Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (6200, 5)\n",
      "\n",
      "Features: Index(['temperature', 'humidity', 'ph', 'rainfall', 'label'], dtype='object')\n",
      "\n",
      "Feature matrix:\n",
      "    temperature   humidity        ph    rainfall\n",
      "0    25.547599  91.641948  5.702485  212.867626\n",
      "1    31.655312  60.132637  6.526692   66.690968\n",
      "2    23.461683  23.221976  5.645436   95.842534\n",
      "3    23.086593  83.555461  7.227746   71.840807\n",
      "4    23.750331  92.881605  5.570021  117.660283\n",
      "\n",
      "Response vector:\n",
      " 0         COCONUT\n",
      "1      BLACK GRAM\n",
      "2    KIDNEY BEANS\n",
      "3          COTTON\n",
      "4           APPLE\n",
      "Name: label, dtype: object\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "import pandas as pd \n",
    "\n",
    "# reading csv file \n",
    "data = pd.read_csv('cpdata.csv') \n",
    "\n",
    "# shape of dataset \n",
    "print(\"Shape:\", data.shape) \n",
    "\n",
    "# column names \n",
    "print(\"\\nFeatures:\", data.columns) \n",
    "\n",
    "# storing the feature matrix (X) and response vector (y) \n",
    "X = data[data.columns[:-1]] \n",
    "y = data[data.columns[-1]] \n",
    "\n",
    "# printing first 5 rows of feature matrix \n",
    "print(\"\\nFeature matrix:\\n\", X.head()) \n",
    "\n",
    "# printing first 5 values of response vector \n",
    "print(\"\\nResponse vector:\\n\", y.head())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3720, 4)\n",
      "(2480, 4)\n",
      "(3720,)\n",
      "(2480,)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=1) \n",
    "  \n",
    "# printing the shapes of the new X objects \n",
    "print(X_train.shape) \n",
    "print(X_test.shape) \n",
    "  \n",
    "# printing the shapes of the new y objects \n",
    "print(y_train.shape) \n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kNN model accuracy: 0.8625\n",
      "Predictions: ['COCONUT' 'PIGEON PEAS']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=1) \n",
    "  \n",
    "# training the model on training set \n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "knn = KNeighborsClassifier(n_neighbors=3) \n",
    "knn.fit(X_train, y_train) \n",
    "  \n",
    "# making predictions on the testing set \n",
    "y_pred = knn.predict(X_test) \n",
    "from sklearn import metrics \n",
    "print(\"kNN model accuracy:\", metrics.accuracy_score(y_test, y_pred)) \n",
    "\n",
    "# making prediction for out of sample data \n",
    "sample = [[25, 91, 5, 212],[26, 8, 6, 200]]\n",
    "preds = knn.predict(sample) \n",
    " \n",
    "print(\"Predictions:\", preds) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['crop_prediction.pkl']"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib \n",
    "joblib.dump(knn, 'crop_prediction.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load(open('crop_prediction.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['COCONUT', 'PIGEON PEAS'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: joblib\r\n",
      "Version: 0.14.1\r\n",
      "Summary: Lightweight pipelining: using Python functions as pipeline jobs.\r\n",
      "Home-page: https://joblib.readthedocs.io\r\n",
      "Author: Gael Varoquaux\r\n",
      "Author-email: gael.varoquaux@normalesup.org\r\n",
      "License: BSD\r\n",
      "Location: /home/spchandgude/.local/lib/python2.7/site-packages\r\n",
      "Requires: \r\n"
     ]
    }
   ],
   "source": [
    "!pip show joblib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.externals import joblib "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "tenure = pd.read_csv('tenure.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crops</th>\n",
       "      <th>min.time</th>\n",
       "      <th>max.time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>COCONUT</td>\n",
       "      <td>60.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BLACK GRAM</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KIDNEY BEANS</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COTTON</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>APPLE</td>\n",
       "      <td>72.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MOTH BEANS</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>JUTE</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MANGO</td>\n",
       "      <td>60.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MUNG BEAN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>WHEAT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>POMEGRANATE</td>\n",
       "      <td>36.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ADZUKI BEANS</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RUBBER</td>\n",
       "      <td>100.0</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GRAPES</td>\n",
       "      <td>30.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>TEA</td>\n",
       "      <td>36.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TOBACCO</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SUGARCANE</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PAPAYA</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PEAS</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GROUNDNUT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BANANA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LENTIL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>WATERMELON</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MILLET</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>36.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PIGEON PEAS</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CHICKPEA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>COFFEE</td>\n",
       "      <td>36.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>MAIZE</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>RICE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>MUSK MELON</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>CHICK PEAS</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Crops  min.time  max.time\n",
       "0        COCONUT      60.0      72.0\n",
       "1     BLACK GRAM       3.0       3.5\n",
       "2   KIDNEY BEANS       3.0       3.5\n",
       "3         COTTON       5.0       8.0\n",
       "4          APPLE      72.0     120.0\n",
       "5     MOTH BEANS       2.5       3.0\n",
       "6           JUTE       3.0       5.0\n",
       "7          MANGO      60.0     100.0\n",
       "8      MUNG BEAN       3.0       4.0\n",
       "9          WHEAT       2.0       5.0\n",
       "10   POMEGRANATE      36.0      48.0\n",
       "11  ADZUKI BEANS       3.0       4.0\n",
       "12        RUBBER     100.0     150.0\n",
       "13        GRAPES      30.0      36.0\n",
       "14           TEA      36.0      50.0\n",
       "15       TOBACCO       1.5       3.0\n",
       "16     SUGARCANE      12.0      18.0\n",
       "17        PAPAYA       6.0      11.0\n",
       "18          PEAS       2.0       2.5\n",
       "19     GROUNDNUT       4.0       6.0\n",
       "20        BANANA      10.0      15.0\n",
       "21        LENTIL       3.0       4.0\n",
       "22    WATERMELON       2.0       3.0\n",
       "23        MILLET       2.0       2.5\n",
       "24        ORANGE      36.0      48.0\n",
       "25   PIGEON PEAS       3.5       5.0\n",
       "26      CHICKPEA       3.0       4.0\n",
       "27        COFFEE      36.0      48.0\n",
       "28         MAIZE       2.0       3.4\n",
       "29          RICE       4.0       5.0\n",
       "30    MUSK MELON       2.5       3.5\n",
       "31  CHICK PEAS         3.0       6.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tenure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tenure.pkl']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(tenure, 'tenure.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "tenure_filtered=tenure[tenure['Crops'] == 'COCONUT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "min=tenure_filtered['min.time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    60.0\n",
       "Name: min.time, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "max=tenure_filtered['max.time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "d= str(max)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0    72.0\\nName: max.time, dtype: float64'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=d.split(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0    72.0', 'Name: max.time, dtype: float64']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=d[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0    72.0'"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=d.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'72.0'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=float(min[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-6fecebd0febc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "tenure_filtered=tenure[tenure['Crops'] == 'COCONUT']\n",
    "a=tenure_filtered['min.time']\n",
    "b=tenure_filtered['max.time']\n",
    "\n",
    "l = str(a)\n",
    "l=l.split(\"\\n\")\n",
    "l=l[0]\n",
    "l=l.split()\n",
    "output2=l[1]\n",
    "\n",
    "m = str(b)\n",
    "m=m.split(\"\\n\")\n",
    "m=m[0]\n",
    "m=m.split()\n",
    "output3=m[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'72.0'"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "cost = pd.read_csv('cost_of_production.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crop</th>\n",
       "      <th>Field Preparation</th>\n",
       "      <th>Nursery and planting sowing</th>\n",
       "      <th>Weeding</th>\n",
       "      <th>Protection</th>\n",
       "      <th>Fertilizers</th>\n",
       "      <th>Wages</th>\n",
       "      <th>Staking, transport and other expenses</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wheat</td>\n",
       "      <td>5500</td>\n",
       "      <td>4500</td>\n",
       "      <td>3300</td>\n",
       "      <td>3000</td>\n",
       "      <td>6000</td>\n",
       "      <td>700</td>\n",
       "      <td>1000</td>\n",
       "      <td>24000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mungbean</td>\n",
       "      <td>4000</td>\n",
       "      <td>3300</td>\n",
       "      <td>3400</td>\n",
       "      <td>1900</td>\n",
       "      <td>7000</td>\n",
       "      <td>1100</td>\n",
       "      <td>2300</td>\n",
       "      <td>23000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tea</td>\n",
       "      <td>20000</td>\n",
       "      <td>90000</td>\n",
       "      <td>15000</td>\n",
       "      <td>20000</td>\n",
       "      <td>30000</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Millet</td>\n",
       "      <td>4000</td>\n",
       "      <td>10000</td>\n",
       "      <td>3500</td>\n",
       "      <td>3500</td>\n",
       "      <td>8000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>31000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Maize</td>\n",
       "      <td>3500</td>\n",
       "      <td>4400</td>\n",
       "      <td>3100</td>\n",
       "      <td>1000</td>\n",
       "      <td>4500</td>\n",
       "      <td>500</td>\n",
       "      <td>1000</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lentil</td>\n",
       "      <td>8000</td>\n",
       "      <td>12000</td>\n",
       "      <td>8000</td>\n",
       "      <td>4000</td>\n",
       "      <td>7000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>42000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jute</td>\n",
       "      <td>35000</td>\n",
       "      <td>65000</td>\n",
       "      <td>55000</td>\n",
       "      <td>8000</td>\n",
       "      <td>75000</td>\n",
       "      <td>2000</td>\n",
       "      <td>10000</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cofee</td>\n",
       "      <td>40000</td>\n",
       "      <td>95000</td>\n",
       "      <td>55000</td>\n",
       "      <td>2000</td>\n",
       "      <td>80000</td>\n",
       "      <td>2000</td>\n",
       "      <td>18000</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cotton</td>\n",
       "      <td>17500</td>\n",
       "      <td>12000</td>\n",
       "      <td>5500</td>\n",
       "      <td>2000</td>\n",
       "      <td>18500</td>\n",
       "      <td>1500</td>\n",
       "      <td>2000</td>\n",
       "      <td>59000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Groundnut</td>\n",
       "      <td>17000</td>\n",
       "      <td>12500</td>\n",
       "      <td>4500</td>\n",
       "      <td>3000</td>\n",
       "      <td>15000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>52000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Peas</td>\n",
       "      <td>8000</td>\n",
       "      <td>9000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2500</td>\n",
       "      <td>7000</td>\n",
       "      <td>500</td>\n",
       "      <td>1000</td>\n",
       "      <td>31000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rubber</td>\n",
       "      <td>12000</td>\n",
       "      <td>23000</td>\n",
       "      <td>9000</td>\n",
       "      <td>8000</td>\n",
       "      <td>12000</td>\n",
       "      <td>1000</td>\n",
       "      <td>3000</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sugarcane</td>\n",
       "      <td>23000</td>\n",
       "      <td>30000</td>\n",
       "      <td>7000</td>\n",
       "      <td>4500</td>\n",
       "      <td>14500</td>\n",
       "      <td>1500</td>\n",
       "      <td>10000</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Tobacco</td>\n",
       "      <td>5000</td>\n",
       "      <td>8000</td>\n",
       "      <td>4000</td>\n",
       "      <td>3000</td>\n",
       "      <td>4000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>26000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kidney beans</td>\n",
       "      <td>7000</td>\n",
       "      <td>7000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4000</td>\n",
       "      <td>7000</td>\n",
       "      <td>4000</td>\n",
       "      <td>2000</td>\n",
       "      <td>36000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Moth beans</td>\n",
       "      <td>6500</td>\n",
       "      <td>7500</td>\n",
       "      <td>4500</td>\n",
       "      <td>4500</td>\n",
       "      <td>8000</td>\n",
       "      <td>3500</td>\n",
       "      <td>2500</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Pigeon peas</td>\n",
       "      <td>5000</td>\n",
       "      <td>9000</td>\n",
       "      <td>6500</td>\n",
       "      <td>5000</td>\n",
       "      <td>13000</td>\n",
       "      <td>500</td>\n",
       "      <td>1000</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Chick peas</td>\n",
       "      <td>4000</td>\n",
       "      <td>11000</td>\n",
       "      <td>4000</td>\n",
       "      <td>2000</td>\n",
       "      <td>7000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1500</td>\n",
       "      <td>31500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Banana</td>\n",
       "      <td>25000</td>\n",
       "      <td>60000</td>\n",
       "      <td>15000</td>\n",
       "      <td>3000</td>\n",
       "      <td>40000</td>\n",
       "      <td>2000</td>\n",
       "      <td>6000</td>\n",
       "      <td>148000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Grapes</td>\n",
       "      <td>40000</td>\n",
       "      <td>110000</td>\n",
       "      <td>40000</td>\n",
       "      <td>30000</td>\n",
       "      <td>30000</td>\n",
       "      <td>80000</td>\n",
       "      <td>20000</td>\n",
       "      <td>350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Apple</td>\n",
       "      <td>3000</td>\n",
       "      <td>15000</td>\n",
       "      <td>3500</td>\n",
       "      <td>8000</td>\n",
       "      <td>12000</td>\n",
       "      <td>2000</td>\n",
       "      <td>3500</td>\n",
       "      <td>47000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mango</td>\n",
       "      <td>5000</td>\n",
       "      <td>17000</td>\n",
       "      <td>5500</td>\n",
       "      <td>10000</td>\n",
       "      <td>14000</td>\n",
       "      <td>2000</td>\n",
       "      <td>5500</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Muskmelon</td>\n",
       "      <td>7000</td>\n",
       "      <td>7000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4500</td>\n",
       "      <td>5500</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Orange</td>\n",
       "      <td>1000</td>\n",
       "      <td>4000</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>3500</td>\n",
       "      <td>500</td>\n",
       "      <td>2250</td>\n",
       "      <td>13750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Papaya</td>\n",
       "      <td>3000</td>\n",
       "      <td>4000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>1000</td>\n",
       "      <td>3000</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Watermelon</td>\n",
       "      <td>5000</td>\n",
       "      <td>10000</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>12000</td>\n",
       "      <td>2000</td>\n",
       "      <td>6000</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Pomegranate</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4000</td>\n",
       "      <td>7000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>27000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ADZUKI BEANS</td>\n",
       "      <td>4000</td>\n",
       "      <td>2000</td>\n",
       "      <td>4000</td>\n",
       "      <td>3000</td>\n",
       "      <td>5000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>21000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>BLACK GRAM</td>\n",
       "      <td>4000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>17000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>RICE</td>\n",
       "      <td>10000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>27000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Coconut</td>\n",
       "      <td>12000</td>\n",
       "      <td>1000</td>\n",
       "      <td>4000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>28000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Crop  Field Preparation  Nursery and planting sowing  Weeding  \\\n",
       "0          Wheat               5500                         4500     3300   \n",
       "1       Mungbean               4000                         3300     3400   \n",
       "2            Tea              20000                        90000    15000   \n",
       "3         Millet               4000                        10000     3500   \n",
       "4          Maize               3500                         4400     3100   \n",
       "5         Lentil               8000                        12000     8000   \n",
       "6           Jute              35000                        65000    55000   \n",
       "7          Cofee              40000                        95000    55000   \n",
       "8         Cotton              17500                        12000     5500   \n",
       "9      Groundnut              17000                        12500     4500   \n",
       "10          Peas               8000                         9000     3000   \n",
       "11        Rubber              12000                        23000     9000   \n",
       "12     Sugarcane              23000                        30000     7000   \n",
       "13       Tobacco               5000                         8000     4000   \n",
       "14  Kidney beans               7000                         7000     5000   \n",
       "15    Moth beans               6500                         7500     4500   \n",
       "16   Pigeon peas               5000                         9000     6500   \n",
       "17    Chick peas               4000                        11000     4000   \n",
       "18        Banana              25000                        60000    15000   \n",
       "19        Grapes              40000                       110000    40000   \n",
       "20         Apple               3000                        15000     3500   \n",
       "21         Mango               5000                        17000     5500   \n",
       "22     Muskmelon               7000                         7000     5000   \n",
       "23        Orange               1000                         4000     1500   \n",
       "24        Papaya               3000                         4000     2000   \n",
       "25    Watermelon               5000                        10000     5000   \n",
       "26   Pomegranate               4000                         4000     5000   \n",
       "27  ADZUKI BEANS               4000                         2000     4000   \n",
       "28    BLACK GRAM               4000                         1000     5000   \n",
       "29          RICE              10000                         1000     5000   \n",
       "30       Coconut              12000                         1000     4000   \n",
       "\n",
       "    Protection  Fertilizers  Wages  Staking, transport and other expenses  \\\n",
       "0         3000         6000    700                                   1000   \n",
       "1         1900         7000   1100                                   2300   \n",
       "2        20000        30000  10000                                  15000   \n",
       "3         3500         8000   1000                                   1000   \n",
       "4         1000         4500    500                                   1000   \n",
       "5         4000         7000   2000                                   1000   \n",
       "6         8000        75000   2000                                  10000   \n",
       "7         2000        80000   2000                                  18000   \n",
       "8         2000        18500   1500                                   2000   \n",
       "9         3000        15000   1000                                   1000   \n",
       "10        2500         7000    500                                   1000   \n",
       "11        8000        12000   1000                                   3000   \n",
       "12        4500        14500   1500                                  10000   \n",
       "13        3000         4000   1000                                   1000   \n",
       "14        4000         7000   4000                                   2000   \n",
       "15        4500         8000   3500                                   2500   \n",
       "16        5000        13000    500                                   1000   \n",
       "17        2000         7000   2000                                   1500   \n",
       "18        3000        40000   2000                                   6000   \n",
       "19       30000        30000  80000                                  20000   \n",
       "20        8000        12000   2000                                   3500   \n",
       "21       10000        14000   2000                                   5500   \n",
       "22        4500         5500   1000                                   5000   \n",
       "23        1000         3500    500                                   2250   \n",
       "24        1000         5000   1000                                   3000   \n",
       "25        5000        12000   2000                                   6000   \n",
       "26        4000         7000   2000                                   1000   \n",
       "27        3000         5000   2000                                   1000   \n",
       "28        2000         2000   2000                                   1000   \n",
       "29        3000         2000   3000                                   3000   \n",
       "30        3000         2000   3000                                   3000   \n",
       "\n",
       "     Total  \n",
       "0    24000  \n",
       "1    23000  \n",
       "2   200000  \n",
       "3    31000  \n",
       "4    18000  \n",
       "5    42000  \n",
       "6   250000  \n",
       "7   300000  \n",
       "8    59000  \n",
       "9    52000  \n",
       "10   31000  \n",
       "11   70000  \n",
       "12   90000  \n",
       "13   26000  \n",
       "14   36000  \n",
       "15   35000  \n",
       "16   40000  \n",
       "17   31500  \n",
       "18  148000  \n",
       "19  350000  \n",
       "20   47000  \n",
       "21   55000  \n",
       "22   35000  \n",
       "23   13750  \n",
       "24   18000  \n",
       "25   45000  \n",
       "26   27000  \n",
       "27   21000  \n",
       "28   17000  \n",
       "29   27000  \n",
       "30   28000  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "cost['Crop'] = cost['Crop'].str.upper() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crop</th>\n",
       "      <th>Field Preparation</th>\n",
       "      <th>Nursery and planting sowing</th>\n",
       "      <th>Weeding</th>\n",
       "      <th>Protection</th>\n",
       "      <th>Fertilizers</th>\n",
       "      <th>Wages</th>\n",
       "      <th>Staking, transport and other expenses</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WHEAT</td>\n",
       "      <td>5500</td>\n",
       "      <td>4500</td>\n",
       "      <td>3300</td>\n",
       "      <td>3000</td>\n",
       "      <td>6000</td>\n",
       "      <td>700</td>\n",
       "      <td>1000</td>\n",
       "      <td>24000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MUNGBEAN</td>\n",
       "      <td>4000</td>\n",
       "      <td>3300</td>\n",
       "      <td>3400</td>\n",
       "      <td>1900</td>\n",
       "      <td>7000</td>\n",
       "      <td>1100</td>\n",
       "      <td>2300</td>\n",
       "      <td>23000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TEA</td>\n",
       "      <td>20000</td>\n",
       "      <td>90000</td>\n",
       "      <td>15000</td>\n",
       "      <td>20000</td>\n",
       "      <td>30000</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MILLET</td>\n",
       "      <td>4000</td>\n",
       "      <td>10000</td>\n",
       "      <td>3500</td>\n",
       "      <td>3500</td>\n",
       "      <td>8000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>31000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MAIZE</td>\n",
       "      <td>3500</td>\n",
       "      <td>4400</td>\n",
       "      <td>3100</td>\n",
       "      <td>1000</td>\n",
       "      <td>4500</td>\n",
       "      <td>500</td>\n",
       "      <td>1000</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LENTIL</td>\n",
       "      <td>8000</td>\n",
       "      <td>12000</td>\n",
       "      <td>8000</td>\n",
       "      <td>4000</td>\n",
       "      <td>7000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>42000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>JUTE</td>\n",
       "      <td>35000</td>\n",
       "      <td>65000</td>\n",
       "      <td>55000</td>\n",
       "      <td>8000</td>\n",
       "      <td>75000</td>\n",
       "      <td>2000</td>\n",
       "      <td>10000</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>COFEE</td>\n",
       "      <td>40000</td>\n",
       "      <td>95000</td>\n",
       "      <td>55000</td>\n",
       "      <td>2000</td>\n",
       "      <td>80000</td>\n",
       "      <td>2000</td>\n",
       "      <td>18000</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>COTTON</td>\n",
       "      <td>17500</td>\n",
       "      <td>12000</td>\n",
       "      <td>5500</td>\n",
       "      <td>2000</td>\n",
       "      <td>18500</td>\n",
       "      <td>1500</td>\n",
       "      <td>2000</td>\n",
       "      <td>59000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GROUNDNUT</td>\n",
       "      <td>17000</td>\n",
       "      <td>12500</td>\n",
       "      <td>4500</td>\n",
       "      <td>3000</td>\n",
       "      <td>15000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>52000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PEAS</td>\n",
       "      <td>8000</td>\n",
       "      <td>9000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2500</td>\n",
       "      <td>7000</td>\n",
       "      <td>500</td>\n",
       "      <td>1000</td>\n",
       "      <td>31000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>RUBBER</td>\n",
       "      <td>12000</td>\n",
       "      <td>23000</td>\n",
       "      <td>9000</td>\n",
       "      <td>8000</td>\n",
       "      <td>12000</td>\n",
       "      <td>1000</td>\n",
       "      <td>3000</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SUGARCANE</td>\n",
       "      <td>23000</td>\n",
       "      <td>30000</td>\n",
       "      <td>7000</td>\n",
       "      <td>4500</td>\n",
       "      <td>14500</td>\n",
       "      <td>1500</td>\n",
       "      <td>10000</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>TOBACCO</td>\n",
       "      <td>5000</td>\n",
       "      <td>8000</td>\n",
       "      <td>4000</td>\n",
       "      <td>3000</td>\n",
       "      <td>4000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>26000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>KIDNEY BEANS</td>\n",
       "      <td>7000</td>\n",
       "      <td>7000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4000</td>\n",
       "      <td>7000</td>\n",
       "      <td>4000</td>\n",
       "      <td>2000</td>\n",
       "      <td>36000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MOTH BEANS</td>\n",
       "      <td>6500</td>\n",
       "      <td>7500</td>\n",
       "      <td>4500</td>\n",
       "      <td>4500</td>\n",
       "      <td>8000</td>\n",
       "      <td>3500</td>\n",
       "      <td>2500</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PIGEON PEAS</td>\n",
       "      <td>5000</td>\n",
       "      <td>9000</td>\n",
       "      <td>6500</td>\n",
       "      <td>5000</td>\n",
       "      <td>13000</td>\n",
       "      <td>500</td>\n",
       "      <td>1000</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CHICK PEAS</td>\n",
       "      <td>4000</td>\n",
       "      <td>11000</td>\n",
       "      <td>4000</td>\n",
       "      <td>2000</td>\n",
       "      <td>7000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1500</td>\n",
       "      <td>31500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BANANA</td>\n",
       "      <td>25000</td>\n",
       "      <td>60000</td>\n",
       "      <td>15000</td>\n",
       "      <td>3000</td>\n",
       "      <td>40000</td>\n",
       "      <td>2000</td>\n",
       "      <td>6000</td>\n",
       "      <td>148000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GRAPES</td>\n",
       "      <td>40000</td>\n",
       "      <td>110000</td>\n",
       "      <td>40000</td>\n",
       "      <td>30000</td>\n",
       "      <td>30000</td>\n",
       "      <td>80000</td>\n",
       "      <td>20000</td>\n",
       "      <td>350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>APPLE</td>\n",
       "      <td>3000</td>\n",
       "      <td>15000</td>\n",
       "      <td>3500</td>\n",
       "      <td>8000</td>\n",
       "      <td>12000</td>\n",
       "      <td>2000</td>\n",
       "      <td>3500</td>\n",
       "      <td>47000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MANGO</td>\n",
       "      <td>5000</td>\n",
       "      <td>17000</td>\n",
       "      <td>5500</td>\n",
       "      <td>10000</td>\n",
       "      <td>14000</td>\n",
       "      <td>2000</td>\n",
       "      <td>5500</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MUSKMELON</td>\n",
       "      <td>7000</td>\n",
       "      <td>7000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4500</td>\n",
       "      <td>5500</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>1000</td>\n",
       "      <td>4000</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>3500</td>\n",
       "      <td>500</td>\n",
       "      <td>2250</td>\n",
       "      <td>13750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PAPAYA</td>\n",
       "      <td>3000</td>\n",
       "      <td>4000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>1000</td>\n",
       "      <td>3000</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>WATERMELON</td>\n",
       "      <td>5000</td>\n",
       "      <td>10000</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>12000</td>\n",
       "      <td>2000</td>\n",
       "      <td>6000</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>POMEGRANATE</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4000</td>\n",
       "      <td>7000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>27000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ADZUKI BEANS</td>\n",
       "      <td>4000</td>\n",
       "      <td>2000</td>\n",
       "      <td>4000</td>\n",
       "      <td>3000</td>\n",
       "      <td>5000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>21000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>BLACK GRAM</td>\n",
       "      <td>4000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>17000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>RICE</td>\n",
       "      <td>10000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>27000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>COCONUT</td>\n",
       "      <td>12000</td>\n",
       "      <td>1000</td>\n",
       "      <td>4000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>28000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Crop  Field Preparation  Nursery and planting sowing  Weeding  \\\n",
       "0          WHEAT               5500                         4500     3300   \n",
       "1       MUNGBEAN               4000                         3300     3400   \n",
       "2            TEA              20000                        90000    15000   \n",
       "3         MILLET               4000                        10000     3500   \n",
       "4          MAIZE               3500                         4400     3100   \n",
       "5         LENTIL               8000                        12000     8000   \n",
       "6           JUTE              35000                        65000    55000   \n",
       "7          COFEE              40000                        95000    55000   \n",
       "8         COTTON              17500                        12000     5500   \n",
       "9      GROUNDNUT              17000                        12500     4500   \n",
       "10          PEAS               8000                         9000     3000   \n",
       "11        RUBBER              12000                        23000     9000   \n",
       "12     SUGARCANE              23000                        30000     7000   \n",
       "13       TOBACCO               5000                         8000     4000   \n",
       "14  KIDNEY BEANS               7000                         7000     5000   \n",
       "15    MOTH BEANS               6500                         7500     4500   \n",
       "16   PIGEON PEAS               5000                         9000     6500   \n",
       "17    CHICK PEAS               4000                        11000     4000   \n",
       "18        BANANA              25000                        60000    15000   \n",
       "19        GRAPES              40000                       110000    40000   \n",
       "20         APPLE               3000                        15000     3500   \n",
       "21         MANGO               5000                        17000     5500   \n",
       "22     MUSKMELON               7000                         7000     5000   \n",
       "23        ORANGE               1000                         4000     1500   \n",
       "24        PAPAYA               3000                         4000     2000   \n",
       "25    WATERMELON               5000                        10000     5000   \n",
       "26   POMEGRANATE               4000                         4000     5000   \n",
       "27  ADZUKI BEANS               4000                         2000     4000   \n",
       "28    BLACK GRAM               4000                         1000     5000   \n",
       "29          RICE              10000                         1000     5000   \n",
       "30       COCONUT              12000                         1000     4000   \n",
       "\n",
       "    Protection  Fertilizers  Wages  Staking, transport and other expenses  \\\n",
       "0         3000         6000    700                                   1000   \n",
       "1         1900         7000   1100                                   2300   \n",
       "2        20000        30000  10000                                  15000   \n",
       "3         3500         8000   1000                                   1000   \n",
       "4         1000         4500    500                                   1000   \n",
       "5         4000         7000   2000                                   1000   \n",
       "6         8000        75000   2000                                  10000   \n",
       "7         2000        80000   2000                                  18000   \n",
       "8         2000        18500   1500                                   2000   \n",
       "9         3000        15000   1000                                   1000   \n",
       "10        2500         7000    500                                   1000   \n",
       "11        8000        12000   1000                                   3000   \n",
       "12        4500        14500   1500                                  10000   \n",
       "13        3000         4000   1000                                   1000   \n",
       "14        4000         7000   4000                                   2000   \n",
       "15        4500         8000   3500                                   2500   \n",
       "16        5000        13000    500                                   1000   \n",
       "17        2000         7000   2000                                   1500   \n",
       "18        3000        40000   2000                                   6000   \n",
       "19       30000        30000  80000                                  20000   \n",
       "20        8000        12000   2000                                   3500   \n",
       "21       10000        14000   2000                                   5500   \n",
       "22        4500         5500   1000                                   5000   \n",
       "23        1000         3500    500                                   2250   \n",
       "24        1000         5000   1000                                   3000   \n",
       "25        5000        12000   2000                                   6000   \n",
       "26        4000         7000   2000                                   1000   \n",
       "27        3000         5000   2000                                   1000   \n",
       "28        2000         2000   2000                                   1000   \n",
       "29        3000         2000   3000                                   3000   \n",
       "30        3000         2000   3000                                   3000   \n",
       "\n",
       "     Total  \n",
       "0    24000  \n",
       "1    23000  \n",
       "2   200000  \n",
       "3    31000  \n",
       "4    18000  \n",
       "5    42000  \n",
       "6   250000  \n",
       "7   300000  \n",
       "8    59000  \n",
       "9    52000  \n",
       "10   31000  \n",
       "11   70000  \n",
       "12   90000  \n",
       "13   26000  \n",
       "14   36000  \n",
       "15   35000  \n",
       "16   40000  \n",
       "17   31500  \n",
       "18  148000  \n",
       "19  350000  \n",
       "20   47000  \n",
       "21   55000  \n",
       "22   35000  \n",
       "23   13750  \n",
       "24   18000  \n",
       "25   45000  \n",
       "26   27000  \n",
       "27   21000  \n",
       "28   17000  \n",
       "29   27000  \n",
       "30   28000  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crops</th>\n",
       "      <th>min.time</th>\n",
       "      <th>max.time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ADZUKI BEANS</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>APPLE</td>\n",
       "      <td>72.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BANANA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BLACK GRAM</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>CHICK PEAS</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CHICKPEA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>COCONUT</td>\n",
       "      <td>60.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>COFFEE</td>\n",
       "      <td>36.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COTTON</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GRAPES</td>\n",
       "      <td>30.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GROUNDNUT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>JUTE</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KIDNEY BEANS</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LENTIL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>MAIZE</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MANGO</td>\n",
       "      <td>60.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MILLET</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MOTH BEANS</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MUNG BEAN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>MUSK MELON</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>36.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PAPAYA</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PEAS</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PIGEON PEAS</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>POMEGRANATE</td>\n",
       "      <td>36.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>RICE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RUBBER</td>\n",
       "      <td>100.0</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SUGARCANE</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>TEA</td>\n",
       "      <td>36.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TOBACCO</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>WATERMELON</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>WHEAT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Crops  min.time  max.time\n",
       "11  ADZUKI BEANS       3.0       4.0\n",
       "4          APPLE      72.0     120.0\n",
       "20        BANANA      10.0      15.0\n",
       "1     BLACK GRAM       3.0       3.5\n",
       "31  CHICK PEAS         3.0       6.0\n",
       "26      CHICKPEA       3.0       4.0\n",
       "0        COCONUT      60.0      72.0\n",
       "27        COFFEE      36.0      48.0\n",
       "3         COTTON       5.0       8.0\n",
       "13        GRAPES      30.0      36.0\n",
       "19     GROUNDNUT       4.0       6.0\n",
       "6           JUTE       3.0       5.0\n",
       "2   KIDNEY BEANS       3.0       3.5\n",
       "21        LENTIL       3.0       4.0\n",
       "28         MAIZE       2.0       3.4\n",
       "7          MANGO      60.0     100.0\n",
       "23        MILLET       2.0       2.5\n",
       "5     MOTH BEANS       2.5       3.0\n",
       "8      MUNG BEAN       3.0       4.0\n",
       "30    MUSK MELON       2.5       3.5\n",
       "24        ORANGE      36.0      48.0\n",
       "17        PAPAYA       6.0      11.0\n",
       "18          PEAS       2.0       2.5\n",
       "25   PIGEON PEAS       3.5       5.0\n",
       "10   POMEGRANATE      36.0      48.0\n",
       "29          RICE       4.0       5.0\n",
       "12        RUBBER     100.0     150.0\n",
       "16     SUGARCANE      12.0      18.0\n",
       "14           TEA      36.0      50.0\n",
       "15       TOBACCO       1.5       3.0\n",
       "22    WATERMELON       2.0       3.0\n",
       "9          WHEAT       2.0       5.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tenure.sort_values('Crops')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crop</th>\n",
       "      <th>Field Preparation</th>\n",
       "      <th>Nursery and planting sowing</th>\n",
       "      <th>Weeding</th>\n",
       "      <th>Protection</th>\n",
       "      <th>Fertilizers</th>\n",
       "      <th>Wages</th>\n",
       "      <th>Staking, transport and other expenses</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ADZUKI BEANS</td>\n",
       "      <td>4000</td>\n",
       "      <td>2000</td>\n",
       "      <td>4000</td>\n",
       "      <td>3000</td>\n",
       "      <td>5000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>21000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>APPLE</td>\n",
       "      <td>3000</td>\n",
       "      <td>15000</td>\n",
       "      <td>3500</td>\n",
       "      <td>8000</td>\n",
       "      <td>12000</td>\n",
       "      <td>2000</td>\n",
       "      <td>3500</td>\n",
       "      <td>47000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BANANA</td>\n",
       "      <td>25000</td>\n",
       "      <td>60000</td>\n",
       "      <td>15000</td>\n",
       "      <td>3000</td>\n",
       "      <td>40000</td>\n",
       "      <td>2000</td>\n",
       "      <td>6000</td>\n",
       "      <td>148000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>BLACK GRAM</td>\n",
       "      <td>4000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>17000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CHICK PEAS</td>\n",
       "      <td>4000</td>\n",
       "      <td>11000</td>\n",
       "      <td>4000</td>\n",
       "      <td>2000</td>\n",
       "      <td>7000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1500</td>\n",
       "      <td>31500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>COCONUT</td>\n",
       "      <td>12000</td>\n",
       "      <td>1000</td>\n",
       "      <td>4000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>COFEE</td>\n",
       "      <td>40000</td>\n",
       "      <td>95000</td>\n",
       "      <td>55000</td>\n",
       "      <td>2000</td>\n",
       "      <td>80000</td>\n",
       "      <td>2000</td>\n",
       "      <td>18000</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>COTTON</td>\n",
       "      <td>17500</td>\n",
       "      <td>12000</td>\n",
       "      <td>5500</td>\n",
       "      <td>2000</td>\n",
       "      <td>18500</td>\n",
       "      <td>1500</td>\n",
       "      <td>2000</td>\n",
       "      <td>59000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GRAPES</td>\n",
       "      <td>40000</td>\n",
       "      <td>110000</td>\n",
       "      <td>40000</td>\n",
       "      <td>30000</td>\n",
       "      <td>30000</td>\n",
       "      <td>80000</td>\n",
       "      <td>20000</td>\n",
       "      <td>350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GROUNDNUT</td>\n",
       "      <td>17000</td>\n",
       "      <td>12500</td>\n",
       "      <td>4500</td>\n",
       "      <td>3000</td>\n",
       "      <td>15000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>52000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>JUTE</td>\n",
       "      <td>35000</td>\n",
       "      <td>65000</td>\n",
       "      <td>55000</td>\n",
       "      <td>8000</td>\n",
       "      <td>75000</td>\n",
       "      <td>2000</td>\n",
       "      <td>10000</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>KIDNEY BEANS</td>\n",
       "      <td>7000</td>\n",
       "      <td>7000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4000</td>\n",
       "      <td>7000</td>\n",
       "      <td>4000</td>\n",
       "      <td>2000</td>\n",
       "      <td>36000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LENTIL</td>\n",
       "      <td>8000</td>\n",
       "      <td>12000</td>\n",
       "      <td>8000</td>\n",
       "      <td>4000</td>\n",
       "      <td>7000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>42000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MAIZE</td>\n",
       "      <td>3500</td>\n",
       "      <td>4400</td>\n",
       "      <td>3100</td>\n",
       "      <td>1000</td>\n",
       "      <td>4500</td>\n",
       "      <td>500</td>\n",
       "      <td>1000</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MANGO</td>\n",
       "      <td>5000</td>\n",
       "      <td>17000</td>\n",
       "      <td>5500</td>\n",
       "      <td>10000</td>\n",
       "      <td>14000</td>\n",
       "      <td>2000</td>\n",
       "      <td>5500</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MILLET</td>\n",
       "      <td>4000</td>\n",
       "      <td>10000</td>\n",
       "      <td>3500</td>\n",
       "      <td>3500</td>\n",
       "      <td>8000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>31000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MOTH BEANS</td>\n",
       "      <td>6500</td>\n",
       "      <td>7500</td>\n",
       "      <td>4500</td>\n",
       "      <td>4500</td>\n",
       "      <td>8000</td>\n",
       "      <td>3500</td>\n",
       "      <td>2500</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MUNGBEAN</td>\n",
       "      <td>4000</td>\n",
       "      <td>3300</td>\n",
       "      <td>3400</td>\n",
       "      <td>1900</td>\n",
       "      <td>7000</td>\n",
       "      <td>1100</td>\n",
       "      <td>2300</td>\n",
       "      <td>23000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MUSKMELON</td>\n",
       "      <td>7000</td>\n",
       "      <td>7000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4500</td>\n",
       "      <td>5500</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>1000</td>\n",
       "      <td>4000</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>3500</td>\n",
       "      <td>500</td>\n",
       "      <td>2250</td>\n",
       "      <td>13750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PAPAYA</td>\n",
       "      <td>3000</td>\n",
       "      <td>4000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>1000</td>\n",
       "      <td>3000</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PEAS</td>\n",
       "      <td>8000</td>\n",
       "      <td>9000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2500</td>\n",
       "      <td>7000</td>\n",
       "      <td>500</td>\n",
       "      <td>1000</td>\n",
       "      <td>31000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PIGEON PEAS</td>\n",
       "      <td>5000</td>\n",
       "      <td>9000</td>\n",
       "      <td>6500</td>\n",
       "      <td>5000</td>\n",
       "      <td>13000</td>\n",
       "      <td>500</td>\n",
       "      <td>1000</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>POMEGRANATE</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4000</td>\n",
       "      <td>7000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>27000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>RICE</td>\n",
       "      <td>10000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>27000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>RUBBER</td>\n",
       "      <td>12000</td>\n",
       "      <td>23000</td>\n",
       "      <td>9000</td>\n",
       "      <td>8000</td>\n",
       "      <td>12000</td>\n",
       "      <td>1000</td>\n",
       "      <td>3000</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SUGARCANE</td>\n",
       "      <td>23000</td>\n",
       "      <td>30000</td>\n",
       "      <td>7000</td>\n",
       "      <td>4500</td>\n",
       "      <td>14500</td>\n",
       "      <td>1500</td>\n",
       "      <td>10000</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TEA</td>\n",
       "      <td>20000</td>\n",
       "      <td>90000</td>\n",
       "      <td>15000</td>\n",
       "      <td>20000</td>\n",
       "      <td>30000</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>TOBACCO</td>\n",
       "      <td>5000</td>\n",
       "      <td>8000</td>\n",
       "      <td>4000</td>\n",
       "      <td>3000</td>\n",
       "      <td>4000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>26000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>WATERMELON</td>\n",
       "      <td>5000</td>\n",
       "      <td>10000</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>12000</td>\n",
       "      <td>2000</td>\n",
       "      <td>6000</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WHEAT</td>\n",
       "      <td>5500</td>\n",
       "      <td>4500</td>\n",
       "      <td>3300</td>\n",
       "      <td>3000</td>\n",
       "      <td>6000</td>\n",
       "      <td>700</td>\n",
       "      <td>1000</td>\n",
       "      <td>24000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Crop  Field Preparation  Nursery and planting sowing  Weeding  \\\n",
       "27  ADZUKI BEANS               4000                         2000     4000   \n",
       "20         APPLE               3000                        15000     3500   \n",
       "18        BANANA              25000                        60000    15000   \n",
       "28    BLACK GRAM               4000                         1000     5000   \n",
       "17    CHICK PEAS               4000                        11000     4000   \n",
       "30       COCONUT              12000                         1000     4000   \n",
       "7          COFEE              40000                        95000    55000   \n",
       "8         COTTON              17500                        12000     5500   \n",
       "19        GRAPES              40000                       110000    40000   \n",
       "9      GROUNDNUT              17000                        12500     4500   \n",
       "6           JUTE              35000                        65000    55000   \n",
       "14  KIDNEY BEANS               7000                         7000     5000   \n",
       "5         LENTIL               8000                        12000     8000   \n",
       "4          MAIZE               3500                         4400     3100   \n",
       "21         MANGO               5000                        17000     5500   \n",
       "3         MILLET               4000                        10000     3500   \n",
       "15    MOTH BEANS               6500                         7500     4500   \n",
       "1       MUNGBEAN               4000                         3300     3400   \n",
       "22     MUSKMELON               7000                         7000     5000   \n",
       "23        ORANGE               1000                         4000     1500   \n",
       "24        PAPAYA               3000                         4000     2000   \n",
       "10          PEAS               8000                         9000     3000   \n",
       "16   PIGEON PEAS               5000                         9000     6500   \n",
       "26   POMEGRANATE               4000                         4000     5000   \n",
       "29          RICE              10000                         1000     5000   \n",
       "11        RUBBER              12000                        23000     9000   \n",
       "12     SUGARCANE              23000                        30000     7000   \n",
       "2            TEA              20000                        90000    15000   \n",
       "13       TOBACCO               5000                         8000     4000   \n",
       "25    WATERMELON               5000                        10000     5000   \n",
       "0          WHEAT               5500                         4500     3300   \n",
       "\n",
       "    Protection  Fertilizers  Wages  Staking, transport and other expenses  \\\n",
       "27        3000         5000   2000                                   1000   \n",
       "20        8000        12000   2000                                   3500   \n",
       "18        3000        40000   2000                                   6000   \n",
       "28        2000         2000   2000                                   1000   \n",
       "17        2000         7000   2000                                   1500   \n",
       "30        3000         2000   3000                                   3000   \n",
       "7         2000        80000   2000                                  18000   \n",
       "8         2000        18500   1500                                   2000   \n",
       "19       30000        30000  80000                                  20000   \n",
       "9         3000        15000   1000                                   1000   \n",
       "6         8000        75000   2000                                  10000   \n",
       "14        4000         7000   4000                                   2000   \n",
       "5         4000         7000   2000                                   1000   \n",
       "4         1000         4500    500                                   1000   \n",
       "21       10000        14000   2000                                   5500   \n",
       "3         3500         8000   1000                                   1000   \n",
       "15        4500         8000   3500                                   2500   \n",
       "1         1900         7000   1100                                   2300   \n",
       "22        4500         5500   1000                                   5000   \n",
       "23        1000         3500    500                                   2250   \n",
       "24        1000         5000   1000                                   3000   \n",
       "10        2500         7000    500                                   1000   \n",
       "16        5000        13000    500                                   1000   \n",
       "26        4000         7000   2000                                   1000   \n",
       "29        3000         2000   3000                                   3000   \n",
       "11        8000        12000   1000                                   3000   \n",
       "12        4500        14500   1500                                  10000   \n",
       "2        20000        30000  10000                                  15000   \n",
       "13        3000         4000   1000                                   1000   \n",
       "25        5000        12000   2000                                   6000   \n",
       "0         3000         6000    700                                   1000   \n",
       "\n",
       "     Total  \n",
       "27   21000  \n",
       "20   47000  \n",
       "18  148000  \n",
       "28   17000  \n",
       "17   31500  \n",
       "30   28000  \n",
       "7   300000  \n",
       "8    59000  \n",
       "19  350000  \n",
       "9    52000  \n",
       "6   250000  \n",
       "14   36000  \n",
       "5    42000  \n",
       "4    18000  \n",
       "21   55000  \n",
       "3    31000  \n",
       "15   35000  \n",
       "1    23000  \n",
       "22   35000  \n",
       "23   13750  \n",
       "24   18000  \n",
       "10   31000  \n",
       "16   40000  \n",
       "26   27000  \n",
       "29   27000  \n",
       "11   70000  \n",
       "12   90000  \n",
       "2   200000  \n",
       "13   26000  \n",
       "25   45000  \n",
       "0    24000  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost.sort_values('Crop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cost.pkl']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(cost, 'cost.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "cost_filtered=cost[cost['Crop'] == 'COCONUT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crop</th>\n",
       "      <th>Diseases</th>\n",
       "      <th>Pesticide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WHEAT</td>\n",
       "      <td>Barley Yellow Dwarf</td>\n",
       "      <td>Bulldock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WHEAT</td>\n",
       "      <td>Crown and Root Rot</td>\n",
       "      <td>Banol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WHEAT</td>\n",
       "      <td>Leaf Rust</td>\n",
       "      <td>Stature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WHEAT</td>\n",
       "      <td>Fusarium Head Blight</td>\n",
       "      <td>streptomycin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WHEAT</td>\n",
       "      <td>Tan spot</td>\n",
       "      <td>sulfur spray</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>MOTH BEANS</td>\n",
       "      <td>Bacterial blight</td>\n",
       "      <td>streptomycin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>MOTH BEANS</td>\n",
       "      <td>Bacterial brown spot</td>\n",
       "      <td>sulfur spray</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>MOTH BEANS</td>\n",
       "      <td>Halo blight</td>\n",
       "      <td>streptomycin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>MOTH BEANS</td>\n",
       "      <td>Damping-off</td>\n",
       "      <td>Mycostop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>MOTH BEANS</td>\n",
       "      <td>Mosaic</td>\n",
       "      <td>Thyla-P</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Crop              Diseases     Pesticide\n",
       "0         WHEAT   Barley Yellow Dwarf      Bulldock\n",
       "1         WHEAT    Crown and Root Rot         Banol\n",
       "2         WHEAT             Leaf Rust      Stature \n",
       "3         WHEAT  Fusarium Head Blight  streptomycin\n",
       "4         WHEAT              Tan spot  sulfur spray\n",
       "..          ...                   ...           ...\n",
       "152  MOTH BEANS      Bacterial blight  streptomycin\n",
       "153  MOTH BEANS  Bacterial brown spot  sulfur spray\n",
       "154  MOTH BEANS           Halo blight  streptomycin\n",
       "155  MOTH BEANS           Damping-off     Mycostop \n",
       "156  MOTH BEANS               Mosaic        Thyla-P\n",
       "\n",
       "[157 rows x 3 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crop</th>\n",
       "      <th>Field Preparation</th>\n",
       "      <th>Nursery and planting sowing</th>\n",
       "      <th>Weeding</th>\n",
       "      <th>Protection</th>\n",
       "      <th>Fertilizers</th>\n",
       "      <th>Wages</th>\n",
       "      <th>Staking, transport and other expenses</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>COCONUT</td>\n",
       "      <td>12000</td>\n",
       "      <td>1000</td>\n",
       "      <td>4000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>28000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Crop  Field Preparation  Nursery and planting sowing  Weeding  \\\n",
       "30  COCONUT              12000                         1000     4000   \n",
       "\n",
       "    Protection  Fertilizers  Wages  Staking, transport and other expenses  \\\n",
       "30        3000         2000   3000                                   3000   \n",
       "\n",
       "    Total  \n",
       "30  28000  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost_filtered\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "a1=cost_filtered['Field Preparation']\n",
    "a2=cost_filtered['Nursery and planting sowing']\n",
    "a3=cost_filtered['Weeding']\n",
    "a4=cost_filtered['Protection']\n",
    "a5=cost_filtered['Fertilizers']\n",
    "a6=cost_filtered['Wages']\n",
    "a7=cost_filtered['Staking, transport and other expenses']\n",
    "a8=cost_filtered['Total']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30    4000\n",
       "Name: Weeding, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "x3 = str(a3)\n",
    "x3=x3.split(\"\\n\")\n",
    "x3=x3[0]\n",
    "x3=x3.split()\n",
    "output6=x3[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4000'"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pesticides.pkl']"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pesticides= pd.read_csv('pesticides.csv')\n",
    "joblib.dump(pesticides, 'pesticides.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['WHEAT', 'MUNG BEAN', 'TEA', 'MILLET', 'MAIZE', 'LENTIL', 'JUTE',\n",
       "       'COFFEE', 'COTTON', 'GROUND NUT', 'PEAS', 'POMEGRANATE',\n",
       "       'WATERMELON', 'PAPAYA', 'ORANGE', 'MUSKMELON', 'MANGO', 'APPLE',\n",
       "       'GRAPES', 'BANANA', 'CHICK PEAS', 'PIGEON PEAS', 'BLACK GRAM',\n",
       "       'ADZUKI BEANS', 'KIDNEY BEANS', 'RUBBER', 'COCONUT', 'TOBACCO',\n",
       "       'SUGARCANE', 'MOTH BEANS'], dtype=object)"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pesticides['Crop'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pesticides['Crop'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "pesticides_filtered=pesticides[pesticides['Crop'] == 'COCONUT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crop</th>\n",
       "      <th>Diseases</th>\n",
       "      <th>Pesticide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>COCONUT</td>\n",
       "      <td>Bud rot and nutfall</td>\n",
       "      <td>Banol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>COCONUT</td>\n",
       "      <td>Ganoderma butt rot</td>\n",
       "      <td>Banol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>COCONUT</td>\n",
       "      <td>Gray leaf blight</td>\n",
       "      <td>streptomycin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>COCONUT</td>\n",
       "      <td>Lethal bole rot</td>\n",
       "      <td>Banol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>COCONUT</td>\n",
       "      <td>Stem bleeding disease</td>\n",
       "      <td>benomyl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Crop               Diseases     Pesticide\n",
       "132  COCONUT    Bud rot and nutfall         Banol\n",
       "133  COCONUT     Ganoderma butt rot         Banol\n",
       "134  COCONUT       Gray leaf blight  streptomycin\n",
       "135  COCONUT        Lethal bole rot         Banol\n",
       "136  COCONUT  Stem bleeding disease       benomyl"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pesticides_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pesticides_filtered['Diseases']\n",
    "d=pesticides_filtered.set_index('Diseases')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crop</th>\n",
       "      <th>Pesticide</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diseases</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bud rot and nutfall</th>\n",
       "      <td>COCONUT</td>\n",
       "      <td>Banol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ganoderma butt rot</th>\n",
       "      <td>COCONUT</td>\n",
       "      <td>Banol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gray leaf blight</th>\n",
       "      <td>COCONUT</td>\n",
       "      <td>streptomycin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lethal bole rot</th>\n",
       "      <td>COCONUT</td>\n",
       "      <td>Banol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stem bleeding disease</th>\n",
       "      <td>COCONUT</td>\n",
       "      <td>benomyl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Crop     Pesticide\n",
       "Diseases                                    \n",
       "Bud rot and nutfall    COCONUT         Banol\n",
       "Ganoderma butt rot     COCONUT         Banol\n",
       "Gray leaf blight       COCONUT  streptomycin\n",
       "Lethal bole rot        COCONUT         Banol\n",
       "Stem bleeding disease  COCONUT       benomyl"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Crop'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/home/spchandgude/.local/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Crop'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-243-6b9f60f1f733>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mdel\u001b[0m \u001b[0mpesticides_filtered\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Crop'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/home/spchandgude/.local/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__delitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3757\u001b[0m             \u001b[0;31m# there was no match, this call should raise the appropriate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3758\u001b[0m             \u001b[0;31m# exception:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3759\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3761\u001b[0m         \u001b[0;31m# delete from the caches\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/spchandgude/.local/lib/python3.6/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mdelete\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1000\u001b[0m         \u001b[0mDelete\u001b[0m \u001b[0mselected\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnon\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1001\u001b[0m         \"\"\"\n\u001b[0;32m-> 1002\u001b[0;31m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1003\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1004\u001b[0m         \u001b[0mis_deleted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbool_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/spchandgude/.local/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2646\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Crop'"
     ]
    }
   ],
   "source": [
    "del pesticides_filtered['Crop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of ['Diseases'] are in the columns\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-218-45c4d4f9e6ed>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpesticides_filtered\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpesticides_filtered\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Diseases'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/home/spchandgude/.local/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mset_index\u001b[0;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[1;32m   4301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4302\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4303\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of {missing} are in the columns\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4305\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"None of ['Diseases'] are in the columns\""
     ]
    }
   ],
   "source": [
    "pesticides_filtered=pesticides_filtered.set_index('Diseases')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pesticide</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diseases</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bud rot and nutfall</th>\n",
       "      <td>Banol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ganoderma butt rot</th>\n",
       "      <td>Banol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gray leaf blight</th>\n",
       "      <td>streptomycin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lethal bole rot</th>\n",
       "      <td>Banol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stem bleeding disease</th>\n",
       "      <td>benomyl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pesticide\n",
       "Diseases                           \n",
       "Bud rot and nutfall           Banol\n",
       "Ganoderma butt rot            Banol\n",
       "Gray leaf blight       streptomycin\n",
       "Lethal bole rot               Banol\n",
       "Stem bleeding disease       benomyl"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pesticides_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "b1=pesticides_filtered[0:1]\n",
    "b2=pesticides_filtered[1:2]\n",
    "b3=pesticides_filtered[2:3]\n",
    "b4=pesticides_filtered[3:4]\n",
    "b5=pesticides_filtered[4:5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pesticide</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diseases</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bud rot and nutfall</th>\n",
       "      <td>Banol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Pesticide\n",
       "Diseases                     \n",
       "Bud rot and nutfall     Banol"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Diseases'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/home/spchandgude/.local/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Diseases'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-230-4d1948e6298a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mk1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mb1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Diseases\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/home/spchandgude/.local/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2798\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2799\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2800\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2801\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/spchandgude/.local/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2646\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Diseases'"
     ]
    }
   ],
   "source": [
    "k1=b1[\"Diseases\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132    Bud rot and nutfall\n",
       "Name: Diseases, dtype: object"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "y1 = str(k1)\n",
    "y1=y1.split(\"\\n\")\n",
    "y1=y1[0]\n",
    "y1=y1.split()\n",
    "output6=y1[1:len(k1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['132', 'Bud', 'rot', 'and', 'nutfall']"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132    Bud rot and nutfall\n",
       "Name: Diseases, dtype: object"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans= pd.read_csv('loans.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan under</th>\n",
       "      <th>interest</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kisan Credit Card Scheme</td>\n",
       "      <td>7.00</td>\n",
       "      <td>3 lakh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>State Bank Of India Agricultural Loans</td>\n",
       "      <td>7.00</td>\n",
       "      <td>3 lakh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ICICI Bank agriculture loan interest rates</td>\n",
       "      <td>13.28</td>\n",
       "      <td>3-10lakh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HDFC Bank Agricultural Loans</td>\n",
       "      <td>9.45</td>\n",
       "      <td>3-10 lakh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allahabad Bank Agricultural Loans</td>\n",
       "      <td>11.00</td>\n",
       "      <td>3-10 lakh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bank of Baroda Agricultural Loans</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3-10 lakh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Punjab National Bank Agricultural Loans</td>\n",
       "      <td>7.00</td>\n",
       "      <td>3-20 lakhs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Axis Bank Agricultural Loans</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3-10 lakhs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   loan under  interest      amount\n",
       "0                    Kisan Credit Card Scheme      7.00      3 lakh\n",
       "1      State Bank Of India Agricultural Loans      7.00      3 lakh\n",
       "2  ICICI Bank agriculture loan interest rates     13.28    3-10lakh\n",
       "3                HDFC Bank Agricultural Loans      9.45   3-10 lakh\n",
       "4           Allahabad Bank Agricultural Loans     11.00   3-10 lakh\n",
       "5           Bank of Baroda Agricultural Loans     10.00   3-10 lakh\n",
       "6     Punjab National Bank Agricultural Loans      7.00  3-20 lakhs\n",
       "7                Axis Bank Agricultural Loans     10.00  3-10 lakhs"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans=loans.set_index('loan under')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loans.pkl']"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(loans, 'loans.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>interest</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan under</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kisan Credit Card Scheme</th>\n",
       "      <td>7.00</td>\n",
       "      <td>3 lakh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State Bank Of India Agricultural Loans</th>\n",
       "      <td>7.00</td>\n",
       "      <td>3 lakh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICICI Bank agriculture loan interest rates</th>\n",
       "      <td>13.28</td>\n",
       "      <td>3-10lakh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDFC Bank Agricultural Loans</th>\n",
       "      <td>9.45</td>\n",
       "      <td>3-10 lakh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allahabad Bank Agricultural Loans</th>\n",
       "      <td>11.00</td>\n",
       "      <td>3-10 lakh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bank of Baroda Agricultural Loans</th>\n",
       "      <td>10.00</td>\n",
       "      <td>3-10 lakh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Punjab National Bank Agricultural Loans</th>\n",
       "      <td>7.00</td>\n",
       "      <td>3-20 lakhs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Axis Bank Agricultural Loans</th>\n",
       "      <td>10.00</td>\n",
       "      <td>3-10 lakhs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            interest      amount\n",
       "loan under                                                      \n",
       "Kisan Credit Card Scheme                        7.00      3 lakh\n",
       "State Bank Of India Agricultural Loans          7.00      3 lakh\n",
       "ICICI Bank agriculture loan interest rates     13.28    3-10lakh\n",
       "HDFC Bank Agricultural Loans                    9.45   3-10 lakh\n",
       "Allahabad Bank Agricultural Loans              11.00   3-10 lakh\n",
       "Bank of Baroda Agricultural Loans              10.00   3-10 lakh\n",
       "Punjab National Bank Agricultural Loans         7.00  3-20 lakhs\n",
       "Axis Bank Agricultural Loans                   10.00  3-10 lakhs"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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