Auto_MPG.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MPG Cars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "The following exercise utilizes data from [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Auto+MPG)\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the first dataset [cars1](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars1.csv) and [cars2](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars2.csv).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ### Step 3. Assign each to a variable called cars1 and cars2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cars1 = df = pd.read_csv(\"https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model</th>\n",
       "      <th>origin</th>\n",
       "      <th>car</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>24.0</td>\n",
       "      <td>6</td>\n",
       "      <td>200</td>\n",
       "      <td>81</td>\n",
       "      <td>3012</td>\n",
       "      <td>17.6</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>ford maverick</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>22.5</td>\n",
       "      <td>6</td>\n",
       "      <td>232</td>\n",
       "      <td>90</td>\n",
       "      <td>3085</td>\n",
       "      <td>17.6</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>amc hornet</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>29.0</td>\n",
       "      <td>4</td>\n",
       "      <td>85</td>\n",
       "      <td>52</td>\n",
       "      <td>2035</td>\n",
       "      <td>22.2</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevette</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>24.5</td>\n",
       "      <td>4</td>\n",
       "      <td>98</td>\n",
       "      <td>60</td>\n",
       "      <td>2164</td>\n",
       "      <td>22.1</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet woody</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>29.0</td>\n",
       "      <td>4</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>1937</td>\n",
       "      <td>14.2</td>\n",
       "      <td>76</td>\n",
       "      <td>2</td>\n",
       "      <td>vw rabbit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  model  \\\n",
       "0    18.0          8           307        130    3504          12.0     70   \n",
       "1    15.0          8           350        165    3693          11.5     70   \n",
       "2    18.0          8           318        150    3436          11.0     70   \n",
       "3    16.0          8           304        150    3433          12.0     70   \n",
       "4    17.0          8           302        140    3449          10.5     70   \n",
       "..    ...        ...           ...        ...     ...           ...    ...   \n",
       "193  24.0          6           200         81    3012          17.6     76   \n",
       "194  22.5          6           232         90    3085          17.6     76   \n",
       "195  29.0          4            85         52    2035          22.2     76   \n",
       "196  24.5          4            98         60    2164          22.1     76   \n",
       "197  29.0          4            90         70    1937          14.2     76   \n",
       "\n",
       "     origin                        car  Unnamed: 9  Unnamed: 10  Unnamed: 11  \\\n",
       "0         1  chevrolet chevelle malibu         NaN          NaN          NaN   \n",
       "1         1          buick skylark 320         NaN          NaN          NaN   \n",
       "2         1         plymouth satellite         NaN          NaN          NaN   \n",
       "3         1              amc rebel sst         NaN          NaN          NaN   \n",
       "4         1                ford torino         NaN          NaN          NaN   \n",
       "..      ...                        ...         ...          ...          ...   \n",
       "193       1              ford maverick         NaN          NaN          NaN   \n",
       "194       1                 amc hornet         NaN          NaN          NaN   \n",
       "195       1         chevrolet chevette         NaN          NaN          NaN   \n",
       "196       1            chevrolet woody         NaN          NaN          NaN   \n",
       "197       2                  vw rabbit         NaN          NaN          NaN   \n",
       "\n",
       "     Unnamed: 12  Unnamed: 13  \n",
       "0            NaN          NaN  \n",
       "1            NaN          NaN  \n",
       "2            NaN          NaN  \n",
       "3            NaN          NaN  \n",
       "4            NaN          NaN  \n",
       "..           ...          ...  \n",
       "193          NaN          NaN  \n",
       "194          NaN          NaN  \n",
       "195          NaN          NaN  \n",
       "196          NaN          NaN  \n",
       "197          NaN          NaN  \n",
       "\n",
       "[198 rows x 14 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "cars2 = df = pd.read_csv(\"https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "      <th>acceleration</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33.0</td>\n",
       "      <td>4</td>\n",
       "      <td>91</td>\n",
       "      <td>53</td>\n",
       "      <td>1795</td>\n",
       "      <td>17.4</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>honda civic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.0</td>\n",
       "      <td>6</td>\n",
       "      <td>225</td>\n",
       "      <td>100</td>\n",
       "      <td>3651</td>\n",
       "      <td>17.7</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge aspen se</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>6</td>\n",
       "      <td>250</td>\n",
       "      <td>78</td>\n",
       "      <td>3574</td>\n",
       "      <td>21.0</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>ford granada ghia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.5</td>\n",
       "      <td>6</td>\n",
       "      <td>250</td>\n",
       "      <td>110</td>\n",
       "      <td>3645</td>\n",
       "      <td>16.2</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>pontiac ventura sj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.5</td>\n",
       "      <td>6</td>\n",
       "      <td>258</td>\n",
       "      <td>95</td>\n",
       "      <td>3193</td>\n",
       "      <td>17.8</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>amc pacer d/l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>86</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang gl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>52</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135</td>\n",
       "      <td>84</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge rampage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford ranger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119</td>\n",
       "      <td>82</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  model  \\\n",
       "0    33.0          4            91         53    1795          17.4     76   \n",
       "1    20.0          6           225        100    3651          17.7     76   \n",
       "2    18.0          6           250         78    3574          21.0     76   \n",
       "3    18.5          6           250        110    3645          16.2     76   \n",
       "4    17.5          6           258         95    3193          17.8     76   \n",
       "..    ...        ...           ...        ...     ...           ...    ...   \n",
       "195  27.0          4           140         86    2790          15.6     82   \n",
       "196  44.0          4            97         52    2130          24.6     82   \n",
       "197  32.0          4           135         84    2295          11.6     82   \n",
       "198  28.0          4           120         79    2625          18.6     82   \n",
       "199  31.0          4           119         82    2720          19.4     82   \n",
       "\n",
       "     origin                 car  \n",
       "0         3         honda civic  \n",
       "1         1      dodge aspen se  \n",
       "2         1   ford granada ghia  \n",
       "3         1  pontiac ventura sj  \n",
       "4         1       amc pacer d/l  \n",
       "..      ...                 ...  \n",
       "195       1     ford mustang gl  \n",
       "196       2           vw pickup  \n",
       "197       1       dodge rampage  \n",
       "198       1         ford ranger  \n",
       "199       1          chevy s-10  \n",
       "\n",
       "[200 rows x 9 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Oops, it seems our first dataset has some unnamed blank columns, fix cars1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thought about deleting these columns, so I before doing so I might as well check if they are completely empty.First I create alist of all columns I want to check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "columns_to_remove = ['Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12','Unnamed: 13']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 9     True\n",
       "Unnamed: 10    True\n",
       "Unnamed: 11    True\n",
       "Unnamed: 12    True\n",
       "Unnamed: 13    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_columns = cars1[columns_to_remove].isna().all()\n",
    "check_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it appears that all columns are empty .Deleting these rows :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "cars1.drop(columns=columns_to_remove, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model</th>\n",
       "      <th>origin</th>\n",
       "      <th>car</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
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       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
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       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
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       "      <td>3693</td>\n",
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       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
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       "    <tr>\n",
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       "      <td>16.0</td>\n",
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       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
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       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>3012</td>\n",
       "      <td>17.6</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>ford maverick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>22.5</td>\n",
       "      <td>6</td>\n",
       "      <td>232</td>\n",
       "      <td>90</td>\n",
       "      <td>3085</td>\n",
       "      <td>17.6</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>amc hornet</td>\n",
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       "      <td>29.0</td>\n",
       "      <td>4</td>\n",
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       "      <td>2035</td>\n",
       "      <td>22.2</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevette</td>\n",
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       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>24.5</td>\n",
       "      <td>4</td>\n",
       "      <td>98</td>\n",
       "      <td>60</td>\n",
       "      <td>2164</td>\n",
       "      <td>22.1</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet woody</td>\n",
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       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>29.0</td>\n",
       "      <td>4</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>1937</td>\n",
       "      <td>14.2</td>\n",
       "      <td>76</td>\n",
       "      <td>2</td>\n",
       "      <td>vw rabbit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  model  \\\n",
       "0    18.0          8           307        130    3504          12.0     70   \n",
       "1    15.0          8           350        165    3693          11.5     70   \n",
       "2    18.0          8           318        150    3436          11.0     70   \n",
       "3    16.0          8           304        150    3433          12.0     70   \n",
       "4    17.0          8           302        140    3449          10.5     70   \n",
       "..    ...        ...           ...        ...     ...           ...    ...   \n",
       "193  24.0          6           200         81    3012          17.6     76   \n",
       "194  22.5          6           232         90    3085          17.6     76   \n",
       "195  29.0          4            85         52    2035          22.2     76   \n",
       "196  24.5          4            98         60    2164          22.1     76   \n",
       "197  29.0          4            90         70    1937          14.2     76   \n",
       "\n",
       "     origin                        car  \n",
       "0         1  chevrolet chevelle malibu  \n",
       "1         1          buick skylark 320  \n",
       "2         1         plymouth satellite  \n",
       "3         1              amc rebel sst  \n",
       "4         1                ford torino  \n",
       "..      ...                        ...  \n",
       "193       1              ford maverick  \n",
       "194       1                 amc hornet  \n",
       "195       1         chevrolet chevette  \n",
       "196       1            chevrolet woody  \n",
       "197       2                  vw rabbit  \n",
       "\n",
       "[198 rows x 9 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explanation\n",
    "#### Identify and Validate Specific Columns:\n",
    "\n",
    "- The valid columns are checked for being entirely NaN.\n",
    "- The empty columns are identified and stored in columns_to_remove.\n",
    "#### Remove the Columns:\n",
    "\n",
    "- The drop method is used to remove the specified columns from the DataFrame.\n",
    "- The inplace=True parameter ensures the changes are applied directly to the DataFrame without needing to reassign it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. What is the number of observations in each dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 198 entries, 0 to 197\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   mpg           198 non-null    float64\n",
      " 1   cylinders     198 non-null    int64  \n",
      " 2   displacement  198 non-null    int64  \n",
      " 3   horsepower    198 non-null    object \n",
      " 4   weight        198 non-null    int64  \n",
      " 5   acceleration  198 non-null    float64\n",
      " 6   model         198 non-null    int64  \n",
      " 7   origin        198 non-null    int64  \n",
      " 8   car           198 non-null    object \n",
      "dtypes: float64(2), int64(5), object(2)\n",
      "memory usage: 14.1+ KB\n"
     ]
    }
   ],
   "source": [
    "cars1.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   mpg           200 non-null    float64\n",
      " 1   cylinders     200 non-null    int64  \n",
      " 2   displacement  200 non-null    int64  \n",
      " 3   horsepower    200 non-null    object \n",
      " 4   weight        200 non-null    int64  \n",
      " 5   acceleration  200 non-null    float64\n",
      " 6   model         200 non-null    int64  \n",
      " 7   origin        200 non-null    int64  \n",
      " 8   car           200 non-null    object \n",
      "dtypes: float64(2), int64(5), object(2)\n",
      "memory usage: 14.2+ KB\n"
     ]
    },
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
       "      <td>pontiac ventura sj</td>\n",
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       "      <th>4</th>\n",
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       "      <td>1</td>\n",
       "      <td>amc pacer d/l</td>\n",
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       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang gl</td>\n",
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       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>52</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
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       "    <tr>\n",
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       "      <td>82</td>\n",
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       "      <td>dodge rampage</td>\n",
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       "      <td>2625</td>\n",
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       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford ranger</td>\n",
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       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
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       "      <td>82</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  model  \\\n",
       "0    33.0          4            91         53    1795          17.4     76   \n",
       "1    20.0          6           225        100    3651          17.7     76   \n",
       "2    18.0          6           250         78    3574          21.0     76   \n",
       "3    18.5          6           250        110    3645          16.2     76   \n",
       "4    17.5          6           258         95    3193          17.8     76   \n",
       "..    ...        ...           ...        ...     ...           ...    ...   \n",
       "195  27.0          4           140         86    2790          15.6     82   \n",
       "196  44.0          4            97         52    2130          24.6     82   \n",
       "197  32.0          4           135         84    2295          11.6     82   \n",
       "198  28.0          4           120         79    2625          18.6     82   \n",
       "199  31.0          4           119         82    2720          19.4     82   \n",
       "\n",
       "     origin                 car  \n",
       "0         3         honda civic  \n",
       "1         1      dodge aspen se  \n",
       "2         1   ford granada ghia  \n",
       "3         1  pontiac ventura sj  \n",
       "4         1       amc pacer d/l  \n",
       "..      ...                 ...  \n",
       "195       1     ford mustang gl  \n",
       "196       2           vw pickup  \n",
       "197       1       dodge rampage  \n",
       "198       1         ford ranger  \n",
       "199       1          chevy s-10  \n",
       "\n",
       "[200 rows x 9 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars2.info()\n",
    "cars2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Join cars1 and cars2 into a single DataFrame called cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result = pd.concat([cars1,cars2], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>86</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang gl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>52</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135</td>\n",
       "      <td>84</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge rampage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford ranger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119</td>\n",
       "      <td>82</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  model  \\\n",
       "0    18.0          8           307        130    3504          12.0     70   \n",
       "1    15.0          8           350        165    3693          11.5     70   \n",
       "2    18.0          8           318        150    3436          11.0     70   \n",
       "3    16.0          8           304        150    3433          12.0     70   \n",
       "4    17.0          8           302        140    3449          10.5     70   \n",
       "..    ...        ...           ...        ...     ...           ...    ...   \n",
       "393  27.0          4           140         86    2790          15.6     82   \n",
       "394  44.0          4            97         52    2130          24.6     82   \n",
       "395  32.0          4           135         84    2295          11.6     82   \n",
       "396  28.0          4           120         79    2625          18.6     82   \n",
       "397  31.0          4           119         82    2720          19.4     82   \n",
       "\n",
       "     origin                        car  \n",
       "0         1  chevrolet chevelle malibu  \n",
       "1         1          buick skylark 320  \n",
       "2         1         plymouth satellite  \n",
       "3         1              amc rebel sst  \n",
       "4         1                ford torino  \n",
       "..      ...                        ...  \n",
       "393       1            ford mustang gl  \n",
       "394       2                  vw pickup  \n",
       "395       1              dodge rampage  \n",
       "396       1                ford ranger  \n",
       "397       1                 chevy s-10  \n",
       "\n",
       "[398 rows x 9 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)  \n",
    "\n",
    "198 Entries"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "\n",
    "200 Entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 398 entries, 0 to 397\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   mpg           398 non-null    float64\n",
      " 1   cylinders     398 non-null    int64  \n",
      " 2   displacement  398 non-null    int64  \n",
      " 3   horsepower    398 non-null    object \n",
      " 4   weight        398 non-null    int64  \n",
      " 5   acceleration  398 non-null    float64\n",
      " 6   model         398 non-null    int64  \n",
      " 7   origin        398 non-null    int64  \n",
      " 8   car           398 non-null    object \n",
      "dtypes: float64(2), int64(5), object(2)\n",
      "memory usage: 28.1+ KB\n"
     ]
    }
   ],
   "source": [
    "result.info()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "\n",
    "Total entries after concatenation of two Dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Oops, there is a column missing, called owners. Create a random number Series from 15,000 to 73,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To generate a random number for each row, we first need to know how many rows are in the DataFrame. This is done using result.shape[0], where shape[0] returns the number of rows.We know how many rows are in the datashape(look above) but we could also use shape[] to extract this information and use it whilst adding a new column with new random numbers as values :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_rows = result.shape[0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to create random integers between 15,000 and 73,000 for each row. We use numpy's randint function to generate these numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_numbers = np.random.randint(15000, 73001, size= num_rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 15000: The lower bound (inclusive).\n",
    "- 73001: The upper bound (exclusive). We use 73001 because randint does not include the upper bound, so this ensures we get numbers up to 73,000.\n",
    "- size=num_rows: The number of random integers to generate, equal to the number of rows in the DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add these random numbers as a new column named owners to the DataFrame. This is done by assigning the random_numbers array to result['owners']:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "result['owners'] = random_numbers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model</th>\n",
       "      <th>origin</th>\n",
       "      <th>car</th>\n",
       "      <th>owners</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "      <td>36295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "      <td>28222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "      <td>16256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "      <td>44194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "      <td>27349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>86</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang gl</td>\n",
       "      <td>55791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>52</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
       "      <td>52989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135</td>\n",
       "      <td>84</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge rampage</td>\n",
       "      <td>40836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford ranger</td>\n",
       "      <td>15259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119</td>\n",
       "      <td>82</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "      <td>27230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  model  \\\n",
       "0    18.0          8           307        130    3504          12.0     70   \n",
       "1    15.0          8           350        165    3693          11.5     70   \n",
       "2    18.0          8           318        150    3436          11.0     70   \n",
       "3    16.0          8           304        150    3433          12.0     70   \n",
       "4    17.0          8           302        140    3449          10.5     70   \n",
       "..    ...        ...           ...        ...     ...           ...    ...   \n",
       "393  27.0          4           140         86    2790          15.6     82   \n",
       "394  44.0          4            97         52    2130          24.6     82   \n",
       "395  32.0          4           135         84    2295          11.6     82   \n",
       "396  28.0          4           120         79    2625          18.6     82   \n",
       "397  31.0          4           119         82    2720          19.4     82   \n",
       "\n",
       "     origin                        car  owners  \n",
       "0         1  chevrolet chevelle malibu   36295  \n",
       "1         1          buick skylark 320   28222  \n",
       "2         1         plymouth satellite   16256  \n",
       "3         1              amc rebel sst   44194  \n",
       "4         1                ford torino   27349  \n",
       "..      ...                        ...     ...  \n",
       "393       1            ford mustang gl   55791  \n",
       "394       2                  vw pickup   52989  \n",
       "395       1              dodge rampage   40836  \n",
       "396       1                ford ranger   15259  \n",
       "397       1                 chevy s-10   27230  \n",
       "\n",
       "[398 rows x 10 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "result"
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   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
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