2 mtcars_DataPreporcessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "#### Mtcars, an automobile company in Chambersburg, United States, has recorded the production of its cars within a dataset. The company is coming up with a new model based on the feedback given by its customers. It has to explore the current dataset to derive further insights from it.\n",
    "\n",
    "## explore for dimensionality, and type and average value of the horsepower across all the cars. Also, identify a few of mostly correlated features, which would help in modification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>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",
       "    <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",
       "      <td>1</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",
       "      <td>4</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",
       "      <td>2</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",
       "      <td>2</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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>4</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",
       "      <td>4</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",
       "      <td>4</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",
       "      <td>1</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",
       "      <td>2</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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>2</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",
       "      <td>2</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",
       "      <td>4</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",
       "      <td>2</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",
       "      <td>1</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",
       "      <td>2</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",
       "      <td>2</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",
       "      <td>4</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",
       "      <td>6</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",
       "      <td>8</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",
       "      <td>2</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",
       "\n",
       "    gear  carb  \n",
       "0      4     4  \n",
       "1      4     4  \n",
       "2      4     1  \n",
       "3      3     1  \n",
       "4      3     2  \n",
       "5      3     1  \n",
       "6      3     4  \n",
       "7      4     2  \n",
       "8      4     2  \n",
       "9      4     4  \n",
       "10     4     4  \n",
       "11     3     3  \n",
       "12     3     3  \n",
       "13     3     3  \n",
       "14     3     4  \n",
       "15     3     4  \n",
       "16     3     4  \n",
       "17     4     1  \n",
       "18     4     2  \n",
       "19     4     1  \n",
       "20     3     1  \n",
       "21     3     2  \n",
       "22     3     2  \n",
       "23     3     4  \n",
       "24     3     2  \n",
       "25     4     1  \n",
       "26     5     2  \n",
       "27     5     2  \n",
       "28     5     4  \n",
       "29     5     6  \n",
       "30     5     8  \n",
       "31     4     2  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('mtcars.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 12)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['model'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>count</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.090625</td>\n",
       "      <td>6.187500</td>\n",
       "      <td>230.721875</td>\n",
       "      <td>146.687500</td>\n",
       "      <td>3.596563</td>\n",
       "      <td>3.217250</td>\n",
       "      <td>17.848750</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.406250</td>\n",
       "      <td>3.687500</td>\n",
       "      <td>2.8125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.026948</td>\n",
       "      <td>1.785922</td>\n",
       "      <td>123.938694</td>\n",
       "      <td>68.562868</td>\n",
       "      <td>0.534679</td>\n",
       "      <td>0.978457</td>\n",
       "      <td>1.786943</td>\n",
       "      <td>0.504016</td>\n",
       "      <td>0.498991</td>\n",
       "      <td>0.737804</td>\n",
       "      <td>1.6152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.400000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>71.100000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2.760000</td>\n",
       "      <td>1.513000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.425000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>120.825000</td>\n",
       "      <td>96.500000</td>\n",
       "      <td>3.080000</td>\n",
       "      <td>2.581250</td>\n",
       "      <td>16.892500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.200000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>196.300000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>3.695000</td>\n",
       "      <td>3.325000</td>\n",
       "      <td>17.710000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22.800000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>3.920000</td>\n",
       "      <td>3.610000</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>33.900000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>472.000000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>4.930000</td>\n",
       "      <td>5.424000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mpg        cyl        disp          hp       drat         wt  \\\n",
       "count  32.000000  32.000000   32.000000   32.000000  32.000000  32.000000   \n",
       "mean   20.090625   6.187500  230.721875  146.687500   3.596563   3.217250   \n",
       "std     6.026948   1.785922  123.938694   68.562868   0.534679   0.978457   \n",
       "min    10.400000   4.000000   71.100000   52.000000   2.760000   1.513000   \n",
       "25%    15.425000   4.000000  120.825000   96.500000   3.080000   2.581250   \n",
       "50%    19.200000   6.000000  196.300000  123.000000   3.695000   3.325000   \n",
       "75%    22.800000   8.000000  326.000000  180.000000   3.920000   3.610000   \n",
       "max    33.900000   8.000000  472.000000  335.000000   4.930000   5.424000   \n",
       "\n",
       "            qsec         vs         am       gear     carb  \n",
       "count  32.000000  32.000000  32.000000  32.000000  32.0000  \n",
       "mean   17.848750   0.437500   0.406250   3.687500   2.8125  \n",
       "std     1.786943   0.504016   0.498991   0.737804   1.6152  \n",
       "min    14.500000   0.000000   0.000000   3.000000   1.0000  \n",
       "25%    16.892500   0.000000   0.000000   3.000000   2.0000  \n",
       "50%    17.710000   0.000000   0.000000   4.000000   2.0000  \n",
       "75%    18.900000   1.000000   1.000000   4.000000   4.0000  \n",
       "max    22.900000   1.000000   1.000000   5.000000   8.0000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146.6875"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hp'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hp'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    110\n",
       "1    175\n",
       "2    180\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hp'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>-0.847551</td>\n",
       "      <td>-0.776168</td>\n",
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       "      <td>-0.867659</td>\n",
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       "      <td>-0.492687</td>\n",
       "      <td>0.526988</td>\n",
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       "    <tr>\n",
       "      <th>disp</th>\n",
       "      <td>-0.847551</td>\n",
       "      <td>0.902033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.790949</td>\n",
       "      <td>-0.710214</td>\n",
       "      <td>0.887980</td>\n",
       "      <td>-0.433698</td>\n",
       "      <td>-0.710416</td>\n",
       "      <td>-0.591227</td>\n",
       "      <td>-0.555569</td>\n",
       "      <td>0.394977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hp</th>\n",
       "      <td>-0.776168</td>\n",
       "      <td>0.832447</td>\n",
       "      <td>0.790949</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.448759</td>\n",
       "      <td>0.658748</td>\n",
       "      <td>-0.708223</td>\n",
       "      <td>-0.723097</td>\n",
       "      <td>-0.243204</td>\n",
       "      <td>-0.125704</td>\n",
       "      <td>0.749812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drat</th>\n",
       "      <td>0.681172</td>\n",
       "      <td>-0.699938</td>\n",
       "      <td>-0.710214</td>\n",
       "      <td>-0.448759</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.712441</td>\n",
       "      <td>0.091205</td>\n",
       "      <td>0.440278</td>\n",
       "      <td>0.712711</td>\n",
       "      <td>0.699610</td>\n",
       "      <td>-0.090790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wt</th>\n",
       "      <td>-0.867659</td>\n",
       "      <td>0.782496</td>\n",
       "      <td>0.887980</td>\n",
       "      <td>0.658748</td>\n",
       "      <td>-0.712441</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.174716</td>\n",
       "      <td>-0.554916</td>\n",
       "      <td>-0.692495</td>\n",
       "      <td>-0.583287</td>\n",
       "      <td>0.427606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qsec</th>\n",
       "      <td>0.418684</td>\n",
       "      <td>-0.591242</td>\n",
       "      <td>-0.433698</td>\n",
       "      <td>-0.708223</td>\n",
       "      <td>0.091205</td>\n",
       "      <td>-0.174716</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.744535</td>\n",
       "      <td>-0.229861</td>\n",
       "      <td>-0.212682</td>\n",
       "      <td>-0.656249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vs</th>\n",
       "      <td>0.664039</td>\n",
       "      <td>-0.810812</td>\n",
       "      <td>-0.710416</td>\n",
       "      <td>-0.723097</td>\n",
       "      <td>0.440278</td>\n",
       "      <td>-0.554916</td>\n",
       "      <td>0.744535</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.168345</td>\n",
       "      <td>0.206023</td>\n",
       "      <td>-0.569607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>am</th>\n",
       "      <td>0.599832</td>\n",
       "      <td>-0.522607</td>\n",
       "      <td>-0.591227</td>\n",
       "      <td>-0.243204</td>\n",
       "      <td>0.712711</td>\n",
       "      <td>-0.692495</td>\n",
       "      <td>-0.229861</td>\n",
       "      <td>0.168345</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.794059</td>\n",
       "      <td>0.057534</td>\n",
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       "    <tr>\n",
       "      <th>gear</th>\n",
       "      <td>0.480285</td>\n",
       "      <td>-0.492687</td>\n",
       "      <td>-0.555569</td>\n",
       "      <td>-0.125704</td>\n",
       "      <td>0.699610</td>\n",
       "      <td>-0.583287</td>\n",
       "      <td>-0.212682</td>\n",
       "      <td>0.206023</td>\n",
       "      <td>0.794059</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.274073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carb</th>\n",
       "      <td>-0.550925</td>\n",
       "      <td>0.526988</td>\n",
       "      <td>0.394977</td>\n",
       "      <td>0.749812</td>\n",
       "      <td>-0.090790</td>\n",
       "      <td>0.427606</td>\n",
       "      <td>-0.656249</td>\n",
       "      <td>-0.569607</td>\n",
       "      <td>0.057534</td>\n",
       "      <td>0.274073</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mpg       cyl      disp        hp      drat        wt      qsec  \\\n",
       "mpg   1.000000 -0.852162 -0.847551 -0.776168  0.681172 -0.867659  0.418684   \n",
       "cyl  -0.852162  1.000000  0.902033  0.832447 -0.699938  0.782496 -0.591242   \n",
       "disp -0.847551  0.902033  1.000000  0.790949 -0.710214  0.887980 -0.433698   \n",
       "hp   -0.776168  0.832447  0.790949  1.000000 -0.448759  0.658748 -0.708223   \n",
       "drat  0.681172 -0.699938 -0.710214 -0.448759  1.000000 -0.712441  0.091205   \n",
       "wt   -0.867659  0.782496  0.887980  0.658748 -0.712441  1.000000 -0.174716   \n",
       "qsec  0.418684 -0.591242 -0.433698 -0.708223  0.091205 -0.174716  1.000000   \n",
       "vs    0.664039 -0.810812 -0.710416 -0.723097  0.440278 -0.554916  0.744535   \n",
       "am    0.599832 -0.522607 -0.591227 -0.243204  0.712711 -0.692495 -0.229861   \n",
       "gear  0.480285 -0.492687 -0.555569 -0.125704  0.699610 -0.583287 -0.212682   \n",
       "carb -0.550925  0.526988  0.394977  0.749812 -0.090790  0.427606 -0.656249   \n",
       "\n",
       "            vs        am      gear      carb  \n",
       "mpg   0.664039  0.599832  0.480285 -0.550925  \n",
       "cyl  -0.810812 -0.522607 -0.492687  0.526988  \n",
       "disp -0.710416 -0.591227 -0.555569  0.394977  \n",
       "hp   -0.723097 -0.243204 -0.125704  0.749812  \n",
       "drat  0.440278  0.712711  0.699610 -0.090790  \n",
       "wt   -0.554916 -0.692495 -0.583287  0.427606  \n",
       "qsec  0.744535 -0.229861 -0.212682 -0.656249  \n",
       "vs    1.000000  0.168345  0.206023 -0.569607  \n",
       "am    0.168345  1.000000  0.794059  0.057534  \n",
       "gear  0.206023  0.794059  1.000000  0.274073  \n",
       "carb -0.569607  0.057534  0.274073  1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlations=df.corr()\n",
    "correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(correlations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for missing values and outliers within the horsepower column and remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model    False\n",
       "mpg      False\n",
       "cyl      False\n",
       "disp     False\n",
       "hp       False\n",
       "drat     False\n",
       "wt       False\n",
       "qsec     False\n",
       "vs       False\n",
       "am       False\n",
       "gear     False\n",
       "carb     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any()"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "#### if any missing value\n",
    "missing vaues substitution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    " #df['col']=df['col'].fillna(df['col'].mean())\n",
    "\n",
    "#or\n",
    "\n",
    "#df=df.fillna(df.mean())\n",
    "\n",
    "#or\n",
    "\n",
    "#from sklearn.impute import SimpleImputer\n",
    "#imp=SimpleImputer(missing_values=np.nan, strategy=\"most_frequent\")\n",
    "#df=imp.fit_transform(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "#### oulier in HP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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d5LvA+cB7VrsYaTHefkBaRJIe8PdVdd9q1yL1y2EZSWqQR+6S1CCP3CWpQYa7JDXIcJekBhnuktQgw12SGvR/as/CIoF3JPIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Detecting Outlier\n",
    "df.boxplot(['hp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>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",
       "    <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",
       "      <td>1</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",
       "      <td>4</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",
       "      <td>2</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",
       "      <td>2</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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>4</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",
       "      <td>4</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",
       "      <td>4</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",
       "      <td>1</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",
       "      <td>2</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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>2</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",
       "      <td>2</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",
       "      <td>4</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",
       "      <td>2</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",
       "      <td>1</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",
       "      <td>2</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",
       "      <td>2</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",
       "      <td>4</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",
       "      <td>6</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",
       "      <td>2</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",
       "31           Volvo 142E  21.4    4  121.0  109  4.11  2.780  18.60   1   1   \n",
       "\n",
       "    gear  carb  \n",
       "0      4     4  \n",
       "1      4     4  \n",
       "2      4     1  \n",
       "3      3     1  \n",
       "4      3     2  \n",
       "5      3     1  \n",
       "6      3     4  \n",
       "7      4     2  \n",
       "8      4     2  \n",
       "9      4     4  \n",
       "10     4     4  \n",
       "11     3     3  \n",
       "12     3     3  \n",
       "13     3     3  \n",
       "14     3     4  \n",
       "15     3     4  \n",
       "16     3     4  \n",
       "17     4     1  \n",
       "18     4     2  \n",
       "19     4     1  \n",
       "20     3     1  \n",
       "21     3     2  \n",
       "22     3     2  \n",
       "23     3     4  \n",
       "24     3     2  \n",
       "25     4     1  \n",
       "26     5     2  \n",
       "27     5     2  \n",
       "28     5     4  \n",
       "29     5     6  \n",
       "31     4     2  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removing Outlier\n",
    "filt=df['hp']<280\n",
    "df_n=df[filt]\n",
    "df_n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All oulier in Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 11)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#taking numeric columns\n",
    "df1=df.iloc[:,1:]\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>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>count</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.090625</td>\n",
       "      <td>6.187500</td>\n",
       "      <td>230.721875</td>\n",
       "      <td>146.687500</td>\n",
       "      <td>3.596563</td>\n",
       "      <td>3.217250</td>\n",
       "      <td>17.848750</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.406250</td>\n",
       "      <td>3.687500</td>\n",
       "      <td>2.8125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.026948</td>\n",
       "      <td>1.785922</td>\n",
       "      <td>123.938694</td>\n",
       "      <td>68.562868</td>\n",
       "      <td>0.534679</td>\n",
       "      <td>0.978457</td>\n",
       "      <td>1.786943</td>\n",
       "      <td>0.504016</td>\n",
       "      <td>0.498991</td>\n",
       "      <td>0.737804</td>\n",
       "      <td>1.6152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.400000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>71.100000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2.760000</td>\n",
       "      <td>1.513000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.425000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>120.825000</td>\n",
       "      <td>96.500000</td>\n",
       "      <td>3.080000</td>\n",
       "      <td>2.581250</td>\n",
       "      <td>16.892500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.200000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>196.300000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>3.695000</td>\n",
       "      <td>3.325000</td>\n",
       "      <td>17.710000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22.800000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>3.920000</td>\n",
       "      <td>3.610000</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>33.900000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>472.000000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>4.930000</td>\n",
       "      <td>5.424000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mpg        cyl        disp          hp       drat         wt  \\\n",
       "count  32.000000  32.000000   32.000000   32.000000  32.000000  32.000000   \n",
       "mean   20.090625   6.187500  230.721875  146.687500   3.596563   3.217250   \n",
       "std     6.026948   1.785922  123.938694   68.562868   0.534679   0.978457   \n",
       "min    10.400000   4.000000   71.100000   52.000000   2.760000   1.513000   \n",
       "25%    15.425000   4.000000  120.825000   96.500000   3.080000   2.581250   \n",
       "50%    19.200000   6.000000  196.300000  123.000000   3.695000   3.325000   \n",
       "75%    22.800000   8.000000  326.000000  180.000000   3.920000   3.610000   \n",
       "max    33.900000   8.000000  472.000000  335.000000   4.930000   5.424000   \n",
       "\n",
       "            qsec         vs         am       gear     carb  \n",
       "count  32.000000  32.000000  32.000000  32.000000  32.0000  \n",
       "mean   17.848750   0.437500   0.406250   3.687500   2.8125  \n",
       "std     1.786943   0.504016   0.498991   0.737804   1.6152  \n",
       "min    14.500000   0.000000   0.000000   3.000000   1.0000  \n",
       "25%    16.892500   0.000000   0.000000   3.000000   2.0000  \n",
       "50%    17.710000   0.000000   0.000000   4.000000   2.0000  \n",
       "75%    18.900000   1.000000   1.000000   4.000000   4.0000  \n",
       "max    22.900000   1.000000   1.000000   5.000000   8.0000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAALpElEQVR4nO3df6zd9V3H8debdkARs4I1hsCyztVkonFu4lycMXOaDDfGNIsJuiWbmZIpqSQzWWLUZM5/TJbosGomQROcmcBIFhmZ4qIz/gpsBYE5nHIFnDZxFBAzpKCUj3+cL3K5FHoP99zvu6OPR3LT0/M993zf/d5Pnz33e3rOrTFGAJjfKd0DAJysBBigiQADNBFggCYCDNBk5zI33rNnz9i7d+82jQLw4rNnz57cdNNNN40xLty4bakA7927NwcPHlzdZAAngarac6zrnYIAaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigyVI/E+6FOnDgQNbW1ubYFSehQ4cOZffu3bnqqqu6R4GlzBLgtbW13P4P/5ijZ5w9x+44yez46oM5cuRI9xiwtFkCnCRHzzg7R171lrl2x0nkzNs+1j0CvCDOAQM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNNk5x04OHTqUUx57dI5dAazUgQMHkiT79+9f+X3PEuAjR46knvzfOXYFsFJra2vbdt9OQQA0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNNnZPQBs2ZNHc+TIkVx++eXdk/AitLa2ll27dm3LfR/3EXBVXVpVB6vq4OHDh7dlCICT0XEfAY8xrkxyZZJccMEFY9sngmWdsiO7Tj81V1xxRfckvAht53dWzgEDNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmO+fYya5du/LV/xlz7Apgpfbt27dt9z1LgM8999z8x+NfmWNXACu1f//+bbtvpyAAmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0GTnXDva8ehD2fWlT8+1O04mR59Icmr3FLC0WQK8b9++OXbDSerQoSeye/fu7jFgabMEeP/+/XPsBuBrinPAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCY1xtj8jasOJ/nXF7ivPUkeeIGfu53MtRxzLcdcy3kxzvVAkowxLty4YakAb0VVHRxjXDDLzpZgruWYaznmWs7JNpdTEABNBBigyZwBvnLGfS3DXMsx13LMtZyTaq7ZzgED8ExOQQA0EWCAJlsOcFWdXlWfq6o7quqLVfUrx7jNaVV1bVWtVdUtVbV33bZfmK7/p6p681bnWXKu91fVXVV1Z1X9eVW9fN22o1V1+/Rxw8xzvaeqDq/b/0+t2/buqrp7+nj3zHP9xrqZ/rmqHl63bVuO17r731FVf19VNx5j2+zraxMzzb62lpht9vW1ybla1ldV3VdVX5ju++AxtldV/ea0ju6sqteu27a14zXG2NJHkkpy5nT5JUluSfL6Dbf52SQfnS5fkuTa6fL5Se5IclqSVyT5lyQ7tjrTEnP9QJIzpss/89Rc0+8fWcUcL3Cu9yT5rWN87tlJ7pl+PWu6fNZcc224/f4kv7/dx2vd/b8/yceT3HiMbbOvr03MNPvaWmK22dfXZubqWl9J7kuy53m2vyXJn0x/R16f5JZVHa8tPwIeC49Mv33J9LHxmb23J7l6unx9kh+sqpquv2aM8fgY494ka0let9WZNjvXGOOzY4xHp9/enOS8Vex7q3M9jzcn+cwY46Exxn8m+UySZ726Zqa5fjzJH61i38dTVecleWuSq57jJrOvr+PN1LG2Njvb89i29fUC5pptfW3C25P8wfR35OYku6vqnKzgeK3kHPD0bcXtSe6fBrplw03OTfJvSTLGeCLJfyX5hvXXT/59um4lNjHXeu/N4l+5p5xeVQer6uaq+pFVzbTEXO+Yvt25vqpeNl13Qhyv6dvpVyT5i3VXb9vxSvKRJB9I8uRzbO9YX8ebab3Z1tYSs82+vjY5V8f6Gkn+rKpurapLj7H9uY7Llo/XSgI8xjg6xvjOLP6Vf11Vffsq7nerNjtXVb0ryQVJPrzu6pePxUsPfyLJR6rqlTPO9akke8cY35HFv6pXZwZLfB0vSXL9GOPouuu25XhV1UVJ7h9j3LqK+1uFZWaae21tcrbZ19eSX8fZ1tfk+8YYr03yw0kuq6rvX+F9P6+V/i+IMcbDST6bZz8MP5TkZUlSVTuTvDTJg+uvn5w3XbdSzzNXquqHkvxikovHGI+v+5xD06/3JPnLJK+Za64xxoPrZrkqyXdNl9uP1+SSbPj2cBuP1xuSXFxV9yW5JsmbquoPN9xm7vW1mZm61tZxZ2taX5s6ZpM519f6+74/ySfz7NNUz3Vctn68VnAC+xuT7J4u70ry10ku2nCby/LMJ0mumy5/W575JMk9Wd2TcJuZ6zVZPDHzLRuuPyvJadPlPUnuTnL+jHOds+7yjya5eTx90v/eab6zpstnzzXXtO1VWTxpUXMcrw37fmOO/aTS7OtrEzPNvraWmG329bWZuTrWV5KvS/L16y7/XZILN9zmrXnmk3CfW9Xx2pmtOyfJ1VW1I4tH1NeNMW6sqg8lOTjGuCHJ7yX5WFWtJXkoi78kGWN8saquS3JXkieSXDae+W3Hds/14SRnJvnE4jmbfHmMcXGSb03yu1X15PS5vzbGuGvGuX6uqi7O4pg8lMWz1hljPFRVv5rk89N9fWiM8dCMcyWLr901Y1qBk+08Xsd0Aqyv483UsbY2O1vH+trMXMn86+ubknxy+hrtTPLxMcafVtX7kmSM8dEkn87if0KsJXk0yU9O27Z8vLwUGaCJV8IBNBFggCYCDNBEgAGaCDBAEwEGaCLAnFSmV8rBCcFi5IRWVb+c5F1JDmfxxie3ZvFy0d/O4tV7jyb56THGl6rqbUl+KcmpWbwU+Z1jjK9U1QeTvDLJNyf5chbvtAXtBJgTVlV9d5J3JHl1Fm+PeVsWAb4yyfvGGHdX1fck+Z0kb0ryN1m8h/GoxZuMfyDJz093d34Wb7pyZOY/BjwnAeZE9oYkfzzGeCzJY1X1qSSnJ/nePP0S32TxXg/J4s1Qrp3eq/XULF6b/5QbxJcTjQDzteaUJA+PxdtmbnQgya+PMW6oqjcm+eC6bf+97ZPBkjwJx4nsb5O8rRY/r+7MJBdlcc733qr6seT/f17Xq6fbvzRPvx3gyn+eGayaAHPCGmN8PskNSe7M4u0Av5DFT7t4Z5L3VtUdSb6YxY+MSRaPeD9RVbcmeWD2gWFJ3g2NE1pVnTnGeKSqzkjyV0kuHWPc1j0XrIJzwJzorqyq87N48u1q8eXFxCNggCbOAQM0EWCAJgIM0ESAAZoIMECT/wMgsbVMbckCIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAALJklEQVR4nO3df6zdd13H8dd7vZB1U0C6ZakdsZgmMwgJkAYRCH+w1RSGyD8kkuhK4oJ/mK5ME6OJifiH/mWMW0lMlqK2QKZuzMRI09CFqUCU0Y4fA7bEGgquDtd11rGtiG0//nHPklp2b3+de9/ntI9HcpN7T8/5ft+9ued5v+dzzvneGmMEgNV3VfcAAFcqAQZoIsAATQQYoIkAAzRZuJArX3fddWPjxo0rNArA5engwYNPjzGuP/vyCwrwxo0bc+DAgelNBXAFqKrvvNTlliAAmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZockF/E+5KcPvtt+f48ePZsGFD9yiXpU2bNmX79u3dY8BMEOCzPPnkk3nu+Rfyvf/xrZm2NS880z0CzBSVeSlrFnLiZ97TPcVlZ+3je7tHgJliDRigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoMmqBHjnzp3ZuXPnauwKrkjuY/NpYTV2cujQodXYDVyx3MfmkyUIgCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYYBnHjh3LHXfckWPHjk192wIMsIzdu3fn0UcfzZ49e6a+bQEGWMKxY8eyb9++jDGyb9++qR8FL0x1a0s4cuRITpw4kR07dqzG7i7JiRMnktE9xeXpqh88m0OHvj8XPwfz5tChQ1m7dm33GJed3bt35/Tp00mSU6dOZc+ePbnzzjuntv1zHgFX1Yer6kBVHTh69OjUdgww6x588MGcPHkySXLy5Mns379/qts/5xHwGOOeJPckyebNmy/q2HDDhg1Jkrvuuutibr6qbr311jz3gx92j3FZOn31K7Lpp2+Yi5+DeeNRxcq45ZZbsnfv3pw8eTILCwvZsmXLVLdvDRhgCdu2bctVVy1mcs2aNbntttumun0BBljCunXrsnXr1lRVtm7dmnXr1k11+6vyJBzAvNq2bVsOHz489aPfRIABlrVu3brcfffdK7JtSxAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaLKzGTjZt2rQau4ErlvvYfFqVAG/fvn01dgNXLPex+WQJAqCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNFroHmEmnTmbt43u7p7jsrHnhmSQ3dI8BM0OAz7J+/focP348GzYIxfTdkE2bNnUPATNDgM+ya9eu7hGAK4Q1YIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMECTGmOc/5Wrjib5zkXu67okT1/kbVfbPM2azNe88zRrMl/zztOsyXzNe6mz/tQY4/qzL7ygAF+Kqjowxti8Kju7RPM0azJf887TrMl8zTtPsybzNe9KzWoJAqCJAAM0Wc0A37OK+7pU8zRrMl/zztOsyXzNO0+zJvM174rMumprwAD8f5YgAJoIMECTFQ9wVf15VT1VVd9Y6X1dqqp6TVU9VFXfqqpvVtWO7pmWUlVXV9XDVfW1yax/0D3T+aiqNVX1lar6++5ZllNVh6vq0ar6alUd6J7nXKrqVVV1f1U9XlWPVdXPd8/0Uqrqpsn39MWPZ6vqI91zLaeq7pzcx75RVfdW1dVT2/ZKrwFX1TuTPJdkzxjj9Su6s0tUVeuTrB9jPFJVP57kYJL3jzG+1Tzaj6iqSnLtGOO5qnpZki8k2THG+Jfm0ZZVVb+ZZHOSV4wx3ts9z1Kq6nCSzWOMuXijQFXtTvL5Mcauqnp5kmvGGMebx1pWVa1JciTJz40xLvYNXiuqqjZk8b71ujHGiar6myR7xxh/OY3tr/gR8Bjjn5I8s9L7mYYxxpNjjEcmn38/yWNJNvRO9dLGoucmX75s8jHTz6hW1Y1Jbk2yq3uWy0lVvTLJO5N8PEnGGD+c9fhO3Jzk32Y1vmdYSLK2qhaSXJPkP6a1YWvAS6iqjUnelORLzaMsafJw/qtJnkqyf4wxs7NO/GmS305yunmO8zGSfLaqDlbVh7uHOYfXJjma5C8myzu7qura7qHOwy8nubd7iOWMMY4k+eMk303yZJL/HmN8dlrbF+CXUFU/luTTST4yxni2e56ljDFOjTHemOTGJG+pqpld4qmq9yZ5aoxxsHuW8/SOMcabk7w7yW9MltJm1UKSNyf5szHGm5I8n+R3ekda3mSZ5H1J7uueZTlV9RNJfimLv+R+Msm1VfUr09q+AJ9lsp766SSfGmM80D3P+Zg83HwoydbmUZbz9iTvm6yt/lWSd1XVJ3tHWtrkyCdjjKeS/G2St/ROtKwnkjxxxiOg+7MY5Fn27iSPjDH+s3uQc7glybfHGEfHGP+b5IEkb5vWxgX4DJMntj6e5LExxp90z7Ocqrq+ql41+Xxtki1JHm8dahljjN8dY9w4xtiYxYeenxtjTO1IYpqq6trJk7CZPJT/hSQz+yqeMcb3kvx7Vd00uejmJDP3xPFZPpgZX36Y+G6St1bVNZM+3JzF54amYjVehnZvkn9OclNVPVFVv7bS+7wEb0/yq1k8OnvxZTLv6R5qCeuTPFRVX0/y5SyuAc/0S7vmyA1JvlBVX0vycJLPjDH2Nc90LtuTfGry8/DGJH/UO87SJr/UtmTxaHKmTR5V3J/kkSSPZrGZU3tbsrciAzSxBAHQRIABmggwQBMBBmgiwABNBJjLVlV9qKo+1j0HLEWAuSxNTpwCM02AmXlVdVtVfX1y7uNPVNUvVtWXJieeebCqbphc76OTf/9ikk9Mbv6aqvqHqvrXqvr9vv8F/ChHCcy0qvrZJL+X5G1jjKer6tVZPFPZW8cYo6puz+IZ1n5rcpPXZfFEOieq6kNZPIfD65O8kOTLVfWZMcbMn2CdK4MAM+veleS+F0+MPsZ4pqrekOSvJyfQf3mSb59x/b8bY5w44+v9Y4xjSVJVDyR5RxIBZiZYgmAe7UzysTHGG5L8epIz/0TM82dd9+z32nvvPTNDgJl1n0vygapalySTJYhXZvFP2STJtnPcfktVvXpyxrj3J/niSg0KF8oSBDNtjPHNqvrDJP9YVaeSfCXJR5PcV1X/lcVAv3aZTTycxfM735jkk9Z/mSXOhgbQxBIEQBMBBmgiwABNBBigiQADNBFggCYCDNDk/wArUFhITrTPmQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for column in df1:\n",
    "    plt.figure()\n",
    "    sns.boxplot(df1[column])   #or #df1.boxplot([column]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1      True\n",
       "2      True\n",
       "3      True\n",
       "4      True\n",
       "5      True\n",
       "6      True\n",
       "7      True\n",
       "8     False\n",
       "9      True\n",
       "10     True\n",
       "11     True\n",
       "12     True\n",
       "13     True\n",
       "14    False\n",
       "15    False\n",
       "16    False\n",
       "17     True\n",
       "18     True\n",
       "19     True\n",
       "20     True\n",
       "21     True\n",
       "22     True\n",
       "23     True\n",
       "24     True\n",
       "25     True\n",
       "26     True\n",
       "27     True\n",
       "28     True\n",
       "29    False\n",
       "30    False\n",
       "31     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter=(df1['hp']<280) & (df1['wt']<4.3) & (df1['qsec']<21) & (df1['carb']<6)\n",
    "filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>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>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>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>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>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>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",
       "    <tr>\n",
       "      <th>5</th>\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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\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",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\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",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\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",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\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",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\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",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\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",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\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",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\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",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  carb\n",
       "0   21.0    6  160.0  110  3.90  2.620  16.46   0   1     4     4\n",
       "1   21.0    6  160.0  110  3.90  2.875  17.02   0   1     4     4\n",
       "2   22.8    4  108.0   93  3.85  2.320  18.61   1   1     4     1\n",
       "3   21.4    6  258.0  110  3.08  3.215  19.44   1   0     3     1\n",
       "4   18.7    8  360.0  175  3.15  3.440  17.02   0   0     3     2\n",
       "5   18.1    6  225.0  105  2.76  3.460  20.22   1   0     3     1\n",
       "6   14.3    8  360.0  245  3.21  3.570  15.84   0   0     3     4\n",
       "7   24.4    4  146.7   62  3.69  3.190  20.00   1   0     4     2\n",
       "8   19.2    6  167.6  123  3.92  3.440  18.30   1   0     4     4\n",
       "9   17.8    6  167.6  123  3.92  3.440  18.90   1   0     4     4\n",
       "10  16.4    8  275.8  180  3.07  4.070  17.40   0   0     3     3\n",
       "11  17.3    8  275.8  180  3.07  3.730  17.60   0   0     3     3\n",
       "12  15.2    8  275.8  180  3.07  3.780  18.00   0   0     3     3\n",
       "13  32.4    4   78.7   66  4.08  2.200  19.47   1   1     4     1\n",
       "14  30.4    4   75.7   52  4.93  1.615  18.52   1   1     4     2\n",
       "15  33.9    4   71.1   65  4.22  1.835  19.90   1   1     4     1\n",
       "16  21.5    4  120.1   97  3.70  2.465  20.01   1   0     3     1\n",
       "17  15.5    8  318.0  150  2.76  3.520  16.87   0   0     3     2\n",
       "18  15.2    8  304.0  150  3.15  3.435  17.30   0   0     3     2\n",
       "19  13.3    8  350.0  245  3.73  3.840  15.41   0   0     3     4\n",
       "20  19.2    8  400.0  175  3.08  3.845  17.05   0   0     3     2\n",
       "21  27.3    4   79.0   66  4.08  1.935  18.90   1   1     4     1\n",
       "22  26.0    4  120.3   91  4.43  2.140  16.70   0   1     5     2\n",
       "23  30.4    4   95.1  113  3.77  1.513  16.90   1   1     5     2\n",
       "24  15.8    8  351.0  264  4.22  3.170  14.50   0   1     5     4\n",
       "25  21.4    4  121.0  109  4.11  2.780  18.60   1   1     4     2"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=df1[filter]\n",
    "df1=df1.reset_index(drop=True)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26, 11)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAJqUlEQVR4nO3dW4icdxnH8d+TRGsUz4lS1uoqUbQoqATRGw8oIhFaRBEFUaFVVKgXXgneiN4ooheCoKWIB/AMSsDqhYdSEKumVK1naj21Rk2tqWhqTN2/FzNgDK076c68T8f9fGBhdvbN+z7/nd3v7ryzM6kxRgCY3p7uAQB2KwEGaCLAAE0EGKCJAAM02Xc+Gx84cGBsbm6uaBSA/0/XX3/9bWOMg+def14B3tzczLFjx5Y3FcAuUFW/ubvrnYIAaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigyXn9n3D31uWXX56TJ09mY2NjisMBLNWhQ4dyxRVXLH2/kwT4+PHj+dvfT+UPpyc5HMDS7D11+8r2PV0R9+7LnU8+MtnhAJZh/8+uXtm+nQMGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmiyb4qDnD59OtnamuJQAEu15x9/za233rWafa9kr+fY2tpKxpjiUABLVVtncuedd65k305BADQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJtsGuKreWFXHqurYiRMnppgJYFfYNsBjjCvHGIfHGIcPHjw4xUwAu4JTEABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoMkkAd6zZ09SNcWhAJZq7Llf9u/fv5J9TxLgCy64INmzd4pDASzV1gMeko2NjZXs2ykIgCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzTZN9mR/nVX9v/s6skOB7AMe0/dnuTRK9n3JAG+8MILc/LkyWxsrGYRAKvz6Bw6dGgle54kwFddddUUhwFYK84BAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZrUGGPxjatOJPnNvTzWgSS33ct/u66seXfYbWvebetNdr7mx40xDp575XkFeCeq6tgY4/AkB7uPsObdYbetebetN1ndmp2CAGgiwABNpgzwlRMe677CmneH3bbm3bbeZEVrnuwcMAD/zSkIgCYCDNBk6QGuqpdU1c+r6qaqevvdfPyCqvrs/OPfqarNZc8wpQXW+7aq+klV/bCqvl5Vj+uYc5m2W/NZ2728qkZVrf2fLC2y5qp65fy2/nFVfWrqGZdtga/tx1bVN6vqhvnX95GOOZelqj5aVX+qqh/dw8erqj44/3z8sKqeueODjjGW9pZkb5JfJnlCkvsn+UGSi8/Z5i1JPjy//Kokn13mDFO+LbjeFyR54Pzym9d5vYuueb7dg5Ncm+S6JIe7557gdn5ikhuSPHz+/qO6555gzVcmefP88sVJft099w7X/Nwkz0zyo3v4+JEkX0lSSZ6d5Ds7PeayfwN+VpKbxhg3jzH+meQzSS49Z5tLk3x8fvkLSV5YVbXkOaay7XrHGN8cY5yav3tdksdMPOOyLXIbJ8m7k7w3yT+mHG5FFlnzG5J8aIzxlyQZY/xp4hmXbZE1jyQPmV9+aJLfTzjf0o0xrk1y+//Y5NIknxgz1yV5WFVduJNjLjvAG0l+d9b7t8yvu9ttxhh3JbkjySOXPMdUFlnv2S7L7CfoOtt2zfO7ZheNMb485WArtMjt/KQkT6qqb1XVdVX1ksmmW41F1vzOJK+pqluSXJ3kimlGa3O+3+/b2rejcVhYVb0myeEkz+ueZZWqak+SDyR5ffMoU9uX2WmI52d2L+faqnraGONk51Ar9uokHxtjvL+qnpPkk1X11DHGVvdg62LZvwHfmuSis95/zPy6u92mqvZldtflz0ueYyqLrDdV9aIk70hyyRjj9ESzrcp2a35wkqcmuaaqfp3ZubKja/5A3CK38y1Jjo4xzowxfpXkF5kFeV0tsubLknwuScYY307ygMxetOb/1ULf7+dj2QH+XpInVtXjq+r+mT3IdvScbY4med388iuSfGPMz3CvoW3XW1XPSPKRzOK77ucFk23WPMa4Y4xxYIyxOcbYzOy89yVjjGM94y7FIl/XX8rst99U1YHMTkncPOGMy7bImn+b5IVJUlVPySzAJyadclpHk7x2/tcQz05yxxjj+I72uIJHEo9k9tP/l0neMb/uXZl9EyazG+nzSW5K8t0kT+h+9HPF6/1akj8m+f787Wj3zKte8znbXpM1/yuIBW/nyuzUy0+S3JjkVd0zT7Dmi5N8K7O/kPh+khd3z7zD9X46yfEkZzK7R3NZkjcledNZt/GH5p+PG5fxde2pyABNPBMOoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwKyVqvpSVV0/f83dN86v+1tVvW9+3deq6llVdU1V3VxVl3TPDPfEEzFYK1X1iDHG7VW1P7Onyz4vyW1JjowxvlJVX0zyoCQvzeyZWh8fYzy9bWD4H7waGuvmrVX1svnlizJ7wZt/Jvnq/Lobk5weY5ypqhuTbE4/IixGgFkbVfX8JC9K8pwxxqmquiaz1xY5M/5zV24ryekkGWNszV9xD+6TnANmnTw0yV/m8X1yZi91CWtLgFknX02yr6p+muQ9mb3UJawtD8IBNPEbMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQ5N9BisPA02QjXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "for c in df1:\n",
    "    plt.figure()\n",
    "    sns.boxplot(df1[c])   #or #df1.boxplot([column]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "# Groupby:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{3: [3, 4, 5, 6, 11, 12, 13, 14, 15, 16, 20, 21, 22, 23, 24], 4: [0, 1, 2, 7, 8, 9, 10, 17, 18, 19, 25, 31], 5: [26, 27, 28, 29, 30]}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('gear').groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{4: [2, 7, 8, 17, 18, 19, 20, 25, 26, 27, 31], 6: [0, 1, 3, 5, 9, 10, 29], 8: [4, 6, 11, 12, 13, 14, 15, 16, 21, 22, 23, 24, 28, 30]}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('cyl').groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "t=df.groupby('cyl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>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>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",
       "      <td>2</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",
       "      <td>2</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",
       "      <td>1</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",
       "      <td>2</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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>2</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",
       "      <td>2</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",
       "      <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",
       "2       Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4   \n",
       "7        Merc 240D  24.4    4  146.7   62  3.69  3.190  20.00   1   0     4   \n",
       "8         Merc 230  22.8    4  140.8   95  3.92  3.150  22.90   1   0     4   \n",
       "17        Fiat 128  32.4    4   78.7   66  4.08  2.200  19.47   1   1     4   \n",
       "18     Honda Civic  30.4    4   75.7   52  4.93  1.615  18.52   1   1     4   \n",
       "19  Toyota Corolla  33.9    4   71.1   65  4.22  1.835  19.90   1   1     4   \n",
       "20   Toyota Corona  21.5    4  120.1   97  3.70  2.465  20.01   1   0     3   \n",
       "25       Fiat X1-9  27.3    4   79.0   66  4.08  1.935  18.90   1   1     4   \n",
       "26   Porsche 914-2  26.0    4  120.3   91  4.43  2.140  16.70   0   1     5   \n",
       "27    Lotus Europa  30.4    4   95.1  113  3.77  1.513  16.90   1   1     5   \n",
       "31      Volvo 142E  21.4    4  121.0  109  4.11  2.780  18.60   1   1     4   \n",
       "\n",
       "    carb  \n",
       "2      1  \n",
       "7      2  \n",
       "8      2  \n",
       "17     1  \n",
       "18     2  \n",
       "19     1  \n",
       "20     1  \n",
       "25     1  \n",
       "26     2  \n",
       "27     2  \n",
       "31     2  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.get_group(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(3, 4): [20], (3, 6): [3, 5], (3, 8): [4, 6, 11, 12, 13, 14, 15, 16, 21, 22, 23, 24], (4, 4): [2, 7, 8, 17, 18, 19, 25, 31], (4, 6): [0, 1, 9, 10], (5, 4): [26, 27], (5, 6): [29], (5, 8): [28, 30]}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['gear','cyl']).groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(4, 3): [20], (4, 4): [2, 7, 8, 17, 18, 19, 25, 31], (4, 5): [26, 27], (6, 3): [3, 5], (6, 4): [0, 1, 9, 10], (6, 5): [29], (8, 3): [4, 6, 11, 12, 13, 14, 15, 16, 21, 22, 23, 24], (8, 5): [28, 30]}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['cyl','gear']).groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cyl\n",
       "4     82.636364\n",
       "6    122.285714\n",
       "8    209.214286\n",
       "Name: hp, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('cyl')['hp'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>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>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",
       "      <td>2</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",
       "      <td>2</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",
       "      <td>1</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",
       "      <td>2</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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>2</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",
       "      <td>2</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",
       "      <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",
       "2       Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4   \n",
       "7        Merc 240D  24.4    4  146.7   62  3.69  3.190  20.00   1   0     4   \n",
       "8         Merc 230  22.8    4  140.8   95  3.92  3.150  22.90   1   0     4   \n",
       "17        Fiat 128  32.4    4   78.7   66  4.08  2.200  19.47   1   1     4   \n",
       "18     Honda Civic  30.4    4   75.7   52  4.93  1.615  18.52   1   1     4   \n",
       "19  Toyota Corolla  33.9    4   71.1   65  4.22  1.835  19.90   1   1     4   \n",
       "20   Toyota Corona  21.5    4  120.1   97  3.70  2.465  20.01   1   0     3   \n",
       "25       Fiat X1-9  27.3    4   79.0   66  4.08  1.935  18.90   1   1     4   \n",
       "26   Porsche 914-2  26.0    4  120.3   91  4.43  2.140  16.70   0   1     5   \n",
       "27    Lotus Europa  30.4    4   95.1  113  3.77  1.513  16.90   1   1     5   \n",
       "31      Volvo 142E  21.4    4  121.0  109  4.11  2.780  18.60   1   1     4   \n",
       "\n",
       "    carb  \n",
       "2      1  \n",
       "7      2  \n",
       "8      2  \n",
       "17     1  \n",
       "18     2  \n",
       "19     1  \n",
       "20     1  \n",
       "25     1  \n",
       "26     2  \n",
       "27     2  \n",
       "31     2  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = t.get_group(4)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</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>1</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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</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",
       "      <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       Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4   \n",
       "1        Merc 240D  24.4    4  146.7   62  3.69  3.190  20.00   1   0     4   \n",
       "2         Merc 230  22.8    4  140.8   95  3.92  3.150  22.90   1   0     4   \n",
       "3         Fiat 128  32.4    4   78.7   66  4.08  2.200  19.47   1   1     4   \n",
       "4      Honda Civic  30.4    4   75.7   52  4.93  1.615  18.52   1   1     4   \n",
       "5   Toyota Corolla  33.9    4   71.1   65  4.22  1.835  19.90   1   1     4   \n",
       "6    Toyota Corona  21.5    4  120.1   97  3.70  2.465  20.01   1   0     3   \n",
       "7        Fiat X1-9  27.3    4   79.0   66  4.08  1.935  18.90   1   1     4   \n",
       "8    Porsche 914-2  26.0    4  120.3   91  4.43  2.140  16.70   0   1     5   \n",
       "9     Lotus Europa  30.4    4   95.1  113  3.77  1.513  16.90   1   1     5   \n",
       "10      Volvo 142E  21.4    4  121.0  109  4.11  2.780  18.60   1   1     4   \n",
       "\n",
       "    carb  \n",
       "0      1  \n",
       "1      2  \n",
       "2      2  \n",
       "3      1  \n",
       "4      2  \n",
       "5      1  \n",
       "6      1  \n",
       "7      1  \n",
       "8      2  \n",
       "9      2  \n",
       "10     2  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.reset_index(drop=True)"
   ]
  },
  {
   "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": null,
   "metadata": {},
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