AiFarmerHelper-checkpoints.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>20.879744</td>\n",
       "      <td>82.002744</td>\n",
       "      <td>6.502985</td>\n",
       "      <td>202.935536</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>21.770462</td>\n",
       "      <td>80.319644</td>\n",
       "      <td>7.038096</td>\n",
       "      <td>226.655537</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>44</td>\n",
       "      <td>23.004459</td>\n",
       "      <td>82.320763</td>\n",
       "      <td>7.840207</td>\n",
       "      <td>263.964248</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>26.491096</td>\n",
       "      <td>80.158363</td>\n",
       "      <td>6.980401</td>\n",
       "      <td>242.864034</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>20.130175</td>\n",
       "      <td>81.604873</td>\n",
       "      <td>7.628473</td>\n",
       "      <td>262.717340</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N   P   K  temperature   humidity        ph    rainfall label\n",
       "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
       "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
       "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice\n",
       "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice\n",
       "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/upflairs-pvt-ltd/3rd_july_datascience/master/farmer_guider/farmer.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "rice           100\n",
       "maize          100\n",
       "chickpea       100\n",
       "kidneybeans    100\n",
       "pigeonpeas     100\n",
       "mothbeans      100\n",
       "mungbean       100\n",
       "blackgram      100\n",
       "lentil         100\n",
       "pomegranate    100\n",
       "banana         100\n",
       "mango          100\n",
       "grapes         100\n",
       "watermelon     100\n",
       "muskmelon      100\n",
       "apple          100\n",
       "orange         100\n",
       "papaya         100\n",
       "coconut        100\n",
       "cotton         100\n",
       "jute           100\n",
       "coffee         100\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "group = df.groupby('label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">temperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>22.630942</td>\n",
       "      <td>21.036527</td>\n",
       "      <td>23.996862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>27.376798</td>\n",
       "      <td>25.010185</td>\n",
       "      <td>29.908885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blackgram</th>\n",
       "      <td>29.973340</td>\n",
       "      <td>25.097374</td>\n",
       "      <td>34.946616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chickpea</th>\n",
       "      <td>18.872847</td>\n",
       "      <td>17.024985</td>\n",
       "      <td>20.995022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coconut</th>\n",
       "      <td>27.409892</td>\n",
       "      <td>25.008724</td>\n",
       "      <td>29.869083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coffee</th>\n",
       "      <td>25.540477</td>\n",
       "      <td>23.059519</td>\n",
       "      <td>27.923744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cotton</th>\n",
       "      <td>23.988958</td>\n",
       "      <td>22.000851</td>\n",
       "      <td>25.992374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grapes</th>\n",
       "      <td>23.849575</td>\n",
       "      <td>8.825675</td>\n",
       "      <td>41.948657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jute</th>\n",
       "      <td>24.958376</td>\n",
       "      <td>23.094338</td>\n",
       "      <td>26.985822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kidneybeans</th>\n",
       "      <td>20.115085</td>\n",
       "      <td>15.330426</td>\n",
       "      <td>24.923601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lentil</th>\n",
       "      <td>24.509052</td>\n",
       "      <td>18.064861</td>\n",
       "      <td>29.944139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maize</th>\n",
       "      <td>22.389204</td>\n",
       "      <td>18.041855</td>\n",
       "      <td>26.549864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mango</th>\n",
       "      <td>31.208770</td>\n",
       "      <td>27.003155</td>\n",
       "      <td>35.990097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothbeans</th>\n",
       "      <td>28.194920</td>\n",
       "      <td>24.018254</td>\n",
       "      <td>31.999286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mungbean</th>\n",
       "      <td>28.525775</td>\n",
       "      <td>27.014704</td>\n",
       "      <td>29.914544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>muskmelon</th>\n",
       "      <td>28.663066</td>\n",
       "      <td>27.024151</td>\n",
       "      <td>29.943492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>22.765725</td>\n",
       "      <td>10.010813</td>\n",
       "      <td>34.906653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>papaya</th>\n",
       "      <td>33.723859</td>\n",
       "      <td>23.012402</td>\n",
       "      <td>43.675493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pigeonpeas</th>\n",
       "      <td>27.741762</td>\n",
       "      <td>18.319104</td>\n",
       "      <td>36.977944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pomegranate</th>\n",
       "      <td>21.837842</td>\n",
       "      <td>18.071330</td>\n",
       "      <td>24.962732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rice</th>\n",
       "      <td>23.689332</td>\n",
       "      <td>20.045414</td>\n",
       "      <td>26.929951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>watermelon</th>\n",
       "      <td>25.591767</td>\n",
       "      <td>24.043558</td>\n",
       "      <td>26.986037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature                      \n",
       "                   mean        min        max\n",
       "label                                        \n",
       "apple         22.630942  21.036527  23.996862\n",
       "banana        27.376798  25.010185  29.908885\n",
       "blackgram     29.973340  25.097374  34.946616\n",
       "chickpea      18.872847  17.024985  20.995022\n",
       "coconut       27.409892  25.008724  29.869083\n",
       "coffee        25.540477  23.059519  27.923744\n",
       "cotton        23.988958  22.000851  25.992374\n",
       "grapes        23.849575   8.825675  41.948657\n",
       "jute          24.958376  23.094338  26.985822\n",
       "kidneybeans   20.115085  15.330426  24.923601\n",
       "lentil        24.509052  18.064861  29.944139\n",
       "maize         22.389204  18.041855  26.549864\n",
       "mango         31.208770  27.003155  35.990097\n",
       "mothbeans     28.194920  24.018254  31.999286\n",
       "mungbean      28.525775  27.014704  29.914544\n",
       "muskmelon     28.663066  27.024151  29.943492\n",
       "orange        22.765725  10.010813  34.906653\n",
       "papaya        33.723859  23.012402  43.675493\n",
       "pigeonpeas    27.741762  18.319104  36.977944\n",
       "pomegranate   21.837842  18.071330  24.962732\n",
       "rice          23.689332  20.045414  26.929951\n",
       "watermelon    25.591767  24.043558  26.986037"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = group[['temperature']].agg(['mean', 'min', 'max'])\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['apple', 'banana', 'blackgram', 'chickpea', 'coconut', 'coffee',\n",
       "       'cotton', 'grapes', 'jute', 'kidneybeans', 'lentil', 'maize', 'mango',\n",
       "       'mothbeans', 'mungbean', 'muskmelon', 'orange', 'papaya', 'pigeonpeas',\n",
       "       'pomegranate', 'rice', 'watermelon'],\n",
       "      dtype='object', name='label')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_temperature = group['temperature'].mean()\n",
    "min_temperature.plot.bar(color='crimson')\n",
    "\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('Avg Temperature')\n",
    "plt.title('Avg Temperature by Label')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_temperature = group['temperature'].min()\n",
    "min_temperature.plot.bar(color='dodgerblue')\n",
    "\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('min Temperature')\n",
    "plt.title('min Temperature by Label')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>20.879744</td>\n",
       "      <td>82.002744</td>\n",
       "      <td>6.502985</td>\n",
       "      <td>202.935536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>21.770462</td>\n",
       "      <td>80.319644</td>\n",
       "      <td>7.038096</td>\n",
       "      <td>226.655537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>44</td>\n",
       "      <td>23.004459</td>\n",
       "      <td>82.320763</td>\n",
       "      <td>7.840207</td>\n",
       "      <td>263.964248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>26.491096</td>\n",
       "      <td>80.158363</td>\n",
       "      <td>6.980401</td>\n",
       "      <td>242.864034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>20.130175</td>\n",
       "      <td>81.604873</td>\n",
       "      <td>7.628473</td>\n",
       "      <td>262.717340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N   P   K  temperature   humidity        ph    rainfall\n",
       "0  90  42  43    20.879744  82.002744  6.502985  202.935536\n",
       "1  85  58  41    21.770462  80.319644  7.038096  226.655537\n",
       "2  60  55  44    23.004459  82.320763  7.840207  263.964248\n",
       "3  74  35  40    26.491096  80.158363  6.980401  242.864034\n",
       "4  78  42  42    20.130175  81.604873  7.628473  262.717340"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.drop('label',axis='columns')\n",
    "x.head()\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N              0\n",
       "P              0\n",
       "K              0\n",
       "temperature    0\n",
       "humidity       0\n",
       "ph             0\n",
       "rainfall       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.isnull().sum()  # our data is missing value free"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2200 entries, 0 to 2199\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   N            2200 non-null   int64  \n",
      " 1   P            2200 non-null   int64  \n",
      " 2   K            2200 non-null   int64  \n",
      " 3   temperature  2200 non-null   float64\n",
      " 4   humidity     2200 non-null   float64\n",
      " 5   ph           2200 non-null   float64\n",
      " 6   rainfall     2200 non-null   float64\n",
      " 7   label        2200 non-null   object \n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 137.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.duplicated().sum() # duplicates free\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N                int64\n",
       "P                int64\n",
       "K                int64\n",
       "temperature    float64\n",
       "humidity       float64\n",
       "ph             float64\n",
       "rainfall       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dtypes\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='temperature', ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(x['temperature'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "# import seaborn\n",
    "\n",
    "# # declaring data\n",
    "# data = [44, 45, 40, 41, 39]\n",
    "# keys = ['Class 1', 'Class 2', 'CLass 3', 'Class 4', 'Class 5']\n",
    "\n",
    "# # define Seaborn color palette to use\n",
    "# palette_color = seaborn.color_palette('bright')\n",
    "\n",
    "# # plotting data on chart\n",
    "# plt.pie(data, labels=keys, colors=palette_color, autopct='%.0f%%')\n",
    "\n",
    "# # displaying chart\n",
    "# plt.show()\n",
    "     "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}