{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da25ef72",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9e339892",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5e0733be",
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('housing.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "92d783c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['median_income'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aded2c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6466567021344465"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['median_income'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "135df0f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_inc=np.log(df['median_income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "276bed29",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='median_income', ylabel='Density'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(log_inc)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}