DA_scout_car.ipynb

{
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  {
   "cell_type": "code",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\turan\\anaconda3\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>make_model</th>\n",
       "      <th>short_description</th>\n",
       "      <th>body_type</th>\n",
       "      <th>price</th>\n",
       "      <th>vat</th>\n",
       "      <th>km</th>\n",
       "      <th>registration</th>\n",
       "      <th>prev_owner</th>\n",
       "      <th>kw</th>\n",
       "      <th>hp</th>\n",
       "      <th>...</th>\n",
       "      <th>description</th>\n",
       "      <th>emission_label</th>\n",
       "      <th>gears</th>\n",
       "      <th>country_version</th>\n",
       "      <th>electricity_consumption</th>\n",
       "      <th>last_service_date</th>\n",
       "      <th>other_fuel_types</th>\n",
       "      <th>availability</th>\n",
       "      <th>last_timing_belt_service_date</th>\n",
       "      <th>available_from</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Audi A1</td>\n",
       "      <td>Sportback 1.4 TDI S-tronic Xenon Navi Klima</td>\n",
       "      <td>Sedans</td>\n",
       "      <td>15770</td>\n",
       "      <td>VAT deductible</td>\n",
       "      <td>56,013 km</td>\n",
       "      <td>01/2016</td>\n",
       "      <td>2 previous owners</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66 kW</td>\n",
       "      <td>...</td>\n",
       "      <td>[\\n, Sicherheit:,  , Deaktivierung für Beifahr...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>Audi A1</td>\n",
       "      <td>1.8 TFSI sport</td>\n",
       "      <td>Sedans</td>\n",
       "      <td>14500</td>\n",
       "      <td>Price negotiable</td>\n",
       "      <td>80,000 km</td>\n",
       "      <td>03/2017</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>141 kW</td>\n",
       "      <td>...</td>\n",
       "      <td>[\\nLangstreckenfahrzeug daher die hohe Kilomet...</td>\n",
       "      <td>[\\n4 (Green)\\n]</td>\n",
       "      <td>[\\n7\\n]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>Audi A1</td>\n",
       "      <td>Sportback 1.6 TDI S tronic Einparkhilfe plus+m...</td>\n",
       "      <td>Sedans</td>\n",
       "      <td>14640</td>\n",
       "      <td>VAT deductible</td>\n",
       "      <td>83,450 km</td>\n",
       "      <td>02/2016</td>\n",
       "      <td>1 previous owner</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85 kW</td>\n",
       "      <td>...</td>\n",
       "      <td>[\\n, Fahrzeug-Nummer: AM-95365,  , Ehem. UPE 2...</td>\n",
       "      <td>[\\n4 (Green)\\n]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>3</th>\n",
       "      <td>Audi A1</td>\n",
       "      <td>1.4 TDi Design S tronic</td>\n",
       "      <td>Sedans</td>\n",
       "      <td>14500</td>\n",
       "      <td>None</td>\n",
       "      <td>73,000 km</td>\n",
       "      <td>08/2016</td>\n",
       "      <td>1 previous owner</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66 kW</td>\n",
       "      <td>...</td>\n",
       "      <td>[\\nAudi A1: , - 1e eigenaar , - Perfecte staat...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\\n6\\n]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>Audi A1</td>\n",
       "      <td>Sportback 1.4 TDI S-Tronic S-Line Ext. admired...</td>\n",
       "      <td>Sedans</td>\n",
       "      <td>16790</td>\n",
       "      <td>None</td>\n",
       "      <td>16,200 km</td>\n",
       "      <td>05/2016</td>\n",
       "      <td>1 previous owner</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66 kW</td>\n",
       "      <td>...</td>\n",
       "      <td>[\\n, Technik &amp; Sicherheit:, Xenon plus, Klimaa...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\\nGermany\\n]</td>\n",
       "      <td>NaN</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>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  make_model                                  short_description body_type  \\\n",
       "0    Audi A1        Sportback 1.4 TDI S-tronic Xenon Navi Klima    Sedans   \n",
       "1    Audi A1                                     1.8 TFSI sport    Sedans   \n",
       "2    Audi A1  Sportback 1.6 TDI S tronic Einparkhilfe plus+m...    Sedans   \n",
       "3    Audi A1                            1.4 TDi Design S tronic    Sedans   \n",
       "4    Audi A1  Sportback 1.4 TDI S-Tronic S-Line Ext. admired...    Sedans   \n",
       "\n",
       "   price               vat         km registration         prev_owner  kw  \\\n",
       "0  15770    VAT deductible  56,013 km      01/2016  2 previous owners NaN   \n",
       "1  14500  Price negotiable  80,000 km      03/2017               None NaN   \n",
       "2  14640    VAT deductible  83,450 km      02/2016   1 previous owner NaN   \n",
       "3  14500              None  73,000 km      08/2016   1 previous owner NaN   \n",
       "4  16790              None  16,200 km      05/2016   1 previous owner NaN   \n",
       "\n",
       "       hp  ...                                        description  \\\n",
       "0   66 kW  ...  [\\n, Sicherheit:,  , Deaktivierung für Beifahr...   \n",
       "1  141 kW  ...  [\\nLangstreckenfahrzeug daher die hohe Kilomet...   \n",
       "2   85 kW  ...  [\\n, Fahrzeug-Nummer: AM-95365,  , Ehem. UPE 2...   \n",
       "3   66 kW  ...  [\\nAudi A1: , - 1e eigenaar , - Perfecte staat...   \n",
       "4   66 kW  ...  [\\n, Technik & Sicherheit:, Xenon plus, Klimaa...   \n",
       "\n",
       "    emission_label    gears country_version electricity_consumption  \\\n",
       "0              NaN      NaN             NaN                     NaN   \n",
       "1  [\\n4 (Green)\\n]  [\\n7\\n]             NaN                     NaN   \n",
       "2  [\\n4 (Green)\\n]      NaN             NaN                     NaN   \n",
       "3              NaN  [\\n6\\n]             NaN                     NaN   \n",
       "4              NaN      NaN   [\\nGermany\\n]                     NaN   \n",
       "\n",
       "  last_service_date other_fuel_types availability  \\\n",
       "0               NaN              NaN          NaN   \n",
       "1               NaN              NaN          NaN   \n",
       "2               NaN              NaN          NaN   \n",
       "3               NaN              NaN          NaN   \n",
       "4               NaN              NaN          NaN   \n",
       "\n",
       "  last_timing_belt_service_date available_from  \n",
       "0                           NaN            NaN  \n",
       "1                           NaN            NaN  \n",
       "2                           NaN            NaN  \n",
       "3                           NaN            NaN  \n",
       "4                           NaN            NaN  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os \n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import re \n",
    "import json \n",
    "import copy\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "from collections import Counter\n",
    "from datetime import datetime\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "df=pd.read_json(\"scout_car.json\",lines=True)\n",
    "df.columns=[i.replace(\" \",\"_\").lower() for i in df.columns]\n",
    "urls=df.iloc[:,0]\n",
    "df=df.iloc[:,1:]\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>next_inspection</th>\n",
       "      <th>inspection_new</th>\n",
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       "      <th>full_service</th>\n",
       "      <th>non-smoking_vehicle</th>\n",
       "      <th>null</th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>offer_number</th>\n",
       "      <th>first_registration</th>\n",
       "      <th>body_color</th>\n",
       "      <th>paint_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[\\n06/2021\\n, \\n99 g CO2/km (comb)\\n]</td>\n",
       "      <td>[\\nYes\\n, \\nEuro 6\\n]</td>\n",
       "      <td>[\\n, \\n, \\n4 (Green)\\n]</td>\n",
       "      <td>[\\n, \\n]</td>\n",
       "      <td>[\\n, \\n]</td>\n",
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       "      <td>[\\n, 2016, \\n]</td>\n",
       "      <td>[\\n, Black, \\n]</td>\n",
       "      <td>[\\nMetallic\\n]</td>\n",
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       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
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       "      <td>[\\n, Red, \\n]</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\\n, \\n, \\n99 g CO2/km (comb)\\n]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\nAudi\\n</td>\n",
       "      <td>[\\n, A1, \\n]</td>\n",
       "      <td>[\\nAM-95365\\n]</td>\n",
       "      <td>[\\n, 2016, \\n]</td>\n",
       "      <td>[\\n, Black, \\n]</td>\n",
       "      <td>[\\nMetallic\\n]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\\n, \\n, \\n99 g CO2/km (comb)\\n]</td>\n",
       "      <td>[\\n, \\n, \\nEuro 6\\n]</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\nAudi\\n</td>\n",
       "      <td>[\\n, A1, \\n]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\\n, 2016, \\n]</td>\n",
       "      <td>[\\n, Brown, \\n]</td>\n",
       "      <td>[\\nMetallic\\n]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>[\\nYes\\n, \\n109 g CO2/km (comb)\\n]</td>\n",
       "      <td>[\\n, \\n, \\nEuro 6\\n]</td>\n",
       "      <td>[\\n, \\n, \\n4 (Green)\\n]</td>\n",
       "      <td>[\\n, \\n]</td>\n",
       "      <td>[]</td>\n",
       "      <td>\\nAudi\\n</td>\n",
       "      <td>[\\n, A1, \\n]</td>\n",
       "      <td>[\\nC1626\\n]</td>\n",
       "      <td>[\\n, 2016, \\n]</td>\n",
       "      <td>[\\n, Black, \\n]</td>\n",
       "      <td>[\\nMetallic\\n]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         next_inspection                      inspection_new  \\\n",
       "0  [\\n06/2021\\n, \\n99 g CO2/km (comb)\\n]               [\\nYes\\n, \\nEuro 6\\n]   \n",
       "1                                    NaN                                 NaN   \n",
       "2                                    NaN                                 NaN   \n",
       "3                                    NaN                                 NaN   \n",
       "4                                    NaN  [\\nYes\\n, \\n109 g CO2/km (comb)\\n]   \n",
       "\n",
       "                           warranty                      full_service  \\\n",
       "0           [\\n, \\n, \\n4 (Green)\\n]                          [\\n, \\n]   \n",
       "1                               NaN                               NaN   \n",
       "2  [\\n, \\n, \\n99 g CO2/km (comb)\\n]                               NaN   \n",
       "3                               NaN  [\\n, \\n, \\n99 g CO2/km (comb)\\n]   \n",
       "4              [\\n, \\n, \\nEuro 6\\n]           [\\n, \\n, \\n4 (Green)\\n]   \n",
       "\n",
       "    non-smoking_vehicle null      make         model     offer_number  \\\n",
       "0              [\\n, \\n]   []  \\nAudi\\n  [\\n, A1, \\n]  [\\nLR-062483\\n]   \n",
       "1                   NaN   []  \\nAudi\\n  [\\n, A1, \\n]              NaN   \n",
       "2                   NaN   []  \\nAudi\\n  [\\n, A1, \\n]   [\\nAM-95365\\n]   \n",
       "3  [\\n, \\n, \\nEuro 6\\n]   []  \\nAudi\\n  [\\n, A1, \\n]              NaN   \n",
       "4              [\\n, \\n]   []  \\nAudi\\n  [\\n, A1, \\n]      [\\nC1626\\n]   \n",
       "\n",
       "  first_registration       body_color      paint_type  \n",
       "0     [\\n, 2016, \\n]  [\\n, Black, \\n]  [\\nMetallic\\n]  \n",
       "1     [\\n, 2017, \\n]    [\\n, Red, \\n]             NaN  \n",
       "2     [\\n, 2016, \\n]  [\\n, Black, \\n]  [\\nMetallic\\n]  \n",
       "3     [\\n, 2016, \\n]  [\\n, Brown, \\n]  [\\nMetallic\\n]  \n",
       "4     [\\n, 2016, \\n]  [\\n, Black, \\n]  [\\nMetallic\\n]  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,12:24].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>body_color_original</th>\n",
       "      <th>upholstery</th>\n",
       "      <th>body</th>\n",
       "      <th>nr._of_doors</th>\n",
       "      <th>nr._of_seats</th>\n",
       "      <th>model_code</th>\n",
       "      <th>gearing_type</th>\n",
       "      <th>displacement</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>weight</th>\n",
       "      <th>...</th>\n",
       "      <th>description</th>\n",
       "      <th>emission_label</th>\n",
       "      <th>gears</th>\n",
       "      <th>country_version</th>\n",
       "      <th>electricity_consumption</th>\n",
       "      <th>last_service_date</th>\n",
       "      <th>other_fuel_types</th>\n",
       "      <th>availability</th>\n",
       "      <th>last_timing_belt_service_date</th>\n",
       "      <th>available_from</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[\\nMythosschwarz\\n]</td>\n",
       "      <td>[\\nCloth, Black\\n]</td>\n",
       "      <td>[\\n, Sedans, \\n]</td>\n",
       "      <td>[\\n5\\n]</td>\n",
       "      <td>[\\n5\\n]</td>\n",
       "      <td>[\\n0588/BDF\\n]</td>\n",
       "      <td>[\\n, Automatic, \\n]</td>\n",
       "      <td>[\\n1,422 cc\\n]</td>\n",
       "      <td>[\\n3\\n]</td>\n",
       "      <td>[\\n1,220 kg\\n]</td>\n",
       "      <td>...</td>\n",
       "      <td>[\\n, Sicherheit:,  , Deaktivierung für Beifahr...</td>\n",
       "      <td>NaN</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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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>[\\nCloth, Grey\\n]</td>\n",
       "      <td>[\\n, Sedans, \\n]</td>\n",
       "      <td>[\\n3\\n]</td>\n",
       "      <td>[\\n4\\n]</td>\n",
       "      <td>[\\n0588/BCY\\n]</td>\n",
       "      <td>[\\n, Automatic, \\n]</td>\n",
       "      <td>[\\n1,798 cc\\n]</td>\n",
       "      <td>[\\n4\\n]</td>\n",
       "      <td>[\\n1,255 kg\\n]</td>\n",
       "      <td>...</td>\n",
       "      <td>[\\nLangstreckenfahrzeug daher die hohe Kilomet...</td>\n",
       "      <td>[\\n4 (Green)\\n]</td>\n",
       "      <td>[\\n7\\n]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>[\\nmythosschwarz metallic\\n]</td>\n",
       "      <td>[\\nCloth, Black\\n]</td>\n",
       "      <td>[\\n, Sedans, \\n]</td>\n",
       "      <td>[\\n4\\n]</td>\n",
       "      <td>[\\n4\\n]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\\n, Automatic, \\n]</td>\n",
       "      <td>[\\n1,598 cc\\n]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>[\\n, Fahrzeug-Nummer: AM-95365,  , Ehem. UPE 2...</td>\n",
       "      <td>[\\n4 (Green)\\n]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\\n, Sedans, \\n]</td>\n",
       "      <td>[\\n3\\n]</td>\n",
       "      <td>[\\n4\\n]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\\n, Automatic, \\n]</td>\n",
       "      <td>[\\n1,422 cc\\n]</td>\n",
       "      <td>[\\n3\\n]</td>\n",
       "      <td>[\\n1,195 kg\\n]</td>\n",
       "      <td>...</td>\n",
       "      <td>[\\nAudi A1: , - 1e eigenaar , - Perfecte staat...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\\n6\\n]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>[\\nMythosschwarz Metallic\\n]</td>\n",
       "      <td>[\\nCloth, Black\\n]</td>\n",
       "      <td>[\\n, Sedans, \\n]</td>\n",
       "      <td>[\\n5\\n]</td>\n",
       "      <td>[\\n5\\n]</td>\n",
       "      <td>[\\n0588/BDF\\n]</td>\n",
       "      <td>[\\n, Automatic, \\n]</td>\n",
       "      <td>[\\n1,422 cc\\n]</td>\n",
       "      <td>[\\n3\\n]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>[\\n, Technik &amp; Sicherheit:, Xenon plus, Klimaa...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\\nGermany\\n]</td>\n",
       "      <td>NaN</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>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            body_color_original          upholstery              body  \\\n",
       "0           [\\nMythosschwarz\\n]  [\\nCloth, Black\\n]  [\\n, Sedans, \\n]   \n",
       "1                           NaN   [\\nCloth, Grey\\n]  [\\n, Sedans, \\n]   \n",
       "2  [\\nmythosschwarz metallic\\n]  [\\nCloth, Black\\n]  [\\n, Sedans, \\n]   \n",
       "3                           NaN                 NaN  [\\n, Sedans, \\n]   \n",
       "4  [\\nMythosschwarz Metallic\\n]  [\\nCloth, Black\\n]  [\\n, Sedans, \\n]   \n",
       "\n",
       "  nr._of_doors nr._of_seats      model_code         gearing_type  \\\n",
       "0      [\\n5\\n]      [\\n5\\n]  [\\n0588/BDF\\n]  [\\n, Automatic, \\n]   \n",
       "1      [\\n3\\n]      [\\n4\\n]  [\\n0588/BCY\\n]  [\\n, Automatic, \\n]   \n",
       "2      [\\n4\\n]      [\\n4\\n]             NaN  [\\n, Automatic, \\n]   \n",
       "3      [\\n3\\n]      [\\n4\\n]             NaN  [\\n, Automatic, \\n]   \n",
       "4      [\\n5\\n]      [\\n5\\n]  [\\n0588/BDF\\n]  [\\n, Automatic, \\n]   \n",
       "\n",
       "     displacement cylinders          weight  ...  \\\n",
       "0  [\\n1,422 cc\\n]   [\\n3\\n]  [\\n1,220 kg\\n]  ...   \n",
       "1  [\\n1,798 cc\\n]   [\\n4\\n]  [\\n1,255 kg\\n]  ...   \n",
       "2  [\\n1,598 cc\\n]       NaN             NaN  ...   \n",
       "3  [\\n1,422 cc\\n]   [\\n3\\n]  [\\n1,195 kg\\n]  ...   \n",
       "4  [\\n1,422 cc\\n]   [\\n3\\n]             NaN  ...   \n",
       "\n",
       "                                         description   emission_label  \\\n",
       "0  [\\n, Sicherheit:,  , Deaktivierung für Beifahr...              NaN   \n",
       "1  [\\nLangstreckenfahrzeug daher die hohe Kilomet...  [\\n4 (Green)\\n]   \n",
       "2  [\\n, Fahrzeug-Nummer: AM-95365,  , Ehem. UPE 2...  [\\n4 (Green)\\n]   \n",
       "3  [\\nAudi A1: , - 1e eigenaar , - Perfecte staat...              NaN   \n",
       "4  [\\n, Technik & Sicherheit:, Xenon plus, Klimaa...              NaN   \n",
       "\n",
       "     gears country_version electricity_consumption last_service_date  \\\n",
       "0      NaN             NaN                     NaN               NaN   \n",
       "1  [\\n7\\n]             NaN                     NaN               NaN   \n",
       "2      NaN             NaN                     NaN               NaN   \n",
       "3  [\\n6\\n]             NaN                     NaN               NaN   \n",
       "4      NaN   [\\nGermany\\n]                     NaN               NaN   \n",
       "\n",
       "  other_fuel_types availability last_timing_belt_service_date available_from  \n",
       "0              NaN          NaN                           NaN            NaN  \n",
       "1              NaN          NaN                           NaN            NaN  \n",
       "2              NaN          NaN                           NaN            NaN  \n",
       "3              NaN          NaN                           NaN            NaN  \n",
       "4              NaN          NaN                           NaN            NaN  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,24:].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns=[i.replace(\" \",\"_\") for i in df.columns]#strip white spaces from the colum names\n",
    "df.columns=[i.strip(\"\\n\").replace(\".\",\"\").replace(\"&\",\"\") for i in df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "make_model                           0\n",
       "short_description                   46\n",
       "body_type                           60\n",
       "price                                0\n",
       "vat                               4513\n",
       "km                                   0\n",
       "registration                         0\n",
       "prev_owner                        6828\n",
       "kw                               15919\n",
       "hp                                   0\n",
       "type                                 2\n",
       "previous_owners                   6640\n",
       "next_inspection                  12384\n",
       "inspection_new                   11987\n",
       "warranty                          5420\n",
       "full_service                      7704\n",
       "non-smoking_vehicle               8742\n",
       "null                                 0\n",
       "make                                 0\n",
       "model                                0\n",
       "offer_number                      3175\n",
       "first_registration                1597\n",
       "body_color                         597\n",
       "paint_type                        5772\n",
       "body_color_original               3759\n",
       "upholstery                        3720\n",
       "body                                60\n",
       "nr_of_doors                        212\n",
       "nr_of_seats                        977\n",
       "model_code                       10941\n",
       "gearing_type                         0\n",
       "displacement                       496\n",
       "cylinders                         5680\n",
       "weight                            6974\n",
       "drive_chain                       6858\n",
       "fuel                                 0\n",
       "consumption                       1906\n",
       "co2_emission                      1808\n",
       "emission_class                    3021\n",
       "comfort__convenience               920\n",
       "entertainment__media              1374\n",
       "extras                            2962\n",
       "safety__security                   982\n",
       "description                          0\n",
       "emission_label                   11934\n",
       "gears                             4712\n",
       "country_version                   8333\n",
       "electricity_consumption          15782\n",
       "last_service_date                15353\n",
       "other_fuel_types                 15039\n",
       "availability                     15284\n",
       "last_timing_belt_service_date    15903\n",
       "available_from                   15647\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"description\",1,inplace=True)#dropping the German description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "na_above_5000=[i for i in df.columns if df[i].isna().sum()>5000]#leaving the features with more than 5000 NAs aside\n",
    "\n",
    "df=df[[i for i in df.columns if df[i].isna().sum()<=5000]]#working with features with less than 5000 NAs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NewData:\n",
    "    \"\"\" \n",
    "    This class combine the cleaned features of the dataset into a dataframe\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        self.data_list=[]\n",
    "        self.data_frame=None\n",
    "        self.name_of_list=[]\n",
    "\n",
    "    def add(self,data,name):\n",
    "        self.name=name\n",
    "        self.data=data\n",
    "        if type(self.data)==pd.core.series.Series:\n",
    "            self.data=self.data.to_list()\n",
    "        \n",
    "        if self.data not in self.data_list:\n",
    "            # self.data=pd.Series(self.data)\n",
    "            self.data_list.append(self.data)\n",
    "            \n",
    "            \n",
    "            if len(self.data)!=len(self.data_list[0]):\n",
    "                    raise ValueError(\"check the data length\") \n",
    "                          \n",
    "            self.data_frame=pd.concat([pd.Series(i) for i in self.data_list],1)    \n",
    "       \n",
    "\n",
    "        self.column_names()\n",
    "        self.data_frame.columns=self.name_of_list\n",
    "   \n",
    "    def column_names(self):\n",
    "        if self.name not in self.name_of_list:\n",
    "            self.name_of_list.append(self.name) \n",
    "            return self\n",
    "    def remove_name(self,name_to_remove):\n",
    "        self.name_of_list.remove(name_to_remove)\n",
    "\n",
    "    def remove_data_list(self,data_list_to_remove):\n",
    "        self.data_list.remove(data_list_to_remove)    \n",
    "\n",
    "\n",
    "    def remove_data_frame(self,data_frame_to_remove):   \n",
    "        self.data_frame.drop(data_frame_to_remove,1,inplace=True) \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "new_data=NewData()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unpack_numeric_list(data,*args):\n",
    "    \"\"\"unpack numeric columns\"\"\"\n",
    "    unpacked_numeric_list=[]\n",
    "    for i in data:\n",
    "        try:\n",
    "            if len(i)==1:\n",
    "                a=([i[0].replace(j,\"\").strip(\"\\n\") for j in args])\n",
    "                unpacked_numeric_list.append(int(a[1]))\n",
    "            else:\n",
    "                b=([i.strip(j).replace(\"\\n\",\"\").replace(\",\",\"\") for j in args])\n",
    "                unpacked_numeric_list.append(int(b[0]))    \n",
    "        except:\n",
    "            # print(i) \n",
    "            unpacked_numeric_list.append(None)\n",
    "\n",
    "\n",
    "    return unpacked_numeric_list        \n",
    "\n",
    "co2_emission=unpack_numeric_list(df.co2_emission,\"\\n\",\" g CO2/km (comb)\")\n",
    "\n",
    "new_data.add(co2_emission,\"co2_emission\")      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_numeric_data_new(data,*args):\n",
    "    data_list=[]\n",
    "    for i in data:\n",
    "        try:\n",
    "            \n",
    "            a=i[0].strip(args).replace(\"\\n\",\"\").replace(\",\",\"\")\n",
    "            data_list.append((a))\n",
    "        except:\n",
    "            data_list.append(None)   \n",
    "    # print(strip)        \n",
    "    return data_list   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_numeric_data(data,to_clean):\n",
    "    data_list=[]\n",
    "    for i in data:\n",
    "        try:\n",
    "            data_list.append(int(i.strip(to_clean).replace(\",\",\"\")))\n",
    "        except:\n",
    "            data_list.append(None)   \n",
    "    return data_list        \n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"make\"]= [i.split()[0] for i in df.make_model]\n",
    "df[\"model\"]=[i.split()[1] for i in df.make_model]\n",
    "df.drop(\"make_model\",1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data.add(df.make,\"make\")\n",
    "new_data.add(df.model,\"model\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_numeric_data(data,to_clean):\n",
    "    data_list=[]\n",
    "    for i in data:\n",
    "        try:\n",
    "            data_list.append(int(i.strip(to_clean).replace(\",\",\"\")))\n",
    "        except:\n",
    "            data_list.append(None)   \n",
    "    return data_list        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.km=clean_numeric_data(df.km, \" km\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.hp=clean_numeric_data(df.hp,\" kW\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>make</th>\n",
       "      <th>model</th>\n",
       "      <th>km</th>\n",
       "      <th>hp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>56013.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>83450.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15914</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15915</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15916</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>15.0</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15917</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>10.0</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15918</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15919 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          make   model       km     hp\n",
       "0         Audi      A1  56013.0   66.0\n",
       "1         Audi      A1  80000.0  141.0\n",
       "2         Audi      A1  83450.0   85.0\n",
       "3         Audi      A1  73000.0   66.0\n",
       "4         Audi      A1  16200.0   66.0\n",
       "...        ...     ...      ...    ...\n",
       "15914  Renault  Espace      NaN  147.0\n",
       "15915  Renault  Espace   9900.0  165.0\n",
       "15916  Renault  Espace     15.0  146.0\n",
       "15917  Renault  Espace     10.0  147.0\n",
       "15918  Renault  Espace      NaN  165.0\n",
       "\n",
       "[15919 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "new_data.add(df.km,\"km\")\n",
    "new_data.add(df.hp,\"hp\")\n",
    "new_data.data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15919"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "def empty_columns(column):\n",
    "    \"\"\"This function counts the number of cells with empty lists ([])\"\"\"\n",
    "    count=0\n",
    "    for i in column:\n",
    "        if i==[]: \n",
    "            count+=1\n",
    "\n",
    "    return count        \n",
    "\n",
    "empty_columns(df.null) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('null', 15919)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(i,empty_columns(df[i])) for i in df.columns if empty_columns(df[i])>100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"null\",1,inplace=True)#null contains only empty lists"
   ]
  },
  {
   "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>short_description</th>\n",
       "      <th>body_type</th>\n",
       "      <th>price</th>\n",
       "      <th>vat</th>\n",
       "      <th>km</th>\n",
       "      <th>registration</th>\n",
       "      <th>hp</th>\n",
       "      <th>type</th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>...</th>\n",
       "      <th>displacement</th>\n",
       "      <th>fuel</th>\n",
       "      <th>consumption</th>\n",
       "      <th>co2_emission</th>\n",
       "      <th>emission_class</th>\n",
       "      <th>comfort__convenience</th>\n",
       "      <th>entertainment__media</th>\n",
       "      <th>extras</th>\n",
       "      <th>safety__security</th>\n",
       "      <th>gears</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sportback 1.4 TDI S-tronic Xenon Navi Klima</td>\n",
       "      <td>Sedans</td>\n",
       "      <td>15770</td>\n",
       "      <td>VAT deductible</td>\n",
       "      <td>56013.0</td>\n",
       "      <td>01/2016</td>\n",
       "      <td>66.0</td>\n",
       "      <td>[, Used, , Diesel (Particulate Filter)]</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>...</td>\n",
       "      <td>[\\n1,422 cc\\n]</td>\n",
       "      <td>[\\n, Diesel (Particulate Filter), \\n]</td>\n",
       "      <td>[[3.8 l/100 km (comb)], [4.3 l/100 km (city)],...</td>\n",
       "      <td>[\\n99 g CO2/km (comb)\\n]</td>\n",
       "      <td>[\\nEuro 6\\n]</td>\n",
       "      <td>[Air conditioning, Armrest, Automatic climate ...</td>\n",
       "      <td>[Bluetooth, Hands-free equipment, On-board com...</td>\n",
       "      <td>[Alloy wheels, Catalytic Converter, Voice Cont...</td>\n",
       "      <td>[ABS, Central door lock, Daytime running light...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.8 TFSI sport</td>\n",
       "      <td>Sedans</td>\n",
       "      <td>14500</td>\n",
       "      <td>Price negotiable</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>03/2017</td>\n",
       "      <td>141.0</td>\n",
       "      <td>[, Used, , Gasoline]</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>...</td>\n",
       "      <td>[\\n1,798 cc\\n]</td>\n",
       "      <td>[\\n, Gasoline, \\n]</td>\n",
       "      <td>[[5.6 l/100 km (comb)], [7.1 l/100 km (city)],...</td>\n",
       "      <td>[\\n129 g CO2/km (comb)\\n]</td>\n",
       "      <td>[\\nEuro 6\\n]</td>\n",
       "      <td>[Air conditioning, Automatic climate control, ...</td>\n",
       "      <td>[Bluetooth, Hands-free equipment, On-board com...</td>\n",
       "      <td>[Alloy wheels, Sport seats, Sport suspension, ...</td>\n",
       "      <td>[ABS, Central door lock, Central door lock wit...</td>\n",
       "      <td>[\\n7\\n]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sportback 1.6 TDI S tronic Einparkhilfe plus+m...</td>\n",
       "      <td>Sedans</td>\n",
       "      <td>14640</td>\n",
       "      <td>VAT deductible</td>\n",
       "      <td>83450.0</td>\n",
       "      <td>02/2016</td>\n",
       "      <td>85.0</td>\n",
       "      <td>[, Used, , Diesel (Particulate Filter)]</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>...</td>\n",
       "      <td>[\\n1,598 cc\\n]</td>\n",
       "      <td>[\\n, Diesel (Particulate Filter), \\n]</td>\n",
       "      <td>[[3.8 l/100 km (comb)], [4.4 l/100 km (city)],...</td>\n",
       "      <td>[\\n99 g CO2/km (comb)\\n]</td>\n",
       "      <td>[\\nEuro 6\\n]</td>\n",
       "      <td>[Air conditioning, Cruise control, Electrical ...</td>\n",
       "      <td>[MP3, On-board computer]</td>\n",
       "      <td>[Alloy wheels, Voice Control]</td>\n",
       "      <td>[ABS, Central door lock, Daytime running light...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.4 TDi Design S tronic</td>\n",
       "      <td>Sedans</td>\n",
       "      <td>14500</td>\n",
       "      <td>None</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>08/2016</td>\n",
       "      <td>66.0</td>\n",
       "      <td>[, Used, , Diesel (Particulate Filter)]</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>...</td>\n",
       "      <td>[\\n1,422 cc\\n]</td>\n",
       "      <td>[\\n, Diesel (Particulate Filter), \\n]</td>\n",
       "      <td>[[3.8 l/100 km (comb)], [4.3 l/100 km (city)],...</td>\n",
       "      <td>[\\n99 g CO2/km (comb)\\n]</td>\n",
       "      <td>[\\nEuro 6\\n]</td>\n",
       "      <td>[Air suspension, Armrest, Auxiliary heating, E...</td>\n",
       "      <td>[Bluetooth, CD player, Hands-free equipment, M...</td>\n",
       "      <td>[Alloy wheels, Sport seats, Voice Control]</td>\n",
       "      <td>[ABS, Alarm system, Central door lock with rem...</td>\n",
       "      <td>[\\n6\\n]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sportback 1.4 TDI S-Tronic S-Line Ext. admired...</td>\n",
       "      <td>Sedans</td>\n",
       "      <td>16790</td>\n",
       "      <td>None</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>05/2016</td>\n",
       "      <td>66.0</td>\n",
       "      <td>[, Used, , Diesel (Particulate Filter)]</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>...</td>\n",
       "      <td>[\\n1,422 cc\\n]</td>\n",
       "      <td>[\\n, Diesel (Particulate Filter), \\n]</td>\n",
       "      <td>[[4.1 l/100 km (comb)], [4.6 l/100 km (city)],...</td>\n",
       "      <td>[\\n109 g CO2/km (comb)\\n]</td>\n",
       "      <td>[\\nEuro 6\\n]</td>\n",
       "      <td>[Air conditioning, Armrest, Automatic climate ...</td>\n",
       "      <td>[Bluetooth, CD player, Hands-free equipment, M...</td>\n",
       "      <td>[Alloy wheels, Sport package, Sport suspension...</td>\n",
       "      <td>[ABS, Central door lock, Driver-side airbag, E...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   short_description body_type  price  \\\n",
       "0        Sportback 1.4 TDI S-tronic Xenon Navi Klima    Sedans  15770   \n",
       "1                                     1.8 TFSI sport    Sedans  14500   \n",
       "2  Sportback 1.6 TDI S tronic Einparkhilfe plus+m...    Sedans  14640   \n",
       "3                            1.4 TDi Design S tronic    Sedans  14500   \n",
       "4  Sportback 1.4 TDI S-Tronic S-Line Ext. admired...    Sedans  16790   \n",
       "\n",
       "                vat       km registration     hp  \\\n",
       "0    VAT deductible  56013.0      01/2016   66.0   \n",
       "1  Price negotiable  80000.0      03/2017  141.0   \n",
       "2    VAT deductible  83450.0      02/2016   85.0   \n",
       "3              None  73000.0      08/2016   66.0   \n",
       "4              None  16200.0      05/2016   66.0   \n",
       "\n",
       "                                      type  make model  ...    displacement  \\\n",
       "0  [, Used, , Diesel (Particulate Filter)]  Audi    A1  ...  [\\n1,422 cc\\n]   \n",
       "1                     [, Used, , Gasoline]  Audi    A1  ...  [\\n1,798 cc\\n]   \n",
       "2  [, Used, , Diesel (Particulate Filter)]  Audi    A1  ...  [\\n1,598 cc\\n]   \n",
       "3  [, Used, , Diesel (Particulate Filter)]  Audi    A1  ...  [\\n1,422 cc\\n]   \n",
       "4  [, Used, , Diesel (Particulate Filter)]  Audi    A1  ...  [\\n1,422 cc\\n]   \n",
       "\n",
       "                                    fuel  \\\n",
       "0  [\\n, Diesel (Particulate Filter), \\n]   \n",
       "1                     [\\n, Gasoline, \\n]   \n",
       "2  [\\n, Diesel (Particulate Filter), \\n]   \n",
       "3  [\\n, Diesel (Particulate Filter), \\n]   \n",
       "4  [\\n, Diesel (Particulate Filter), \\n]   \n",
       "\n",
       "                                         consumption  \\\n",
       "0  [[3.8 l/100 km (comb)], [4.3 l/100 km (city)],...   \n",
       "1  [[5.6 l/100 km (comb)], [7.1 l/100 km (city)],...   \n",
       "2  [[3.8 l/100 km (comb)], [4.4 l/100 km (city)],...   \n",
       "3  [[3.8 l/100 km (comb)], [4.3 l/100 km (city)],...   \n",
       "4  [[4.1 l/100 km (comb)], [4.6 l/100 km (city)],...   \n",
       "\n",
       "                co2_emission emission_class  \\\n",
       "0   [\\n99 g CO2/km (comb)\\n]   [\\nEuro 6\\n]   \n",
       "1  [\\n129 g CO2/km (comb)\\n]   [\\nEuro 6\\n]   \n",
       "2   [\\n99 g CO2/km (comb)\\n]   [\\nEuro 6\\n]   \n",
       "3   [\\n99 g CO2/km (comb)\\n]   [\\nEuro 6\\n]   \n",
       "4  [\\n109 g CO2/km (comb)\\n]   [\\nEuro 6\\n]   \n",
       "\n",
       "                                comfort__convenience  \\\n",
       "0  [Air conditioning, Armrest, Automatic climate ...   \n",
       "1  [Air conditioning, Automatic climate control, ...   \n",
       "2  [Air conditioning, Cruise control, Electrical ...   \n",
       "3  [Air suspension, Armrest, Auxiliary heating, E...   \n",
       "4  [Air conditioning, Armrest, Automatic climate ...   \n",
       "\n",
       "                                entertainment__media  \\\n",
       "0  [Bluetooth, Hands-free equipment, On-board com...   \n",
       "1  [Bluetooth, Hands-free equipment, On-board com...   \n",
       "2                           [MP3, On-board computer]   \n",
       "3  [Bluetooth, CD player, Hands-free equipment, M...   \n",
       "4  [Bluetooth, CD player, Hands-free equipment, M...   \n",
       "\n",
       "                                              extras  \\\n",
       "0  [Alloy wheels, Catalytic Converter, Voice Cont...   \n",
       "1  [Alloy wheels, Sport seats, Sport suspension, ...   \n",
       "2                      [Alloy wheels, Voice Control]   \n",
       "3         [Alloy wheels, Sport seats, Voice Control]   \n",
       "4  [Alloy wheels, Sport package, Sport suspension...   \n",
       "\n",
       "                                    safety__security    gears  \n",
       "0  [ABS, Central door lock, Daytime running light...      NaN  \n",
       "1  [ABS, Central door lock, Central door lock wit...  [\\n7\\n]  \n",
       "2  [ABS, Central door lock, Daytime running light...      NaN  \n",
       "3  [ABS, Alarm system, Central door lock with rem...  [\\n6\\n]  \n",
       "4  [ABS, Central door lock, Driver-side airbag, E...      NaN  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>make</th>\n",
       "      <th>model</th>\n",
       "      <th>km</th>\n",
       "      <th>hp</th>\n",
       "      <th>transmission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>56013.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>83450.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15914</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147.0</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15915</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15916</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>15.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15917</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>10.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15918</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15919 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          make   model       km     hp transmission\n",
       "0         Audi      A1  56013.0   66.0    Automatic\n",
       "1         Audi      A1  80000.0  141.0    Automatic\n",
       "2         Audi      A1  83450.0   85.0    Automatic\n",
       "3         Audi      A1  73000.0   66.0    Automatic\n",
       "4         Audi      A1  16200.0   66.0    Automatic\n",
       "...        ...     ...      ...    ...          ...\n",
       "15914  Renault  Espace      NaN  147.0    Automatic\n",
       "15915  Renault  Espace   9900.0  165.0    Automatic\n",
       "15916  Renault  Espace     15.0  146.0    Automatic\n",
       "15917  Renault  Espace     10.0  147.0    Automatic\n",
       "15918  Renault  Espace      NaN  165.0    Automatic\n",
       "\n",
       "[15919 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unpack and clean \n",
    "df[\"transmission\"]=[i[1] for i in df.gearing_type]\n",
    "new_data.add(df.transmission,\"transmission\")\n",
    "new_data.data_frame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine_size=[]\n",
    "for i in df.short_description:\n",
    "   if i:\n",
    "    engine_cc=re.findall(\"[1-9][.][0-9]\",i)\n",
    "    # print(engine_size)\n",
    "    if  len(engine_cc)>0:\n",
    "        engine_size.append(float(engine_cc[0]))\n",
    "    else:\n",
    "        engine_size.append(None)  \n",
    "   else:\n",
    "    engine_size.append(None) \n",
    "\n",
    "\n",
    "df[\"engine_size\"]=engine_size\n",
    "\n",
    "new_data.add(df.engine_size,\"engine_size\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15919"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(engine_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Sportback', '1.4', 'TDI', 'S-tronic', 'Xenon', 'Navi', 'Klima'],\n",
       " ['1.8', 'TFSI', 'sport'],\n",
       " ['Sportback', '1.6', 'TDI', 'S', 'tronic', 'Einparkhilfe', 'plus+music'],\n",
       " ['1.4', 'TDi', 'Design', 'S', 'tronic'],\n",
       " ['Sportback', '1.4', 'TDI', 'S-Tronic', 'S-Line', 'Ext.', 'admired,', 'Xe']]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i.split() if i else i for i in df.short_description][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#unpacking sportback cars\n",
    "sport_back=[]\n",
    "for i in df.short_description:\n",
    "    if i and len(i)!=0:\n",
    "        sportback=re.findall(\"Sportback|sport|SPORTBACK\",i)\n",
    "        if sportback:\n",
    "            sport_back.append(1)\n",
    "           \n",
    "        else:\n",
    "            sport_back.append(0)\n",
    "           \n",
    "\n",
    "    else:\n",
    "        sport_back.append(0)  \n",
    "                 \n",
    "\n",
    "df[\"sport_back\"]=sport_back\n",
    "new_data.add(df.sport_back,\"sport_back\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unpack_list(data):\n",
    "    \"\"\"Urpack the columns that contain data in list at row level\"\"\"\n",
    "    unpacked_list=[]\n",
    "    for i in data:\n",
    "        if i and type(i)!=float:\n",
    "            unpacked_list.append(i[0].strip())\n",
    "        else:\n",
    "            unpacked_list.append(None)\n",
    "\n",
    "    return unpacked_list        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"number_offer\"]= unpack_list(df.offer_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#unpacking offer_number\n",
    "number_offer=[]\n",
    "for i in df.offer_number:\n",
    "    if i and type(i)!=float:\n",
    "        number_offer.append(i[0].strip())\n",
    "    else:\n",
    "        number_offer.append(None) \n",
    "\n",
    "df[\"number_offer\"]=number_offer   \n",
    "new_data.add(df.number_offer,\"number_offer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#unpacking displacmenet\n",
    "cylinder=[]\n",
    "for i in df.displacement:\n",
    "    try:\n",
    "        cylinder.append(int(i[0].strip(\"\\n1,\").strip(\" cc\")))\n",
    "        \n",
    "    except:\n",
    "        cylinder.append(None) \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "df[\"cylinder\"]=cylinder\n",
    "new_data.add(df.cylinder,\"cylinder\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>make</th>\n",
       "      <th>model</th>\n",
       "      <th>km</th>\n",
       "      <th>hp</th>\n",
       "      <th>transmission</th>\n",
       "      <th>engine_size</th>\n",
       "      <th>sport_back</th>\n",
       "      <th>number_offer</th>\n",
       "      <th>cylinder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>56013.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>LR-062483</td>\n",
       "      <td>422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>798.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>83450.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>AM-95365</td>\n",
       "      <td>598.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>C1626</td>\n",
       "      <td>422.0</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>15914</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10988301</td>\n",
       "      <td>997.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15915</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>507370_3223</td>\n",
       "      <td>798.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15916</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>15.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Espace16</td>\n",
       "      <td>997.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15917</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>10.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2691331</td>\n",
       "      <td>997.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15918</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Re_30000008029</td>\n",
       "      <td>798.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15919 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          make   model       km     hp transmission  engine_size  sport_back  \\\n",
       "0         Audi      A1  56013.0   66.0    Automatic          1.4           1   \n",
       "1         Audi      A1  80000.0  141.0    Automatic          1.8           1   \n",
       "2         Audi      A1  83450.0   85.0    Automatic          1.6           1   \n",
       "3         Audi      A1  73000.0   66.0    Automatic          1.4           0   \n",
       "4         Audi      A1  16200.0   66.0    Automatic          1.4           1   \n",
       "...        ...     ...      ...    ...          ...          ...         ...   \n",
       "15914  Renault  Espace      NaN  147.0    Automatic          NaN           0   \n",
       "15915  Renault  Espace   9900.0  165.0    Automatic          NaN           0   \n",
       "15916  Renault  Espace     15.0  146.0    Automatic          NaN           0   \n",
       "15917  Renault  Espace     10.0  147.0    Automatic          NaN           0   \n",
       "15918  Renault  Espace      NaN  165.0    Automatic          NaN           0   \n",
       "\n",
       "         number_offer  cylinder  \n",
       "0           LR-062483     422.0  \n",
       "1                None     798.0  \n",
       "2            AM-95365     598.0  \n",
       "3                None     422.0  \n",
       "4               C1626     422.0  \n",
       "...               ...       ...  \n",
       "15914        10988301     997.0  \n",
       "15915     507370_3223     798.0  \n",
       "15916        Espace16     997.0  \n",
       "15917         2691331     997.0  \n",
       "15918  Re_30000008029     798.0  \n",
       "\n",
       "[15919 rows x 9 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>short_description</th>\n",
       "      <th>body_type</th>\n",
       "      <th>price</th>\n",
       "      <th>vat</th>\n",
       "      <th>km</th>\n",
       "      <th>registration</th>\n",
       "      <th>hp</th>\n",
       "      <th>type</th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>offer_number</th>\n",
       "      <th>first_registration</th>\n",
       "      <th>body_color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sportback 1.4 TDI S-tronic Xenon Navi Klima</td>\n",
       "      <td>Sedans</td>\n",
       "      <td>15770</td>\n",
       "      <td>VAT deductible</td>\n",
       "      <td>56013.0</td>\n",
       "      <td>01/2016</td>\n",
       "      <td>66.0</td>\n",
       "      <td>[, Used, , Diesel (Particulate Filter)]</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>[\\nLR-062483\\n]</td>\n",
       "      <td>[\\n, 2016, \\n]</td>\n",
       "      <td>[\\n, Black, \\n]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.8 TFSI sport</td>\n",
       "      <td>Sedans</td>\n",
       "      <td>14500</td>\n",
       "      <td>Price negotiable</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>03/2017</td>\n",
       "      <td>141.0</td>\n",
       "      <td>[, Used, , Gasoline]</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\\n, 2017, \\n]</td>\n",
       "      <td>[\\n, Red, \\n]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sportback 1.6 TDI S tronic Einparkhilfe plus+m...</td>\n",
       "      <td>Sedans</td>\n",
       "      <td>14640</td>\n",
       "      <td>VAT deductible</td>\n",
       "      <td>83450.0</td>\n",
       "      <td>02/2016</td>\n",
       "      <td>85.0</td>\n",
       "      <td>[, Used, , Diesel (Particulate Filter)]</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>[\\nAM-95365\\n]</td>\n",
       "      <td>[\\n, 2016, \\n]</td>\n",
       "      <td>[\\n, Black, \\n]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.4 TDi Design S tronic</td>\n",
       "      <td>Sedans</td>\n",
       "      <td>14500</td>\n",
       "      <td>None</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>08/2016</td>\n",
       "      <td>66.0</td>\n",
       "      <td>[, Used, , Diesel (Particulate Filter)]</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\\n, 2016, \\n]</td>\n",
       "      <td>[\\n, Brown, \\n]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sportback 1.4 TDI S-Tronic S-Line Ext. admired...</td>\n",
       "      <td>Sedans</td>\n",
       "      <td>16790</td>\n",
       "      <td>None</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>05/2016</td>\n",
       "      <td>66.0</td>\n",
       "      <td>[, Used, , Diesel (Particulate Filter)]</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>[\\nC1626\\n]</td>\n",
       "      <td>[\\n, 2016, \\n]</td>\n",
       "      <td>[\\n, Black, \\n]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   short_description body_type  price  \\\n",
       "0        Sportback 1.4 TDI S-tronic Xenon Navi Klima    Sedans  15770   \n",
       "1                                     1.8 TFSI sport    Sedans  14500   \n",
       "2  Sportback 1.6 TDI S tronic Einparkhilfe plus+m...    Sedans  14640   \n",
       "3                            1.4 TDi Design S tronic    Sedans  14500   \n",
       "4  Sportback 1.4 TDI S-Tronic S-Line Ext. admired...    Sedans  16790   \n",
       "\n",
       "                vat       km registration     hp  \\\n",
       "0    VAT deductible  56013.0      01/2016   66.0   \n",
       "1  Price negotiable  80000.0      03/2017  141.0   \n",
       "2    VAT deductible  83450.0      02/2016   85.0   \n",
       "3              None  73000.0      08/2016   66.0   \n",
       "4              None  16200.0      05/2016   66.0   \n",
       "\n",
       "                                      type  make model     offer_number  \\\n",
       "0  [, Used, , Diesel (Particulate Filter)]  Audi    A1  [\\nLR-062483\\n]   \n",
       "1                     [, Used, , Gasoline]  Audi    A1              NaN   \n",
       "2  [, Used, , Diesel (Particulate Filter)]  Audi    A1   [\\nAM-95365\\n]   \n",
       "3  [, Used, , Diesel (Particulate Filter)]  Audi    A1              NaN   \n",
       "4  [, Used, , Diesel (Particulate Filter)]  Audi    A1      [\\nC1626\\n]   \n",
       "\n",
       "  first_registration       body_color  \n",
       "0     [\\n, 2016, \\n]  [\\n, Black, \\n]  \n",
       "1     [\\n, 2017, \\n]    [\\n, Red, \\n]  \n",
       "2     [\\n, 2016, \\n]  [\\n, Black, \\n]  \n",
       "3     [\\n, 2016, \\n]  [\\n, Brown, \\n]  \n",
       "4     [\\n, 2016, \\n]  [\\n, Black, \\n]  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,:13].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>[\\nMythosschwarz\\n]</td>\n",
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       "      <td>[[3.8 l/100 km (comb)], [4.3 l/100 km (city)],...</td>\n",
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       "      <td>[[5.6 l/100 km (comb)], [7.1 l/100 km (city)],...</td>\n",
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       "      <td>[\\n99 g CO2/km (comb)\\n]</td>\n",
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       "      <td>[\\n, Diesel (Particulate Filter), \\n]</td>\n",
       "      <td>[[4.1 l/100 km (comb)], [4.6 l/100 km (city)],...</td>\n",
       "      <td>[\\n109 g CO2/km (comb)\\n]</td>\n",
       "      <td>[\\nEuro 6\\n]</td>\n",
       "      <td>[Air conditioning, Armrest, Automatic climate ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            body_color_original          upholstery              body  \\\n",
       "0           [\\nMythosschwarz\\n]  [\\nCloth, Black\\n]  [\\n, Sedans, \\n]   \n",
       "1                           NaN   [\\nCloth, Grey\\n]  [\\n, Sedans, \\n]   \n",
       "2  [\\nmythosschwarz metallic\\n]  [\\nCloth, Black\\n]  [\\n, Sedans, \\n]   \n",
       "3                           NaN                 NaN  [\\n, Sedans, \\n]   \n",
       "4  [\\nMythosschwarz Metallic\\n]  [\\nCloth, Black\\n]  [\\n, Sedans, \\n]   \n",
       "\n",
       "  nr_of_doors nr_of_seats         gearing_type    displacement  \\\n",
       "0     [\\n5\\n]     [\\n5\\n]  [\\n, Automatic, \\n]  [\\n1,422 cc\\n]   \n",
       "1     [\\n3\\n]     [\\n4\\n]  [\\n, Automatic, \\n]  [\\n1,798 cc\\n]   \n",
       "2     [\\n4\\n]     [\\n4\\n]  [\\n, Automatic, \\n]  [\\n1,598 cc\\n]   \n",
       "3     [\\n3\\n]     [\\n4\\n]  [\\n, Automatic, \\n]  [\\n1,422 cc\\n]   \n",
       "4     [\\n5\\n]     [\\n5\\n]  [\\n, Automatic, \\n]  [\\n1,422 cc\\n]   \n",
       "\n",
       "                                    fuel  \\\n",
       "0  [\\n, Diesel (Particulate Filter), \\n]   \n",
       "1                     [\\n, Gasoline, \\n]   \n",
       "2  [\\n, Diesel (Particulate Filter), \\n]   \n",
       "3  [\\n, Diesel (Particulate Filter), \\n]   \n",
       "4  [\\n, Diesel (Particulate Filter), \\n]   \n",
       "\n",
       "                                         consumption  \\\n",
       "0  [[3.8 l/100 km (comb)], [4.3 l/100 km (city)],...   \n",
       "1  [[5.6 l/100 km (comb)], [7.1 l/100 km (city)],...   \n",
       "2  [[3.8 l/100 km (comb)], [4.4 l/100 km (city)],...   \n",
       "3  [[3.8 l/100 km (comb)], [4.3 l/100 km (city)],...   \n",
       "4  [[4.1 l/100 km (comb)], [4.6 l/100 km (city)],...   \n",
       "\n",
       "                co2_emission emission_class  \\\n",
       "0   [\\n99 g CO2/km (comb)\\n]   [\\nEuro 6\\n]   \n",
       "1  [\\n129 g CO2/km (comb)\\n]   [\\nEuro 6\\n]   \n",
       "2   [\\n99 g CO2/km (comb)\\n]   [\\nEuro 6\\n]   \n",
       "3   [\\n99 g CO2/km (comb)\\n]   [\\nEuro 6\\n]   \n",
       "4  [\\n109 g CO2/km (comb)\\n]   [\\nEuro 6\\n]   \n",
       "\n",
       "                                comfort__convenience  \n",
       "0  [Air conditioning, Armrest, Automatic climate ...  \n",
       "1  [Air conditioning, Automatic climate control, ...  \n",
       "2  [Air conditioning, Cruise control, Electrical ...  \n",
       "3  [Air suspension, Armrest, Auxiliary heating, E...  \n",
       "4  [Air conditioning, Armrest, Automatic climate ...  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,13:25].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>entertainment__media</th>\n",
       "      <th>extras</th>\n",
       "      <th>safety__security</th>\n",
       "      <th>gears</th>\n",
       "      <th>transmission</th>\n",
       "      <th>engine_size</th>\n",
       "      <th>sport_back</th>\n",
       "      <th>number_offer</th>\n",
       "      <th>cylinder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[Bluetooth, Hands-free equipment, On-board com...</td>\n",
       "      <td>[Alloy wheels, Catalytic Converter, Voice Cont...</td>\n",
       "      <td>[ABS, Central door lock, Daytime running light...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>LR-062483</td>\n",
       "      <td>422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Bluetooth, Hands-free equipment, On-board com...</td>\n",
       "      <td>[Alloy wheels, Sport seats, Sport suspension, ...</td>\n",
       "      <td>[ABS, Central door lock, Central door lock wit...</td>\n",
       "      <td>[\\n7\\n]</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>798.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[MP3, On-board computer]</td>\n",
       "      <td>[Alloy wheels, Voice Control]</td>\n",
       "      <td>[ABS, Central door lock, Daytime running light...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>AM-95365</td>\n",
       "      <td>598.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[Bluetooth, CD player, Hands-free equipment, M...</td>\n",
       "      <td>[Alloy wheels, Sport seats, Voice Control]</td>\n",
       "      <td>[ABS, Alarm system, Central door lock with rem...</td>\n",
       "      <td>[\\n6\\n]</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[Bluetooth, CD player, Hands-free equipment, M...</td>\n",
       "      <td>[Alloy wheels, Sport package, Sport suspension...</td>\n",
       "      <td>[ABS, Central door lock, Driver-side airbag, E...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>C1626</td>\n",
       "      <td>422.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                entertainment__media  \\\n",
       "0  [Bluetooth, Hands-free equipment, On-board com...   \n",
       "1  [Bluetooth, Hands-free equipment, On-board com...   \n",
       "2                           [MP3, On-board computer]   \n",
       "3  [Bluetooth, CD player, Hands-free equipment, M...   \n",
       "4  [Bluetooth, CD player, Hands-free equipment, M...   \n",
       "\n",
       "                                              extras  \\\n",
       "0  [Alloy wheels, Catalytic Converter, Voice Cont...   \n",
       "1  [Alloy wheels, Sport seats, Sport suspension, ...   \n",
       "2                      [Alloy wheels, Voice Control]   \n",
       "3         [Alloy wheels, Sport seats, Voice Control]   \n",
       "4  [Alloy wheels, Sport package, Sport suspension...   \n",
       "\n",
       "                                    safety__security    gears transmission  \\\n",
       "0  [ABS, Central door lock, Daytime running light...      NaN    Automatic   \n",
       "1  [ABS, Central door lock, Central door lock wit...  [\\n7\\n]    Automatic   \n",
       "2  [ABS, Central door lock, Daytime running light...      NaN    Automatic   \n",
       "3  [ABS, Alarm system, Central door lock with rem...  [\\n6\\n]    Automatic   \n",
       "4  [ABS, Central door lock, Driver-side airbag, E...      NaN    Automatic   \n",
       "\n",
       "   engine_size  sport_back number_offer  cylinder  \n",
       "0          1.4           1    LR-062483     422.0  \n",
       "1          1.8           1         None     798.0  \n",
       "2          1.6           1     AM-95365     598.0  \n",
       "3          1.4           0         None     422.0  \n",
       "4          1.4           1        C1626     422.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,25:].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "gears=[]\n",
    "for i in df.gears:\n",
    "    if i and type(i)!=float:\n",
    "        gears.append(i[0].strip())\n",
    "    else:\n",
    "        gears.append(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>make</th>\n",
       "      <th>model</th>\n",
       "      <th>km</th>\n",
       "      <th>hp</th>\n",
       "      <th>transmission</th>\n",
       "      <th>engine_size</th>\n",
       "      <th>sport_back</th>\n",
       "      <th>number_offer</th>\n",
       "      <th>cylinder</th>\n",
       "      <th>gears</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>56013.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>LR-062483</td>\n",
       "      <td>422.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>798.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>83450.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>AM-95365</td>\n",
       "      <td>598.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>422.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>C1626</td>\n",
       "      <td>422.0</td>\n",
       "      <td>None</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>15914</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10988301</td>\n",
       "      <td>997.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15915</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>507370_3223</td>\n",
       "      <td>798.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15916</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>15.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Espace16</td>\n",
       "      <td>997.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15917</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>10.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2691331</td>\n",
       "      <td>997.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15918</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Re_30000008029</td>\n",
       "      <td>798.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15919 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          make   model       km     hp transmission  engine_size  sport_back  \\\n",
       "0         Audi      A1  56013.0   66.0    Automatic          1.4           1   \n",
       "1         Audi      A1  80000.0  141.0    Automatic          1.8           1   \n",
       "2         Audi      A1  83450.0   85.0    Automatic          1.6           1   \n",
       "3         Audi      A1  73000.0   66.0    Automatic          1.4           0   \n",
       "4         Audi      A1  16200.0   66.0    Automatic          1.4           1   \n",
       "...        ...     ...      ...    ...          ...          ...         ...   \n",
       "15914  Renault  Espace      NaN  147.0    Automatic          NaN           0   \n",
       "15915  Renault  Espace   9900.0  165.0    Automatic          NaN           0   \n",
       "15916  Renault  Espace     15.0  146.0    Automatic          NaN           0   \n",
       "15917  Renault  Espace     10.0  147.0    Automatic          NaN           0   \n",
       "15918  Renault  Espace      NaN  165.0    Automatic          NaN           0   \n",
       "\n",
       "         number_offer  cylinder gears  \n",
       "0           LR-062483     422.0  None  \n",
       "1                None     798.0     7  \n",
       "2            AM-95365     598.0  None  \n",
       "3                None     422.0     6  \n",
       "4               C1626     422.0  None  \n",
       "...               ...       ...   ...  \n",
       "15914        10988301     997.0     6  \n",
       "15915     507370_3223     798.0     7  \n",
       "15916        Espace16     997.0     6  \n",
       "15917         2691331     997.0     6  \n",
       "15918  Re_30000008029     798.0  None  \n",
       "\n",
       "[15919 rows x 10 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gears=unpack_list(df.gears)#unpacking the gears\n",
    "\n",
    "new_data.add(gears,\"gears\")\n",
    "new_data.data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "192074"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_safety_features=[]\n",
    "safety_features=[]\n",
    "for i in df.safety__security:\n",
    "    if i not in unique_safety_features:\n",
    "        unique_safety_features.append(i)\n",
    "        \n",
    "\n",
    "    if type(i)!=float:\n",
    "        for feature in i:\n",
    "            safety_features.append(feature)\n",
    "    else:\n",
    "        safety_features.append(None)        \n",
    "\n",
    "   \n",
    "len(safety_features)     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ABS',\n",
       " 'Central door lock',\n",
       " 'Daytime running lights',\n",
       " 'Driver-side airbag',\n",
       " 'Electronic stability control']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "safety_features[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "192074"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(safety_features) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4444"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(unique_safety_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "sss={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "982"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.safety__security.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['ABS',\n",
       "  'Central door lock',\n",
       "  'Daytime running lights',\n",
       "  'Driver-side airbag',\n",
       "  'Electronic stability control',\n",
       "  'Fog lights',\n",
       "  'Immobilizer',\n",
       "  'Isofix',\n",
       "  'Passenger-side airbag',\n",
       "  'Power steering',\n",
       "  'Side airbag',\n",
       "  'Tire pressure monitoring system',\n",
       "  'Traction control',\n",
       "  'Xenon headlights'],\n",
       " ['ABS',\n",
       "  'Central door lock',\n",
       "  'Central door lock with remote control',\n",
       "  'Daytime running lights',\n",
       "  'Driver-side airbag',\n",
       "  'Electronic stability control',\n",
       "  'Head airbag',\n",
       "  'Immobilizer',\n",
       "  'Isofix',\n",
       "  'Passenger-side airbag',\n",
       "  'Power steering',\n",
       "  'Side airbag',\n",
       "  'Tire pressure monitoring system',\n",
       "  'Traction control',\n",
       "  'Xenon headlights'],\n",
       " ['ABS',\n",
       "  'Central door lock',\n",
       "  'Daytime running lights',\n",
       "  'Driver-side airbag',\n",
       "  'Electronic stability control',\n",
       "  'Immobilizer',\n",
       "  'Isofix',\n",
       "  'Passenger-side airbag',\n",
       "  'Power steering',\n",
       "  'Side airbag',\n",
       "  'Tire pressure monitoring system',\n",
       "  'Traction control'],\n",
       " ['ABS',\n",
       "  'Alarm system',\n",
       "  'Central door lock with remote control',\n",
       "  'Driver drowsiness detection',\n",
       "  'Driver-side airbag',\n",
       "  'Electronic stability control',\n",
       "  'Emergency system',\n",
       "  'Head airbag',\n",
       "  'Immobilizer',\n",
       "  'Isofix',\n",
       "  'Passenger-side airbag',\n",
       "  'Power steering',\n",
       "  'Side airbag',\n",
       "  'Tire pressure monitoring system'],\n",
       " ['ABS',\n",
       "  'Central door lock',\n",
       "  'Driver-side airbag',\n",
       "  'Electronic stability control',\n",
       "  'Fog lights',\n",
       "  'Immobilizer',\n",
       "  'Isofix',\n",
       "  'Passenger-side airbag',\n",
       "  'Power steering',\n",
       "  'Side airbag',\n",
       "  'Tire pressure monitoring system',\n",
       "  'Traction control',\n",
       "  'Xenon headlights']]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for i in df.safety__security][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_fuel=[]\n",
    "for i in df.fuel:\n",
    "    unique_fuel.append(i[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "fuel_type=[]\n",
    "for i in df.fuel:\n",
    "    if  re.findall(\"Benzine|Gasoline|Super\",i[1]):\n",
    "        fuel_type.append(\"Benzine\")\n",
    "    elif  re.findall(\"LPG\",i[1]):    \n",
    "        fuel_type.append(\"LPG\")\n",
    "    elif re.findall(\"Diesel\",i[1]):\n",
    "        fuel_type.append(\"Diesel\")\n",
    "    elif re.findall(\"CNG\",i[1]):\n",
    "        fuel_type.append(\"CNG\")\n",
    "    elif re.findall(\"Electric\",i[1]):\n",
    "        fuel_type.append(\"Electric\")\n",
    "    else:\n",
    "        fuel_type.append(\"Others\")       \n",
    "             \n",
    "\n",
    "# new_data.add(fuel_type,\"fuel_type\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data.add(fuel_type,\"fuel_type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Benzine', 'CNG', 'Diesel', 'Electric', 'LPG', 'Others'],\n",
       "      dtype='<U8')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(fuel_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Diesel': 7299,\n",
       "         'Benzine': 8549,\n",
       "         'Others': 8,\n",
       "         'CNG': 36,\n",
       "         'LPG': 26,\n",
       "         'Electric': 1})"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(fuel_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>make</th>\n",
       "      <th>model</th>\n",
       "      <th>km</th>\n",
       "      <th>hp</th>\n",
       "      <th>transmission</th>\n",
       "      <th>engine_size</th>\n",
       "      <th>sport_back</th>\n",
       "      <th>number_offer</th>\n",
       "      <th>cylinder</th>\n",
       "      <th>gears</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>co2_emission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>56013.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>LR-062483</td>\n",
       "      <td>422.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>798.0</td>\n",
       "      <td>7</td>\n",
       "      <td>Benzine</td>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>83450.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>AM-95365</td>\n",
       "      <td>598.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>422.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>C1626</td>\n",
       "      <td>422.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>109.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15914</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10988301</td>\n",
       "      <td>997.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15915</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>507370_3223</td>\n",
       "      <td>798.0</td>\n",
       "      <td>7</td>\n",
       "      <td>Benzine</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15916</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>15.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Espace16</td>\n",
       "      <td>997.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15917</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>10.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2691331</td>\n",
       "      <td>997.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15918</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Re_30000008029</td>\n",
       "      <td>798.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Benzine</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15919 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          make   model       km     hp transmission  engine_size  sport_back  \\\n",
       "0         Audi      A1  56013.0   66.0    Automatic          1.4           1   \n",
       "1         Audi      A1  80000.0  141.0    Automatic          1.8           1   \n",
       "2         Audi      A1  83450.0   85.0    Automatic          1.6           1   \n",
       "3         Audi      A1  73000.0   66.0    Automatic          1.4           0   \n",
       "4         Audi      A1  16200.0   66.0    Automatic          1.4           1   \n",
       "...        ...     ...      ...    ...          ...          ...         ...   \n",
       "15914  Renault  Espace      NaN  147.0    Automatic          NaN           0   \n",
       "15915  Renault  Espace   9900.0  165.0    Automatic          NaN           0   \n",
       "15916  Renault  Espace     15.0  146.0    Automatic          NaN           0   \n",
       "15917  Renault  Espace     10.0  147.0    Automatic          NaN           0   \n",
       "15918  Renault  Espace      NaN  165.0    Automatic          NaN           0   \n",
       "\n",
       "         number_offer  cylinder gears fuel_type  co2_emission  \n",
       "0           LR-062483     422.0  None    Diesel          99.0  \n",
       "1                None     798.0     7   Benzine         129.0  \n",
       "2            AM-95365     598.0  None    Diesel          99.0  \n",
       "3                None     422.0     6    Diesel          99.0  \n",
       "4               C1626     422.0  None    Diesel         109.0  \n",
       "...               ...       ...   ...       ...           ...  \n",
       "15914        10988301     997.0     6    Diesel         139.0  \n",
       "15915     507370_3223     798.0     7   Benzine         168.0  \n",
       "15916        Espace16     997.0     6    Diesel         139.0  \n",
       "15917         2691331     997.0     6    Diesel         139.0  \n",
       "15918  Re_30000008029     798.0  None   Benzine         153.0  \n",
       "\n",
       "[15919 rows x 12 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data.add(df.body_type,\"body_typ\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data.add(df.price,\"price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_doors=unpack_numeric_list(df.nr_of_doors,\"\\n\",\"\\n\")\n",
    "new_data.add(number_of_doors,\"number_of_doors\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data.add(df.vat,\"vat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_seats=unpack_numeric_list(df.nr_of_seats,\"\\n\",\"\\n\")\n",
    "new_data.add(number_of_seats,\"number_of_seats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "emission_class=[]\n",
    "for i in df.emission_class:\n",
    "    try:\n",
    "        # if [] in i:\n",
    "        #     emission_class.append(None)\n",
    "        emission_class.append(i[0].strip(\"\\n \"))\n",
    "\n",
    "    except:\n",
    "        emission_class.append(None)    \n",
    "\n",
    "new_data.add(emission_class,\"emission_class\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15919, (15919,))"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(emission_class),df.emission_class.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "consumption_combined=[]\n",
    "for i in df.consumption:\n",
    "    try:\n",
    "        consumption_combined.append(float(i[0][0].split()[0]))\n",
    "    except:\n",
    "        consumption_combined.append(None)\n",
    "\n",
    "new_data.add(consumption_combined,\"consumption_combined\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "consumption_city=[]\n",
    "for i in df.consumption:\n",
    "    try:\n",
    "        consumption_city.append(float(i[1][0].split()[0]))\n",
    "    except:\n",
    "        consumption_city.append(None)\n",
    "\n",
    "\n",
    "new_data.add(consumption_city,\"consumption_city\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "registration=[]\n",
    "for i in df.registration:\n",
    "    try:\n",
    "       dt=pd.to_datetime(i)\n",
    "       registration.append(dt)\n",
    "    except:\n",
    "        registration.append(None)    \n",
    "\n",
    "new_data.add(registration,\"registration\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Since all cars have similar safety features, and these features are many\n",
    "I counted the number of features as a measurement of safety of a car.\n",
    "\n",
    "\"\"\"\n",
    "safety_features=[]\n",
    "for i in df.safety__security:\n",
    "    try:\n",
    "        safety_features.append(len(i))\n",
    "\n",
    "    except:\n",
    "        safety_features.append(None)\n",
    "new_data.add(safety_features,\"safety_features\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "upholstery_color=[]\n",
    "upholstery=[]\n",
    "for i in df.upholstery:\n",
    "    try:\n",
    "        feat=i[0].strip(\"\\n\").split(\",\")\n",
    "        upholstery_color.append(feat[1])\n",
    "        upholstery.append(feat[0])  \n",
    "\n",
    "    except:\n",
    "        upholstery.append(None)\n",
    "        upholstery_color.append(None)  \n",
    "\n",
    "new_data.add(upholstery_color,\"upholstery_color\")\n",
    "new_data.add(upholstery,\"upholstery\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Since all cars have similar safety features, and these features are many\n",
    "I counted the number of features as a measurement of safety of a car.\n",
    "\n",
    "\"\"\"\n",
    "extras=[]\n",
    "for i in df.extras:\n",
    "    try:\n",
    "        extras.append(len(i))\n",
    "\n",
    "    except:\n",
    "        extras.append(None)    \n",
    "\n",
    "new_data.add(extras,\"extras\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1374"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.entertainment__media.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Since all cars have similar safety features, and these features are many\n",
    "I counted the number of features as a measurement of safety of a car.\n",
    "\n",
    "\"\"\"\n",
    "entertainment=[]\n",
    "for i in df.entertainment__media:\n",
    "    try:\n",
    "        entertainment.append(len(i))\n",
    "\n",
    "    except:\n",
    "        entertainment.append(None)  \n",
    "\n",
    "new_data.add(entertainment,\"entertainment\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "body_original_color=[]\n",
    "for i in df.body_color_original:\n",
    "    try:\n",
    "        body_original_color.append(i[0].strip(\"\\n\").capitalize())\n",
    "      \n",
    "\n",
    "    except:\n",
    "        body_original_color.append(None)  \n",
    "\n",
    "new_data.add(body_original_color,\"body_original_color\")   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Since all cars have similar safety features, and these features are many\n",
    "I counted the number of features as a measurement of safety of a car.\n",
    "\n",
    "\"\"\"\n",
    "comfort_convenience=[]\n",
    "for i in df.comfort__convenience:\n",
    "    try:\n",
    "        comfort_convenience.append(len(i))\n",
    "    except:\n",
    "        comfort_convenience.append(None)  \n",
    "\n",
    "\n",
    "new_data.add(comfort_convenience,\"comfort_convenience\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31934.262573964497"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data =copy.deepcopy( new_data.data_frame)\n",
    "\n",
    "data.groupby(\"make\")[\"km\"].mean().to_dict()[\"Renault\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filling the missing km values with relevant car brand average\n",
    "for car in data.make.unique():\n",
    "    data.km.fillna(data.groupby(\"make\")[\"km\"].mean().to_dict()[car],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>make</th>\n",
       "      <th>model</th>\n",
       "      <th>km</th>\n",
       "      <th>hp</th>\n",
       "      <th>transmission</th>\n",
       "      <th>engine_size</th>\n",
       "      <th>sport_back</th>\n",
       "      <th>number_offer</th>\n",
       "      <th>cylinder</th>\n",
       "      <th>gears</th>\n",
       "      <th>...</th>\n",
       "      <th>consumption_combined</th>\n",
       "      <th>consumption_city</th>\n",
       "      <th>registration</th>\n",
       "      <th>safety_features</th>\n",
       "      <th>upholstery_color</th>\n",
       "      <th>upholstery</th>\n",
       "      <th>extras</th>\n",
       "      <th>entertainment</th>\n",
       "      <th>body_original_color</th>\n",
       "      <th>comfort_convenience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>56013.000000</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>LR-062483</td>\n",
       "      <td>422.0</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>Cloth</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Mythosschwarz</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>80000.000000</td>\n",
       "      <td>141.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>798.0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>5.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Grey</td>\n",
       "      <td>Cloth</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>None</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>83450.000000</td>\n",
       "      <td>85.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>AM-95365</td>\n",
       "      <td>598.0</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>Cloth</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Mythosschwarz metallic</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>73000.000000</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>422.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>14.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>None</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A1</td>\n",
       "      <td>16200.000000</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>C1626</td>\n",
       "      <td>422.0</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>Cloth</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Mythosschwarz metallic</td>\n",
       "      <td>13.0</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",
       "      <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>15914</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>33005.972719</td>\n",
       "      <td>147.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10988301</td>\n",
       "      <td>997.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>5.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>NaT</td>\n",
       "      <td>19.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Grigio scuro</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15915</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>9900.000000</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>507370_3223</td>\n",
       "      <td>798.0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>19.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Stahl-grau metallic</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15916</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>146.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Espace16</td>\n",
       "      <td>997.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>5.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>Full leather</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Arktis-weiß</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15917</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>147.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2691331</td>\n",
       "      <td>997.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>5.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>12.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Grigio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15918</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Espace</td>\n",
       "      <td>33005.972719</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Re_30000008029</td>\n",
       "      <td>798.0</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Brown</td>\n",
       "      <td>Full leather</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Titanium-grau metallic</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15919 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          make   model            km     hp transmission  engine_size  \\\n",
       "0         Audi      A1  56013.000000   66.0    Automatic          1.4   \n",
       "1         Audi      A1  80000.000000  141.0    Automatic          1.8   \n",
       "2         Audi      A1  83450.000000   85.0    Automatic          1.6   \n",
       "3         Audi      A1  73000.000000   66.0    Automatic          1.4   \n",
       "4         Audi      A1  16200.000000   66.0    Automatic          1.4   \n",
       "...        ...     ...           ...    ...          ...          ...   \n",
       "15914  Renault  Espace  33005.972719  147.0    Automatic          NaN   \n",
       "15915  Renault  Espace   9900.000000  165.0    Automatic          NaN   \n",
       "15916  Renault  Espace     15.000000  146.0    Automatic          NaN   \n",
       "15917  Renault  Espace     10.000000  147.0    Automatic          NaN   \n",
       "15918  Renault  Espace  33005.972719  165.0    Automatic          NaN   \n",
       "\n",
       "       sport_back    number_offer  cylinder gears  ... consumption_combined  \\\n",
       "0               1       LR-062483     422.0  None  ...                  3.8   \n",
       "1               1            None     798.0     7  ...                  5.6   \n",
       "2               1        AM-95365     598.0  None  ...                  3.8   \n",
       "3               0            None     422.0     6  ...                  3.8   \n",
       "4               1           C1626     422.0  None  ...                  4.1   \n",
       "...           ...             ...       ...   ...  ...                  ...   \n",
       "15914           0        10988301     997.0     6  ...                  5.3   \n",
       "15915           0     507370_3223     798.0     7  ...                  NaN   \n",
       "15916           0        Espace16     997.0     6  ...                  5.3   \n",
       "15917           0         2691331     997.0     6  ...                  5.3   \n",
       "15918           0  Re_30000008029     798.0  None  ...                  6.8   \n",
       "\n",
       "       consumption_city registration  safety_features  upholstery_color  \\\n",
       "0                   4.3   2016-01-01             14.0             Black   \n",
       "1                   7.1   2017-03-01             15.0              Grey   \n",
       "2                   4.4   2016-02-01             12.0             Black   \n",
       "3                   4.3   2016-08-01             14.0              None   \n",
       "4                   4.6   2016-05-01             13.0             Black   \n",
       "...                 ...          ...              ...               ...   \n",
       "15914               6.2          NaT             19.0              None   \n",
       "15915               7.0   2019-01-01             19.0              None   \n",
       "15916               6.2   2019-03-01             18.0             Black   \n",
       "15917               6.2   2019-06-01             12.0              None   \n",
       "15918               8.7   2019-01-01             14.0             Brown   \n",
       "\n",
       "         upholstery  extras entertainment     body_original_color  \\\n",
       "0             Cloth     3.0           4.0           Mythosschwarz   \n",
       "1             Cloth     4.0           5.0                    None   \n",
       "2             Cloth     2.0           2.0  Mythosschwarz metallic   \n",
       "3              None     3.0           8.0                    None   \n",
       "4             Cloth     4.0           7.0  Mythosschwarz metallic   \n",
       "...             ...     ...           ...                     ...   \n",
       "15914          None     2.0           6.0            Grigio scuro   \n",
       "15915          None     3.0           5.0     Stahl-grau metallic   \n",
       "15916  Full leather     1.0           4.0             Arktis-weiß   \n",
       "15917          None     2.0           4.0                  Grigio   \n",
       "15918  Full leather     2.0           1.0  Titanium-grau metallic   \n",
       "\n",
       "       comfort_convenience  \n",
       "0                     16.0  \n",
       "1                      9.0  \n",
       "2                     13.0  \n",
       "3                     16.0  \n",
       "4                     13.0  \n",
       "...                    ...  \n",
       "15914                 20.0  \n",
       "15915                 23.0  \n",
       "15916                 24.0  \n",
       "15917                 10.0  \n",
       "15918                 11.0  \n",
       "\n",
       "[15919 rows x 28 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                          Diesel (Particulate Filter)\n",
       "1                                             Gasoline\n",
       "2                          Diesel (Particulate Filter)\n",
       "3                          Diesel (Particulate Filter)\n",
       "4                          Diesel (Particulate Filter)\n",
       "                             ...                      \n",
       "15914                      Diesel (Particulate Filter)\n",
       "15915    Super 95 / Super Plus 98 (Particulate Filter)\n",
       "15916                                           Diesel\n",
       "15917                                           Diesel\n",
       "15918                                         Super 95\n",
       "Name: fuel, Length: 15919, dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fuel.str.join(\"\").str.strip(\"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "make                       0\n",
       "model                      0\n",
       "km                         0\n",
       "hp                        88\n",
       "transmission               0\n",
       "engine_size             5124\n",
       "sport_back                 0\n",
       "number_offer            3175\n",
       "cylinder                 601\n",
       "gears                   4712\n",
       "fuel_type                  0\n",
       "co2_emission            2443\n",
       "body_typ                  60\n",
       "price                      0\n",
       "number_of_doors          212\n",
       "vat                     4513\n",
       "number_of_seats          977\n",
       "emission_class          3628\n",
       "consumption_combined    2883\n",
       "consumption_city        2422\n",
       "registration            1597\n",
       "safety_features          982\n",
       "upholstery_color        5861\n",
       "upholstery              5861\n",
       "extras                  2962\n",
       "entertainment           1374\n",
       "body_original_color     3759\n",
       "comfort_convenience      920\n",
       "dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.hp.fillna(data.hp.mode()[0],inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.engine_size.fillna(data.engine_size.mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 66., 141.,  85.,  70.,  92., 112.,  60.,  71.,  67., 110.,  93.,\n",
       "       147.,  86., 140.,  87.,  81.,  82., 135., 132., 100.,  96., 162.,\n",
       "       150., 294., 228., 270., 137.,   9., 133.,  77., 101.,  78., 103.,\n",
       "         1.,  74., 118.,  84.,  88.,  80.,  76., 149.,  44.,  51.,  55.,\n",
       "        52.,  63.,  40.,  65.,  75., 125., 120., 184., 239., 121., 143.,\n",
       "       191.,  89., 195., 127., 122., 154., 155., 104., 123., 146.,  90.,\n",
       "        53.,  54.,  56., 164.,   4., 163.,  57., 119., 165., 117., 115.,\n",
       "        98., 168., 167.])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.hp.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "make                       0\n",
       "model                      0\n",
       "km                         0\n",
       "hp                         0\n",
       "transmission               0\n",
       "engine_size                0\n",
       "sport_back                 0\n",
       "number_offer            3175\n",
       "cylinder                 601\n",
       "gears                   4712\n",
       "fuel_type                  0\n",
       "co2_emission            2443\n",
       "body_typ                  60\n",
       "price                      0\n",
       "number_of_doors          212\n",
       "vat                     4513\n",
       "number_of_seats          977\n",
       "emission_class          3628\n",
       "consumption_combined    2883\n",
       "consumption_city        2422\n",
       "registration            1597\n",
       "safety_features          982\n",
       "upholstery_color        5861\n",
       "upholstery              5861\n",
       "extras                  2962\n",
       "entertainment           1374\n",
       "body_original_color     3759\n",
       "comfort_convenience      920\n",
       "dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "fill_with_mode=[\"hp\",\"number_of_seats\",\"emission_class\",\"safety_features\",\"extras\",\"comfort_convenience\",\"entertainment\",\"registration\",\\\n",
    "    \"body_original_color\",\"vat\",\"co2_emission\",\"body_typ\",\"gears\",\"upholstery\",\"upholstery_color\",\"cylinder\",\"engine_size\",\"number_of_doors\"]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in fill_with_mode:\n",
    "    data[i].fillna(data[i].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hp                     0\n",
       "number_of_seats        0\n",
       "emission_class         0\n",
       "safety_features        0\n",
       "extras                 0\n",
       "comfort_convenience    0\n",
       "entertainment          0\n",
       "body_original_color    0\n",
       "vat                    0\n",
       "co2_emission           0\n",
       "body_typ               0\n",
       "gears                  0\n",
       "upholstery             0\n",
       "upholstery_color       0\n",
       "cylinder               0\n",
       "engine_size            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[fill_with_mode].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>consumption_combined</th>\n",
       "      <th>consumption_city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.8</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.6</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.8</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.8</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.1</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15914</th>\n",
       "      <td>5.3</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15915</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15916</th>\n",
       "      <td>5.3</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15917</th>\n",
       "      <td>5.3</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15918</th>\n",
       "      <td>6.8</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15919 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       consumption_combined  consumption_city\n",
       "0                       3.8               4.3\n",
       "1                       5.6               7.1\n",
       "2                       3.8               4.4\n",
       "3                       3.8               4.3\n",
       "4                       4.1               4.6\n",
       "...                     ...               ...\n",
       "15914                   5.3               6.2\n",
       "15915                   NaN               7.0\n",
       "15916                   5.3               6.2\n",
       "15917                   5.3               6.2\n",
       "15918                   6.8               8.7\n",
       "\n",
       "[15919 rows x 2 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fill_with_mean=[\"consumption_combined\",\"consumption_city\"]\n",
    "data[fill_with_mean]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in fill_with_mean:\n",
    "    data[col].fillna(data[col].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "make                       0\n",
       "model                      0\n",
       "km                         0\n",
       "hp                         0\n",
       "transmission               0\n",
       "engine_size                0\n",
       "sport_back                 0\n",
       "number_offer            3175\n",
       "cylinder                   0\n",
       "gears                      0\n",
       "fuel_type                  0\n",
       "co2_emission               0\n",
       "body_typ                   0\n",
       "price                      0\n",
       "number_of_doors            0\n",
       "vat                        0\n",
       "number_of_seats            0\n",
       "emission_class             0\n",
       "consumption_combined       0\n",
       "consumption_city           0\n",
       "registration               0\n",
       "safety_features            0\n",
       "upholstery_color           0\n",
       "upholstery                 0\n",
       "extras                     0\n",
       "entertainment              0\n",
       "body_original_color        0\n",
       "comfort_convenience        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(\"number_of_doors\",1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "make                            object\n",
       "model                           object\n",
       "km                             float64\n",
       "hp                             float64\n",
       "transmission                    object\n",
       "engine_size                    float64\n",
       "sport_back                       int64\n",
       "number_offer                    object\n",
       "cylinder                       float64\n",
       "gears                           object\n",
       "fuel_type                       object\n",
       "co2_emission                   float64\n",
       "body_typ                        object\n",
       "price                            int64\n",
       "vat                             object\n",
       "number_of_seats                float64\n",
       "emission_class                  object\n",
       "consumption_combined           float64\n",
       "consumption_city               float64\n",
       "registration            datetime64[ns]\n",
       "safety_features                float64\n",
       "upholstery_color                object\n",
       "upholstery                      object\n",
       "extras                         float64\n",
       "entertainment                  float64\n",
       "body_original_color             object\n",
       "comfort_convenience            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "km                      float64\n",
       "hp                      float64\n",
       "engine_size             float64\n",
       "cylinder                float64\n",
       "co2_emission            float64\n",
       "number_of_seats         float64\n",
       "consumption_combined    float64\n",
       "consumption_city        float64\n",
       "safety_features         float64\n",
       "extras                  float64\n",
       "entertainment           float64\n",
       "comfort_convenience     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[numeric_data].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_data=[col for col in data.columns if data[col].dtype==float]\n",
    "len(numeric_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.histplot(data.price);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1080 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(\"seaborn\")\n",
    "fig,ax=plt.subplots(3,4,figsize=(20,15))\n",
    "\n",
    "ax=ax.ravel()\n",
    "for i in range(len(numeric_data)):\n",
    "    ax[i].boxplot(data[numeric_data[i]])\n",
    "    ax[i].set_title(numeric_data[i])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "10bd42e55e01058be9474698467774bde09fdfefde00ef9783435b86aa6e56c3"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}