Module_01.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jSqkbjBF1iD_"
      },
      "source": [
        "# Module:1 Data Validation Process and Preprocessing Technique"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fmVqSvXc1iEI"
      },
      "outputs": [],
      "source": [
        "#import libraries for access and functional purpose\n",
        "import pandas as p\n",
        "import numpy as n\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as s"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RpNx2BeN1iEK"
      },
      "outputs": [],
      "source": [
        "#read the given dataset\n",
        "df = p.read_csv(\"df4.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J3qUOBhX1iEL",
        "outputId": "5adb8d86-b545-4492-d398-05cdf718cf6d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State_Name</th>\n",
              "      <th>District_Name</th>\n",
              "      <th>Crop_Year</th>\n",
              "      <th>Season</th>\n",
              "      <th>Crop</th>\n",
              "      <th>Area</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>Average Humidity</th>\n",
              "      <th>Mean Temp</th>\n",
              "      <th>Cost of Cultivation (`/Hectare) C2</th>\n",
              "      <th>Cost of Production (`/Quintal) C2</th>\n",
              "      <th>Yield (Quintal/ Hectare)</th>\n",
              "      <th>cost of production per yield</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>NICOBARS</td>\n",
              "      <td>2000</td>\n",
              "      <td>Kharif</td>\n",
              "      <td>Arecanut</td>\n",
              "      <td>1254.0</td>\n",
              "      <td>0.012360</td>\n",
              "      <td>57</td>\n",
              "      <td>62</td>\n",
              "      <td>23076.74</td>\n",
              "      <td>1941.55</td>\n",
              "      <td>9.83</td>\n",
              "      <td>19085.4365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>NICOBARS</td>\n",
              "      <td>2001</td>\n",
              "      <td>Kharif</td>\n",
              "      <td>Arecanut</td>\n",
              "      <td>1254.0</td>\n",
              "      <td>0.084119</td>\n",
              "      <td>56</td>\n",
              "      <td>58</td>\n",
              "      <td>12610.85</td>\n",
              "      <td>1691.66</td>\n",
              "      <td>6.83</td>\n",
              "      <td>11554.0378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>NICOBARS</td>\n",
              "      <td>2002</td>\n",
              "      <td>Whole Year</td>\n",
              "      <td>Arecanut</td>\n",
              "      <td>1258.0</td>\n",
              "      <td>0.080064</td>\n",
              "      <td>58</td>\n",
              "      <td>53</td>\n",
              "      <td>32683.46</td>\n",
              "      <td>3207.35</td>\n",
              "      <td>9.33</td>\n",
              "      <td>29924.5755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>NICOBARS</td>\n",
              "      <td>2003</td>\n",
              "      <td>Whole Year</td>\n",
              "      <td>Arecanut</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.181051</td>\n",
              "      <td>57</td>\n",
              "      <td>58</td>\n",
              "      <td>13209.32</td>\n",
              "      <td>2228.97</td>\n",
              "      <td>5.90</td>\n",
              "      <td>13150.9230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>NICOBARS</td>\n",
              "      <td>2004</td>\n",
              "      <td>Whole Year</td>\n",
              "      <td>Arecanut</td>\n",
              "      <td>1264.7</td>\n",
              "      <td>0.035446</td>\n",
              "      <td>63</td>\n",
              "      <td>67</td>\n",
              "      <td>22560.30</td>\n",
              "      <td>1595.56</td>\n",
              "      <td>13.57</td>\n",
              "      <td>21651.7492</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    State_Name District_Name  Crop_Year       Season  \\\n",
              "0  Andaman and Nicobar Islands      NICOBARS       2000  Kharif        \n",
              "1  Andaman and Nicobar Islands      NICOBARS       2001  Kharif        \n",
              "2  Andaman and Nicobar Islands      NICOBARS       2002  Whole Year    \n",
              "3  Andaman and Nicobar Islands      NICOBARS       2003  Whole Year    \n",
              "4  Andaman and Nicobar Islands      NICOBARS       2004  Whole Year    \n",
              "\n",
              "       Crop    Area  rainfall  Average Humidity  Mean Temp  \\\n",
              "0  Arecanut  1254.0  0.012360                57         62   \n",
              "1  Arecanut  1254.0  0.084119                56         58   \n",
              "2  Arecanut  1258.0  0.080064                58         53   \n",
              "3  Arecanut  1261.0  0.181051                57         58   \n",
              "4  Arecanut  1264.7  0.035446                63         67   \n",
              "\n",
              "   Cost of Cultivation (`/Hectare) C2  Cost of Production (`/Quintal) C2  \\\n",
              "0                            23076.74                            1941.55   \n",
              "1                            12610.85                            1691.66   \n",
              "2                            32683.46                            3207.35   \n",
              "3                            13209.32                            2228.97   \n",
              "4                            22560.30                            1595.56   \n",
              "\n",
              "   Yield (Quintal/ Hectare)   cost of production per yield  \n",
              "0                       9.83                    19085.4365  \n",
              "1                       6.83                    11554.0378  \n",
              "2                       9.33                    29924.5755  \n",
              "3                       5.90                    13150.9230  \n",
              "4                      13.57                    21651.7492  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ER6NSvxz1iEN",
        "outputId": "8024e86e-eac7-42d7-f59a-0de8dd6ed954"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Arecanut', 'Arecanut', 'Arecanut', 'Arecanut', 'Arecanut', ..., 'Moong(Green Gram)', 'Moong(Green Gram)', 'Moong(Green Gram)', 'Moong(Green Gram)', 'Moong(Green Gram)']\n",
              "Length: 9542\n",
              "Categories (74, object): ['Arecanut', 'Arhar/Tur', 'Bajra', 'Banana', ..., 'Varagu', 'Water Melon', 'Wheat', 'Yam']"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "listcrops = p.Categorical(df['Crop'])\n",
        "listcrops"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L6m_paX81iEO",
        "outputId": "08e62109-3917-4d63-d5d3-b720b9b9af24"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Moong(Green Gram)    932\n",
              "Urad                 510\n",
              "Rice                 498\n",
              "Sugarcane            482\n",
              "Ragi                 456\n",
              "                    ... \n",
              "Horse-gram             6\n",
              "Plums                  6\n",
              "Peach                  4\n",
              "Jute                   2\n",
              "Turnip                 2\n",
              "Name: Crop, Length: 74, dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Crop'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "INNWPKBZ1iEQ",
        "outputId": "134376e1-3b1c-4310-bfde-3da884791ef5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "74"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Crop'].nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1kcGQ-mc1iES",
        "outputId": "abb6b147-7c26-4dbb-a951-9b9de7b2f44f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Arecanut', 'Horse-gram', 'Banana', 'Jowar', 'Jute', 'Korra',\n",
              "       'Lab-Lab', 'Litchi', 'Maize', 'Mango', 'Mesta',\n",
              "       'Moong(Green Gram)', 'Niger seed', 'Onion', 'Orange',\n",
              "       'Other Cereals & Millets', 'Other Citrus Fruit',\n",
              "       'Other Fresh Fruits', 'Other Kharif pulses', 'Other Vegetables',\n",
              "       'Papaya', 'Peach', 'Pear', 'Pineapple', 'Plums', 'Pome Fruit',\n",
              "       'Pome Granet', 'Potato', 'Pulses total', 'Pump Kin', 'Ragi',\n",
              "       'Redish', 'Ribed Guard', 'Rice', 'Samai', 'Sannhamp', 'Sesamum',\n",
              "       'Small millets', 'Snak Guard', 'Soyabean', 'Sugarcane',\n",
              "       'Sunflower', 'Sweet potato', 'Tapioca', 'Tobacco', 'Tomato',\n",
              "       'Total foodgrain', 'Turmeric', 'Turnip', 'Urad', 'Varagu',\n",
              "       'Water Melon', 'Wheat', 'Yam', 'Arhar/Tur', 'Bajra',\n",
              "       'Beans & Mutter(Vegetable)', 'Bhindi', 'Bottle Gourd', 'Brinjal',\n",
              "       'Cabbage', 'Cashewnut', 'Castor seed', 'Citrus Fruit', 'Coconut ',\n",
              "       'Coriander', 'Cotton(lint)', 'Cowpea(Lobia)', 'Cucumber',\n",
              "       'Dry chillies', 'Dry ginger', 'Garlic', 'Ginger', 'Gram'],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Crop'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GIsnZF901iET",
        "outputId": "452c029b-32e1-462b-eefd-90892b26beb7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(9542, 13)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QwEh9TUZ1iEU",
        "outputId": "d4fee502-2195-4299-fb7d-b92c4747a3e0"
      },
      "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>Crop_Year</th>\n",
              "      <th>Area</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>Average Humidity</th>\n",
              "      <th>Mean Temp</th>\n",
              "      <th>Cost of Cultivation (`/Hectare) C2</th>\n",
              "      <th>Cost of Production (`/Quintal) C2</th>\n",
              "      <th>Yield (Quintal/ Hectare)</th>\n",
              "      <th>cost of production per yield</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9542.000000</td>\n",
              "      <td>9542.000000</td>\n",
              "      <td>9542.000000</td>\n",
              "      <td>9542.000000</td>\n",
              "      <td>9542.000000</td>\n",
              "      <td>9542.000000</td>\n",
              "      <td>9542.000000</td>\n",
              "      <td>9542.000000</td>\n",
              "      <td>9.542000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2004.811360</td>\n",
              "      <td>10195.213935</td>\n",
              "      <td>0.054538</td>\n",
              "      <td>48.200587</td>\n",
              "      <td>75.982079</td>\n",
              "      <td>35493.628852</td>\n",
              "      <td>1615.055764</td>\n",
              "      <td>94.521492</td>\n",
              "      <td>7.076037e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.807089</td>\n",
              "      <td>29057.327016</td>\n",
              "      <td>0.084302</td>\n",
              "      <td>18.757970</td>\n",
              "      <td>12.039919</td>\n",
              "      <td>24699.634470</td>\n",
              "      <td>1114.276186</td>\n",
              "      <td>238.334783</td>\n",
              "      <td>1.795160e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1997.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>7868.640000</td>\n",
              "      <td>85.790000</td>\n",
              "      <td>1.320000</td>\n",
              "      <td>8.842152e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2001.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>19083.550000</td>\n",
              "      <td>731.250000</td>\n",
              "      <td>9.590000</td>\n",
              "      <td>1.399085e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2004.000000</td>\n",
              "      <td>728.000000</td>\n",
              "      <td>0.004642</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>28144.500000</td>\n",
              "      <td>1595.560000</td>\n",
              "      <td>13.700000</td>\n",
              "      <td>2.542234e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2009.000000</td>\n",
              "      <td>5835.750000</td>\n",
              "      <td>0.082980</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>45239.510000</td>\n",
              "      <td>2228.970000</td>\n",
              "      <td>36.610000</td>\n",
              "      <td>4.828261e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2014.000000</td>\n",
              "      <td>367554.000000</td>\n",
              "      <td>0.348660</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>91442.630000</td>\n",
              "      <td>5777.480000</td>\n",
              "      <td>1015.450000</td>\n",
              "      <td>2.206025e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Crop_Year           Area     rainfall  Average Humidity    Mean Temp  \\\n",
              "count  9542.000000    9542.000000  9542.000000       9542.000000  9542.000000   \n",
              "mean   2004.811360   10195.213935     0.054538         48.200587    75.982079   \n",
              "std       4.807089   29057.327016     0.084302         18.757970    12.039919   \n",
              "min    1997.000000       1.000000     0.000000         11.000000    52.000000   \n",
              "25%    2001.000000      56.000000     0.000000         36.000000    66.000000   \n",
              "50%    2004.000000     728.000000     0.004642         47.000000    79.000000   \n",
              "75%    2009.000000    5835.750000     0.082980         62.000000    85.000000   \n",
              "max    2014.000000  367554.000000     0.348660        100.000000   100.000000   \n",
              "\n",
              "       Cost of Cultivation (`/Hectare) C2  Cost of Production (`/Quintal) C2  \\\n",
              "count                         9542.000000                        9542.000000   \n",
              "mean                         35493.628852                        1615.055764   \n",
              "std                          24699.634470                        1114.276186   \n",
              "min                           7868.640000                          85.790000   \n",
              "25%                          19083.550000                         731.250000   \n",
              "50%                          28144.500000                        1595.560000   \n",
              "75%                          45239.510000                        2228.970000   \n",
              "max                          91442.630000                        5777.480000   \n",
              "\n",
              "       Yield (Quintal/ Hectare)   cost of production per yield  \n",
              "count                9542.000000                  9.542000e+03  \n",
              "mean                   94.521492                  7.076037e+04  \n",
              "std                   238.334783                  1.795160e+05  \n",
              "min                     1.320000                  8.842152e+02  \n",
              "25%                     9.590000                  1.399085e+04  \n",
              "50%                    13.700000                  2.542234e+04  \n",
              "75%                    36.610000                  4.828261e+04  \n",
              "max                  1015.450000                  2.206025e+06  "
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#To describe the dataframe\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0AsbyMr61iEV",
        "outputId": "c68142a1-9b96-4e0a-f90f-d0a6cb35973b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9542 entries, 0 to 9541\n",
            "Data columns (total 13 columns):\n",
            " #   Column                              Non-Null Count  Dtype  \n",
            "---  ------                              --------------  -----  \n",
            " 0   State_Name                          9542 non-null   object \n",
            " 1   District_Name                       9542 non-null   object \n",
            " 2   Crop_Year                           9542 non-null   int64  \n",
            " 3   Season                              9542 non-null   object \n",
            " 4   Crop                                9542 non-null   object \n",
            " 5   Area                                9542 non-null   float64\n",
            " 6   rainfall                            9542 non-null   float64\n",
            " 7   Average Humidity                    9542 non-null   int64  \n",
            " 8   Mean Temp                           9542 non-null   int64  \n",
            " 9   Cost of Cultivation (`/Hectare) C2  9542 non-null   float64\n",
            " 10  Cost of Production (`/Quintal) C2   9542 non-null   float64\n",
            " 11  Yield (Quintal/ Hectare)            9542 non-null   float64\n",
            " 12  cost of production per yield        9542 non-null   float64\n",
            "dtypes: float64(6), int64(3), object(4)\n",
            "memory usage: 969.2+ KB\n"
          ]
        }
      ],
      "source": [
        "#Checking datatype and information about dataset\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kw-nbBD91iEW",
        "outputId": "23132329-5598-4e55-c59b-ec5c1fbbf266"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>Cost of Cultivation (`/Hectare) C2</th>\n",
              "      <th>Cost of Production (`/Quintal) C2</th>\n",
              "      <th>Yield (Quintal/ Hectare)</th>\n",
              "      <th>cost of production per yield</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9542.000000</td>\n",
              "      <td>9542.000000</td>\n",
              "      <td>9542.000000</td>\n",
              "      <td>9542.000000</td>\n",
              "      <td>9542.000000</td>\n",
              "      <td>9.542000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>10195.213935</td>\n",
              "      <td>0.054538</td>\n",
              "      <td>35493.628852</td>\n",
              "      <td>1615.055764</td>\n",
              "      <td>94.521492</td>\n",
              "      <td>7.076037e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>29057.327016</td>\n",
              "      <td>0.084302</td>\n",
              "      <td>24699.634470</td>\n",
              "      <td>1114.276186</td>\n",
              "      <td>238.334783</td>\n",
              "      <td>1.795160e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7868.640000</td>\n",
              "      <td>85.790000</td>\n",
              "      <td>1.320000</td>\n",
              "      <td>8.842152e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>56.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>19083.550000</td>\n",
              "      <td>731.250000</td>\n",
              "      <td>9.590000</td>\n",
              "      <td>1.399085e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>728.000000</td>\n",
              "      <td>0.004642</td>\n",
              "      <td>28144.500000</td>\n",
              "      <td>1595.560000</td>\n",
              "      <td>13.700000</td>\n",
              "      <td>2.542234e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5835.750000</td>\n",
              "      <td>0.082980</td>\n",
              "      <td>45239.510000</td>\n",
              "      <td>2228.970000</td>\n",
              "      <td>36.610000</td>\n",
              "      <td>4.828261e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>367554.000000</td>\n",
              "      <td>0.348660</td>\n",
              "      <td>91442.630000</td>\n",
              "      <td>5777.480000</td>\n",
              "      <td>1015.450000</td>\n",
              "      <td>2.206025e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Area     rainfall  Cost of Cultivation (`/Hectare) C2  \\\n",
              "count    9542.000000  9542.000000                         9542.000000   \n",
              "mean    10195.213935     0.054538                        35493.628852   \n",
              "std     29057.327016     0.084302                        24699.634470   \n",
              "min         1.000000     0.000000                         7868.640000   \n",
              "25%        56.000000     0.000000                        19083.550000   \n",
              "50%       728.000000     0.004642                        28144.500000   \n",
              "75%      5835.750000     0.082980                        45239.510000   \n",
              "max    367554.000000     0.348660                        91442.630000   \n",
              "\n",
              "       Cost of Production (`/Quintal) C2  Yield (Quintal/ Hectare)   \\\n",
              "count                        9542.000000                9542.000000   \n",
              "mean                         1615.055764                  94.521492   \n",
              "std                          1114.276186                 238.334783   \n",
              "min                            85.790000                   1.320000   \n",
              "25%                           731.250000                   9.590000   \n",
              "50%                          1595.560000                  13.700000   \n",
              "75%                          2228.970000                  36.610000   \n",
              "max                          5777.480000                1015.450000   \n",
              "\n",
              "       cost of production per yield  \n",
              "count                  9.542000e+03  \n",
              "mean                   7.076037e+04  \n",
              "std                    1.795160e+05  \n",
              "min                    8.842152e+02  \n",
              "25%                    1.399085e+04  \n",
              "50%                    2.542234e+04  \n",
              "75%                    4.828261e+04  \n",
              "max                    2.206025e+06  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[df.dtypes[df.dtypes == 'float64'].index].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VkhNpv9P1iEX",
        "outputId": "ea89cb56-243d-40f5-e46a-d1c11e48cf39"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>counts</th>\n",
              "      <th>freqs</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>categories</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Andaman and Nicobar Islands</th>\n",
              "      <td>6</td>\n",
              "      <td>0.000629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bihar</th>\n",
              "      <td>6</td>\n",
              "      <td>0.000629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chhattisgarh</th>\n",
              "      <td>55</td>\n",
              "      <td>0.005764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tamil Nadu</th>\n",
              "      <td>7360</td>\n",
              "      <td>0.771327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Telangana</th>\n",
              "      <td>1683</td>\n",
              "      <td>0.176378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Uttar Pradesh</th>\n",
              "      <td>432</td>\n",
              "      <td>0.045274</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             counts     freqs\n",
              "categories                                   \n",
              "Andaman and Nicobar Islands       6  0.000629\n",
              "Bihar                             6  0.000629\n",
              "Chhattisgarh                     55  0.005764\n",
              "Tamil Nadu                     7360  0.771327\n",
              "Telangana                      1683  0.176378\n",
              "Uttar Pradesh                   432  0.045274"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "p.Categorical(df['State_Name']).describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hOXNbhur1iEY",
        "outputId": "322fece7-0e20-4661-891c-c4fa187486a8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>counts</th>\n",
              "      <th>freqs</th>\n",
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              "    <tr>\n",
              "      <th>categories</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ADILABAD</th>\n",
              "      <td>188</td>\n",
              "      <td>0.019702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ARIYALUR</th>\n",
              "      <td>67</td>\n",
              "      <td>0.007022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BAGHPAT</th>\n",
              "      <td>20</td>\n",
              "      <td>0.002096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BAHRAICH</th>\n",
              "      <td>21</td>\n",
              "      <td>0.002201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BALLIA</th>\n",
              "      <td>25</td>\n",
              "      <td>0.002620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TUTICORIN</th>\n",
              "      <td>268</td>\n",
              "      <td>0.028086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VELLORE</th>\n",
              "      <td>293</td>\n",
              "      <td>0.030706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VILLUPURAM</th>\n",
              "      <td>269</td>\n",
              "      <td>0.028191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VIRUDHUNAGAR</th>\n",
              "      <td>248</td>\n",
              "      <td>0.025990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WARANGAL</th>\n",
              "      <td>163</td>\n",
              "      <td>0.017082</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>67 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              counts     freqs\n",
              "categories                    \n",
              "ADILABAD         188  0.019702\n",
              "ARIYALUR          67  0.007022\n",
              "BAGHPAT           20  0.002096\n",
              "BAHRAICH          21  0.002201\n",
              "BALLIA            25  0.002620\n",
              "...              ...       ...\n",
              "TUTICORIN        268  0.028086\n",
              "VELLORE          293  0.030706\n",
              "VILLUPURAM       269  0.028191\n",
              "VIRUDHUNAGAR     248  0.025990\n",
              "WARANGAL         163  0.017082\n",
              "\n",
              "[67 rows x 2 columns]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "p.Categorical(df['District_Name']).describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WmL4tfNW1iEZ",
        "outputId": "ca9653df-0b2e-4330-da77-f97da1a95179"
      },
      "outputs": [
        {
          "data": {
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              "      <td>376</td>\n",
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              "      <th>63</th>\n",
              "      <td>53</td>\n",
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              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>173</td>\n",
              "      <td>0.018130</td>\n",
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              "    <tr>\n",
              "      <th>65</th>\n",
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              "      <th>66</th>\n",
              "      <td>150</td>\n",
              "      <td>0.015720</td>\n",
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              "    <tr>\n",
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              "      <td>0.005135</td>\n",
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              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>315</td>\n",
              "      <td>0.033012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>46</td>\n",
              "      <td>0.004821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>404</td>\n",
              "      <td>0.042339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>58</td>\n",
              "      <td>0.006078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>329</td>\n",
              "      <td>0.034479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>101</td>\n",
              "      <td>0.010585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>189</td>\n",
              "      <td>0.019807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>153</td>\n",
              "      <td>0.016034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>255</td>\n",
              "      <td>0.026724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>119</td>\n",
              "      <td>0.012471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>252</td>\n",
              "      <td>0.026410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>102</td>\n",
              "      <td>0.010690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>467</td>\n",
              "      <td>0.048942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>232</td>\n",
              "      <td>0.024314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>698</td>\n",
              "      <td>0.073150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>222</td>\n",
              "      <td>0.023266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>584</td>\n",
              "      <td>0.061203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>261</td>\n",
              "      <td>0.027353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>576</td>\n",
              "      <td>0.060365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>103</td>\n",
              "      <td>0.010794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>254</td>\n",
              "      <td>0.026619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>40</td>\n",
              "      <td>0.004192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>273</td>\n",
              "      <td>0.028610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>74</td>\n",
              "      <td>0.007755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>267</td>\n",
              "      <td>0.027982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>86</td>\n",
              "      <td>0.009013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>211</td>\n",
              "      <td>0.022113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>102</td>\n",
              "      <td>0.010690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>105</td>\n",
              "      <td>0.011004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>98</td>\n",
              "      <td>0.010270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>32</td>\n",
              "      <td>0.003354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>24</td>\n",
              "      <td>0.002515</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            counts     freqs\n",
              "categories                  \n",
              "52              25  0.002620\n",
              "53              95  0.009956\n",
              "54             150  0.015720\n",
              "55              77  0.008070\n",
              "56             318  0.033326\n",
              "57             120  0.012576\n",
              "58             376  0.039405\n",
              "59             154  0.016139\n",
              "60             335  0.035108\n",
              "61              95  0.009956\n",
              "62             219  0.022951\n",
              "63              53  0.005554\n",
              "64             173  0.018130\n",
              "65             121  0.012681\n",
              "66             150  0.015720\n",
              "67              49  0.005135\n",
              "68             315  0.033012\n",
              "69              46  0.004821\n",
              "70             404  0.042339\n",
              "71              58  0.006078\n",
              "72             329  0.034479\n",
              "73             101  0.010585\n",
              "74             189  0.019807\n",
              "75             153  0.016034\n",
              "76             255  0.026724\n",
              "77             119  0.012471\n",
              "78             252  0.026410\n",
              "79             102  0.010690\n",
              "80             467  0.048942\n",
              "81             232  0.024314\n",
              "82             698  0.073150\n",
              "83             222  0.023266\n",
              "84             584  0.061203\n",
              "85             261  0.027353\n",
              "86             576  0.060365\n",
              "87             103  0.010794\n",
              "88             254  0.026619\n",
              "89              40  0.004192\n",
              "90             273  0.028610\n",
              "91              74  0.007755\n",
              "92             267  0.027982\n",
              "93              86  0.009013\n",
              "94             211  0.022113\n",
              "95             102  0.010690\n",
              "96             105  0.011004\n",
              "98              98  0.010270\n",
              "99              32  0.003354\n",
              "100             24  0.002515"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "p.Categorical(df['Mean Temp']).describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rXGrajpq1iEa",
        "outputId": "ba269d0f-0161-4a93-a279-954e21496806"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>counts</th>\n",
              "      <th>freqs</th>\n",
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              "      <th>categories</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Kharif</th>\n",
              "      <td>3974</td>\n",
              "      <td>0.416475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rabi</th>\n",
              "      <td>775</td>\n",
              "      <td>0.081220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Summer</th>\n",
              "      <td>198</td>\n",
              "      <td>0.020750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Whole Year</th>\n",
              "      <td>4595</td>\n",
              "      <td>0.481555</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             counts     freqs\n",
              "categories                   \n",
              "Kharif         3974  0.416475\n",
              "Rabi            775  0.081220\n",
              "Summer          198  0.020750\n",
              "Whole Year     4595  0.481555"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "p.Categorical(df['Season']).describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XN8PjsH71iEa",
        "outputId": "cff260de-ae5a-4cb2-e73f-c3e725870f94"
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      "outputs": [
        {
          "data": {
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              "      <th>16</th>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>49</td>\n",
              "      <td>0.005135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>52</td>\n",
              "      <td>0.005450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>25</td>\n",
              "      <td>0.002620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>22</td>\n",
              "      <td>0.002306</td>\n",
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              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>20</td>\n",
              "      <td>0.002096</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            counts     freqs\n",
              "categories                  \n",
              "11              30  0.003144\n",
              "13              28  0.002934\n",
              "14              26  0.002725\n",
              "15              49  0.005135\n",
              "16              74  0.007755\n",
              "...            ...       ...\n",
              "91              49  0.005135\n",
              "93              52  0.005450\n",
              "96              25  0.002620\n",
              "98              22  0.002306\n",
              "100             20  0.002096\n",
              "\n",
              "[80 rows x 2 columns]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "p.Categorical(df['Average Humidity']).describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2YbeqCRh1iEb",
        "outputId": "24b27bc4-20b6-49d9-ef0a-34404f5dfa62"
      },
      "outputs": [
        {
          "data": {
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              "      <td>25</td>\n",
              "      <td>0.002620</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>214 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            counts     freqs\n",
              "categories                  \n",
              "0.0           4319  0.452630\n",
              "0.00014373      25  0.002620\n",
              "0.00038962      25  0.002620\n",
              "0.0012627       27  0.002830\n",
              "0.00153143      27  0.002830\n",
              "...            ...       ...\n",
              "0.301119        23  0.002410\n",
              "0.31073026      25  0.002620\n",
              "0.3132829       25  0.002620\n",
              "0.33147006      29  0.003039\n",
              "0.34866006      25  0.002620\n",
              "\n",
              "[214 rows x 2 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "p.Categorical(df['rainfall']).describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wp0iR8nL1iEd",
        "outputId": "6c4bb9a7-dd1e-4b2d-96ed-7b323829c447"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "      <th></th>\n",
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              "  <tbody>\n",
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              "      <th>Arecanut</th>\n",
              "      <td>6</td>\n",
              "      <td>0.000629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arhar/Tur</th>\n",
              "      <td>218</td>\n",
              "      <td>0.022846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bajra</th>\n",
              "      <td>184</td>\n",
              "      <td>0.019283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Banana</th>\n",
              "      <td>149</td>\n",
              "      <td>0.015615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beans &amp; Mutter(Vegetable)</th>\n",
              "      <td>18</td>\n",
              "      <td>0.001886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Urad</th>\n",
              "      <td>510</td>\n",
              "      <td>0.053448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Varagu</th>\n",
              "      <td>43</td>\n",
              "      <td>0.004506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Water Melon</th>\n",
              "      <td>32</td>\n",
              "      <td>0.003354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wheat</th>\n",
              "      <td>25</td>\n",
              "      <td>0.002620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yam</th>\n",
              "      <td>36</td>\n",
              "      <td>0.003773</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>74 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                           counts     freqs\n",
              "categories                                 \n",
              "Arecanut                        6  0.000629\n",
              "Arhar/Tur                     218  0.022846\n",
              "Bajra                         184  0.019283\n",
              "Banana                        149  0.015615\n",
              "Beans & Mutter(Vegetable)      18  0.001886\n",
              "...                           ...       ...\n",
              "Urad                          510  0.053448\n",
              "Varagu                         43  0.004506\n",
              "Water Melon                    32  0.003354\n",
              "Wheat                          25  0.002620\n",
              "Yam                            36  0.003773\n",
              "\n",
              "[74 rows x 2 columns]"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "p.Categorical(df['Crop']).describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B53Ub1ul1iEd",
        "outputId": "ec175bbd-b7b0-47a6-af6d-1edb153a976c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0       False\n",
              "1       False\n",
              "2       False\n",
              "3       False\n",
              "4       False\n",
              "        ...  \n",
              "9537    False\n",
              "9538    False\n",
              "9539    False\n",
              "9540    False\n",
              "9541    False\n",
              "Length: 9542, dtype: bool"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Checking for duplicate data\n",
        "df.duplicated()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sYxGQQY31iEf",
        "outputId": "30f9d910-3a50-4fa2-a7cb-1f343aba8e6c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#find sum of duplicate data\n",
        "sum(df.duplicated())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xCqLNz181iEf",
        "outputId": "839a2494-ac66-4e60-b136-bc9642650aca"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "State_Name                               6\n",
              "District_Name                           67\n",
              "Crop_Year                               18\n",
              "Season                                   4\n",
              "Crop                                    74\n",
              "Area                                  4689\n",
              "rainfall                               214\n",
              "Average Humidity                        80\n",
              "Mean Temp                               48\n",
              "Cost of Cultivation (`/Hectare) C2      49\n",
              "Cost of Production (`/Quintal) C2       49\n",
              "Yield (Quintal/ Hectare)                49\n",
              "cost of production per yield           337\n",
              "dtype: int64"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mts4QIK41iEg",
        "outputId": "5b8e456d-ac00-410c-f083-aefd779aec13"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Crop_Year</th>\n",
              "      <th>Area</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>Average Humidity</th>\n",
              "      <th>Mean Temp</th>\n",
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              "      <th>cost of production per yield</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Crop_Year</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.033998</td>\n",
              "      <td>0.001955</td>\n",
              "      <td>-0.012323</td>\n",
              "      <td>0.053708</td>\n",
              "      <td>-0.008896</td>\n",
              "      <td>0.004706</td>\n",
              "      <td>0.017974</td>\n",
              "      <td>0.006590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Area</th>\n",
              "      <td>-0.033998</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.020967</td>\n",
              "      <td>0.007052</td>\n",
              "      <td>0.002213</td>\n",
              "      <td>0.001313</td>\n",
              "      <td>0.004999</td>\n",
              "      <td>-0.004052</td>\n",
              "      <td>-0.001414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rainfall</th>\n",
              "      <td>0.001955</td>\n",
              "      <td>-0.020967</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.009805</td>\n",
              "      <td>-0.311162</td>\n",
              "      <td>-0.030486</td>\n",
              "      <td>-0.014171</td>\n",
              "      <td>0.069267</td>\n",
              "      <td>-0.046942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Average Humidity</th>\n",
              "      <td>-0.012323</td>\n",
              "      <td>0.007052</td>\n",
              "      <td>-0.009805</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.252588</td>\n",
              "      <td>-0.009753</td>\n",
              "      <td>0.030869</td>\n",
              "      <td>-0.131063</td>\n",
              "      <td>-0.090237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean Temp</th>\n",
              "      <td>0.053708</td>\n",
              "      <td>0.002213</td>\n",
              "      <td>-0.311162</td>\n",
              "      <td>-0.252588</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.098394</td>\n",
              "      <td>-0.041967</td>\n",
              "      <td>0.057664</td>\n",
              "      <td>0.015582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cost of Cultivation (`/Hectare) C2</th>\n",
              "      <td>-0.008896</td>\n",
              "      <td>0.001313</td>\n",
              "      <td>-0.030486</td>\n",
              "      <td>-0.009753</td>\n",
              "      <td>0.098394</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.025679</td>\n",
              "      <td>0.028143</td>\n",
              "      <td>-0.055532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cost of Production (`/Quintal) C2</th>\n",
              "      <td>0.004706</td>\n",
              "      <td>0.004999</td>\n",
              "      <td>-0.014171</td>\n",
              "      <td>0.030869</td>\n",
              "      <td>-0.041967</td>\n",
              "      <td>0.025679</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.308414</td>\n",
              "      <td>-0.056326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yield (Quintal/ Hectare)</th>\n",
              "      <td>0.017974</td>\n",
              "      <td>-0.004052</td>\n",
              "      <td>0.069267</td>\n",
              "      <td>-0.131063</td>\n",
              "      <td>0.057664</td>\n",
              "      <td>0.028143</td>\n",
              "      <td>-0.308414</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.743026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cost of production per yield</th>\n",
              "      <td>0.006590</td>\n",
              "      <td>-0.001414</td>\n",
              "      <td>-0.046942</td>\n",
              "      <td>-0.090237</td>\n",
              "      <td>0.015582</td>\n",
              "      <td>-0.055532</td>\n",
              "      <td>-0.056326</td>\n",
              "      <td>0.743026</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    Crop_Year      Area  rainfall  \\\n",
              "Crop_Year                            1.000000 -0.033998  0.001955   \n",
              "Area                                -0.033998  1.000000 -0.020967   \n",
              "rainfall                             0.001955 -0.020967  1.000000   \n",
              "Average Humidity                    -0.012323  0.007052 -0.009805   \n",
              "Mean Temp                            0.053708  0.002213 -0.311162   \n",
              "Cost of Cultivation (`/Hectare) C2  -0.008896  0.001313 -0.030486   \n",
              "Cost of Production (`/Quintal) C2    0.004706  0.004999 -0.014171   \n",
              "Yield (Quintal/ Hectare)             0.017974 -0.004052  0.069267   \n",
              "cost of production per yield         0.006590 -0.001414 -0.046942   \n",
              "\n",
              "                                    Average Humidity  Mean Temp  \\\n",
              "Crop_Year                                  -0.012323   0.053708   \n",
              "Area                                        0.007052   0.002213   \n",
              "rainfall                                   -0.009805  -0.311162   \n",
              "Average Humidity                            1.000000  -0.252588   \n",
              "Mean Temp                                  -0.252588   1.000000   \n",
              "Cost of Cultivation (`/Hectare) C2         -0.009753   0.098394   \n",
              "Cost of Production (`/Quintal) C2           0.030869  -0.041967   \n",
              "Yield (Quintal/ Hectare)                   -0.131063   0.057664   \n",
              "cost of production per yield               -0.090237   0.015582   \n",
              "\n",
              "                                    Cost of Cultivation (`/Hectare) C2  \\\n",
              "Crop_Year                                                    -0.008896   \n",
              "Area                                                          0.001313   \n",
              "rainfall                                                     -0.030486   \n",
              "Average Humidity                                             -0.009753   \n",
              "Mean Temp                                                     0.098394   \n",
              "Cost of Cultivation (`/Hectare) C2                            1.000000   \n",
              "Cost of Production (`/Quintal) C2                             0.025679   \n",
              "Yield (Quintal/ Hectare)                                      0.028143   \n",
              "cost of production per yield                                 -0.055532   \n",
              "\n",
              "                                    Cost of Production (`/Quintal) C2  \\\n",
              "Crop_Year                                                    0.004706   \n",
              "Area                                                         0.004999   \n",
              "rainfall                                                    -0.014171   \n",
              "Average Humidity                                             0.030869   \n",
              "Mean Temp                                                   -0.041967   \n",
              "Cost of Cultivation (`/Hectare) C2                           0.025679   \n",
              "Cost of Production (`/Quintal) C2                            1.000000   \n",
              "Yield (Quintal/ Hectare)                                    -0.308414   \n",
              "cost of production per yield                                -0.056326   \n",
              "\n",
              "                                    Yield (Quintal/ Hectare)   \\\n",
              "Crop_Year                                            0.017974   \n",
              "Area                                                -0.004052   \n",
              "rainfall                                             0.069267   \n",
              "Average Humidity                                    -0.131063   \n",
              "Mean Temp                                            0.057664   \n",
              "Cost of Cultivation (`/Hectare) C2                   0.028143   \n",
              "Cost of Production (`/Quintal) C2                   -0.308414   \n",
              "Yield (Quintal/ Hectare)                             1.000000   \n",
              "cost of production per yield                         0.743026   \n",
              "\n",
              "                                    cost of production per yield  \n",
              "Crop_Year                                               0.006590  \n",
              "Area                                                   -0.001414  \n",
              "rainfall                                               -0.046942  \n",
              "Average Humidity                                       -0.090237  \n",
              "Mean Temp                                               0.015582  \n",
              "Cost of Cultivation (`/Hectare) C2                     -0.055532  \n",
              "Cost of Production (`/Quintal) C2                      -0.056326  \n",
              "Yield (Quintal/ Hectare)                                0.743026  \n",
              "cost of production per yield                            1.000000  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Correlation\n",
        "df.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y7d3l5wr1iEh",
        "outputId": "85e248ae-8dac-4896-e788-ffef1709fffe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Minimum yield of crops is  (100kg/2.47 acre): 1.32\n",
            "Maximun yield of crops is  (100kg/2.47 acre): 1015.45\n"
          ]
        }
      ],
      "source": [
        "#Checking minimum or maximum yields (100kg/2.47 acre)\n",
        "print(\"Minimum yield of crops is  (100kg/2.47 acre):\", df[\"Yield (Quintal/ Hectare) \"].min())\n",
        "print(\"Maximun yield of crops is  (100kg/2.47 acre):\", df[\"Yield (Quintal/ Hectare) \"].max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cQ5YQW0n1iEi",
        "outputId": "62c10dca-176a-4fc6-a9c8-a45796e88bf9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Minimum cost production for c2 scheme(per 2.47 acre): 85.79\n",
            "Maximun cost production for c2 scheme(per 2.47 acre): 5777.48\n"
          ]
        }
      ],
      "source": [
        "#Checking minimum or maximum cost production for c2 scheme (per 2.47 acre)\n",
        "print(\"Minimum cost production for c2 scheme(per 2.47 acre):\", df[\"Cost of Production (`/Quintal) C2\"].min())\n",
        "print(\"Maximun cost production for c2 scheme(per 2.47 acre):\", df[\"Cost of Production (`/Quintal) C2\"].max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_UHD41101iEi",
        "outputId": "9844db7f-1f48-4ef4-ff37-a9f96e11a0fb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State_Name</th>\n",
              "      <th>District_Name</th>\n",
              "      <th>Crop_Year</th>\n",
              "      <th>Season</th>\n",
              "      <th>Crop</th>\n",
              "      <th>Area</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>Average Humidity</th>\n",
              "      <th>Mean Temp</th>\n",
              "      <th>CC</th>\n",
              "      <th>CP</th>\n",
              "      <th>Y</th>\n",
              "      <th>cost of production per yield</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>NICOBARS</td>\n",
              "      <td>2000</td>\n",
              "      <td>Kharif</td>\n",
              "      <td>Arecanut</td>\n",
              "      <td>1254.0</td>\n",
              "      <td>0.012360</td>\n",
              "      <td>57</td>\n",
              "      <td>62</td>\n",
              "      <td>23076.74</td>\n",
              "      <td>1941.55</td>\n",
              "      <td>9.83</td>\n",
              "      <td>19085.4365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>NICOBARS</td>\n",
              "      <td>2001</td>\n",
              "      <td>Kharif</td>\n",
              "      <td>Arecanut</td>\n",
              "      <td>1254.0</td>\n",
              "      <td>0.084119</td>\n",
              "      <td>56</td>\n",
              "      <td>58</td>\n",
              "      <td>12610.85</td>\n",
              "      <td>1691.66</td>\n",
              "      <td>6.83</td>\n",
              "      <td>11554.0378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>NICOBARS</td>\n",
              "      <td>2002</td>\n",
              "      <td>Whole Year</td>\n",
              "      <td>Arecanut</td>\n",
              "      <td>1258.0</td>\n",
              "      <td>0.080064</td>\n",
              "      <td>58</td>\n",
              "      <td>53</td>\n",
              "      <td>32683.46</td>\n",
              "      <td>3207.35</td>\n",
              "      <td>9.33</td>\n",
              "      <td>29924.5755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>NICOBARS</td>\n",
              "      <td>2003</td>\n",
              "      <td>Whole Year</td>\n",
              "      <td>Arecanut</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.181051</td>\n",
              "      <td>57</td>\n",
              "      <td>58</td>\n",
              "      <td>13209.32</td>\n",
              "      <td>2228.97</td>\n",
              "      <td>5.90</td>\n",
              "      <td>13150.9230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>NICOBARS</td>\n",
              "      <td>2004</td>\n",
              "      <td>Whole Year</td>\n",
              "      <td>Arecanut</td>\n",
              "      <td>1264.7</td>\n",
              "      <td>0.035446</td>\n",
              "      <td>63</td>\n",
              "      <td>67</td>\n",
              "      <td>22560.30</td>\n",
              "      <td>1595.56</td>\n",
              "      <td>13.57</td>\n",
              "      <td>21651.7492</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    State_Name District_Name  Crop_Year       Season  \\\n",
              "0  Andaman and Nicobar Islands      NICOBARS       2000  Kharif        \n",
              "1  Andaman and Nicobar Islands      NICOBARS       2001  Kharif        \n",
              "2  Andaman and Nicobar Islands      NICOBARS       2002  Whole Year    \n",
              "3  Andaman and Nicobar Islands      NICOBARS       2003  Whole Year    \n",
              "4  Andaman and Nicobar Islands      NICOBARS       2004  Whole Year    \n",
              "\n",
              "       Crop    Area  rainfall  Average Humidity  Mean Temp        CC       CP  \\\n",
              "0  Arecanut  1254.0  0.012360                57         62  23076.74  1941.55   \n",
              "1  Arecanut  1254.0  0.084119                56         58  12610.85  1691.66   \n",
              "2  Arecanut  1258.0  0.080064                58         53  32683.46  3207.35   \n",
              "3  Arecanut  1261.0  0.181051                57         58  13209.32  2228.97   \n",
              "4  Arecanut  1264.7  0.035446                63         67  22560.30  1595.56   \n",
              "\n",
              "       Y  cost of production per yield  \n",
              "0   9.83                    19085.4365  \n",
              "1   6.83                    11554.0378  \n",
              "2   9.33                    29924.5755  \n",
              "3   5.90                    13150.9230  \n",
              "4  13.57                    21651.7492  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Rename the data\n",
        "df.rename(columns={'Cost of Cultivation (`/Hectare) C2':'CC'}, inplace=True)\n",
        "df.rename(columns={'Cost of Production (`/Quintal) C2':'CP'}, inplace=True)\n",
        "df.rename(columns={'Yield (Quintal/ Hectare) ':'Y'}, inplace=True)\n",
        "#show the dataframe\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wYKf_rta1iEj",
        "outputId": "c5ae7433-b8bf-4bb8-f691-a7e4b480e5df"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\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>Crop</th>\n",
              "      <th>Arecanut</th>\n",
              "      <th>Arhar/Tur</th>\n",
              "      <th>Bajra</th>\n",
              "      <th>Banana</th>\n",
              "      <th>Beans &amp; Mutter(Vegetable)</th>\n",
              "      <th>Bhindi</th>\n",
              "      <th>Bottle Gourd</th>\n",
              "      <th>Brinjal</th>\n",
              "      <th>Cabbage</th>\n",
              "      <th>Cashewnut</th>\n",
              "      <th>...</th>\n",
              "      <th>Tobacco</th>\n",
              "      <th>Tomato</th>\n",
              "      <th>Total foodgrain</th>\n",
              "      <th>Turmeric</th>\n",
              "      <th>Turnip</th>\n",
              "      <th>Urad</th>\n",
              "      <th>Varagu</th>\n",
              "      <th>Water Melon</th>\n",
              "      <th>Wheat</th>\n",
              "      <th>Yam</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>State_Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Andaman and Nicobar Islands</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bihar</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chhattisgarh</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tamil Nadu</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>146</td>\n",
              "      <td>55</td>\n",
              "      <td>56</td>\n",
              "      <td>359</td>\n",
              "      <td>2</td>\n",
              "      <td>510</td>\n",
              "      <td>43</td>\n",
              "      <td>32</td>\n",
              "      <td>25</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Telangana</th>\n",
              "      <td>0</td>\n",
              "      <td>218</td>\n",
              "      <td>184</td>\n",
              "      <td>94</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>15</td>\n",
              "      <td>46</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Uttar Pradesh</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6 rows × 74 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "Crop                         Arecanut  Arhar/Tur  Bajra  Banana  \\\n",
              "State_Name                                                        \n",
              "Andaman and Nicobar Islands         6          0      0       0   \n",
              "Bihar                               0          0      0       0   \n",
              "Chhattisgarh                        0          0      0      55   \n",
              "Tamil Nadu                          0          0      0       0   \n",
              "Telangana                           0        218    184      94   \n",
              "Uttar Pradesh                       0          0      0       0   \n",
              "\n",
              "Crop                         Beans & Mutter(Vegetable)  Bhindi  Bottle Gourd  \\\n",
              "State_Name                                                                     \n",
              "Andaman and Nicobar Islands                          0       0             0   \n",
              "Bihar                                                0       0             0   \n",
              "Chhattisgarh                                         0       0             0   \n",
              "Tamil Nadu                                           0       0             0   \n",
              "Telangana                                           18      18            18   \n",
              "Uttar Pradesh                                        0       0             0   \n",
              "\n",
              "Crop                         Brinjal  Cabbage  Cashewnut  ...  Tobacco  \\\n",
              "State_Name                                                ...            \n",
              "Andaman and Nicobar Islands        0        0          0  ...        0   \n",
              "Bihar                              0        0          0  ...        0   \n",
              "Chhattisgarh                       0        0          0  ...        0   \n",
              "Tamil Nadu                         0        0          0  ...      146   \n",
              "Telangana                         18       15         46  ...        0   \n",
              "Uttar Pradesh                      0        0          0  ...        0   \n",
              "\n",
              "Crop                         Tomato  Total foodgrain  Turmeric  Turnip  Urad  \\\n",
              "State_Name                                                                     \n",
              "Andaman and Nicobar Islands       0                0         0       0     0   \n",
              "Bihar                             0                0         0       0     0   \n",
              "Chhattisgarh                      0                0         0       0     0   \n",
              "Tamil Nadu                       55               56       359       2   510   \n",
              "Telangana                         0                0         0       0     0   \n",
              "Uttar Pradesh                     0                0         0       0     0   \n",
              "\n",
              "Crop                         Varagu  Water Melon  Wheat  Yam  \n",
              "State_Name                                                    \n",
              "Andaman and Nicobar Islands       0            0      0    0  \n",
              "Bihar                             0            0      0    0  \n",
              "Chhattisgarh                      0            0      0    0  \n",
              "Tamil Nadu                       43           32     25   36  \n",
              "Telangana                         0            0      0    0  \n",
              "Uttar Pradesh                     0            0      0    0  \n",
              "\n",
              "[6 rows x 74 columns]"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "p.crosstab(df.State_Name,df.Crop)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jgZuElr91iEk"
      },
      "outputs": [],
      "source": [
        "df.rename(columns={'Mean Temp':'T'}, inplace=True)\n",
        "df.rename(columns={'Average Humidity':'H'}, inplace=True)\n",
        "df.rename(columns={'rainfall':'R'}, inplace=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x1VzHaG51iEk"
      },
      "outputs": [],
      "source": [
        "#Rename the data\n",
        "df.rename(columns={'Cost of Cultivation (`/Hectare) C2':'CC'}, inplace=True)\n",
        "df.rename(columns={'Cost of Production (`/Quintal) C2':'CP'}, inplace=True)\n",
        "df.rename(columns={'Yield (Quintal/ Hectare) ':'Y'}, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PCX-TT5t1iEl"
      },
      "outputs": [],
      "source": [
        "df.rename(columns={'cost of production per yield':'CPPY'}, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qjMLCnU-1iEl",
        "outputId": "136bc49a-5d1e-4f90-b511-0062c646836e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([   9.83,    6.83,    9.33,    5.9 ,   13.57,   39.83,   36.61,\n",
              "         19.05, 1015.45,   67.41,   13.7 ,   10.93,   34.99,   56.  ,\n",
              "         31.1 ,   19.9 ,    1.32,  986.21,    7.47,   12.69,   39.04,\n",
              "          8.05,   12.94,   10.29,   19.94,    6.7 ,   42.95,   11.61,\n",
              "          8.72,   11.98,   23.59,   16.69,   37.19,   32.42,    6.42,\n",
              "         24.39,   13.45,    4.05,   11.97,  448.89,    4.71,   23.56,\n",
              "         42.68,   17.83,   13.54,  744.01,    3.01,  757.92,    9.59])"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Y'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EwHR14Sn1iEm",
        "outputId": "2dfe27b8-9187-4ab3-fe29-94a84309d433"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['State_Name', 'District_Name', 'Crop_Year', 'Season', 'Crop', 'Area',\n",
              "       'R', 'H', 'T', 'CC', 'CP', 'Y', 'CPPY'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5YP8fB761iEm"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "var_mod = ['State_Name', 'District_Name', 'Crop_Year', 'Season', 'Crop', 'Area',\n",
        "       'R', 'H', 'T', 'CC', 'CP', 'Y', 'CPPY']\n",
        "le = LabelEncoder()\n",
        "for i in var_mod:\n",
        "    df[i] = le.fit_transform(df[i]).astype(str)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bgbGeqv-1iEn",
        "outputId": "7d7499be-6727-4687-b69e-fe737335ac2c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State_Name</th>\n",
              "      <th>District_Name</th>\n",
              "      <th>Crop_Year</th>\n",
              "      <th>Season</th>\n",
              "      <th>Crop</th>\n",
              "      <th>Area</th>\n",
              "      <th>R</th>\n",
              "      <th>H</th>\n",
              "      <th>T</th>\n",
              "      <th>CC</th>\n",
              "      <th>CP</th>\n",
              "      <th>Y</th>\n",
              "      <th>CPPY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1015</td>\n",
              "      <td>33</td>\n",
              "      <td>45</td>\n",
              "      <td>10</td>\n",
              "      <td>21</td>\n",
              "      <td>30</td>\n",
              "      <td>13</td>\n",
              "      <td>126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1015</td>\n",
              "      <td>121</td>\n",
              "      <td>44</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>26</td>\n",
              "      <td>7</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1016</td>\n",
              "      <td>118</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>33</td>\n",
              "      <td>45</td>\n",
              "      <td>11</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1018</td>\n",
              "      <td>172</td>\n",
              "      <td>45</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>36</td>\n",
              "      <td>4</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1019</td>\n",
              "      <td>75</td>\n",
              "      <td>51</td>\n",
              "      <td>15</td>\n",
              "      <td>20</td>\n",
              "      <td>24</td>\n",
              "      <td>23</td>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  State_Name District_Name Crop_Year Season Crop  Area    R   H   T  CC  CP  \\\n",
              "0          0            42         3      0    0  1015   33  45  10  21  30   \n",
              "1          0            42         4      0    0  1015  121  44   6   3  26   \n",
              "2          0            42         5      3    0  1016  118  46   1  33  45   \n",
              "3          0            42         6      3    0  1018  172  45   6   4  36   \n",
              "4          0            42         7      3    0  1019   75  51  15  20  24   \n",
              "\n",
              "    Y CPPY  \n",
              "0  13  126  \n",
              "1   7   72  \n",
              "2  11  200  \n",
              "3   4   78  \n",
              "4  23  144  "
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mATARDAO1iEo"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TCg5SJR-1iEo"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.12"
    },
    "colab": {
      "name": "Module_01_CYCC.ipynb",
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}