Basics.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Exploring Ebay Car Sales Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esse é um projeto guiado, parte do curso [Data Analyst em Python](https://app.dataquest.io/path/data-analyst), do Dataquest.io  \n",
    "\n",
    "O dataset foi originalmente criado no Kaggle por [orgesleka](https://www.kaggle.com/orgesleka). \n",
    "\n",
    "O dataset original não está mais disponível no Kaggle, mas você pode encontrá-lo clicando [aqui](https://data.world/data-society/used-cars-data)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para efeitos de estudos foram feitas algumas modificações no Dataset:\n",
    "\n",
    "* Serão utilizados 50000 amostras desses dados para garantir que o código seja executado rapidamente;\n",
    "* O dataset foi ligeiramente bagunçado, para se parecer mais com um conjunto de dados da vida real, pois os dados do Kaggle podem ser um pouco mais fáceis de trabalhar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dicionário dos dados:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```dateCrawled``` -  Quando este anúncio foi rastreado pela primeira vez. (Todos os valores de campo são obtidos a partir desta data.)    \n",
    "```name``` - Modelo e marca do carro.    \n",
    "```seller``` - Se o vendedor é particular ou revendedor.    \n",
    "```offerType``` - O tipo do anúncio.  \n",
    "```price``` - O preço de venda do veículo.  \n",
    "```abtest``` - Se o anúncio foi incluído ou não no test A/B.  \n",
    "```vehicleType``` - O tipo do veículo.  \n",
    "```yearOfRegistration``` - O ano de registro do veículo.  \n",
    "```gearbox``` - O tipo de transmissão do carro (manual, automático).  \n",
    "```powerPS``` - A potência do carro.  \n",
    "```model``` - O nome do modelo do carro.  \n",
    "```kilometer``` - A quilometragem do veículo.  \n",
    "```monthOfRegistration``` - O mês de registro do carro.  \n",
    "```fuelType``` - O tipo de combustível.  \n",
    "```brand``` - A marca do carro.  \n",
    "```notRepairedDamage``` - Se o carro apresenta algum dano que não foi consertado.  \n",
    "```dateCreated``` - A data de criação do anúncio.  \n",
    "```nrOfPictures``` - O número de fotos no anúncio.  \n",
    "```postalCode``` - O código postal da localização do veículo.  \n",
    "```lastSeenOnline``` - Quando o anúncio foi visto pela última vez online.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1/9\n",
    "#### Instructions\n",
    "\n",
    "- [X] Start by writing a paragraph in a markdown cell introducing the project and the dataset.\n",
    "- [x] Import the pandas and NumPy libraries\n",
    "- [x] Read the autos.csv CSV file into pandas, and assign it to the variable name autos.\n",
    "    * Try without specifying any encoding (which will default to UTF-8)\n",
    "    * If you get an encoding error, try the next two most popular encodings (Latin-1 and Windows-1252) until you are able to read the file without error.\n",
    "- [x] Create a new cell with just the variable autos and run this cell.\n",
    "    * A neat feature of jupyter notebook is its ability to render the first few and last few values of any pandas object.\n",
    "- [x] Use the ```DataFrame.info()``` and ```DataFrame.head()``` methods to print information about the autos dataframe, as well as the first few rows.\n",
    "    * Write a markdown cell briefly describing your observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "autos = pd.read_csv('autos.csv', encoding='Latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>dateCrawled</th>\n",
       "      <th>name</th>\n",
       "      <th>seller</th>\n",
       "      <th>offerType</th>\n",
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       "      <th>abtest</th>\n",
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       "      <th>model</th>\n",
       "      <th>odometer</th>\n",
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       "      <th>fuelType</th>\n",
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       "      <th>nrOfPictures</th>\n",
       "      <th>postalCode</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <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",
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       "    <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",
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       "      <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",
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       "    <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",
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       "<p>50000 rows × 20 columns</p>\n",
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      "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",
       "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",
       "...       ...       ...      ...      ...         ...                 ...   \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",
       "...          ...      ...     ...        ...                  ...      ...   \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",
       "...           ...               ...                  ...           ...   \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",
       "...           ...                  ...  \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]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "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",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   dateCrawled          50000 non-null  object\n",
      " 1   name                 50000 non-null  object\n",
      " 2   seller               50000 non-null  object\n",
      " 3   offerType            50000 non-null  object\n",
      " 4   price                50000 non-null  object\n",
      " 5   abtest               50000 non-null  object\n",
      " 6   vehicleType          44905 non-null  object\n",
      " 7   yearOfRegistration   50000 non-null  int64 \n",
      " 8   gearbox              47320 non-null  object\n",
      " 9   powerPS              50000 non-null  int64 \n",
      " 10  model                47242 non-null  object\n",
      " 11  odometer             50000 non-null  object\n",
      " 12  monthOfRegistration  50000 non-null  int64 \n",
      " 13  fuelType             45518 non-null  object\n",
      " 14  brand                50000 non-null  object\n",
      " 15  notRepairedDamage    40171 non-null  object\n",
      " 16  dateCreated          50000 non-null  object\n",
      " 17  nrOfPictures         50000 non-null  int64 \n",
      " 18  postalCode           50000 non-null  int64 \n",
      " 19  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": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 20)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O dataset tem 20 colunas e 50000 linhas e algumas colunas tem valores nulos.  \n",
    "Os nomes das colunas estão em ```camelcase``` o que não é o formato recomendados para o Python.  \n",
    "Por isso iremos converter os nomes das colunas para ```snakecase```, preenchendo os espaços com underscore."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2/9 \n",
    "## Clean Columns\n",
    "\n",
    "* [X] Use the DataFrame.columns attribute to print an array of the existing column names.\n",
    "* [X] Copy that array and make the following edits to columns names:\n",
    "    * yearOfRegistration to registration_year\n",
    "    * monthOfRegistration to registration_month\n",
    "    * notRepairedDamage to unrepaired_damage\n",
    "    * dateCreated to ad_created\n",
    "    * The rest of the column names from camelcase to snakecase.\n",
    "* [X] Assign the modified column names back to the DataFrame.columns attribute.\n",
    "* [X] Use DataFrame.head() to look at the current state of the autos dataframe.\n",
    "* [X] Write a markdown cell explaining the changes you made and why."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_columns = ['date_crawled', 'name', 'seller', 'offer_type', 'price', 'abtest',\n",
    "       'vehicle_type', 'registration_year', 'gearbox', 'power_PS', 'model',\n",
    "       'odometer', 'registration_month', 'fuel_type', 'brand',\n",
    "       'unrepaired_damage', 'ad_created', 'nr_of_pictures', 'postal_code',\n",
    "       'last_seen']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "autos.columns = new_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_crawled</th>\n",
       "      <th>name</th>\n",
       "      <th>seller</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>price</th>\n",
       "      <th>abtest</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>nr_of_pictures</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   abtest 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  nr_of_pictures  \\\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",
       "   postal_code            last_seen  \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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora vamos fazer uma exploração básica dos dados para determinar algumas alterações que podem ser realizadas, por exemplo:\n",
    "\n",
    "- Colunas do tipo texto, cujos valores sejam os mesmos. Esses valores podem ser descartados já que não contém informações relevantes para a análise.\n",
    "- Dados numéricos que estão erroneamente configurados como texto e precisam ser convertidos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3/9 \n",
    "## Initial Data Exploration and Cleaning\n",
    "\n",
    "* [X] Use ```DataFrame.describe()``` to look at descriptive statistics for all columns.\n",
    "* [X] Write a markdown cell noting:\n",
    "    * Any columns that have mostly one value that are candidates to be dropped\n",
    "    * Any columns that need more investigation.\n",
    "    * Any examples of numeric data stored as text that needs to be cleaned.\n",
    "* [X] If you need to investigate any columns more, do so and write up any additional things you found.\n",
    "* [X] You likely found that the price and odometer columns are numeric values stored as text. For each column:\n",
    "    * Remove any non-numeric characters.\n",
    "    * Convert the column to a numeric dtype.\n",
    "    * Use DataFrame.rename() to rename the column to odometer_km."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_crawled</th>\n",
       "      <th>name</th>\n",
       "      <th>seller</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>price</th>\n",
       "      <th>abtest</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>nr_of_pictures</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>2016-03-08 10:40:35</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 abtest  \\\n",
       "count                 50000        50000   50000      50000  50000  50000   \n",
       "unique                48213        38754       2          2   2357      2   \n",
       "top     2016-03-08 10:40:35  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  nr_of_pictures   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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['nr_of_pictures'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Any columns that have mostly one value that are candidates to be dropped\n",
    "A coluna nr_of_pictures não contém valores relevantes, todas as linhas são iguais a 0. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_crawled             0\n",
       "name                     0\n",
       "seller                   0\n",
       "offer_type               0\n",
       "price                    0\n",
       "abtest                   0\n",
       "vehicle_type          5095\n",
       "registration_year        0\n",
       "gearbox               2680\n",
       "power_PS                 0\n",
       "model                 2758\n",
       "odometer                 0\n",
       "registration_month       0\n",
       "fuel_type             4482\n",
       "brand                    0\n",
       "unrepaired_damage     9829\n",
       "ad_created               0\n",
       "nr_of_pictures           0\n",
       "postal_code              0\n",
       "last_seen                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Any columns that need more investigation.  \n",
    "\n",
    "As colunas vehicle_type, gearbox, model, fuel_type e unrepaired_damage contém valores nulos. Pode ser necessário utilizar algum método de limpeza desses dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_crawled          object\n",
       "name                  object\n",
       "seller                object\n",
       "offer_type            object\n",
       "price                 object\n",
       "abtest                object\n",
       "vehicle_type          object\n",
       "registration_year      int64\n",
       "gearbox               object\n",
       "power_PS               int64\n",
       "model                 object\n",
       "odometer              object\n",
       "registration_month     int64\n",
       "fuel_type             object\n",
       "brand                 object\n",
       "unrepaired_damage     object\n",
       "ad_created            object\n",
       "nr_of_pictures         int64\n",
       "postal_code            int64\n",
       "last_seen             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Any examples of numeric data stored as text that needs to be cleaned.\n",
    "As colunas price e odometer são do tipo object e deveriam ser do tipo int ou float.\n",
    "\n",
    "If you need to investigate any columns more, do so and write up any additional things you found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.price.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-16-3e0e8c2fb3ea>:3: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will*not* be treated as literal strings when regex=True.\n",
      "  autos['price'] = autos['price'].str.replace('$', '')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .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>abtest</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>nr_of_pictures</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   abtest 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  nr_of_pictures  \\\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",
       "   postal_code            last_seen  \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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A coluna price não contém valores nulos. \n",
    "# Utilizaremos o método str.replace para substituir o símbolo $ \n",
    "autos['price'] = autos['price'].str.replace('$', '')\n",
    "autos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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>abtest</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>nr_of_pictures</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>5000</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>8500</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>8990</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>4350</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>1350</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   abtest vehicle_type  registration_year  \\\n",
       "0  privat    Angebot  5000  control          bus               2004   \n",
       "1  privat    Angebot  8500  control    limousine               1997   \n",
       "2  privat    Angebot  8990     test    limousine               2009   \n",
       "3  privat    Angebot  4350  control   kleinwagen               2007   \n",
       "4  privat    Angebot  1350     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  nr_of_pictures  \\\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",
       "   postal_code            last_seen  \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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# E aqui, aplicando novamente o método str.replace para substituir a vírgula\n",
    "autos['price'] = autos['price'].str.replace(',', '')\n",
    "autos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utilizaremos o método as type para converter price de object para int\n",
    "autos['price'] = autos['price'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_crawled          object\n",
       "name                  object\n",
       "seller                object\n",
       "offer_type            object\n",
       "price                  int64\n",
       "abtest                object\n",
       "vehicle_type          object\n",
       "registration_year      int64\n",
       "gearbox               object\n",
       "power_PS               int64\n",
       "model                 object\n",
       "odometer              object\n",
       "registration_month     int64\n",
       "fuel_type             object\n",
       "brand                 object\n",
       "unrepaired_damage     object\n",
       "ad_created            object\n",
       "nr_of_pictures         int64\n",
       "postal_code            int64\n",
       "last_seen             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# E agora aplicaremos o mesmo processo a odometer, \n",
    "# porém utilizaremos o método replace para substituir a vírgula e a str \"km\"\n",
    "# ao mesmo tempo:\n",
    "autos[\"odometer\"] = (autos[\"odometer\"]\n",
    "                             .str.replace(\"km\",\"\")\n",
    "                             .str.replace(\",\",\"\")\n",
    "                             .astype(int)\n",
    "                             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>brand</th>\n",
       "      <th>unrepaired_damage</th>\n",
       "      <th>ad_created</th>\n",
       "      <th>nr_of_pictures</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>5000</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>150000</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>8500</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>150000</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>8990</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>70000</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>4350</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>70000</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>1350</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>150000</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>...</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>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>24900</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>100000</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>1980</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>150000</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>13200</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>5000</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>22900</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>40000</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>1250</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>150000</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": [
       "              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",
       "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 offer_type  price   abtest vehicle_type  registration_year  \\\n",
       "0      privat    Angebot   5000  control          bus               2004   \n",
       "1      privat    Angebot   8500  control    limousine               1997   \n",
       "2      privat    Angebot   8990     test    limousine               2009   \n",
       "3      privat    Angebot   4350  control   kleinwagen               2007   \n",
       "4      privat    Angebot   1350     test        kombi               2003   \n",
       "...       ...        ...    ...      ...          ...                ...   \n",
       "49995  privat    Angebot  24900  control    limousine               2011   \n",
       "49996  privat    Angebot   1980  control       cabrio               1996   \n",
       "49997  privat    Angebot  13200     test       cabrio               2014   \n",
       "49998  privat    Angebot  22900  control        kombi               2013   \n",
       "49999  privat    Angebot   1250  control    limousine               1996   \n",
       "\n",
       "         gearbox  power_PS   model  odometer  registration_month fuel_type  \\\n",
       "0        manuell       158  andere    150000                   3       lpg   \n",
       "1      automatik       286     7er    150000                   6    benzin   \n",
       "2        manuell       102    golf     70000                   7    benzin   \n",
       "3      automatik        71  fortwo     70000                   6    benzin   \n",
       "4        manuell         0   focus    150000                   7    benzin   \n",
       "...          ...       ...     ...       ...                 ...       ...   \n",
       "49995  automatik       239      q5    100000                   1    diesel   \n",
       "49996    manuell        75   astra    150000                   5    benzin   \n",
       "49997  automatik        69     500      5000                  11    benzin   \n",
       "49998    manuell       150      a3     40000                  11    diesel   \n",
       "49999    manuell       101  vectra    150000                   1    benzin   \n",
       "\n",
       "            brand unrepaired_damage           ad_created  nr_of_pictures  \\\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",
       "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",
       "       postal_code            last_seen  \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",
       "...            ...                  ...  \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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_crawled</th>\n",
       "      <th>name</th>\n",
       "      <th>seller</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>price</th>\n",
       "      <th>abtest</th>\n",
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       "      <th>registration_year</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>power_PS</th>\n",
       "      <th>model</th>\n",
       "      <th>odometer_km</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>nr_of_pictures</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>5000</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>150000</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>8500</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>150000</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>8990</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>70000</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>4350</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>70000</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>1350</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>150000</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>...</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>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>24900</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>100000</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>1980</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>150000</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>13200</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>5000</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>22900</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>40000</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>1250</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>150000</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": [
       "              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",
       "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 offer_type  price   abtest vehicle_type  registration_year  \\\n",
       "0      privat    Angebot   5000  control          bus               2004   \n",
       "1      privat    Angebot   8500  control    limousine               1997   \n",
       "2      privat    Angebot   8990     test    limousine               2009   \n",
       "3      privat    Angebot   4350  control   kleinwagen               2007   \n",
       "4      privat    Angebot   1350     test        kombi               2003   \n",
       "...       ...        ...    ...      ...          ...                ...   \n",
       "49995  privat    Angebot  24900  control    limousine               2011   \n",
       "49996  privat    Angebot   1980  control       cabrio               1996   \n",
       "49997  privat    Angebot  13200     test       cabrio               2014   \n",
       "49998  privat    Angebot  22900  control        kombi               2013   \n",
       "49999  privat    Angebot   1250  control    limousine               1996   \n",
       "\n",
       "         gearbox  power_PS   model  odometer_km  registration_month fuel_type  \\\n",
       "0        manuell       158  andere       150000                   3       lpg   \n",
       "1      automatik       286     7er       150000                   6    benzin   \n",
       "2        manuell       102    golf        70000                   7    benzin   \n",
       "3      automatik        71  fortwo        70000                   6    benzin   \n",
       "4        manuell         0   focus       150000                   7    benzin   \n",
       "...          ...       ...     ...          ...                 ...       ...   \n",
       "49995  automatik       239      q5       100000                   1    diesel   \n",
       "49996    manuell        75   astra       150000                   5    benzin   \n",
       "49997  automatik        69     500         5000                  11    benzin   \n",
       "49998    manuell       150      a3        40000                  11    diesel   \n",
       "49999    manuell       101  vectra       150000                   1    benzin   \n",
       "\n",
       "            brand unrepaired_damage           ad_created  nr_of_pictures  \\\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",
       "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",
       "       postal_code            last_seen  \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",
       "...            ...                  ...  \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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Por último iremos renomear a coluna odometer para odometer_km\n",
    "# utilizando o método rename:\n",
    "autos.rename({\"odometer\": \"odometer_km\"}, axis=1, inplace=True)\n",
    "autos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4/9\n",
    "## Exploring Odometer and Price\n",
    "\n",
    "From the last screen, we learned that there are a number of text columns where almost all of the values are the same (seller and offer_type).  \n",
    "\n",
    "We also converted the price and odometer columns to numeric types and renamed odometer to odometer_km.\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",
    "* 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",
    "        * chained to .head() if there are lots of values.\n",
    "        * Because Series.value_counts() returns a series, we can use Series.sort_index() with ascending= True or False to view the highest and lowest values with their counts (can also chain to head() here).\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",
    "\n",
    "* For each of the odometer_km and price columns:\n",
    "    * Use the techniques above to explore the data\n",
    "    * If you find there are outliers, remove them and write a markdown paragraph explaining your decision.\n",
    "    * After you have removed the outliers, make some observations about the remaining values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['odometer_km'].unique().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2357,)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['price'].unique().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     50000.000000\n",
       "mean     125732.700000\n",
       "std       40042.211706\n",
       "min        5000.000000\n",
       "25%      125000.000000\n",
       "50%      150000.000000\n",
       "75%      150000.000000\n",
       "max      150000.000000\n",
       "Name: odometer_km, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['odometer_km'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['price'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150000    32424\n",
       "125000     5170\n",
       "100000     2169\n",
       "90000      1757\n",
       "80000      1436\n",
       "Name: odometer_km, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['odometer_km'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1421\n",
       "500      781\n",
       "1500     734\n",
       "2500     643\n",
       "1000     639\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['price'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150000    32424\n",
       "125000     5170\n",
       "100000     2169\n",
       "90000      1757\n",
       "80000      1436\n",
       "Name: odometer_km, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['odometer_km'].value_counts().sort_index(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99999999    1\n",
       "27322222    1\n",
       "12345678    3\n",
       "11111111    2\n",
       "10000000    1\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['price'].value_counts().sort_index(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos = autos[autos['price'].between(1,351000)]\n",
    "autos['price'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5/9 \n",
    "## Exploring the date columns\n",
    "\n",
    "* [X] Use the workflow we just described to calculate the distribution of values in the ```date_crawled```, ```ad_created```, and ```last_seen``` columns (all string columns) as percentages.\n",
    "    * To include missing values in the distribution and to use percentages instead of counts, chain the ```Series.value_counts(normalize=True, dropna=False)``` method.\n",
    "    * To rank by date in ascending order (earliest to latest), chain the ```Series.sort_index()``` method.\n",
    "    * Write a markdown cell after each column exploration to explain your observations.\n",
    "* [X] Use ```Series.describe()``` to understand the distribution of ```registration_year```.\n",
    "    * Write a markdown cell explaining your observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos[['date_crawled','ad_created','last_seen']][0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "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": 33,
     "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": 34,
   "metadata": {},
   "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",
       "2016-03-30    0.033687\n",
       "2016-04-01    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": 34,
     "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": [
    "Analisando a distribuição de ```data_crawled``` observa-se que o site foi rastreado pelo menos 1 vez ao dia durante o período de 5 de março a 7 de abril. A distribuição das listagens rastreadas em cada dia é aproximadamente uniforme."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "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": 35,
     "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": [
    "O rastreador registrou a data em que viu qualquer anúncio pela última vez, o que nos permite determinar em que dia uma listagem foi removida, provavelmente porque o carro foi vendido.\n",
    "\n",
    "Os últimos três dias contêm uma quantidade desproporcional de valores 'vistos pela última vez'. Dado que esses valores são de 6 a 10 vezes os valores dos dias anteriores, é improvável que tenha ocorrido um grande aumento nas vendas, e mais provavelmente esses valores estão relacionados ao término do período de rastreamento e não indicam vendas de carros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "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",
       "                ...   \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": 36,
     "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": [
    "Existe uma grande variedade de datas de criação de anúncios. A maioria cai dentro de 1 a 2 meses da data de listagem, mas alguns são bastante antigos, com o mais antigo em torno de 9 meses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['registration_year'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O ano em que o carro foi registrado pela primeira vez provavelmente indicará a idade do carro. Olhando para esta coluna, notamos alguns valores estranhos. O valor mínimo é 1000, muito antes de os carros serem inventados, e o máximo é 9999, muitos anos no futuro.\n",
    "\n",
    "Como um carro não pode ser registrado pela primeira vez após a listagem ser vista, qualquer veículo com um ano de registro acima de 2016 é definitivamente impreciso. Determinar o primeiro ano válido é mais difícil. Realisticamente, pode ser em algum lugar nas primeiras décadas do século XX.\n",
    "\n",
    "Vamos contar o número de listagens com carros que estão fora do intervalo de 1900 a 2016 e ver se é seguro remover essas linhas totalmente ou se precisamos de mais lógica personalizada."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6/9 \n",
    "## Dealing with Incorrect Registration Year Data\n",
    "\n",
    "\n",
    "* [X] Decide which the highest and lowest acceptable values are for the ```registration_year``` column.\n",
    "    * Write a markdown cell explaining your decision and why.\n",
    "* [X] Remove the values outside those upper and lower bounds and calculate the distribution of the remaining values using ```Series.value_counts(normalize=True)```.\n",
    "    * Write a markdown cell explaining your observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.038793369710697"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(~autos['registration_year'].between(1900,2016)).sum() / autos.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dado que isso representa menos de 4% de nossos dados, removeremos essas linhas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos = autos[autos['registration_year'].between(1900,2016)]\n",
    "autos['registration_year'].value_counts(normalize=True).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizando between fizemos uma seleção dos carros registrados entre 1900 e 2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7/9\n",
    "## Exploring Price by Brand\n",
    "\n",
    "* [X] Explore the unique values in the ```brand``` column, and decide on which brands you want to aggregate by.\n",
    "    * You might want to select the top 20, or you might want to select those that have over a certain percentage of the total values (e.g. > 5%).\n",
    "    * Remember that ```Series.value_counts()``` produces a series with index labels, so you can use ```Series.index``` attribute to access the labels, should you wish.\n",
    "* [X] Write a short paragraph describing the brand data, and explaining which brands you've chosen to aggregate on.\n",
    "* [X] Create an empty dictionary to hold your aggregate data.\n",
    "    * Loop over your selected brands, and assign the mean price to the dictionary, with the brand name as the key.\n",
    "    * Print your dictionary of aggregate data, and write a paragraph analyzing the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['brand'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2160x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.dpi'] = 360\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,4))\n",
    "ax = sns.countplot(x = autos['brand'], palette='pastel')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=55) \n",
    "plt.ylabel('')\n",
    "plt.xlabel('')\n",
    "plt.xticks(size=6)\n",
    "plt.title('Autos by Brand')\n",
    "sns.despine(left=True)\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('autosbybrand.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os fabricantes alemães representam quatro das cinco principais marcas, quase 50% das listagens gerais. A Volkswagen é de longe a marca mais popular, com aproximadamente o dobro dos carros à venda das duas marcas seguintes juntas.\n",
    "\n",
    "Existem muitas marcas que não têm uma porcentagem significativa de listagens, portanto, limitaremos nossa análise às marcas que representam mais de 5% do total de listagens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'volkswagen': 5402,\n",
       " 'bmw': 8332,\n",
       " 'opel': 2975,\n",
       " 'mercedes_benz': 8628,\n",
       " 'audi': 9336,\n",
       " 'ford': 3749}"
      ]
     },
     "execution_count": 43,
     "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": [
    "Das 5 principais marcas, há uma diferença de preço distinta:\n",
    "\n",
    "* Audi, BMW e Mercedes Benz são mais caros\n",
    "* Ford e Opel são menos caros\n",
    "* A Volkswagen está no meio - isso pode explicar sua popularidade, pode ser uma opção do melhor de ambos os mundos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8/9\n",
    "## Exploring Mileage\n",
    "\n",
    "* [X] Use the loop method from the last screen to calculate the mean mileage and mean price for each of the top brands, storing the results in a dictionary.\n",
    "* [X] Convert both dictionaries to series objects, using the series constructor.\n",
    "* [X] Create a dataframe from the first series object using the dataframe constructor.\n",
    "* [X] Assign the other series as a new column in this dataframe.\n",
    "* [X] Pretty print the dataframe, and write a paragraph analyzing the aggregate data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>volkswagen</th>\n",
       "      <td>5402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmw</th>\n",
       "      <td>8332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opel</th>\n",
       "      <td>2975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercedes_benz</th>\n",
       "      <td>8628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>audi</th>\n",
       "      <td>9336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ford</th>\n",
       "      <td>3749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean_price\n",
       "volkswagen           5402\n",
       "bmw                  8332\n",
       "opel                 2975\n",
       "mercedes_benz        8628\n",
       "audi                 9336\n",
       "ford                 3749"
      ]
     },
     "execution_count": 44,
     "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": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "brand_mean_mileage = {}\n",
    "\n",
    "for brand in common_brands:\n",
    "    brand_only = autos[autos['brand'] == brand]\n",
    "    mean_mileage = brand_only['odometer_km'].mean()\n",
    "    brand_mean_mileage[brand] = int(mean_mileage)\n",
    "\n",
    "mean_mileage = pd.Series(brand_mean_mileage).sort_values(ascending=False)\n",
    "mean_prices = pd.Series(brand_mean_prices).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_mileage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bmw</th>\n",
       "      <td>132572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercedes_benz</th>\n",
       "      <td>130788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opel</th>\n",
       "      <td>129310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>audi</th>\n",
       "      <td>129157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volkswagen</th>\n",
       "      <td>128707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ford</th>\n",
       "      <td>124266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean_mileage\n",
       "bmw                  132572\n",
       "mercedes_benz        130788\n",
       "opel                 129310\n",
       "audi                 129157\n",
       "volkswagen           128707\n",
       "ford                 124266"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_info = pd.DataFrame(mean_mileage,columns=['mean_mileage'])\n",
    "brand_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_mileage</th>\n",
       "      <th>mean_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bmw</th>\n",
       "      <td>132572</td>\n",
       "      <td>8332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercedes_benz</th>\n",
       "      <td>130788</td>\n",
       "      <td>8628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opel</th>\n",
       "      <td>129310</td>\n",
       "      <td>2975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>audi</th>\n",
       "      <td>129157</td>\n",
       "      <td>9336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volkswagen</th>\n",
       "      <td>128707</td>\n",
       "      <td>5402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ford</th>\n",
       "      <td>124266</td>\n",
       "      <td>3749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean_mileage  mean_price\n",
       "bmw                  132572        8332\n",
       "mercedes_benz        130788        8628\n",
       "opel                 129310        2975\n",
       "audi                 129157        9336\n",
       "volkswagen           128707        5402\n",
       "ford                 124266        3749"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_info['mean_price'] = mean_prices\n",
    "brand_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A gama de quilometragem do carro não varia tanto quanto os preços por marca, em vez disso, todos caem dentro de 10% para as principais marcas. Há uma ligeira tendência para os veículos mais caros terem maior quilometragem, enquanto os veículos mais baratos terão menor quilometragem."
   ]
  }
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