EDA on Mtcars Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"../Data/mtcars.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>93</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6</td>\n",
       "      <td>258.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>18.7</td>\n",
       "      <td>8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model   mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  \\\n",
       "0          Mazda RX4  21.0    6  160.0  110  3.90  2.620  16.46   0   1     4   \n",
       "1      Mazda RX4 Wag  21.0    6  160.0  110  3.90  2.875  17.02   0   1     4   \n",
       "2         Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4   \n",
       "3     Hornet 4 Drive  21.4    6  258.0  110  3.08  3.215  19.44   1   0     3   \n",
       "4  Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3   \n",
       "\n",
       "   carb  \n",
       "0     4  \n",
       "1     4  \n",
       "2     1  \n",
       "3     1  \n",
       "4     2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 33 entries, 0 to 32\n",
      "Data columns (total 12 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   model   33 non-null     object \n",
      " 1   mpg     33 non-null     float64\n",
      " 2   cyl     33 non-null     int64  \n",
      " 3   disp    33 non-null     float64\n",
      " 4   hp      33 non-null     int64  \n",
      " 5   drat    33 non-null     float64\n",
      " 6   wt      33 non-null     float64\n",
      " 7   qsec    33 non-null     float64\n",
      " 8   vs      33 non-null     int64  \n",
      " 9   am      33 non-null     int64  \n",
      " 10  gear    33 non-null     int64  \n",
      " 11  carb    33 non-null     int64  \n",
      "dtypes: float64(5), int64(6), object(1)\n",
      "memory usage: 3.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model     object\n",
       "mpg      float64\n",
       "cyl        int64\n",
       "disp     float64\n",
       "hp         int64\n",
       "drat     float64\n",
       "wt       float64\n",
       "qsec     float64\n",
       "vs         int64\n",
       "am         int64\n",
       "gear       int64\n",
       "carb       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>93</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6</td>\n",
       "      <td>258.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>18.7</td>\n",
       "      <td>8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Valiant</td>\n",
       "      <td>18.1</td>\n",
       "      <td>6</td>\n",
       "      <td>225.0</td>\n",
       "      <td>105</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.460</td>\n",
       "      <td>20.22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Duster 360</td>\n",
       "      <td>14.3</td>\n",
       "      <td>8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>245</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.570</td>\n",
       "      <td>15.84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Merc 240D</td>\n",
       "      <td>24.4</td>\n",
       "      <td>4</td>\n",
       "      <td>146.7</td>\n",
       "      <td>62</td>\n",
       "      <td>3.69</td>\n",
       "      <td>3.190</td>\n",
       "      <td>20.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Merc 230</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4</td>\n",
       "      <td>140.8</td>\n",
       "      <td>95</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.150</td>\n",
       "      <td>22.90</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Merc 280</td>\n",
       "      <td>19.2</td>\n",
       "      <td>6</td>\n",
       "      <td>167.6</td>\n",
       "      <td>123</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.440</td>\n",
       "      <td>18.30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Merc 280C</td>\n",
       "      <td>17.8</td>\n",
       "      <td>6</td>\n",
       "      <td>167.6</td>\n",
       "      <td>123</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.440</td>\n",
       "      <td>18.90</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Merc 450SE</td>\n",
       "      <td>16.4</td>\n",
       "      <td>8</td>\n",
       "      <td>275.8</td>\n",
       "      <td>180</td>\n",
       "      <td>3.07</td>\n",
       "      <td>4.070</td>\n",
       "      <td>17.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Merc 450SL</td>\n",
       "      <td>17.3</td>\n",
       "      <td>8</td>\n",
       "      <td>275.8</td>\n",
       "      <td>180</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.730</td>\n",
       "      <td>17.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Merc 450SLC</td>\n",
       "      <td>15.2</td>\n",
       "      <td>8</td>\n",
       "      <td>275.8</td>\n",
       "      <td>180</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.780</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cadillac Fleetwood</td>\n",
       "      <td>10.4</td>\n",
       "      <td>8</td>\n",
       "      <td>472.0</td>\n",
       "      <td>205</td>\n",
       "      <td>2.93</td>\n",
       "      <td>5.250</td>\n",
       "      <td>17.98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Lincoln Continental</td>\n",
       "      <td>10.4</td>\n",
       "      <td>8</td>\n",
       "      <td>460.0</td>\n",
       "      <td>215</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.424</td>\n",
       "      <td>17.82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Chrysler Imperial</td>\n",
       "      <td>14.7</td>\n",
       "      <td>8</td>\n",
       "      <td>440.0</td>\n",
       "      <td>230</td>\n",
       "      <td>3.23</td>\n",
       "      <td>5.345</td>\n",
       "      <td>17.42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Fiat 128</td>\n",
       "      <td>32.4</td>\n",
       "      <td>4</td>\n",
       "      <td>78.7</td>\n",
       "      <td>66</td>\n",
       "      <td>4.08</td>\n",
       "      <td>2.200</td>\n",
       "      <td>19.47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Honda Civic</td>\n",
       "      <td>30.4</td>\n",
       "      <td>4</td>\n",
       "      <td>75.7</td>\n",
       "      <td>52</td>\n",
       "      <td>4.93</td>\n",
       "      <td>1.615</td>\n",
       "      <td>18.52</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Toyota Corolla</td>\n",
       "      <td>33.9</td>\n",
       "      <td>4</td>\n",
       "      <td>71.1</td>\n",
       "      <td>65</td>\n",
       "      <td>4.22</td>\n",
       "      <td>1.835</td>\n",
       "      <td>19.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Toyota Corona</td>\n",
       "      <td>21.5</td>\n",
       "      <td>4</td>\n",
       "      <td>120.1</td>\n",
       "      <td>97</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.465</td>\n",
       "      <td>20.01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Dodge Challenger</td>\n",
       "      <td>15.5</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.520</td>\n",
       "      <td>16.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>AMC Javelin</td>\n",
       "      <td>15.2</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.435</td>\n",
       "      <td>17.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Camaro Z28</td>\n",
       "      <td>13.3</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>245</td>\n",
       "      <td>3.73</td>\n",
       "      <td>3.840</td>\n",
       "      <td>15.41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Pontiac Firebird</td>\n",
       "      <td>19.2</td>\n",
       "      <td>8</td>\n",
       "      <td>400.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.845</td>\n",
       "      <td>17.05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Fiat X1-9</td>\n",
       "      <td>27.3</td>\n",
       "      <td>4</td>\n",
       "      <td>79.0</td>\n",
       "      <td>66</td>\n",
       "      <td>4.08</td>\n",
       "      <td>1.935</td>\n",
       "      <td>18.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Porsche 914-2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.3</td>\n",
       "      <td>91</td>\n",
       "      <td>4.43</td>\n",
       "      <td>2.140</td>\n",
       "      <td>16.70</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Lotus Europa</td>\n",
       "      <td>30.4</td>\n",
       "      <td>4</td>\n",
       "      <td>95.1</td>\n",
       "      <td>113</td>\n",
       "      <td>3.77</td>\n",
       "      <td>1.513</td>\n",
       "      <td>16.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Ford Pantera L</td>\n",
       "      <td>15.8</td>\n",
       "      <td>8</td>\n",
       "      <td>351.0</td>\n",
       "      <td>264</td>\n",
       "      <td>4.22</td>\n",
       "      <td>3.170</td>\n",
       "      <td>14.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Ferrari Dino</td>\n",
       "      <td>19.7</td>\n",
       "      <td>6</td>\n",
       "      <td>145.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.770</td>\n",
       "      <td>15.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Maserati Bora</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>301.0</td>\n",
       "      <td>335</td>\n",
       "      <td>3.54</td>\n",
       "      <td>3.570</td>\n",
       "      <td>14.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Volvo 142E</td>\n",
       "      <td>21.4</td>\n",
       "      <td>4</td>\n",
       "      <td>121.0</td>\n",
       "      <td>109</td>\n",
       "      <td>4.11</td>\n",
       "      <td>2.780</td>\n",
       "      <td>18.60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Volvo 142E</td>\n",
       "      <td>21.4</td>\n",
       "      <td>4</td>\n",
       "      <td>121.0</td>\n",
       "      <td>109</td>\n",
       "      <td>4.11</td>\n",
       "      <td>2.780</td>\n",
       "      <td>18.60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  model   mpg  cyl   disp   hp  drat     wt   qsec  vs  am  \\\n",
       "0             Mazda RX4  21.0    6  160.0  110  3.90  2.620  16.46   0   1   \n",
       "1         Mazda RX4 Wag  21.0    6  160.0  110  3.90  2.875  17.02   0   1   \n",
       "2            Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1   \n",
       "3        Hornet 4 Drive  21.4    6  258.0  110  3.08  3.215  19.44   1   0   \n",
       "4     Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   0   \n",
       "5               Valiant  18.1    6  225.0  105  2.76  3.460  20.22   1   0   \n",
       "6            Duster 360  14.3    8  360.0  245  3.21  3.570  15.84   0   0   \n",
       "7             Merc 240D  24.4    4  146.7   62  3.69  3.190  20.00   1   0   \n",
       "8              Merc 230  22.8    4  140.8   95  3.92  3.150  22.90   1   0   \n",
       "9              Merc 280  19.2    6  167.6  123  3.92  3.440  18.30   1   0   \n",
       "10            Merc 280C  17.8    6  167.6  123  3.92  3.440  18.90   1   0   \n",
       "11           Merc 450SE  16.4    8  275.8  180  3.07  4.070  17.40   0   0   \n",
       "12           Merc 450SL  17.3    8  275.8  180  3.07  3.730  17.60   0   0   \n",
       "13          Merc 450SLC  15.2    8  275.8  180  3.07  3.780  18.00   0   0   \n",
       "14   Cadillac Fleetwood  10.4    8  472.0  205  2.93  5.250  17.98   0   0   \n",
       "15  Lincoln Continental  10.4    8  460.0  215  3.00  5.424  17.82   0   0   \n",
       "16    Chrysler Imperial  14.7    8  440.0  230  3.23  5.345  17.42   0   0   \n",
       "17             Fiat 128  32.4    4   78.7   66  4.08  2.200  19.47   1   1   \n",
       "18          Honda Civic  30.4    4   75.7   52  4.93  1.615  18.52   1   1   \n",
       "19       Toyota Corolla  33.9    4   71.1   65  4.22  1.835  19.90   1   1   \n",
       "20        Toyota Corona  21.5    4  120.1   97  3.70  2.465  20.01   1   0   \n",
       "21     Dodge Challenger  15.5    8  318.0  150  2.76  3.520  16.87   0   0   \n",
       "22          AMC Javelin  15.2    8  304.0  150  3.15  3.435  17.30   0   0   \n",
       "23           Camaro Z28  13.3    8  350.0  245  3.73  3.840  15.41   0   0   \n",
       "24     Pontiac Firebird  19.2    8  400.0  175  3.08  3.845  17.05   0   0   \n",
       "25            Fiat X1-9  27.3    4   79.0   66  4.08  1.935  18.90   1   1   \n",
       "26        Porsche 914-2  26.0    4  120.3   91  4.43  2.140  16.70   0   1   \n",
       "27         Lotus Europa  30.4    4   95.1  113  3.77  1.513  16.90   1   1   \n",
       "28       Ford Pantera L  15.8    8  351.0  264  4.22  3.170  14.50   0   1   \n",
       "29         Ferrari Dino  19.7    6  145.0  175  3.62  2.770  15.50   0   1   \n",
       "30        Maserati Bora  15.0    8  301.0  335  3.54  3.570  14.60   0   1   \n",
       "31           Volvo 142E  21.4    4  121.0  109  4.11  2.780  18.60   1   1   \n",
       "32           Volvo 142E  21.4    4  121.0  109  4.11  2.780  18.60   1   1   \n",
       "\n",
       "    gear  \n",
       "0      4  \n",
       "1      4  \n",
       "2      4  \n",
       "3      3  \n",
       "4      3  \n",
       "5      3  \n",
       "6      3  \n",
       "7      4  \n",
       "8      4  \n",
       "9      4  \n",
       "10     4  \n",
       "11     3  \n",
       "12     3  \n",
       "13     3  \n",
       "14     3  \n",
       "15     3  \n",
       "16     3  \n",
       "17     4  \n",
       "18     4  \n",
       "19     4  \n",
       "20     3  \n",
       "21     3  \n",
       "22     3  \n",
       "23     3  \n",
       "24     3  \n",
       "25     4  \n",
       "26     5  \n",
       "27     5  \n",
       "28     5  \n",
       "29     5  \n",
       "30     5  \n",
       "31     4  \n",
       "32     4  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#irrelavant columns\n",
    "df.drop(columns=[\"carb\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "5     False\n",
       "6     False\n",
       "7     False\n",
       "8     False\n",
       "9     False\n",
       "10    False\n",
       "11    False\n",
       "12    False\n",
       "13    False\n",
       "14    False\n",
       "15    False\n",
       "16    False\n",
       "17    False\n",
       "18    False\n",
       "19    False\n",
       "20    False\n",
       "21    False\n",
       "22    False\n",
       "23    False\n",
       "24    False\n",
       "25    False\n",
       "26    False\n",
       "27    False\n",
       "28    False\n",
       "29    False\n",
       "30    False\n",
       "31    False\n",
       "32     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#duplicate rows\n",
    "df.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33, 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "duplicate_df=df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 12)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate_df.shape"
   ]
  },
  {
   "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>model</th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Volvo 142E</td>\n",
       "      <td>21.4</td>\n",
       "      <td>4</td>\n",
       "      <td>121.0</td>\n",
       "      <td>109</td>\n",
       "      <td>4.11</td>\n",
       "      <td>2.78</td>\n",
       "      <td>18.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         model   mpg  cyl   disp   hp  drat    wt  qsec  vs  am  gear  carb\n",
       "32  Volvo 142E  21.4    4  121.0  109  4.11  2.78  18.6   1   1     4     2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33, 12)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-e062e58b7cc5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop_duplicates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "df=df.drop_duplicates()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model    0\n",
       "mpg      0\n",
       "cyl      0\n",
       "disp     0\n",
       "hp       0\n",
       "drat     0\n",
       "wt       0\n",
       "qsec     0\n",
       "vs       0\n",
       "am       0\n",
       "gear     0\n",
       "carb     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(figsize=(10,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg       7.37500\n",
       "cyl       4.00000\n",
       "disp    205.17500\n",
       "hp       83.50000\n",
       "drat      0.84000\n",
       "wt        1.02875\n",
       "qsec      2.00750\n",
       "vs        1.00000\n",
       "am        1.00000\n",
       "gear      1.00000\n",
       "carb      2.00000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#IQR -> Q3 -Q1\n",
    "Q1=df.quantile(0.25)\n",
    "Q3=df.quantile(0.75)\n",
    "IQR=Q3-Q1\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26, 12)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q1-1.5*IQR to Q3+1.5*IQR\n",
    "o_df=df[~((df<(Q1-1.5*IQR)) | (df> (Q3+1.5*IQR))).any(axis=1)]\n",
    "o_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "o_df.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'mpg'}>,\n",
       "        <AxesSubplot:title={'center':'cyl'}>,\n",
       "        <AxesSubplot:title={'center':'disp'}>],\n",
       "       [<AxesSubplot:title={'center':'hp'}>,\n",
       "        <AxesSubplot:title={'center':'drat'}>,\n",
       "        <AxesSubplot:title={'center':'wt'}>],\n",
       "       [<AxesSubplot:title={'center':'qsec'}>,\n",
       "        <AxesSubplot:title={'center':'vs'}>,\n",
       "        <AxesSubplot:title={'center':'am'}>],\n",
       "       [<AxesSubplot:title={'center':'gear'}>,\n",
       "        <AxesSubplot:title={'center':'carb'}>, <AxesSubplot:>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>,\n",
       "       <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>,\n",
       "       <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(subplots=True,figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas_profiling in c:\\programdata\\anaconda3\\lib\\site-packages (3.0.0)\n",
      "Requirement already satisfied: scipy>=1.4.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas_profiling) (1.5.2)\n",
      "Requirement already satisfied: phik>=0.11.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas_profiling) (0.11.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: joblib in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas_profiling) (0.17.0)\n",
      "Requirement already satisfied: htmlmin>=0.1.12 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas_profiling) (0.1.12)\n",
      "Requirement already satisfied: seaborn>=0.10.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas_profiling) (0.11.0)\n",
      "Requirement already satisfied: pydantic>=1.8.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas_profiling) (1.8.2)\n",
      "Requirement already satisfied: pandas!=1.0.0,!=1.0.1,!=1.0.2,!=1.1.0,>=0.25.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas_profiling) (1.1.3)\n",
      "Requirement already satisfied: visions[type_image_path]==0.7.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas_profiling) (0.7.1)\n",
      "Requirement already satisfied: numpy>=1.16.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas_profiling) (1.19.2)\n",
      "Requirement already satisfied: PyYAML>=5.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas_profiling) (5.3.1)\n",
      "Requirement already satisfied: tangled-up-in-unicode==0.1.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas_profiling) (0.1.0)\n",
      "Requirement already satisfied: missingno>=0.4.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas_profiling) (0.4.2)\n",
      "Requirement already satisfied: matplotlib>=3.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas_profiling) (3.3.2)\n",
      "Requirement already satisfied: tqdm>=4.48.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas_profiling) (4.50.2)\n",
      "Requirement already satisfied: requests>=2.24.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas_profiling) (2.24.0)\n",
      "Requirement already satisfied: jinja2>=2.11.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas_profiling) (2.11.2)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pydantic>=1.8.1->pandas_profiling) (3.7.4.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,!=1.1.0,>=0.25.3->pandas_profiling) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,!=1.1.0,>=0.25.3->pandas_profiling) (2020.1)\n",
      "Requirement already satisfied: bottleneck in c:\\programdata\\anaconda3\\lib\\site-packages (from visions[type_image_path]==0.7.1->pandas_profiling) (1.3.2)\n",
      "Requirement already satisfied: networkx>=2.4 in c:\\programdata\\anaconda3\\lib\\site-packages (from visions[type_image_path]==0.7.1->pandas_profiling) (2.5)\n",
      "Requirement already satisfied: attrs>=19.3.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from visions[type_image_path]==0.7.1->pandas_profiling) (20.3.0)\n",
      "Requirement already satisfied: multimethod==1.4 in c:\\programdata\\anaconda3\\lib\\site-packages (from visions[type_image_path]==0.7.1->pandas_profiling) (1.4)\n",
      "Requirement already satisfied: imagehash; extra == \"type_image_path\" in c:\\programdata\\anaconda3\\lib\\site-packages (from visions[type_image_path]==0.7.1->pandas_profiling) (4.2.0)\n",
      "Requirement already satisfied: Pillow; extra == \"type_image_path\" in c:\\programdata\\anaconda3\\lib\\site-packages (from visions[type_image_path]==0.7.1->pandas_profiling) (8.0.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3.2.0->pandas_profiling) (2.4.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3.2.0->pandas_profiling) (1.3.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3.2.0->pandas_profiling) (0.10.0)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3.2.0->pandas_profiling) (2020.6.20)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.24.0->pandas_profiling) (3.0.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.24.0->pandas_profiling) (2.10)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.24.0->pandas_profiling) (1.25.11)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in c:\\programdata\\anaconda3\\lib\\site-packages (from jinja2>=2.11.1->pandas_profiling) (1.1.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas!=1.0.0,!=1.0.1,!=1.0.2,!=1.1.0,>=0.25.3->pandas_profiling) (1.15.0)\n",
      "Requirement already satisfied: decorator>=4.3.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from networkx>=2.4->visions[type_image_path]==0.7.1->pandas_profiling) (4.4.2)\n",
      "Requirement already satisfied: PyWavelets in c:\\programdata\\anaconda3\\lib\\site-packages (from imagehash; extra == \"type_image_path\"->visions[type_image_path]==0.7.1->pandas_profiling) (1.1.1)\n"
     ]
    }
   ],
   "source": [
    "pip install pandas_profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1ffbdd02e66240768e5c424c2f148a9b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Summarize dataset'), FloatProgress(value=0.0, max=25.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f8e07f148f864f30a2e06d38fd57b7ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Generate report structure'), FloatProgress(value=0.0, max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1194e116f92546f7a3542315a8172c78",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Render HTML'), FloatProgress(value=0.0, max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6e8d3bc26a084d60b33cf5bd7aac1be8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Export report to file'), FloatProgress(value=0.0, max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from pandas_profiling import ProfileReport\n",
    "report=ProfileReport(df)\n",
    "report.to_file(output_file=\"mtcars_report.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method to_file in module pandas_profiling.profile_report:\n",
      "\n",
      "to_file(output_file: Union[str, pathlib.Path], silent: bool = True) -> None method of pandas_profiling.profile_report.ProfileReport instance\n",
      "    Write the report to a file.\n",
      "    \n",
      "    By default a name is generated.\n",
      "    \n",
      "    Args:\n",
      "        output_file: The name or the path of the file to generate including the extension (.html, .json).\n",
      "        silent: if False, opens the file in the default browser or download it in a Google Colab environment\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(report.to_file)"
   ]
  }
 ],
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