PandasAndNumPyFundamentals/Exploring+Ebay+Car+Sales-Copy1.ipynb

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    "# Exploring Ebay Car Sales Data\n",
    "\n",
    "## Description:\n",
    "In this guided project, I will work with a dataset of used cars from <i>ebay Kleinanzeigen</i> a section of the German eBay website.\n",
    "\n",
    "The dataset was originally scraped and uploaded to Kaggle, you can visit it here:\n",
    "\n",
    "A few modifications have been made to the dataset, as follows:\n",
    "-50,000 data points were sameples from the full dataset\n",
    "\n",
    "-The dataset has been given dirty values to help understand what to expect in a scraped dataset.\n",
    "\n",
    "The data dictionary is as follows:\n",
    "\n",
    "- dateCrawled: When this ad was first crawled. \n",
    "All field-values were taken from this date.\n",
    "- name: Name of the car.\n",
    "- seller: Whether the seller is private or a dealer.\n",
    "- offerType: The type of listing\n",
    "- price: The price on the ad to sell the car.\n",
    "- abtest:Whether the listing is included in an A/B test.\n",
    "- vehicleType: The vehicle Type.\n",
    "- yearOfRegistration: The year in which the car was first registered.\n",
    "- gearbox: The transmission type.\n",
    "- powerPS: The power of the car in PS.\n",
    "- model: The car model name.\n",
    "- kilometer: How many kilometers the car has driven.\n",
    "- monthOfRegistration: The month in which the car was first registered.\n",
    "- fuelType: What type of fuel the car uses.\n",
    "- brand: The brand of the car.\n",
    "- notRepairedDamage: If the car has a damage which is not yet repaired.\n",
    "- dateCreated: The date on which the eBay listing was created.\n",
    "- nrOfPictures: The number of pictures in the ad.\n",
    "- postalCode: The postal code for the location of the vehicle.\n",
    "- lastSeenOnline: When the crawler saw this ad last online.\n",
    "\n",
    "## Purpose:\n",
    "The aim of the project is to be able to clean the data and analyze the included car listing. We will also look in better detail some of the benefits that jupyter notebook has for those who work with pandas.\n",
    "\n",
    "So lets begin, by importing the libraries we need, and reading the dataset into pandas.\n",
    "\n"
   ]
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "autos = pd.read_csv(\"autos.csv\", encoding='Latin-1')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateCrawled</th>\n",
       "      <th>name</th>\n",
       "      <th>seller</th>\n",
       "      <th>offerType</th>\n",
       "      <th>price</th>\n",
       "      <th>abtest</th>\n",
       "      <th>vehicleType</th>\n",
       "      <th>yearOfRegistration</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>powerPS</th>\n",
       "      <th>model</th>\n",
       "      <th>odometer</th>\n",
       "      <th>monthOfRegistration</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>brand</th>\n",
       "      <th>notRepairedDamage</th>\n",
       "      <th>dateCreated</th>\n",
       "      <th>nrOfPictures</th>\n",
       "      <th>postalCode</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-26 17:47:46</td>\n",
       "      <td>Peugeot_807_160_NAVTECH_ON_BOARD</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$5,000</td>\n",
       "      <td>control</td>\n",
       "      <td>bus</td>\n",
       "      <td>2004</td>\n",
       "      <td>manuell</td>\n",
       "      <td>158</td>\n",
       "      <td>andere</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>3</td>\n",
       "      <td>lpg</td>\n",
       "      <td>peugeot</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-26 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>79588</td>\n",
       "      <td>2016-04-06 06:45:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-04-04 13:38:56</td>\n",
       "      <td>BMW_740i_4_4_Liter_HAMANN_UMBAU_Mega_Optik</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$8,500</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>1997</td>\n",
       "      <td>automatik</td>\n",
       "      <td>286</td>\n",
       "      <td>7er</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>6</td>\n",
       "      <td>benzin</td>\n",
       "      <td>bmw</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-04-04 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>71034</td>\n",
       "      <td>2016-04-06 14:45:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-26 18:57:24</td>\n",
       "      <td>Volkswagen_Golf_1.6_United</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$8,990</td>\n",
       "      <td>test</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2009</td>\n",
       "      <td>manuell</td>\n",
       "      <td>102</td>\n",
       "      <td>golf</td>\n",
       "      <td>70,000km</td>\n",
       "      <td>7</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-26 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>35394</td>\n",
       "      <td>2016-04-06 20:15:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-12 16:58:10</td>\n",
       "      <td>Smart_smart_fortwo_coupe_softouch/F1/Klima/Pan...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$4,350</td>\n",
       "      <td>control</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2007</td>\n",
       "      <td>automatik</td>\n",
       "      <td>71</td>\n",
       "      <td>fortwo</td>\n",
       "      <td>70,000km</td>\n",
       "      <td>6</td>\n",
       "      <td>benzin</td>\n",
       "      <td>smart</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-12 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>33729</td>\n",
       "      <td>2016-03-15 03:16:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-04-01 14:38:50</td>\n",
       "      <td>Ford_Focus_1_6_Benzin_TÜV_neu_ist_sehr_gepfleg...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$1,350</td>\n",
       "      <td>test</td>\n",
       "      <td>kombi</td>\n",
       "      <td>2003</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>focus</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>7</td>\n",
       "      <td>benzin</td>\n",
       "      <td>ford</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>39218</td>\n",
       "      <td>2016-04-01 14:38:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-03-21 13:47:45</td>\n",
       "      <td>Chrysler_Grand_Voyager_2.8_CRD_Aut.Limited_Sto...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$7,900</td>\n",
       "      <td>test</td>\n",
       "      <td>bus</td>\n",
       "      <td>2006</td>\n",
       "      <td>automatik</td>\n",
       "      <td>150</td>\n",
       "      <td>voyager</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>4</td>\n",
       "      <td>diesel</td>\n",
       "      <td>chrysler</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-21 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>22962</td>\n",
       "      <td>2016-04-06 09:45:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-03-20 17:55:21</td>\n",
       "      <td>VW_Golf_III_GT_Special_Electronic_Green_Metall...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$300</td>\n",
       "      <td>test</td>\n",
       "      <td>limousine</td>\n",
       "      <td>1995</td>\n",
       "      <td>manuell</td>\n",
       "      <td>90</td>\n",
       "      <td>golf</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>8</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-20 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>31535</td>\n",
       "      <td>2016-03-23 02:48:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-03-16 18:55:19</td>\n",
       "      <td>Golf_IV_1.9_TDI_90PS</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$1,990</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>1998</td>\n",
       "      <td>manuell</td>\n",
       "      <td>90</td>\n",
       "      <td>golf</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>12</td>\n",
       "      <td>diesel</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-16 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>53474</td>\n",
       "      <td>2016-04-07 03:17:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-03-22 16:51:34</td>\n",
       "      <td>Seat_Arosa</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$250</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>arosa</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>seat</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-22 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>7426</td>\n",
       "      <td>2016-03-26 18:18:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-03-16 13:47:02</td>\n",
       "      <td>Renault_Megane_Scenic_1.6e_RT_Klimaanlage</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$590</td>\n",
       "      <td>control</td>\n",
       "      <td>bus</td>\n",
       "      <td>1997</td>\n",
       "      <td>manuell</td>\n",
       "      <td>90</td>\n",
       "      <td>megane</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>7</td>\n",
       "      <td>benzin</td>\n",
       "      <td>renault</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-16 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>15749</td>\n",
       "      <td>2016-04-06 10:46:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016-03-15 01:41:36</td>\n",
       "      <td>VW_Golf_Tuning_in_siber/grau</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$999</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>manuell</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>4</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-14 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>86157</td>\n",
       "      <td>2016-04-07 03:16:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2016-03-16 18:45:34</td>\n",
       "      <td>Mercedes_A140_Motorschaden</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$350</td>\n",
       "      <td>control</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>0</td>\n",
       "      <td>benzin</td>\n",
       "      <td>mercedes_benz</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-16 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>17498</td>\n",
       "      <td>2016-03-16 18:45:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2016-03-31 19:48:22</td>\n",
       "      <td>Smart_smart_fortwo_coupe_softouch_pure_MHD_Pan...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$5,299</td>\n",
       "      <td>control</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2010</td>\n",
       "      <td>automatik</td>\n",
       "      <td>71</td>\n",
       "      <td>fortwo</td>\n",
       "      <td>50,000km</td>\n",
       "      <td>9</td>\n",
       "      <td>benzin</td>\n",
       "      <td>smart</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-31 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>34590</td>\n",
       "      <td>2016-04-06 14:17:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2016-03-23 10:48:32</td>\n",
       "      <td>Audi_A3_1.6_tuning</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$1,350</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>1999</td>\n",
       "      <td>manuell</td>\n",
       "      <td>101</td>\n",
       "      <td>a3</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>11</td>\n",
       "      <td>benzin</td>\n",
       "      <td>audi</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-23 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>12043</td>\n",
       "      <td>2016-04-01 14:17:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016-03-23 11:50:46</td>\n",
       "      <td>Renault_Clio_3__Dynamique_1.2__16_V;_viele_Ver...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$3,999</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2007</td>\n",
       "      <td>manuell</td>\n",
       "      <td>75</td>\n",
       "      <td>clio</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>9</td>\n",
       "      <td>benzin</td>\n",
       "      <td>renault</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-23 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>81737</td>\n",
       "      <td>2016-04-01 15:46:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016-04-01 12:06:20</td>\n",
       "      <td>Corvette_C3_Coupe_T_Top_Crossfire_Injection</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$18,900</td>\n",
       "      <td>test</td>\n",
       "      <td>coupe</td>\n",
       "      <td>1982</td>\n",
       "      <td>automatik</td>\n",
       "      <td>203</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80,000km</td>\n",
       "      <td>6</td>\n",
       "      <td>benzin</td>\n",
       "      <td>sonstige_autos</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>61276</td>\n",
       "      <td>2016-04-02 21:10:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-03-16 14:59:02</td>\n",
       "      <td>Opel_Vectra_B_Kombi</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$350</td>\n",
       "      <td>test</td>\n",
       "      <td>kombi</td>\n",
       "      <td>1999</td>\n",
       "      <td>manuell</td>\n",
       "      <td>101</td>\n",
       "      <td>vectra</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>5</td>\n",
       "      <td>benzin</td>\n",
       "      <td>opel</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-16 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>57299</td>\n",
       "      <td>2016-03-18 05:29:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016-03-29 11:46:22</td>\n",
       "      <td>Volkswagen_Scirocco_2_G60</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$5,500</td>\n",
       "      <td>test</td>\n",
       "      <td>coupe</td>\n",
       "      <td>1990</td>\n",
       "      <td>manuell</td>\n",
       "      <td>205</td>\n",
       "      <td>scirocco</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>6</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-29 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>74821</td>\n",
       "      <td>2016-04-05 20:46:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016-03-26 19:57:44</td>\n",
       "      <td>Verkaufen_mein_bmw_e36_320_i_touring</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$300</td>\n",
       "      <td>control</td>\n",
       "      <td>bus</td>\n",
       "      <td>1995</td>\n",
       "      <td>manuell</td>\n",
       "      <td>150</td>\n",
       "      <td>3er</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>0</td>\n",
       "      <td>benzin</td>\n",
       "      <td>bmw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-26 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>54329</td>\n",
       "      <td>2016-04-02 12:16:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016-03-17 13:36:21</td>\n",
       "      <td>mazda_tribute_2.0_mit_gas_und_tuev_neu_2018</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$4,150</td>\n",
       "      <td>control</td>\n",
       "      <td>suv</td>\n",
       "      <td>2004</td>\n",
       "      <td>manuell</td>\n",
       "      <td>124</td>\n",
       "      <td>andere</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>2</td>\n",
       "      <td>lpg</td>\n",
       "      <td>mazda</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-17 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>40878</td>\n",
       "      <td>2016-03-17 14:45:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2016-03-05 19:57:31</td>\n",
       "      <td>Audi_A4_Avant_1.9_TDI_*6_Gang*AHK*Klimatronik*...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$3,500</td>\n",
       "      <td>test</td>\n",
       "      <td>kombi</td>\n",
       "      <td>2003</td>\n",
       "      <td>manuell</td>\n",
       "      <td>131</td>\n",
       "      <td>a4</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>5</td>\n",
       "      <td>diesel</td>\n",
       "      <td>audi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-05 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>53913</td>\n",
       "      <td>2016-03-07 05:46:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016-03-06 19:07:10</td>\n",
       "      <td>Porsche_911_Carrera_4S_Cabrio</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$41,500</td>\n",
       "      <td>test</td>\n",
       "      <td>cabrio</td>\n",
       "      <td>2004</td>\n",
       "      <td>manuell</td>\n",
       "      <td>320</td>\n",
       "      <td>911</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>4</td>\n",
       "      <td>benzin</td>\n",
       "      <td>porsche</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-06 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>65428</td>\n",
       "      <td>2016-04-05 23:46:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016-03-28 20:50:54</td>\n",
       "      <td>MINI_Cooper_S_Cabrio</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$25,450</td>\n",
       "      <td>control</td>\n",
       "      <td>cabrio</td>\n",
       "      <td>2015</td>\n",
       "      <td>manuell</td>\n",
       "      <td>184</td>\n",
       "      <td>cooper</td>\n",
       "      <td>10,000km</td>\n",
       "      <td>1</td>\n",
       "      <td>benzin</td>\n",
       "      <td>mini</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-28 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>44789</td>\n",
       "      <td>2016-04-01 06:45:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2016-03-10 19:55:34</td>\n",
       "      <td>Peugeot_Boxer_2_2_HDi_120_Ps_9_Sitzer_inkl_Klima</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$7,999</td>\n",
       "      <td>control</td>\n",
       "      <td>bus</td>\n",
       "      <td>2010</td>\n",
       "      <td>manuell</td>\n",
       "      <td>120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>2</td>\n",
       "      <td>diesel</td>\n",
       "      <td>peugeot</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-10 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>30900</td>\n",
       "      <td>2016-03-17 08:45:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2016-04-03 11:57:02</td>\n",
       "      <td>BMW_535i_xDrive_Sport_Aut.</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$48,500</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2014</td>\n",
       "      <td>automatik</td>\n",
       "      <td>306</td>\n",
       "      <td>5er</td>\n",
       "      <td>30,000km</td>\n",
       "      <td>12</td>\n",
       "      <td>benzin</td>\n",
       "      <td>bmw</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-04-03 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>22547</td>\n",
       "      <td>2016-04-07 13:16:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2016-03-21 21:56:18</td>\n",
       "      <td>Ford_escort_kombi_an_bastler_mit_ghia_ausstattung</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$90</td>\n",
       "      <td>control</td>\n",
       "      <td>kombi</td>\n",
       "      <td>1996</td>\n",
       "      <td>manuell</td>\n",
       "      <td>116</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>4</td>\n",
       "      <td>benzin</td>\n",
       "      <td>ford</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-03-21 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>27574</td>\n",
       "      <td>2016-04-01 05:16:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2016-04-03 22:46:28</td>\n",
       "      <td>Volkswagen_Polo_Fox</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$777</td>\n",
       "      <td>control</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>1992</td>\n",
       "      <td>manuell</td>\n",
       "      <td>54</td>\n",
       "      <td>polo</td>\n",
       "      <td>125,000km</td>\n",
       "      <td>2</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-04-03 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>38110</td>\n",
       "      <td>2016-04-05 23:46:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2016-03-27 18:45:01</td>\n",
       "      <td>Hat_einer_Ahnung_mit_Ford_Galaxy_HILFE</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$0</td>\n",
       "      <td>control</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-27 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>66701</td>\n",
       "      <td>2016-03-27 18:45:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2016-03-19 21:56:19</td>\n",
       "      <td>MINI_Cooper_D</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$5,250</td>\n",
       "      <td>control</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2007</td>\n",
       "      <td>manuell</td>\n",
       "      <td>110</td>\n",
       "      <td>cooper</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>7</td>\n",
       "      <td>diesel</td>\n",
       "      <td>mini</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-03-19 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>15745</td>\n",
       "      <td>2016-04-07 14:58:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2016-04-02 12:45:44</td>\n",
       "      <td>Mercedes_Benz_E_320_T_CDI_Avantgarde_DPF7_Sitz...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$4,999</td>\n",
       "      <td>test</td>\n",
       "      <td>kombi</td>\n",
       "      <td>2004</td>\n",
       "      <td>automatik</td>\n",
       "      <td>204</td>\n",
       "      <td>e_klasse</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>10</td>\n",
       "      <td>diesel</td>\n",
       "      <td>mercedes_benz</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-04-02 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>47638</td>\n",
       "      <td>2016-04-02 12:45:44</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49970</th>\n",
       "      <td>2016-03-21 22:47:37</td>\n",
       "      <td>c4_Grand_Picasso_mit_Automatik_Leder_Navi_Temp...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$15,800</td>\n",
       "      <td>control</td>\n",
       "      <td>bus</td>\n",
       "      <td>2010</td>\n",
       "      <td>automatik</td>\n",
       "      <td>136</td>\n",
       "      <td>c4</td>\n",
       "      <td>60,000km</td>\n",
       "      <td>4</td>\n",
       "      <td>diesel</td>\n",
       "      <td>citroen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-21 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>14947</td>\n",
       "      <td>2016-04-07 04:17:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49971</th>\n",
       "      <td>2016-03-29 14:54:12</td>\n",
       "      <td>W.Lupo_1.0</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$950</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2001</td>\n",
       "      <td>manuell</td>\n",
       "      <td>50</td>\n",
       "      <td>lupo</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>4</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-29 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>65197</td>\n",
       "      <td>2016-03-29 20:41:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49972</th>\n",
       "      <td>2016-03-26 22:25:23</td>\n",
       "      <td>Mercedes_Benz_Vito_115_CDI_Extralang_Aut.</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$3,300</td>\n",
       "      <td>control</td>\n",
       "      <td>bus</td>\n",
       "      <td>2004</td>\n",
       "      <td>automatik</td>\n",
       "      <td>150</td>\n",
       "      <td>vito</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>10</td>\n",
       "      <td>diesel</td>\n",
       "      <td>mercedes_benz</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-03-26 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>65326</td>\n",
       "      <td>2016-03-28 11:28:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49973</th>\n",
       "      <td>2016-03-27 05:32:39</td>\n",
       "      <td>Mercedes_Benz_SLK_200_Kompressor</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$6,000</td>\n",
       "      <td>control</td>\n",
       "      <td>cabrio</td>\n",
       "      <td>2004</td>\n",
       "      <td>manuell</td>\n",
       "      <td>163</td>\n",
       "      <td>slk</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>11</td>\n",
       "      <td>benzin</td>\n",
       "      <td>mercedes_benz</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-27 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>53567</td>\n",
       "      <td>2016-03-27 08:25:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49974</th>\n",
       "      <td>2016-03-20 10:52:31</td>\n",
       "      <td>Golf_1_Cabrio_Tuev_Neu_viele_Extras_alles_eing...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$0</td>\n",
       "      <td>control</td>\n",
       "      <td>cabrio</td>\n",
       "      <td>1983</td>\n",
       "      <td>manuell</td>\n",
       "      <td>70</td>\n",
       "      <td>golf</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>2</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-20 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>8209</td>\n",
       "      <td>2016-03-27 19:48:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49975</th>\n",
       "      <td>2016-03-27 20:51:39</td>\n",
       "      <td>Honda_Jazz_1.3_DSi_i_VTEC_IMA_CVT_Comfort</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$9,700</td>\n",
       "      <td>control</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2012</td>\n",
       "      <td>automatik</td>\n",
       "      <td>88</td>\n",
       "      <td>jazz</td>\n",
       "      <td>100,000km</td>\n",
       "      <td>11</td>\n",
       "      <td>hybrid</td>\n",
       "      <td>honda</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-27 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>84385</td>\n",
       "      <td>2016-04-05 19:45:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49976</th>\n",
       "      <td>2016-03-19 18:56:05</td>\n",
       "      <td>Audi_80_Avant_2.6_E__Vollausstattung!!_Einziga...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$5,900</td>\n",
       "      <td>test</td>\n",
       "      <td>kombi</td>\n",
       "      <td>1992</td>\n",
       "      <td>automatik</td>\n",
       "      <td>150</td>\n",
       "      <td>80</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>12</td>\n",
       "      <td>benzin</td>\n",
       "      <td>audi</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-19 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>36100</td>\n",
       "      <td>2016-04-07 06:16:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49977</th>\n",
       "      <td>2016-03-31 18:37:18</td>\n",
       "      <td>Mercedes_Benz_C200_Cdi_W203</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$5,500</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2003</td>\n",
       "      <td>manuell</td>\n",
       "      <td>116</td>\n",
       "      <td>c_klasse</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>2</td>\n",
       "      <td>diesel</td>\n",
       "      <td>mercedes_benz</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-31 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>33739</td>\n",
       "      <td>2016-04-06 12:16:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49978</th>\n",
       "      <td>2016-04-04 10:37:14</td>\n",
       "      <td>Mercedes_Benz_E_200_Classic</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$900</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>1996</td>\n",
       "      <td>automatik</td>\n",
       "      <td>136</td>\n",
       "      <td>e_klasse</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>9</td>\n",
       "      <td>benzin</td>\n",
       "      <td>mercedes_benz</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-04-04 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>24405</td>\n",
       "      <td>2016-04-06 12:44:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49979</th>\n",
       "      <td>2016-03-20 18:38:40</td>\n",
       "      <td>Volkswagen_Polo_1.6_TDI_Style</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$11,000</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2011</td>\n",
       "      <td>manuell</td>\n",
       "      <td>90</td>\n",
       "      <td>polo</td>\n",
       "      <td>70,000km</td>\n",
       "      <td>11</td>\n",
       "      <td>diesel</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-20 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>48455</td>\n",
       "      <td>2016-04-07 01:45:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49980</th>\n",
       "      <td>2016-03-12 10:55:54</td>\n",
       "      <td>Ford_Escort_Turnier_16V</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$400</td>\n",
       "      <td>control</td>\n",
       "      <td>kombi</td>\n",
       "      <td>1995</td>\n",
       "      <td>manuell</td>\n",
       "      <td>105</td>\n",
       "      <td>escort</td>\n",
       "      <td>125,000km</td>\n",
       "      <td>3</td>\n",
       "      <td>benzin</td>\n",
       "      <td>ford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-12 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>56218</td>\n",
       "      <td>2016-04-06 17:16:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49981</th>\n",
       "      <td>2016-03-15 09:38:21</td>\n",
       "      <td>Opel_Astra_Kombi_mit_Anhaengerkupplung</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$2,000</td>\n",
       "      <td>control</td>\n",
       "      <td>kombi</td>\n",
       "      <td>1998</td>\n",
       "      <td>manuell</td>\n",
       "      <td>115</td>\n",
       "      <td>astra</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>12</td>\n",
       "      <td>benzin</td>\n",
       "      <td>opel</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-15 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>86859</td>\n",
       "      <td>2016-04-05 17:21:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49982</th>\n",
       "      <td>2016-03-29 18:51:08</td>\n",
       "      <td>Skoda_Fabia_4_Tuerer_Bj:2004__85.000Tkm</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$1,950</td>\n",
       "      <td>control</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2004</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>fabia</td>\n",
       "      <td>90,000km</td>\n",
       "      <td>7</td>\n",
       "      <td>benzin</td>\n",
       "      <td>skoda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-29 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>45884</td>\n",
       "      <td>2016-03-29 18:51:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49983</th>\n",
       "      <td>2016-03-06 12:43:04</td>\n",
       "      <td>Ford_focus_99</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$600</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>1999</td>\n",
       "      <td>manuell</td>\n",
       "      <td>101</td>\n",
       "      <td>focus</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>4</td>\n",
       "      <td>benzin</td>\n",
       "      <td>ford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-06 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>52477</td>\n",
       "      <td>2016-03-09 06:16:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49984</th>\n",
       "      <td>2016-03-31 22:48:48</td>\n",
       "      <td>Student_sucht_ein__Anfaengerauto___ab_2000_BJ_...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$0</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sonstige_autos</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-31 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>12103</td>\n",
       "      <td>2016-04-02 19:44:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49985</th>\n",
       "      <td>2016-04-02 16:38:23</td>\n",
       "      <td>Verkaufe_meinen_vw_vento!</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$1,000</td>\n",
       "      <td>control</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995</td>\n",
       "      <td>automatik</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>0</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-04-02 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>30900</td>\n",
       "      <td>2016-04-06 15:17:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49986</th>\n",
       "      <td>2016-04-04 20:46:02</td>\n",
       "      <td>Chrysler_300C_3.0_CRD_DPF_Automatik_Voll_Ausst...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$15,900</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2010</td>\n",
       "      <td>automatik</td>\n",
       "      <td>218</td>\n",
       "      <td>300c</td>\n",
       "      <td>125,000km</td>\n",
       "      <td>11</td>\n",
       "      <td>diesel</td>\n",
       "      <td>chrysler</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-04-04 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>73527</td>\n",
       "      <td>2016-04-06 23:16:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49987</th>\n",
       "      <td>2016-03-22 20:47:27</td>\n",
       "      <td>Audi_A3_Limousine_2.0_TDI_DPF_Ambition__NAVI__...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$21,990</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2013</td>\n",
       "      <td>manuell</td>\n",
       "      <td>150</td>\n",
       "      <td>a3</td>\n",
       "      <td>50,000km</td>\n",
       "      <td>11</td>\n",
       "      <td>diesel</td>\n",
       "      <td>audi</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-22 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>94362</td>\n",
       "      <td>2016-03-26 22:46:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49988</th>\n",
       "      <td>2016-03-28 19:49:51</td>\n",
       "      <td>BMW_330_Ci</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$9,550</td>\n",
       "      <td>control</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2001</td>\n",
       "      <td>manuell</td>\n",
       "      <td>231</td>\n",
       "      <td>3er</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>10</td>\n",
       "      <td>benzin</td>\n",
       "      <td>bmw</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-28 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>83646</td>\n",
       "      <td>2016-04-07 02:17:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49989</th>\n",
       "      <td>2016-03-11 19:50:37</td>\n",
       "      <td>VW_Polo_zum_Ausschlachten_oder_Wiederaufbau</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$150</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>1997</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>polo</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>5</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-03-11 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>21244</td>\n",
       "      <td>2016-03-12 10:17:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49990</th>\n",
       "      <td>2016-03-21 19:54:19</td>\n",
       "      <td>Mercedes_Benz_A_200__BlueEFFICIENCY__Urban</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$17,500</td>\n",
       "      <td>test</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2012</td>\n",
       "      <td>manuell</td>\n",
       "      <td>156</td>\n",
       "      <td>a_klasse</td>\n",
       "      <td>30,000km</td>\n",
       "      <td>12</td>\n",
       "      <td>benzin</td>\n",
       "      <td>mercedes_benz</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-21 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>58239</td>\n",
       "      <td>2016-04-06 22:46:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49991</th>\n",
       "      <td>2016-03-06 15:25:19</td>\n",
       "      <td>Kleinwagen</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$500</td>\n",
       "      <td>control</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>twingo</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>0</td>\n",
       "      <td>benzin</td>\n",
       "      <td>renault</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-06 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>61350</td>\n",
       "      <td>2016-03-06 18:24:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49992</th>\n",
       "      <td>2016-03-10 19:37:38</td>\n",
       "      <td>Fiat_Grande_Punto_1.4_T_Jet_16V_Sport</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$4,800</td>\n",
       "      <td>control</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2009</td>\n",
       "      <td>manuell</td>\n",
       "      <td>120</td>\n",
       "      <td>andere</td>\n",
       "      <td>125,000km</td>\n",
       "      <td>9</td>\n",
       "      <td>lpg</td>\n",
       "      <td>fiat</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-10 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>68642</td>\n",
       "      <td>2016-03-13 01:44:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49993</th>\n",
       "      <td>2016-03-15 18:47:35</td>\n",
       "      <td>Audi_A3__1_8l__Silber;_schoenes_Fahrzeug</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$1,650</td>\n",
       "      <td>control</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>1997</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>7</td>\n",
       "      <td>benzin</td>\n",
       "      <td>audi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-15 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>65203</td>\n",
       "      <td>2016-04-06 19:46:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49994</th>\n",
       "      <td>2016-03-22 17:36:42</td>\n",
       "      <td>Audi_A6__S6__Avant_4.2_quattro_eventuell_Tausc...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$5,000</td>\n",
       "      <td>control</td>\n",
       "      <td>kombi</td>\n",
       "      <td>2001</td>\n",
       "      <td>automatik</td>\n",
       "      <td>299</td>\n",
       "      <td>a6</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>1</td>\n",
       "      <td>benzin</td>\n",
       "      <td>audi</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-22 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>46537</td>\n",
       "      <td>2016-04-06 08:16:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>2016-03-27 14:38:19</td>\n",
       "      <td>Audi_Q5_3.0_TDI_qu._S_tr.__Navi__Panorama__Xenon</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$24,900</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2011</td>\n",
       "      <td>automatik</td>\n",
       "      <td>239</td>\n",
       "      <td>q5</td>\n",
       "      <td>100,000km</td>\n",
       "      <td>1</td>\n",
       "      <td>diesel</td>\n",
       "      <td>audi</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-27 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>82131</td>\n",
       "      <td>2016-04-01 13:47:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>2016-03-28 10:50:25</td>\n",
       "      <td>Opel_Astra_F_Cabrio_Bertone_Edition___TÜV_neu+...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$1,980</td>\n",
       "      <td>control</td>\n",
       "      <td>cabrio</td>\n",
       "      <td>1996</td>\n",
       "      <td>manuell</td>\n",
       "      <td>75</td>\n",
       "      <td>astra</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>5</td>\n",
       "      <td>benzin</td>\n",
       "      <td>opel</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-28 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>44807</td>\n",
       "      <td>2016-04-02 14:18:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>2016-04-02 14:44:48</td>\n",
       "      <td>Fiat_500_C_1.2_Dualogic_Lounge</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$13,200</td>\n",
       "      <td>test</td>\n",
       "      <td>cabrio</td>\n",
       "      <td>2014</td>\n",
       "      <td>automatik</td>\n",
       "      <td>69</td>\n",
       "      <td>500</td>\n",
       "      <td>5,000km</td>\n",
       "      <td>11</td>\n",
       "      <td>benzin</td>\n",
       "      <td>fiat</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-04-02 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>73430</td>\n",
       "      <td>2016-04-04 11:47:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>2016-03-08 19:25:42</td>\n",
       "      <td>Audi_A3_2.0_TDI_Sportback_Ambition</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$22,900</td>\n",
       "      <td>control</td>\n",
       "      <td>kombi</td>\n",
       "      <td>2013</td>\n",
       "      <td>manuell</td>\n",
       "      <td>150</td>\n",
       "      <td>a3</td>\n",
       "      <td>40,000km</td>\n",
       "      <td>11</td>\n",
       "      <td>diesel</td>\n",
       "      <td>audi</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-08 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>35683</td>\n",
       "      <td>2016-04-05 16:45:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>2016-03-14 00:42:12</td>\n",
       "      <td>Opel_Vectra_1.6_16V</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$1,250</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>1996</td>\n",
       "      <td>manuell</td>\n",
       "      <td>101</td>\n",
       "      <td>vectra</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>1</td>\n",
       "      <td>benzin</td>\n",
       "      <td>opel</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-13 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>45897</td>\n",
       "      <td>2016-04-06 21:18:48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               dateCrawled                                               name  \\\n",
       "0      2016-03-26 17:47:46                   Peugeot_807_160_NAVTECH_ON_BOARD   \n",
       "1      2016-04-04 13:38:56         BMW_740i_4_4_Liter_HAMANN_UMBAU_Mega_Optik   \n",
       "2      2016-03-26 18:57:24                         Volkswagen_Golf_1.6_United   \n",
       "3      2016-03-12 16:58:10  Smart_smart_fortwo_coupe_softouch/F1/Klima/Pan...   \n",
       "4      2016-04-01 14:38:50  Ford_Focus_1_6_Benzin_TÜV_neu_ist_sehr_gepfleg...   \n",
       "5      2016-03-21 13:47:45  Chrysler_Grand_Voyager_2.8_CRD_Aut.Limited_Sto...   \n",
       "6      2016-03-20 17:55:21  VW_Golf_III_GT_Special_Electronic_Green_Metall...   \n",
       "7      2016-03-16 18:55:19                               Golf_IV_1.9_TDI_90PS   \n",
       "8      2016-03-22 16:51:34                                         Seat_Arosa   \n",
       "9      2016-03-16 13:47:02          Renault_Megane_Scenic_1.6e_RT_Klimaanlage   \n",
       "10     2016-03-15 01:41:36                       VW_Golf_Tuning_in_siber/grau   \n",
       "11     2016-03-16 18:45:34                         Mercedes_A140_Motorschaden   \n",
       "12     2016-03-31 19:48:22  Smart_smart_fortwo_coupe_softouch_pure_MHD_Pan...   \n",
       "13     2016-03-23 10:48:32                                 Audi_A3_1.6_tuning   \n",
       "14     2016-03-23 11:50:46  Renault_Clio_3__Dynamique_1.2__16_V;_viele_Ver...   \n",
       "15     2016-04-01 12:06:20        Corvette_C3_Coupe_T_Top_Crossfire_Injection   \n",
       "16     2016-03-16 14:59:02                                Opel_Vectra_B_Kombi   \n",
       "17     2016-03-29 11:46:22                          Volkswagen_Scirocco_2_G60   \n",
       "18     2016-03-26 19:57:44               Verkaufen_mein_bmw_e36_320_i_touring   \n",
       "19     2016-03-17 13:36:21        mazda_tribute_2.0_mit_gas_und_tuev_neu_2018   \n",
       "20     2016-03-05 19:57:31  Audi_A4_Avant_1.9_TDI_*6_Gang*AHK*Klimatronik*...   \n",
       "21     2016-03-06 19:07:10                      Porsche_911_Carrera_4S_Cabrio   \n",
       "22     2016-03-28 20:50:54                               MINI_Cooper_S_Cabrio   \n",
       "23     2016-03-10 19:55:34   Peugeot_Boxer_2_2_HDi_120_Ps_9_Sitzer_inkl_Klima   \n",
       "24     2016-04-03 11:57:02                         BMW_535i_xDrive_Sport_Aut.   \n",
       "25     2016-03-21 21:56:18  Ford_escort_kombi_an_bastler_mit_ghia_ausstattung   \n",
       "26     2016-04-03 22:46:28                                Volkswagen_Polo_Fox   \n",
       "27     2016-03-27 18:45:01             Hat_einer_Ahnung_mit_Ford_Galaxy_HILFE   \n",
       "28     2016-03-19 21:56:19                                      MINI_Cooper_D   \n",
       "29     2016-04-02 12:45:44  Mercedes_Benz_E_320_T_CDI_Avantgarde_DPF7_Sitz...   \n",
       "...                    ...                                                ...   \n",
       "49970  2016-03-21 22:47:37  c4_Grand_Picasso_mit_Automatik_Leder_Navi_Temp...   \n",
       "49971  2016-03-29 14:54:12                                         W.Lupo_1.0   \n",
       "49972  2016-03-26 22:25:23          Mercedes_Benz_Vito_115_CDI_Extralang_Aut.   \n",
       "49973  2016-03-27 05:32:39                   Mercedes_Benz_SLK_200_Kompressor   \n",
       "49974  2016-03-20 10:52:31  Golf_1_Cabrio_Tuev_Neu_viele_Extras_alles_eing...   \n",
       "49975  2016-03-27 20:51:39          Honda_Jazz_1.3_DSi_i_VTEC_IMA_CVT_Comfort   \n",
       "49976  2016-03-19 18:56:05  Audi_80_Avant_2.6_E__Vollausstattung!!_Einziga...   \n",
       "49977  2016-03-31 18:37:18                        Mercedes_Benz_C200_Cdi_W203   \n",
       "49978  2016-04-04 10:37:14                        Mercedes_Benz_E_200_Classic   \n",
       "49979  2016-03-20 18:38:40                      Volkswagen_Polo_1.6_TDI_Style   \n",
       "49980  2016-03-12 10:55:54                            Ford_Escort_Turnier_16V   \n",
       "49981  2016-03-15 09:38:21             Opel_Astra_Kombi_mit_Anhaengerkupplung   \n",
       "49982  2016-03-29 18:51:08            Skoda_Fabia_4_Tuerer_Bj:2004__85.000Tkm   \n",
       "49983  2016-03-06 12:43:04                                      Ford_focus_99   \n",
       "49984  2016-03-31 22:48:48  Student_sucht_ein__Anfaengerauto___ab_2000_BJ_...   \n",
       "49985  2016-04-02 16:38:23                          Verkaufe_meinen_vw_vento!   \n",
       "49986  2016-04-04 20:46:02  Chrysler_300C_3.0_CRD_DPF_Automatik_Voll_Ausst...   \n",
       "49987  2016-03-22 20:47:27  Audi_A3_Limousine_2.0_TDI_DPF_Ambition__NAVI__...   \n",
       "49988  2016-03-28 19:49:51                                         BMW_330_Ci   \n",
       "49989  2016-03-11 19:50:37        VW_Polo_zum_Ausschlachten_oder_Wiederaufbau   \n",
       "49990  2016-03-21 19:54:19         Mercedes_Benz_A_200__BlueEFFICIENCY__Urban   \n",
       "49991  2016-03-06 15:25:19                                         Kleinwagen   \n",
       "49992  2016-03-10 19:37:38              Fiat_Grande_Punto_1.4_T_Jet_16V_Sport   \n",
       "49993  2016-03-15 18:47:35           Audi_A3__1_8l__Silber;_schoenes_Fahrzeug   \n",
       "49994  2016-03-22 17:36:42  Audi_A6__S6__Avant_4.2_quattro_eventuell_Tausc...   \n",
       "49995  2016-03-27 14:38:19   Audi_Q5_3.0_TDI_qu._S_tr.__Navi__Panorama__Xenon   \n",
       "49996  2016-03-28 10:50:25  Opel_Astra_F_Cabrio_Bertone_Edition___TÜV_neu+...   \n",
       "49997  2016-04-02 14:44:48                     Fiat_500_C_1.2_Dualogic_Lounge   \n",
       "49998  2016-03-08 19:25:42                 Audi_A3_2.0_TDI_Sportback_Ambition   \n",
       "49999  2016-03-14 00:42:12                                Opel_Vectra_1.6_16V   \n",
       "\n",
       "       seller offerType    price   abtest vehicleType  yearOfRegistration  \\\n",
       "0      privat   Angebot   $5,000  control         bus                2004   \n",
       "1      privat   Angebot   $8,500  control   limousine                1997   \n",
       "2      privat   Angebot   $8,990     test   limousine                2009   \n",
       "3      privat   Angebot   $4,350  control  kleinwagen                2007   \n",
       "4      privat   Angebot   $1,350     test       kombi                2003   \n",
       "5      privat   Angebot   $7,900     test         bus                2006   \n",
       "6      privat   Angebot     $300     test   limousine                1995   \n",
       "7      privat   Angebot   $1,990  control   limousine                1998   \n",
       "8      privat   Angebot     $250     test         NaN                2000   \n",
       "9      privat   Angebot     $590  control         bus                1997   \n",
       "10     privat   Angebot     $999     test         NaN                2017   \n",
       "11     privat   Angebot     $350  control         NaN                2000   \n",
       "12     privat   Angebot   $5,299  control  kleinwagen                2010   \n",
       "13     privat   Angebot   $1,350  control   limousine                1999   \n",
       "14     privat   Angebot   $3,999     test  kleinwagen                2007   \n",
       "15     privat   Angebot  $18,900     test       coupe                1982   \n",
       "16     privat   Angebot     $350     test       kombi                1999   \n",
       "17     privat   Angebot   $5,500     test       coupe                1990   \n",
       "18     privat   Angebot     $300  control         bus                1995   \n",
       "19     privat   Angebot   $4,150  control         suv                2004   \n",
       "20     privat   Angebot   $3,500     test       kombi                2003   \n",
       "21     privat   Angebot  $41,500     test      cabrio                2004   \n",
       "22     privat   Angebot  $25,450  control      cabrio                2015   \n",
       "23     privat   Angebot   $7,999  control         bus                2010   \n",
       "24     privat   Angebot  $48,500  control   limousine                2014   \n",
       "25     privat   Angebot      $90  control       kombi                1996   \n",
       "26     privat   Angebot     $777  control  kleinwagen                1992   \n",
       "27     privat   Angebot       $0  control         NaN                2005   \n",
       "28     privat   Angebot   $5,250  control  kleinwagen                2007   \n",
       "29     privat   Angebot   $4,999     test       kombi                2004   \n",
       "...       ...       ...      ...      ...         ...                 ...   \n",
       "49970  privat   Angebot  $15,800  control         bus                2010   \n",
       "49971  privat   Angebot     $950     test  kleinwagen                2001   \n",
       "49972  privat   Angebot   $3,300  control         bus                2004   \n",
       "49973  privat   Angebot   $6,000  control      cabrio                2004   \n",
       "49974  privat   Angebot       $0  control      cabrio                1983   \n",
       "49975  privat   Angebot   $9,700  control  kleinwagen                2012   \n",
       "49976  privat   Angebot   $5,900     test       kombi                1992   \n",
       "49977  privat   Angebot   $5,500  control   limousine                2003   \n",
       "49978  privat   Angebot     $900  control   limousine                1996   \n",
       "49979  privat   Angebot  $11,000     test  kleinwagen                2011   \n",
       "49980  privat   Angebot     $400  control       kombi                1995   \n",
       "49981  privat   Angebot   $2,000  control       kombi                1998   \n",
       "49982  privat   Angebot   $1,950  control  kleinwagen                2004   \n",
       "49983  privat   Angebot     $600     test  kleinwagen                1999   \n",
       "49984  privat   Angebot       $0     test         NaN                2000   \n",
       "49985  privat   Angebot   $1,000  control         NaN                1995   \n",
       "49986  privat   Angebot  $15,900  control   limousine                2010   \n",
       "49987  privat   Angebot  $21,990  control   limousine                2013   \n",
       "49988  privat   Angebot   $9,550  control       coupe                2001   \n",
       "49989  privat   Angebot     $150     test  kleinwagen                1997   \n",
       "49990  privat   Angebot  $17,500     test   limousine                2012   \n",
       "49991  privat   Angebot     $500  control         NaN                2016   \n",
       "49992  privat   Angebot   $4,800  control  kleinwagen                2009   \n",
       "49993  privat   Angebot   $1,650  control  kleinwagen                1997   \n",
       "49994  privat   Angebot   $5,000  control       kombi                2001   \n",
       "49995  privat   Angebot  $24,900  control   limousine                2011   \n",
       "49996  privat   Angebot   $1,980  control      cabrio                1996   \n",
       "49997  privat   Angebot  $13,200     test      cabrio                2014   \n",
       "49998  privat   Angebot  $22,900  control       kombi                2013   \n",
       "49999  privat   Angebot   $1,250  control   limousine                1996   \n",
       "\n",
       "         gearbox  powerPS     model   odometer  monthOfRegistration fuelType  \\\n",
       "0        manuell      158    andere  150,000km                    3      lpg   \n",
       "1      automatik      286       7er  150,000km                    6   benzin   \n",
       "2        manuell      102      golf   70,000km                    7   benzin   \n",
       "3      automatik       71    fortwo   70,000km                    6   benzin   \n",
       "4        manuell        0     focus  150,000km                    7   benzin   \n",
       "5      automatik      150   voyager  150,000km                    4   diesel   \n",
       "6        manuell       90      golf  150,000km                    8   benzin   \n",
       "7        manuell       90      golf  150,000km                   12   diesel   \n",
       "8        manuell        0     arosa  150,000km                   10      NaN   \n",
       "9        manuell       90    megane  150,000km                    7   benzin   \n",
       "10       manuell       90       NaN  150,000km                    4   benzin   \n",
       "11           NaN        0       NaN  150,000km                    0   benzin   \n",
       "12     automatik       71    fortwo   50,000km                    9   benzin   \n",
       "13       manuell      101        a3  150,000km                   11   benzin   \n",
       "14       manuell       75      clio  150,000km                    9   benzin   \n",
       "15     automatik      203       NaN   80,000km                    6   benzin   \n",
       "16       manuell      101    vectra  150,000km                    5   benzin   \n",
       "17       manuell      205  scirocco  150,000km                    6   benzin   \n",
       "18       manuell      150       3er  150,000km                    0   benzin   \n",
       "19       manuell      124    andere  150,000km                    2      lpg   \n",
       "20       manuell      131        a4  150,000km                    5   diesel   \n",
       "21       manuell      320       911  150,000km                    4   benzin   \n",
       "22       manuell      184    cooper   10,000km                    1   benzin   \n",
       "23       manuell      120       NaN  150,000km                    2   diesel   \n",
       "24     automatik      306       5er   30,000km                   12   benzin   \n",
       "25       manuell      116       NaN  150,000km                    4   benzin   \n",
       "26       manuell       54      polo  125,000km                    2   benzin   \n",
       "27           NaN        0       NaN  150,000km                    0      NaN   \n",
       "28       manuell      110    cooper  150,000km                    7   diesel   \n",
       "29     automatik      204  e_klasse  150,000km                   10   diesel   \n",
       "...          ...      ...       ...        ...                  ...      ...   \n",
       "49970  automatik      136        c4   60,000km                    4   diesel   \n",
       "49971    manuell       50      lupo  150,000km                    4   benzin   \n",
       "49972  automatik      150      vito  150,000km                   10   diesel   \n",
       "49973    manuell      163       slk  150,000km                   11   benzin   \n",
       "49974    manuell       70      golf  150,000km                    2   benzin   \n",
       "49975  automatik       88      jazz  100,000km                   11   hybrid   \n",
       "49976  automatik      150        80  150,000km                   12   benzin   \n",
       "49977    manuell      116  c_klasse  150,000km                    2   diesel   \n",
       "49978  automatik      136  e_klasse  150,000km                    9   benzin   \n",
       "49979    manuell       90      polo   70,000km                   11   diesel   \n",
       "49980    manuell      105    escort  125,000km                    3   benzin   \n",
       "49981    manuell      115     astra  150,000km                   12   benzin   \n",
       "49982    manuell        0     fabia   90,000km                    7   benzin   \n",
       "49983    manuell      101     focus  150,000km                    4   benzin   \n",
       "49984        NaN        0       NaN  150,000km                    0      NaN   \n",
       "49985  automatik        0       NaN  150,000km                    0   benzin   \n",
       "49986  automatik      218      300c  125,000km                   11   diesel   \n",
       "49987    manuell      150        a3   50,000km                   11   diesel   \n",
       "49988    manuell      231       3er  150,000km                   10   benzin   \n",
       "49989    manuell        0      polo  150,000km                    5   benzin   \n",
       "49990    manuell      156  a_klasse   30,000km                   12   benzin   \n",
       "49991    manuell        0    twingo  150,000km                    0   benzin   \n",
       "49992    manuell      120    andere  125,000km                    9      lpg   \n",
       "49993    manuell        0       NaN  150,000km                    7   benzin   \n",
       "49994  automatik      299        a6  150,000km                    1   benzin   \n",
       "49995  automatik      239        q5  100,000km                    1   diesel   \n",
       "49996    manuell       75     astra  150,000km                    5   benzin   \n",
       "49997  automatik       69       500    5,000km                   11   benzin   \n",
       "49998    manuell      150        a3   40,000km                   11   diesel   \n",
       "49999    manuell      101    vectra  150,000km                    1   benzin   \n",
       "\n",
       "                brand notRepairedDamage          dateCreated  nrOfPictures  \\\n",
       "0             peugeot              nein  2016-03-26 00:00:00             0   \n",
       "1                 bmw              nein  2016-04-04 00:00:00             0   \n",
       "2          volkswagen              nein  2016-03-26 00:00:00             0   \n",
       "3               smart              nein  2016-03-12 00:00:00             0   \n",
       "4                ford              nein  2016-04-01 00:00:00             0   \n",
       "5            chrysler               NaN  2016-03-21 00:00:00             0   \n",
       "6          volkswagen               NaN  2016-03-20 00:00:00             0   \n",
       "7          volkswagen              nein  2016-03-16 00:00:00             0   \n",
       "8                seat              nein  2016-03-22 00:00:00             0   \n",
       "9             renault              nein  2016-03-16 00:00:00             0   \n",
       "10         volkswagen              nein  2016-03-14 00:00:00             0   \n",
       "11      mercedes_benz               NaN  2016-03-16 00:00:00             0   \n",
       "12              smart              nein  2016-03-31 00:00:00             0   \n",
       "13               audi              nein  2016-03-23 00:00:00             0   \n",
       "14            renault               NaN  2016-03-23 00:00:00             0   \n",
       "15     sonstige_autos              nein  2016-04-01 00:00:00             0   \n",
       "16               opel              nein  2016-03-16 00:00:00             0   \n",
       "17         volkswagen              nein  2016-03-29 00:00:00             0   \n",
       "18                bmw               NaN  2016-03-26 00:00:00             0   \n",
       "19              mazda              nein  2016-03-17 00:00:00             0   \n",
       "20               audi               NaN  2016-03-05 00:00:00             0   \n",
       "21            porsche              nein  2016-03-06 00:00:00             0   \n",
       "22               mini              nein  2016-03-28 00:00:00             0   \n",
       "23            peugeot              nein  2016-03-10 00:00:00             0   \n",
       "24                bmw              nein  2016-04-03 00:00:00             0   \n",
       "25               ford                ja  2016-03-21 00:00:00             0   \n",
       "26         volkswagen              nein  2016-04-03 00:00:00             0   \n",
       "27               ford               NaN  2016-03-27 00:00:00             0   \n",
       "28               mini                ja  2016-03-19 00:00:00             0   \n",
       "29      mercedes_benz              nein  2016-04-02 00:00:00             0   \n",
       "...               ...               ...                  ...           ...   \n",
       "49970         citroen              nein  2016-03-21 00:00:00             0   \n",
       "49971      volkswagen              nein  2016-03-29 00:00:00             0   \n",
       "49972   mercedes_benz                ja  2016-03-26 00:00:00             0   \n",
       "49973   mercedes_benz              nein  2016-03-27 00:00:00             0   \n",
       "49974      volkswagen              nein  2016-03-20 00:00:00             0   \n",
       "49975           honda              nein  2016-03-27 00:00:00             0   \n",
       "49976            audi              nein  2016-03-19 00:00:00             0   \n",
       "49977   mercedes_benz              nein  2016-03-31 00:00:00             0   \n",
       "49978   mercedes_benz                ja  2016-04-04 00:00:00             0   \n",
       "49979      volkswagen              nein  2016-03-20 00:00:00             0   \n",
       "49980            ford               NaN  2016-03-12 00:00:00             0   \n",
       "49981            opel              nein  2016-03-15 00:00:00             0   \n",
       "49982           skoda               NaN  2016-03-29 00:00:00             0   \n",
       "49983            ford               NaN  2016-03-06 00:00:00             0   \n",
       "49984  sonstige_autos               NaN  2016-03-31 00:00:00             0   \n",
       "49985      volkswagen               NaN  2016-04-02 00:00:00             0   \n",
       "49986        chrysler              nein  2016-04-04 00:00:00             0   \n",
       "49987            audi              nein  2016-03-22 00:00:00             0   \n",
       "49988             bmw              nein  2016-03-28 00:00:00             0   \n",
       "49989      volkswagen                ja  2016-03-11 00:00:00             0   \n",
       "49990   mercedes_benz              nein  2016-03-21 00:00:00             0   \n",
       "49991         renault               NaN  2016-03-06 00:00:00             0   \n",
       "49992            fiat              nein  2016-03-10 00:00:00             0   \n",
       "49993            audi               NaN  2016-03-15 00:00:00             0   \n",
       "49994            audi              nein  2016-03-22 00:00:00             0   \n",
       "49995            audi              nein  2016-03-27 00:00:00             0   \n",
       "49996            opel              nein  2016-03-28 00:00:00             0   \n",
       "49997            fiat              nein  2016-04-02 00:00:00             0   \n",
       "49998            audi              nein  2016-03-08 00:00:00             0   \n",
       "49999            opel              nein  2016-03-13 00:00:00             0   \n",
       "\n",
       "       postalCode             lastSeen  \n",
       "0           79588  2016-04-06 06:45:54  \n",
       "1           71034  2016-04-06 14:45:08  \n",
       "2           35394  2016-04-06 20:15:37  \n",
       "3           33729  2016-03-15 03:16:28  \n",
       "4           39218  2016-04-01 14:38:50  \n",
       "5           22962  2016-04-06 09:45:21  \n",
       "6           31535  2016-03-23 02:48:59  \n",
       "7           53474  2016-04-07 03:17:32  \n",
       "8            7426  2016-03-26 18:18:10  \n",
       "9           15749  2016-04-06 10:46:35  \n",
       "10          86157  2016-04-07 03:16:21  \n",
       "11          17498  2016-03-16 18:45:34  \n",
       "12          34590  2016-04-06 14:17:52  \n",
       "13          12043  2016-04-01 14:17:13  \n",
       "14          81737  2016-04-01 15:46:47  \n",
       "15          61276  2016-04-02 21:10:48  \n",
       "16          57299  2016-03-18 05:29:37  \n",
       "17          74821  2016-04-05 20:46:26  \n",
       "18          54329  2016-04-02 12:16:41  \n",
       "19          40878  2016-03-17 14:45:58  \n",
       "20          53913  2016-03-07 05:46:46  \n",
       "21          65428  2016-04-05 23:46:19  \n",
       "22          44789  2016-04-01 06:45:30  \n",
       "23          30900  2016-03-17 08:45:17  \n",
       "24          22547  2016-04-07 13:16:50  \n",
       "25          27574  2016-04-01 05:16:49  \n",
       "26          38110  2016-04-05 23:46:48  \n",
       "27          66701  2016-03-27 18:45:01  \n",
       "28          15745  2016-04-07 14:58:48  \n",
       "29          47638  2016-04-02 12:45:44  \n",
       "...           ...                  ...  \n",
       "49970       14947  2016-04-07 04:17:34  \n",
       "49971       65197  2016-03-29 20:41:51  \n",
       "49972       65326  2016-03-28 11:28:18  \n",
       "49973       53567  2016-03-27 08:25:24  \n",
       "49974        8209  2016-03-27 19:48:16  \n",
       "49975       84385  2016-04-05 19:45:34  \n",
       "49976       36100  2016-04-07 06:16:44  \n",
       "49977       33739  2016-04-06 12:16:11  \n",
       "49978       24405  2016-04-06 12:44:20  \n",
       "49979       48455  2016-04-07 01:45:12  \n",
       "49980       56218  2016-04-06 17:16:49  \n",
       "49981       86859  2016-04-05 17:21:46  \n",
       "49982       45884  2016-03-29 18:51:08  \n",
       "49983       52477  2016-03-09 06:16:08  \n",
       "49984       12103  2016-04-02 19:44:53  \n",
       "49985       30900  2016-04-06 15:17:52  \n",
       "49986       73527  2016-04-06 23:16:00  \n",
       "49987       94362  2016-03-26 22:46:06  \n",
       "49988       83646  2016-04-07 02:17:40  \n",
       "49989       21244  2016-03-12 10:17:55  \n",
       "49990       58239  2016-04-06 22:46:57  \n",
       "49991       61350  2016-03-06 18:24:19  \n",
       "49992       68642  2016-03-13 01:44:51  \n",
       "49993       65203  2016-04-06 19:46:53  \n",
       "49994       46537  2016-04-06 08:16:39  \n",
       "49995       82131  2016-04-01 13:47:40  \n",
       "49996       44807  2016-04-02 14:18:02  \n",
       "49997       73430  2016-04-04 11:47:27  \n",
       "49998       35683  2016-04-05 16:45:07  \n",
       "49999       45897  2016-04-06 21:18:48  \n",
       "\n",
       "[50000 rows x 20 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets run \"autos\" as a cell, we should see some output of our dataset below:\n",
    "autos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will use `DataFrame.info()` and `DataFrame.head()` to print information about the `autos` dataframe, and the first few rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 20 columns):\n",
      "dateCrawled            50000 non-null object\n",
      "name                   50000 non-null object\n",
      "seller                 50000 non-null object\n",
      "offerType              50000 non-null object\n",
      "price                  50000 non-null object\n",
      "abtest                 50000 non-null object\n",
      "vehicleType            44905 non-null object\n",
      "yearOfRegistration     50000 non-null int64\n",
      "gearbox                47320 non-null object\n",
      "powerPS                50000 non-null int64\n",
      "model                  47242 non-null object\n",
      "odometer               50000 non-null object\n",
      "monthOfRegistration    50000 non-null int64\n",
      "fuelType               45518 non-null object\n",
      "brand                  50000 non-null object\n",
      "notRepairedDamage      40171 non-null object\n",
      "dateCreated            50000 non-null object\n",
      "nrOfPictures           50000 non-null int64\n",
      "postalCode             50000 non-null int64\n",
      "lastSeen               50000 non-null object\n",
      "dtypes: int64(5), object(15)\n",
      "memory usage: 7.6+ MB\n"
     ]
    }
   ],
   "source": [
    "autos.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateCrawled</th>\n",
       "      <th>name</th>\n",
       "      <th>seller</th>\n",
       "      <th>offerType</th>\n",
       "      <th>price</th>\n",
       "      <th>abtest</th>\n",
       "      <th>vehicleType</th>\n",
       "      <th>yearOfRegistration</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>powerPS</th>\n",
       "      <th>model</th>\n",
       "      <th>odometer</th>\n",
       "      <th>monthOfRegistration</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>brand</th>\n",
       "      <th>notRepairedDamage</th>\n",
       "      <th>dateCreated</th>\n",
       "      <th>nrOfPictures</th>\n",
       "      <th>postalCode</th>\n",
       "      <th>lastSeen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-26 17:47:46</td>\n",
       "      <td>Peugeot_807_160_NAVTECH_ON_BOARD</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$5,000</td>\n",
       "      <td>control</td>\n",
       "      <td>bus</td>\n",
       "      <td>2004</td>\n",
       "      <td>manuell</td>\n",
       "      <td>158</td>\n",
       "      <td>andere</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>3</td>\n",
       "      <td>lpg</td>\n",
       "      <td>peugeot</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-26 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>79588</td>\n",
       "      <td>2016-04-06 06:45:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-04-04 13:38:56</td>\n",
       "      <td>BMW_740i_4_4_Liter_HAMANN_UMBAU_Mega_Optik</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$8,500</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>1997</td>\n",
       "      <td>automatik</td>\n",
       "      <td>286</td>\n",
       "      <td>7er</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>6</td>\n",
       "      <td>benzin</td>\n",
       "      <td>bmw</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-04-04 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>71034</td>\n",
       "      <td>2016-04-06 14:45:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-26 18:57:24</td>\n",
       "      <td>Volkswagen_Golf_1.6_United</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$8,990</td>\n",
       "      <td>test</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2009</td>\n",
       "      <td>manuell</td>\n",
       "      <td>102</td>\n",
       "      <td>golf</td>\n",
       "      <td>70,000km</td>\n",
       "      <td>7</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-26 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>35394</td>\n",
       "      <td>2016-04-06 20:15:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-12 16:58:10</td>\n",
       "      <td>Smart_smart_fortwo_coupe_softouch/F1/Klima/Pan...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$4,350</td>\n",
       "      <td>control</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2007</td>\n",
       "      <td>automatik</td>\n",
       "      <td>71</td>\n",
       "      <td>fortwo</td>\n",
       "      <td>70,000km</td>\n",
       "      <td>6</td>\n",
       "      <td>benzin</td>\n",
       "      <td>smart</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-12 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>33729</td>\n",
       "      <td>2016-03-15 03:16:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-04-01 14:38:50</td>\n",
       "      <td>Ford_Focus_1_6_Benzin_TÜV_neu_ist_sehr_gepfleg...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$1,350</td>\n",
       "      <td>test</td>\n",
       "      <td>kombi</td>\n",
       "      <td>2003</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>focus</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>7</td>\n",
       "      <td>benzin</td>\n",
       "      <td>ford</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>39218</td>\n",
       "      <td>2016-04-01 14:38:50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dateCrawled                                               name  \\\n",
       "0  2016-03-26 17:47:46                   Peugeot_807_160_NAVTECH_ON_BOARD   \n",
       "1  2016-04-04 13:38:56         BMW_740i_4_4_Liter_HAMANN_UMBAU_Mega_Optik   \n",
       "2  2016-03-26 18:57:24                         Volkswagen_Golf_1.6_United   \n",
       "3  2016-03-12 16:58:10  Smart_smart_fortwo_coupe_softouch/F1/Klima/Pan...   \n",
       "4  2016-04-01 14:38:50  Ford_Focus_1_6_Benzin_TÜV_neu_ist_sehr_gepfleg...   \n",
       "\n",
       "   seller offerType   price   abtest vehicleType  yearOfRegistration  \\\n",
       "0  privat   Angebot  $5,000  control         bus                2004   \n",
       "1  privat   Angebot  $8,500  control   limousine                1997   \n",
       "2  privat   Angebot  $8,990     test   limousine                2009   \n",
       "3  privat   Angebot  $4,350  control  kleinwagen                2007   \n",
       "4  privat   Angebot  $1,350     test       kombi                2003   \n",
       "\n",
       "     gearbox  powerPS   model   odometer  monthOfRegistration fuelType  \\\n",
       "0    manuell      158  andere  150,000km                    3      lpg   \n",
       "1  automatik      286     7er  150,000km                    6   benzin   \n",
       "2    manuell      102    golf   70,000km                    7   benzin   \n",
       "3  automatik       71  fortwo   70,000km                    6   benzin   \n",
       "4    manuell        0   focus  150,000km                    7   benzin   \n",
       "\n",
       "        brand notRepairedDamage          dateCreated  nrOfPictures  \\\n",
       "0     peugeot              nein  2016-03-26 00:00:00             0   \n",
       "1         bmw              nein  2016-04-04 00:00:00             0   \n",
       "2  volkswagen              nein  2016-03-26 00:00:00             0   \n",
       "3       smart              nein  2016-03-12 00:00:00             0   \n",
       "4        ford              nein  2016-04-01 00:00:00             0   \n",
       "\n",
       "   postalCode             lastSeen  \n",
       "0       79588  2016-04-06 06:45:54  \n",
       "1       71034  2016-04-06 14:45:08  \n",
       "2       35394  2016-04-06 20:15:37  \n",
       "3       33729  2016-03-15 03:16:28  \n",
       "4       39218  2016-04-01 14:38:50  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the information extracted we can see that:\n",
    "- The number of entries is arranged from `0-49999` (`50,000` total).\n",
    "- Some of the attributes of the dataframe are objects, and some are actually int values.\n",
    "- There are 20 data columns in total within this dataset.\n",
    "- The `.head()` function takes in an int value and displays the first \"n\" values of the dataset.\n",
    "- The `.info()` function gives us insight of the datatypes used in the dataset, and how the data is arranged."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Cleaning Column Names\n",
    "Now that we know what is in the data set, lets begin by cleaning some of the column names for readability.\n",
    "\n",
    "To begin, lets revisit the columns that we have in our present dataset though the `.columns` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['dateCrawled', 'name', 'seller', 'offerType', 'price', 'abtest',\n",
       "       'vehicleType', 'yearOfRegistration', 'gearbox', 'powerPS', 'model',\n",
       "       'odometer', 'monthOfRegistration', 'fuelType', 'brand',\n",
       "       'notRepairedDamage', 'dateCreated', 'nrOfPictures', 'postalCode',\n",
       "       'lastSeen'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.columns #Useing the .columns attribute to print all column names:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that all of the columns use camelcase.. (example: offerType)\n",
    "\n",
    "Lets change that! Instead of the useing a capital letter after every new word in the column's name, lets instead use an underscore to divide the two words (that is, instead use `offer_type` instead of `offerType`).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_crawled</th>\n",
       "      <th>name</th>\n",
       "      <th>seller</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>price</th>\n",
       "      <th>ab_test</th>\n",
       "      <th>vehicle_type</th>\n",
       "      <th>registration_year</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>power_PS</th>\n",
       "      <th>model</th>\n",
       "      <th>odometer</th>\n",
       "      <th>registration_month</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>brand</th>\n",
       "      <th>unrepaired_damage</th>\n",
       "      <th>ad_created</th>\n",
       "      <th>num_photos</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>last_seen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-26 17:47:46</td>\n",
       "      <td>Peugeot_807_160_NAVTECH_ON_BOARD</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$5,000</td>\n",
       "      <td>control</td>\n",
       "      <td>bus</td>\n",
       "      <td>2004</td>\n",
       "      <td>manuell</td>\n",
       "      <td>158</td>\n",
       "      <td>andere</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>3</td>\n",
       "      <td>lpg</td>\n",
       "      <td>peugeot</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-26 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>79588</td>\n",
       "      <td>2016-04-06 06:45:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-04-04 13:38:56</td>\n",
       "      <td>BMW_740i_4_4_Liter_HAMANN_UMBAU_Mega_Optik</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$8,500</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>1997</td>\n",
       "      <td>automatik</td>\n",
       "      <td>286</td>\n",
       "      <td>7er</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>6</td>\n",
       "      <td>benzin</td>\n",
       "      <td>bmw</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-04-04 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>71034</td>\n",
       "      <td>2016-04-06 14:45:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-26 18:57:24</td>\n",
       "      <td>Volkswagen_Golf_1.6_United</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$8,990</td>\n",
       "      <td>test</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2009</td>\n",
       "      <td>manuell</td>\n",
       "      <td>102</td>\n",
       "      <td>golf</td>\n",
       "      <td>70,000km</td>\n",
       "      <td>7</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-26 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>35394</td>\n",
       "      <td>2016-04-06 20:15:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-12 16:58:10</td>\n",
       "      <td>Smart_smart_fortwo_coupe_softouch/F1/Klima/Pan...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$4,350</td>\n",
       "      <td>control</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2007</td>\n",
       "      <td>automatik</td>\n",
       "      <td>71</td>\n",
       "      <td>fortwo</td>\n",
       "      <td>70,000km</td>\n",
       "      <td>6</td>\n",
       "      <td>benzin</td>\n",
       "      <td>smart</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-12 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>33729</td>\n",
       "      <td>2016-03-15 03:16:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-04-01 14:38:50</td>\n",
       "      <td>Ford_Focus_1_6_Benzin_TÜV_neu_ist_sehr_gepfleg...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$1,350</td>\n",
       "      <td>test</td>\n",
       "      <td>kombi</td>\n",
       "      <td>2003</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>focus</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>7</td>\n",
       "      <td>benzin</td>\n",
       "      <td>ford</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>39218</td>\n",
       "      <td>2016-04-01 14:38:50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date_crawled                                               name  \\\n",
       "0  2016-03-26 17:47:46                   Peugeot_807_160_NAVTECH_ON_BOARD   \n",
       "1  2016-04-04 13:38:56         BMW_740i_4_4_Liter_HAMANN_UMBAU_Mega_Optik   \n",
       "2  2016-03-26 18:57:24                         Volkswagen_Golf_1.6_United   \n",
       "3  2016-03-12 16:58:10  Smart_smart_fortwo_coupe_softouch/F1/Klima/Pan...   \n",
       "4  2016-04-01 14:38:50  Ford_Focus_1_6_Benzin_TÜV_neu_ist_sehr_gepfleg...   \n",
       "\n",
       "   seller offer_type   price  ab_test vehicle_type  registration_year  \\\n",
       "0  privat    Angebot  $5,000  control          bus               2004   \n",
       "1  privat    Angebot  $8,500  control    limousine               1997   \n",
       "2  privat    Angebot  $8,990     test    limousine               2009   \n",
       "3  privat    Angebot  $4,350  control   kleinwagen               2007   \n",
       "4  privat    Angebot  $1,350     test        kombi               2003   \n",
       "\n",
       "     gearbox  power_PS   model   odometer  registration_month fuel_type  \\\n",
       "0    manuell       158  andere  150,000km                   3       lpg   \n",
       "1  automatik       286     7er  150,000km                   6    benzin   \n",
       "2    manuell       102    golf   70,000km                   7    benzin   \n",
       "3  automatik        71  fortwo   70,000km                   6    benzin   \n",
       "4    manuell         0   focus  150,000km                   7    benzin   \n",
       "\n",
       "        brand unrepaired_damage           ad_created  num_photos  postal_code  \\\n",
       "0     peugeot              nein  2016-03-26 00:00:00           0        79588   \n",
       "1         bmw              nein  2016-04-04 00:00:00           0        71034   \n",
       "2  volkswagen              nein  2016-03-26 00:00:00           0        35394   \n",
       "3       smart              nein  2016-03-12 00:00:00           0        33729   \n",
       "4        ford              nein  2016-04-01 00:00:00           0        39218   \n",
       "\n",
       "             last_seen  \n",
       "0  2016-04-06 06:45:54  \n",
       "1  2016-04-06 14:45:08  \n",
       "2  2016-04-06 20:15:37  \n",
       "3  2016-03-15 03:16:28  \n",
       "4  2016-04-01 14:38:50  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#An explicit assignment for snake_case:\n",
    "autos.columns = ['date_crawled', 'name', 'seller', 'offer_type', 'price',\n",
    "                'ab_test', 'vehicle_type', 'registration_year', 'gearbox', \n",
    "                'power_PS', 'model', 'odometer', 'registration_month', \n",
    "                'fuel_type', 'brand', 'unrepaired_damage', 'ad_created',\n",
    "                'num_photos', 'postal_code', 'last_seen']\n",
    "\n",
    "#Now, lets see the results:\n",
    "autos.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploration and Cleaning\n",
    "\n",
    "Now let's do some basic data exploration to determine what other cleaning tasks need to be done. In this section, we will look for: \n",
    "\n",
    "- Text columns where all or almost all values are the same. These can often be dropped as they don't have useful information for analysis. \n",
    "\n",
    "- Examples of numeric data stored as text which can be cleaned and converted.\n",
    "\n",
    "Lets begin by looking at some descriptive statustics with the use of the function `DataFrame.describe()` with the option `include='all'`to obtain both categorical and numeric columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "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>date_crawled</th>\n",
       "      <th>name</th>\n",
       "      <th>seller</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>price</th>\n",
       "      <th>ab_test</th>\n",
       "      <th>vehicle_type</th>\n",
       "      <th>registration_year</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>power_PS</th>\n",
       "      <th>model</th>\n",
       "      <th>odometer</th>\n",
       "      <th>registration_month</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>brand</th>\n",
       "      <th>unrepaired_damage</th>\n",
       "      <th>ad_created</th>\n",
       "      <th>num_photos</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>last_seen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000</td>\n",
       "      <td>50000</td>\n",
       "      <td>50000</td>\n",
       "      <td>50000</td>\n",
       "      <td>50000</td>\n",
       "      <td>50000</td>\n",
       "      <td>44905</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>47320</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>47242</td>\n",
       "      <td>50000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>45518</td>\n",
       "      <td>50000</td>\n",
       "      <td>40171</td>\n",
       "      <td>50000</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>48213</td>\n",
       "      <td>38754</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2357</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>245</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2016-03-29 23:42:13</td>\n",
       "      <td>Ford_Fiesta</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$0</td>\n",
       "      <td>test</td>\n",
       "      <td>limousine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>manuell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>golf</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>NaN</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-04-03 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-04-07 06:17:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>49999</td>\n",
       "      <td>49999</td>\n",
       "      <td>1421</td>\n",
       "      <td>25756</td>\n",
       "      <td>12859</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4024</td>\n",
       "      <td>32424</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30107</td>\n",
       "      <td>10687</td>\n",
       "      <td>35232</td>\n",
       "      <td>1946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\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>2005.073280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.355920</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.723360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50813.627300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\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>105.712813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>209.216627</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.711984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25779.747957</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>1000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\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>1999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30451.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\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>2003.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49577.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\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>2008.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71540.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\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>9999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17700.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99998.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               date_crawled         name  seller offer_type  price ab_test  \\\n",
       "count                 50000        50000   50000      50000  50000   50000   \n",
       "unique                48213        38754       2          2   2357       2   \n",
       "top     2016-03-29 23:42:13  Ford_Fiesta  privat    Angebot     $0    test   \n",
       "freq                      3           78   49999      49999   1421   25756   \n",
       "mean                    NaN          NaN     NaN        NaN    NaN     NaN   \n",
       "std                     NaN          NaN     NaN        NaN    NaN     NaN   \n",
       "min                     NaN          NaN     NaN        NaN    NaN     NaN   \n",
       "25%                     NaN          NaN     NaN        NaN    NaN     NaN   \n",
       "50%                     NaN          NaN     NaN        NaN    NaN     NaN   \n",
       "75%                     NaN          NaN     NaN        NaN    NaN     NaN   \n",
       "max                     NaN          NaN     NaN        NaN    NaN     NaN   \n",
       "\n",
       "       vehicle_type  registration_year  gearbox      power_PS  model  \\\n",
       "count         44905       50000.000000    47320  50000.000000  47242   \n",
       "unique            8                NaN        2           NaN    245   \n",
       "top       limousine                NaN  manuell           NaN   golf   \n",
       "freq          12859                NaN    36993           NaN   4024   \n",
       "mean            NaN        2005.073280      NaN    116.355920    NaN   \n",
       "std             NaN         105.712813      NaN    209.216627    NaN   \n",
       "min             NaN        1000.000000      NaN      0.000000    NaN   \n",
       "25%             NaN        1999.000000      NaN     70.000000    NaN   \n",
       "50%             NaN        2003.000000      NaN    105.000000    NaN   \n",
       "75%             NaN        2008.000000      NaN    150.000000    NaN   \n",
       "max             NaN        9999.000000      NaN  17700.000000    NaN   \n",
       "\n",
       "         odometer  registration_month fuel_type       brand unrepaired_damage  \\\n",
       "count       50000        50000.000000     45518       50000             40171   \n",
       "unique         13                 NaN         7          40                 2   \n",
       "top     150,000km                 NaN    benzin  volkswagen              nein   \n",
       "freq        32424                 NaN     30107       10687             35232   \n",
       "mean          NaN            5.723360       NaN         NaN               NaN   \n",
       "std           NaN            3.711984       NaN         NaN               NaN   \n",
       "min           NaN            0.000000       NaN         NaN               NaN   \n",
       "25%           NaN            3.000000       NaN         NaN               NaN   \n",
       "50%           NaN            6.000000       NaN         NaN               NaN   \n",
       "75%           NaN            9.000000       NaN         NaN               NaN   \n",
       "max           NaN           12.000000       NaN         NaN               NaN   \n",
       "\n",
       "                 ad_created  num_photos   postal_code            last_seen  \n",
       "count                 50000     50000.0  50000.000000                50000  \n",
       "unique                   76         NaN           NaN                39481  \n",
       "top     2016-04-03 00:00:00         NaN           NaN  2016-04-07 06:17:27  \n",
       "freq                   1946         NaN           NaN                    8  \n",
       "mean                    NaN         0.0  50813.627300                  NaN  \n",
       "std                     NaN         0.0  25779.747957                  NaN  \n",
       "min                     NaN         0.0   1067.000000                  NaN  \n",
       "25%                     NaN         0.0  30451.000000                  NaN  \n",
       "50%                     NaN         0.0  49577.000000                  NaN  \n",
       "75%                     NaN         0.0  71540.000000                  NaN  \n",
       "max                     NaN         0.0  99998.000000                  NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets try useing .describe() with include='all' option\n",
    "autos.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Our initial observations:\n",
    "\n",
    "There are a number of text columns where all (or nearly all) of the values are the same:\n",
    "- seller\n",
    "- offer_type\n",
    "\n",
    "The `num_photos` column looks odd, and we will look into investigating this further.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    50000\n",
       "Name: num_photos, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos[\"num_photos\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since `num_photos` has just zeros for the column, lets drop it from the data table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "autos = autos.drop([\"num_photos\", \"seller\", \"offer_type\"], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note also that there are two columns (price and odometer) that have numeric values stored as text, as well as abbreviations. \n",
    "We can fix these useing a set of commands:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5000\n",
       "1    8500\n",
       "2    8990\n",
       "3    4350\n",
       "4    1350\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos[\"price\"] = (autos[\"price\"].str.replace(\"$\", \"\")\n",
    "                  .str.replace(\",\", \"\")\n",
    "                   .astype(int))\n",
    "\n",
    "autos[\"price\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "autos.rename({\"odometer\":\"odometer_km\"}, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    150,000km\n",
       "1    150,000km\n",
       "2     70,000km\n",
       "3     70,000km\n",
       "4    150,000km\n",
       "Name: odometer_km, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos[\"odometer_km\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Odometer and Price Columns\n",
    "\n",
    "Let's continue exploring the data, specifically looking for data that doesn't look right. We'll start by analyzing the `odometer_km` and `price` columns. Here's the steps we'll take:\n",
    "\n",
    "- Analyze the columns using minimum and maximum values and look for any values that look unrealistically high or low (outliers) that we might want to remove.\n",
    "    \n",
    "__We'll use:__\n",
    "- `Series.unique().shape` to see how many unique values\n",
    "- `Series.describe()` to view min/max/median/mean etc\n",
    "- `Series.value_counts()`, with some variations:\n",
    "\n",
    "- chained to `.head()` if there are lots of values.\n",
    "           \n",
    "- Because `Series.value_counts()` returns a series, we can use\n",
    "           \n",
    "- `Series.sort_index()` with ascending= True or False to view the \n",
    "    highest and lowest values with their counts (can also chain to \n",
    "    head() here).\n",
    "\n",
    "- When removing outliers, we can do: `df[(df[\"col\"] > x ) & (df[\"col\"] < y )]`, but it's more readable to use: `df[df[\"col\"].between(x,y)]`\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets first try to use some of these on the `odometer` column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150,000km    32424\n",
       "125,000km     5170\n",
       "100,000km     2169\n",
       "90,000km      1757\n",
       "80,000km      1436\n",
       "70,000km      1230\n",
       "60,000km      1164\n",
       "50,000km      1027\n",
       "5,000km        967\n",
       "40,000km       819\n",
       "30,000km       789\n",
       "20,000km       784\n",
       "10,000km       264\n",
       "Name: odometer_km, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos[\"odometer_km\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can clearly see that in this dataset there is a subtatial amount of cars for sale that exceed 100,000 km in their mileage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2357,)\n",
      "count    5.000000e+04\n",
      "mean     9.840044e+03\n",
      "std      4.811044e+05\n",
      "min      0.000000e+00\n",
      "25%      1.100000e+03\n",
      "50%      2.950000e+03\n",
      "75%      7.200000e+03\n",
      "max      1.000000e+08\n",
      "Name: price, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0       1421\n",
       "500      781\n",
       "1500     734\n",
       "2500     643\n",
       "1000     639\n",
       "1200     639\n",
       "600      531\n",
       "800      498\n",
       "3500     498\n",
       "2000     460\n",
       "999      434\n",
       "750      433\n",
       "900      420\n",
       "650      419\n",
       "850      410\n",
       "700      395\n",
       "4500     394\n",
       "300      384\n",
       "2200     382\n",
       "950      379\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(autos[\"price\"].unique().shape)\n",
    "print(autos[\"price\"].describe())\n",
    "autos[\"price\"].value_counts().head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that we have 1421 cases of \"zero\" for the prices of the car sales, we can drop all of the rows that lead to this result.\n",
    "\n",
    "The highest price for a car exceeds 950,000 dollars. Lets examine the highest limit for a car price in our dataset for a moment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99999999    1\n",
       "27322222    1\n",
       "12345678    3\n",
       "11111111    2\n",
       "10000000    1\n",
       "3890000     1\n",
       "1300000     1\n",
       "1234566     1\n",
       "999999      2\n",
       "999990      1\n",
       "350000      1\n",
       "345000      1\n",
       "299000      1\n",
       "295000      1\n",
       "265000      1\n",
       "259000      1\n",
       "250000      1\n",
       "220000      1\n",
       "198000      1\n",
       "197000      1\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos[\"price\"].value_counts().sort_index(ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1421\n",
       "1      156\n",
       "2        3\n",
       "3        1\n",
       "5        2\n",
       "8        1\n",
       "9        1\n",
       "10       7\n",
       "11       2\n",
       "12       3\n",
       "13       2\n",
       "14       1\n",
       "15       2\n",
       "17       3\n",
       "18       1\n",
       "20       4\n",
       "25       5\n",
       "29       1\n",
       "30       7\n",
       "35       1\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos[\"price\"].value_counts().sort_index(ascending=True).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From what we can see here, there are more than 1,400 listings that have an opening price value of zero, but also a high number of expensive listings including one that exceeds $1 million.\n",
    "\n",
    "From most auctions that can be found on Ebay, its particularly normal for an auction to begin at $1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     48565.000000\n",
       "mean       5888.935591\n",
       "std        9059.854754\n",
       "min           1.000000\n",
       "25%        1200.000000\n",
       "50%        3000.000000\n",
       "75%        7490.000000\n",
       "max      350000.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos = autos[autos[\"price\"].between(1, 351000)] #Selecting an interval\n",
    "autos[\"price\"].describe() #In this interval, describe data statistically:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However if we shorten the scope of our findings to an interval between 1 and 351,000 we can see that 50% of the listings are equal to or greater than 3,000 dollars. The `.describe()` function comes in handy here, giving the mean and certain percentile information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Date Columns\n",
    "\n",
    "In this dataset there are a total of 5 columns that represent date values. Some of the columns were created by the crawler, some came from the website itself. \n",
    "\n",
    "The `date_crawled`, `last_seen`, and `ad_created` columns are all identified as string values by pandas. Because they are of a string datatype, we will need to convert the data into a numerical representation so we can understand it quantitatively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "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>date_crawled</th>\n",
       "      <th>ad_created</th>\n",
       "      <th>last_seen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-26 17:47:46</td>\n",
       "      <td>2016-03-26 00:00:00</td>\n",
       "      <td>2016-04-06 06:45:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-04-04 13:38:56</td>\n",
       "      <td>2016-04-04 00:00:00</td>\n",
       "      <td>2016-04-06 14:45:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-26 18:57:24</td>\n",
       "      <td>2016-03-26 00:00:00</td>\n",
       "      <td>2016-04-06 20:15:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-12 16:58:10</td>\n",
       "      <td>2016-03-12 00:00:00</td>\n",
       "      <td>2016-03-15 03:16:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-04-01 14:38:50</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>2016-04-01 14:38:50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date_crawled           ad_created            last_seen\n",
       "0  2016-03-26 17:47:46  2016-03-26 00:00:00  2016-04-06 06:45:54\n",
       "1  2016-04-04 13:38:56  2016-04-04 00:00:00  2016-04-06 14:45:08\n",
       "2  2016-03-26 18:57:24  2016-03-26 00:00:00  2016-04-06 20:15:37\n",
       "3  2016-03-12 16:58:10  2016-03-12 00:00:00  2016-03-15 03:16:28\n",
       "4  2016-04-01 14:38:50  2016-04-01 00:00:00  2016-04-01 14:38:50"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos[['date_crawled','ad_created','last_seen']][0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-03-05    0.025327\n",
       "2016-03-06    0.014043\n",
       "2016-03-07    0.036014\n",
       "2016-03-08    0.033296\n",
       "2016-03-09    0.033090\n",
       "2016-03-10    0.032184\n",
       "2016-03-11    0.032575\n",
       "2016-03-12    0.036920\n",
       "2016-03-13    0.015670\n",
       "2016-03-14    0.036549\n",
       "2016-03-15    0.034284\n",
       "2016-03-16    0.029610\n",
       "2016-03-17    0.031628\n",
       "2016-03-18    0.012911\n",
       "2016-03-19    0.034778\n",
       "2016-03-20    0.037887\n",
       "2016-03-21    0.037373\n",
       "2016-03-22    0.032987\n",
       "2016-03-23    0.032225\n",
       "2016-03-24    0.029342\n",
       "2016-03-25    0.031607\n",
       "2016-03-26    0.032204\n",
       "2016-03-27    0.031092\n",
       "2016-03-28    0.034860\n",
       "2016-03-29    0.034099\n",
       "2016-03-30    0.033687\n",
       "2016-03-31    0.031834\n",
       "2016-04-01    0.033687\n",
       "2016-04-02    0.035478\n",
       "2016-04-03    0.038608\n",
       "2016-04-04    0.036487\n",
       "2016-04-05    0.013096\n",
       "2016-04-06    0.003171\n",
       "2016-04-07    0.001400\n",
       "Name: date_crawled, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(autos[\"date_crawled\"]\n",
    "        .str[:10]\n",
    "        .value_counts(normalize=True, dropna=False)\n",
    "        .sort_index()\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-04-07    0.001400\n",
       "2016-04-06    0.003171\n",
       "2016-03-18    0.012911\n",
       "2016-04-05    0.013096\n",
       "2016-03-06    0.014043\n",
       "2016-03-13    0.015670\n",
       "2016-03-05    0.025327\n",
       "2016-03-24    0.029342\n",
       "2016-03-16    0.029610\n",
       "2016-03-27    0.031092\n",
       "2016-03-25    0.031607\n",
       "2016-03-17    0.031628\n",
       "2016-03-31    0.031834\n",
       "2016-03-10    0.032184\n",
       "2016-03-26    0.032204\n",
       "2016-03-23    0.032225\n",
       "2016-03-11    0.032575\n",
       "2016-03-22    0.032987\n",
       "2016-03-09    0.033090\n",
       "2016-03-08    0.033296\n",
       "2016-04-01    0.033687\n",
       "2016-03-30    0.033687\n",
       "2016-03-29    0.034099\n",
       "2016-03-15    0.034284\n",
       "2016-03-19    0.034778\n",
       "2016-03-28    0.034860\n",
       "2016-04-02    0.035478\n",
       "2016-03-07    0.036014\n",
       "2016-04-04    0.036487\n",
       "2016-03-14    0.036549\n",
       "2016-03-12    0.036920\n",
       "2016-03-21    0.037373\n",
       "2016-03-20    0.037887\n",
       "2016-04-03    0.038608\n",
       "Name: date_crawled, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(autos[\"date_crawled\"]\n",
    "        .str[:10]\n",
    "        .value_counts(normalize=True, dropna=False)\n",
    "        .sort_values()\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like the site was crawled daily over roughly a one month period in March and April 2016. The distribution of listings crawled on each day is roughly uniform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-03-05    0.001071\n",
       "2016-03-06    0.004324\n",
       "2016-03-07    0.005395\n",
       "2016-03-08    0.007413\n",
       "2016-03-09    0.009595\n",
       "2016-03-10    0.010666\n",
       "2016-03-11    0.012375\n",
       "2016-03-12    0.023783\n",
       "2016-03-13    0.008895\n",
       "2016-03-14    0.012602\n",
       "2016-03-15    0.015876\n",
       "2016-03-16    0.016452\n",
       "2016-03-17    0.028086\n",
       "2016-03-18    0.007351\n",
       "2016-03-19    0.015834\n",
       "2016-03-20    0.020653\n",
       "2016-03-21    0.020632\n",
       "2016-03-22    0.021373\n",
       "2016-03-23    0.018532\n",
       "2016-03-24    0.019767\n",
       "2016-03-25    0.019211\n",
       "2016-03-26    0.016802\n",
       "2016-03-27    0.015649\n",
       "2016-03-28    0.020859\n",
       "2016-03-29    0.022341\n",
       "2016-03-30    0.024771\n",
       "2016-03-31    0.023783\n",
       "2016-04-01    0.022794\n",
       "2016-04-02    0.024915\n",
       "2016-04-03    0.025203\n",
       "2016-04-04    0.024483\n",
       "2016-04-05    0.124761\n",
       "2016-04-06    0.221806\n",
       "2016-04-07    0.131947\n",
       "Name: last_seen, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(autos[\"last_seen\"]\n",
    "        .str[:10]\n",
    "        .value_counts(normalize=True, dropna=False)\n",
    "        .sort_index()\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The crawler recorded the date it last saw any listing, which allows us to determine on what day a listing was removed, presumably because the car was sold.\n",
    "\n",
    "The last three days contain a disproportionate amount of 'last seen' values. Given that these are 6-10x the values from the previous days, it's unlikely that there was a massive spike in sales, and more likely that these values are to do with the crawling period ending and don't indicate car sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(76,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2015-06-11    0.000021\n",
       "2015-08-10    0.000021\n",
       "2015-09-09    0.000021\n",
       "2015-11-10    0.000021\n",
       "2015-12-05    0.000021\n",
       "2015-12-30    0.000021\n",
       "2016-01-03    0.000021\n",
       "2016-01-07    0.000021\n",
       "2016-01-10    0.000041\n",
       "2016-01-13    0.000021\n",
       "2016-01-14    0.000021\n",
       "2016-01-16    0.000021\n",
       "2016-01-22    0.000021\n",
       "2016-01-27    0.000062\n",
       "2016-01-29    0.000021\n",
       "2016-02-01    0.000021\n",
       "2016-02-02    0.000041\n",
       "2016-02-05    0.000041\n",
       "2016-02-07    0.000021\n",
       "2016-02-08    0.000021\n",
       "2016-02-09    0.000021\n",
       "2016-02-11    0.000021\n",
       "2016-02-12    0.000041\n",
       "2016-02-14    0.000041\n",
       "2016-02-16    0.000021\n",
       "2016-02-17    0.000021\n",
       "2016-02-18    0.000041\n",
       "2016-02-19    0.000062\n",
       "2016-02-20    0.000041\n",
       "2016-02-21    0.000062\n",
       "                ...   \n",
       "2016-03-09    0.033151\n",
       "2016-03-10    0.031895\n",
       "2016-03-11    0.032904\n",
       "2016-03-12    0.036755\n",
       "2016-03-13    0.017008\n",
       "2016-03-14    0.035190\n",
       "2016-03-15    0.034016\n",
       "2016-03-16    0.030125\n",
       "2016-03-17    0.031278\n",
       "2016-03-18    0.013590\n",
       "2016-03-19    0.033687\n",
       "2016-03-20    0.037949\n",
       "2016-03-21    0.037579\n",
       "2016-03-22    0.032801\n",
       "2016-03-23    0.032060\n",
       "2016-03-24    0.029280\n",
       "2016-03-25    0.031751\n",
       "2016-03-26    0.032266\n",
       "2016-03-27    0.030989\n",
       "2016-03-28    0.034984\n",
       "2016-03-29    0.034037\n",
       "2016-03-30    0.033501\n",
       "2016-03-31    0.031875\n",
       "2016-04-01    0.033687\n",
       "2016-04-02    0.035149\n",
       "2016-04-03    0.038855\n",
       "2016-04-04    0.036858\n",
       "2016-04-05    0.011819\n",
       "2016-04-06    0.003253\n",
       "2016-04-07    0.001256\n",
       "Name: ad_created, Length: 76, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(autos[\"ad_created\"].str[:10].unique().shape)\n",
    "(autos[\"ad_created\"]\n",
    "        .str[:10]\n",
    "        .value_counts(normalize=True, dropna=False)\n",
    "        .sort_index()\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note here that there is a large variety of ad created dates. Most fall within 1-2 months of the listing date, but a few are quite old, with the oldest at around 9 months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    48565.000000\n",
       "mean      2004.755421\n",
       "std         88.643887\n",
       "min       1000.000000\n",
       "25%       1999.000000\n",
       "50%       2004.000000\n",
       "75%       2008.000000\n",
       "max       9999.000000\n",
       "Name: registration_year, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos[\"registration_year\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The year that the car was first registered will likely indicate the age of the car. Looking at this column, we note some odd values. The minimum value is 1000, long before cars were invented and the maximum is 9999, many years into the future."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dealing With Incorrect Information About Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because a car can't be first registered before the listing was seen, any vehicle with a registration year above 2016 is definitely inaccurate. Determining the earliest valid year is more difficult. Realistically, it could be somewhere in the first few decades of the 1900s.\n",
    "\n",
    "One option is to remove the listings with these values. Let's determine what percentage of our data has invalid values in this column:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.038793369710697"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(~autos[\"registration_year\"].between(1900,2016)).sum() / autos.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Since this represents less than 4% of the data we will remove these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000    0.067608\n",
       "2005    0.062895\n",
       "1999    0.062060\n",
       "2004    0.057904\n",
       "2003    0.057818\n",
       "2006    0.057197\n",
       "2001    0.056468\n",
       "2002    0.053255\n",
       "1998    0.050620\n",
       "2007    0.048778\n",
       "Name: registration_year, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Many ways to select rows in a dataframe that fall within a value range for a column.\n",
    "# Using `Series.between()` is one way.\n",
    "autos = autos[autos[\"registration_year\"].between(1900,2016)]\n",
    "autos[\"registration_year\"].value_counts(normalize=True).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that most of the vehicles were first registered in the past 20 years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Price By Brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "volkswagen        0.211264\n",
       "bmw               0.110045\n",
       "opel              0.107581\n",
       "mercedes_benz     0.096463\n",
       "audi              0.086566\n",
       "ford              0.069900\n",
       "renault           0.047150\n",
       "peugeot           0.029841\n",
       "fiat              0.025642\n",
       "seat              0.018273\n",
       "skoda             0.016409\n",
       "nissan            0.015274\n",
       "mazda             0.015188\n",
       "smart             0.014160\n",
       "citroen           0.014010\n",
       "toyota            0.012703\n",
       "hyundai           0.010025\n",
       "sonstige_autos    0.009811\n",
       "volvo             0.009147\n",
       "mini              0.008762\n",
       "mitsubishi        0.008226\n",
       "honda             0.007840\n",
       "kia               0.007069\n",
       "alfa_romeo        0.006641\n",
       "porsche           0.006127\n",
       "suzuki            0.005934\n",
       "chevrolet         0.005698\n",
       "chrysler          0.003513\n",
       "dacia             0.002635\n",
       "daihatsu          0.002506\n",
       "jeep              0.002271\n",
       "subaru            0.002142\n",
       "land_rover        0.002099\n",
       "saab              0.001649\n",
       "jaguar            0.001564\n",
       "daewoo            0.001500\n",
       "trabant           0.001392\n",
       "rover             0.001328\n",
       "lancia            0.001071\n",
       "lada              0.000578\n",
       "Name: brand, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos[\"brand\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "German manufacturers represent four out of the top five brands, almost 50% of the overall listings. Volkswagen is by far the most popular brand, with approximately double the cars for sale of the next two brands combined.\n",
    "\n",
    "There are lots of brands that don't have a significant percentage of listings, so we will limit our analysis to brands representing more than 5% of total listings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['volkswagen', 'bmw', 'opel', 'mercedes_benz', 'audi', 'ford'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "brand_counts = autos[\"brand\"].value_counts(normalize=True)\n",
    "common_brands = brand_counts[brand_counts > .05].index\n",
    "print(common_brands)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'audi': 9336,\n",
       " 'bmw': 8332,\n",
       " 'ford': 3749,\n",
       " 'mercedes_benz': 8628,\n",
       " 'opel': 2975,\n",
       " 'volkswagen': 5402}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_mean_prices = {}\n",
    "\n",
    "for brand in common_brands:\n",
    "    brand_only = autos[autos[\"brand\"] == brand]\n",
    "    mean_price = brand_only[\"price\"].mean()\n",
    "    brand_mean_prices[brand] = int(mean_price)\n",
    "\n",
    "brand_mean_prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the top 5 brands, there is a distinct price gap:\n",
    "\n",
    "- Audi, BMW and Mercedes Benz are more expensive\n",
    "- Ford and Opel are less expensive\n",
    "- Volkswagen is in between - this may explain its popularity, it may be a 'best of 'both worlds' option."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Prices With A Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "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>mean_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>audi</th>\n",
       "      <td>9336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmw</th>\n",
       "      <td>8332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ford</th>\n",
       "      <td>3749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercedes_benz</th>\n",
       "      <td>8628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opel</th>\n",
       "      <td>2975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volkswagen</th>\n",
       "      <td>5402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean_price\n",
       "audi                 9336\n",
       "bmw                  8332\n",
       "ford                 3749\n",
       "mercedes_benz        8628\n",
       "opel                 2975\n",
       "volkswagen           5402"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmp_series = pd.Series(brand_mean_prices)\n",
    "pd.DataFrame(bmp_series, columns=[\"mean_price\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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