soil.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import researchpy as rp\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "import numpy as np    \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r'C:\\Users\\ananyya srivastava\\Desktop\\SoilS.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Block</th>\n",
       "      <th>Panchayath</th>\n",
       "      <th>SampleCode</th>\n",
       "      <th>FarmerName</th>\n",
       "      <th>ContactAddress</th>\n",
       "      <th>f_dateofsampling</th>\n",
       "      <th>f_sex</th>\n",
       "      <th>f_surveyno</th>\n",
       "      <th>f_latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>Soil_s</th>\n",
       "      <th>Soil_Zn</th>\n",
       "      <th>Soil_b</th>\n",
       "      <th>Soil_Fe</th>\n",
       "      <th>Soil_Cu</th>\n",
       "      <th>Soil_Mn</th>\n",
       "      <th>Zinc_Method</th>\n",
       "      <th>Copper_Method</th>\n",
       "      <th>Iron_Method</th>\n",
       "      <th>Manganese_Method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Thrissur</td>\n",
       "      <td>Anthikkad Block</td>\n",
       "      <td>Manaloor</td>\n",
       "      <td>TCR/TRS/MNL/215/3/182896</td>\n",
       "      <td>sunil.K.A</td>\n",
       "      <td>kunnath house\\r\\n Manaloor post</td>\n",
       "      <td>3/26/2011</td>\n",
       "      <td>male</td>\n",
       "      <td>215/3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>34.40</td>\n",
       "      <td>8.09</td>\n",
       "      <td>0.59</td>\n",
       "      <td>48.20</td>\n",
       "      <td>9.45</td>\n",
       "      <td>21.32</td>\n",
       "      <td>HCl</td>\n",
       "      <td>HCl</td>\n",
       "      <td>HCl</td>\n",
       "      <td>HCl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kottayam</td>\n",
       "      <td>Erattupetta Block</td>\n",
       "      <td>Melukavu</td>\n",
       "      <td>KTM/MNC/MLK/729/11/182897</td>\n",
       "      <td>T.M. Joseph</td>\n",
       "      <td>Valluvadikkal House\\r\\nKizhakkanmattom\\r\\n</td>\n",
       "      <td>3/18/2011</td>\n",
       "      <td>male</td>\n",
       "      <td>729/11</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.00</td>\n",
       "      <td>3.77</td>\n",
       "      <td>1.68</td>\n",
       "      <td>23.60</td>\n",
       "      <td>1.63</td>\n",
       "      <td>14.00</td>\n",
       "      <td>HCl</td>\n",
       "      <td>HCl</td>\n",
       "      <td>HCl</td>\n",
       "      <td>HCl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kollam</td>\n",
       "      <td>Sasthamcotta Block</td>\n",
       "      <td>Kunnathur</td>\n",
       "      <td>KLM/KNT/KNT/60/6/182898</td>\n",
       "      <td>SUMANGALAAMMA</td>\n",
       "      <td>THENGANAYYATHU\\r\\nIVERKALA PADI NORTH\\r\\nPUTHA...</td>\n",
       "      <td>4/4/2011</td>\n",
       "      <td>female</td>\n",
       "      <td>60/6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.77</td>\n",
       "      <td>6.89</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.83</td>\n",
       "      <td>6.80</td>\n",
       "      <td>HCl</td>\n",
       "      <td>HCl</td>\n",
       "      <td>HCl</td>\n",
       "      <td>HCl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kottayam</td>\n",
       "      <td>Erattupetta Block</td>\n",
       "      <td>Melukavu</td>\n",
       "      <td>KTM/MNC/MLK/669/2/182899</td>\n",
       "      <td>P.V. Georgekutty</td>\n",
       "      <td>Paikadayil House\\r\\nKonnippadu P.O\\r\\n</td>\n",
       "      <td>3/18/2011</td>\n",
       "      <td>male</td>\n",
       "      <td>669/2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.00</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.82</td>\n",
       "      <td>18.30</td>\n",
       "      <td>1.66</td>\n",
       "      <td>4.20</td>\n",
       "      <td>HCl</td>\n",
       "      <td>HCl</td>\n",
       "      <td>HCl</td>\n",
       "      <td>HCl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thrissur</td>\n",
       "      <td>Anthikkad Block</td>\n",
       "      <td>Manaloor</td>\n",
       "      <td>TCR/TRS/MNL/918/1/182900</td>\n",
       "      <td>Abhilash</td>\n",
       "      <td>Thottupura house\\r\\n Manaloor post</td>\n",
       "      <td>3/26/2011</td>\n",
       "      <td>male</td>\n",
       "      <td>918/1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>35.60</td>\n",
       "      <td>13.89</td>\n",
       "      <td>1.66</td>\n",
       "      <td>92.90</td>\n",
       "      <td>9.90</td>\n",
       "      <td>21.31</td>\n",
       "      <td>HCl</td>\n",
       "      <td>HCl</td>\n",
       "      <td>HCl</td>\n",
       "      <td>HCl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   District               Block Panchayath                 SampleCode  \\\n",
       "0  Thrissur     Anthikkad Block   Manaloor   TCR/TRS/MNL/215/3/182896   \n",
       "1  Kottayam   Erattupetta Block   Melukavu  KTM/MNC/MLK/729/11/182897   \n",
       "2    Kollam  Sasthamcotta Block  Kunnathur    KLM/KNT/KNT/60/6/182898   \n",
       "3  Kottayam   Erattupetta Block   Melukavu   KTM/MNC/MLK/669/2/182899   \n",
       "4  Thrissur     Anthikkad Block   Manaloor   TCR/TRS/MNL/918/1/182900   \n",
       "\n",
       "         FarmerName                                     ContactAddress  \\\n",
       "0         sunil.K.A                   kunnath house\\r\\n Manaloor post    \n",
       "1       T.M. Joseph         Valluvadikkal House\\r\\nKizhakkanmattom\\r\\n   \n",
       "2     SUMANGALAAMMA  THENGANAYYATHU\\r\\nIVERKALA PADI NORTH\\r\\nPUTHA...   \n",
       "3  P.V. Georgekutty             Paikadayil House\\r\\nKonnippadu P.O\\r\\n   \n",
       "4          Abhilash                Thottupura house\\r\\n Manaloor post    \n",
       "\n",
       "  f_dateofsampling   f_sex f_surveyno  f_latitude       ...         Soil_s  \\\n",
       "0        3/26/2011    male      215/3           0       ...          34.40   \n",
       "1        3/18/2011    male     729/11           0       ...          25.00   \n",
       "2         4/4/2011  female       60/6           0       ...           0.77   \n",
       "3        3/18/2011    male      669/2           0       ...          25.00   \n",
       "4        3/26/2011    male      918/1           0       ...          35.60   \n",
       "\n",
       "  Soil_Zn  Soil_b  Soil_Fe  Soil_Cu  Soil_Mn  Zinc_Method  Copper_Method  \\\n",
       "0    8.09    0.59    48.20     9.45    21.32          HCl            HCl   \n",
       "1    3.77    1.68    23.60     1.63    14.00          HCl            HCl   \n",
       "2    6.89    0.53     2.99     0.83     6.80          HCl            HCl   \n",
       "3    2.43    1.82    18.30     1.66     4.20          HCl            HCl   \n",
       "4   13.89    1.66    92.90     9.90    21.31          HCl            HCl   \n",
       "\n",
       "   Iron_Method Manganese_Method  \n",
       "0          HCl              HCl  \n",
       "1          HCl              HCl  \n",
       "2          HCl              HCl  \n",
       "3          HCl              HCl  \n",
       "4          HCl              HCl  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 40 columns):\n",
      "District            2000 non-null object\n",
      "Block               2000 non-null object\n",
      "Panchayath          2000 non-null object\n",
      "SampleCode          2000 non-null object\n",
      "FarmerName          2000 non-null object\n",
      "ContactAddress      2000 non-null object\n",
      "f_dateofsampling    2000 non-null object\n",
      "f_sex               2000 non-null object\n",
      "f_surveyno          2000 non-null object\n",
      "f_latitude          2000 non-null int64\n",
      "f_longitude         2000 non-null int64\n",
      "f_ownership         2000 non-null object\n",
      "f_ownername         0 non-null float64\n",
      "f_panchayat         2000 non-null int64\n",
      "f_pincode           546 non-null float64\n",
      "f_district          2000 non-null int64\n",
      "f_taluk             2000 non-null int64\n",
      "f_block             2000 non-null int64\n",
      "f_mob_no            387 non-null float64\n",
      "SoilType            2000 non-null object\n",
      "crop1               2000 non-null object\n",
      "crop2               2000 non-null object\n",
      "crop3               2000 non-null object\n",
      "Soil_pH             2000 non-null float64\n",
      "Soil_ec             2000 non-null float64\n",
      "Soil_OrganicC       2000 non-null float64\n",
      "Soil_P              2000 non-null float64\n",
      "Soil_k              2000 non-null float64\n",
      "Soil_Ca             2000 non-null float64\n",
      "Soil_Mg             2000 non-null float64\n",
      "Soil_s              2000 non-null float64\n",
      "Soil_Zn             2000 non-null float64\n",
      "Soil_b              2000 non-null float64\n",
      "Soil_Fe             2000 non-null float64\n",
      "Soil_Cu             2000 non-null float64\n",
      "Soil_Mn             2000 non-null float64\n",
      "Zinc_Method         2000 non-null object\n",
      "Copper_Method       2000 non-null object\n",
      "Iron_Method         2000 non-null object\n",
      "Manganese_Method    2000 non-null object\n",
      "dtypes: float64(16), int64(6), object(18)\n",
      "memory usage: 625.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f_ownership</th>\n",
       "      <th>f_ownername</th>\n",
       "      <th>f_panchayat</th>\n",
       "      <th>f_pincode</th>\n",
       "      <th>f_district</th>\n",
       "      <th>f_taluk</th>\n",
       "      <th>f_block</th>\n",
       "      <th>f_mob_no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ownland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>524</td>\n",
       "      <td>680617.0</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>111</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ownland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>294</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ownland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ownland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>294</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ownland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>524</td>\n",
       "      <td>680617.0</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>111</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  f_ownership  f_ownername  f_panchayat  f_pincode  f_district  f_taluk  \\\n",
       "0     ownland          NaN          524   680617.0           8       41   \n",
       "1     ownland          NaN          294        NaN           5       24   \n",
       "2     ownland          NaN          143        NaN           2        8   \n",
       "3     ownland          NaN          294        NaN           5       24   \n",
       "4     ownland          NaN          524   680617.0           8       41   \n",
       "\n",
       "   f_block  f_mob_no  \n",
       "0      111       NaN  \n",
       "1       63       NaN  \n",
       "2       29       NaN  \n",
       "3       63       NaN  \n",
       "4      111       NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['f_ownership','f_ownername','f_panchayat','f_pincode','f_district','f_taluk','f_block','f_mob_no']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "soil_col=['Soil_pH','Soil_ec','Soil_OrganicC','Soil_P','Soil_k','Soil_Ca','Soil_Mg','Soil_s',\n",
    "          'Soil_Zn','Soil_b','Soil_Fe','Soil_Cu','Soil_Mn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "methods_col=['Zinc_Method','Copper_Method','Iron_Method','Manganese_Method']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_col=['FarmerName','ContactAddress','f_dateofsampling','f_sex', 'f_surveyno','f_latitude', 'f_longitude', 'f_ownership', 'f_ownername',\n",
    "       'f_panchayat', 'f_pincode', 'f_district', 'f_taluk', 'f_block','f_mob_no',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Soil_pH            5.573874\n",
       "Soil_ec            0.227181\n",
       "Soil_OrganicC      1.206468\n",
       "Soil_P            75.865930\n",
       "Soil_k           209.237950\n",
       "Soil_Ca          373.570914\n",
       "Soil_Mg           51.942797\n",
       "Soil_s            11.555962\n",
       "Soil_Zn            5.680801\n",
       "Soil_b             0.577789\n",
       "Soil_Fe           68.614304\n",
       "Soil_Cu            3.673857\n",
       "Soil_Mn           23.975925\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[soil_col].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f_latitude</th>\n",
       "      <th>f_longitude</th>\n",
       "      <th>f_ownername</th>\n",
       "      <th>f_panchayat</th>\n",
       "      <th>f_pincode</th>\n",
       "      <th>f_district</th>\n",
       "      <th>f_taluk</th>\n",
       "      <th>f_block</th>\n",
       "      <th>f_mob_no</th>\n",
       "      <th>Soil_pH</th>\n",
       "      <th>...</th>\n",
       "      <th>Soil_P</th>\n",
       "      <th>Soil_k</th>\n",
       "      <th>Soil_Ca</th>\n",
       "      <th>Soil_Mg</th>\n",
       "      <th>Soil_s</th>\n",
       "      <th>Soil_Zn</th>\n",
       "      <th>Soil_b</th>\n",
       "      <th>Soil_Fe</th>\n",
       "      <th>Soil_Cu</th>\n",
       "      <th>Soil_Mn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2000.0</td>\n",
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       "      <td>3.870000e+02</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>...</td>\n",
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       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>413.121000</td>\n",
       "      <td>681422.868132</td>\n",
       "      <td>5.958500</td>\n",
       "      <td>27.595000</td>\n",
       "      <td>84.964000</td>\n",
       "      <td>9.395787e+09</td>\n",
       "      <td>5.573874</td>\n",
       "      <td>...</td>\n",
       "      <td>75.86593</td>\n",
       "      <td>209.237950</td>\n",
       "      <td>373.570914</td>\n",
       "      <td>51.942797</td>\n",
       "      <td>11.555962</td>\n",
       "      <td>5.680801</td>\n",
       "      <td>0.577789</td>\n",
       "      <td>68.614304</td>\n",
       "      <td>3.673857</td>\n",
       "      <td>23.975925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>283.888768</td>\n",
       "      <td>29355.254943</td>\n",
       "      <td>3.620958</td>\n",
       "      <td>17.942518</td>\n",
       "      <td>57.293664</td>\n",
       "      <td>1.312579e+09</td>\n",
       "      <td>0.673939</td>\n",
       "      <td>...</td>\n",
       "      <td>57.40095</td>\n",
       "      <td>202.927122</td>\n",
       "      <td>379.162491</td>\n",
       "      <td>105.679611</td>\n",
       "      <td>11.653144</td>\n",
       "      <td>6.835201</td>\n",
       "      <td>0.916663</td>\n",
       "      <td>128.355449</td>\n",
       "      <td>26.856054</td>\n",
       "      <td>18.705390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.410170e+05</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>680734.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>9.447146e+09</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>35.00000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>163.820000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>2.275000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.077500</td>\n",
       "      <td>13.900000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>10.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>337.000000</td>\n",
       "      <td>680734.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9.526616e+09</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>65.00000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>317.100000</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>6.810000</td>\n",
       "      <td>2.890000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>19.325000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>566.000000</td>\n",
       "      <td>685501.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>9.846641e+09</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>99.00000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>52.200000</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>7.032500</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>86.125000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>31.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1006.000000</td>\n",
       "      <td>689572.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>9.995965e+09</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>472.00000</td>\n",
       "      <td>4225.000000</td>\n",
       "      <td>4627.000000</td>\n",
       "      <td>3041.000000</td>\n",
       "      <td>95.900000</td>\n",
       "      <td>86.100000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>3807.000000</td>\n",
       "      <td>1097.000000</td>\n",
       "      <td>133.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       f_latitude  f_longitude  f_ownername  f_panchayat      f_pincode  \\\n",
       "count      2000.0       2000.0          0.0  2000.000000     546.000000   \n",
       "mean          0.0          0.0          NaN   413.121000  681422.868132   \n",
       "std           0.0          0.0          NaN   283.888768   29355.254943   \n",
       "min           0.0          0.0          NaN    98.000000       6.000000   \n",
       "25%           0.0          0.0          NaN   152.000000  680734.000000   \n",
       "50%           0.0          0.0          NaN   337.000000  680734.000000   \n",
       "75%           0.0          0.0          NaN   566.000000  685501.000000   \n",
       "max           0.0          0.0          NaN  1006.000000  689572.000000   \n",
       "\n",
       "        f_district      f_taluk      f_block      f_mob_no      Soil_pH  \\\n",
       "count  2000.000000  2000.000000  2000.000000  3.870000e+02  2000.000000   \n",
       "mean      5.958500    27.595000    84.964000  9.395787e+09     5.573874   \n",
       "std       3.620958    17.942518    57.293664  1.312579e+09     0.673939   \n",
       "min       2.000000     7.000000    20.000000  2.410170e+05     0.600000   \n",
       "25%       2.000000     8.000000    30.000000  9.447146e+09     5.200000   \n",
       "50%       5.000000    23.000000    69.000000  9.526616e+09     5.600000   \n",
       "75%       8.000000    40.000000   119.000000  9.846641e+09     6.000000   \n",
       "max      13.000000    61.000000   198.000000  9.995965e+09     9.900000   \n",
       "\n",
       "          ...           Soil_P       Soil_k      Soil_Ca      Soil_Mg  \\\n",
       "count     ...       2000.00000  2000.000000  2000.000000  2000.000000   \n",
       "mean      ...         75.86593   209.237950   373.570914    51.942797   \n",
       "std       ...         57.40095   202.927122   379.162491   105.679611   \n",
       "min       ...          0.00000     0.700000     0.410000     0.200000   \n",
       "25%       ...         35.00000    86.000000   163.820000    14.900000   \n",
       "50%       ...         65.00000   187.000000   317.100000    24.500000   \n",
       "75%       ...         99.00000   263.000000   480.000000    52.200000   \n",
       "max       ...        472.00000  4225.000000  4627.000000  3041.000000   \n",
       "\n",
       "            Soil_s      Soil_Zn       Soil_b      Soil_Fe      Soil_Cu  \\\n",
       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
       "mean     11.555962     5.680801     0.577789    68.614304     3.673857   \n",
       "std      11.653144     6.835201     0.916663   128.355449    26.856054   \n",
       "min       0.060000     0.010000     0.000000     0.180000     0.010000   \n",
       "25%       2.275000     1.300000     0.077500    13.900000     0.970000   \n",
       "50%       6.810000     2.890000     0.560000    26.500000     2.500000   \n",
       "75%      23.100000     7.032500     0.840000    86.125000     4.300000   \n",
       "max      95.900000    86.100000    34.000000  3807.000000  1097.000000   \n",
       "\n",
       "           Soil_Mn  \n",
       "count  2000.000000  \n",
       "mean     23.975925  \n",
       "std      18.705390  \n",
       "min       0.100000  \n",
       "25%      10.900000  \n",
       "50%      19.325000  \n",
       "75%      31.800000  \n",
       "max     133.200000  \n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "District            2000\n",
       "Block               2000\n",
       "Panchayath          2000\n",
       "SampleCode          2000\n",
       "FarmerName          2000\n",
       "ContactAddress      2000\n",
       "f_dateofsampling    2000\n",
       "f_sex               2000\n",
       "f_surveyno          2000\n",
       "f_latitude          2000\n",
       "f_longitude         2000\n",
       "f_ownership         2000\n",
       "f_ownername            0\n",
       "f_panchayat         2000\n",
       "f_pincode            546\n",
       "f_district          2000\n",
       "f_taluk             2000\n",
       "f_block             2000\n",
       "f_mob_no             387\n",
       "SoilType            2000\n",
       "crop1               2000\n",
       "crop2               2000\n",
       "crop3               2000\n",
       "Soil_pH             2000\n",
       "Soil_ec             2000\n",
       "Soil_OrganicC       2000\n",
       "Soil_P              2000\n",
       "Soil_k              2000\n",
       "Soil_Ca             2000\n",
       "Soil_Mg             2000\n",
       "Soil_s              2000\n",
       "Soil_Zn             2000\n",
       "Soil_b              2000\n",
       "Soil_Fe             2000\n",
       "Soil_Cu             2000\n",
       "Soil_Mn             2000\n",
       "Zinc_Method         2000\n",
       "Copper_Method       2000\n",
       "Iron_Method         2000\n",
       "Manganese_Method    2000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['District' 'Block' 'Panchayath' 'SampleCode' 'FarmerName'\n",
      " 'ContactAddress' 'f_dateofsampling' 'f_sex' 'f_surveyno' 'f_latitude'\n",
      " 'f_longitude' 'f_ownership' 'f_ownername' 'f_panchayat' 'f_pincode'\n",
      " 'f_district' 'f_taluk' 'f_block' 'f_mob_no' 'SoilType' 'crop1' 'crop2'\n",
      " 'crop3' 'Soil_pH' 'Soil_ec' 'Soil_OrganicC' 'Soil_P' 'Soil_k' 'Soil_Ca'\n",
      " 'Soil_Mg' 'Soil_s' 'Soil_Zn' 'Soil_b' 'Soil_Fe' 'Soil_Cu' 'Soil_Mn'\n",
      " 'Zinc_Method' 'Copper_Method' 'Iron_Method' 'Manganese_Method']\n"
     ]
    }
   ],
   "source": [
    "print(df.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7]), <a list of 8 Text xticklabel objects>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.boxplot(x='District',y='Soil_pH',data=df)\n",
    "plt.title(\" pH in different districts\")\n",
    "plt.xticks(rotation=75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7]), <a list of 8 Text xticklabel objects>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.boxplot(x='District',y='Soil_k',data=df)\n",
    "plt.title(\" potassium in different districts\")\n",
    "plt.xticks(rotation=75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7]), <a list of 8 Text xticklabel objects>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.boxplot(x='District',y='Soil_Ca',data=df)\n",
    "plt.title(\"Calcium in different districts\")\n",
    "plt.xticks(rotation=75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mangnese distribution')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.boxplot(x='District',y='Soil_Mn',data=df)\n",
    "plt.xticks(rotation=65)\n",
    "plt.title('Mangnese distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f240e08ef0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rpM9GxOljVKZGFgO3AscAn8ulvwO4VNLawPLAo7l1v46IRcAiSU8Da6X0RyNiVpq/E5jcxP6vjYhn662IiEFgMFt6qPgx+83MRtDNQaZZzXZgGJK0am0hNdn9fUllalgO4OPAdpJOzqWfDpwREe8hq81Nyq1blJtfyhsBeKT0JbzxueTzAXi5/aKbmZVjQM1P3arZYHRsRCyoLUTEc8Cx5RSpsYh4BfgIcJikY1LyKsATaf7IDncxF9gmzR/UYV5mZqXrh2a6ZoPRgHIj8UmaQNYcVonUVLYP8BVJ+wOnAD+TdDPwTIfZnwp8RtKtwJod5mVmVroie9OlTl9PS7o3l3aKpCckzUrTvrl1X5Y0R9J/Sdo7l75PSpsj6aRR99vM46IlfYfsmspZQAB/BzweEV8Y9cXj0vi7ZrRkaGHheQ683tekOC8ufrzQ/P5q+cmF5gfFP9K7Vx47vnio2FbwMh47PhRLCs9zQJt2/GFu/7Nbmj7Ivzt454b7k7QL8BJwQURsntJOAV6KiFOHbbspcDFZB7G3A9cBG6fVDwF7AvOAO4BDI+L+kfbb7P0yXyK7FvMZQGRdm89t8rVmZlaiIpvfIuKmdPtOM/YHLkmdxB6VNIcsMAHMiYhHsvLpkrRtZ8EoIoaAM9NkZmZdZIyuBR0v6QhgJvCF1HdgHeC23DbzUhrA48PSd2iU+Wg3vV6W/s6WdM/wqcU3YmZmJWilN13+nsg0TW1iF2cCGwFbAfOB76b0emEwGqSPaLSaUe1eno+Msp2ZmVVkYELz2y57T2TTr3mqNi/pHOBXaXEesG5u03cAT6b5kdLrGu2m1/mp59yPIuKDTZbbzMzGUNnNdJLWjoj5afFAoNbT7krgp5K+R9aBYQrwO7Ka0RRJG5DddvMJ4JON9jHqNaOIWCrpFUmrRMTz7b0VMzMriwqMRpIuBnYlG5N0HjAN2FXSVmRNbXPJOrQREfelyzn3kw0YcFxELE35HA9cDUwAzouI+xrtt9nedK8CsyVdS24Ugog4odk3aGZm5Si4N92hdZJ/1GD7bwLfrJN+FXBVs/ttNhj9Ok3L7KvZnZiZWXm6eWSFZjUbjFZNg6a+TtLnRtrYxp/s0mKxFi1dMPpGLZo0YbWCcxwqOD/IWjWKU8YNqmWYOPCmqoswqjK+50Xoh2DU7HBA9cZ7O6rAcpiZWZsmDjQ/dauGNSNJh5L1gNhA0pW5VX8F/LnMgpmZWXMG1PtXTUZrpruV7AanNXnjJieAFwHf9Gpm1gW6+dEQzRrtPqM/An+U9EFgYUQMSdqY7Omps8eigGZm1lgXt741rdn3cBMwSdI6wHTgaOAnZRXKzMyaN6BoeupWzQYjpYfafRQ4PSIOBDYtr1hmZtasfnjSa7NduyVpJ+AwoPZ01WZfa2ZmJZrYxUGmWc0GlM8DXwauSMM/bAjcUF6xzMysWeri5rdmNfs8oxuBG3PLjwAeCsjMrAt0c/Nbs0Z7ntG/pL+/lHTl8Glsili3XC/l5veV9LCk9Rpsf4qkE9P8TyQdNBblNDMbCwMtTN1qtJrRhenvqQ23qoikPYDTgb0i4rGqy2NmVoVu7iXXrNHuM7oz/b1R0lvS/J/GomCjkfQB4Bxg34j4Q0pbHzgPeAvwJ+DoRkFK0leB/YAVyW7w/XREhKQZwN3ANimvI8iumb0HuDQivlLW+zIza1U/dGAYrZlOqYnrGeBB4CFJf0on8SqtAPwCOCAiHsylnwFcEBFbABcBp42SzxkRsV1EbE4WkPJPtH0tInYBzkr7Og7YHDhK0hoFvQ8zs471Q9fu0ZoQPw+8H9guItaIiNWAHYD3S/qH0ks3ssVkNZljhqXvBPw0zV8I7DxKPrtJul3SbGB3YLPcuto1sdnAfRExPyIWAY+w7ON0gWWfKz84eGlr78bMrAP9cNPraNeMjgD2jIhnagkR8YikvwWuAb5fZuEaGAI+Dlwn6eSI+NYI2434yUuaBPwQ2DYiHpd0CjApt8mi3L4W5dKHqPO5Lftc+Ye694ibWd/p5hpPs0arGS2XD0Q16brRcuUUqTlpRIiPAIdJqtWQbiV71jpkN+je0iCLWuB5RtLKgHvYmVlPGg+96V5rc92YiIhnJe0D3JSua50AnCfpi6QODA1eu0DSOWTNcHOBO8agyGZmhevm5rdmKWLkNyFpKfByvVXApIiotHbUvcZfM93SKP63yWtLXyg8zwEVO4rV8hPeXGh+ACr4Sa+9IkZuVW9LGU+4LbqMAOJdHRf0+P+8oemCnbHTbl3ZqDda1+7x+b/CzKyHdHPzW7M82KmZWY/rh2Y6ByMzsx7XD73pHIzMzHqcm+nMzKxyrhmZmVnlJgz4mpGZmVXMzXRmZlY596YzSyKWFp7noqEFhef54uJiy7nOm1YqND+Aou/V7J2baIcKzq+M9110GYvRD9eM+qF2Z2Y2rhX5CAlJ50l6WtK9ubTVJV2bnqp9raTVUroknSZpjqR7JG2de82RafuHJR056nto762bmVm3WE7R9NSEnwD7DEs7CZgeEVOA6WkZ4EPAlDRNBc6ELHgB08geObQ9MK0WwEbiYGRm1uOKrBlFxE3As8OS9wfOT/PnAwfk0i+IzG3AqpLWBvYGro2IZyPiOeBa/jLALcPXjMzMetwYXDNaKyLmA0TEfElvTenrAI/ntpuX0kZKH5GDkZlZj5vQQjCSNJWsSa1mMD0ctB319hwN0kfkYGRm1uNaqRkt+1Tqpj0lae1UK1obeDqlzwPWzW33DuDJlL7rsPQZjXbga0ZmZj1uQNH01KYrgVqPuCOBX+TSj0i96nYEnk/NeVcDe0laLXVc2Culjcg1IzOzHrdcgdeMJF1MVqtZU9I8sl5x3wYuk3QM8BhwcNr8KmBfYA7wCunp2ukp3N/gjSdofz0ihneKWIaDkZlZjyuyA0NEHDrCqj3qbBvAcSPkcx5wXrP7rbSZTtJLufl9081R6zXY/gBJm+aWj5L09rLLaWbWzcagma50XXHNSNIewOnAPhHxWINNDwA2zS0fBTgYmdm4NkHNT92q8mAk6QPAOcCHI+IPKW19SdPT8BLTJa0n6X3A3wDfkTRL0peAbYGL0vKKkr4q6Q5J90oaTBfVNpJ0V25/UyTdmeb/YvuUPkPS9yXdJOkBSdtJ+nmquf3TWH9GZmaNFHnTa1WqDkYrkPXKOCAiHsyln0F2V+8WwEXAaRFxK1nPjS9GxFYR8c/ATOCwtLwQOCMitouIzYEVgY+kAPe8pK1S3keTDXdBve1zZXgtInYBzkplPA7YHDhK0hpFfxBmZu2aOND81K2qLtpi4FbgmGHpOwE/TfMXAjs3md9ukm6XNBvYHdgspZ8LHC1pAnBILu+Rtocs8AHMBu6LiPkRsQh4hGX71QPZjWSSZkqaOTh4aZPFNTPr3ARF01O3qro33RDwceA6SSdHxLdG2G7UT1DSJOCHwLYR8bikU4BJafXlZN0TrwfujIg/j7I9wKJcGRfl0oeo87kteyPZQ917xM2s71RdqyhC5e8hIl4hax47LPVhh6y29Ik0fxhwS5p/EXhz7uX55VogeUbSysBBuX28SnbD1ZnAj0fb3sysl/TDNaOqa0bA6zdI7QPcJOkZ4ATgPElfBP5EupEKuAQ4R9IJZMHjJ8BZkhaSNe2dQ9asNpc3braquQj4KHBN2ucCSY22NzPrCd0cZJql7J6l/ifpRGCViPjH8vc2/prplgwtLDzPl5Y8UXiexT/pdcNC8wOQim2w6JUnvQbFHpsy3nfRZQQQ7+44lFwx9zdNn3MOnPyhrgxdXVEzKpukK4CNyDopmJn1lW7uJdescRGMIuLAqstgZlaWfmimGxfByMysn3XzyArNcjAyM+tx3TzmXLMcjMzMelwfXDJyMDIz63W+ZmRmZpVbbsDNdGZmVjHXjMySIZYUnmcZNy0uHir+psXi9cMVgNZFDBWaXzYucrGKLiOACggkDkZmZla5fvj54mBkZtbjiqhdVc3ByMysx7mZzszMKudmOjMzq5w8AoOZmVWtD1rpHIzMzHqdOzCYmVnl+iAWORiZmfW6fniERGmdMCS9lJvfV9LDktZrMY9T0uPC6627Nf3dVdKvWnltE/udLOnedl5rZjbWpOanblV6zUjSHsDpwF4R8VhR+UbE+4rKy8ysl3VxjGlaqd3TJX0AOAf4cET8IaWtL2m6pHvS3/VS+qzctFDSX6dsNpU0Q9Ijkk7I5f1Snf1tJ+luSRsOSz9W0m8krShpK0m3pf1fIWm1tM02kn4v6T+B43KvnSDpO5LuSK/5dNGfk5lZJ9TC1K3KDEYrAL8ADoiIB3PpZwAXRMQWwEXAaQARsVVEbAX8IzATuDVtvwmwN7A9ME3ScvV2Jul9wFnA/hHxSC79eGC/VI6FwAXAl9L+ZwPT0qY/Bk6IiJ2GZX0M8HxEbAdsBxwraYOWPw0zs5IMqPlpNJLmSpqdKgYzU9rqkq5Nl1uuzf2Il6TTJM1JP9a3bvs9tPvCJiwmCyjHDEvfCfhpmr8Q2Lm2QtIU4DvAIRGxOCX/OiIWRcQzwNPAWnX29W5gENhvWFPg4cCHgI9FxCJJqwCrRsSNaf35wC510i/M5bEXcISkWcDtwBrAlOEFkDRV0kxJMwcHLx3hIzEzK14JNaPdUgVh27R8EjA9IqYA09MyZOfXKWmaCpzZ7nso85rREPBx4DpJJ0fEt0bYLgAkrQRcBhwbEU/m1i/KzS+lfpnnA5OA9wL5194LbAW8A3i0QVlVK8cI6z4bEVc3eD0RMUgWEIGHev92aDPrGQPlj8CwP7Brmj8fmAF8KaVfEBEB3CZpVUlrR8T8VndQ6jWjiHgF+AhwmKRaDelW4BNp/jDgljT/Y+DHEXFzG7taAHwY+JakXXPpdwOfBq6U9PaIeB54Ll3LgqzmdGNELACel1SrpR2Wy+Nq4DO15kFJG6fAaWbWFVrpTZdvxUnT1GHZBXCNpDtz69aqBZj0960pfR3g8dxr56W0lpXemy4inpW0D3CTpGeAE4DzJH0R+BNwtKT1gYOAjSX9j/TST7W4n6ck7Qf8JpcHEXFL6uL9a0l7AkcCZ0l6E/AIcHTa9OhUrlfIAlDNucBk4C5JSmU+oJWymZmVqZVaxbKtOHW9PyKelPRW4FpJDzbYtl7LX1vVNGW1KyvW+Gume23oxcLzXLjkmcLz/POi1wrNb/LKG46+UYukYn8jqqv7UL1h6PXLxMUYqN/XqSNFlxFgQJt1fID++NIvmz7nrL/yfk3vT9IpwEvAscCuETFf0trAjIh4l6Sz0/zFafv/qm3X0hugP0YeNzMb14rqwCBpJUlvrs2TdeC6F7iSrFWJ9PcXaf5Ksg5ekrQjWc/jlgMReDggM7OeV+DD9dYCrsiuSDAR+GlE/IekO4DL0rX/x4CD0/ZXAfsCc4BXeOOyR8scjMzMelxRwSjdo7llnfQ/A3vUSQ9ygwR0wsHIzKzH9cZVwcYcjMzMepyf9GpmZpVzzcjMzCrXzY+GaJaDkZlZj5tQdQEK4GBkhVAJ/x0maIXC84Rib3oNlhaaH4DG6X/LJfFqofktX8JNr0XfkFwU14zMzKwL9H40cjAyM+txvTLkUyMORmZmPU7q/ZHdHIzMzHqea0ZmZlYx9cGY1w5GZmY9zs10ZmbWBdxMZ2ZmFXNvOjMzq5yDkZmZVU7q/QGBujYYSVoDmJ4W3wYsBf4ETAaejIhNm8jj74BXIuKCssppZlY914xKk54suBWApFOAlyLiVEmTgV+N9npJEyPirLLKJ2lCRBQ/MJmZWYv6oZmuV/sDTpB0jqT7JF0jaUUASTMkfUvSjcDnJJ0i6cS07gRJ90u6R9IlKe2vJc1K092S3ixpV0mvBztJZ0g6Ks3PlfRVSbfwxjPgzcwqNtDC1J26t2SNTQF+EBGbAQuAj+XWrRoRfx0R3x32mpOA90bEFsDfpbQTgeMiYivgA8DCJvb9akTsHBGX5BMlTZU0U9LMwcFL23lPZmZtUQv/ulXXNtON4tGImJXm7yS7jlQzUiS4B7hI0r8D/57Sfgt8T9JFwM8jYp5GH4u9bv4RMQgMZksP9f4zgM2sZzRx3up6vVozWpSbX8qyQfXlEV7zYeAHwDbAnenBwJUwAAARrUlEQVSa0reBTwErArdJ2gRYwrKfy6Rh+YyUv5lZJcSEpqdu1as1o5YoGytj3Yi4IV3v+SSwsqQ1ImI2MFvSTsAmZDWtTSWtQBaI9gBuqarsZmaj6/2a0bgIRmRP5f03SauQHbXvR8QCSd+QtBtZ7ep+4DcRsUjSZWTNeg8Dd1dWajOzJvRDM50ifHmjeOPvmtHioVcKz3PR0gWF5/n0qy8Wmt/6K69faH4AAwU/br2bL1rnvTZU7LFZfuDNheYHEBT/X1u8q+MD9NrQnU0XbPmBbbryCzFeakZmZn3Lj5AwM7Mu0JWVnZY4GJmZ9bgBP8/IzMyq1/vBqPffgZnZOFfkCAyS9pH0X5LmSDppDIoPOBiZmfUBtTA1yCV7FsUPgA8BmwKHShr1CQlFcDAyM+txkpqeRrE9MCciHomI14BLgP1LfwP4mpGZWc8rcJifdYDHc8vzgB2KyryhiPBU0QRMHY95uozdm6fL2N15FlUuYGZumppbdzBwbm75cOD0sSiXm+mqNXWc5ukydm+eLmN359mxiBiMiG1z02Bu9Txg3dzyO4Anx6JcDkZmZlZzBzBF0gaSlgc+AVw5Fjv2NSMzMwMgIpZIOh64mmyA6fMi4r6x2LeDUbUGR9+kL/N0Gbs3T5exu/MsXURcBVw11vv1qN1mZlY5XzMyM7PKORiZmVnlHIzMzKxy7sBgy5C0HbBmRPxmWPrfAE9ExJ0t5jcJ+DvgncBs4EcRsaSgsm4zvDyS9ouIX3aY71uBSbXliHisg7xWr5P8YkQs7iDPg4H/iIgXJX0F2Br4p4i4q4M83wdMJndOiIgLOshvEvD3wM5AALcAZ0bEqx3kuT4wJSKuk7QiMDEi2n48rKQj6qV38r5TvhOAtVj2s2z7OzReuAPDGJE0G+o+s1hARMQWHeR9LXBwRCxIy6sBl0TE3m3kNQM4KiLmDkt/JzAYEbu3mN+lwGLgZrLBF/8YEZ9rtVwj5H0XcGREzE7LhwKfj4i2hi9JAfe7wNuBp4H1gQciYrMOyjiX7CbC58iO9arA/JT/sa0G95TnPRGxhaSdgf8DnAqc3MH7vhDYCJgFLE3JEREntJNfyvMy4EXg31LSocBqEXFwm/kdS3YT6eoRsZGkKcBZEbFHB2U8Pbc4CdgDuCsiDuogz88C04CngKGU3NH/73Gj6qEpxstEdmJbn+zX53255fWB9TvM++5m0prMa3aDdb/vJD+yX4p3FfiZbgjcBbwbOJYs4K3SQX6/B9aofXbAbmQBuJMyngXsnVveC/gesCNweyfHmywQfbKT451e+wDph2mBx+YvvivtfH9yr50FLJ9/n42+q23uYxXgyg7zmAOsUWS5xsvka0ZjJCL+mKa5wKLc8h8j4o8dZj8kab3aQmrOaLfKu2KDdSu1kd/rzVFRUPNcLr9HyO4Qvxw4CNgrIp7vIMvFEfFnYEDSQETcAGzVYTG3jYirawsRcQ2wS0TcBqzQZp5PSDob+DhwlaQV6Oz6773A2zp4fT13S9qxtiBpB+C3HeS3KLJRpGv5TaT97/hIXgGmdJjH40An38Fxy9eM+sP/Bm6RdGNa3oX2x8W6TtI3ga9E+qkHIOlrwPVt5LelpBdq2QArpuVa8+RftZphnSbP1cnuFr9dEtF+k8gCSSsDNwEXSXoa6DSAPivpS2RD8QMcAjyXrisMjfyyhj4O7AOcGhELJK0NfLG2UtJqEfFcC/mtCdwv6XfAolpiRPxNqwXLHZvlgCMkPZaW1wfubzW/nBslnUz2/dmT7HpUp9cGf8kb36MBsuf3XNZJnsAjwAxJv2bZz/J7Hebb93zNaIxI2jq3eBHwSXJPuooOLj6n/Ncka/oR8J8R8Uyb+awEnEv2XJNZKXlLstF9PxURL3VSzgb7bfoEmmp+I6rVNFs9Kaf3vpDsxHQYWbPNRam21JZ0XKaRXcgX2YX8r5H9el4vIua0m3eDfd4VEVuPvuXr2/91vfSIuLFe+ih5lXVsBoBjyJo5RTZczbnRwQls2PteQnY9c167+aU8p9VLj4ivdZLveOBgNEYk3dBgdUSLHQOG5S2yk+eGEfH11GT3toj4XQd5bgjULtzfl5rEStPqCbTIPFPnjLUi4rfD0nch60H4hyLLVTZJd0fEe5vYbu98E+KwdQdHxM+KL93r+Td7bNaLgnuiNTjeHwCe7LXj3S/cTDdGImK3ErP/IVmTz+7A18l6MV0ObNdBnlsC19euwUhaFdg1Iv69w7KOZNRHUJaY578AJ9dJfyWt26/lHUsNRzpupwmsBc3+wrxK0k3A30bEE8PWfRkoLRjR/LH5d7Ku60i6PCI+VsC+RzreC+nN490XHIzGWJ37L24m66La9v0XwA4RsbWkuwEi4rk0/HsnpkXEFbWFdG1iGtnJoQxlVNGbzXNyRNzzFy+OmClpcpv73onsYvbFwO2UE2w7dQ/wU+A2Sf9zWE2o7PI2e2zy5diwoH2P1+Pd1RyMxt4FZDWX2j0OhwIXkj1hsV2L0wXxAJD0Ftq/OF5Tr3dWv35fJjVY16h3YSNvA/YkO76fBH4NXBxjMxx/syfCiIhzUseXiyTtCxwXEa9Qzo+DdsQI853ot+PdF9y1e+y9KyKOiYgb0jQV2LjDPE8DrgDemnrC3QJ8q8M8Z0r6nqSNJG0o6ftAyzdotqDKZro70k2Vy75YOoY233NELI2I/4iII8k6lswh62X12XbyS+VZvdGU27SlG0Ej4iGyX/ZPkXXJbuvm2RY1e2y2lPSCpBeBLdL8C5JezPXSbFVPHO/xxh0Yxpikn5A1y92WlncgG0Xg7zvMdxOyk5CA6RHxQG5dq119az3L/hH4YMrzGrIhZ15uMZ96w+G8LiKerW1Xmx/rPCWtRRbMX+ONk9G2ZDdZHhgR/91MuerkuwLwYbJfy5PJnph5Xp3rM83m9yhZ7aDeiTwioqVmrHodHSTtCpwHvCUi3txGGQs/3k3ut5XemD1xvMcbB6MxJukB4F1ArYfQemR3wA9R0rAhZfRUa2HfhZ5Ay8oz5bsbsHlavC8irh+2vpUT3vkpr9+QDc10bztlKpOkA+p1SFE2nNSnI+LbteUW3ncpx6aJ/bb8HR9vx7vbORiNsdHuwwBeaLUW08Q+m+rqm7b9l4j4/LAbAl83nnsFtXLCkzQE1GqR+c+xk5t9N4mIB4fds/a6Tu9Va7Dfyn7MNKuV73gLeVZ6vMebfr0g3bVilKF/lA3+WfR//FZ+cVyY/p5axI7LOIFWdVKmhetaEdHU9dgWm1D/J9nIGt+tt0uyrv1laPp9V3hsyvhVXfXxHlccjLpPpV1CI40iHW3cfT+CMk6gVZ2UyzjhTafJHx+ps0vZ96zV3XUL21Z1bMpQ6fEebxyMuk+lv/Bef4H0fuAUsjHFJvJGc0NLbf5lnEArPCmXoZ1jsxzwGbIxCAFmAGdHB89IKkqFx6ZX7uvplXKOOQejHtZszyVa7Oqb/Aj4B7LeRktH2XZUZZxAKzgpl3EiaefHx5lkA5H+MC0fntI+VVShhqksYJb8HR919yXk6Yv0I3AHhi7TYmeD0nouSbo92nxY2wj5nUt2Aj0/JR0OLI2Itk+gReVZVXfklGc7vcB+HxFbjpbWRD6lve8Cj00ZvTF76niPF64ZjZEyfuFFxAYdFaqxGyR9B/g5yw6F3+4F6O2GnSyvl/T7TgpYYJ530uCERxqGpugTU9LOr++lkjaKNKCnskFt26m9lvm+Czk2JX3He+14jwsORmOn8P8AJfdcqtWKts1nSfsXoIs6gRaeZxknvJKbl75I9mOhNpL6ZODoVjMp+cdMIcemjO94Dx7vccHNdD1M0mBETFX9x1NEdPBYiqJJ2gP4MdnDxyCdQCN7mmqleZbU/byM5qXtgMcj4r/T3f6fJhsh47+Bk9poSivtx0yBx6bw73ivHO/xxsFojFR4/0XbJH2Y7JlGrw8sGRFfbzGPQk+gZeTZK0E93YP2wYh4Vtmzli4BPkv2aPR3R8RBLeZXxom+8ONdtF453uONg9EYKfM/QEk91c4C3gTsRvbk14OA30XEMS3mU+gJtKw8i1bSr+/XOylI+gHwp4g4JS3PioitOilzEco6Nt3cnR1688dmt3Ew6gMl9VS7JyK2yP1dGfh5ROzVYj6Fn0DLOikXecIrqdZxL7BVRCyR9CAwNSJuqq2LiM0b5zBivkW+77KOTRnf8a4+3uONOzCMsZJ+4ZXRU21h+vuKpLcDfwbaufA7QdLEiFhCdvF2am5du9+/MvKEAu/fiXJu/rwYuFHSM2TH52YAZY/Rfr6DfIu8b6msY1PGd7zbj/e44mA09sq4YbGMnmq/Uvao8e8Ad5FdnD23jXzKOIGWdVIu/IRX5I+PiPimpOnA2sA18UazxgBZU1i7inzfZR2bMr7jXX28xxs3040xFXTD4rDXF95TbVj+KwCTIqKtk4mkHXnjBPpyStsYWLndtvSS8rwLOHjYCe//RQc3KZbRvFS0ot93ScemjN6Y4/J4dysHozFW5H+AMnsuSfponeTngdkR8XS7+Xazkk54hf/4KFrZP2Y6UfJ3fFwe727lx46PvdoNizMkzQCuB77QZl5nkz2tErKbVE8i+0X2FDDYYTmPIWuWOyxN55CNyPxbSYd3mHdXkbSdpLdFxHRgCtmoEy+QPd220+sSSyVtlNtXEc1LhSj5fRel8O/4eD3e3c7BaIyU9B9gQu6X4SHAYERcHhH/CLyzwyIPkXXF/VhEfAzYlGxYoB2AL3WYd7cpM6gX+eOjaGW+76KU8R0fr8e7qzkYjZ0y/gNMkFTrhLIH2Re/ptPOKZMj4qnc8tPAxunE0G8XYws/4fVIraPMHzNFKeM7Pl6Pd1dzMBo7ZfzHr/Vc+gXF9lwCuFnSryQdKelI4BfATZJWAhZ0mHe3KeOE1xO1jhJ/zBSljO/4eD3eXa1bvnDjQeH3X5TY1RfgOOCjwM5k421dAFye9tFv91KU0R257o8P4HJJszotcEHK6oZdmJK+4+P1eHc196YbI5L+N7Av8AywHrB1RET6D3B+RLy/0gLmSJoAXB0RH6y6LGOl6O7IKmm0hKKV0Q27F4zX493NXDMaIyXXYgoVEUslvSJplXbvLeo1EXFbnbSHOsiy62sdUMr77gnj9Xh3M9eMrC5JlwE7AtcCL9fSI+KEygrVY8ZrrWO88vHujIOR1ZU6LfyFiDi/XrqZWSccjKwuSZPIevkF8IeIeLXiIplZH3PXbluGpImS/i8wj6xr6r8Bj0v6v2kQSDOzwjkY2XDfAVYHNoiIbSLivcBGwKrAqZWWzMz6lpvpbBmSHiYbaSGGpU8AHoyIKdWUzMz6mWtGNlwMD0QpcSnZ9SMzs8I5GNlw90s6YniipL8FHqygPGY2DriZzpYhaR2yQR4XAneS1Ya2A1YEDoyIJyosnpn1KQcjq0vS7sBmZOPS3ZdGI86vXy0inqukcGbWdxyMrC2S7urk8cxmZnm+ZmTtUtUFMLP+4WBk7XKV2swK42BkZmaVczCydrmZzswK4w4MtgxJqzdaX3uapaTVc0+2NDPriIORLUPSo2TXg+rVfCIiNhzjIpnZOOBgZGZmlfNjx20ZkjaJiAcl1b2HyE+sNLMyuGZky5A0GBFTJd1QZ3VExO5jXigz63sORmZmVjk301ld6amunwF2SUkzgLMjYnFlhTKzvuWakdUl6VxgObJHjwMcDiyNiE9VVyoz61cORlaXpN9HxJajpZmZFcEjMNhIlkraqLYgaUNgaYXlMbM+5mtGNpIvAjdIeiQtTwaOrq44ZtbPXDOyZUjaTtLb0sP0ppA99fUF4Brg95UWzsz6loORDXc28Fqa3wE4iawTw1PAYFWFMrP+5mY6G25CbgDUQ4DBiLgcuFzSrArLZWZ9zDUjG26CpNqPlD2A63Pr/OPFzErhk4sNdzFwo6RngIXAzQCS3gk8X2XBzKx/+T4j+wuSdgTWBq6JiJdT2sbAyh4o1czK4GBkZmaV8zUjMzOrnIORmZlVzsHIzMwq52BkZmaVczAyM7PK/X8+o8ZC/N2uUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.heatmap(mean,cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean=df.groupby('District')[soil_col].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f240ebeb38>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['District'].value_counts(normalize=True).plot(kind='Bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "district_ph=df[['District','Soil_pH']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Soil_pH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Thrissur</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kottayam</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kollam</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kottayam</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thrissur</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   District  Soil_pH\n",
       "0  Thrissur      6.7\n",
       "1  Kottayam      4.3\n",
       "2    Kollam      6.1\n",
       "3  Kottayam      4.4\n",
       "4  Thrissur      6.3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district_ph.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f240f3b828>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "district_ph.groupby('District')['Soil_pH'].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_ph=district_ph.groupby('District')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.DataFrameGroupBy object at 0x000001F240EACB00>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "group_ph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 108,  109,  115,  119,  273,  274,  275,  276,  278,  279,  306,\n",
       "             307,  338,  341,  344,  346,  347,  468,  469,  470,  471,  472,\n",
       "             473,  474,  475,  476,  477,  478, 1183, 1185, 1277, 1292, 1297,\n",
       "            1303, 1308, 1314, 1328, 1338, 1339, 1346, 1350, 1356, 1359, 1360,\n",
       "            1365, 1373, 1376, 1421, 1422, 1428, 1430, 1434, 1437, 1440, 1441,\n",
       "            1463, 1464, 1466, 1467, 1665, 1666, 1667, 1670, 1674, 1679, 1682,\n",
       "            1686, 1691, 1692, 1694, 1695, 1696, 1697, 1698, 1699, 1700, 1701,\n",
       "            1702, 1703, 1704, 1705, 1706, 1707, 1708, 1710, 1714, 1715, 1717,\n",
       "            1718, 1803, 1806],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "group_ph.groups['Alappuzha']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   0,    4,    6,    9,   10,   11,   13,   15,   18,   23,\n",
       "            ...\n",
       "            1958, 1970, 1971, 1972, 1975, 1979, 1984, 1988, 1992, 1998],\n",
       "           dtype='int64', length=513)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_ph.groups['Thrissur']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=114.13127661362887, pvalue=5.604370845690677e-141)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#one way anova\n",
    "stats.f_oneway(group_ph.groups['Alappuzha'],\n",
    "               group_ph.groups['Ernakulam'],\n",
    "               group_ph.groups['Idukki'],\n",
    "              group_ph.groups['Kannur'],group_ph.groups['Kollam'],group_ph.groups['Kottayam'],\n",
    "             group_ph.groups['Kozhikkode'],group_ph.groups['Thrissur'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#as p value<0.05 we can reject null hypothesis which says ph in all district is same.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "district_ca=df[['District','Soil_Ca']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Soil_Ca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Thrissur</td>\n",
       "      <td>913.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kottayam</td>\n",
       "      <td>131.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kollam</td>\n",
       "      <td>321.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kottayam</td>\n",
       "      <td>131.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thrissur</td>\n",
       "      <td>814.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   District  Soil_Ca\n",
       "0  Thrissur    913.8\n",
       "1  Kottayam    131.1\n",
       "2    Kollam    321.2\n",
       "3  Kottayam    131.1\n",
       "4  Thrissur    814.8"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district_ca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f240f2d710>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "district_ca.groupby('District')['Soil_Ca'].mean().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f24100f470>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "district_ca.groupby('District')['Soil_Ca'].mean().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "group_ca=district_ca.groupby('District')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=114.13127661362887, pvalue=5.604370845690677e-141)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(group_ca.groups['Alappuzha'],\n",
    "               group_ca.groups['Ernakulam'],\n",
    "               group_ca.groups['Idukki'],\n",
    "              group_ca.groups['Kannur'],group_ca.groups['Kollam'],group_ca.groups['Kottayam'],\n",
    "             group_ca.groups['Kozhikkode'],group_ca.groups['Thrissur'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#as p value<0.05 we can reject null hypothesis which says calcium content in all district is same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MULTIPLE COMPARISONS USING TUKEY HSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.multicomp import (pairwise_tukeyhsd,MultiComparison)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "Multi_ph = MultiComparison(district_ph['Soil_pH'],district_ph['District'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "Multi_ca = MultiComparison(district_ca['Soil_Ca'],district_ca['District'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Multiple Comparison of Means - Tukey HSD,FWER=0.05     \n",
      "=============================================================\n",
      "  group1     group2    meandiff    lower      upper    reject\n",
      "-------------------------------------------------------------\n",
      "Alappuzha  Ernakulam  2515.4541  2390.4295  2640.4787   True \n",
      "Alappuzha    Idukki    61.7018    -19.6234   143.0271  False \n",
      "Alappuzha    Kannur    -22.5474   -92.6397   47.5449   False \n",
      "Alappuzha    Kollam   -173.2006  -238.0853   -108.316   True \n",
      "Alappuzha   Kottayam   134.6811   66.6263    202.7358   True \n",
      "Alappuzha  Kozhikkode  182.3886  -157.0836   521.8608  False \n",
      "Alappuzha   Thrissur   183.9228   118.1175   249.728    True \n",
      "Ernakulam    Idukki   -2453.7523 -2575.7733 -2331.7314  True \n",
      "Ernakulam    Kannur   -2538.0015 -2652.8416 -2423.1614  True \n",
      "Ernakulam    Kollam   -2688.6548 -2800.3925 -2576.917   True \n",
      "Ernakulam   Kottayam  -2380.773  -2494.381  -2267.1651  True \n",
      "Ernakulam  Kozhikkode -2333.0655 -2684.5149 -1981.6161  True \n",
      "Ernakulam   Thrissur  -2331.5314 -2443.8062 -2219.2565  True \n",
      "  Idukki     Kannur    -84.2492  -148.8317   -19.6667   True \n",
      "  Idukki     Kollam   -234.9024   -293.792  -176.0129   True \n",
      "  Idukki    Kottayam   72.9793    10.6141    135.3445   True \n",
      "  Idukki   Kozhikkode  120.6868  -217.6907   459.0644  False \n",
      "  Idukki    Thrissur   122.221    62.3185    182.1234   True \n",
      "  Kannur     Kollam   -150.6532  -192.6903  -108.6161   True \n",
      "  Kannur    Kottayam   157.2285   110.4465   204.0105   True \n",
      "  Kannur   Kozhikkode  204.936   -130.9188   540.7909  False \n",
      "  Kannur    Thrissur   206.4702   163.0255   249.9148   True \n",
      "  Kollam    Kottayam   307.8817   269.3378   346.4256   True \n",
      "  Kollam   Kozhikkode  355.5893   20.7825    690.396    True \n",
      "  Kollam    Thrissur   357.1234   322.7066   391.5402   True \n",
      " Kottayam  Kozhikkode  47.7076    -287.728   383.1431  False \n",
      " Kottayam   Thrissur   49.2417     9.1673     89.316    True \n",
      "Kozhikkode  Thrissur    1.5341   -333.4523   336.5205  False \n",
      "-------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(Multi_ca.tukeyhsd().summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Multiple Comparison of Means - Tukey HSD,FWER=0.05 \n",
      "=====================================================\n",
      "  group1     group2   meandiff  lower   upper  reject\n",
      "-----------------------------------------------------\n",
      "Alappuzha  Ernakulam   0.1643  -0.2174  0.5459 False \n",
      "Alappuzha    Idukki   -0.0714  -0.3197  0.1768 False \n",
      "Alappuzha    Kannur    0.1027  -0.1113  0.3167 False \n",
      "Alappuzha    Kollam    0.6383   0.4402  0.8363  True \n",
      "Alappuzha   Kottayam  -0.0386  -0.2464  0.1691 False \n",
      "Alappuzha  Kozhikkode  1.0679   0.0316  2.1042  True \n",
      "Alappuzha   Thrissur  -0.1919  -0.3928  0.009  False \n",
      "Ernakulam    Idukki   -0.2357  -0.6082  0.1368 False \n",
      "Ernakulam    Kannur   -0.0616  -0.4121  0.289  False \n",
      "Ernakulam    Kollam    0.474    0.1329  0.8151  True \n",
      "Ernakulam   Kottayam  -0.2029  -0.5497  0.1439 False \n",
      "Ernakulam  Kozhikkode  0.9037  -0.1692  1.9766 False \n",
      "Ernakulam   Thrissur  -0.3561  -0.6989 -0.0134  True \n",
      "  Idukki     Kannur    0.1741   -0.023  0.3713 False \n",
      "  Idukki     Kollam    0.7097   0.5299  0.8895  True \n",
      "  Idukki    Kottayam   0.0328  -0.1575  0.2232 False \n",
      "  Idukki   Kozhikkode  1.1394   0.1064  2.1723  True \n",
      "  Idukki    Thrissur  -0.1204  -0.3033  0.0624 False \n",
      "  Kannur     Kollam    0.5356   0.4072  0.6639  True \n",
      "  Kannur    Kottayam  -0.1413  -0.2841  0.0015 False \n",
      "  Kannur   Kozhikkode  0.9653   -0.06   1.9905 False \n",
      "  Kannur    Thrissur  -0.2946  -0.4272  -0.162  True \n",
      "  Kollam    Kottayam  -0.6769  -0.7945 -0.5592  True \n",
      "  Kollam   Kozhikkode  0.4297  -0.5924  1.4517 False \n",
      "  Kollam    Thrissur  -0.8302  -0.9352 -0.7251  True \n",
      " Kottayam  Kozhikkode  1.1066   0.0826  2.1305  True \n",
      " Kottayam   Thrissur  -0.1533  -0.2756 -0.0309  True \n",
      "Kozhikkode  Thrissur  -1.2598  -2.2824 -0.2372  True \n",
      "-----------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(Multi_ph.tukeyhsd().summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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