean()))\n",
    "print(0.05*(Working_Directory[~rows_to_remove]['Maize_Production'].mean()))\n",
    "print(0.08*(Working_Directory[~rows_to_remove]['Maize_Production'].mean()))\n",
    "\n",
    "Target = Working_Directory[~rows_to_remove]['Maize_Production'].mean() + delta_O\n",
    "print(Target)\n",
    "rows_to_remove = (Case_file == 0).any(axis=1)\n",
    "\n",
    "rows_to_remove |= Working_Directory['Maize_Production']  < 1000\n",
    "\n",
    "# rows_to_remove |= Working_Directory['Maize_Production'] > 500000\n",
    "\n",
    "# Update Case_file and y\n",
    "Case_file = Case_file[~rows_to_remove].reset_index(drop=True)\n",
    "y = Working_Directory['Maize_Production'][~rows_to_remove].reset_index(drop=True)\n",
    "\n",
    "# Verify the length of Case_file\n",
    "case_file_length = len(Case_file)\n",
    "print(case_file_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
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   "source": [
    "import plotly.express as px\n",
    "import pandas as pd\n",
    "\n",
    "# Melt the DataFrame to a long format for easier plotting\n",
    "Case_file_melt = Case_file.melt(var_name='Columns', value_name='Values')\n",
    "\n",
    "# Create box plots for each column\n",
    "fig = px.box(Case_file_melt, x='Columns', y='Values', title='Box Plots of Case_file Columns and Maize Production',points = \"all\")\n",
    "\n",
    "# Show the figure\n",
    "fig.show()\n"
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        "title": {
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    "Case_file_melt = Working_Directory[['Maize_Production']].melt(var_name='Maize_Production', value_name='Production')\n",
    "\n",
    "# Create box plots for each column\n",
    "fig = px.box(Case_file_melt[1:], x='Maize_Production', y='Production', title='Box Plot of Maize Production',points = \"all\")\n",
    "\n",
    "# Show the figure\n",
    "fig.show()"
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   "source": [
    "TotalNPK_data = Case_file[['TotalNPK']]\n",
    "\n",
    "fig = px.box(TotalNPK_data, y='TotalNPK', title='Box Plot of TotalNPK', points='all')\n",
    "\n",
    "fig.show()"
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  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
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       "      <th></th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>TotalNPK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2567.461578</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>438.180210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25037.139270</td>\n",
       "      <td>14867.0</td>\n",
       "      <td>3036.080250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7729.614277</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>222.225258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.0</td>\n",
       "      <td>10902.406570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.0</td>\n",
       "      <td>25702.835480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2278.829821</td>\n",
       "      <td>640.0</td>\n",
       "      <td>220.782906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>71206.945310</td>\n",
       "      <td>59377.0</td>\n",
       "      <td>8196.896749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>104722.000400</td>\n",
       "      <td>43827.0</td>\n",
       "      <td>10428.246260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37944.093760</td>\n",
       "      <td>24207.0</td>\n",
       "      <td>2746.313635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>40572.445630</td>\n",
       "      <td>25831.0</td>\n",
       "      <td>8156.290370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>151008.140300</td>\n",
       "      <td>79058.0</td>\n",
       "      <td>17039.273890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7599.582730</td>\n",
       "      <td>3448.0</td>\n",
       "      <td>3519.022414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8774.134623</td>\n",
       "      <td>4892.0</td>\n",
       "      <td>1448.830355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>61547.727440</td>\n",
       "      <td>37283.0</td>\n",
       "      <td>6945.331415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>80396.926680</td>\n",
       "      <td>38569.0</td>\n",
       "      <td>7289.684770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>159047.086200</td>\n",
       "      <td>131163.0</td>\n",
       "      <td>48288.815130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>48619.507080</td>\n",
       "      <td>40207.0</td>\n",
       "      <td>10420.071650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>75578.052000</td>\n",
       "      <td>44300.0</td>\n",
       "      <td>22940.521250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>33424.645600</td>\n",
       "      <td>30900.0</td>\n",
       "      <td>6447.725408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>34261.981770</td>\n",
       "      <td>34163.0</td>\n",
       "      <td>5591.244159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>95150.640270</td>\n",
       "      <td>170696.0</td>\n",
       "      <td>21731.170720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22563.771080</td>\n",
       "      <td>4576.0</td>\n",
       "      <td>1025.616827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>8404.154608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2371.831678</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>249.381182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6221.617910</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>710.101614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1253.317144</td>\n",
       "      <td>961.0</td>\n",
       "      <td>368.617469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>38936.400170</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>10997.550400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>8404.154608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    LivestockCensus_2012_Cattle  AreaUnderCereals_Maize      TotalNPK\n",
       "0                   2567.461578                  1129.0    438.180210\n",
       "1                  25037.139270                 14867.0   3036.080250\n",
       "2                   7729.614277                  1987.0    222.225258\n",
       "3                  83821.593090                 92194.0  10902.406570\n",
       "4                  85657.632090                173400.0  25702.835480\n",
       "5                   2278.829821                   640.0    220.782906\n",
       "6                  71206.945310                 59377.0   8196.896749\n",
       "7                 104722.000400                 43827.0  10428.246260\n",
       "8                  37944.093760                 24207.0   2746.313635\n",
       "9                  40572.445630                 25831.0   8156.290370\n",
       "10                151008.140300                 79058.0  17039.273890\n",
       "11                  7599.582730                  3448.0   3519.022414\n",
       "12                  8774.134623                  4892.0   1448.830355\n",
       "13                 61547.727440                 37283.0   6945.331415\n",
       "14                 80396.926680                 38569.0   7289.684770\n",
       "15                159047.086200                131163.0  48288.815130\n",
       "16                 48619.507080                 40207.0  10420.071650\n",
       "17                 75578.052000                 44300.0  22940.521250\n",
       "18                 33424.645600                 30900.0   6447.725408\n",
       "19                 34261.981770                 34163.0   5591.244159\n",
       "20                 95150.640270                170696.0  21731.170720\n",
       "21                 22563.771080                  4576.0   1025.616827\n",
       "22                 23446.847900                 49869.5   8404.154608\n",
       "23                  2371.831678                  1382.0    249.381182\n",
       "24                  6221.617910                  2634.0    710.101614\n",
       "25                  1253.317144                   961.0    368.617469\n",
       "26                 38936.400170                 48210.0  10997.550400\n",
       "27                 23446.847900                 49869.5   8404.154608"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Case_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "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>const</th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>TotalNPK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2567.461578</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>438.180210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>25037.139270</td>\n",
       "      <td>14867.0</td>\n",
       "      <td>3036.080250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7729.614277</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>222.225258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.0</td>\n",
       "      <td>10902.406570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.0</td>\n",
       "      <td>25702.835480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   const  LivestockCensus_2012_Cattle  AreaUnderCereals_Maize      TotalNPK\n",
       "0    1.0                  2567.461578                  1129.0    438.180210\n",
       "1    1.0                 25037.139270                 14867.0   3036.080250\n",
       "2    1.0                  7729.614277                  1987.0    222.225258\n",
       "3    1.0                 83821.593090                 92194.0  10902.406570\n",
       "4    1.0                 85657.632090                173400.0  25702.835480"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Case_file =sm.add_constant(Case_file)\n",
    "Case_file.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Case_file_1 = Case_file[['const','NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea','AreaUnderCereals_Maize','TotalNPK']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "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>District</th>\n",
       "      <th>Maize_Production</th>\n",
       "      <th>TotalNPK</th>\n",
       "      <th>SowingSeedsDistributed_Maize</th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea</th>\n",
       "      <th>Maize_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>2879</td>\n",
       "      <td>438.180210</td>\n",
       "      <td>4.450</td>\n",
       "      <td>2567.461578</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>9348</td>\n",
       "      <td>0.018798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BENGALURU(R)</td>\n",
       "      <td>42583</td>\n",
       "      <td>3036.080250</td>\n",
       "      <td>134.730</td>\n",
       "      <td>25037.139270</td>\n",
       "      <td>14867.0</td>\n",
       "      <td>20281</td>\n",
       "      <td>0.157645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RAMANAGARA</td>\n",
       "      <td>10450</td>\n",
       "      <td>222.225258</td>\n",
       "      <td>260.150</td>\n",
       "      <td>7729.614277</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>23868</td>\n",
       "      <td>0.029741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHITRADURGA</td>\n",
       "      <td>271975</td>\n",
       "      <td>10902.406570</td>\n",
       "      <td>5004.000</td>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.0</td>\n",
       "      <td>92506</td>\n",
       "      <td>0.303824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DAVANAGERE</td>\n",
       "      <td>593544</td>\n",
       "      <td>25702.835480</td>\n",
       "      <td>6124.600</td>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.0</td>\n",
       "      <td>78364</td>\n",
       "      <td>0.257947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KOLAR</td>\n",
       "      <td>1759</td>\n",
       "      <td>220.782906</td>\n",
       "      <td>51.500</td>\n",
       "      <td>2278.829821</td>\n",
       "      <td>640.0</td>\n",
       "      <td>20140</td>\n",
       "      <td>0.009950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CHIKKABALLAPURA</td>\n",
       "      <td>105491</td>\n",
       "      <td>8196.896749</td>\n",
       "      <td>3538.150</td>\n",
       "      <td>71206.945310</td>\n",
       "      <td>59377.0</td>\n",
       "      <td>50370</td>\n",
       "      <td>0.289061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SHIVAMOGGA</td>\n",
       "      <td>226532</td>\n",
       "      <td>10428.246260</td>\n",
       "      <td>3814.020</td>\n",
       "      <td>104722.000400</td>\n",
       "      <td>43827.0</td>\n",
       "      <td>32347</td>\n",
       "      <td>0.184215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TUMAKURU</td>\n",
       "      <td>66868</td>\n",
       "      <td>2746.313635</td>\n",
       "      <td>1639.850</td>\n",
       "      <td>37944.093760</td>\n",
       "      <td>24207.0</td>\n",
       "      <td>144708</td>\n",
       "      <td>0.071991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHIKKAMAGALURU</td>\n",
       "      <td>61353</td>\n",
       "      <td>8156.290370</td>\n",
       "      <td>2923.790</td>\n",
       "      <td>40572.445630</td>\n",
       "      <td>25831.0</td>\n",
       "      <td>26174</td>\n",
       "      <td>0.120188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DAKSHINA KANNADA</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16589</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UDUPI</td>\n",
       "      <td>109</td>\n",
       "      <td>2.304863</td>\n",
       "      <td>0.000</td>\n",
       "      <td>94.499008</td>\n",
       "      <td>8.0</td>\n",
       "      <td>699</td>\n",
       "      <td>0.000375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HASSAN</td>\n",
       "      <td>333937</td>\n",
       "      <td>17039.273890</td>\n",
       "      <td>8925.330</td>\n",
       "      <td>151008.140300</td>\n",
       "      <td>79058.0</td>\n",
       "      <td>23393</td>\n",
       "      <td>0.248999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KODAGU</td>\n",
       "      <td>17921</td>\n",
       "      <td>3519.022414</td>\n",
       "      <td>246.410</td>\n",
       "      <td>7599.582730</td>\n",
       "      <td>3448.0</td>\n",
       "      <td>106</td>\n",
       "      <td>0.085941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MANDYA</td>\n",
       "      <td>19387</td>\n",
       "      <td>1448.830355</td>\n",
       "      <td>794.030</td>\n",
       "      <td>8774.134623</td>\n",
       "      <td>4892.0</td>\n",
       "      <td>16025</td>\n",
       "      <td>0.023925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MYSURU</td>\n",
       "      <td>137154</td>\n",
       "      <td>6945.331415</td>\n",
       "      <td>1573.430</td>\n",
       "      <td>61547.727440</td>\n",
       "      <td>37283.0</td>\n",
       "      <td>19842</td>\n",
       "      <td>0.111996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CHAMARAJANAGAR</td>\n",
       "      <td>147886</td>\n",
       "      <td>7289.684770</td>\n",
       "      <td>2054.600</td>\n",
       "      <td>80396.926680</td>\n",
       "      <td>38569.0</td>\n",
       "      <td>44070</td>\n",
       "      <td>0.306251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BELAGAVI</td>\n",
       "      <td>576758</td>\n",
       "      <td>48288.815130</td>\n",
       "      <td>11883.580</td>\n",
       "      <td>159047.086200</td>\n",
       "      <td>131163.0</td>\n",
       "      <td>139708</td>\n",
       "      <td>0.268283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>VIJAYAPURA</td>\n",
       "      <td>226327</td>\n",
       "      <td>10420.071650</td>\n",
       "      <td>2682.760</td>\n",
       "      <td>48619.507080</td>\n",
       "      <td>40207.0</td>\n",
       "      <td>113515</td>\n",
       "      <td>0.192153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BAGALKOT</td>\n",
       "      <td>251699</td>\n",
       "      <td>22940.521250</td>\n",
       "      <td>5678.150</td>\n",
       "      <td>75578.052000</td>\n",
       "      <td>44300.0</td>\n",
       "      <td>117497</td>\n",
       "      <td>0.270487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DHARAWAD</td>\n",
       "      <td>90634</td>\n",
       "      <td>6447.725408</td>\n",
       "      <td>2471.350</td>\n",
       "      <td>33424.645600</td>\n",
       "      <td>30900.0</td>\n",
       "      <td>20816</td>\n",
       "      <td>0.172487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>GADAG</td>\n",
       "      <td>138476</td>\n",
       "      <td>5591.244159</td>\n",
       "      <td>4111.000</td>\n",
       "      <td>34261.981770</td>\n",
       "      <td>34163.0</td>\n",
       "      <td>41664</td>\n",
       "      <td>0.240174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>HAVERI</td>\n",
       "      <td>413487</td>\n",
       "      <td>21731.170720</td>\n",
       "      <td>11325.600</td>\n",
       "      <td>95150.640270</td>\n",
       "      <td>170696.0</td>\n",
       "      <td>63016</td>\n",
       "      <td>0.334321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>UTTARA KANNADA</td>\n",
       "      <td>24088</td>\n",
       "      <td>1025.616827</td>\n",
       "      <td>1469.200</td>\n",
       "      <td>22563.771080</td>\n",
       "      <td>4576.0</td>\n",
       "      <td>8460</td>\n",
       "      <td>0.066964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BALLARI</td>\n",
       "      <td>104045</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>2859.005</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>75198</td>\n",
       "      <td>0.136288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BIDAR</td>\n",
       "      <td>2791</td>\n",
       "      <td>249.381182</td>\n",
       "      <td>24.550</td>\n",
       "      <td>2371.831678</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>19908</td>\n",
       "      <td>0.010114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KALABURAGI</td>\n",
       "      <td>14621</td>\n",
       "      <td>710.101614</td>\n",
       "      <td>165.000</td>\n",
       "      <td>6221.617910</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>48802</td>\n",
       "      <td>0.013150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>YADGIRI</td>\n",
       "      <td>2673</td>\n",
       "      <td>368.617469</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1253.317144</td>\n",
       "      <td>961.0</td>\n",
       "      <td>22035</td>\n",
       "      <td>0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RAICHUR</td>\n",
       "      <td>723</td>\n",
       "      <td>61.547519</td>\n",
       "      <td>14.350</td>\n",
       "      <td>188.537479</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>26571</td>\n",
       "      <td>0.000525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>KOPPAL</td>\n",
       "      <td>190252</td>\n",
       "      <td>10997.550400</td>\n",
       "      <td>5292.000</td>\n",
       "      <td>38936.400170</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>82552</td>\n",
       "      <td>0.149521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>VIJAYANAGARA</td>\n",
       "      <td>104045</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>2859.005</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>75198</td>\n",
       "      <td>0.136288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            District  Maize_Production      TotalNPK  \\\n",
       "0          BENGALURU              2879    438.180210   \n",
       "1       BENGALURU(R)             42583   3036.080250   \n",
       "2         RAMANAGARA             10450    222.225258   \n",
       "3        CHITRADURGA            271975  10902.406570   \n",
       "4         DAVANAGERE            593544  25702.835480   \n",
       "5              KOLAR              1759    220.782906   \n",
       "6    CHIKKABALLAPURA            105491   8196.896749   \n",
       "7         SHIVAMOGGA            226532  10428.246260   \n",
       "8           TUMAKURU             66868   2746.313635   \n",
       "9     CHIKKAMAGALURU             61353   8156.290370   \n",
       "10  DAKSHINA KANNADA                 0      0.000000   \n",
       "11             UDUPI               109      2.304863   \n",
       "12            HASSAN            333937  17039.273890   \n",
       "13            KODAGU             17921   3519.022414   \n",
       "14            MANDYA             19387   1448.830355   \n",
       "15            MYSURU            137154   6945.331415   \n",
       "16    CHAMARAJANAGAR            147886   7289.684770   \n",
       "17          BELAGAVI            576758  48288.815130   \n",
       "18        VIJAYAPURA            226327  10420.071650   \n",
       "19          BAGALKOT            251699  22940.521250   \n",
       "20          DHARAWAD             90634   6447.725408   \n",
       "21             GADAG            138476   5591.244159   \n",
       "22            HAVERI            413487  21731.170720   \n",
       "23    UTTARA KANNADA             24088   1025.616827   \n",
       "24           BALLARI            104045   8404.154608   \n",
       "25             BIDAR              2791    249.381182   \n",
       "26        KALABURAGI             14621    710.101614   \n",
       "27           YADGIRI              2673    368.617469   \n",
       "28           RAICHUR               723     61.547519   \n",
       "29            KOPPAL            190252  10997.550400   \n",
       "30      VIJAYANAGARA            104045   8404.154608   \n",
       "\n",
       "    SowingSeedsDistributed_Maize  LivestockCensus_2012_Cattle  \\\n",
       "0                          4.450                  2567.461578   \n",
       "1                        134.730                 25037.139270   \n",
       "2                        260.150                  7729.614277   \n",
       "3                       5004.000                 83821.593090   \n",
       "4                       6124.600                 85657.632090   \n",
       "5                         51.500                  2278.829821   \n",
       "6                       3538.150                 71206.945310   \n",
       "7                       3814.020                104722.000400   \n",
       "8                       1639.850                 37944.093760   \n",
       "9                       2923.790                 40572.445630   \n",
       "10                         0.000                     0.000000   \n",
       "11                         0.000                    94.499008   \n",
       "12                      8925.330                151008.140300   \n",
       "13                       246.410                  7599.582730   \n",
       "14                       794.030                  8774.134623   \n",
       "15                      1573.430                 61547.727440   \n",
       "16                      2054.600                 80396.926680   \n",
       "17                     11883.580                159047.086200   \n",
       "18                      2682.760                 48619.507080   \n",
       "19                      5678.150                 75578.052000   \n",
       "20                      2471.350                 33424.645600   \n",
       "21                      4111.000                 34261.981770   \n",
       "22                     11325.600                 95150.640270   \n",
       "23                      1469.200                 22563.771080   \n",
       "24                      2859.005                 23446.847900   \n",
       "25                        24.550                  2371.831678   \n",
       "26                       165.000                  6221.617910   \n",
       "27                         0.000                  1253.317144   \n",
       "28                        14.350                   188.537479   \n",
       "29                      5292.000                 38936.400170   \n",
       "30                      2859.005                 23446.847900   \n",
       "\n",
       "    AreaUnderCereals_Maize  \\\n",
       "0                   1129.0   \n",
       "1                  14867.0   \n",
       "2                   1987.0   \n",
       "3                  92194.0   \n",
       "4                 173400.0   \n",
       "5                    640.0   \n",
       "6                  59377.0   \n",
       "7                  43827.0   \n",
       "8                  24207.0   \n",
       "9                  25831.0   \n",
       "10                     0.0   \n",
       "11                     8.0   \n",
       "12                 79058.0   \n",
       "13                  3448.0   \n",
       "14                  4892.0   \n",
       "15                 37283.0   \n",
       "16                 38569.0   \n",
       "17                131163.0   \n",
       "18                 40207.0   \n",
       "19                 44300.0   \n",
       "20                 30900.0   \n",
       "21                 34163.0   \n",
       "22                170696.0   \n",
       "23                  4576.0   \n",
       "24                 49869.5   \n",
       "25                  1382.0   \n",
       "26                  2634.0   \n",
       "27                   961.0   \n",
       "28                  2600.0   \n",
       "29                 48210.0   \n",
       "30                 49869.5   \n",
       "\n",
       "    NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea  \\\n",
       "0                                                9348                  \n",
       "1                                               20281                  \n",
       "2                                               23868                  \n",
       "3                                               92506                  \n",
       "4                                               78364                  \n",
       "5                                               20140                  \n",
       "6                                               50370                  \n",
       "7                                               32347                  \n",
       "8                                              144708                  \n",
       "9                                               26174                  \n",
       "10                                              16589                  \n",
       "11                                                699                  \n",
       "12                                              23393                  \n",
       "13                                                106                  \n",
       "14                                              16025                  \n",
       "15                                              19842                  \n",
       "16                                              44070                  \n",
       "17                                             139708                  \n",
       "18                                             113515                  \n",
       "19                                             117497                  \n",
       "20                                              20816                  \n",
       "21                                              41664                  \n",
       "22                                              63016                  \n",
       "23                                               8460                  \n",
       "24                                              75198                  \n",
       "25                                              19908                  \n",
       "26                                              48802                  \n",
       "27                                              22035                  \n",
       "28                                              26571                  \n",
       "29                                              82552                  \n",
       "30                                              75198                  \n",
       "\n",
       "    Maize_Ratio  \n",
       "0      0.018798  \n",
       "1      0.157645  \n",
       "2      0.029741  \n",
       "3      0.303824  \n",
       "4      0.257947  \n",
       "5      0.009950  \n",
       "6      0.289061  \n",
       "7      0.184215  \n",
       "8      0.071991  \n",
       "9      0.120188  \n",
       "10     0.000000  \n",
       "11     0.000375  \n",
       "12     0.248999  \n",
       "13     0.085941  \n",
       "14     0.023925  \n",
       "15     0.111996  \n",
       "16     0.306251  \n",
       "17     0.268283  \n",
       "18     0.192153  \n",
       "19     0.270487  \n",
       "20     0.172487  \n",
       "21     0.240174  \n",
       "22     0.334321  \n",
       "23     0.066964  \n",
       "24     0.136288  \n",
       "25     0.010114  \n",
       "26     0.013150  \n",
       "27     0.004033  \n",
       "28     0.000525  \n",
       "29     0.149521  \n",
       "30     0.136288  "
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Working_Directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "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>Maize_Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>271975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>593544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Maize_Production\n",
       "0              2879\n",
       "1             42583\n",
       "2             10450\n",
       "3            271975\n",
       "4            593544"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "columns_to_standardize = ['LivestockCensus_2012_Cattle','AreaUnderCereals_Maize','TotalNPK']\n",
    "\n",
    "Case_file_1 = Case_file[['const'] + columns_to_standardize].copy()\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_st = pd.DataFrame()\n",
    "X_st['const'] = Case_file['const']\n",
    "# X_st[columns_to_standardize] = scaler.fit_transform(Case_file_1[columns_to_standardize])\n",
    "X_st[columns_to_standardize] = Case_file_1[columns_to_standardize]\n",
    "\n",
    "y_st = pd.DataFrame()\n",
    "y_st['Maize_Production'] = Working_Directory[~rows_to_remove]['Maize_Production'].reset_index(drop=True)\n",
    "# y_st['Target_Production'] = (1+Target)*y_st['Maize_Production']\n",
    "# columns_to_standardize = ['Maize_Production','Target_Production']\n",
    "\n",
    "columns_to_standardize = ['Maize_Production']\n",
    "# y_st[columns_to_standardize] = scaler.fit_transform(y_st[columns_to_standardize])\n",
    "# columns_to_standardize = ['GroundNut Production','Target Production']\n",
    "y_st.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "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>Maize_Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>271975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>593544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>105491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>226532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>66868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>61353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>333937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>137154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>147886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>576758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>226327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>251699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>90634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>138476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>413487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>104045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>14621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>190252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>104045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Maize_Production\n",
       "0               2879\n",
       "1              42583\n",
       "2              10450\n",
       "3             271975\n",
       "4             593544\n",
       "5               1759\n",
       "6             105491\n",
       "7             226532\n",
       "8              66868\n",
       "9              61353\n",
       "10            333937\n",
       "11             17921\n",
       "12             19387\n",
       "13            137154\n",
       "14            147886\n",
       "15            576758\n",
       "16            226327\n",
       "17            251699\n",
       "18             90634\n",
       "19            138476\n",
       "20            413487\n",
       "21             24088\n",
       "22            104045\n",
       "23              2791\n",
       "24             14621\n",
       "25              2673\n",
       "26            190252\n",
       "27            104045"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Row with maximum Maize_Production:\n",
      "      District  Maize_Production     TotalNPK  SowingSeedsDistributed_Maize  \\\n",
      "4  DAVANAGERE            593544  25702.83548                        6124.6   \n",
      "\n",
      "   LivestockCensus_2012_Cattle  AreaUnderCereals_Maize  \\\n",
      "4                  85657.63209                173400.0   \n",
      "\n",
      "   NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea  \\\n",
      "4                                              78364                  \n",
      "\n",
      "   Maize_Ratio  \n",
      "4     0.257947  \n",
      "\n",
      "Row with minimum Maize_Production:\n",
      "   District  Maize_Production    TotalNPK  SowingSeedsDistributed_Maize  \\\n",
      "5    KOLAR              1759  220.782906                          51.5   \n",
      "\n",
      "   LivestockCensus_2012_Cattle  AreaUnderCereals_Maize  \\\n",
      "5                  2278.829821                   640.0   \n",
      "\n",
      "   NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea  \\\n",
      "5                                              20140                  \n",
      "\n",
      "   Maize_Ratio  \n",
      "5      0.00995  \n"
     ]
    }
   ],
   "source": [
    "# Get the row with the maximum value in 'Maize_Production'\n",
    "max_row = Working_Directory[~rows_to_remove][Working_Directory[~rows_to_remove]['Maize_Production'] == Working_Directory[~rows_to_remove]['Maize_Production'].max()]\n",
    "\n",
    "# Get the row with the minimum value in 'Maize_Production'\n",
    "min_row = Working_Directory[~rows_to_remove][Working_Directory[~rows_to_remove]['Maize_Production'] == Working_Directory[~rows_to_remove]['Maize_Production'].min()]\n",
    "\n",
    "# Print the rows\n",
    "print(\"Row with maximum Maize_Production:\\n\", max_row)\n",
    "print(\"\\nRow with minimum Maize_Production:\\n\", min_row)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:       Maize_Production   R-squared:                       0.953\n",
      "Model:                            OLS   Adj. R-squared:                  0.947\n",
      "Method:                 Least Squares   F-statistic:                     160.9\n",
      "Date:                Wed, 30 Apr 2025   Prob (F-statistic):           5.01e-16\n",
      "Time:                        10:35:12   Log-Likelihood:                -332.85\n",
      "No. Observations:                  28   AIC:                             673.7\n",
      "Df Residuals:                      24   BIC:                             679.0\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================================\n",
      "                                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------\n",
      "const                       -7629.7025   1.08e+04     -0.704      0.488      -3e+04    1.47e+04\n",
      "LivestockCensus_2012_Cattle     0.5784      0.304      1.901      0.069      -0.050       1.206\n",
      "AreaUnderCereals_Maize          1.7786      0.272      6.546      0.000       1.218       2.339\n",
      "TotalNPK                        5.8348      1.444      4.040      0.000       2.854       8.815\n",
      "==============================================================================\n",
      "Omnibus:                        3.140   Durbin-Watson:                   2.453\n",
      "Prob(Omnibus):                  0.208   Jarque-Bera (JB):                1.842\n",
      "Skew:                           0.206   Prob(JB):                        0.398\n",
      "Kurtosis:                       4.187   Cond. No.                     1.34e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.34e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.57835467, 1.77855209, 5.83482942])"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model=sm.OLS(y,Case_file_1).fit()\n",
    "# print(model.summary())\n",
    "# model.params.values\n",
    "# coef=model.params.values[1:]\n",
    "# coef\n",
    "\n",
    "model=sm.OLS(y_st['Maize_Production'],X_st).fit()\n",
    "Case_file = X_st\n",
    "print(model.summary())\n",
    "model.params.values\n",
    "coef=model.params.values[1:]\n",
    "coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>TotalNPK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2567.461578</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>438.180210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25037.139270</td>\n",
       "      <td>14867.0</td>\n",
       "      <td>3036.080250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7729.614277</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>222.225258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.0</td>\n",
       "      <td>10902.406570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.0</td>\n",
       "      <td>25702.835480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LivestockCensus_2012_Cattle  AreaUnderCereals_Maize      TotalNPK\n",
       "0                  2567.461578                  1129.0    438.180210\n",
       "1                 25037.139270                 14867.0   3036.080250\n",
       "2                  7729.614277                  1987.0    222.225258\n",
       "3                 83821.593090                 92194.0  10902.406570\n",
       "4                 85657.632090                173400.0  25702.835480"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Case_file.drop('const',axis=1,inplace=True)\n",
    "Case_file[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<itertools.product at 0x1e5839a5140>"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Case_file.loc[:,'Beta_0'] = model.params.values[0]\n",
    "Case_file.loc[:,'Beta_1'] = coef[0]\n",
    "Case_file.loc[:,'Beta_2'] = coef[1]\n",
    "Case_file.loc[:,'Beta_3'] = coef[2]\n",
    "# Case_file.loc[:,'Beta_4'] = coef[3]\n",
    "# Case_file.loc[:,'Beta_5'] = coef[4]\n",
    "\n",
    "intervals = np.arange(0, 1.1, 0.1)\n",
    "# columns=['Sensitivity_SowingSeedsDistributed_Maize', 'Sensitivity_LivestockCensus_2012_Cattle' , 'Sensitivity_AreaUnderCereals_Maize', 'Sensitivity_TotalNPK','Sensitivity_NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea']\n",
    "\n",
    "columns=['Sensitivity_LivestockCensus_2012_Cattle' , 'Sensitivity_AreaUnderCereals_Maize', 'Sensitivity_TotalNPK']\n",
    "# Generate all possible combinations of elements in arr\n",
    "\n",
    "num = len(columns)\n",
    "combinations = product(intervals, repeat=len(columns))\n",
    "combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sensitivity_LivestockCensus_2012_Cattle</th>\n",
       "      <th>Sensitivity_AreaUnderCereals_Maize</th>\n",
       "      <th>Sensitivity_TotalNPK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1329</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1330</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1331 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sensitivity_LivestockCensus_2012_Cattle  \\\n",
       "0                                         0.0   \n",
       "1                                         0.0   \n",
       "2                                         0.0   \n",
       "3                                         0.0   \n",
       "4                                         0.0   \n",
       "...                                       ...   \n",
       "1326                                      1.0   \n",
       "1327                                      1.0   \n",
       "1328                                      1.0   \n",
       "1329                                      1.0   \n",
       "1330                                      1.0   \n",
       "\n",
       "      Sensitivity_AreaUnderCereals_Maize  Sensitivity_TotalNPK  \n",
       "0                                    0.0                   0.0  \n",
       "1                                    0.0                   0.1  \n",
       "2                                    0.0                   0.2  \n",
       "3                                    0.0                   0.3  \n",
       "4                                    0.0                   0.4  \n",
       "...                                  ...                   ...  \n",
       "1326                                 1.0                   0.6  \n",
       "1327                                 1.0                   0.7  \n",
       "1328                                 1.0                   0.8  \n",
       "1329                                 1.0                   0.9  \n",
       "1330                                 1.0                   1.0  \n",
       "\n",
       "[1331 rows x 3 columns]"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha_df = pd.DataFrame(combinations, columns=columns)\n",
    "alpha_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Sensitivity_LivestockCensus_2012_Cattle  \\\n",
      "0                                          0.0   \n",
      "1                                          0.0   \n",
      "2                                          0.0   \n",
      "3                                          0.0   \n",
      "4                                          0.0   \n",
      "...                                        ...   \n",
      "37263                                      1.0   \n",
      "37264                                      1.0   \n",
      "37265                                      1.0   \n",
      "37266                                      1.0   \n",
      "37267                                      1.0   \n",
      "\n",
      "       Sensitivity_AreaUnderCereals_Maize  Sensitivity_TotalNPK  \\\n",
      "0                                     0.0                   0.0   \n",
      "1                                     0.0                   0.0   \n",
      "2                                     0.0                   0.0   \n",
      "3                                     0.0                   0.0   \n",
      "4                                     0.0                   0.0   \n",
      "...                                   ...                   ...   \n",
      "37263                                 1.0                   1.0   \n",
      "37264                                 1.0                   1.0   \n",
      "37265                                 1.0                   1.0   \n",
      "37266                                 1.0                   1.0   \n",
      "37267                                 1.0                   1.0   \n",
      "\n",
      "       LivestockCensus_2012_Cattle  AreaUnderCereals_Maize      TotalNPK  \\\n",
      "0                      2567.461578                  1129.0    438.180210   \n",
      "1                     25037.139270                 14867.0   3036.080250   \n",
      "2                      7729.614277                  1987.0    222.225258   \n",
      "3                     83821.593090                 92194.0  10902.406570   \n",
      "4                     85657.632090                173400.0  25702.835480   \n",
      "...                            ...                     ...           ...   \n",
      "37263                  2371.831678                  1382.0    249.381182   \n",
      "37264                  6221.617910                  2634.0    710.101614   \n",
      "37265                  1253.317144                   961.0    368.617469   \n",
      "37266                 38936.400170                 48210.0  10997.550400   \n",
      "37267                 23446.847900                 49869.5   8404.154608   \n",
      "\n",
      "           Beta_0    Beta_1    Beta_2    Beta_3  \n",
      "0     -7629.70252  0.578355  1.778552  5.834829  \n",
      "1     -7629.70252  0.578355  1.778552  5.834829  \n",
      "2     -7629.70252  0.578355  1.778552  5.834829  \n",
      "3     -7629.70252  0.578355  1.778552  5.834829  \n",
      "4     -7629.70252  0.578355  1.778552  5.834829  \n",
      "...           ...       ...       ...       ...  \n",
      "37263 -7629.70252  0.578355  1.778552  5.834829  \n",
      "37264 -7629.70252  0.578355  1.778552  5.834829  \n",
      "37265 -7629.70252  0.578355  1.778552  5.834829  \n",
      "37266 -7629.70252  0.578355  1.778552  5.834829  \n",
      "37267 -7629.70252  0.578355  1.778552  5.834829  \n",
      "\n",
      "[37268 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "Case_file['key'] = 1\n",
    "alpha_df['key'] = 1\n",
    "\n",
    "# Performing the Cartesian product\n",
    "result_df = pd.merge( alpha_df, Case_file, on='key').drop('key', axis=1)\n",
    "\n",
    "print(result_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "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>Sensitivity_LivestockCensus_2012_Cattle</th>\n",
       "      <th>Sensitivity_AreaUnderCereals_Maize</th>\n",
       "      <th>Sensitivity_TotalNPK</th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>TotalNPK</th>\n",
       "      <th>Beta_0</th>\n",
       "      <th>Beta_1</th>\n",
       "      <th>Beta_2</th>\n",
       "      <th>Beta_3</th>\n",
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       "      <th>1</th>\n",
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       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7729.614277</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83821.593090</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.0</td>\n",
       "      <td>25702.835480</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sensitivity_LivestockCensus_2012_Cattle  \\\n",
       "0                                      0.0   \n",
       "1                                      0.0   \n",
       "2                                      0.0   \n",
       "3                                      0.0   \n",
       "4                                      0.0   \n",
       "\n",
       "   Sensitivity_AreaUnderCereals_Maize  Sensitivity_TotalNPK  \\\n",
       "0                                 0.0                   0.0   \n",
       "1                                 0.0                   0.0   \n",
       "2                                 0.0                   0.0   \n",
       "3                                 0.0                   0.0   \n",
       "4                                 0.0                   0.0   \n",
       "\n",
       "   LivestockCensus_2012_Cattle  AreaUnderCereals_Maize      TotalNPK  \\\n",
       "0                  2567.461578                  1129.0    438.180210   \n",
       "1                 25037.139270                 14867.0   3036.080250   \n",
       "2                  7729.614277                  1987.0    222.225258   \n",
       "3                 83821.593090                 92194.0  10902.406570   \n",
       "4                 85657.632090                173400.0  25702.835480   \n",
       "\n",
       "       Beta_0    Beta_1    Beta_2    Beta_3  \n",
       "0 -7629.70252  0.578355  1.778552  5.834829  \n",
       "1 -7629.70252  0.578355  1.778552  5.834829  \n",
       "2 -7629.70252  0.578355  1.778552  5.834829  \n",
       "3 -7629.70252  0.578355  1.778552  5.834829  \n",
       "4 -7629.70252  0.578355  1.778552  5.834829  "
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(134853.12903225806)"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Working_Directory['Maize_Production'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['District', 'Maize_Production', 'TotalNPK',\n",
       "       'SowingSeedsDistributed_Maize', 'LivestockCensus_2012_Cattle',\n",
       "       'AreaUnderCereals_Maize',\n",
       "       'NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea',\n",
       "       'Maize_Ratio'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Working_Directory.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(range(len(y_st)), y_st, color='blue', label='y_st')\n",
    "\n",
    "# Adding labels and title\n",
    "plt.title('Scatter Plot of y_st')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Standardized Values')\n",
    "\n",
    "# Show legend and plot\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "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>Maize_Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>271975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>593544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>105491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>226532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>66868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>61353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>333937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>137154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>147886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>576758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>226327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>251699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>90634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>138476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>413487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>104045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>14621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>190252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>104045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Maize_Production\n",
       "0               2879\n",
       "1              42583\n",
       "2              10450\n",
       "3             271975\n",
       "4             593544\n",
       "5               1759\n",
       "6             105491\n",
       "7             226532\n",
       "8              66868\n",
       "9              61353\n",
       "10            333937\n",
       "11             17921\n",
       "12             19387\n",
       "13            137154\n",
       "14            147886\n",
       "15            576758\n",
       "16            226327\n",
       "17            251699\n",
       "18             90634\n",
       "19            138476\n",
       "20            413487\n",
       "21             24088\n",
       "22            104045\n",
       "23              2791\n",
       "24             14621\n",
       "25              2673\n",
       "26            190252\n",
       "27            104045"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(125002.2323076923)"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "THRESHOLD = Target\n",
    "DEFAULT_DELTA_O = delta_O\n",
    "\n",
    "y_st['delta_O'] = np.where(y_st['Maize_Production'] > THRESHOLD, 0, DEFAULT_DELTA_O)\n",
    "\n",
    "result_df['delta_O'] = np.tile(y_st['delta_O'].values, 1331)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>Maize_Production</th>\n",
       "      <th>delta_O</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2879</td>\n",
       "      <td>9259.424615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42583</td>\n",
       "      <td>9259.424615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10450</td>\n",
       "      <td>9259.424615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>271975</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>593544</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1759</td>\n",
       "      <td>9259.424615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>105491</td>\n",
       "      <td>9259.424615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>226532</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>66868</td>\n",
       "      <td>9259.424615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>61353</td>\n",
       "      <td>9259.424615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>333937</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17921</td>\n",
       "      <td>9259.424615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19387</td>\n",
       "      <td>9259.424615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>137154</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>147886</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>576758</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>226327</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>251699</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>90634</td>\n",
       "      <td>9259.424615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>138476</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>413487</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24088</td>\n",
       "      <td>9259.424615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>104045</td>\n",
       "      <td>9259.424615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2791</td>\n",
       "      <td>9259.424615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>14621</td>\n",
       "      <td>9259.424615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2673</td>\n",
       "      <td>9259.424615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>190252</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>104045</td>\n",
       "      <td>9259.424615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Maize_Production      delta_O\n",
       "0               2879  9259.424615\n",
       "1              42583  9259.424615\n",
       "2              10450  9259.424615\n",
       "3             271975     0.000000\n",
       "4             593544     0.000000\n",
       "5               1759  9259.424615\n",
       "6             105491  9259.424615\n",
       "7             226532     0.000000\n",
       "8              66868  9259.424615\n",
       "9              61353  9259.424615\n",
       "10            333937     0.000000\n",
       "11             17921  9259.424615\n",
       "12             19387  9259.424615\n",
       "13            137154     0.000000\n",
       "14            147886     0.000000\n",
       "15            576758     0.000000\n",
       "16            226327     0.000000\n",
       "17            251699     0.000000\n",
       "18             90634  9259.424615\n",
       "19            138476     0.000000\n",
       "20            413487     0.000000\n",
       "21             24088  9259.424615\n",
       "22            104045  9259.424615\n",
       "23              2791  9259.424615\n",
       "24             14621  9259.424615\n",
       "25              2673  9259.424615\n",
       "26            190252     0.000000\n",
       "27            104045  9259.424615"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        9259.424615\n",
       "1        9259.424615\n",
       "2        9259.424615\n",
       "3           0.000000\n",
       "4           0.000000\n",
       "            ...     \n",
       "37263    9259.424615\n",
       "37264    9259.424615\n",
       "37265    9259.424615\n",
       "37266       0.000000\n",
       "37267    9259.424615\n",
       "Name: delta_O, Length: 37268, dtype: float64"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df['delta_O']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_st values:\n",
      " [9259.42461538 9259.42461538 9259.42461538    0.            0.        ]\n",
      "result_df values:\n",
      " [9259.42461538 9259.42461538 9259.42461538    0.            0.        ]\n"
     ]
    }
   ],
   "source": [
    "print(\"y_st values:\\n\", y_st['delta_O'].values[:5])\n",
    "print(\"result_df values:\\n\", result_df['delta_O'].values[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delta_O = 5000\n",
    "\n",
    "# result_df['New_F1'] = result_df['SowingSeedsDistributed_Maize'] + (result_df['Sensitivity_SowingSeedsDistributed_Maize']*delta_O)/result_df['Beta_1']\n",
    "result_df['New_F1'] = result_df['LivestockCensus_2012_Cattle'] + (result_df['Sensitivity_LivestockCensus_2012_Cattle'] * result_df['delta_O']) / result_df['Beta_1']\n",
    "result_df['New_F2'] = result_df['AreaUnderCereals_Maize'] + (result_df['Sensitivity_AreaUnderCereals_Maize'] * result_df['delta_O']) / result_df['Beta_2']\n",
    "result_df['New_F3'] = result_df['TotalNPK'] + (result_df['Sensitivity_TotalNPK'] * result_df['delta_O']) / result_df['Beta_3']\n",
    "\n",
    "# result_df['New_F1'] = result_df['LivestockCensus_2012_Cattle'] + (result_df['Sensitivity_LivestockCensus_2012_Cattle'] * delta_O) / result_df['Beta_1']\n",
    "# result_df['New_F2'] = result_df['AreaUnderCereals_Maize'] + (result_df['Sensitivity_AreaUnderCereals_Maize'] * delta_O) / result_df['Beta_2']\n",
    "# result_df['New_F3'] = result_df['TotalNPK'] + (result_df['Sensitivity_TotalNPK'] * delta_O) / result_df['Beta_3']\n",
    "# result_df['New_F4'] = result_df['NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea'] + (result_df['Sensitivity_NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea']*delta_O)/result_df['Beta_4']\n",
    "result_df['District'] = np.tile(Working_Directory[~rows_to_remove]['District'],11**num)\n",
    "\n",
    "result_df['New_F1'] = result_df['New_F1'].apply(lambda x: max(0, x))\n",
    "result_df['New_F2'] = result_df['New_F2'].apply(lambda x: max(0, x))\n",
    "result_df['New_F3'] = result_df['New_F3'].apply(lambda x: max(0, x))\n",
    "# result_df['New_F4'] = result_df['New_F4'].apply(lambda x: max(0, x))\n",
    "# result_df['New_F5'] = result_df['New_F5'].apply(lambda x: max(0, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sensitivity_LivestockCensus_2012_Cattle</th>\n",
       "      <th>Sensitivity_AreaUnderCereals_Maize</th>\n",
       "      <th>Sensitivity_TotalNPK</th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>TotalNPK</th>\n",
       "      <th>Beta_0</th>\n",
       "      <th>Beta_1</th>\n",
       "      <th>Beta_2</th>\n",
       "      <th>Beta_3</th>\n",
       "      <th>delta_O</th>\n",
       "      <th>New_F1</th>\n",
       "      <th>New_F2</th>\n",
       "      <th>New_F3</th>\n",
       "      <th>District</th>\n",
       "      <th>New_Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2567.461578</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>438.180210</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>2567.461578</td>\n",
       "      <td>1129.000000</td>\n",
       "      <td>438.180210</td>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>2879.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25037.139270</td>\n",
       "      <td>14867.0</td>\n",
       "      <td>3036.080250</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>25037.139270</td>\n",
       "      <td>14867.000000</td>\n",
       "      <td>3036.080250</td>\n",
       "      <td>BENGALURU(R)</td>\n",
       "      <td>51007.388234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7729.614277</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>222.225258</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>7729.614277</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>222.225258</td>\n",
       "      <td>RAMANAGARA</td>\n",
       "      <td>10450.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.0</td>\n",
       "      <td>10902.406570</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.000000</td>\n",
       "      <td>10902.406570</td>\n",
       "      <td>CHITRADURGA</td>\n",
       "      <td>271975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.0</td>\n",
       "      <td>25702.835480</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.000000</td>\n",
       "      <td>25702.835480</td>\n",
       "      <td>DAVANAGERE</td>\n",
       "      <td>593544.000000</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",
       "      <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>37263</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2371.831678</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>249.381182</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>18381.773500</td>\n",
       "      <td>6588.158793</td>\n",
       "      <td>1836.304114</td>\n",
       "      <td>BIDAR</td>\n",
       "      <td>25433.386906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37264</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6221.617910</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>710.101614</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>22231.559732</td>\n",
       "      <td>7840.158793</td>\n",
       "      <td>2297.024546</td>\n",
       "      <td>KALABURAGI</td>\n",
       "      <td>32574.901104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37265</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1253.317144</td>\n",
       "      <td>961.0</td>\n",
       "      <td>368.617469</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>17263.258966</td>\n",
       "      <td>6167.158793</td>\n",
       "      <td>1955.540401</td>\n",
       "      <td>YADGIRI</td>\n",
       "      <td>24733.441767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37266</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38936.400170</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>10997.550400</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38936.400170</td>\n",
       "      <td>48210.000000</td>\n",
       "      <td>10997.550400</td>\n",
       "      <td>KOPPAL</td>\n",
       "      <td>190252.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37267</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>39456.789722</td>\n",
       "      <td>55075.658793</td>\n",
       "      <td>9991.077540</td>\n",
       "      <td>VIJAYANAGARA</td>\n",
       "      <td>171441.477479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37268 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sensitivity_LivestockCensus_2012_Cattle  \\\n",
       "0                                          0.0   \n",
       "1                                          0.0   \n",
       "2                                          0.0   \n",
       "3                                          0.0   \n",
       "4                                          0.0   \n",
       "...                                        ...   \n",
       "37263                                      1.0   \n",
       "37264                                      1.0   \n",
       "37265                                      1.0   \n",
       "37266                                      1.0   \n",
       "37267                                      1.0   \n",
       "\n",
       "       Sensitivity_AreaUnderCereals_Maize  Sensitivity_TotalNPK  \\\n",
       "0                                     0.0                   0.0   \n",
       "1                                     0.0                   0.0   \n",
       "2                                     0.0                   0.0   \n",
       "3                                     0.0                   0.0   \n",
       "4                                     0.0                   0.0   \n",
       "...                                   ...                   ...   \n",
       "37263                                 1.0                   1.0   \n",
       "37264                                 1.0                   1.0   \n",
       "37265                                 1.0                   1.0   \n",
       "37266                                 1.0                   1.0   \n",
       "37267                                 1.0                   1.0   \n",
       "\n",
       "       LivestockCensus_2012_Cattle  AreaUnderCereals_Maize      TotalNPK  \\\n",
       "0                      2567.461578                  1129.0    438.180210   \n",
       "1                     25037.139270                 14867.0   3036.080250   \n",
       "2                      7729.614277                  1987.0    222.225258   \n",
       "3                     83821.593090                 92194.0  10902.406570   \n",
       "4                     85657.632090                173400.0  25702.835480   \n",
       "...                            ...                     ...           ...   \n",
       "37263                  2371.831678                  1382.0    249.381182   \n",
       "37264                  6221.617910                  2634.0    710.101614   \n",
       "37265                  1253.317144                   961.0    368.617469   \n",
       "37266                 38936.400170                 48210.0  10997.550400   \n",
       "37267                 23446.847900                 49869.5   8404.154608   \n",
       "\n",
       "           Beta_0    Beta_1    Beta_2    Beta_3      delta_O        New_F1  \\\n",
       "0     -7629.70252  0.578355  1.778552  5.834829  9259.424615   2567.461578   \n",
       "1     -7629.70252  0.578355  1.778552  5.834829  9259.424615  25037.139270   \n",
       "2     -7629.70252  0.578355  1.778552  5.834829  9259.424615   7729.614277   \n",
       "3     -7629.70252  0.578355  1.778552  5.834829     0.000000  83821.593090   \n",
       "4     -7629.70252  0.578355  1.778552  5.834829     0.000000  85657.632090   \n",
       "...           ...       ...       ...       ...          ...           ...   \n",
       "37263 -7629.70252  0.578355  1.778552  5.834829  9259.424615  18381.773500   \n",
       "37264 -7629.70252  0.578355  1.778552  5.834829  9259.424615  22231.559732   \n",
       "37265 -7629.70252  0.578355  1.778552  5.834829  9259.424615  17263.258966   \n",
       "37266 -7629.70252  0.578355  1.778552  5.834829     0.000000  38936.400170   \n",
       "37267 -7629.70252  0.578355  1.778552  5.834829  9259.424615  39456.789722   \n",
       "\n",
       "              New_F2        New_F3      District  New_Production  \n",
       "0        1129.000000    438.180210     BENGALURU     2879.000000  \n",
       "1       14867.000000   3036.080250  BENGALURU(R)    51007.388234  \n",
       "2        1987.000000    222.225258    RAMANAGARA    10450.000000  \n",
       "3       92194.000000  10902.406570   CHITRADURGA   271975.000000  \n",
       "4      173400.000000  25702.835480    DAVANAGERE   593544.000000  \n",
       "...              ...           ...           ...             ...  \n",
       "37263    6588.158793   1836.304114         BIDAR    25433.386906  \n",
       "37264    7840.158793   2297.024546    KALABURAGI    32574.901104  \n",
       "37265    6167.158793   1955.540401       YADGIRI    24733.441767  \n",
       "37266   48210.000000  10997.550400        KOPPAL   190252.000000  \n",
       "37267   55075.658793   9991.077540  VIJAYANAGARA   171441.477479  \n",
       "\n",
       "[37268 rows x 16 columns]"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1: Tile the original production values\n",
    "original_production = np.tile(y_st['Maize_Production'].values, 1331)\n",
    "\n",
    "# Step 2: Calculate New_Production\n",
    "result_df['New_Production'] = (\n",
    "    result_df['Beta_1'] * result_df['New_F1'] +\n",
    "    result_df['Beta_2'] * result_df['New_F2'] +\n",
    "    result_df['Beta_3'] * result_df['New_F3'] +\n",
    "    result_df['Beta_0']\n",
    ")\n",
    "\n",
    "# Step 3: Replace with original if new is lower\n",
    "result_df['New_Production'] = np.where(\n",
    "    result_df['New_Production'] < original_production,\n",
    "    original_production,\n",
    "    result_df['New_Production']\n",
    ")\n",
    "\n",
    "# + result_df['Beta_4'] * result_df['New_F4']  + result_df['Beta_5'] * result_df['New_F5'] + result_df['Beta_0']\n",
    "result_df['New_Production'] = result_df['New_Production'].apply(lambda x: max(0, x))\n",
    "\n",
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sensitivity_LivestockCensus_2012_Cattle</th>\n",
       "      <th>Sensitivity_AreaUnderCereals_Maize</th>\n",
       "      <th>Sensitivity_TotalNPK</th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>TotalNPK</th>\n",
       "      <th>Beta_0</th>\n",
       "      <th>Beta_1</th>\n",
       "      <th>Beta_2</th>\n",
       "      <th>Beta_3</th>\n",
       "      <th>delta_O</th>\n",
       "      <th>New_F1</th>\n",
       "      <th>New_F2</th>\n",
       "      <th>New_F3</th>\n",
       "      <th>District</th>\n",
       "      <th>New_Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>6221.617910</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>710.101614</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>6221.617910</td>\n",
       "      <td>4195.847638</td>\n",
       "      <td>2138.332253</td>\n",
       "      <td>KALABURAGI</td>\n",
       "      <td>15907.936797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1253.317144</td>\n",
       "      <td>961.0</td>\n",
       "      <td>368.617469</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>1253.317144</td>\n",
       "      <td>2522.847638</td>\n",
       "      <td>1796.848108</td>\n",
       "      <td>YADGIRI</td>\n",
       "      <td>8066.477460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>38936.400170</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>10997.550400</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38936.400170</td>\n",
       "      <td>48210.000000</td>\n",
       "      <td>10997.550400</td>\n",
       "      <td>KOPPAL</td>\n",
       "      <td>190252.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>51431.347638</td>\n",
       "      <td>9832.385246</td>\n",
       "      <td>VIJAYANAGARA</td>\n",
       "      <td>154774.513172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2567.461578</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>438.180210</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>2567.461578</td>\n",
       "      <td>2690.847638</td>\n",
       "      <td>2025.103141</td>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>10457.144966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25037.139270</td>\n",
       "      <td>14867.0</td>\n",
       "      <td>3036.080250</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>25037.139270</td>\n",
       "      <td>16428.847638</td>\n",
       "      <td>4623.003182</td>\n",
       "      <td>BENGALURU(R)</td>\n",
       "      <td>63044.640234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7729.614277</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>222.225258</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>7729.614277</td>\n",
       "      <td>3548.847638</td>\n",
       "      <td>1809.148190</td>\n",
       "      <td>RAMANAGARA</td>\n",
       "      <td>13708.637475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.0</td>\n",
       "      <td>10902.406570</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.000000</td>\n",
       "      <td>10902.406570</td>\n",
       "      <td>CHITRADURGA</td>\n",
       "      <td>271975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.0</td>\n",
       "      <td>25702.835480</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.000000</td>\n",
       "      <td>25702.835480</td>\n",
       "      <td>DAVANAGERE</td>\n",
       "      <td>593544.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1209</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2278.829821</td>\n",
       "      <td>640.0</td>\n",
       "      <td>220.782906</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>2278.829821</td>\n",
       "      <td>2201.847638</td>\n",
       "      <td>1807.705838</td>\n",
       "      <td>KOLAR</td>\n",
       "      <td>8152.025286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1210</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71206.945310</td>\n",
       "      <td>59377.0</td>\n",
       "      <td>8196.896749</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>71206.945310</td>\n",
       "      <td>60938.847638</td>\n",
       "      <td>9783.819681</td>\n",
       "      <td>CHIKKABALLAPURA</td>\n",
       "      <td>199023.000766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1211</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>104722.000400</td>\n",
       "      <td>43827.0</td>\n",
       "      <td>10428.246260</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>104722.000400</td>\n",
       "      <td>43827.000000</td>\n",
       "      <td>10428.246260</td>\n",
       "      <td>SHIVAMOGGA</td>\n",
       "      <td>226532.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1212</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37944.093760</td>\n",
       "      <td>24207.0</td>\n",
       "      <td>2746.313635</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>37944.093760</td>\n",
       "      <td>25768.847638</td>\n",
       "      <td>4333.236567</td>\n",
       "      <td>TUMAKURU</td>\n",
       "      <td>85430.375410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1213</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40572.445630</td>\n",
       "      <td>25831.0</td>\n",
       "      <td>8156.290370</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>40572.445630</td>\n",
       "      <td>27392.847638</td>\n",
       "      <td>9743.213302</td>\n",
       "      <td>CHIKKAMAGALURU</td>\n",
       "      <td>121405.155023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1214</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>151008.140300</td>\n",
       "      <td>79058.0</td>\n",
       "      <td>17039.273890</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>151008.140300</td>\n",
       "      <td>79058.000000</td>\n",
       "      <td>17039.273890</td>\n",
       "      <td>HASSAN</td>\n",
       "      <td>333937.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1215</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7599.582730</td>\n",
       "      <td>3448.0</td>\n",
       "      <td>3519.022414</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>7599.582730</td>\n",
       "      <td>5009.847638</td>\n",
       "      <td>5105.945346</td>\n",
       "      <td>KODAGU</td>\n",
       "      <td>35468.146782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8774.134623</td>\n",
       "      <td>4892.0</td>\n",
       "      <td>1448.830355</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>8774.134623</td>\n",
       "      <td>6453.847638</td>\n",
       "      <td>3035.753287</td>\n",
       "      <td>MANDYA</td>\n",
       "      <td>26636.466036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1217</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>61547.727440</td>\n",
       "      <td>37283.0</td>\n",
       "      <td>6945.331415</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>61547.727440</td>\n",
       "      <td>37283.000000</td>\n",
       "      <td>6945.331415</td>\n",
       "      <td>MYSURU</td>\n",
       "      <td>137154.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1218</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80396.926680</td>\n",
       "      <td>38569.0</td>\n",
       "      <td>7289.684770</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>80396.926680</td>\n",
       "      <td>38569.000000</td>\n",
       "      <td>7289.684770</td>\n",
       "      <td>CHAMARAJANAGAR</td>\n",
       "      <td>149999.278312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1219</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>159047.086200</td>\n",
       "      <td>131163.0</td>\n",
       "      <td>48288.815130</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>159047.086200</td>\n",
       "      <td>131163.000000</td>\n",
       "      <td>48288.815130</td>\n",
       "      <td>BELAGAVI</td>\n",
       "      <td>599393.150009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48619.507080</td>\n",
       "      <td>40207.0</td>\n",
       "      <td>10420.071650</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>48619.507080</td>\n",
       "      <td>40207.000000</td>\n",
       "      <td>10420.071650</td>\n",
       "      <td>VIJAYAPURA</td>\n",
       "      <td>226327.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1221</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75578.052000</td>\n",
       "      <td>44300.0</td>\n",
       "      <td>22940.521250</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>75578.052000</td>\n",
       "      <td>44300.000000</td>\n",
       "      <td>22940.521250</td>\n",
       "      <td>BAGALKOT</td>\n",
       "      <td>251699.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1222</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33424.645600</td>\n",
       "      <td>30900.0</td>\n",
       "      <td>6447.725408</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>33424.645600</td>\n",
       "      <td>32461.847638</td>\n",
       "      <td>8034.648340</td>\n",
       "      <td>DHARAWAD</td>\n",
       "      <td>116317.486942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1223</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34261.981770</td>\n",
       "      <td>34163.0</td>\n",
       "      <td>5591.244159</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34261.981770</td>\n",
       "      <td>34163.000000</td>\n",
       "      <td>5591.244159</td>\n",
       "      <td>GADAG</td>\n",
       "      <td>138476.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95150.640270</td>\n",
       "      <td>170696.0</td>\n",
       "      <td>21731.170720</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>95150.640270</td>\n",
       "      <td>170696.000000</td>\n",
       "      <td>21731.170720</td>\n",
       "      <td>HAVERI</td>\n",
       "      <td>477790.516964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1225</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22563.771080</td>\n",
       "      <td>4576.0</td>\n",
       "      <td>1025.616827</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>22563.771080</td>\n",
       "      <td>6137.847638</td>\n",
       "      <td>2612.539759</td>\n",
       "      <td>UTTARA KANNADA</td>\n",
       "      <td>31580.365468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1226</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>51431.347638</td>\n",
       "      <td>9991.077540</td>\n",
       "      <td>BALLARI</td>\n",
       "      <td>155700.455633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2371.831678</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>249.381182</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>2371.831678</td>\n",
       "      <td>2943.847638</td>\n",
       "      <td>1836.304114</td>\n",
       "      <td>BIDAR</td>\n",
       "      <td>9692.365060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6221.617910</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>710.101614</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>6221.617910</td>\n",
       "      <td>4195.847638</td>\n",
       "      <td>2297.024546</td>\n",
       "      <td>KALABURAGI</td>\n",
       "      <td>16833.879258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1253.317144</td>\n",
       "      <td>961.0</td>\n",
       "      <td>368.617469</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>1253.317144</td>\n",
       "      <td>2522.847638</td>\n",
       "      <td>1955.540401</td>\n",
       "      <td>YADGIRI</td>\n",
       "      <td>8992.419921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sensitivity_LivestockCensus_2012_Cattle  \\\n",
       "1200                                      0.0   \n",
       "1201                                      0.0   \n",
       "1202                                      0.0   \n",
       "1203                                      0.0   \n",
       "1204                                      0.0   \n",
       "1205                                      0.0   \n",
       "1206                                      0.0   \n",
       "1207                                      0.0   \n",
       "1208                                      0.0   \n",
       "1209                                      0.0   \n",
       "1210                                      0.0   \n",
       "1211                                      0.0   \n",
       "1212                                      0.0   \n",
       "1213                                      0.0   \n",
       "1214                                      0.0   \n",
       "1215                                      0.0   \n",
       "1216                                      0.0   \n",
       "1217                                      0.0   \n",
       "1218                                      0.0   \n",
       "1219                                      0.0   \n",
       "1220                                      0.0   \n",
       "1221                                      0.0   \n",
       "1222                                      0.0   \n",
       "1223                                      0.0   \n",
       "1224                                      0.0   \n",
       "1225                                      0.0   \n",
       "1226                                      0.0   \n",
       "1227                                      0.0   \n",
       "1228                                      0.0   \n",
       "1229                                      0.0   \n",
       "\n",
       "      Sensitivity_AreaUnderCereals_Maize  Sensitivity_TotalNPK  \\\n",
       "1200                                 0.3                   0.9   \n",
       "1201                                 0.3                   0.9   \n",
       "1202                                 0.3                   0.9   \n",
       "1203                                 0.3                   0.9   \n",
       "1204                                 0.3                   1.0   \n",
       "1205                                 0.3                   1.0   \n",
       "1206                                 0.3                   1.0   \n",
       "1207                                 0.3                   1.0   \n",
       "1208                                 0.3                   1.0   \n",
       "1209                                 0.3                   1.0   \n",
       "1210                                 0.3                   1.0   \n",
       "1211                                 0.3                   1.0   \n",
       "1212                                 0.3                   1.0   \n",
       "1213                                 0.3                   1.0   \n",
       "1214                                 0.3                   1.0   \n",
       "1215                                 0.3                   1.0   \n",
       "1216                                 0.3                   1.0   \n",
       "1217                                 0.3                   1.0   \n",
       "1218                                 0.3                   1.0   \n",
       "1219                                 0.3                   1.0   \n",
       "1220                                 0.3                   1.0   \n",
       "1221                                 0.3                   1.0   \n",
       "1222                                 0.3                   1.0   \n",
       "1223                                 0.3                   1.0   \n",
       "1224                                 0.3                   1.0   \n",
       "1225                                 0.3                   1.0   \n",
       "1226                                 0.3                   1.0   \n",
       "1227                                 0.3                   1.0   \n",
       "1228                                 0.3                   1.0   \n",
       "1229                                 0.3                   1.0   \n",
       "\n",
       "      LivestockCensus_2012_Cattle  AreaUnderCereals_Maize      TotalNPK  \\\n",
       "1200                  6221.617910                  2634.0    710.101614   \n",
       "1201                  1253.317144                   961.0    368.617469   \n",
       "1202                 38936.400170                 48210.0  10997.550400   \n",
       "1203                 23446.847900                 49869.5   8404.154608   \n",
       "1204                  2567.461578                  1129.0    438.180210   \n",
       "1205                 25037.139270                 14867.0   3036.080250   \n",
       "1206                  7729.614277                  1987.0    222.225258   \n",
       "1207                 83821.593090                 92194.0  10902.406570   \n",
       "1208                 85657.632090                173400.0  25702.835480   \n",
       "1209                  2278.829821                   640.0    220.782906   \n",
       "1210                 71206.945310                 59377.0   8196.896749   \n",
       "1211                104722.000400                 43827.0  10428.246260   \n",
       "1212                 37944.093760                 24207.0   2746.313635   \n",
       "1213                 40572.445630                 25831.0   8156.290370   \n",
       "1214                151008.140300                 79058.0  17039.273890   \n",
       "1215                  7599.582730                  3448.0   3519.022414   \n",
       "1216                  8774.134623                  4892.0   1448.830355   \n",
       "1217                 61547.727440                 37283.0   6945.331415   \n",
       "1218                 80396.926680                 38569.0   7289.684770   \n",
       "1219                159047.086200                131163.0  48288.815130   \n",
       "1220                 48619.507080                 40207.0  10420.071650   \n",
       "1221                 75578.052000                 44300.0  22940.521250   \n",
       "1222                 33424.645600                 30900.0   6447.725408   \n",
       "1223                 34261.981770                 34163.0   5591.244159   \n",
       "1224                 95150.640270                170696.0  21731.170720   \n",
       "1225                 22563.771080                  4576.0   1025.616827   \n",
       "1226                 23446.847900                 49869.5   8404.154608   \n",
       "1227                  2371.831678                  1382.0    249.381182   \n",
       "1228                  6221.617910                  2634.0    710.101614   \n",
       "1229                  1253.317144                   961.0    368.617469   \n",
       "\n",
       "          Beta_0    Beta_1    Beta_2    Beta_3      delta_O         New_F1  \\\n",
       "1200 -7629.70252  0.578355  1.778552  5.834829  9259.424615    6221.617910   \n",
       "1201 -7629.70252  0.578355  1.778552  5.834829  9259.424615    1253.317144   \n",
       "1202 -7629.70252  0.578355  1.778552  5.834829     0.000000   38936.400170   \n",
       "1203 -7629.70252  0.578355  1.778552  5.834829  9259.424615   23446.847900   \n",
       "1204 -7629.70252  0.578355  1.778552  5.834829  9259.424615    2567.461578   \n",
       "1205 -7629.70252  0.578355  1.778552  5.834829  9259.424615   25037.139270   \n",
       "1206 -7629.70252  0.578355  1.778552  5.834829  9259.424615    7729.614277   \n",
       "1207 -7629.70252  0.578355  1.778552  5.834829     0.000000   83821.593090   \n",
       "1208 -7629.70252  0.578355  1.778552  5.834829     0.000000   85657.632090   \n",
       "1209 -7629.70252  0.578355  1.778552  5.834829  9259.424615    2278.829821   \n",
       "1210 -7629.70252  0.578355  1.778552  5.834829  9259.424615   71206.945310   \n",
       "1211 -7629.70252  0.578355  1.778552  5.834829     0.000000  104722.000400   \n",
       "1212 -7629.70252  0.578355  1.778552  5.834829  9259.424615   37944.093760   \n",
       "1213 -7629.70252  0.578355  1.778552  5.834829  9259.424615   40572.445630   \n",
       "1214 -7629.70252  0.578355  1.778552  5.834829     0.000000  151008.140300   \n",
       "1215 -7629.70252  0.578355  1.778552  5.834829  9259.424615    7599.582730   \n",
       "1216 -7629.70252  0.578355  1.778552  5.834829  9259.424615    8774.134623   \n",
       "1217 -7629.70252  0.578355  1.778552  5.834829     0.000000   61547.727440   \n",
       "1218 -7629.70252  0.578355  1.778552  5.834829     0.000000   80396.926680   \n",
       "1219 -7629.70252  0.578355  1.778552  5.834829     0.000000  159047.086200   \n",
       "1220 -7629.70252  0.578355  1.778552  5.834829     0.000000   48619.507080   \n",
       "1221 -7629.70252  0.578355  1.778552  5.834829     0.000000   75578.052000   \n",
       "1222 -7629.70252  0.578355  1.778552  5.834829  9259.424615   33424.645600   \n",
       "1223 -7629.70252  0.578355  1.778552  5.834829     0.000000   34261.981770   \n",
       "1224 -7629.70252  0.578355  1.778552  5.834829     0.000000   95150.640270   \n",
       "1225 -7629.70252  0.578355  1.778552  5.834829  9259.424615   22563.771080   \n",
       "1226 -7629.70252  0.578355  1.778552  5.834829  9259.424615   23446.847900   \n",
       "1227 -7629.70252  0.578355  1.778552  5.834829  9259.424615    2371.831678   \n",
       "1228 -7629.70252  0.578355  1.778552  5.834829  9259.424615    6221.617910   \n",
       "1229 -7629.70252  0.578355  1.778552  5.834829  9259.424615    1253.317144   \n",
       "\n",
       "             New_F2        New_F3         District  New_Production  \n",
       "1200    4195.847638   2138.332253       KALABURAGI    15907.936797  \n",
       "1201    2522.847638   1796.848108          YADGIRI     8066.477460  \n",
       "1202   48210.000000  10997.550400           KOPPAL   190252.000000  \n",
       "1203   51431.347638   9832.385246     VIJAYANAGARA   154774.513172  \n",
       "1204    2690.847638   2025.103141        BENGALURU    10457.144966  \n",
       "1205   16428.847638   4623.003182     BENGALURU(R)    63044.640234  \n",
       "1206    3548.847638   1809.148190       RAMANAGARA    13708.637475  \n",
       "1207   92194.000000  10902.406570      CHITRADURGA   271975.000000  \n",
       "1208  173400.000000  25702.835480       DAVANAGERE   593544.000000  \n",
       "1209    2201.847638   1807.705838            KOLAR     8152.025286  \n",
       "1210   60938.847638   9783.819681  CHIKKABALLAPURA   199023.000766  \n",
       "1211   43827.000000  10428.246260       SHIVAMOGGA   226532.000000  \n",
       "1212   25768.847638   4333.236567         TUMAKURU    85430.375410  \n",
       "1213   27392.847638   9743.213302   CHIKKAMAGALURU   121405.155023  \n",
       "1214   79058.000000  17039.273890           HASSAN   333937.000000  \n",
       "1215    5009.847638   5105.945346           KODAGU    35468.146782  \n",
       "1216    6453.847638   3035.753287           MANDYA    26636.466036  \n",
       "1217   37283.000000   6945.331415           MYSURU   137154.000000  \n",
       "1218   38569.000000   7289.684770   CHAMARAJANAGAR   149999.278312  \n",
       "1219  131163.000000  48288.815130         BELAGAVI   599393.150009  \n",
       "1220   40207.000000  10420.071650       VIJAYAPURA   226327.000000  \n",
       "1221   44300.000000  22940.521250         BAGALKOT   251699.000000  \n",
       "1222   32461.847638   8034.648340         DHARAWAD   116317.486942  \n",
       "1223   34163.000000   5591.244159            GADAG   138476.000000  \n",
       "1224  170696.000000  21731.170720           HAVERI   477790.516964  \n",
       "1225    6137.847638   2612.539759   UTTARA KANNADA    31580.365468  \n",
       "1226   51431.347638   9991.077540          BALLARI   155700.455633  \n",
       "1227    2943.847638   1836.304114            BIDAR     9692.365060  \n",
       "1228    4195.847638   2297.024546       KALABURAGI    16833.879258  \n",
       "1229    2522.847638   1955.540401          YADGIRI     8992.419921  "
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df[1200:1230]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_list(lst):\n",
    "    min_val = min(lst)\n",
    "    max_val = max(lst)\n",
    "    normalized_lst = [(x - min_val) / (max_val - min_val) for x in lst]\n",
    "    return normalized_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    1   2   3   4   5   6   7   8   9   10  ...  22  23  24  25  26  27  28  \\\n",
      "1    0   1   1   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
      "2    1   0   1   0   0   1   1   0   1   0  ...   0   0   0   0   0   0   0   \n",
      "3    1   1   0   0   0   0   0   0   1   0  ...   0   0   0   0   0   0   0   \n",
      "4    0   0   0   0   1   0   0   0   1   1  ...   0   0   0   1   0   0   0   \n",
      "5    0   0   0   1   0   0   0   1   0   1  ...   0   1   0   1   0   0   0   \n",
      "6    0   1   0   0   0   0   1   0   0   0  ...   0   0   0   0   0   0   0   \n",
      "7    0   1   0   0   0   1   0   0   1   0  ...   0   0   0   0   0   0   0   \n",
      "8    0   0   0   0   1   0   0   0   0   1  ...   0   1   1   0   0   0   0   \n",
      "9    0   1   1   1   0   0   1   0   0   1  ...   0   0   0   0   0   0   0   \n",
      "10   0   0   0   1   1   0   0   1   1   0  ...   0   0   0   0   0   0   0   \n",
      "11   0   0   0   0   0   0   0   0   0   1  ...   0   0   0   0   0   0   0   \n",
      "12   0   0   0   0   0   0   0   1   0   1  ...   0   0   1   0   0   0   0   \n",
      "13   0   0   0   0   0   0   0   0   1   1  ...   0   0   0   0   0   0   0   \n",
      "14   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
      "15   0   0   1   0   0   0   0   0   1   0  ...   0   0   0   0   0   0   0   \n",
      "16   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
      "17   0   0   1   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
      "18   0   0   0   0   0   0   0   0   0   0  ...   1   0   1   0   0   0   0   \n",
      "19   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   1   1   \n",
      "20   0   0   0   0   0   0   0   0   0   0  ...   1   0   0   0   0   0   0   \n",
      "21   0   0   0   0   0   0   0   0   0   0  ...   1   1   1   0   0   0   0   \n",
      "22   0   0   0   0   0   0   0   0   0   0  ...   0   1   0   1   0   0   0   \n",
      "23   0   0   0   0   1   0   0   1   0   0  ...   1   0   1   1   0   0   0   \n",
      "24   0   0   0   0   0   0   0   1   0   0  ...   0   1   0   0   0   0   0   \n",
      "25   0   0   0   1   1   0   0   0   0   0  ...   1   1   0   0   0   0   0   \n",
      "26   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   1   0   \n",
      "27   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   1   0   1   \n",
      "28   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   1   0   \n",
      "29   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   1   0   0   1   \n",
      "30   0   0   0   0   0   0   0   0   0   0  ...   1   0   0   1   0   0   0   \n",
      "31   0   0   0   1   1   0   0   0   0   0  ...   1   1   0   1   0   0   0   \n",
      "\n",
      "    29  30  31  \n",
      "1    0   0   0  \n",
      "2    0   0   0  \n",
      "3    0   0   0  \n",
      "4    0   0   1  \n",
      "5    0   0   1  \n",
      "6    0   0   0  \n",
      "7    0   0   0  \n",
      "8    0   0   0  \n",
      "9    0   0   0  \n",
      "10   0   0   0  \n",
      "11   0   0   0  \n",
      "12   0   0   0  \n",
      "13   0   0   0  \n",
      "14   0   0   0  \n",
      "15   0   0   0  \n",
      "16   0   0   0  \n",
      "17   0   0   0  \n",
      "18   0   0   0  \n",
      "19   1   0   0  \n",
      "20   1   1   0  \n",
      "21   0   0   0  \n",
      "22   0   1   1  \n",
      "23   0   0   1  \n",
      "24   0   0   0  \n",
      "25   1   1   1  \n",
      "26   0   0   0  \n",
      "27   0   0   0  \n",
      "28   1   0   0  \n",
      "29   0   1   0  \n",
      "30   1   0   1  \n",
      "31   0   1   0  \n",
      "\n",
      "[31 rows x 31 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "adjacency_excel = pd.read_excel('Karnataka_District_Adjacency_File.xlsx')\n",
    "\n",
    "district_codes = sorted(set(adjacency_excel['District_Code']))\n",
    "\n",
    "n = len(district_codes)\n",
    "adj_matrix = np.zeros((n, n), dtype=int)\n",
    "\n",
    "code_to_index = {code: idx for idx, code in enumerate(district_codes)}\n",
    "\n",
    "for i in range(len(adjacency_excel)):\n",
    "    district_code = adjacency_excel['District_Code'][i]\n",
    "    neighbors = [int(num) for num in str(adjacency_excel['District_Neighbors'][i]).split(',') if num.strip().isdigit()]\n",
    "    \n",
    "    for neighbor in neighbors:\n",
    "        adj_matrix[code_to_index[district_code]][code_to_index[neighbor]] = 1\n",
    "        adj_matrix[code_to_index[neighbor]][code_to_index[district_code]] = 1  # Assuming adjacency is bidirectional\n",
    "\n",
    "adj_matrix_df = pd.DataFrame(adj_matrix, index=district_codes, columns=district_codes)\n",
    "\n",
    "print(adj_matrix_df)\n",
    "\n",
    "adj_matrix_np = adj_matrix_df.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "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>Maize_Production</th>\n",
       "      <th>District</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2879</td>\n",
       "      <td>BENGALURU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42583</td>\n",
       "      <td>BENGALURU(R)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10450</td>\n",
       "      <td>RAMANAGARA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>271975</td>\n",
       "      <td>CHITRADURGA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>593544</td>\n",
       "      <td>DAVANAGERE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1759</td>\n",
       "      <td>KOLAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>105491</td>\n",
       "      <td>CHIKKABALLAPURA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>226532</td>\n",
       "      <td>SHIVAMOGGA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>66868</td>\n",
       "      <td>TUMAKURU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>61353</td>\n",
       "      <td>CHIKKAMAGALURU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>333937</td>\n",
       "      <td>HASSAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17921</td>\n",
       "      <td>KODAGU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19387</td>\n",
       "      <td>MANDYA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>137154</td>\n",
       "      <td>MYSURU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>147886</td>\n",
       "      <td>CHAMARAJANAGAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>576758</td>\n",
       "      <td>BELAGAVI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>226327</td>\n",
       "      <td>VIJAYAPURA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>251699</td>\n",
       "      <td>BAGALKOT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>90634</td>\n",
       "      <td>DHARAWAD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>138476</td>\n",
       "      <td>GADAG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>413487</td>\n",
       "      <td>HAVERI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24088</td>\n",
       "      <td>UTTARA KANNADA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>104045</td>\n",
       "      <td>BALLARI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2791</td>\n",
       "      <td>BIDAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>14621</td>\n",
       "      <td>KALABURAGI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2673</td>\n",
       "      <td>YADGIRI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>190252</td>\n",
       "      <td>KOPPAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>104045</td>\n",
       "      <td>VIJAYANAGARA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Maize_Production         District\n",
       "0               2879        BENGALURU\n",
       "1              42583     BENGALURU(R)\n",
       "2              10450       RAMANAGARA\n",
       "3             271975      CHITRADURGA\n",
       "4             593544       DAVANAGERE\n",
       "5               1759            KOLAR\n",
       "6             105491  CHIKKABALLAPURA\n",
       "7             226532       SHIVAMOGGA\n",
       "8              66868         TUMAKURU\n",
       "9              61353   CHIKKAMAGALURU\n",
       "10            333937           HASSAN\n",
       "11             17921           KODAGU\n",
       "12             19387           MANDYA\n",
       "13            137154           MYSURU\n",
       "14            147886   CHAMARAJANAGAR\n",
       "15            576758         BELAGAVI\n",
       "16            226327       VIJAYAPURA\n",
       "17            251699         BAGALKOT\n",
       "18             90634         DHARAWAD\n",
       "19            138476            GADAG\n",
       "20            413487           HAVERI\n",
       "21             24088   UTTARA KANNADA\n",
       "22            104045          BALLARI\n",
       "23              2791            BIDAR\n",
       "24             14621       KALABURAGI\n",
       "25              2673          YADGIRI\n",
       "26            190252           KOPPAL\n",
       "27            104045     VIJAYANAGARA"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tre = pd.DataFrame()\n",
    "tre['Maize_Production'] =  y_st['Maize_Production']\n",
    "tre['District'] = result_df['District']\n",
    "tre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_avg_capability(capability, neighbors):\n",
    "    num_neighbors = len(neighbors)\n",
    "    if num_neighbors == 0:\n",
    "        return 0\n",
    "    sum_capabilities = sum(capability[n] for n in neighbors)\n",
    "    return sum_capabilities / num_neighbors\n",
    "\n",
    "def calculate_stress_stability(capability, adjacency_matrix):\n",
    "    n = len(capability)\n",
    "    stress = np.zeros(n)\n",
    "    stability = np.zeros(n)\n",
    "    \n",
    "    for i in range(n):\n",
    "        neighbors = [j for j in range(n) if adjacency_matrix[i][j] == 1 and i != j]\n",
    "        if len(neighbors) > 0:\n",
    "            avg_capability = calculate_avg_capability(capability, neighbors)\n",
    "            stress[i] = abs(avg_capability - capability[i])\n",
    "            # stability[i] = 1 - stress[i]\n",
    "        else:\n",
    "            stress[i] = 0  # If no neighbors, stress is zero\n",
    "            # stability[i] = 1\n",
    "    min_stress = min(stress)\n",
    "    max_stress = max(stress)\n",
    "    \n",
    "    if max_stress - min_stress > 0:\n",
    "        stress = (stress - min_stress) / (max_stress - min_stress)\n",
    "    else:\n",
    "        stress = np.zeros(n)\n",
    "    \n",
    "    stability = 1 - stress\n",
    "        \n",
    "    return stress, stability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1331\n",
      "28\n",
      "37268 37268\n"
     ]
    }
   ],
   "source": [
    "stress_values_list = []\n",
    "stability_values_list = []\n",
    "\n",
    "# Define batch size\n",
    "batch_size = case_file_length\n",
    "\n",
    "# Iterate over the New_Production column in batches of 31\n",
    "num_batches = len(result_df) // batch_size\n",
    "\n",
    "for i in range(num_batches):\n",
    "    # Get the batch of 31 production values\n",
    "    start_idx = i * batch_size\n",
    "    end_idx = start_idx + batch_size\n",
    "    batch = result_df['New_Production'][start_idx:end_idx].values\n",
    "    \n",
    "    # Calculate the stress and stability values for the batch\n",
    "    stress_batch, stability_batch = calculate_stress_stability(batch, adj_matrix_np)\n",
    "    \n",
    "    stress_values_list.extend(stress_batch)\n",
    "    stability_values_list.extend(stability_batch)\n",
    "\n",
    "\n",
    "print(num_batches)\n",
    "print(len(stress_batch))\n",
    "print(len(stress_values_list), len(stability_values_list))\n",
    "\n",
    "result_df['Stress_Values'] = stress_values_list\n",
    "result_df['Stability_Values'] = stability_values_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sensitivity_LivestockCensus_2012_Cattle',\n",
       "       'Sensitivity_AreaUnderCereals_Maize', 'Sensitivity_TotalNPK',\n",
       "       'LivestockCensus_2012_Cattle', 'AreaUnderCereals_Maize', 'TotalNPK',\n",
       "       'Beta_0', 'Beta_1', 'Beta_2', 'Beta_3', 'delta_O', 'New_F1', 'New_F2',\n",
       "       'New_F3', 'District', 'New_Production', 'Stress_Values',\n",
       "       'Stability_Values'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [],
   "source": [
    "viz_df = result_df[['District','Sensitivity_LivestockCensus_2012_Cattle','Sensitivity_AreaUnderCereals_Maize',\n",
    "                    'Sensitivity_TotalNPK',\t'New_Production','Stability_Values']].copy()\n",
    "\n",
    "# viz_df = result_df[['District','Sensitivity_SowingSeedsDistributed_Maize','Sensitivity_LivestockCensus_2012_Cattle','Sensitivity_AreaUnderCereals_Maize',\n",
    "#                     'Sensitivity_TotalNPK',\t'Sensitivity_NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea','New_Production','Stability_Values']].copy()\n",
    "\n",
    "abbreviation_mapping = {\n",
    "    'BENGALURU': 'BLR',\n",
    "    'BENGALURU(R)': 'BLR(R)',\n",
    "    'RAMANAGARA': 'RGA',\n",
    "    'CHITRADURGA': 'CDA',\n",
    "    'DAVANAGERE': 'DVG',\n",
    "    'KOLAR': 'KLR',\n",
    "    'CHIKKABALLAPURA': 'CKA',\n",
    "    'SHIVAMOGGA': 'SMG',\n",
    "    'TUMAKURU': 'TKR',\n",
    "    'CHIKKAMAGALURU': 'CMG',\n",
    "    'DAKSHINA KANNADA': 'DKA',\n",
    "    'UDUPI': 'UPI',\n",
    "    'HASSAN': 'HSN',\n",
    "    'KODAGU': 'KDG',\n",
    "    'MANDYA': 'MDY',\n",
    "    'MYSURU': 'MYS',\n",
    "    'CHAMARAJANAGAR': 'CNR',\n",
    "    'BELAGAVI': 'BLG',\n",
    "    'VIJAYAPURA': 'VJP',\n",
    "    'BAGALKOT': 'BKT',\n",
    "    'DHARAWAD': 'DWD',\n",
    "    'GADAG': 'GDG',\n",
    "    'HAVERI': 'HVR',\n",
    "    'UTTARA KANNADA': 'UTK',\n",
    "    'BALLARI': 'BLL',\n",
    "    'BIDAR': 'BDR',\n",
    "    'KALABURAGI': 'KLB',\n",
    "    'YADGIRI': 'YDR',\n",
    "    'RAICHUR': 'RCR',\n",
    "    'KOPPAL': 'KPL',\n",
    "    'VIJAYANAGARA' : 'VJN'\n",
    "}\n",
    "\n",
    "viz_df['Abbreviation'] = viz_df['District'].map(abbreviation_mapping)\n",
    "\n",
    "legend_labels = [{'Abbreviation': abb, 'Full Form': full_form} \n",
    "                    for abb, full_form in zip(viz_df['Abbreviation'].unique(), viz_df['District'].unique())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "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>District</th>\n",
       "      <th>Maize_Production</th>\n",
       "      <th>TotalNPK</th>\n",
       "      <th>SowingSeedsDistributed_Maize</th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea</th>\n",
       "      <th>Maize_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>2879</td>\n",
       "      <td>438.180210</td>\n",
       "      <td>4.450</td>\n",
       "      <td>2567.461578</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>9348</td>\n",
       "      <td>0.018798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BENGALURU(R)</td>\n",
       "      <td>42583</td>\n",
       "      <td>3036.080250</td>\n",
       "      <td>134.730</td>\n",
       "      <td>25037.139270</td>\n",
       "      <td>14867.0</td>\n",
       "      <td>20281</td>\n",
       "      <td>0.157645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RAMANAGARA</td>\n",
       "      <td>10450</td>\n",
       "      <td>222.225258</td>\n",
       "      <td>260.150</td>\n",
       "      <td>7729.614277</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>23868</td>\n",
       "      <td>0.029741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHITRADURGA</td>\n",
       "      <td>271975</td>\n",
       "      <td>10902.406570</td>\n",
       "      <td>5004.000</td>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.0</td>\n",
       "      <td>92506</td>\n",
       "      <td>0.303824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DAVANAGERE</td>\n",
       "      <td>593544</td>\n",
       "      <td>25702.835480</td>\n",
       "      <td>6124.600</td>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.0</td>\n",
       "      <td>78364</td>\n",
       "      <td>0.257947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KOLAR</td>\n",
       "      <td>1759</td>\n",
       "      <td>220.782906</td>\n",
       "      <td>51.500</td>\n",
       "      <td>2278.829821</td>\n",
       "      <td>640.0</td>\n",
       "      <td>20140</td>\n",
       "      <td>0.009950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CHIKKABALLAPURA</td>\n",
       "      <td>105491</td>\n",
       "      <td>8196.896749</td>\n",
       "      <td>3538.150</td>\n",
       "      <td>71206.945310</td>\n",
       "      <td>59377.0</td>\n",
       "      <td>50370</td>\n",
       "      <td>0.289061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SHIVAMOGGA</td>\n",
       "      <td>226532</td>\n",
       "      <td>10428.246260</td>\n",
       "      <td>3814.020</td>\n",
       "      <td>104722.000400</td>\n",
       "      <td>43827.0</td>\n",
       "      <td>32347</td>\n",
       "      <td>0.184215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TUMAKURU</td>\n",
       "      <td>66868</td>\n",
       "      <td>2746.313635</td>\n",
       "      <td>1639.850</td>\n",
       "      <td>37944.093760</td>\n",
       "      <td>24207.0</td>\n",
       "      <td>144708</td>\n",
       "      <td>0.071991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHIKKAMAGALURU</td>\n",
       "      <td>61353</td>\n",
       "      <td>8156.290370</td>\n",
       "      <td>2923.790</td>\n",
       "      <td>40572.445630</td>\n",
       "      <td>25831.0</td>\n",
       "      <td>26174</td>\n",
       "      <td>0.120188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DAKSHINA KANNADA</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16589</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UDUPI</td>\n",
       "      <td>109</td>\n",
       "      <td>2.304863</td>\n",
       "      <td>0.000</td>\n",
       "      <td>94.499008</td>\n",
       "      <td>8.0</td>\n",
       "      <td>699</td>\n",
       "      <td>0.000375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HASSAN</td>\n",
       "      <td>333937</td>\n",
       "      <td>17039.273890</td>\n",
       "      <td>8925.330</td>\n",
       "      <td>151008.140300</td>\n",
       "      <td>79058.0</td>\n",
       "      <td>23393</td>\n",
       "      <td>0.248999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KODAGU</td>\n",
       "      <td>17921</td>\n",
       "      <td>3519.022414</td>\n",
       "      <td>246.410</td>\n",
       "      <td>7599.582730</td>\n",
       "      <td>3448.0</td>\n",
       "      <td>106</td>\n",
       "      <td>0.085941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MANDYA</td>\n",
       "      <td>19387</td>\n",
       "      <td>1448.830355</td>\n",
       "      <td>794.030</td>\n",
       "      <td>8774.134623</td>\n",
       "      <td>4892.0</td>\n",
       "      <td>16025</td>\n",
       "      <td>0.023925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MYSURU</td>\n",
       "      <td>137154</td>\n",
       "      <td>6945.331415</td>\n",
       "      <td>1573.430</td>\n",
       "      <td>61547.727440</td>\n",
       "      <td>37283.0</td>\n",
       "      <td>19842</td>\n",
       "      <td>0.111996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CHAMARAJANAGAR</td>\n",
       "      <td>147886</td>\n",
       "      <td>7289.684770</td>\n",
       "      <td>2054.600</td>\n",
       "      <td>80396.926680</td>\n",
       "      <td>38569.0</td>\n",
       "      <td>44070</td>\n",
       "      <td>0.306251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BELAGAVI</td>\n",
       "      <td>576758</td>\n",
       "      <td>48288.815130</td>\n",
       "      <td>11883.580</td>\n",
       "      <td>159047.086200</td>\n",
       "      <td>131163.0</td>\n",
       "      <td>139708</td>\n",
       "      <td>0.268283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>VIJAYAPURA</td>\n",
       "      <td>226327</td>\n",
       "      <td>10420.071650</td>\n",
       "      <td>2682.760</td>\n",
       "      <td>48619.507080</td>\n",
       "      <td>40207.0</td>\n",
       "      <td>113515</td>\n",
       "      <td>0.192153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BAGALKOT</td>\n",
       "      <td>251699</td>\n",
       "      <td>22940.521250</td>\n",
       "      <td>5678.150</td>\n",
       "      <td>75578.052000</td>\n",
       "      <td>44300.0</td>\n",
       "      <td>117497</td>\n",
       "      <td>0.270487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DHARAWAD</td>\n",
       "      <td>90634</td>\n",
       "      <td>6447.725408</td>\n",
       "      <td>2471.350</td>\n",
       "      <td>33424.645600</td>\n",
       "      <td>30900.0</td>\n",
       "      <td>20816</td>\n",
       "      <td>0.172487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>GADAG</td>\n",
       "      <td>138476</td>\n",
       "      <td>5591.244159</td>\n",
       "      <td>4111.000</td>\n",
       "      <td>34261.981770</td>\n",
       "      <td>34163.0</td>\n",
       "      <td>41664</td>\n",
       "      <td>0.240174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>HAVERI</td>\n",
       "      <td>413487</td>\n",
       "      <td>21731.170720</td>\n",
       "      <td>11325.600</td>\n",
       "      <td>95150.640270</td>\n",
       "      <td>170696.0</td>\n",
       "      <td>63016</td>\n",
       "      <td>0.334321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>UTTARA KANNADA</td>\n",
       "      <td>24088</td>\n",
       "      <td>1025.616827</td>\n",
       "      <td>1469.200</td>\n",
       "      <td>22563.771080</td>\n",
       "      <td>4576.0</td>\n",
       "      <td>8460</td>\n",
       "      <td>0.066964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BALLARI</td>\n",
       "      <td>104045</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>2859.005</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>75198</td>\n",
       "      <td>0.136288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BIDAR</td>\n",
       "      <td>2791</td>\n",
       "      <td>249.381182</td>\n",
       "      <td>24.550</td>\n",
       "      <td>2371.831678</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>19908</td>\n",
       "      <td>0.010114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KALABURAGI</td>\n",
       "      <td>14621</td>\n",
       "      <td>710.101614</td>\n",
       "      <td>165.000</td>\n",
       "      <td>6221.617910</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>48802</td>\n",
       "      <td>0.013150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>YADGIRI</td>\n",
       "      <td>2673</td>\n",
       "      <td>368.617469</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1253.317144</td>\n",
       "      <td>961.0</td>\n",
       "      <td>22035</td>\n",
       "      <td>0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RAICHUR</td>\n",
       "      <td>723</td>\n",
       "      <td>61.547519</td>\n",
       "      <td>14.350</td>\n",
       "      <td>188.537479</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>26571</td>\n",
       "      <td>0.000525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>KOPPAL</td>\n",
       "      <td>190252</td>\n",
       "      <td>10997.550400</td>\n",
       "      <td>5292.000</td>\n",
       "      <td>38936.400170</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>82552</td>\n",
       "      <td>0.149521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>VIJAYANAGARA</td>\n",
       "      <td>104045</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>2859.005</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>75198</td>\n",
       "      <td>0.136288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            District  Maize_Production      TotalNPK  \\\n",
       "0          BENGALURU              2879    438.180210   \n",
       "1       BENGALURU(R)             42583   3036.080250   \n",
       "2         RAMANAGARA             10450    222.225258   \n",
       "3        CHITRADURGA            271975  10902.406570   \n",
       "4         DAVANAGERE            593544  25702.835480   \n",
       "5              KOLAR              1759    220.782906   \n",
       "6    CHIKKABALLAPURA            105491   8196.896749   \n",
       "7         SHIVAMOGGA            226532  10428.246260   \n",
       "8           TUMAKURU             66868   2746.313635   \n",
       "9     CHIKKAMAGALURU             61353   8156.290370   \n",
       "10  DAKSHINA KANNADA                 0      0.000000   \n",
       "11             UDUPI               109      2.304863   \n",
       "12            HASSAN            333937  17039.273890   \n",
       "13            KODAGU             17921   3519.022414   \n",
       "14            MANDYA             19387   1448.830355   \n",
       "15            MYSURU            137154   6945.331415   \n",
       "16    CHAMARAJANAGAR            147886   7289.684770   \n",
       "17          BELAGAVI            576758  48288.815130   \n",
       "18        VIJAYAPURA            226327  10420.071650   \n",
       "19          BAGALKOT            251699  22940.521250   \n",
       "20          DHARAWAD             90634   6447.725408   \n",
       "21             GADAG            138476   5591.244159   \n",
       "22            HAVERI            413487  21731.170720   \n",
       "23    UTTARA KANNADA             24088   1025.616827   \n",
       "24           BALLARI            104045   8404.154608   \n",
       "25             BIDAR              2791    249.381182   \n",
       "26        KALABURAGI             14621    710.101614   \n",
       "27           YADGIRI              2673    368.617469   \n",
       "28           RAICHUR               723     61.547519   \n",
       "29            KOPPAL            190252  10997.550400   \n",
       "30      VIJAYANAGARA            104045   8404.154608   \n",
       "\n",
       "    SowingSeedsDistributed_Maize  LivestockCensus_2012_Cattle  \\\n",
       "0                          4.450                  2567.461578   \n",
       "1                        134.730                 25037.139270   \n",
       "2                        260.150                  7729.614277   \n",
       "3                       5004.000                 83821.593090   \n",
       "4                       6124.600                 85657.632090   \n",
       "5                         51.500                  2278.829821   \n",
       "6                       3538.150                 71206.945310   \n",
       "7                       3814.020                104722.000400   \n",
       "8                       1639.850                 37944.093760   \n",
       "9                       2923.790                 40572.445630   \n",
       "10                         0.000                     0.000000   \n",
       "11                         0.000                    94.499008   \n",
       "12                      8925.330                151008.140300   \n",
       "13                       246.410                  7599.582730   \n",
       "14                       794.030                  8774.134623   \n",
       "15                      1573.430                 61547.727440   \n",
       "16                      2054.600                 80396.926680   \n",
       "17                     11883.580                159047.086200   \n",
       "18                      2682.760                 48619.507080   \n",
       "19                      5678.150                 75578.052000   \n",
       "20                      2471.350                 33424.645600   \n",
       "21                      4111.000                 34261.981770   \n",
       "22                     11325.600                 95150.640270   \n",
       "23                      1469.200                 22563.771080   \n",
       "24                      2859.005                 23446.847900   \n",
       "25                        24.550                  2371.831678   \n",
       "26                       165.000                  6221.617910   \n",
       "27                         0.000                  1253.317144   \n",
       "28                        14.350                   188.537479   \n",
       "29                      5292.000                 38936.400170   \n",
       "30                      2859.005                 23446.847900   \n",
       "\n",
       "    AreaUnderCereals_Maize  \\\n",
       "0                   1129.0   \n",
       "1                  14867.0   \n",
       "2                   1987.0   \n",
       "3                  92194.0   \n",
       "4                 173400.0   \n",
       "5                    640.0   \n",
       "6                  59377.0   \n",
       "7                  43827.0   \n",
       "8                  24207.0   \n",
       "9                  25831.0   \n",
       "10                     0.0   \n",
       "11                     8.0   \n",
       "12                 79058.0   \n",
       "13                  3448.0   \n",
       "14                  4892.0   \n",
       "15                 37283.0   \n",
       "16                 38569.0   \n",
       "17                131163.0   \n",
       "18                 40207.0   \n",
       "19                 44300.0   \n",
       "20                 30900.0   \n",
       "21                 34163.0   \n",
       "22                170696.0   \n",
       "23                  4576.0   \n",
       "24                 49869.5   \n",
       "25                  1382.0   \n",
       "26                  2634.0   \n",
       "27                   961.0   \n",
       "28                  2600.0   \n",
       "29                 48210.0   \n",
       "30                 49869.5   \n",
       "\n",
       "    NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea  \\\n",
       "0                                                9348                  \n",
       "1                                               20281                  \n",
       "2                                               23868                  \n",
       "3                                               92506                  \n",
       "4                                               78364                  \n",
       "5                                               20140                  \n",
       "6                                               50370                  \n",
       "7                                               32347                  \n",
       "8                                              144708                  \n",
       "9                                               26174                  \n",
       "10                                              16589                  \n",
       "11                                                699                  \n",
       "12                                              23393                  \n",
       "13                                                106                  \n",
       "14                                              16025                  \n",
       "15                                              19842                  \n",
       "16                                              44070                  \n",
       "17                                             139708                  \n",
       "18                                             113515                  \n",
       "19                                             117497                  \n",
       "20                                              20816                  \n",
       "21                                              41664                  \n",
       "22                                              63016                  \n",
       "23                                               8460                  \n",
       "24                                              75198                  \n",
       "25                                              19908                  \n",
       "26                                              48802                  \n",
       "27                                              22035                  \n",
       "28                                              26571                  \n",
       "29                                              82552                  \n",
       "30                                              75198                  \n",
       "\n",
       "    Maize_Ratio  \n",
       "0      0.018798  \n",
       "1      0.157645  \n",
       "2      0.029741  \n",
       "3      0.303824  \n",
       "4      0.257947  \n",
       "5      0.009950  \n",
       "6      0.289061  \n",
       "7      0.184215  \n",
       "8      0.071991  \n",
       "9      0.120188  \n",
       "10     0.000000  \n",
       "11     0.000375  \n",
       "12     0.248999  \n",
       "13     0.085941  \n",
       "14     0.023925  \n",
       "15     0.111996  \n",
       "16     0.306251  \n",
       "17     0.268283  \n",
       "18     0.192153  \n",
       "19     0.270487  \n",
       "20     0.172487  \n",
       "21     0.240174  \n",
       "22     0.334321  \n",
       "23     0.066964  \n",
       "24     0.136288  \n",
       "25     0.010114  \n",
       "26     0.013150  \n",
       "27     0.004033  \n",
       "28     0.000525  \n",
       "29     0.149521  \n",
       "30     0.136288  "
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Working_Directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y_st['Maize_Production']\n",
    "\n",
    "viz_df['Percentage Change'] = viz_df['New_Production'] - np.tile(y.values, len(viz_df) // case_file_length + 1)[:len(viz_df)]\n",
    "for i in range(0, len(viz_df), case_file_length):\n",
    "    viz_df.loc[i:i+case_file_length-1, 'Percentage Change'] = viz_df.loc[i:i+case_file_length-1, 'Percentage Change'] * 100 / y.values + 1e-9\n",
    "    # viz_df.loc[i:i+case_file_length-1, 'Normalized Percentage Change'] = normalize_list(viz_df.loc[i:i+case_file_length-1, 'Percentage Change'])\n",
    "\n",
    "viz_df['Percentage Change'] = viz_df['Percentage Change'].replace([np.inf, -np.inf], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df['Percentage Change'] = viz_df['Percentage Change']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(9259.424615384616)"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta_O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1137.3999999999999)"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Working_Directory['Maize_Production'].quantile(0.08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2879\n",
       "1      42583\n",
       "2      10450\n",
       "3     271975\n",
       "4     593544\n",
       "5       1759\n",
       "6     105491\n",
       "7     226532\n",
       "8      66868\n",
       "9      61353\n",
       "10         0\n",
       "11       109\n",
       "12    333937\n",
       "13     17921\n",
       "14     19387\n",
       "15    137154\n",
       "16    147886\n",
       "17    576758\n",
       "18    226327\n",
       "19    251699\n",
       "20     90634\n",
       "21    138476\n",
       "22    413487\n",
       "23     24088\n",
       "24    104045\n",
       "25      2791\n",
       "26     14621\n",
       "27      2673\n",
       "28       723\n",
       "29    190252\n",
       "30    104045\n",
       "Name: Maize_Production, dtype: int64"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Working_Directory['Maize_Production']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "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>District</th>\n",
       "      <th>Maize_Production</th>\n",
       "      <th>TotalNPK</th>\n",
       "      <th>SowingSeedsDistributed_Maize</th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea</th>\n",
       "      <th>Maize_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>2879</td>\n",
       "      <td>438.180210</td>\n",
       "      <td>4.450</td>\n",
       "      <td>2567.461578</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>9348</td>\n",
       "      <td>0.018798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BENGALURU(R)</td>\n",
       "      <td>42583</td>\n",
       "      <td>3036.080250</td>\n",
       "      <td>134.730</td>\n",
       "      <td>25037.139270</td>\n",
       "      <td>14867.0</td>\n",
       "      <td>20281</td>\n",
       "      <td>0.157645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RAMANAGARA</td>\n",
       "      <td>10450</td>\n",
       "      <td>222.225258</td>\n",
       "      <td>260.150</td>\n",
       "      <td>7729.614277</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>23868</td>\n",
       "      <td>0.029741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHITRADURGA</td>\n",
       "      <td>271975</td>\n",
       "      <td>10902.406570</td>\n",
       "      <td>5004.000</td>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.0</td>\n",
       "      <td>92506</td>\n",
       "      <td>0.303824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DAVANAGERE</td>\n",
       "      <td>593544</td>\n",
       "      <td>25702.835480</td>\n",
       "      <td>6124.600</td>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.0</td>\n",
       "      <td>78364</td>\n",
       "      <td>0.257947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KOLAR</td>\n",
       "      <td>1759</td>\n",
       "      <td>220.782906</td>\n",
       "      <td>51.500</td>\n",
       "      <td>2278.829821</td>\n",
       "      <td>640.0</td>\n",
       "      <td>20140</td>\n",
       "      <td>0.009950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CHIKKABALLAPURA</td>\n",
       "      <td>105491</td>\n",
       "      <td>8196.896749</td>\n",
       "      <td>3538.150</td>\n",
       "      <td>71206.945310</td>\n",
       "      <td>59377.0</td>\n",
       "      <td>50370</td>\n",
       "      <td>0.289061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SHIVAMOGGA</td>\n",
       "      <td>226532</td>\n",
       "      <td>10428.246260</td>\n",
       "      <td>3814.020</td>\n",
       "      <td>104722.000400</td>\n",
       "      <td>43827.0</td>\n",
       "      <td>32347</td>\n",
       "      <td>0.184215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TUMAKURU</td>\n",
       "      <td>66868</td>\n",
       "      <td>2746.313635</td>\n",
       "      <td>1639.850</td>\n",
       "      <td>37944.093760</td>\n",
       "      <td>24207.0</td>\n",
       "      <td>144708</td>\n",
       "      <td>0.071991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHIKKAMAGALURU</td>\n",
       "      <td>61353</td>\n",
       "      <td>8156.290370</td>\n",
       "      <td>2923.790</td>\n",
       "      <td>40572.445630</td>\n",
       "      <td>25831.0</td>\n",
       "      <td>26174</td>\n",
       "      <td>0.120188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DAKSHINA KANNADA</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16589</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UDUPI</td>\n",
       "      <td>109</td>\n",
       "      <td>2.304863</td>\n",
       "      <td>0.000</td>\n",
       "      <td>94.499008</td>\n",
       "      <td>8.0</td>\n",
       "      <td>699</td>\n",
       "      <td>0.000375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HASSAN</td>\n",
       "      <td>333937</td>\n",
       "      <td>17039.273890</td>\n",
       "      <td>8925.330</td>\n",
       "      <td>151008.140300</td>\n",
       "      <td>79058.0</td>\n",
       "      <td>23393</td>\n",
       "      <td>0.248999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KODAGU</td>\n",
       "      <td>17921</td>\n",
       "      <td>3519.022414</td>\n",
       "      <td>246.410</td>\n",
       "      <td>7599.582730</td>\n",
       "      <td>3448.0</td>\n",
       "      <td>106</td>\n",
       "      <td>0.085941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MANDYA</td>\n",
       "      <td>19387</td>\n",
       "      <td>1448.830355</td>\n",
       "      <td>794.030</td>\n",
       "      <td>8774.134623</td>\n",
       "      <td>4892.0</td>\n",
       "      <td>16025</td>\n",
       "      <td>0.023925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MYSURU</td>\n",
       "      <td>137154</td>\n",
       "      <td>6945.331415</td>\n",
       "      <td>1573.430</td>\n",
       "      <td>61547.727440</td>\n",
       "      <td>37283.0</td>\n",
       "      <td>19842</td>\n",
       "      <td>0.111996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CHAMARAJANAGAR</td>\n",
       "      <td>147886</td>\n",
       "      <td>7289.684770</td>\n",
       "      <td>2054.600</td>\n",
       "      <td>80396.926680</td>\n",
       "      <td>38569.0</td>\n",
       "      <td>44070</td>\n",
       "      <td>0.306251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BELAGAVI</td>\n",
       "      <td>576758</td>\n",
       "      <td>48288.815130</td>\n",
       "      <td>11883.580</td>\n",
       "      <td>159047.086200</td>\n",
       "      <td>131163.0</td>\n",
       "      <td>139708</td>\n",
       "      <td>0.268283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>VIJAYAPURA</td>\n",
       "      <td>226327</td>\n",
       "      <td>10420.071650</td>\n",
       "      <td>2682.760</td>\n",
       "      <td>48619.507080</td>\n",
       "      <td>40207.0</td>\n",
       "      <td>113515</td>\n",
       "      <td>0.192153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BAGALKOT</td>\n",
       "      <td>251699</td>\n",
       "      <td>22940.521250</td>\n",
       "      <td>5678.150</td>\n",
       "      <td>75578.052000</td>\n",
       "      <td>44300.0</td>\n",
       "      <td>117497</td>\n",
       "      <td>0.270487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DHARAWAD</td>\n",
       "      <td>90634</td>\n",
       "      <td>6447.725408</td>\n",
       "      <td>2471.350</td>\n",
       "      <td>33424.645600</td>\n",
       "      <td>30900.0</td>\n",
       "      <td>20816</td>\n",
       "      <td>0.172487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>GADAG</td>\n",
       "      <td>138476</td>\n",
       "      <td>5591.244159</td>\n",
       "      <td>4111.000</td>\n",
       "      <td>34261.981770</td>\n",
       "      <td>34163.0</td>\n",
       "      <td>41664</td>\n",
       "      <td>0.240174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>HAVERI</td>\n",
       "      <td>413487</td>\n",
       "      <td>21731.170720</td>\n",
       "      <td>11325.600</td>\n",
       "      <td>95150.640270</td>\n",
       "      <td>170696.0</td>\n",
       "      <td>63016</td>\n",
       "      <td>0.334321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>UTTARA KANNADA</td>\n",
       "      <td>24088</td>\n",
       "      <td>1025.616827</td>\n",
       "      <td>1469.200</td>\n",
       "      <td>22563.771080</td>\n",
       "      <td>4576.0</td>\n",
       "      <td>8460</td>\n",
       "      <td>0.066964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BALLARI</td>\n",
       "      <td>104045</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>2859.005</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>75198</td>\n",
       "      <td>0.136288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BIDAR</td>\n",
       "      <td>2791</td>\n",
       "      <td>249.381182</td>\n",
       "      <td>24.550</td>\n",
       "      <td>2371.831678</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>19908</td>\n",
       "      <td>0.010114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KALABURAGI</td>\n",
       "      <td>14621</td>\n",
       "      <td>710.101614</td>\n",
       "      <td>165.000</td>\n",
       "      <td>6221.617910</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>48802</td>\n",
       "      <td>0.013150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>YADGIRI</td>\n",
       "      <td>2673</td>\n",
       "      <td>368.617469</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1253.317144</td>\n",
       "      <td>961.0</td>\n",
       "      <td>22035</td>\n",
       "      <td>0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RAICHUR</td>\n",
       "      <td>723</td>\n",
       "      <td>61.547519</td>\n",
       "      <td>14.350</td>\n",
       "      <td>188.537479</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>26571</td>\n",
       "      <td>0.000525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>KOPPAL</td>\n",
       "      <td>190252</td>\n",
       "      <td>10997.550400</td>\n",
       "      <td>5292.000</td>\n",
       "      <td>38936.400170</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>82552</td>\n",
       "      <td>0.149521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>VIJAYANAGARA</td>\n",
       "      <td>104045</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>2859.005</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>75198</td>\n",
       "      <td>0.136288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            District  Maize_Production      TotalNPK  \\\n",
       "0          BENGALURU              2879    438.180210   \n",
       "1       BENGALURU(R)             42583   3036.080250   \n",
       "2         RAMANAGARA             10450    222.225258   \n",
       "3        CHITRADURGA            271975  10902.406570   \n",
       "4         DAVANAGERE            593544  25702.835480   \n",
       "5              KOLAR              1759    220.782906   \n",
       "6    CHIKKABALLAPURA            105491   8196.896749   \n",
       "7         SHIVAMOGGA            226532  10428.246260   \n",
       "8           TUMAKURU             66868   2746.313635   \n",
       "9     CHIKKAMAGALURU             61353   8156.290370   \n",
       "10  DAKSHINA KANNADA                 0      0.000000   \n",
       "11             UDUPI               109      2.304863   \n",
       "12            HASSAN            333937  17039.273890   \n",
       "13            KODAGU             17921   3519.022414   \n",
       "14            MANDYA             19387   1448.830355   \n",
       "15            MYSURU            137154   6945.331415   \n",
       "16    CHAMARAJANAGAR            147886   7289.684770   \n",
       "17          BELAGAVI            576758  48288.815130   \n",
       "18        VIJAYAPURA            226327  10420.071650   \n",
       "19          BAGALKOT            251699  22940.521250   \n",
       "20          DHARAWAD             90634   6447.725408   \n",
       "21             GADAG            138476   5591.244159   \n",
       "22            HAVERI            413487  21731.170720   \n",
       "23    UTTARA KANNADA             24088   1025.616827   \n",
       "24           BALLARI            104045   8404.154608   \n",
       "25             BIDAR              2791    249.381182   \n",
       "26        KALABURAGI             14621    710.101614   \n",
       "27           YADGIRI              2673    368.617469   \n",
       "28           RAICHUR               723     61.547519   \n",
       "29            KOPPAL            190252  10997.550400   \n",
       "30      VIJAYANAGARA            104045   8404.154608   \n",
       "\n",
       "    SowingSeedsDistributed_Maize  LivestockCensus_2012_Cattle  \\\n",
       "0                          4.450                  2567.461578   \n",
       "1                        134.730                 25037.139270   \n",
       "2                        260.150                  7729.614277   \n",
       "3                       5004.000                 83821.593090   \n",
       "4                       6124.600                 85657.632090   \n",
       "5                         51.500                  2278.829821   \n",
       "6                       3538.150                 71206.945310   \n",
       "7                       3814.020                104722.000400   \n",
       "8                       1639.850                 37944.093760   \n",
       "9                       2923.790                 40572.445630   \n",
       "10                         0.000                     0.000000   \n",
       "11                         0.000                    94.499008   \n",
       "12                      8925.330                151008.140300   \n",
       "13                       246.410                  7599.582730   \n",
       "14                       794.030                  8774.134623   \n",
       "15                      1573.430                 61547.727440   \n",
       "16                      2054.600                 80396.926680   \n",
       "17                     11883.580                159047.086200   \n",
       "18                      2682.760                 48619.507080   \n",
       "19                      5678.150                 75578.052000   \n",
       "20                      2471.350                 33424.645600   \n",
       "21                      4111.000                 34261.981770   \n",
       "22                     11325.600                 95150.640270   \n",
       "23                      1469.200                 22563.771080   \n",
       "24                      2859.005                 23446.847900   \n",
       "25                        24.550                  2371.831678   \n",
       "26                       165.000                  6221.617910   \n",
       "27                         0.000                  1253.317144   \n",
       "28                        14.350                   188.537479   \n",
       "29                      5292.000                 38936.400170   \n",
       "30                      2859.005                 23446.847900   \n",
       "\n",
       "    AreaUnderCereals_Maize  \\\n",
       "0                   1129.0   \n",
       "1                  14867.0   \n",
       "2                   1987.0   \n",
       "3                  92194.0   \n",
       "4                 173400.0   \n",
       "5                    640.0   \n",
       "6                  59377.0   \n",
       "7                  43827.0   \n",
       "8                  24207.0   \n",
       "9                  25831.0   \n",
       "10                     0.0   \n",
       "11                     8.0   \n",
       "12                 79058.0   \n",
       "13                  3448.0   \n",
       "14                  4892.0   \n",
       "15                 37283.0   \n",
       "16                 38569.0   \n",
       "17                131163.0   \n",
       "18                 40207.0   \n",
       "19                 44300.0   \n",
       "20                 30900.0   \n",
       "21                 34163.0   \n",
       "22                170696.0   \n",
       "23                  4576.0   \n",
       "24                 49869.5   \n",
       "25                  1382.0   \n",
       "26                  2634.0   \n",
       "27                   961.0   \n",
       "28                  2600.0   \n",
       "29                 48210.0   \n",
       "30                 49869.5   \n",
       "\n",
       "    NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea  \\\n",
       "0                                                9348                  \n",
       "1                                               20281                  \n",
       "2                                               23868                  \n",
       "3                                               92506                  \n",
       "4                                               78364                  \n",
       "5                                               20140                  \n",
       "6                                               50370                  \n",
       "7                                               32347                  \n",
       "8                                              144708                  \n",
       "9                                               26174                  \n",
       "10                                              16589                  \n",
       "11                                                699                  \n",
       "12                                              23393                  \n",
       "13                                                106                  \n",
       "14                                              16025                  \n",
       "15                                              19842                  \n",
       "16                                              44070                  \n",
       "17                                             139708                  \n",
       "18                                             113515                  \n",
       "19                                             117497                  \n",
       "20                                              20816                  \n",
       "21                                              41664                  \n",
       "22                                              63016                  \n",
       "23                                               8460                  \n",
       "24                                              75198                  \n",
       "25                                              19908                  \n",
       "26                                              48802                  \n",
       "27                                              22035                  \n",
       "28                                              26571                  \n",
       "29                                              82552                  \n",
       "30                                              75198                  \n",
       "\n",
       "    Maize_Ratio  \n",
       "0      0.018798  \n",
       "1      0.157645  \n",
       "2      0.029741  \n",
       "3      0.303824  \n",
       "4      0.257947  \n",
       "5      0.009950  \n",
       "6      0.289061  \n",
       "7      0.184215  \n",
       "8      0.071991  \n",
       "9      0.120188  \n",
       "10     0.000000  \n",
       "11     0.000375  \n",
       "12     0.248999  \n",
       "13     0.085941  \n",
       "14     0.023925  \n",
       "15     0.111996  \n",
       "16     0.306251  \n",
       "17     0.268283  \n",
       "18     0.192153  \n",
       "19     0.270487  \n",
       "20     0.172487  \n",
       "21     0.240174  \n",
       "22     0.334321  \n",
       "23     0.066964  \n",
       "24     0.136288  \n",
       "25     0.010114  \n",
       "26     0.013150  \n",
       "27     0.004033  \n",
       "28     0.000525  \n",
       "29     0.149521  \n",
       "30     0.136288  "
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Working_Directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c85ad1918f534c939ea3c1abd1377f21",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.5, description='αCT:', max=1.0), FloatSlider(value=0.5, description=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3ea9901341ab4f938b0bc1698b982e28",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Button(description='Update Plot', style=ButtonStyle()), FloatSlider(value=0.5, description='αCT…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.update_plot(alpha1, alpha2, alpha3)>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "from IPython.display import clear_output\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "def update_plot(alpha1, alpha2, alpha3):\n",
    "    \n",
    "    tolerance = 1e-5 \n",
    "    filtered_df = viz_df[\n",
    "        (viz_df['Sensitivity_LivestockCensus_2012_Cattle'].between(alpha1 - tolerance, alpha1 + tolerance)) &\n",
    "        (viz_df['Sensitivity_AreaUnderCereals_Maize'].between(alpha2 - tolerance, alpha2 + tolerance)) & \n",
    "        (viz_df['Sensitivity_TotalNPK'].between(alpha3 - tolerance, alpha3 + tolerance)) \n",
    "    ]\n",
    "\n",
    "    # Create a scatter plot with filtered data\n",
    "    fig = px.scatter(\n",
    "        filtered_df, \n",
    "        x='Stability_Values', \n",
    "        y='Percentage Change', \n",
    "        title='Maize Percentage Change vs Stability',\n",
    "        labels={'Percentage Change': 'Percentage Change', 'Stability_Values': 'Stability'},\n",
    "        text='Abbreviation',\n",
    "        opacity=1,\n",
    "        color=filtered_df['Percentage Change'].apply(lambda val: 'red' if val < 0 else 'darkblue'),  # Conditional coloring\n",
    "        color_discrete_map={'red': 'red', 'darkblue': 'darkblue'}\n",
    "    )\n",
    "\n",
    "    fig.update_traces(textposition='top center', textfont=dict(size=11))\n",
    "\n",
    "    # Update layout to add annotations and legends\n",
    "    fig.update_layout(\n",
    "        annotations=[\n",
    "            dict(\n",
    "                x=0.5,\n",
    "                y=-0.25,\n",
    "                showarrow=False,\n",
    "                text=f' α<sub>CT</sub>: {alpha1:.2f}, α<sub>AM</sub>: {alpha2:.2f}, α<sub>NPK</sub>: {alpha3:.2f}',\n",
    "                xref=\"paper\",\n",
    "                yref=\"paper\",\n",
    "                font=dict(size=13)\n",
    "            )\n",
    "        ],\n",
    "        legend_title_text='Districts',\n",
    "        legend_title=dict(font=dict(size=12)),\n",
    "    )\n",
    "\n",
    "    # Adding custom legends with abbreviations and full forms\n",
    "    legend_labels = [{'Abbreviation': abb, 'Full Form': full_form} \n",
    "                     for abb, full_form in zip(filtered_df['Abbreviation'].unique(), filtered_df['District'].unique())]\n",
    "\n",
    "    for label in legend_labels:\n",
    "        fig.add_trace(go.Scatter(\n",
    "            x=[None],\n",
    "            y=[None],\n",
    "            mode='markers',\n",
    "            marker=dict(color='darkblue'),\n",
    "            name=f\"{label['Abbreviation']}: {label['Full Form']}\"\n",
    "        ))\n",
    "\n",
    "    # Adding average lines for stability and percentage change\n",
    "    avg_stability = filtered_df['Stability_Values'].mean()\n",
    "    avg_precent = filtered_df['Percentage Change'].mean()\n",
    "\n",
    "    fig.add_shape(\n",
    "        type='line',\n",
    "        x0=avg_stability, y0=0, x1=avg_stability, y1=1,\n",
    "        line=dict(color='black', width=1.5, dash='dash'),\n",
    "        yref='paper'\n",
    "    )\n",
    "\n",
    "    fig.add_shape(\n",
    "        type='line',\n",
    "        x0=0, y0=avg_precent, x1=1, y1=avg_precent,\n",
    "        line=dict(color='black', width=1.5, dash='dash'),\n",
    "        xref='paper'\n",
    "    )\n",
    "\n",
    "    fig.add_annotation(\n",
    "        x=avg_stability+0.0075,\n",
    "        y=-0.07,\n",
    "        text=f'Avg Stability: {avg_stability:.2f}',\n",
    "        showarrow=False,\n",
    "        font=dict(size=10.7),\n",
    "        textangle=90  # Rotate text using textangle in annotations\n",
    "    )\n",
    "\n",
    "\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=[0.3],\n",
    "        y=[avg_precent+4.5],\n",
    "        text=[f'Avg Impact: {avg_precent:.2f}'],\n",
    "        mode=\"text\",\n",
    "        showlegend=False,\n",
    "        textfont=dict(size=10.7)\n",
    "    ))\n",
    "\n",
    "    clear_output()\n",
    "\n",
    "    display(fig)\n",
    "    \n",
    "\n",
    "\n",
    "# Creating sliders for interaction\n",
    "alpha1_slider = widgets.FloatSlider(value=0.5, min=0, max=1, step=0.1, description='αCT:')\n",
    "alpha2_slider = widgets.FloatSlider(value=0.5, min=0, max=1, step=0.1, description='αAM:')\n",
    "alpha3_slider = widgets.FloatSlider(value=0.5, min=0, max=1, step=0.1, description='αNPK:')\n",
    "\n",
    "# Creating interactive plot\n",
    "interactive_plot = interact(update_plot, alpha1=alpha1_slider, alpha2=alpha2_slider, alpha3=alpha3_slider)\n",
    "\n",
    "# Create update button to trigger plot update\n",
    "update_button = widgets.Button(description=\"Update Plot\")\n",
    "update_button.on_click(lambda _: update_plot(alpha1_slider.value, alpha2_slider.value, alpha3_slider.value))\n",
    "\n",
    "# Displaying the interactive plot along with the sliders and update button\n",
    "display(widgets.HBox([update_button, alpha1_slider, alpha2_slider, alpha3_slider]))\n",
    "display(interactive_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "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>District</th>\n",
       "      <th>Sensitivity_LivestockCensus_2012_Cattle</th>\n",
       "      <th>Sensitivity_AreaUnderCereals_Maize</th>\n",
       "      <th>Sensitivity_TotalNPK</th>\n",
       "      <th>New_Production</th>\n",
       "      <th>Stability_Values</th>\n",
       "      <th>Abbreviation</th>\n",
       "      <th>Percentage Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11928</th>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>13234.972351</td>\n",
       "      <td>0.940414</td>\n",
       "      <td>BLR</td>\n",
       "      <td>3.597073e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11929</th>\n",
       "      <td>BENGALURU(R)</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>65822.467618</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>BLR(R)</td>\n",
       "      <td>5.457452e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11930</th>\n",
       "      <td>RAMANAGARA</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>16486.464860</td>\n",
       "      <td>0.801618</td>\n",
       "      <td>RGA</td>\n",
       "      <td>5.776521e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11931</th>\n",
       "      <td>CHITRADURGA</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>271975.000000</td>\n",
       "      <td>0.859119</td>\n",
       "      <td>CDA</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11932</th>\n",
       "      <td>DAVANAGERE</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>593544.000000</td>\n",
       "      <td>0.065352</td>\n",
       "      <td>DVG</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11933</th>\n",
       "      <td>KOLAR</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>10929.852671</td>\n",
       "      <td>0.735471</td>\n",
       "      <td>KLR</td>\n",
       "      <td>5.213674e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11934</th>\n",
       "      <td>CHIKKABALLAPURA</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>201800.828150</td>\n",
       "      <td>0.683822</td>\n",
       "      <td>CKA</td>\n",
       "      <td>9.129672e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11935</th>\n",
       "      <td>SHIVAMOGGA</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>226532.000000</td>\n",
       "      <td>0.911865</td>\n",
       "      <td>SMG</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11936</th>\n",
       "      <td>TUMAKURU</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>88208.202794</td>\n",
       "      <td>0.925989</td>\n",
       "      <td>TKR</td>\n",
       "      <td>3.191392e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11937</th>\n",
       "      <td>CHIKKAMAGALURU</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>124182.982408</td>\n",
       "      <td>0.780976</td>\n",
       "      <td>CMG</td>\n",
       "      <td>1.024074e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11938</th>\n",
       "      <td>HASSAN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>333937.000000</td>\n",
       "      <td>0.457488</td>\n",
       "      <td>HSN</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11939</th>\n",
       "      <td>KODAGU</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>38245.974166</td>\n",
       "      <td>0.707085</td>\n",
       "      <td>KDG</td>\n",
       "      <td>1.134143e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11940</th>\n",
       "      <td>MANDYA</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>29414.293421</td>\n",
       "      <td>0.548756</td>\n",
       "      <td>MDY</td>\n",
       "      <td>5.172174e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11941</th>\n",
       "      <td>MYSURU</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>137154.000000</td>\n",
       "      <td>0.604085</td>\n",
       "      <td>MYS</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11942</th>\n",
       "      <td>CHAMARAJANAGAR</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>149999.278312</td>\n",
       "      <td>0.910098</td>\n",
       "      <td>CNR</td>\n",
       "      <td>1.428991e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11943</th>\n",
       "      <td>BELAGAVI</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>599393.150009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>BLG</td>\n",
       "      <td>3.924549e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11944</th>\n",
       "      <td>VIJAYAPURA</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>226327.000000</td>\n",
       "      <td>0.938155</td>\n",
       "      <td>VJP</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11945</th>\n",
       "      <td>BAGALKOT</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>251699.000000</td>\n",
       "      <td>0.795081</td>\n",
       "      <td>BKT</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11946</th>\n",
       "      <td>DHARAWAD</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>119095.314327</td>\n",
       "      <td>0.859027</td>\n",
       "      <td>DWD</td>\n",
       "      <td>3.140247e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11947</th>\n",
       "      <td>GADAG</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>138476.000000</td>\n",
       "      <td>0.993276</td>\n",
       "      <td>GDG</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11948</th>\n",
       "      <td>HAVERI</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>477790.516964</td>\n",
       "      <td>0.216096</td>\n",
       "      <td>HVR</td>\n",
       "      <td>1.555152e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11949</th>\n",
       "      <td>UTTARA KANNADA</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>34358.192853</td>\n",
       "      <td>0.623309</td>\n",
       "      <td>UTK</td>\n",
       "      <td>4.263614e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11950</th>\n",
       "      <td>BALLARI</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>158478.283018</td>\n",
       "      <td>0.851958</td>\n",
       "      <td>BLL</td>\n",
       "      <td>5.231706e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11951</th>\n",
       "      <td>BIDAR</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>12470.192445</td>\n",
       "      <td>0.530021</td>\n",
       "      <td>BDR</td>\n",
       "      <td>3.468002e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11952</th>\n",
       "      <td>KALABURAGI</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>19611.706643</td>\n",
       "      <td>0.471971</td>\n",
       "      <td>KLB</td>\n",
       "      <td>3.413383e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11953</th>\n",
       "      <td>YADGIRI</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>11770.247306</td>\n",
       "      <td>0.615478</td>\n",
       "      <td>YDR</td>\n",
       "      <td>3.403385e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11954</th>\n",
       "      <td>KOPPAL</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>190252.000000</td>\n",
       "      <td>0.798225</td>\n",
       "      <td>KPL</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11955</th>\n",
       "      <td>VIJAYANAGARA</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>158478.283018</td>\n",
       "      <td>0.992457</td>\n",
       "      <td>VJN</td>\n",
       "      <td>5.231706e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              District  Sensitivity_LivestockCensus_2012_Cattle  \\\n",
       "11928        BENGALURU                                      0.3   \n",
       "11929     BENGALURU(R)                                      0.3   \n",
       "11930       RAMANAGARA                                      0.3   \n",
       "11931      CHITRADURGA                                      0.3   \n",
       "11932       DAVANAGERE                                      0.3   \n",
       "11933            KOLAR                                      0.3   \n",
       "11934  CHIKKABALLAPURA                                      0.3   \n",
       "11935       SHIVAMOGGA                                      0.3   \n",
       "11936         TUMAKURU                                      0.3   \n",
       "11937   CHIKKAMAGALURU                                      0.3   \n",
       "11938           HASSAN                                      0.3   \n",
       "11939           KODAGU                                      0.3   \n",
       "11940           MANDYA                                      0.3   \n",
       "11941           MYSURU                                      0.3   \n",
       "11942   CHAMARAJANAGAR                                      0.3   \n",
       "11943         BELAGAVI                                      0.3   \n",
       "11944       VIJAYAPURA                                      0.3   \n",
       "11945         BAGALKOT                                      0.3   \n",
       "11946         DHARAWAD                                      0.3   \n",
       "11947            GADAG                                      0.3   \n",
       "11948           HAVERI                                      0.3   \n",
       "11949   UTTARA KANNADA                                      0.3   \n",
       "11950          BALLARI                                      0.3   \n",
       "11951            BIDAR                                      0.3   \n",
       "11952       KALABURAGI                                      0.3   \n",
       "11953          YADGIRI                                      0.3   \n",
       "11954           KOPPAL                                      0.3   \n",
       "11955     VIJAYANAGARA                                      0.3   \n",
       "\n",
       "       Sensitivity_AreaUnderCereals_Maize  Sensitivity_TotalNPK  \\\n",
       "11928                                 0.5                   0.8   \n",
       "11929                                 0.5                   0.8   \n",
       "11930                                 0.5                   0.8   \n",
       "11931                                 0.5                   0.8   \n",
       "11932                                 0.5                   0.8   \n",
       "11933                                 0.5                   0.8   \n",
       "11934                                 0.5                   0.8   \n",
       "11935                                 0.5                   0.8   \n",
       "11936                                 0.5                   0.8   \n",
       "11937                                 0.5                   0.8   \n",
       "11938                                 0.5                   0.8   \n",
       "11939                                 0.5                   0.8   \n",
       "11940                                 0.5                   0.8   \n",
       "11941                                 0.5                   0.8   \n",
       "11942                                 0.5                   0.8   \n",
       "11943                                 0.5                   0.8   \n",
       "11944                                 0.5                   0.8   \n",
       "11945                                 0.5                   0.8   \n",
       "11946                                 0.5                   0.8   \n",
       "11947                                 0.5                   0.8   \n",
       "11948                                 0.5                   0.8   \n",
       "11949                                 0.5                   0.8   \n",
       "11950                                 0.5                   0.8   \n",
       "11951                                 0.5                   0.8   \n",
       "11952                                 0.5                   0.8   \n",
       "11953                                 0.5                   0.8   \n",
       "11954                                 0.5                   0.8   \n",
       "11955                                 0.5                   0.8   \n",
       "\n",
       "       New_Production  Stability_Values Abbreviation  Percentage Change  \n",
       "11928    13234.972351          0.940414          BLR       3.597073e+02  \n",
       "11929    65822.467618          1.000000       BLR(R)       5.457452e+01  \n",
       "11930    16486.464860          0.801618          RGA       5.776521e+01  \n",
       "11931   271975.000000          0.859119          CDA       1.000000e-09  \n",
       "11932   593544.000000          0.065352          DVG       1.000000e-09  \n",
       "11933    10929.852671          0.735471          KLR       5.213674e+02  \n",
       "11934   201800.828150          0.683822          CKA       9.129672e+01  \n",
       "11935   226532.000000          0.911865          SMG       1.000000e-09  \n",
       "11936    88208.202794          0.925989          TKR       3.191392e+01  \n",
       "11937   124182.982408          0.780976          CMG       1.024074e+02  \n",
       "11938   333937.000000          0.457488          HSN       1.000000e-09  \n",
       "11939    38245.974166          0.707085          KDG       1.134143e+02  \n",
       "11940    29414.293421          0.548756          MDY       5.172174e+01  \n",
       "11941   137154.000000          0.604085          MYS       1.000000e-09  \n",
       "11942   149999.278312          0.910098          CNR       1.428991e+00  \n",
       "11943   599393.150009          0.000000          BLG       3.924549e+00  \n",
       "11944   226327.000000          0.938155          VJP       1.000000e-09  \n",
       "11945   251699.000000          0.795081          BKT       1.000000e-09  \n",
       "11946   119095.314327          0.859027          DWD       3.140247e+01  \n",
       "11947   138476.000000          0.993276          GDG       1.000000e-09  \n",
       "11948   477790.516964          0.216096          HVR       1.555152e+01  \n",
       "11949    34358.192853          0.623309          UTK       4.263614e+01  \n",
       "11950   158478.283018          0.851958          BLL       5.231706e+01  \n",
       "11951    12470.192445          0.530021          BDR       3.468002e+02  \n",
       "11952    19611.706643          0.471971          KLB       3.413383e+01  \n",
       "11953    11770.247306          0.615478          YDR       3.403385e+02  \n",
       "11954   190252.000000          0.798225          KPL       1.000000e-09  \n",
       "11955   158478.283018          0.992457          VJN       5.231706e+01  "
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tolerance = 1e-5\n",
    "filtered_rows = viz_df[\n",
    "    (viz_df['Sensitivity_LivestockCensus_2012_Cattle'].between(0.3 - tolerance, 0.3 + tolerance)) &\n",
    "    (viz_df['Sensitivity_AreaUnderCereals_Maize'].between(0.5 - tolerance, 0.5 + tolerance)) &\n",
    "    (viz_df['Sensitivity_TotalNPK'].between(0.8 - tolerance, 0.8 + tolerance))\n",
    "]\n",
    "filtered_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
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        "title": {
         "text": "Comparison of New and Old Maize Production ( 8% change in State Average Production )"
        },
        "xaxis": {
         "tickangle": -45,
         "title": {
          "text": "Districts"
         }
        },
        "yaxis": {
         "title": {
          "text": "Production Values"
         }
        }
       }
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     },
     "metadata": {},
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   ],
   "source": [
    "new_val = filtered_rows['New_Production']\n",
    "old_val = y_st['Maize_Production']\n",
    "\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# Define the values\n",
    "new_val = filtered_rows['New_Production']\n",
    "old_val = y_st['Maize_Production']\n",
    "abbreviations = filtered_rows['Abbreviation']\n",
    "\n",
    "# Create the figure\n",
    "fig = go.Figure()\n",
    "\n",
    "# Add the Old Production bars\n",
    "fig.add_trace(go.Bar(\n",
    "    x=abbreviations,\n",
    "    y=old_val,\n",
    "    name='Old Production',\n",
    "    marker_color='orange'\n",
    "))\n",
    "\n",
    "\n",
    "# Add the New Production bars\n",
    "fig.add_trace(go.Bar(\n",
    "    x=abbreviations,\n",
    "    y=new_val,\n",
    "    name='New Production',\n",
    "    marker_color='blue'\n",
    "))\n",
    "\n",
    "\n",
    "# Update layout for better readability\n",
    "fig.update_layout(\n",
    "    title='Comparison of New and Old Maize Production ( 8% change in State Average Production )',\n",
    "    xaxis_title='Districts',\n",
    "    yaxis_title='Production Values',\n",
    "    barmode='group',  # To display bars side by side\n",
    "    xaxis_tickangle=-45,  # Rotate x-axis labels for better readability\n",
    "    legend_title='Production Type'\n",
    ")\n",
    "\n",
    "# Show the figure\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [],
   "source": [
    "viz_df['New_Production'] = viz_df['New_Production'].apply(lambda x: max(0, x))\n",
    "y = y_st['Maize_Production']\n",
    "\n",
    "viz_df['Impact'] = viz_df['New_Production'] - np.tile(y.values, len(viz_df) // case_file_length + 1)[:len(viz_df)]\n",
    "for i in range(0, len(viz_df), case_file_length):\n",
    "    viz_df.loc[i:i+case_file_length-1, 'Impact'] = viz_df.loc[i:i+case_file_length-1, 'Impact']\n",
    "    # viz_df.loc[i:i+case_file_length-1, 'Normalized Percentage Change'] = normalize_list(viz_df.loc[i:i+case_file_length-1, 'Percentage Change'])\n",
    "\n",
    "viz_df['Impact'] = viz_df['Impact'].replace([np.inf, -np.inf], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2fba9e5ec0b1449091c23f08a3cd4b9e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.5, description='αCT:', max=1.0), FloatSlider(value=0.5, description=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9d2bc8b58f1c429c85975caaa8d280f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Button(description='Update Plot', style=ButtonStyle()), FloatSlider(value=0.5, description='αCT…"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "from IPython.display import clear_output\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "def update_plot(alpha1, alpha2, alpha3):\n",
    "    \n",
    "    tolerance = 1e-5 \n",
    "    filtered_df = viz_df[\n",
    "        (viz_df['Sensitivity_LivestockCensus_2012_Cattle'].between(alpha1 - tolerance, alpha1 + tolerance)) &\n",
    "        (viz_df['Sensitivity_AreaUnderCereals_Maize'].between(alpha2 - tolerance, alpha2 + tolerance)) & \n",
    "        (viz_df['Sensitivity_TotalNPK'].between(alpha3 - tolerance, alpha3 + tolerance)) \n",
    "    ]\n",
    "\n",
    "    # Create a scatter plot with filtered data\n",
    "    fig = px.scatter(\n",
    "        filtered_df, \n",
    "        x='Stability_Values', \n",
    "        y='Impact', \n",
    "        title='Maize Impact vs Stability',\n",
    "        labels={'Impact': 'Impact', 'Stability_Values': 'Stability'},\n",
    "        text='Abbreviation',\n",
    "        opacity=1,\n",
    "        color=filtered_df['Impact'].apply(lambda val: 'red' if val < 0 else 'darkblue'),  # Conditional coloring\n",
    "        color_discrete_map={'red': 'red', 'darkblue': 'darkblue'}\n",
    "    )\n",
    "\n",
    "    fig.update_traces(textposition='top center', textfont=dict(size=11))\n",
    "\n",
    "    # Update layout to add annotations and legends\n",
    "    fig.update_layout(\n",
    "        annotations=[\n",
    "            dict(\n",
    "                x=0.5,\n",
    "                y=-0.25,\n",
    "                showarrow=False,\n",
    "                text=f' α<sub>CT</sub>: {alpha1:.2f}, α<sub>AM</sub>: {alpha2:.2f}, α<sub>NPK</sub>: {alpha3:.2f}',\n",
    "                xref=\"paper\",\n",
    "                yref=\"paper\",\n",
    "                font=dict(size=13)\n",
    "            )\n",
    "        ],\n",
    "        legend_title_text='Districts',\n",
    "        legend_title=dict(font=dict(size=12)),\n",
    "    )\n",
    "\n",
    "    # Adding custom legends with abbreviations and full forms\n",
    "    legend_labels = [{'Abbreviation': abb, 'Full Form': full_form} \n",
    "                     for abb, full_form in zip(filtered_df['Abbreviation'].unique(), filtered_df['District'].unique())]\n",
    "\n",
    "    for label in legend_labels:\n",
    "        fig.add_trace(go.Scatter(\n",
    "            x=[None],\n",
    "            y=[None],\n",
    "            mode='markers',\n",
    "            marker=dict(color='darkblue'),\n",
    "            name=f\"{label['Abbreviation']}: {label['Full Form']}\"\n",
    "        ))\n",
    "\n",
    "    # Adding average lines for stability and percentage change\n",
    "    avg_stability = filtered_df['Stability_Values'].mean()\n",
    "    avg_precent = filtered_df['Impact'].mean()\n",
    "\n",
    "    fig.add_shape(\n",
    "        type='line',\n",
    "        x0=avg_stability, y0=0, x1=avg_stability, y1=1,\n",
    "        line=dict(color='red', width=1.5, dash='dash'),\n",
    "        yref='paper'\n",
    "    )\n",
    "\n",
    "    fig.add_shape(\n",
    "        type='line',\n",
    "        x0=0, y0=avg_precent, x1=1, y1=avg_precent,\n",
    "        line=dict(color='red', width=1.5, dash='dash'),\n",
    "        xref='paper'\n",
    "    )\n",
    "\n",
    "    # Add annotations for average values\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=[avg_stability],\n",
    "        y=[-0.07],\n",
    "        text=[f'Avg Stability: {avg_stability:.2f}'],\n",
    "        mode=\"text\",\n",
    "        showlegend=False,\n",
    "        textfont=dict(size=10.7)\n",
    "    ))\n",
    "\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=[0.3],\n",
    "        y=[avg_precent-0.04],\n",
    "        text=[f'Avg Impact: {avg_precent:.2f}'],\n",
    "        mode=\"text\",\n",
    "        showlegend=False,\n",
    "        textfont=dict(size=10.7)\n",
    "    ))\n",
    "\n",
    "    clear_output()\n",
    "\n",
    "    display(fig)\n",
    "\n",
    "\n",
    "# Creating sliders for interaction\n",
    "alpha1_slider = widgets.FloatSlider(value=0.5, min=0, max=1, step=0.1, description='αCT:')\n",
    "alpha2_slider = widgets.FloatSlider(value=0.5, min=0, max=1, step=0.1, description='αAM:')\n",
    "alpha3_slider = widgets.FloatSlider(value=0.5, min=0, max=1, step=0.1, description='αNPK:')\n",
    "\n",
    "# Creating interactive plot\n",
    "interactive_plot = interact(update_plot, alpha1=alpha1_slider, alpha2=alpha2_slider, alpha3=alpha3_slider)\n",
    "\n",
    "# Create update button to trigger plot update\n",
    "update_button = widgets.Button(description=\"Update Plot\")\n",
    "update_button.on_click(lambda _: update_plot(alpha1_slider.value, alpha2_slider.value, alpha3_slider.value))\n",
    "\n",
    "# Display the button and sliders\n",
    "widgets.HBox([update_button, alpha1_slider, alpha2_slider, alpha3_slider])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sensitivity_LivestockCensus_2012_Cattle</th>\n",
       "      <th>Sensitivity_AreaUnderCereals_Maize</th>\n",
       "      <th>Sensitivity_TotalNPK</th>\n",
       "      <th>LivestockCensus_2012_Cattle</th>\n",
       "      <th>AreaUnderCereals_Maize</th>\n",
       "      <th>TotalNPK</th>\n",
       "      <th>Beta_0</th>\n",
       "      <th>Beta_1</th>\n",
       "      <th>Beta_2</th>\n",
       "      <th>Beta_3</th>\n",
       "      <th>delta_O</th>\n",
       "      <th>New_F1</th>\n",
       "      <th>New_F2</th>\n",
       "      <th>New_F3</th>\n",
       "      <th>District</th>\n",
       "      <th>New_Production</th>\n",
       "      <th>Stress_Values</th>\n",
       "      <th>Stability_Values</th>\n",
       "      <th>Percentage Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.0</td>\n",
       "      <td>2567.461578</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>438.180210</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>2567.461578</td>\n",
       "      <td>1129.000000</td>\n",
       "      <td>438.180210</td>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>2879.000000</td>\n",
       "      <td>0.052417</td>\n",
       "      <td>0.947583</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25037.139270</td>\n",
       "      <td>14867.0</td>\n",
       "      <td>3036.080250</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>25037.139270</td>\n",
       "      <td>14867.000000</td>\n",
       "      <td>3036.080250</td>\n",
       "      <td>BENGALURU(R)</td>\n",
       "      <td>51007.388234</td>\n",
       "      <td>0.001044</td>\n",
       "      <td>0.998956</td>\n",
       "      <td>1.978345e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7729.614277</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>222.225258</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>7729.614277</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>222.225258</td>\n",
       "      <td>RAMANAGARA</td>\n",
       "      <td>10450.000000</td>\n",
       "      <td>0.187361</td>\n",
       "      <td>0.812639</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.0</td>\n",
       "      <td>10902.406570</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>83821.593090</td>\n",
       "      <td>92194.000000</td>\n",
       "      <td>10902.406570</td>\n",
       "      <td>CHITRADURGA</td>\n",
       "      <td>271975.000000</td>\n",
       "      <td>0.152712</td>\n",
       "      <td>0.847288</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.0</td>\n",
       "      <td>25702.835480</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>85657.632090</td>\n",
       "      <td>173400.000000</td>\n",
       "      <td>25702.835480</td>\n",
       "      <td>DAVANAGERE</td>\n",
       "      <td>593544.000000</td>\n",
       "      <td>0.944086</td>\n",
       "      <td>0.055914</td>\n",
       "      <td>1.000000e-09</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",
       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37263</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2371.831678</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>249.381182</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>18381.773500</td>\n",
       "      <td>6588.158793</td>\n",
       "      <td>1836.304114</td>\n",
       "      <td>BIDAR</td>\n",
       "      <td>25433.386906</td>\n",
       "      <td>0.456385</td>\n",
       "      <td>0.543615</td>\n",
       "      <td>8.112643e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37264</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6221.617910</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>710.101614</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>22231.559732</td>\n",
       "      <td>7840.158793</td>\n",
       "      <td>2297.024546</td>\n",
       "      <td>KALABURAGI</td>\n",
       "      <td>32574.901104</td>\n",
       "      <td>0.517662</td>\n",
       "      <td>0.482338</td>\n",
       "      <td>1.227953e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37265</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1253.317144</td>\n",
       "      <td>961.0</td>\n",
       "      <td>368.617469</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>17263.258966</td>\n",
       "      <td>6167.158793</td>\n",
       "      <td>1955.540401</td>\n",
       "      <td>YADGIRI</td>\n",
       "      <td>24733.441767</td>\n",
       "      <td>0.359057</td>\n",
       "      <td>0.640943</td>\n",
       "      <td>8.253065e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37266</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38936.400170</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>10997.550400</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38936.400170</td>\n",
       "      <td>48210.000000</td>\n",
       "      <td>10997.550400</td>\n",
       "      <td>KOPPAL</td>\n",
       "      <td>190252.000000</td>\n",
       "      <td>0.175023</td>\n",
       "      <td>0.824977</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37267</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23446.847900</td>\n",
       "      <td>49869.5</td>\n",
       "      <td>8404.154608</td>\n",
       "      <td>-7629.70252</td>\n",
       "      <td>0.578355</td>\n",
       "      <td>1.778552</td>\n",
       "      <td>5.834829</td>\n",
       "      <td>9259.424615</td>\n",
       "      <td>39456.789722</td>\n",
       "      <td>55075.658793</td>\n",
       "      <td>9991.077540</td>\n",
       "      <td>VIJAYANAGARA</td>\n",
       "      <td>171441.477479</td>\n",
       "      <td>0.021683</td>\n",
       "      <td>0.978317</td>\n",
       "      <td>6.477628e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37268 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sensitivity_LivestockCensus_2012_Cattle  \\\n",
       "0                                          0.0   \n",
       "1                                          0.0   \n",
       "2                                          0.0   \n",
       "3                                          0.0   \n",
       "4                                          0.0   \n",
       "...                                        ...   \n",
       "37263                                      1.0   \n",
       "37264                                      1.0   \n",
       "37265                                      1.0   \n",
       "37266                                      1.0   \n",
       "37267                                      1.0   \n",
       "\n",
       "       Sensitivity_AreaUnderCereals_Maize  Sensitivity_TotalNPK  \\\n",
       "0                                     0.0                   0.0   \n",
       "1                                     0.0                   0.0   \n",
       "2                                     0.0                   0.0   \n",
       "3                                     0.0                   0.0   \n",
       "4                                     0.0                   0.0   \n",
       "...                                   ...                   ...   \n",
       "37263                                 1.0                   1.0   \n",
       "37264                                 1.0                   1.0   \n",
       "37265                                 1.0                   1.0   \n",
       "37266                                 1.0                   1.0   \n",
       "37267                                 1.0                   1.0   \n",
       "\n",
       "       LivestockCensus_2012_Cattle  AreaUnderCereals_Maize      TotalNPK  \\\n",
       "0                      2567.461578                  1129.0    438.180210   \n",
       "1                     25037.139270                 14867.0   3036.080250   \n",
       "2                      7729.614277                  1987.0    222.225258   \n",
       "3                     83821.593090                 92194.0  10902.406570   \n",
       "4                     85657.632090                173400.0  25702.835480   \n",
       "...                            ...                     ...           ...   \n",
       "37263                  2371.831678                  1382.0    249.381182   \n",
       "37264                  6221.617910                  2634.0    710.101614   \n",
       "37265                  1253.317144                   961.0    368.617469   \n",
       "37266                 38936.400170                 48210.0  10997.550400   \n",
       "37267                 23446.847900                 49869.5   8404.154608   \n",
       "\n",
       "           Beta_0    Beta_1    Beta_2    Beta_3      delta_O        New_F1  \\\n",
       "0     -7629.70252  0.578355  1.778552  5.834829  9259.424615   2567.461578   \n",
       "1     -7629.70252  0.578355  1.778552  5.834829  9259.424615  25037.139270   \n",
       "2     -7629.70252  0.578355  1.778552  5.834829  9259.424615   7729.614277   \n",
       "3     -7629.70252  0.578355  1.778552  5.834829     0.000000  83821.593090   \n",
       "4     -7629.70252  0.578355  1.778552  5.834829     0.000000  85657.632090   \n",
       "...           ...       ...       ...       ...          ...           ...   \n",
       "37263 -7629.70252  0.578355  1.778552  5.834829  9259.424615  18381.773500   \n",
       "37264 -7629.70252  0.578355  1.778552  5.834829  9259.424615  22231.559732   \n",
       "37265 -7629.70252  0.578355  1.778552  5.834829  9259.424615  17263.258966   \n",
       "37266 -7629.70252  0.578355  1.778552  5.834829     0.000000  38936.400170   \n",
       "37267 -7629.70252  0.578355  1.778552  5.834829  9259.424615  39456.789722   \n",
       "\n",
       "              New_F2        New_F3      District  New_Production  \\\n",
       "0        1129.000000    438.180210     BENGALURU     2879.000000   \n",
       "1       14867.000000   3036.080250  BENGALURU(R)    51007.388234   \n",
       "2        1987.000000    222.225258    RAMANAGARA    10450.000000   \n",
       "3       92194.000000  10902.406570   CHITRADURGA   271975.000000   \n",
       "4      173400.000000  25702.835480    DAVANAGERE   593544.000000   \n",
       "...              ...           ...           ...             ...   \n",
       "37263    6588.158793   1836.304114         BIDAR    25433.386906   \n",
       "37264    7840.158793   2297.024546    KALABURAGI    32574.901104   \n",
       "37265    6167.158793   1955.540401       YADGIRI    24733.441767   \n",
       "37266   48210.000000  10997.550400        KOPPAL   190252.000000   \n",
       "37267   55075.658793   9991.077540  VIJAYANAGARA   171441.477479   \n",
       "\n",
       "       Stress_Values  Stability_Values  Percentage Change  \n",
       "0           0.052417          0.947583       1.000000e-09  \n",
       "1           0.001044          0.998956       1.978345e+01  \n",
       "2           0.187361          0.812639       1.000000e-09  \n",
       "3           0.152712          0.847288       1.000000e-09  \n",
       "4           0.944086          0.055914       1.000000e-09  \n",
       "...              ...               ...                ...  \n",
       "37263       0.456385          0.543615       8.112643e+02  \n",
       "37264       0.517662          0.482338       1.227953e+02  \n",
       "37265       0.359057          0.640943       8.253065e+02  \n",
       "37266       0.175023          0.824977       1.000000e-09  \n",
       "37267       0.021683          0.978317       6.477628e+01  \n",
       "\n",
       "[37268 rows x 19 columns]"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "# result_df.to_csv('Prescriptive/Maize_Prescriptive_3.68%.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['District', 'Sensitivity_LivestockCensus_2012_Cattle',\n",
       "       'Sensitivity_AreaUnderCereals_Maize', 'Sensitivity_TotalNPK', 'New_F1',\n",
       "       'New_F2', 'New_F3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viz_df2 = result_df[['District','Sensitivity_SowingSeedsDistributed_Maize','Sensitivity_LivestockCensus_2012_Cattle','Sensitivity_AreaUnderCereals_Maize',\n",
    "#                     'Sensitivity_TotalNPK',\t'Sensitivity_NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea','New_F1','New_F2','New_F3','New_F4','New_F5']].copy()\n",
    "\n",
    "viz_df2 = result_df[['District','Sensitivity_LivestockCensus_2012_Cattle','Sensitivity_AreaUnderCereals_Maize',\n",
    "                    'Sensitivity_TotalNPK',\t'New_F1','New_F2','New_F3']].copy()\n",
    "\n",
    "viz_df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6bbbdd8c40a34ec9a624359256e48aa5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.0, description='αCT:', max=1.0), FloatSlider(value=0.0, description=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "68ba1d29aecf4e90bbb9c69870b8cc91",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(FloatSlider(value=0.0, description='αCT:', max=1.0), FloatSlider(value=0.0, description='αAM:',…"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "from IPython.display import clear_output\n",
    "\n",
    "# Function to update the bar plot based on sensitivity sliders\n",
    "def update_bar_plot(alpha1, alpha2, alpha3):\n",
    "    tolerance = 1e-5\n",
    "    \n",
    "    # Filter the dataframe based on slider sensitivity values\n",
    "    filtered_df = viz_df2[\n",
    "        # (viz_df2['Sensitivity_SowingSeedsDistributed_Maize'].between(alpha1 - tolerance, alpha1 + tolerance)) &\n",
    "        (viz_df2['Sensitivity_LivestockCensus_2012_Cattle'].between(alpha1 - tolerance, alpha1 + tolerance)) &\n",
    "        (viz_df2['Sensitivity_AreaUnderCereals_Maize'].between(alpha2 - tolerance, alpha2 + tolerance)) & \n",
    "        (viz_df2['Sensitivity_TotalNPK'].between(alpha3 - tolerance, alpha3 + tolerance)) \n",
    "        # (viz_df2['Sensitivity_NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea'].between(alpha5 - tolerance, alpha5 + tolerance))\n",
    "    ]\n",
    "    \n",
    "    # Define the factors and their display names\n",
    "    factors = ['New_F1', 'New_F2', 'New_F3']\n",
    "    # display_names = ['Seeds_Maize', 'Cattle', 'Area_Maize', 'TotalNPK', 'TubeWells']\n",
    "    display_names = ['Cattle (Count)', 'Area Sown Under Maize (Ha)', 'NPK Distributed (Tonnes)']\n",
    "\n",
    "    # Calculate mean values for the selected sensitivity group\n",
    "    user_values = filtered_df[factors].mean().round()\n",
    "    \n",
    "    # Get the mean values for the original case where all sensitivities are zero\n",
    "    # original_values = viz_df2[viz_df2[['Sensitivity_SowingSeedsDistributed_Maize_320', 'Sensitivity_LivestockCensus_2012_Cattle_354',\n",
    "    #                                    'Sensitivity_AreaUnderCereals_Maize_193', 'Sensitivity_TotalNPK_315',\n",
    "    #                                    'Sensitivity_NetAreaIrrigatedUnderDifferentSources_TubeWells_NetIrrigatedArea_182']].eq(0).all(axis=1)][factors].mean()\n",
    "\n",
    "    original_values = viz_df2[viz_df2[['Sensitivity_LivestockCensus_2012_Cattle',\n",
    "                                       'Sensitivity_AreaUnderCereals_Maize', 'Sensitivity_TotalNPK']].eq(0).all(axis=1)][factors].mean().round()\n",
    "\n",
    "\n",
    "    # Create the bar plot with interchanged axes\n",
    "    fig = go.Figure(data=[\n",
    "        go.Bar(name='Original Values ', y=display_names, x=original_values, marker_color='#BAC1CC', orientation='h',text=original_values),\n",
    "        go.Bar(name='Prescribed Values', y=display_names, x=user_values, marker_color='#00CC96', orientation='h',text=user_values)\n",
    "    ])\n",
    "\n",
    "    # Customize the layout\n",
    "    fig.update_layout(\n",
    "        title=\"Original vs Prescribed Values\",\n",
    "        yaxis_title=\"Factors\",\n",
    "        xaxis_title=\"Values\",\n",
    "        barmode='group'\n",
    "    )\n",
    "    \n",
    "    fig.update_layout(\n",
    "        annotations=[\n",
    "            dict(\n",
    "                x=0.5,\n",
    "                y=-0.25,\n",
    "                showarrow=False,\n",
    "                text=f' Sensitivities: α<sub>CT</sub>: {alpha1:.2f}, α<sub>AM</sub>: {alpha2:.2f}, α<sub>NPK</sub>: {alpha3:.2f}',\n",
    "                xref=\"paper\",\n",
    "                yref=\"paper\",\n",
    "                font=dict(size=13)\n",
    "            )\n",
    "        ],\n",
    "    )\n",
    "\n",
    "    # Clear previous output and display updated plot\n",
    "    clear_output()\n",
    "    display(fig)\n",
    "\n",
    "# Creating sliders for interaction\n",
    "# alpha1_slider = widgets.FloatSlider(value=0, min=0, max=1, step=0.1, description='αMS:')\n",
    "alpha1_slider = widgets.FloatSlider(value=0, min=0, max=1, step=0.1, description='αCT:')\n",
    "alpha2_slider = widgets.FloatSlider(value=0, min=0, max=1, step=0.1, description='αAM:')\n",
    "alpha3_slider = widgets.FloatSlider(value=0, min=0, max=1, step=0.1, description='αNPK:')\n",
    "# alpha5_slider = widgets.FloatSlider(value=0, min=0, max=1, step=0.1, description='αTBW:')\n",
    "\n",
    "# Creating interactive plot\n",
    "interactive_plot = interact(update_bar_plot, \n",
    "                            alpha1=alpha1_slider, \n",
    "                            alpha2=alpha2_slider, \n",
    "                            alpha3=alpha3_slider)\n",
    "\n",
    "widgets.VBox([alpha1_slider, alpha2_slider, alpha3_slider])"
   ]
  }
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