Exploring Ebay Car Sales Data/main.ipynb

{
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   "source": [
    "# Ebay Car Sales Data analysys\n",
    "## author: Tomás N. P. Barros\n",
    "### email: samotbnp@gmail.com\n",
    "This notebook is a mini project for testing my data cleaning skills and also analyze it with Pandas and Numpy learnings which was scraped from eBay Kleinanzeigen, a classifieds section of the German eBay website. The dataset was originally scraped and uploaded to Kaggle by user [orgeleska](https://www.kaggle.com/orgesleka). The original dataset isn't available on Kaggle anymore, but you can find it [here](https://data.world/data-society/used-cars-data)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- dateCrawled - When this ad was first crawled. All field-values are taken from this date.\n",
    "- name - Name of the car.\n",
    "- seller - Whether the seller is private or a dealer.\n",
    "- offerType - The type of listing\n",
    "- price - The price on the ad to sell the car.\n",
    "- abtest - Whether the listing is included in an A/B test.\n",
    "- vehicleType - The vehicle Type.\n",
    "- yearOfRegistration - The year in which the car was first registered.\n",
    "- gearbox - The transmission type.\n",
    "- powerPS - The power of the car in PS.\n",
    "- model - The car model name.\n",
    "- odometer - How many kilometers the car has driven.\n",
    "- monthOfRegistration - The month in which the car was first registered.\n",
    "- fuelType - What type of fuel the car uses.\n",
    "- brand - The brand of the car.\n",
    "- notRepairedDamage - If the car has a damage which is not yet repaired.\n",
    "- dateCreated - The date on which the eBay listing was created.\n",
    "- nrOfPictures - The number of pictures in the ad.\n",
    "- postalCode - The postal code for the location of the vehicle.\n",
    "- lastSeenOnline - When the crawler saw this ad last online."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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>kilometer</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>2016-03-24 11:52:17</td>\n",
       "      <td>Golf_3_1.6</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>480.0</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-24 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>70435</td>\n",
       "      <td>2016-04-07 03:16:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-03-24 10:58:45</td>\n",
       "      <td>A5_Sportback_2.7_Tdi</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>18300.0</td>\n",
       "      <td>test</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>manuell</td>\n",
       "      <td>190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125000</td>\n",
       "      <td>5</td>\n",
       "      <td>diesel</td>\n",
       "      <td>audi</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-03-24 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>66954</td>\n",
       "      <td>2016-04-07 01:46:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-14 12:52:21</td>\n",
       "      <td>Jeep_Grand_Cherokee_\"Overland\"</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>9800.0</td>\n",
       "      <td>test</td>\n",
       "      <td>suv</td>\n",
       "      <td>2004</td>\n",
       "      <td>automatik</td>\n",
       "      <td>163</td>\n",
       "      <td>grand</td>\n",
       "      <td>125000</td>\n",
       "      <td>8</td>\n",
       "      <td>diesel</td>\n",
       "      <td>jeep</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-14 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>90480</td>\n",
       "      <td>2016-04-05 12:47:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-17 16:54:04</td>\n",
       "      <td>GOLF_4_1_4__3TÜRER</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2001</td>\n",
       "      <td>manuell</td>\n",
       "      <td>75</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-17 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>91074</td>\n",
       "      <td>2016-03-17 17:40:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-03-31 17:25:20</td>\n",
       "      <td>Skoda_Fabia_1.4_TDI_PD_Classic</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2008</td>\n",
       "      <td>manuell</td>\n",
       "      <td>69</td>\n",
       "      <td>fabia</td>\n",
       "      <td>90000</td>\n",
       "      <td>7</td>\n",
       "      <td>diesel</td>\n",
       "      <td>skoda</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-31 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>60437</td>\n",
       "      <td>2016-04-06 10:17:21</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>371523</th>\n",
       "      <td>2016-03-14 17:48:27</td>\n",
       "      <td>Suche_t4___vito_ab_6_sitze</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sonstige_autos</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-14 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>39576</td>\n",
       "      <td>2016-04-06 00:46:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371524</th>\n",
       "      <td>2016-03-05 19:56:21</td>\n",
       "      <td>Smart_smart_leistungssteigerung_100ps</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>test</td>\n",
       "      <td>cabrio</td>\n",
       "      <td>2000</td>\n",
       "      <td>automatik</td>\n",
       "      <td>101</td>\n",
       "      <td>fortwo</td>\n",
       "      <td>125000</td>\n",
       "      <td>3</td>\n",
       "      <td>benzin</td>\n",
       "      <td>smart</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-05 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>26135</td>\n",
       "      <td>2016-03-11 18:17:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371525</th>\n",
       "      <td>2016-03-19 18:57:12</td>\n",
       "      <td>Volkswagen_Multivan_T4_TDI_7DC_UY2</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>9200.0</td>\n",
       "      <td>test</td>\n",
       "      <td>bus</td>\n",
       "      <td>1996</td>\n",
       "      <td>manuell</td>\n",
       "      <td>102</td>\n",
       "      <td>transporter</td>\n",
       "      <td>150000</td>\n",
       "      <td>3</td>\n",
       "      <td>diesel</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-19 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>87439</td>\n",
       "      <td>2016-04-07 07:15:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371526</th>\n",
       "      <td>2016-03-20 19:41:08</td>\n",
       "      <td>VW_Golf_Kombi_1_9l_TDI</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>test</td>\n",
       "      <td>kombi</td>\n",
       "      <td>2002</td>\n",
       "      <td>manuell</td>\n",
       "      <td>100</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>diesel</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-20 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>40764</td>\n",
       "      <td>2016-03-24 12:45:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371527</th>\n",
       "      <td>2016-03-07 19:39:19</td>\n",
       "      <td>BMW_M135i_vollausgestattet_NP_52.720____Euro</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>28990.0</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2013</td>\n",
       "      <td>manuell</td>\n",
       "      <td>320</td>\n",
       "      <td>m_reihe</td>\n",
       "      <td>50000</td>\n",
       "      <td>8</td>\n",
       "      <td>benzin</td>\n",
       "      <td>bmw</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-07 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>73326</td>\n",
       "      <td>2016-03-22 03:17:10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>371528 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                dateCrawled                                          name  \\\n",
       "0       2016-03-24 11:52:17                                    Golf_3_1.6   \n",
       "1       2016-03-24 10:58:45                          A5_Sportback_2.7_Tdi   \n",
       "2       2016-03-14 12:52:21                Jeep_Grand_Cherokee_\"Overland\"   \n",
       "3       2016-03-17 16:54:04                            GOLF_4_1_4__3TÜRER   \n",
       "4       2016-03-31 17:25:20                Skoda_Fabia_1.4_TDI_PD_Classic   \n",
       "...                     ...                                           ...   \n",
       "371523  2016-03-14 17:48:27                    Suche_t4___vito_ab_6_sitze   \n",
       "371524  2016-03-05 19:56:21         Smart_smart_leistungssteigerung_100ps   \n",
       "371525  2016-03-19 18:57:12            Volkswagen_Multivan_T4_TDI_7DC_UY2   \n",
       "371526  2016-03-20 19:41:08                        VW_Golf_Kombi_1_9l_TDI   \n",
       "371527  2016-03-07 19:39:19  BMW_M135i_vollausgestattet_NP_52.720____Euro   \n",
       "\n",
       "        seller offerType    price   abtest vehicleType  yearOfRegistration  \\\n",
       "0       privat   Angebot    480.0     test         NaN                1993   \n",
       "1       privat   Angebot  18300.0     test       coupe                2011   \n",
       "2       privat   Angebot   9800.0     test         suv                2004   \n",
       "3       privat   Angebot   1500.0     test  kleinwagen                2001   \n",
       "4       privat   Angebot   3600.0     test  kleinwagen                2008   \n",
       "...        ...       ...      ...      ...         ...                 ...   \n",
       "371523  privat   Angebot   2200.0     test         NaN                2005   \n",
       "371524  privat   Angebot   1199.0     test      cabrio                2000   \n",
       "371525  privat   Angebot   9200.0     test         bus                1996   \n",
       "371526  privat   Angebot   3400.0     test       kombi                2002   \n",
       "371527  privat   Angebot  28990.0  control   limousine                2013   \n",
       "\n",
       "          gearbox  powerPS        model  kilometer  monthOfRegistration  \\\n",
       "0         manuell        0         golf     150000                    0   \n",
       "1         manuell      190          NaN     125000                    5   \n",
       "2       automatik      163        grand     125000                    8   \n",
       "3         manuell       75         golf     150000                    6   \n",
       "4         manuell       69        fabia      90000                    7   \n",
       "...           ...      ...          ...        ...                  ...   \n",
       "371523        NaN        0          NaN      20000                    1   \n",
       "371524  automatik      101       fortwo     125000                    3   \n",
       "371525    manuell      102  transporter     150000                    3   \n",
       "371526    manuell      100         golf     150000                    6   \n",
       "371527    manuell      320      m_reihe      50000                    8   \n",
       "\n",
       "       fuelType           brand notRepairedDamage          dateCreated  \\\n",
       "0        benzin      volkswagen               NaN  2016-03-24 00:00:00   \n",
       "1        diesel            audi                ja  2016-03-24 00:00:00   \n",
       "2        diesel            jeep               NaN  2016-03-14 00:00:00   \n",
       "3        benzin      volkswagen              nein  2016-03-17 00:00:00   \n",
       "4        diesel           skoda              nein  2016-03-31 00:00:00   \n",
       "...         ...             ...               ...                  ...   \n",
       "371523      NaN  sonstige_autos               NaN  2016-03-14 00:00:00   \n",
       "371524   benzin           smart              nein  2016-03-05 00:00:00   \n",
       "371525   diesel      volkswagen              nein  2016-03-19 00:00:00   \n",
       "371526   diesel      volkswagen               NaN  2016-03-20 00:00:00   \n",
       "371527   benzin             bmw              nein  2016-03-07 00:00:00   \n",
       "\n",
       "        nrOfPictures  postalCode             lastSeen  \n",
       "0                  0       70435  2016-04-07 03:16:57  \n",
       "1                  0       66954  2016-04-07 01:46:50  \n",
       "2                  0       90480  2016-04-05 12:47:46  \n",
       "3                  0       91074  2016-03-17 17:40:17  \n",
       "4                  0       60437  2016-04-06 10:17:21  \n",
       "...              ...         ...                  ...  \n",
       "371523             0       39576  2016-04-06 00:46:52  \n",
       "371524             0       26135  2016-03-11 18:17:12  \n",
       "371525             0       87439  2016-04-07 07:15:26  \n",
       "371526             0       40764  2016-03-24 12:45:21  \n",
       "371527             0       73326  2016-03-22 03:17:10  \n",
       "\n",
       "[371528 rows x 20 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos = pd.read_csv(\"autos.csv\", encoding=\"Latin-1\", dtype={'price' : np.float64})\n",
    "autos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 371528 entries, 0 to 371527\n",
      "Data columns (total 20 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   dateCrawled          371528 non-null  object \n",
      " 1   name                 371528 non-null  object \n",
      " 2   seller               371528 non-null  object \n",
      " 3   offerType            371528 non-null  object \n",
      " 4   price                371528 non-null  float64\n",
      " 5   abtest               371528 non-null  object \n",
      " 6   vehicleType          333659 non-null  object \n",
      " 7   yearOfRegistration   371528 non-null  int64  \n",
      " 8   gearbox              351319 non-null  object \n",
      " 9   powerPS              371528 non-null  int64  \n",
      " 10  model                351044 non-null  object \n",
      " 11  kilometer            371528 non-null  int64  \n",
      " 12  monthOfRegistration  371528 non-null  int64  \n",
      " 13  fuelType             338142 non-null  object \n",
      " 14  brand                371528 non-null  object \n",
      " 15  notRepairedDamage    299468 non-null  object \n",
      " 16  dateCreated          371528 non-null  object \n",
      " 17  nrOfPictures         371528 non-null  int64  \n",
      " 18  postalCode           371528 non-null  int64  \n",
      " 19  lastSeen             371528 non-null  object \n",
      "dtypes: float64(1), int64(6), object(13)\n",
      "memory usage: 56.7+ MB\n"
     ]
    }
   ],
   "source": [
    "autos.info(show_counts=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above information we can see that the columns: _vehicleType_, _gearbox_, _model_, _fuelType_ and _notRepairedDamage_ have null values. But for our luck, the null values correspond to at most ~ 19% of data for the _notRepairedDamage_ column. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting the column names from camelcase to snakecase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['dateCrawled', 'name', 'seller', 'offerType', 'price', 'abtest',\n",
       "       'vehicleType', 'yearOfRegistration', 'gearbox', 'powerPS', 'model',\n",
       "       'kilometer', 'monthOfRegistration', 'fuelType', 'brand',\n",
       "       'notRepairedDamage', 'dateCreated', 'nrOfPictures', 'postalCode',\n",
       "       'lastSeen'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dateCrawled date_crawled\n",
      "name name\n",
      "seller seller\n",
      "offerType offer_type\n",
      "price price\n",
      "abtest abtest\n",
      "vehicleType vehicle_type\n",
      "yearOfRegistration year_registration\n",
      "gearbox gearbox\n",
      "powerPS power_ps\n",
      "model model\n",
      "kilometer kilometer\n",
      "monthOfRegistration month_registration\n",
      "fuelType fuel_type\n",
      "brand brand\n",
      "notRepairedDamage not_repaired_damage\n",
      "dateCreated date_created\n",
      "nrOfPictures nr_pictures\n",
      "postalCode postal_code\n",
      "lastSeen last_seen\n",
      "\n",
      " Index(['date_crawled', 'name', 'seller', 'offer_type', 'price', 'abtest',\n",
      "       'vehicle_type', 'registration_year', 'gearbox', 'power_ps', 'model',\n",
      "       'odometer_km', 'registration_month', 'fuel_type', 'brand',\n",
      "       'unrepaired_damage', 'ad_created', 'nr_pictures', 'postal_code',\n",
      "       'last_seen'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# gonna identify letters wich are in upper case as division of words using ord() function\n",
    "old_columns = autos.columns\n",
    "new_columns = []\n",
    "for col in old_columns:\n",
    "    indexes_to_split = []\n",
    "    words = []\n",
    "    col = col.replace(\"Of\", '')\n",
    "    for i in range(len(col)):\n",
    "        char = col[i]\n",
    "        if ord(char) >= ord('A') and ord(char) <= ord('Z'):\n",
    "            indexes_to_split.append(i)\n",
    "    if col == 'powerPS':\n",
    "        new_columns.append('power_ps')\n",
    "    elif len(indexes_to_split) == 0:\n",
    "        new_columns.append(col)\n",
    "    else:\n",
    "        for i in range(len(indexes_to_split)):\n",
    "            if i == 0:\n",
    "                words.append(col[:indexes_to_split[i]])\n",
    "                if len(indexes_to_split) == 1:\n",
    "                    words.append(col[indexes_to_split[i]:].lower())\n",
    "                else:\n",
    "                    words.append(col[indexes_to_split[i]:indexes_to_split[i + 1]].lower())\n",
    "            elif i == (len(indexes_to_split) - 1):\n",
    "                words.append(col[indexes_to_split[i]:].lower())\n",
    "            else:\n",
    "                words.append(col[indexes_to_split[i]:indexes_to_split[i + 1]].lower())\n",
    "        new_columns.append('_'.join(words))\n",
    "\n",
    "autos.columns = new_columns\n",
    "for i in range(len(old_columns)):\n",
    "    print(old_columns[i], new_columns[i])\n",
    "\n",
    "# better suited columns\n",
    "autos = autos.rename(columns={'year_registration' : 'registration_year', 'month_registration' : 'registration_month', \n",
    "'not_repaired_damage' : 'unrepaired_damage', 'date_created' : 'ad_created', 'kilometer' : 'odometer_km'})\n",
    "\n",
    "print('\\n', autos.columns)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inspecting the columns with more detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 371528 entries, 0 to 371527\n",
      "Data columns (total 20 columns):\n",
      " #   Column              Non-Null Count   Dtype  \n",
      "---  ------              --------------   -----  \n",
      " 0   date_crawled        371528 non-null  object \n",
      " 1   name                371528 non-null  object \n",
      " 2   seller              371528 non-null  object \n",
      " 3   offer_type          371528 non-null  object \n",
      " 4   price               371528 non-null  float64\n",
      " 5   abtest              371528 non-null  object \n",
      " 6   vehicle_type        333659 non-null  object \n",
      " 7   registration_year   371528 non-null  int64  \n",
      " 8   gearbox             351319 non-null  object \n",
      " 9   power_ps            371528 non-null  int64  \n",
      " 10  model               351044 non-null  object \n",
      " 11  odometer_km         371528 non-null  int64  \n",
      " 12  registration_month  371528 non-null  int64  \n",
      " 13  fuel_type           338142 non-null  object \n",
      " 14  brand               371528 non-null  object \n",
      " 15  unrepaired_damage   299468 non-null  object \n",
      " 16  ad_created          371528 non-null  object \n",
      " 17  nr_pictures         371528 non-null  int64  \n",
      " 18  postal_code         371528 non-null  int64  \n",
      " 19  last_seen           371528 non-null  object \n",
      "dtypes: float64(1), int64(6), object(13)\n",
      "memory usage: 56.7+ MB\n"
     ]
    }
   ],
   "source": [
    "autos.info(show_counts=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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_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_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>371528</td>\n",
       "      <td>371528</td>\n",
       "      <td>371528</td>\n",
       "      <td>371528</td>\n",
       "      <td>3.715280e+05</td>\n",
       "      <td>371528</td>\n",
       "      <td>333659</td>\n",
       "      <td>371528.000000</td>\n",
       "      <td>351319</td>\n",
       "      <td>371528.000000</td>\n",
       "      <td>351044</td>\n",
       "      <td>371528.000000</td>\n",
       "      <td>371528.000000</td>\n",
       "      <td>338142</td>\n",
       "      <td>371528</td>\n",
       "      <td>299468</td>\n",
       "      <td>371528</td>\n",
       "      <td>371528.0</td>\n",
       "      <td>371528.00000</td>\n",
       "      <td>371528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>280500</td>\n",
       "      <td>233531</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>251</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>182806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2016-03-24 14:49:47</td>\n",
       "      <td>Ford_Fiesta</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>NaN</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>NaN</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 06:45:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>7</td>\n",
       "      <td>657</td>\n",
       "      <td>371525</td>\n",
       "      <td>371516</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192585</td>\n",
       "      <td>95894</td>\n",
       "      <td>NaN</td>\n",
       "      <td>274214</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30070</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>223857</td>\n",
       "      <td>79640</td>\n",
       "      <td>263182</td>\n",
       "      <td>14450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</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>1.729514e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004.577997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115.549477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125618.688228</td>\n",
       "      <td>5.734445</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50820.66764</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>3.587954e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.866598</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.139578</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40112.337051</td>\n",
       "      <td>3.712412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25799.08247</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>0.000000e+00</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>5000.000000</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>1067.00000</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>1.150000e+03</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>125000.000000</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>30459.00000</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>2.950000e+03</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>150000.000000</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>49610.00000</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>7.200000e+03</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>150000.000000</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>71546.00000</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>2.147484e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150000.000000</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>99998.00000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               date_crawled         name  seller offer_type         price  \\\n",
       "count                371528       371528  371528     371528  3.715280e+05   \n",
       "unique               280500       233531       2          2           NaN   \n",
       "top     2016-03-24 14:49:47  Ford_Fiesta  privat    Angebot           NaN   \n",
       "freq                      7          657  371525     371516           NaN   \n",
       "mean                    NaN          NaN     NaN        NaN  1.729514e+04   \n",
       "std                     NaN          NaN     NaN        NaN  3.587954e+06   \n",
       "min                     NaN          NaN     NaN        NaN  0.000000e+00   \n",
       "25%                     NaN          NaN     NaN        NaN  1.150000e+03   \n",
       "50%                     NaN          NaN     NaN        NaN  2.950000e+03   \n",
       "75%                     NaN          NaN     NaN        NaN  7.200000e+03   \n",
       "max                     NaN          NaN     NaN        NaN  2.147484e+09   \n",
       "\n",
       "        abtest vehicle_type  registration_year  gearbox       power_ps  \\\n",
       "count   371528       333659      371528.000000   351319  371528.000000   \n",
       "unique       2            8                NaN        2            NaN   \n",
       "top       test    limousine                NaN  manuell            NaN   \n",
       "freq    192585        95894                NaN   274214            NaN   \n",
       "mean       NaN          NaN        2004.577997      NaN     115.549477   \n",
       "std        NaN          NaN          92.866598      NaN     192.139578   \n",
       "min        NaN          NaN        1000.000000      NaN       0.000000   \n",
       "25%        NaN          NaN        1999.000000      NaN      70.000000   \n",
       "50%        NaN          NaN        2003.000000      NaN     105.000000   \n",
       "75%        NaN          NaN        2008.000000      NaN     150.000000   \n",
       "max        NaN          NaN        9999.000000      NaN   20000.000000   \n",
       "\n",
       "         model    odometer_km  registration_month fuel_type       brand  \\\n",
       "count   351044  371528.000000       371528.000000    338142      371528   \n",
       "unique     251            NaN                 NaN         7          40   \n",
       "top       golf            NaN                 NaN    benzin  volkswagen   \n",
       "freq     30070            NaN                 NaN    223857       79640   \n",
       "mean       NaN  125618.688228            5.734445       NaN         NaN   \n",
       "std        NaN   40112.337051            3.712412       NaN         NaN   \n",
       "min        NaN    5000.000000            0.000000       NaN         NaN   \n",
       "25%        NaN  125000.000000            3.000000       NaN         NaN   \n",
       "50%        NaN  150000.000000            6.000000       NaN         NaN   \n",
       "75%        NaN  150000.000000            9.000000       NaN         NaN   \n",
       "max        NaN  150000.000000           12.000000       NaN         NaN   \n",
       "\n",
       "       unrepaired_damage           ad_created  nr_pictures   postal_code  \\\n",
       "count             299468               371528     371528.0  371528.00000   \n",
       "unique                 2                  114          NaN           NaN   \n",
       "top                 nein  2016-04-03 00:00:00          NaN           NaN   \n",
       "freq              263182                14450          NaN           NaN   \n",
       "mean                 NaN                  NaN          0.0   50820.66764   \n",
       "std                  NaN                  NaN          0.0   25799.08247   \n",
       "min                  NaN                  NaN          0.0    1067.00000   \n",
       "25%                  NaN                  NaN          0.0   30459.00000   \n",
       "50%                  NaN                  NaN          0.0   49610.00000   \n",
       "75%                  NaN                  NaN          0.0   71546.00000   \n",
       "max                  NaN                  NaN          0.0   99998.00000   \n",
       "\n",
       "                  last_seen  \n",
       "count                371528  \n",
       "unique               182806  \n",
       "top     2016-04-07 06:45:59  \n",
       "freq                     17  \n",
       "mean                    NaN  \n",
       "std                     NaN  \n",
       "min                     NaN  \n",
       "25%                     NaN  \n",
       "50%                     NaN  \n",
       "75%                     NaN  \n",
       "max                     NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.describe(include='all')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above information, the max value for the price seems suspicious.\n",
    "registration_year also seems suspicious since It has values of 1000 and 9999, which don't make sense.\n",
    "power_ps also seems suspicious, since F1 cars have 1050 ps, 20000 seems like an absurd value\n",
    "registration_month with value zero seems wrong too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "autos.drop('date_crawled', inplace=True, axis=1) # since, data_crowled is irrelevant for us, I'll drop the column"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Inspecting 'offer_type' column\n",
    "in this section I'll do basic translation from german to english"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Angebot    371516\n",
       "Gesuch         12\n",
       "Name: offer_type, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['offer_type'].head()\n",
    "autos['offer_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sell    371516\n",
       "buy         12\n",
       "Name: offer_type, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# angebot e gesuch are equivalent to sell and buy offers\n",
    "# therefore i'll translate them to english\n",
    "autos.loc[autos['offer_type'] == 'Angebot', 'offer_type'] = 'sell'\n",
    "autos.loc[autos['offer_type'] == 'Gesuch', 'offer_type'] = 'buy'\n",
    "autos['offer_type'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Inspecting 'seller' column\n",
    "in this section I'll do basic translation from german to english"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "privat        371525\n",
       "gewerblich         3\n",
       "Name: seller, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['seller'].head()\n",
    "autos['seller'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "private       371525\n",
       "commercial         3\n",
       "Name: seller, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# similar to the 'offer_type' column, I'll translate privat to priavate and gewerblich to commercial\n",
    "autos.loc[autos['seller'] == 'privat', 'seller'] = 'private'\n",
    "autos.loc[autos['seller'] == 'gewerblich', 'seller'] = 'commercial'\n",
    "autos['seller'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Inspecting 'price' column\n",
    "In this section, I'll remove the cars with price P, where P < 250,00 eur or P > 1,0e7 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "autos = autos.rename(columns={'price' : 'price_eur'}) # since It is german, the coin used probably is euros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.715280e+05\n",
       "mean     1.729514e+04\n",
       "std      3.587954e+06\n",
       "min      0.000000e+00\n",
       "25%      1.150000e+03\n",
       "50%      2.950000e+03\n",
       "75%      7.200000e+03\n",
       "max      2.147484e+09\n",
       "Name: price_eur, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['price_eur'].head()\n",
    "autos['price_eur'].value_counts(sort=True).sort_index()\n",
    "autos['price_eur'].value_counts()\n",
    "autos['price_eur'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>seller</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>price_eur</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_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_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>Golf_3_1.6</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>480.0</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-24 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>70435</td>\n",
       "      <td>2016-04-07 03:16:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KA_Lufthansa_Edition_450_VB</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>450.0</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>1910</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>ka</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>benzin</td>\n",
       "      <td>ford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-11 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>24148</td>\n",
       "      <td>2016-03-19 08:46:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Polo_6n_1_4</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>300.0</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>polo</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>38871</td>\n",
       "      <td>2016-04-01 12:46:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Stadtflitzer</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>450.0</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>1997</td>\n",
       "      <td>manuell</td>\n",
       "      <td>50</td>\n",
       "      <td>arosa</td>\n",
       "      <td>150000</td>\n",
       "      <td>5</td>\n",
       "      <td>benzin</td>\n",
       "      <td>seat</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-12 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>9526</td>\n",
       "      <td>2016-03-21 01:46:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>VW_Golf_3</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>350.0</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>manuell</td>\n",
       "      <td>75</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>4</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-08 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>19386</td>\n",
       "      <td>2016-03-08 09:44:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>muss_dringend_weg_!!!_2_mal_Alfa_romeo_156_Zu_...</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>250.0</td>\n",
       "      <td>test</td>\n",
       "      <td>kombi</td>\n",
       "      <td>2000</td>\n",
       "      <td>manuell</td>\n",
       "      <td>155</td>\n",
       "      <td>156</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>benzin</td>\n",
       "      <td>alfa_romeo</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>8396</td>\n",
       "      <td>2016-04-05 20:18:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Bastler_Fahrzeug_mit_Rest_Tuev</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>350.0</td>\n",
       "      <td>control</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>1997</td>\n",
       "      <td>manuell</td>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fiat</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-04-03 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>45665</td>\n",
       "      <td>2016-04-05 11:47:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>FORD_KA___TÜV_ABGELAUFEN</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>250.0</td>\n",
       "      <td>control</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>ka</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-16 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>51427</td>\n",
       "      <td>2016-03-21 09:47:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Audi_A4_B5_1_6_Liter</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>499.0</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>1995</td>\n",
       "      <td>manuell</td>\n",
       "      <td>101</td>\n",
       "      <td>a4</td>\n",
       "      <td>150000</td>\n",
       "      <td>5</td>\n",
       "      <td>benzin</td>\n",
       "      <td>audi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-15 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>85414</td>\n",
       "      <td>2016-03-22 20:48:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Ford_Escort_Cabrio</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>300.0</td>\n",
       "      <td>test</td>\n",
       "      <td>cabrio</td>\n",
       "      <td>1992</td>\n",
       "      <td>manuell</td>\n",
       "      <td>105</td>\n",
       "      <td>escort</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ford</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-03-20 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>88515</td>\n",
       "      <td>2016-04-06 13:15:58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  name   seller offer_type  \\\n",
       "0                                           Golf_3_1.6  private       sell   \n",
       "15                        KA_Lufthansa_Edition_450_VB  private       sell   \n",
       "16                                         Polo_6n_1_4  private       sell   \n",
       "23                                        Stadtflitzer  private       sell   \n",
       "35                                           VW_Golf_3  private       sell   \n",
       "82   muss_dringend_weg_!!!_2_mal_Alfa_romeo_156_Zu_...  private       sell   \n",
       "83                      Bastler_Fahrzeug_mit_Rest_Tuev  private       sell   \n",
       "94                            FORD_KA___TÜV_ABGELAUFEN  private       sell   \n",
       "103                               Audi_A4_B5_1_6_Liter  private       sell   \n",
       "108                                 Ford_Escort_Cabrio  private       sell   \n",
       "\n",
       "     price_eur   abtest vehicle_type  registration_year  gearbox  power_ps  \\\n",
       "0        480.0     test          NaN               1993  manuell         0   \n",
       "15       450.0     test   kleinwagen               1910      NaN         0   \n",
       "16       300.0     test          NaN               2016      NaN        60   \n",
       "23       450.0     test   kleinwagen               1997  manuell        50   \n",
       "35       350.0     test          NaN               2016  manuell        75   \n",
       "82       250.0     test        kombi               2000  manuell       155   \n",
       "83       350.0  control   kleinwagen               1997  manuell        54   \n",
       "94       250.0  control          NaN               2000      NaN        60   \n",
       "103      499.0  control    limousine               1995  manuell       101   \n",
       "108      300.0     test       cabrio               1992  manuell       105   \n",
       "\n",
       "      model  odometer_km  registration_month fuel_type       brand  \\\n",
       "0      golf       150000                   0    benzin  volkswagen   \n",
       "15       ka         5000                   0    benzin        ford   \n",
       "16     polo       150000                   0    benzin  volkswagen   \n",
       "23    arosa       150000                   5    benzin        seat   \n",
       "35     golf       150000                   4    benzin  volkswagen   \n",
       "82      156       150000                   0    benzin  alfa_romeo   \n",
       "83      NaN       150000                   3       NaN        fiat   \n",
       "94       ka       150000                   0       NaN        ford   \n",
       "103      a4       150000                   5    benzin        audi   \n",
       "108  escort       150000                   0       NaN        ford   \n",
       "\n",
       "    unrepaired_damage           ad_created  nr_pictures  postal_code  \\\n",
       "0                 NaN  2016-03-24 00:00:00            0        70435   \n",
       "15                NaN  2016-03-11 00:00:00            0        24148   \n",
       "16                NaN  2016-04-01 00:00:00            0        38871   \n",
       "23               nein  2016-03-12 00:00:00            0         9526   \n",
       "35               nein  2016-03-08 00:00:00            0        19386   \n",
       "82                 ja  2016-04-01 00:00:00            0         8396   \n",
       "83                 ja  2016-04-03 00:00:00            0        45665   \n",
       "94                NaN  2016-03-16 00:00:00            0        51427   \n",
       "103               NaN  2016-03-15 00:00:00            0        85414   \n",
       "108                ja  2016-03-20 00:00:00            0        88515   \n",
       "\n",
       "               last_seen  \n",
       "0    2016-04-07 03:16:57  \n",
       "15   2016-03-19 08:46:47  \n",
       "16   2016-04-01 12:46:46  \n",
       "23   2016-03-21 01:46:11  \n",
       "35   2016-03-08 09:44:50  \n",
       "82   2016-04-05 20:18:14  \n",
       "83   2016-04-05 11:47:13  \n",
       "94   2016-03-21 09:47:08  \n",
       "103  2016-03-22 20:48:22  \n",
       "108  2016-04-06 13:15:58  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cheap_cars = autos.loc[(autos['price_eur'] >= 250) & (autos['price_eur'] < 500), :]\n",
    "cheap_cars.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>129969</th>\n",
       "      <td>Kaufe_AUTOS_Zum_basteln</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>2.147484e+09</td>\n",
       "      <td>test</td>\n",
       "      <td>andere</td>\n",
       "      <td>1996</td>\n",
       "      <td>manuell</td>\n",
       "      <td>888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150000</td>\n",
       "      <td>1</td>\n",
       "      <td>andere</td>\n",
       "      <td>sonstige_autos</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-03-11 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>4720</td>\n",
       "      <td>2016-03-20 11:45:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127820</th>\n",
       "      <td>audi_a6_c5_avant</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>test</td>\n",
       "      <td>kombi</td>\n",
       "      <td>1999</td>\n",
       "      <td>manuell</td>\n",
       "      <td>165</td>\n",
       "      <td>a6</td>\n",
       "      <td>5000</td>\n",
       "      <td>12</td>\n",
       "      <td>benzin</td>\n",
       "      <td>audi</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-14 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>28325</td>\n",
       "      <td>2016-03-14 17:49:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77520</th>\n",
       "      <td>Tausch_gegen_gleichwertiges</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>1999</td>\n",
       "      <td>automatik</td>\n",
       "      <td>224</td>\n",
       "      <td>s_klasse</td>\n",
       "      <td>150000</td>\n",
       "      <td>9</td>\n",
       "      <td>benzin</td>\n",
       "      <td>mercedes_benz</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-22 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>73525</td>\n",
       "      <td>2016-04-06 05:15:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150909</th>\n",
       "      <td>Suche_Autos_Model_egal_welches</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sonstige_autos</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-04-02 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>6124</td>\n",
       "      <td>2016-04-02 22:41:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56973</th>\n",
       "      <td>tausche_ford_mondeo_mk3_ghia_gegen_anderes_auto</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2002</td>\n",
       "      <td>manuell</td>\n",
       "      <td>131</td>\n",
       "      <td>mondeo</td>\n",
       "      <td>150000</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-18 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>27574</td>\n",
       "      <td>2016-03-18 18:52:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246635</th>\n",
       "      <td>Suche_Vw_Passat..........</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>passat</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-25 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>7749</td>\n",
       "      <td>2016-04-02 15:15:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189737</th>\n",
       "      <td>Oldtimer_Goggo_Transporter</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>control</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>1956</td>\n",
       "      <td>manuell</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150000</td>\n",
       "      <td>9</td>\n",
       "      <td>benzin</td>\n",
       "      <td>sonstige_autos</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-04-03 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>86899</td>\n",
       "      <td>2016-04-03 12:41:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362782</th>\n",
       "      <td>Passat_35i_1.8_90ps_Kombi_frisch_lackiert_!</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>passat</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-06 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1809</td>\n",
       "      <td>2016-03-20 08:18:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108987</th>\n",
       "      <td>Biete_Hier_zum_Tauschen_an_aber_nur_realistisc...</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>manuell</td>\n",
       "      <td>110</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-28 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>35447</td>\n",
       "      <td>2016-04-05 14:16:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160530</th>\n",
       "      <td>Golf_4_schrott</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-29 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>23992</td>\n",
       "      <td>2016-03-29 12:58:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268173</th>\n",
       "      <td>Schlachte_e36_325i_Umbau</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>test</td>\n",
       "      <td>coupe</td>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>240</td>\n",
       "      <td>3er</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>benzin</td>\n",
       "      <td>bmw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>68782</td>\n",
       "      <td>2016-04-07 05:46:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325589</th>\n",
       "      <td>Tausche/_verkaufe_Golf_1_LX_mit_H_Zulassung</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>test</td>\n",
       "      <td>limousine</td>\n",
       "      <td>1983</td>\n",
       "      <td>manuell</td>\n",
       "      <td>70</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-27 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>28790</td>\n",
       "      <td>2016-04-05 21:44:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366653</th>\n",
       "      <td>BMW_E36_Cabrio_tausch_gegen_ein_Opel_Insignia</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>control</td>\n",
       "      <td>cabrio</td>\n",
       "      <td>1996</td>\n",
       "      <td>manuell</td>\n",
       "      <td>192</td>\n",
       "      <td>3er</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bmw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-20 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>36251</td>\n",
       "      <td>2016-03-20 10:59:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69747</th>\n",
       "      <td>Suche_Mercedes_Benz_r_klasse_cdi</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>control</td>\n",
       "      <td>bus</td>\n",
       "      <td>2006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>andere</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>diesel</td>\n",
       "      <td>mercedes_benz</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-20 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>86916</td>\n",
       "      <td>2016-04-06 07:45:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139052</th>\n",
       "      <td>Mercedes_Benz_CLS_350_CDI_7G_TRONIC</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>control</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2009</td>\n",
       "      <td>automatik</td>\n",
       "      <td>224</td>\n",
       "      <td>andere</td>\n",
       "      <td>150000</td>\n",
       "      <td>11</td>\n",
       "      <td>diesel</td>\n",
       "      <td>mercedes_benz</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-03-14 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>56112</td>\n",
       "      <td>2016-03-14 22:40:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280059</th>\n",
       "      <td>Kaufe_VW_Golf_1_auch_ohne_TÜV_________________...</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>control</td>\n",
       "      <td>andere</td>\n",
       "      <td>1974</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-05 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>13051</td>\n",
       "      <td>2016-03-24 16:15:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251980</th>\n",
       "      <td>Audi_A6_2.7_TDI_quattro_S_Line_Luftfederung_Bose</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>9.900000e+07</td>\n",
       "      <td>test</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2009</td>\n",
       "      <td>automatik</td>\n",
       "      <td>190</td>\n",
       "      <td>a6</td>\n",
       "      <td>150000</td>\n",
       "      <td>11</td>\n",
       "      <td>diesel</td>\n",
       "      <td>audi</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-21 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>60433</td>\n",
       "      <td>2016-03-25 04:45:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120919</th>\n",
       "      <td>???_?_???____???_?_???____???_?_???</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>7.418530e+07</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa_romeo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-25 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>72072</td>\n",
       "      <td>2016-03-25 12:43:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183361</th>\n",
       "      <td>BMW_e60_nur_Tausch_prins_gasanlage_20_Zoll!_No...</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>3.254546e+07</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2004</td>\n",
       "      <td>automatik</td>\n",
       "      <td>175</td>\n",
       "      <td>5er</td>\n",
       "      <td>5000</td>\n",
       "      <td>10</td>\n",
       "      <td>lpg</td>\n",
       "      <td>bmw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-26 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>86316</td>\n",
       "      <td>2016-03-28 00:18:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87799</th>\n",
       "      <td>Leasinguebernahme</td>\n",
       "      <td>private</td>\n",
       "      <td>sell</td>\n",
       "      <td>2.732222e+07</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2014</td>\n",
       "      <td>manuell</td>\n",
       "      <td>163</td>\n",
       "      <td>c4</td>\n",
       "      <td>40000</td>\n",
       "      <td>2</td>\n",
       "      <td>diesel</td>\n",
       "      <td>citroen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-08 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>76532</td>\n",
       "      <td>2016-03-08 20:39:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     name   seller offer_type  \\\n",
       "129969                            Kaufe_AUTOS_Zum_basteln  private       sell   \n",
       "127820                                   audi_a6_c5_avant  private       sell   \n",
       "77520                         Tausch_gegen_gleichwertiges  private       sell   \n",
       "150909                     Suche_Autos_Model_egal_welches  private       sell   \n",
       "56973     tausche_ford_mondeo_mk3_ghia_gegen_anderes_auto  private       sell   \n",
       "246635                          Suche_Vw_Passat..........  private       sell   \n",
       "189737                         Oldtimer_Goggo_Transporter  private       sell   \n",
       "362782        Passat_35i_1.8_90ps_Kombi_frisch_lackiert_!  private       sell   \n",
       "108987  Biete_Hier_zum_Tauschen_an_aber_nur_realistisc...  private       sell   \n",
       "160530                                     Golf_4_schrott  private       sell   \n",
       "268173                           Schlachte_e36_325i_Umbau  private       sell   \n",
       "325589        Tausche/_verkaufe_Golf_1_LX_mit_H_Zulassung  private       sell   \n",
       "366653      BMW_E36_Cabrio_tausch_gegen_ein_Opel_Insignia  private       sell   \n",
       "69747                    Suche_Mercedes_Benz_r_klasse_cdi  private       sell   \n",
       "139052                Mercedes_Benz_CLS_350_CDI_7G_TRONIC  private       sell   \n",
       "280059  Kaufe_VW_Golf_1_auch_ohne_TÜV_________________...  private       sell   \n",
       "251980   Audi_A6_2.7_TDI_quattro_S_Line_Luftfederung_Bose  private       sell   \n",
       "120919                ???_?_???____???_?_???____???_?_???  private       sell   \n",
       "183361  BMW_e60_nur_Tausch_prins_gasanlage_20_Zoll!_No...  private       sell   \n",
       "87799                                   Leasinguebernahme  private       sell   \n",
       "\n",
       "           price_eur   abtest vehicle_type  registration_year    gearbox  \\\n",
       "129969  2.147484e+09     test       andere               1996    manuell   \n",
       "127820  1.000000e+08     test        kombi               1999    manuell   \n",
       "77520   1.000000e+08  control    limousine               1999  automatik   \n",
       "150909  1.000000e+08     test          NaN               1990        NaN   \n",
       "56973   1.000000e+08  control    limousine               2002    manuell   \n",
       "246635  1.000000e+08     test          NaN               2000        NaN   \n",
       "189737  1.000000e+08  control   kleinwagen               1956    manuell   \n",
       "362782  1.000000e+08     test          NaN               1995        NaN   \n",
       "108987  1.000000e+08     test          NaN               2017    manuell   \n",
       "160530  1.000000e+08     test   kleinwagen               2001        NaN   \n",
       "268173  1.000000e+08     test        coupe               1992        NaN   \n",
       "325589  1.000000e+08     test    limousine               1983    manuell   \n",
       "366653  1.000000e+08  control       cabrio               1996    manuell   \n",
       "69747   1.000000e+08  control          bus               2006        NaN   \n",
       "139052  1.000000e+08  control        coupe               2009  automatik   \n",
       "280059  1.000000e+08  control       andere               1974    manuell   \n",
       "251980  9.900000e+07     test    limousine               2009  automatik   \n",
       "120919  7.418530e+07     test          NaN               2000        NaN   \n",
       "183361  3.254546e+07  control    limousine               2004  automatik   \n",
       "87799   2.732222e+07  control    limousine               2014    manuell   \n",
       "\n",
       "        power_ps     model  odometer_km  registration_month fuel_type  \\\n",
       "129969       888       NaN       150000                   1    andere   \n",
       "127820       165        a6         5000                  12    benzin   \n",
       "77520        224  s_klasse       150000                   9    benzin   \n",
       "150909         0       NaN         5000                   1       NaN   \n",
       "56973        131    mondeo       150000                   1       NaN   \n",
       "246635         0    passat         5000                   0       NaN   \n",
       "189737        15       NaN       150000                   9    benzin   \n",
       "362782         0    passat       150000                   0    benzin   \n",
       "108987       110      golf       150000                   0       NaN   \n",
       "160530         0      golf        20000                   0    benzin   \n",
       "268173       240       3er       150000                   0    benzin   \n",
       "325589        70      golf       150000                   3       NaN   \n",
       "366653       192       3er       150000                   0       NaN   \n",
       "69747          0    andere       150000                   0    diesel   \n",
       "139052       224    andere       150000                  11    diesel   \n",
       "280059         0      golf       150000                   6    benzin   \n",
       "251980       190        a6       150000                  11    diesel   \n",
       "120919         0       145        20000                   0       NaN   \n",
       "183361       175       5er         5000                  10       lpg   \n",
       "87799        163        c4        40000                   2    diesel   \n",
       "\n",
       "                 brand unrepaired_damage           ad_created  nr_pictures  \\\n",
       "129969  sonstige_autos                ja  2016-03-11 00:00:00            0   \n",
       "127820            audi              nein  2016-03-14 00:00:00            0   \n",
       "77520    mercedes_benz               NaN  2016-03-22 00:00:00            0   \n",
       "150909  sonstige_autos               NaN  2016-04-02 00:00:00            0   \n",
       "56973             ford               NaN  2016-03-18 00:00:00            0   \n",
       "246635      volkswagen               NaN  2016-03-25 00:00:00            0   \n",
       "189737  sonstige_autos              nein  2016-04-03 00:00:00            0   \n",
       "362782      volkswagen               NaN  2016-03-06 00:00:00            0   \n",
       "108987      volkswagen               NaN  2016-03-28 00:00:00            0   \n",
       "160530      volkswagen               NaN  2016-03-29 00:00:00            0   \n",
       "268173             bmw               NaN  2016-04-01 00:00:00            0   \n",
       "325589      volkswagen              nein  2016-03-27 00:00:00            0   \n",
       "366653             bmw               NaN  2016-03-20 00:00:00            0   \n",
       "69747    mercedes_benz               NaN  2016-03-20 00:00:00            0   \n",
       "139052   mercedes_benz                ja  2016-03-14 00:00:00            0   \n",
       "280059      volkswagen               NaN  2016-03-05 00:00:00            0   \n",
       "251980            audi              nein  2016-03-21 00:00:00            0   \n",
       "120919      alfa_romeo               NaN  2016-03-25 00:00:00            0   \n",
       "183361             bmw               NaN  2016-03-26 00:00:00            0   \n",
       "87799          citroen               NaN  2016-03-08 00:00:00            0   \n",
       "\n",
       "        postal_code            last_seen  \n",
       "129969         4720  2016-03-20 11:45:35  \n",
       "127820        28325  2016-03-14 17:49:54  \n",
       "77520         73525  2016-04-06 05:15:30  \n",
       "150909         6124  2016-04-02 22:41:54  \n",
       "56973         27574  2016-03-18 18:52:27  \n",
       "246635         7749  2016-04-02 15:15:54  \n",
       "189737        86899  2016-04-03 12:41:33  \n",
       "362782         1809  2016-03-20 08:18:07  \n",
       "108987        35447  2016-04-05 14:16:25  \n",
       "160530        23992  2016-03-29 12:58:27  \n",
       "268173        68782  2016-04-07 05:46:52  \n",
       "325589        28790  2016-04-05 21:44:41  \n",
       "366653        36251  2016-03-20 10:59:52  \n",
       "69747         86916  2016-04-06 07:45:57  \n",
       "139052        56112  2016-03-14 22:40:52  \n",
       "280059        13051  2016-03-24 16:15:27  \n",
       "251980        60433  2016-03-25 04:45:53  \n",
       "120919        72072  2016-03-25 12:43:02  \n",
       "183361        86316  2016-03-28 00:18:31  \n",
       "87799         76532  2016-03-08 20:39:05  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expensive_cars = autos.sort_values(by='price_eur', ascending=False).head(20)\n",
    "expensive_cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I'll clear the values below 250 and upper 1,0 * 10^7 euros for safe purposes\n",
    "autos = autos.loc[(autos['price_eur'] >= 250) & (autos['price_eur'] <= 1.0e7), :]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Inspecting 'registration_year' column\n",
    "In this section, I'll remove the cars with year Y, where Y < 1970 or Y > 2016, since the dataset probably is from between 2013~2015 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "autos = autos.loc[(autos['registration_year'] >= 1970) & (autos['registration_year'] <= 2016), :]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Inspecting 'gearbox' column\n",
    "In this section, I'll translate manuell to manual and automatik to automatic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "manuell      249739\n",
       "automatik     72950\n",
       "Name: gearbox, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['gearbox'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "autos.loc[autos['gearbox'] == 'manuell', 'gearbox'] = 'manual'\n",
    "autos.loc[autos['gearbox'] == 'automatik', 'gearbox'] = 'automatic'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Inspecting 'power_ps' column\n",
    "In this section, I'll remove cars with 'power_ps' P, where P < 5 or P > 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    336293.000000\n",
       "mean        119.348547\n",
       "std         184.212931\n",
       "min           0.000000\n",
       "25%          75.000000\n",
       "50%         109.000000\n",
       "75%         150.000000\n",
       "max       20000.000000\n",
       "Name: power_ps, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['power_ps'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    306377.000000\n",
       "mean        127.550896\n",
       "std          62.857548\n",
       "min           5.000000\n",
       "25%          82.000000\n",
       "50%         116.000000\n",
       "75%         150.000000\n",
       "max        1000.000000\n",
       "Name: power_ps, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos = autos.loc[(autos['power_ps'] >= 5) & (autos['power_ps'] <= 1000), :]\n",
    "autos['power_ps'].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Inspecting 'registration_month' column\n",
    "In this section, I'll remove cars with 'registration_month' M, where M < 1 or M > 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    306377.000000\n",
       "mean          6.033717\n",
       "std           3.561495\n",
       "min           0.000000\n",
       "25%           3.000000\n",
       "50%           6.000000\n",
       "75%           9.000000\n",
       "max          12.000000\n",
       "Name: registration_month, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['registration_month'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    289700.000000\n",
       "mean          6.381056\n",
       "std           3.346347\n",
       "min           1.000000\n",
       "25%           4.000000\n",
       "50%           6.000000\n",
       "75%           9.000000\n",
       "max          12.000000\n",
       "Name: registration_month, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos = autos.loc[autos['registration_month'] > 0, :]\n",
    "autos['registration_month'].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Inspecting 'last_seen' column\n",
    "In this section, I'll change the format of last_seen to timestamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                           289700\n",
       "mean     2016-03-30 10:45:27.954245888\n",
       "min                2016-03-05 14:15:08\n",
       "25%      2016-03-23 23:17:28.750000128\n",
       "50%                2016-04-04 16:45:40\n",
       "75%                2016-04-06 11:46:04\n",
       "max                2016-04-07 14:58:51\n",
       "Name: last_seen, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['last_seen'] = pd.to_datetime(autos['last_seen'], format='%Y-%m-%d %H:%M:%S')\n",
    "autos['ad_created'] = pd.to_datetime(autos['ad_created'], format='%Y-%m-%d %H:%M:%S')\n",
    "autos['last_seen'].describe(datetime_is_numeric=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 289700 entries, 1 to 371527\n",
      "Data columns (total 19 columns):\n",
      " #   Column              Non-Null Count   Dtype         \n",
      "---  ------              --------------   -----         \n",
      " 0   name                289700 non-null  object        \n",
      " 1   seller              289700 non-null  object        \n",
      " 2   offer_type          289700 non-null  object        \n",
      " 3   price_eur           289700 non-null  float64       \n",
      " 4   abtest              289700 non-null  object        \n",
      " 5   vehicle_type        282573 non-null  object        \n",
      " 6   registration_year   289700 non-null  int64         \n",
      " 7   gearbox             285980 non-null  object        \n",
      " 8   power_ps            289700 non-null  int64         \n",
      " 9   model               280675 non-null  object        \n",
      " 10  odometer_km         289700 non-null  int64         \n",
      " 11  registration_month  289700 non-null  int64         \n",
      " 12  fuel_type           278983 non-null  object        \n",
      " 13  brand               289700 non-null  object        \n",
      " 14  unrepaired_damage   256424 non-null  object        \n",
      " 15  ad_created          289700 non-null  datetime64[ns]\n",
      " 16  nr_pictures         289700 non-null  int64         \n",
      " 17  postal_code         289700 non-null  int64         \n",
      " 18  last_seen           289700 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(1), int64(6), object(10)\n",
      "memory usage: 44.2+ MB\n"
     ]
    }
   ],
   "source": [
    "autos.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Further Obeservations and decisions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll drop the columns 'seller' and 'offer_tupe' as they have little value variation, thus probably won't give us much insight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>name</th>\n",
       "      <th>price_eur</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_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_pictures</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>last_seen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A5_Sportback_2.7_Tdi</td>\n",
       "      <td>18300.0</td>\n",
       "      <td>test</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>manual</td>\n",
       "      <td>190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125000</td>\n",
       "      <td>5</td>\n",
       "      <td>diesel</td>\n",
       "      <td>audi</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-03-24</td>\n",
       "      <td>0</td>\n",
       "      <td>66954</td>\n",
       "      <td>2016-04-07 01:46:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jeep_Grand_Cherokee_\"Overland\"</td>\n",
       "      <td>9800.0</td>\n",
       "      <td>test</td>\n",
       "      <td>suv</td>\n",
       "      <td>2004</td>\n",
       "      <td>automatic</td>\n",
       "      <td>163</td>\n",
       "      <td>grand</td>\n",
       "      <td>125000</td>\n",
       "      <td>8</td>\n",
       "      <td>diesel</td>\n",
       "      <td>jeep</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-14</td>\n",
       "      <td>0</td>\n",
       "      <td>90480</td>\n",
       "      <td>2016-04-05 12:47:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GOLF_4_1_4__3TÜRER</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2001</td>\n",
       "      <td>manual</td>\n",
       "      <td>75</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-17</td>\n",
       "      <td>0</td>\n",
       "      <td>91074</td>\n",
       "      <td>2016-03-17 17:40:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Skoda_Fabia_1.4_TDI_PD_Classic</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2008</td>\n",
       "      <td>manual</td>\n",
       "      <td>69</td>\n",
       "      <td>fabia</td>\n",
       "      <td>90000</td>\n",
       "      <td>7</td>\n",
       "      <td>diesel</td>\n",
       "      <td>skoda</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>0</td>\n",
       "      <td>60437</td>\n",
       "      <td>2016-04-06 10:17:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BMW_316i___e36_Limousine___Bastlerfahrzeug__Ex...</td>\n",
       "      <td>650.0</td>\n",
       "      <td>test</td>\n",
       "      <td>limousine</td>\n",
       "      <td>1995</td>\n",
       "      <td>manual</td>\n",
       "      <td>102</td>\n",
       "      <td>3er</td>\n",
       "      <td>150000</td>\n",
       "      <td>10</td>\n",
       "      <td>benzin</td>\n",
       "      <td>bmw</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-04-04</td>\n",
       "      <td>0</td>\n",
       "      <td>33775</td>\n",
       "      <td>2016-04-06 19:17:07</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371520</th>\n",
       "      <td>turbo_defekt</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2004</td>\n",
       "      <td>manual</td>\n",
       "      <td>225</td>\n",
       "      <td>leon</td>\n",
       "      <td>150000</td>\n",
       "      <td>5</td>\n",
       "      <td>benzin</td>\n",
       "      <td>seat</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-03-19</td>\n",
       "      <td>0</td>\n",
       "      <td>96465</td>\n",
       "      <td>2016-03-19 20:44:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371524</th>\n",
       "      <td>Smart_smart_leistungssteigerung_100ps</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>test</td>\n",
       "      <td>cabrio</td>\n",
       "      <td>2000</td>\n",
       "      <td>automatic</td>\n",
       "      <td>101</td>\n",
       "      <td>fortwo</td>\n",
       "      <td>125000</td>\n",
       "      <td>3</td>\n",
       "      <td>benzin</td>\n",
       "      <td>smart</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-05</td>\n",
       "      <td>0</td>\n",
       "      <td>26135</td>\n",
       "      <td>2016-03-11 18:17:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371525</th>\n",
       "      <td>Volkswagen_Multivan_T4_TDI_7DC_UY2</td>\n",
       "      <td>9200.0</td>\n",
       "      <td>test</td>\n",
       "      <td>bus</td>\n",
       "      <td>1996</td>\n",
       "      <td>manual</td>\n",
       "      <td>102</td>\n",
       "      <td>transporter</td>\n",
       "      <td>150000</td>\n",
       "      <td>3</td>\n",
       "      <td>diesel</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-19</td>\n",
       "      <td>0</td>\n",
       "      <td>87439</td>\n",
       "      <td>2016-04-07 07:15:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371526</th>\n",
       "      <td>VW_Golf_Kombi_1_9l_TDI</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>test</td>\n",
       "      <td>kombi</td>\n",
       "      <td>2002</td>\n",
       "      <td>manual</td>\n",
       "      <td>100</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>diesel</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-20</td>\n",
       "      <td>0</td>\n",
       "      <td>40764</td>\n",
       "      <td>2016-03-24 12:45:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371527</th>\n",
       "      <td>BMW_M135i_vollausgestattet_NP_52.720____Euro</td>\n",
       "      <td>28990.0</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2013</td>\n",
       "      <td>manual</td>\n",
       "      <td>320</td>\n",
       "      <td>m_reihe</td>\n",
       "      <td>50000</td>\n",
       "      <td>8</td>\n",
       "      <td>benzin</td>\n",
       "      <td>bmw</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-07</td>\n",
       "      <td>0</td>\n",
       "      <td>73326</td>\n",
       "      <td>2016-03-22 03:17:10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289700 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     name  price_eur   abtest  \\\n",
       "1                                    A5_Sportback_2.7_Tdi    18300.0     test   \n",
       "2                          Jeep_Grand_Cherokee_\"Overland\"     9800.0     test   \n",
       "3                                      GOLF_4_1_4__3TÜRER     1500.0     test   \n",
       "4                          Skoda_Fabia_1.4_TDI_PD_Classic     3600.0     test   \n",
       "5       BMW_316i___e36_Limousine___Bastlerfahrzeug__Ex...      650.0     test   \n",
       "...                                                   ...        ...      ...   \n",
       "371520                                       turbo_defekt     3200.0  control   \n",
       "371524              Smart_smart_leistungssteigerung_100ps     1199.0     test   \n",
       "371525                 Volkswagen_Multivan_T4_TDI_7DC_UY2     9200.0     test   \n",
       "371526                             VW_Golf_Kombi_1_9l_TDI     3400.0     test   \n",
       "371527       BMW_M135i_vollausgestattet_NP_52.720____Euro    28990.0  control   \n",
       "\n",
       "       vehicle_type  registration_year    gearbox  power_ps        model  \\\n",
       "1             coupe               2011     manual       190          NaN   \n",
       "2               suv               2004  automatic       163        grand   \n",
       "3        kleinwagen               2001     manual        75         golf   \n",
       "4        kleinwagen               2008     manual        69        fabia   \n",
       "5         limousine               1995     manual       102          3er   \n",
       "...             ...                ...        ...       ...          ...   \n",
       "371520    limousine               2004     manual       225         leon   \n",
       "371524       cabrio               2000  automatic       101       fortwo   \n",
       "371525          bus               1996     manual       102  transporter   \n",
       "371526        kombi               2002     manual       100         golf   \n",
       "371527    limousine               2013     manual       320      m_reihe   \n",
       "\n",
       "        odometer_km  registration_month fuel_type       brand  \\\n",
       "1            125000                   5    diesel        audi   \n",
       "2            125000                   8    diesel        jeep   \n",
       "3            150000                   6    benzin  volkswagen   \n",
       "4             90000                   7    diesel       skoda   \n",
       "5            150000                  10    benzin         bmw   \n",
       "...             ...                 ...       ...         ...   \n",
       "371520       150000                   5    benzin        seat   \n",
       "371524       125000                   3    benzin       smart   \n",
       "371525       150000                   3    diesel  volkswagen   \n",
       "371526       150000                   6    diesel  volkswagen   \n",
       "371527        50000                   8    benzin         bmw   \n",
       "\n",
       "       unrepaired_damage ad_created  nr_pictures  postal_code  \\\n",
       "1                     ja 2016-03-24            0        66954   \n",
       "2                    NaN 2016-03-14            0        90480   \n",
       "3                   nein 2016-03-17            0        91074   \n",
       "4                   nein 2016-03-31            0        60437   \n",
       "5                     ja 2016-04-04            0        33775   \n",
       "...                  ...        ...          ...          ...   \n",
       "371520                ja 2016-03-19            0        96465   \n",
       "371524              nein 2016-03-05            0        26135   \n",
       "371525              nein 2016-03-19            0        87439   \n",
       "371526               NaN 2016-03-20            0        40764   \n",
       "371527              nein 2016-03-07            0        73326   \n",
       "\n",
       "                 last_seen  \n",
       "1      2016-04-07 01:46:50  \n",
       "2      2016-04-05 12:47:46  \n",
       "3      2016-03-17 17:40:17  \n",
       "4      2016-04-06 10:17:21  \n",
       "5      2016-04-06 19:17:07  \n",
       "...                    ...  \n",
       "371520 2016-03-19 20:44:43  \n",
       "371524 2016-03-11 18:17:12  \n",
       "371525 2016-04-07 07:15:26  \n",
       "371526 2016-03-24 12:45:21  \n",
       "371527 2016-03-22 03:17:10  \n",
       "\n",
       "[289700 rows x 17 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.drop(columns=['seller', 'offer_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006    0.063383\n",
       "2005    0.062603\n",
       "1999    0.061015\n",
       "2004    0.059738\n",
       "2003    0.059424\n",
       "2001    0.057684\n",
       "2000    0.057321\n",
       "2002    0.056707\n",
       "2007    0.055847\n",
       "2008    0.051702\n",
       "2009    0.050501\n",
       "1998    0.046189\n",
       "2010    0.040608\n",
       "2011    0.039969\n",
       "1997    0.036355\n",
       "2012    0.031153\n",
       "1996    0.026631\n",
       "2013    0.020473\n",
       "1995    0.020100\n",
       "2016    0.016883\n",
       "2014    0.015854\n",
       "1994    0.011829\n",
       "2015    0.009244\n",
       "1993    0.008378\n",
       "1992    0.007705\n",
       "1991    0.007018\n",
       "1990    0.005236\n",
       "1989    0.003300\n",
       "1988    0.002485\n",
       "1987    0.001722\n",
       "1985    0.001453\n",
       "1986    0.001405\n",
       "1983    0.001160\n",
       "1984    0.001115\n",
       "1982    0.000880\n",
       "1980    0.000825\n",
       "1979    0.000804\n",
       "1978    0.000746\n",
       "1981    0.000739\n",
       "1972    0.000628\n",
       "1971    0.000514\n",
       "1974    0.000501\n",
       "1973    0.000497\n",
       "1970    0.000463\n",
       "1977    0.000459\n",
       "1976    0.000421\n",
       "1975    0.000335\n",
       "Name: registration_year, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['registration_year'].value_counts(normalize=True, dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    289700.000000\n",
       "mean       2003.531819\n",
       "std           6.337602\n",
       "min        1970.000000\n",
       "25%        1999.000000\n",
       "50%        2004.000000\n",
       "75%        2008.000000\n",
       "max        2016.000000\n",
       "Name: registration_year, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['registration_year'].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above observations, we can see that most ads were from cars registered between (1996-2006)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I'll calculate mean price for the 20 most common brands on the site"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'volkswagen': 6018.94466642649,\n",
       " 'bmw': 9178.163946145598,\n",
       " 'opel': 3421.582751540041,\n",
       " 'mercedes_benz': 8997.56057847549,\n",
       " 'audi': 10165.337861028955,\n",
       " 'ford': 4165.077677104859,\n",
       " 'renault': 2805.7711042799715,\n",
       " 'peugeot': 3567.8437678184514,\n",
       " 'fiat': 3255.617323290846,\n",
       " 'seat': 4965.346133141934,\n",
       " 'skoda': 6904.212317666126,\n",
       " 'mazda': 6791.422592259226,\n",
       " 'smart': 3815.603344170924,\n",
       " 'citroen': 4033.5168371164877,\n",
       " 'toyota': 5569.113430583501,\n",
       " 'nissan': 5305.483660130719,\n",
       " 'mini': 10213.58466038349,\n",
       " 'hyundai': 5984.925864909391,\n",
       " 'volvo': 5641.024408014572,\n",
       " 'mitsubishi': 3807.6784932106875}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_mean_prices = {}\n",
    "brand_mean_mileages = {}\n",
    "brand_mean_power = {}\n",
    "\n",
    "top20_brands = autos['brand'].value_counts(normalize=True, sort=True, ascending=False).head(20).index\n",
    "for brand in top20_brands:\n",
    "    brand_bool = autos['brand'] == brand\n",
    "    brand_prices = autos.loc[brand_bool, 'price_eur']\n",
    "    brand_mileages = autos.loc[brand_bool, 'odometer_km']\n",
    "    brand_power = autos.loc[brand_bool, 'power_ps']\n",
    "    brand_mean_prices[brand] = brand_prices.mean(axis=0)\n",
    "    brand_mean_mileages[brand] = brand_mileages.mean(axis=0)\n",
    "    brand_mean_power[brand] = brand_power.mean(axis=0)\n",
    "\n",
    "brand_mean_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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_eur</th>\n",
       "      <th>mean_power_ps</th>\n",
       "      <th>mean_mileage_km</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brand</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mini</th>\n",
       "      <td>10213.584660</td>\n",
       "      <td>126.766656</td>\n",
       "      <td>93233.669158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>audi</th>\n",
       "      <td>10165.337861</td>\n",
       "      <td>167.114689</td>\n",
       "      <td>128388.468982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmw</th>\n",
       "      <td>9178.163946</td>\n",
       "      <td>176.087603</td>\n",
       "      <td>132554.956511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercedes_benz</th>\n",
       "      <td>8997.560578</td>\n",
       "      <td>167.886606</td>\n",
       "      <td>130413.932878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skoda</th>\n",
       "      <td>6904.212318</td>\n",
       "      <td>108.000608</td>\n",
       "      <td>112529.376013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mazda</th>\n",
       "      <td>6791.422592</td>\n",
       "      <td>120.064581</td>\n",
       "      <td>124121.287129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volkswagen</th>\n",
       "      <td>6018.944666</td>\n",
       "      <td>107.980628</td>\n",
       "      <td>127327.481741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hyundai</th>\n",
       "      <td>5984.925865</td>\n",
       "      <td>112.068534</td>\n",
       "      <td>102028.006590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volvo</th>\n",
       "      <td>5641.024408</td>\n",
       "      <td>151.422222</td>\n",
       "      <td>138646.630237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toyota</th>\n",
       "      <td>5569.113431</td>\n",
       "      <td>110.914990</td>\n",
       "      <td>116473.843058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nissan</th>\n",
       "      <td>5305.483660</td>\n",
       "      <td>108.617778</td>\n",
       "      <td>117355.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seat</th>\n",
       "      <td>4965.346133</td>\n",
       "      <td>99.701418</td>\n",
       "      <td>118547.460973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ford</th>\n",
       "      <td>4165.077677</td>\n",
       "      <td>107.095374</td>\n",
       "      <td>122969.851110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citroen</th>\n",
       "      <td>4033.516837</td>\n",
       "      <td>97.570965</td>\n",
       "      <td>119214.267897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smart</th>\n",
       "      <td>3815.603344</td>\n",
       "      <td>62.749187</td>\n",
       "      <td>97789.131444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mitsubishi</th>\n",
       "      <td>3807.678493</td>\n",
       "      <td>119.283837</td>\n",
       "      <td>124943.057381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peugeot</th>\n",
       "      <td>3567.843768</td>\n",
       "      <td>100.929753</td>\n",
       "      <td>122829.855172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opel</th>\n",
       "      <td>3421.582752</td>\n",
       "      <td>102.005613</td>\n",
       "      <td>127460.985626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiat</th>\n",
       "      <td>3255.617323</td>\n",
       "      <td>80.779693</td>\n",
       "      <td>114980.446118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>renault</th>\n",
       "      <td>2805.771104</td>\n",
       "      <td>91.635690</td>\n",
       "      <td>126066.840073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean_price_eur  mean_power_ps  mean_mileage_km\n",
       "brand                                                        \n",
       "mini             10213.584660     126.766656     93233.669158\n",
       "audi             10165.337861     167.114689    128388.468982\n",
       "bmw               9178.163946     176.087603    132554.956511\n",
       "mercedes_benz     8997.560578     167.886606    130413.932878\n",
       "skoda             6904.212318     108.000608    112529.376013\n",
       "mazda             6791.422592     120.064581    124121.287129\n",
       "volkswagen        6018.944666     107.980628    127327.481741\n",
       "hyundai           5984.925865     112.068534    102028.006590\n",
       "volvo             5641.024408     151.422222    138646.630237\n",
       "toyota            5569.113431     110.914990    116473.843058\n",
       "nissan            5305.483660     108.617778    117355.555556\n",
       "seat              4965.346133      99.701418    118547.460973\n",
       "ford              4165.077677     107.095374    122969.851110\n",
       "citroen           4033.516837      97.570965    119214.267897\n",
       "smart             3815.603344      62.749187     97789.131444\n",
       "mitsubishi        3807.678493     119.283837    124943.057381\n",
       "peugeot           3567.843768     100.929753    122829.855172\n",
       "opel              3421.582752     102.005613    127460.985626\n",
       "fiat              3255.617323      80.779693    114980.446118\n",
       "renault           2805.771104      91.635690    126066.840073"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bpm means brand price mean\n",
    "# bpsm means brand ps mean\n",
    "# bmm means brand mileage mean \n",
    "bpm_series = pd.Series(brand_mean_prices)\n",
    "bpsm_series = pd.Series(brand_mean_power)\n",
    "bmm_series = pd.Series(brand_mean_mileages)\n",
    "brands_infos_df = pd.DataFrame(data = {'mean_price_eur' : bpm_series, 'mean_power_ps' : bpsm_series, 'mean_mileage_km' : bmm_series}).sort_values(by='mean_price_eur', ascending=False)\n",
    "brands_infos_df.index.names=['brand']\n",
    "brands_infos_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.bar(brands_infos_df.index, brands_infos_df['mean_price_eur'])\n",
    "fig.autofmt_xdate()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4ublJxowZNc958uSJXL58WT2HiIiI/ttSrGspJCREbt++rW7fu3dPLly4ILly5RIbGxvp27evTJ48WRwcHMTBwUEmT54sWbJkkZYtW4qISPbs2aVTp04yYMAAyZ07t+TKlUsGDhwoRYsWVbOYiIiIPha7rb5NKRZkzpw5I1WqVFG39eNW2rVrJ6tWrZLBgwdLaGiodO/eXV69eiUeHh6yf/9+tYaMiMjs2bMlQ4YM0rRpUwkNDZWqVavKqlWruIYMERERiUgKBhkvLy8BkOTjOp1Oxo4dK2PHjk3yOZkzZ5b58+fL/PnzU6CGREREZOh4rSUiIiIyWAwyREREZLAYZIiIiMhgMcgQERGRwWKQISIiIoPFIENEREQGi0GGiIiIDBaDDBERERmsVL36NRERkaFLrksfiPDyB5+DLTJERERksBhkiIiIyGAxyBAREZHBYpAhIiIig8UgQ0RERAaLQYaIiIgMFoMMERERGSwGGSIiIjJYDDJERERksBhkiIiIyGAxyBAREZHBYpAhIiIig8UgQ0RERAaLQYaIiIgMFoMMERERGSwGGSIiIjJYDDJERERksBhkiIiIyGBlSO0KEBERUeIKDN2dLOXcn+qbLOWkRWyRISIiIoPFIENEREQGi0GGiIiIDBaDDBERERksBhkiIiIyWKkWZAoUKCA6nS7BT48ePUREpH379gkeK1u2bGpVl4iIiNKgVJt+ffr0aYmOjla3L1++LNWrV5cmTZqo+2rVqiUrV65UtzNlyvRV60hERERpW6oFmTx58mhuT506Vezt7aVy5crqPiMjIzE3N//aVSMiIiIDkSbGyERERMi6deukY8eOotPp1P1HjhyRvHnzSuHChaVLly4SGBj4wbLCw8MlKChI80NERETfpjQRZLZt2yavX7+W9u3bq/t8fHxk/fr18scff8jMmTPl9OnT4u3tLeHh4e8ta8qUKZI9e3b1Y21tncK1JyIiotSSJi5RsHz5cvHx8RFLS0t1X7NmzdT/ixQpIu7u7mJrayu7d++Whg0bJlnWsGHDpH///up2UFAQwwwREdE3KtWDzL///isHDx6UrVu3vvd5FhYWYmtrK7du3Xrv84yMjMTIyCg5q0hERERpVKp3La1cuVLy5s0rvr7vv6DVixcv5OHDh2JhYfGVakZERERpXaoGmZiYGFm5cqW0a9dOMmT4v8ahkJAQGThwoPj5+cn9+/flyJEjUrduXTEzM5MGDRqkYo2JiIgoLUnVrqWDBw/KgwcPpGPHjpr706dPL5cuXZI1a9bI69evxcLCQqpUqSKbNm0SExOTVKotERERpTWpGmRq1KghABLcb2xsLL///nsq1IiIiIgMSaqPkSEiIiL6XAwyREREZLAYZIiIiMhgMcgQERGRwWKQISIiIoPFIENEREQGi0GGiIiIDBaDDBERERksBhkiIiIyWAwyREREZLAYZIiIiMhgMcgQERGRwWKQISIiIoPFIENEREQGi0GGiIiIDFaG1K4AERERfX0Fhu5OlnLuT/VNlnI+F1tkiIiIyGAxyBAREZHBYpAhIiIig8UgQ0RERAaLQYaIiIgMFoMMERERGSwGGSIiIjJYDDJERERksBhkiIiIyGAxyBAREZHBYpAhIiIig8UgQ0RERAaLQYaIiIgMFoMMERERGSwGGSIiIjJYDDJERERksBhkiIiIyGAxyBAREZHBSrUgM3bsWNHpdJofc3Nz9TgAGTt2rFhaWoqxsbF4eXnJlStXUqu6RERElAalaouMq6urPHnyRP1cunRJPTZ9+nSZNWuWLFiwQE6fPi3m5uZSvXp1CQ4OTsUaExERUVqSIVV/eYYMmlYYPQAyZ84cGTFihDRs2FBERFavXi358uWTDRs2SNeuXZMsMzw8XMLDw9XtoKCg5K84ERERpQmp2iJz69YtsbS0FDs7O2nevLncvXtXRETu3bsnAQEBUqNGDfVcIyMjqVy5spw4ceK9ZU6ZMkWyZ8+ufqytrVP0byAiIqLUk2pBxsPDQ9asWSO///67LF26VAICAqR8+fLy4sULCQgIEBGRfPnyaV6TL18+9VhShg0bJm/evFE/Dx8+TLG/gYiIiFJXqnUt+fj4qP8XLVpUypUrJ/b29rJ69WopW7asiIjodDrNawAkuC8+IyMjMTIySv4KExERUZqTZqZfZ82aVYoWLSq3bt1S42bit74EBgYmaKUhIiKi/640E2TCw8Pl2rVrYmFhIXZ2dmJubi4HDhxQj0dERMiff/4p5cuXT8VaEhERUVqSal1LAwcOlLp164qNjY0EBgbKxIkTJSgoSNq1ayc6nU769u0rkydPFgcHB3FwcJDJkydLlixZpGXLlqlVZSIiIkpjUi3I+Pv7S4sWLeT58+eSJ08eKVu2rJw8eVJsbW1FRGTw4MESGhoq3bt3l1evXomHh4fs379fTExMUqvKRERElMakWpD55Zdf3vu4TqeTsWPHytixY79OhYiIiMjgpJkxMkRERESfikGGiIiIDBaDDBERERksBhkiIiIyWAwyREREZLAYZIiIiMhgMcgQERGRwWKQISIiIoPFIENEREQGi0GGiIiIDBaDDBERERksBhkiIiIyWAwyREREZLAYZIiIiMhgMcgQERGRwWKQISIiIoPFIENEREQGi0GGiIiIDBaDDBERERksBhkiIiIyWAwyREREZLAYZIiIiMhgMcgQERGRwWKQISIiIoPFIENEREQGi0GGiIiIDBaDDBERERksBhkiIiIyWAwyREREZLAYZIiIiMhgMcgQERGRwWKQISIiIoPFIENEREQGK9WCzJQpU6R06dJiYmIiefPmlfr168uNGzc0z2nfvr3odDrNT9myZVOpxkRERJTWpFqQ+fPPP6VHjx5y8uRJOXDggERFRUmNGjXk7du3mufVqlVLnjx5on727NmTSjUmIiKitCZDav3iffv2aW6vXLlS8ubNK2fPnpVKlSqp+42MjMTc3PxrV4+IiIgMQJoZI/PmzRsREcmVK5fm/iNHjkjevHmlcOHC0qVLFwkMDHxvOeHh4RIUFKT5ISIiom9TmggyAKR///5SsWJFKVKkiLrfx8dH1q9fL3/88YfMnDlTTp8+Ld7e3hIeHp5kWVOmTJHs2bOrH2tr66/xJxAREVEqSLWupbh69uwp//zzjxw/flxzf7NmzdT/ixQpIu7u7mJrayu7d++Whg0bJlrWsGHDpH///up2UFAQwwwREdE3KtWDTK9evWTHjh1y9OhRsbKyeu9zLSwsxNbWVm7dupXkc4yMjMTIyCi5q0lERERpUKoFGQDSq1cv+e233+TIkSNiZ2f3wde8ePFCHj58KBYWFl+hhkRERJTWpdoYmR49esi6detkw4YNYmJiIgEBARIQECChoaEiIhISEiIDBw4UPz8/uX//vhw5ckTq1q0rZmZm0qBBg9SqNhEREaUhqdYis3jxYhER8fLy0ty/cuVKad++vaRPn14uXboka9askdevX4uFhYVUqVJFNm3aJCYmJqlQYyIiIkprUrVr6X2MjY3l999//0q1ISIiIkOUJqZfExEREX0OBhkiIiIyWAwyREREZLAYZIiIiMhgMcgQERGRwWKQISIiIoPFIENEREQGi0GGiIiIDBaDDBERERksBhkiIiIyWAwyREREZLAYZIiIiMhgMcgQERGRwWKQISIiIoPFIENEREQGi0GGiIiIDBaDDBERERksBhkiIiIyWAwyREREZLAYZIiIiMhgMcgQERGRwWKQISIiIoPFIENEREQGi0GGiIiIDBaDDBERERksBhkiIiIyWAwyREREZLAYZIiIiMhgMcgQERGRwWKQISIiIoPFIENEREQGi0GGiIiIDBaDDBERERksgwgyixYtEjs7O8mcObO4ubnJsWPHUrtKRERElAak+SCzadMm6du3r4wYMULOnz8vnp6e4uPjIw8ePEjtqhEREVEqS/NBZtasWdKpUyfp3LmzODs7y5w5c8Ta2loWL16c2lUjIiKiVJYhtSvwPhEREXL27FkZOnSo5v4aNWrIiRMnEn1NeHi4hIeHq9tv3rwREZGgoKBkr19M+LtkKSexurFsbdnJVW5Kls33+uuV/a281ylZNj9HvtfvKzely07OcgG8/4lIwx49egQRwV9//aW5f9KkSShcuHCirxkzZgxEhD/84Q9/+MMf/nwDPw8fPnxvVkjTLTJ6Op1OcxtAgvv0hg0bJv3791e3Y2Ji5OXLl5I7d+4kX5NSgoKCxNraWh4+fCimpqYsOwXLNsQ6s+yvVy7L/nrlsuyvW7Yh1vljAZDg4GCxtLR87/PSdJAxMzOT9OnTS0BAgOb+wMBAyZcvX6KvMTIyEiMjI819OXLkSKkqfhRTU9MU+xKw7K9TLsv+umUbYp0NtWxDrDPL/nrlpnTZH5I9e/YPPidND/bNlCmTuLm5yYEDBzT3HzhwQMqXL59KtSIiIqK0Ik23yIiI9O/fX9q0aSPu7u5Srlw5WbJkiTx48EB++OGH1K4aERERpbI0H2SaNWsmL168kPHjx8uTJ0+kSJEismfPHrG1tU3tqn2QkZGRjBkzJkFXF8tO/rINsc4s++uVy7K/Xrks++uWbYh1Tm464EPzmoiIiIjSpjQ9RoaIiIjofRhkiIiIyGAxyBAREZHBYpD5D+KwKCIi+lYwyPzHREZGJlhgkFLG5cuXU6zsiIgIXgH+G/D8+XMRiV2BPDkdPXpUNm3aJFFRUclaLn0dyf19+NYxyPyHPHr0SOrWrStLly5N9rJTspVn/fr1KVLnlHThwgWpXr26HD9+PNnLfvDggXh6esrBgweTvWz6OkJCQqR169YyevRoCQ8Pl3TpkndXPGPGDJk6dar8888/yVpuXCkVkgwtfKXEvi/u9yE6OjrZyxf5tlrmGWS+QEql5ujo6GT9kl25ckVERCwsLGTu3LkyevToZCtbJHbHE/c6VslZ91evXomfn584OjomW5l6KbkhOzg4yOXLl6VixYrJ/vtsbGzkp59+ko4dOyZLeXEZ4plgSu3oU6pcEZFs2bJJzpw55dKlS8kaSPV1njt3rgQFBcnOnTslODg42coXETlz5oxERUVJhgzJuwzZxYsX5c2bN6rcsLCwZCs7pT7LVatWyYoVK9Tt5GolDQkJkSpVqsj//vc/SZ8+vZw8eVJ++eWXZClbROSXX36RRYsWqdt37txJtrJTA4PMF9CnZv2X90sPAgAkOjpa0qdPLzqdTq5evfrFdfz777+laNGisnnzZkmXLp04OjrKq1evZOLEiV9ctl6GDBkkMjJSVq5cKceOHUu2risAkjNnTlmwYIFUqlQpWXfI0dHRKXoR0axZs0ru3Lnl6NGjMmnSJBFJ3pBQsmRJCQwMlGXLlsmrV6++uDz9jj5+y0Byha+UOMsGIDExMZI+fXoRie2m0df3S+qt/5z05f7222+yadMmefLkyRfWOFZ4eLiIiIwdO1ZERLZt2yZPnz794nIBSPr06SU6Olrs7OykZcuWsnnzZvn777+/uGy9KVOmSJcuXeTChQty4MAB8fT0lBcvXnxxuWfOnJHmzZvL3LlzJSQkROrWrStbtmxJlu9f3O/Iy5cvNfd/qmPHjsmCBQtEJLZ7d/Xq1bJnzx6ZPn26pEuXTqZPn/7F9RURMTY2lmLFismAAQPE19dXKlasKCEhIclStkhsK/fu3btl5syZkjlzZhk5cqRBnsQo7702NmnExMQgKipKc9/06dNhaWn5xWVHRkaq///7779o3LgxdDoddu7c+UXlPnz4EG3btkWxYsXUfatXr0bevHmxcOHCLypbb9WqVciWLRucnZ1hY2MDW1tbHD58GDExMZ9dZvzX7tmzBxUrVsTRo0cTffxjy4z7uqCgIIwdOxazZs3CsWPHEB0d/cll618DaD9DAIiIiMDkyZOh0+lw8+bNz653/N+jN3XqVNjZ2WH9+vWfVaZe3Dpt3rwZAwYMwLx58/DmzZsvKhdIWO9Vq1Zhx44duHPnTqKPv8/jx48TLff69evw8fFB6dKlUb16dfz5559fWOtYz549g5ubG6ysrGBhYQFPT08cOHDgk+sdV9z3+v79+xg8eDA8PDywYsWKz65nYvslAAgPD4eLiwu6du2KJ0+efHb5cQUGBqJUqVKwsrKCiYkJxo4d+9nvRVwxMTEYNmwY8uTJg0yZMqFp06Z4/vz5F5UZ9z05d+4cKlasCHd3d/zwww94+vSp+r0fKzIyEq1bt0bJkiVx8uRJAMCiRYtgZGSEbNmyYcmSJV9U3/h1GTBgAHQ6HYoUKYKgoKAvKhvQfme3b98OIyMjZM6cGTNmzPjislMbg8xnePXqlfr/rl27ULZsWZw6dSpZyu7duzfSpUuHatWqwcrKCrt37/6scqKiotSGceTIERQoUACTJk0CEBtuevbsiVKlSuHZs2efXG5cN2/ehJOTE5YsWYLQ0FA8evQItWrVgpeXlwodnyI6OjrRHeMff/wBT09PdOvW7ZPK27BhA7p06QJAu6NYuHAhcuTIgYoVK6Jy5cooWLDgJ2/Qo0ePxpAhQzT3RURE4M2bN+p9un79OqpWrYratWt/Utl6Sb0fQGwQ8/HxQYsWLXDv3j0An7ZjjvvcW7duwcvLC+bm5mjVqhUsLS1Rp04d/PXXX59V7/ju37+PQoUKIX/+/LC3t4ednd0nHVzPnTsHR0dH/PjjjwD+b6e8cOFC5MuXD507d8amTZvQtWtX2Nra4vfff//suoaEhGDkyJGYNGkShg0bhujoaBw5cgTNmzdH6dKlP/nAHf8zuXLlCpycnFCmTBl4e3vDyMgINWvWxK1btxJ9/seW/fjxY6xfvx6XLl1CQEAAAGDZsmWwtbXFxo0bP6nOevrvsf4k4NixY7CwsECOHDnw008/fVaZgPagGh0djTdv3qBGjRpInz49fH191WNfcjIEAG/fvsWzZ89QtWpV9OvXD+PHj0ehQoXg7e39WfU9evQoqlWrhh9++AEAMHjwYJQsWRKVK1fG6dOnASTcR35M2Yl9p9avX4/vv/8eRkZGaj/9Oe9H3LLDw8PRtm1btGzZElWqVIGHh4cK/p9a77SEQeYD4n/BNmzYAJ1Oh2XLlgEA/vnnH3h4eOCXX375onKvX7+ObNmyoVSpUvDz88OrV69gYWGBS5cufVH9L168iEWLFqF169bIly8fXr9+DQDYvXs3PDw8MGDAgE8uMyAgAHfv3gUQe4Zta2uLO3fuqI3s5s2bcHNzw8iRIxEaGppkOXE3yvgb6Llz57Bu3Tp1gAaACRMmwMPDA7/++iuAjzsrXrlyJXQ6Hc6cOaPuO3z4MDw8PLB582Z1X9OmTVGkSBFs3779o8v+8ccfkSlTJly7dg0AMHv2bOTPnx+VK1dG3759VTmbNm2Cqakpdu3a9dFlA9r35Pjx4+jUqROGDRuGv/76S72v69atQ8mSJTF79uyPKvPdu3cJznQjIyPRr18/tGjRAsHBwQCAGzduIE+ePPj+++8/6+xV7/nz5+jQoQPWr1+PcePGISYmBn///TcqVKjwSQcTf39/dO3aFWXKlFH1f/78OcaNG6dpzVi/fj10Oh1atmyJ27dvf7DcxHbex44dg5ubG0xNTbFt2zZ1/969e+Hs7Izx48cD+LjPMe57pm+xa9asGXx9ffH27Vv4+/tj4cKFyJs3L6ZOnfrB8pIyePBgmJiYoGzZsihcuDC8vLzUY97e3qhXrx5u3Ljx0eXFP7i+ffsWAPDy5UucP38ePj4+aNmy5Se3rMVvPYqIiAAQe4C9fv065s2bB2dnZ3UC9ykH1/h1CAoKgouLC+zt7dGnTx/1+8+dOwcjIyMsX778o8uN+zmOHj0a7u7uqnXuxo0bKFOmDPr06aO2n4/dVuLW+ebNm9iyZYvatwKx73eJEiXQpEmTRP/GT3Hv3j10794dJUuWxKVLl/D06VOUL18e33//vdqmvjQ4phYGmY+k/wKtXr0aOp0OFSpUwKpVqwAA1atXR9euXQF83IaX1HP27Nmj/r9lyxZYW1t/9hcrIiICXbt2RZYsWfD999+jbNmy0Ol06kwiODgYY8aMgZOTE86dO/dJZRcoUACDBw8GACxZsgRmZmaa3wv835lK3JahuAIDA/Hw4UMA2o0nOjoanTt3RrZs2WBrawtnZ2f8/PPPAIBr167hu+++Q4MGDVQgS4q+zLdv38LX1xdly5ZVjz19+hT/+9//VJm+vr4wNTVFsWLFPqrsuHV1c3ND69atceDAAZQtWxZLly7F6NGjkTFjRkybNk39ra1atYKrq+t7y+rTpw8GDhyobgOxO+OOHTvC1NQU7du3R6VKlVCqVClNcGnevDlq1aqlzgiT+s68evUK2bJlU+/nmDFjMGvWLACxLV76HeiUKVOQJ08eFCpUCK6urh+9w4979q5348YN5MiRAxkyZFBBDgD+/vtvZMqU6YMtBTExMSoAHDx4EBUrVkS/fv0AxH7Xzp07pw5OpUqVQuHChdVBfdGiRUluazExMZqDwv379xEeHq7+jrlz58LIyAh79+5Vz3n9+jVGjBgBW1vbRL+78cuPa+DAgZg6dSru37+PEiVKYMqUKZrHW7duDU9PT5w4ceK95SZmy5YtKF26NA4dOgQAePDgAXQ6Hb7//nsAsSHY0tIS8+bNU9vnxzp69Chq166NBg0aYPbs2SocrlmzBiVKlFDfn0+t87Nnz9CnTx98//33WLlyJf79918AwKVLl9CoUSNUrVpVPfdD+9SkutYiIyOxbNkyZMiQIUFr68CBA2FpafnR2zoATR3r1KmDJk2aqC6f0aNHw8PDQ+1XEgscwcHBmDNnjiaoALH7qPbt28PMzAxOTk4oWLCg2g/ExMTgt99+g06nw/Hjxz+6rvF/b58+fVC4cGE0bNgQL1++VI/NmjUL7u7u6liWHN2EqYFBJp74rQQBAQFo3Lgx9u3bByD2S1GpUiU0atQI33//PebOnYu9e/fCzMzsgxtc3C/JixcvMGbMGPzyyy+4evVqgt+/cOFCeHt7IzIy8qM25PhOnTqFwoULq7OGwMBADBgwANmyZVOtE35+fqhRowZatGihXrd//348evQoQX2B/9uh/PDDD2jQoAGA2I07R44c6uCo31Fu3LgROXLk0Gw0eqGhofDy8sKIESM098+bNw9LlixBv3798ODBA1y6dAn9+vWDqampOmP46aef4OHhgTlz5iT5fsStd1RUFI4ePQojIyO1k9HX0c/PDy4uLmjTpg1ev36NuXPnIk+ePO/t647/WRw4cEAF2w0bNmj+lmzZsuHChQsAYluB8ufPr3b88cuJiYnBpEmTkDFjRvj7+6v7165di6pVq2rOpsuUKQM3Nzf88ccfAGIPNqVKlcLIkSOTPFDp3xP9DlwfVA4fPgwg9ow4OjoaPXv2RIkSJVSrl5OTE2rVqqV+f2LftfhnrPpAAABhYWFYunQp0qdPj3/++UdTRvfu3VGgQAF1tv8+z58/x+rVq9GzZ0/Y29trWipfvXqFqlWronfv3qr1qESJEqhSpQr+/vvv95Z75swZlC9fHsWLF0fx4sWxYMECREVF4dGjR/D19dUcUAHg9OnTqFSpEpo3b/7BOgOxZ8GbN2+Go6Mjjh49ihcvXiBv3rwqHIaFhQGAaiUYOHAg3r17l6CcpA7WMTEx8PT0xMSJEwHEtiaVK1cO+fPnx4YNG9Tn3qlTJ1SsWPGTugqnTZuGrFmzon///ujUqROKFCkCZ2dn9XiLFi3g4+Ojae38GAsXLkT27Nnh6+uLNm3aoFy5cqhXr556fPXq1XB1ddV0XX3MCeL169cxfPhwLFiwAJcvX0ZMTAxCQkJQsWJFVK9eHcD/ffceP34MKysrDBs27IPlRkdH46effkK+fPkQEhICAFiwYAE8PDwwf/58ALH71ypVqqBDhw4q5F6/fl2VMWHCBGTKlAmtW7fW7BOfPHmC1q1bo1q1arhy5QoiIiKwY8cO6HQ67N+/H0BsV2eDBg1QqlQpREZGIjw8HKtWrUq0azb+OD29H3/8Efnz50fr1q0B/N82+u7dO9U9rd/GP6YlM61hkIkjqY3F09MTPj4+2LJlC4DYcSyTJk3C77//jvz586N///4oX768phn6fY4dOwZTU1OULFkShQoVQoECBVSTpH7H07lzZzRs2FC95tWrV3jw4AGApM984m44W7ZsgbGxsWajuX79Ory9vVGlShV138KFC2Fubo6NGzfi+fPn0Ol0mDp1qvqiX716NcHOb+DAgfjuu+8QFhaG4OBgdQZ8584d9bo2bdqgXbt2Sb4H8QOO/sCh0+k03V337t1DsWLF1Hvx/PlzdOzYETVq1FBdOkm9H7Nnz4ajoyO6dOkCnU4HJycnzYbeq1cv+Pr6qgPHr7/+ClNTU5QoUQJXrlzRlBX/u3Hy5Em1U9OXrx8AqOfs7IxmzZohKioKISEhGDZsGHQ6XYKzQP1n/vLlS7i5uamQCMQGxSNHjgCIDYeFChWCvb093Nzc0LZtW1WvgQMHomjRovjtt98SLVsfcOrVqwedTocqVaqov1v/nNu3b8PR0VGFGACoWLEiLCwsEowFSsyBAwfg4+ODxo0bY+zYseq9DggIgJubGxo1agTg/z6vBw8ewMLCAsOHD39vuVOmTEGGDBnQokULVKhQATqdTvPd2r59O+zt7dUZ67179+Ds7AydToexY8dqglX8+hYoUAADBgzA2bNnsXLlSlhZWWHUqFEAYruRXV1dsXjxYvWaiIgITJs2DU5OTh8c47Nx40bodDp4e3vj2LFj6v7OnTtrBt8Dsdu3lZUVXF1dsWbNGs1jcb/fwcHBePnypfrcw8LC0KpVK0yYMAHt2rWDqakp+vfvjxcvXgD4vy6hhw8fInPmzJgwYUKCsJvYWfjLly9RsWJFTYvL9evXYWlpqVrEjh49Cg8PD7Rv3x63b99G69atNe9VYtvlhQsXUKNGDbUvBYCRI0dCp9Nh3rx5AGK7EXv16gU7OzucP38es2fPRseOHdUJVnwxMTEYOXIkjI2N0bhxY7i5ucHT01N11e3btw/p0qVTLd76ev3000/Q6XQf1eW2b98+FC9eXLVEP336FK1bt0aNGjXU2KaVK1eqfVXDhg1V2b1794a9vb3m+KDfBzx58gTTpk1TY5r27NmDokWLQqfToWTJkup5165dQ7Zs2VC5cmVky5YNXl5eKrQDCT/D48eP48mTJ6oL+s6dO6hfvz4KFSqkvjv678G2bdtQsmRJ1KlTB61atYJOp1MnHYaCQSYRCxYswMaNG9WHeevWLfTv3x/m5ub4999/MXDgQLVjX7VqFSwtLZE1a1bMmDHjvU1z58+fR61atTBr1iy1szp37hyKFSuGxo0bq+dFR0ejaNGiWLp0KYDY5j+dTpfooFU9fYrXb1QbN25EqVKlEgy4nTBhAnQ6HXbs2AEAuHz5MgYOHAg/Pz8AwLBhw2BtbY3Lly8jMjISFStWhKOjoxo7AsR+8Y2NjdWBUD+YzsrKCrVr14anpyfy5MmjWrHiij84sW3btur2vn37kC9fPnV2qe+n149L0tfx119/hZOTE2bOnKnKjN+tsW7dOuTOnRvr16/HmTNnMGTIEGTOnBmjR48GEHvgaNKkCVq3bq0OuP3790fr1q0xdOhQrF69OtFg+9NPPyF//vxo0aKFGiR3+/ZtZMmSRbUS6euwf/9+ZMyYUXVPnDt3DkOHDkVQUJB6Ttzvy8OHD1Uzsr6LQb+zWb58OZycnNRg1379+iF//vzYtGkTgNj+9TZt2qgwm1Qo9/Pzw8yZM5ElSxYcO3ZM83ls27YNTk5Oqgvo5MmTaNu2LVq1aqU58MQXEhKC77//Hjlz5sTgwYMxaNAguLi4oFWrVuo527dvh06nU6FM/3snTpyIQoUK4e3bt4l+r2/cuAFnZ2esXbsWMTExCA4ORrdu3VCwYEFVpz179iB79uxYs2YNnjx5grFjx2Lq1KlYv379ewezDxo0CO3bt1e3ly5dCp1Oh+HDhyM6OhpPnjzBDz/8oBmXA8R+3/UtKUDSZ8F3796Ft7c38uXLpw5UQOw2Z2JiglGjRqn7t27divr166N27do4ePCg5j3SGz58OMzMzFQo1IeV5s2bI126dKhataqm2yIwMBC9e/dW9x06dEhT1/cNJA8MDESGDBlUi53e8uXLkSlTJhXiZ86ciQoVKsDMzAylSpVSY2bi/p74f8e6devU+1C1alXkzZsX3t7eyJMnjzrBOXnyJHx9fWFlZQVLS8sEAT2us2fPonTp0prZavrybt26hYiICLRt2xaOjo6a14WEhGDSpEnqb4lry5Ytmq7QkJAQTJw4EVZWVuokZ/PmzahQoYKmVWfDhg1o0qQJOnXqpD7bsmXLqv3OrVu3MHbsWPTq1UuFjICAAERFRaF3796wtrbGhAkTcOHCBRgbG2tank+dOoXJkydj69atSb4Xy5cvh5WVFYoWLQoHBwcMHTpUPbZp0ya4urqqbra4n9Gvv/6KVq1aoXXr1gm6vgzBfzrIxN2QY2Ji8O+//6JEiRKwtbWFi4sL8uXLp/pFg4ODUadOHVSvXh2jR4+GhYWFeu2CBQuQOXNmjBkzRm20iR1Izpw5AxMTE+TMmVOTePfv34906dKpbqDXr1+jePHi6Ny5M4oXL458+fJpzpIT8+bNG7i7u6Np06YAYjcYFxcXjBs3TrX2ALFntzqdDqampprXx93Z5MmTR80OevDgAUaMGAETExOsXbsWoaGhePz4MRwdHTU7l+DgYKxbtw59+/bFyJEjE5wFJ/Z+XL58GTqdTo0XePHiBfr16wczMzPNIOHnz5+jQYMGmjEm+rPvuBtj3Nlk9erV07RshIWFYfLkyTAyMlJNv+PGjUOhQoVQrVo1uLm5oUSJErh27RpOnz6NvHnzasZBvHv3Dp06dUKBAgWwbNky/Pvvv5pWpeHDh8Pc3FzTLQQA1apVg729faI7S723b9+iZcuWKFSoEFq3bg2dTodKlSqpx2NiYlCtWjU1gBgARo0ahezZs6NcuXKaacnxLViwAJ06dcKoUaM0OyhnZ2c0aNBAc4B++/Yt3N3d4ejoiNq1ayNz5syYP3++5gw+sc/xjz/+QIcOHXD+/Hl1n/7MTn8W+vbtWzRs2BDu7u6a14aHhyfabaK//eeff8LExESzvVy7dg2NGjVC5cqV1fPq1asHe3t75MyZEwULFsS5c+cSHKj/+usvTbAvXrw4tm3bBj8/P7XNxz9IHDhwAI6OjujRo4e6b9iwYWjTpk2Cz3T16tXYuHGjCtzA/7XKxD+hWLt2LczNzeHk5ITvvvsOxsbG2LJlS6Kh6OzZszhy5AjKli2LzZs346effoKdnR1q1KiBf//9F35+fjA3N8eECRM07+u4ceNQpUqVBDMq44eXc+fOoXPnzhg1ahT27duHt2/f4smTJyhVqpSmTCA2nNnY2Khu2sjISPj7++PixYvqOfp9SXBwMIYPH45Ro0bh119/Vd/TmJgY3L59G6VKlUKnTp3w/Plz/PXXX8iWLRv69++vnhMVFaVaORP7jujfq1mzZqltfd++fShRogQKFSqkQj4QOykjV65cqpVGX8f4SzIAsS1C+un8cd+r8+fPw9vbW7UsRkVFoU+fPqhcubIm8MUNueHh4Rg5ciRcXFzQoUMHWFpawsvLC56enpoZqX///Tfc3NzUfY8ePUL+/PlhZ2f3US1GoaGhGD58uBpT+PLlS6xatQqFCxdWJ3wBAQHo06cPihUrplq34m7bSbVcGoL/bJCJ3zIQHByMEydOYPTo0YiKisK1a9dQsWJFVK1aVR1Uw8PDUatWLTRq1AgZM2ZUG0pISIiaXRP3ix8aGopLly4hMDAQQOwXf8qUKTA2NtZs1NHR0WjWrBmKFy8OIPaMT6fTqabxuPXVb8xLly5VYzn0j+3duxc6nU4FopEjR6qBhfqdU7t27TB9+nRMmTIFYWFhmp2Dvu5r1qxBxowZNRtnv3794O7ujoEDB+LZs2eacRRxy/jQgL8FCxZg5syZqrtq8uTJyJo1qzq7PHXqFJydndGzZ09NefqxKPqWivg7/FGjRsHIyEjtCFq1aqXpmgNim1cLFiyoxgS9evUKW7duRbt27dQZU1KePn0Kd3d3dWDW76z09Xj79q2m2V3v33//xdq1a99btn4HdPHiRVy8eBGjRo1C+vTpVYvcvXv34OXlhUGDBgGIDXbt2rVDnz598NNPPyEyMlJ9dvp//f39Ua5cOdjY2KBPnz4wNzeHh4eHavo/fPgwdDodtmzZovn8rl27hjlz5qB9+/aaA3L8QdsHDx5Ug4v9/f3VgfqXX36BtbU1ypcvD29vbzg5OanAd/bsWeh0OjWuANB+X168eIFffvkF165dU61927dvh7Ozs6ZrBogN5FmzZsWiRYsAxH6W//zzj2r9ilvuvXv3cO7cOeTOnRvt2rVTXbQdOnRAxowZkSdPHkyfPl0Fk7dv32L79u0IDAzE27dvMXfuXPz555+IjIxE3759UaBAAU2IP3nyJOzs7GBra4vSpUurcP78+XNER0ejRo0aKF++fILP/cSJE5g2bRo6d+6MM2fOJHqwPnXqFHQ6HQoVKqTpcvrnn39QtGhRNc5s6NChcHJyQtGiRdG3b184OjqiUKFCagBwfPr9zsiRI5ElSxa0b98eDRs2hIeHhwrMHTt2RP369TWTAY4fPw4zMzN1X9xAAPzfvmDz5s3Inj07vL290bp1a5QtWxb169dX38/Ro0ejdOnSarDsn3/+CVNTU+h0OvW90ou7T3369CmOHz+O4OBg9bu6desGT09PNG3aFNmzZ8eoUaPUGkghISFqX9uvXz84OjqqbTbuPiT+QXzTpk0oWbKkZpBwZGQk5s+fr2ltPnr0KIoXL/7e2Z+9e/eGTqdD/vz58ebNGwQGBqJixYro3LmzOjZMnDgRFhYW6n08fvw4mjZtipw5cyZYYyipFkB9ENX/3U2bNkWGDBmQO3duFVz279+PSpUqqUkf34r/XJCJu4MLCwtDu3btkD17dnh6esLGxkZz0Dl//jwyZMigWXDs/Pnz6Nq1K3Q6HcaMGZNkE/706dORJ08eNfBu+fLlCAsLw/Pnz2FnZ6dmOenrc+3aNZiYmKiZLjt27MCbN2/Uzi3ulzcyMhI9e/aEkZGRprUlKioKjRs3VoHo3bt3GDduHLJly4YSJUrAxMQEPj4+qilT7/Hjxwn6n0uUKAFfX181DiA4OBi//fYbjIyMMHnyZJiZmalmy8Sap6OjozXdPa9fv0bNmjVhZWWFcuXKIV26dPj999/x8OFDFC1aFG3atAEAdeDIkSOHZsxPaGiopnle7+eff0bWrFlhb2+PrFmzqubgQYMGwdvbW3M2GhYWBk9PT+h0Ok1IS+oznDFjhmraPXnyJFxdXTF+/HisWLECAwcORN26deHt7a1G/K9YsQJGRkaJDjBNqhk/NDQU9vb2KrDq3+u+fftqWqb69u0LGxsbVKxYEbly5UKrVq00LVCJvS+lS5dW75m/vz969uyJggULqh37d999h5IlS8LPzw8XL15EixYtEkyXjz+Q98SJEyhWrBhcXFwSjLfYv38/XFxcMH/+fERHR+PUqVNIly6dWngxMjISc+fOTfT9mThxIoyNjeHk5AQLCwtUq1YNr1+/RkREBKytrTFs2DDN2KLVq1fDyMgIFhYWKpjEFxUVpbaT7t27I0+ePDAzM1NLJaxduxa2traacSAA8Pvvv6NmzZqJDmKN202g32bq1q2Lzp07Izw8HOHh4ZgxYwaKFSumzv7/+usvZMyYUf3exA5E8U+Anjx5ot7bHj16IF26dOq7rH/u4MGDUaxYMTx79gyhoaE4ffo0Bg8ejO+//z7BYPjEvns3btyAu7u7pkvGx8cHWbJkwc2bN3H06FF4enrCy8sLJ0+exO3bt9GxY0d4eXmpA3Dc7VwvKCgIdevWxdy5c9V9+i5t/bai71LUf9/GjRuHwYMHY8qUKUl2bfTr1w/Zs2eHg4MDSpYsiXHjxgGI3TYzZswIb29vTYtoUFAQZs+erQbhx91XxjVq1Cg0aNAAo0ePVgPo47Ze6FtmgdjxjVmyZNGMM3zfellBQUFo2LAhGjduDBsbG/VdnTVrFtzc3NQyHufPn1czzSZNmgR7e3ssXrz4vQvhXbt2TbNopf4zWbNmDaysrFCvXj1s2bIFzs7Oav/67t07DBs2DPb29qq34VvwnwsyemfOnMGUKVPQvHlz/PbbbxgyZAh0Oh0WLVqk2ei7d+8Oe3t7TV/7q1evMHfu3ERnFwDAnDlz4OLigl9++QXv3r3D7Nmz4eDgoBakW7VqFdKlS5dgjZi+ffuiQYMGmrPryMhIxMTE4N69e1ixYgXOnj2LqKgoPHv2DIUKFVJdQPrXnD17FpkyZVIbCBDbdLxq1SrNOBcgNjS0bt0a+fPnh7OzMzp16qTOfE+ePAmdTof169drdlSbNm2Cj48PdDod6tSpk+hOOe6B78mTJ3jw4AHu3LmDgQMHIjg4GO/evUPHjh3h4OCAa9euYdOmTZozsRs3bqBChQoJuiHilv/48WPkyJEDlpaWWL16NZ4/f46CBQuqWUJHjx5FhQoV0L17d/W6Z8+eoVGjRnBwcEDz5s3VWVjcFo24BgwYgKxZs6oD1sSJE+Ho6IiiRYuiT58+6NevH7p3765pUXJ2dlafc2Lvh7+/P27fvq1+d3R0NCpWrKgZTBsTE4OzZ8/C1NRUhcWAgADs2bMHvXv31nR/xG0aX7ZsmToo9erVC+XKldPU4/Tp0yhbtqw64w4MDISLiwtcXV2ROXNmFa7174X+s43b4mdjY4MBAwYgMDBQhaSoqChERUWhRYsWaNKkidouli9fjnTp0kGn0+HevXsJWo309uzZgwIFCmDfvn149uwZ/Pz8YGlpqcbYLF++HObm5mq14dDQUPTu3Rs9evTAxIkTk1wBdu/evbC0tMSJEycQGhqK+/fvo3jx4vD29saDBw/w/PlzdOvWDSYmJpgxYwa2b9+Ofv36IWfOnBg6dKjmLD0mJkbTTdCxY0cYGxtj7969sLe315w1x8TEoEePHqhRo4aakdirVy9YWlp+cHrrpEmTkD9/fri7u6N8+fK4cOECTp06hbx582Ly5Mmaz+Xu3buJDlaNP4Ms/mrCeitWrFALNW7duhWOjo5wdnbWDErVD+gtWLAg8uXLh9KlS6sTjLh/y927dzF79mxERkbi8uXLcHR0RGBgIO7evYuaNWvCzMwM06ZNU8Fl//79cHBwgKurKxwcHFC4cGEVHONPjQdiW24dHR1x7NgxnD59GuPHj1f7p7CwMDRu3BhOTk64ceMGgoKC8ObNG4wcORKurq5Jro6+f/9+FChQAGXKlMGIESNQt25d5M+fXx3gDxw4gAoVKmgW4dy2bRvKly+PfPnyqXGGH+PatWuq1QiIDau1a9dGw4YN1arfixcvRs2aNeHk5KSZsRV/xfHdu3fDwsICjo6O8PDwUOOSgNhw7eXlpbqTgNjJKunSpVOB6/bt24meFBqybzbIxO3OiX/G8L///Q/p06eHi4uLGnwIAPXr14e7u7vmDC8wMBB58+ZNMGsjbrnx10GpWrUqFixYACC2taNGjRrImzcvVq5cCSB2RoC3t7eaEqiXWB+lfhBYjhw5ULx4cVhaWqJBgwYIDQ3F6tWrkS5dOs307fv378Pc3BwODg7qPUiMfsxPpUqVcPToUSxZsgReXl4oXry42tk1btwYRYoUSXCGdPHiRfz444/vHfcRExODvn37InPmzChWrBjy5s2rOcBHRUWpA/Xjx49Rt25dVKxYUT22efNmdVaZVFPqjBkz1I5xx44dsLKyUr9b/3jhwoVRoUIFzJo1C15eXmjQoAEePHiAqlWrYunSpZr3/NGjR5r37N27dyhYsKBajyM6Olq1ZujPlH799VeUKFFCTVlMall/feuAsbExXFxc8N133+Hdu3eIiIhA+/btUb9+fc0Byd/fH5aWlsiYMaOmZUpfD32Zev/88w/Mzc1Vt2Lnzp1Rt25dzcyamJgYdOzYEe3atVNh4/79+/j9998142ziby/6loFhw4ZppiMHBQUhOjpafQ+aN2+OcuXK4c2bNwgICED79u2xfPly+Pr6qufo63716lU1vbRbt27w8vJCRESE+t179+5Fzpw51UGob9++sLW1RbFixWBlZaUOqElNSwZiW+U8PDwQEhKinuPn5wdTU1PMmjUL0dHRCA8PR79+/eDh4QF3d3dUrFgxweyzuPTdBDY2Nnj27BmePn0KY2Nj9bfo36s///wT+fPnV61PV65cQZYsWZIctBodHY3BgwfDwcEBGzduxNatW1G+fHl4eHhg48aNmDFjBkxMTDRBZOvWrWoMRfwu3fjvy5s3b9TSBfrZftOmTYOtrS1q1aqF3LlzY+rUqWqW06tXr9T3LiQkBDdu3NC0bsb9Dv7www/IkSMHqlWrhvPnz+PQoUOwt7dH+/btkS1bNrRr106Fg6ioKHXgvnz5MiZMmKBpPYobYI4dO6ZadVxdXTFmzBjN3zho0CBYW1sjKioKr169UvvHKlWqwM7ODoULF8bRo0cTHQfz8uVLdO/eXbWAA//XNa/vkg4NDcW8efOQNWtW9O7dGwsWLED58uWxdOnST77kQ2RkpJoooO/+3LJlC9zd3TVrCsXffyTWbd+iRQvMnTsXhw8fhqOjI2rWrInLly8DiB2vFnfSx4MHD+Dr64vChQursT3fom8yyEyYMAH169fXHID9/f1x8uRJtaE0btwY2bNn1+y0Hj9+jPTp02PWrFmaA9ykSZNQpEgRzJkzJ9FlueN+2W7evIkiRYrg8uXL6N+/P7Jly4ZWrVppdkDA/41RSGzKtr48/dTmihUrqrMV/WJX8+bNw4MHD1CtWjV4e3urL7mfnx+aNm2KbNmyJXoNnlOnTmHv3r24e/cusmfPrplaffLkSVSuXFmdCb98+RLGxsaYOnWqZgDbh/j5+WH27Nlo0aIF9u/fj6VLlyJdunTo0qWLpnl30aJFyJYtG06cOIHjx49rmp4TOyu7ePEiXrx4keg4nNmzZ8PLywvR0dGqrmFhYTh9+jQaNGgAd3d3dOnSRX2uderUgaurKx4+fKimsJqZmcHZ2RnDhw9XZznr169HhgwZNN0hERERCAsLw9WrV+Hl5YU2bdokucYHEHtQ27hxIxo1aoQjR45gy5YtyJo1K3r16gUA2LlzJzw8PNQiWEBsy1qTJk3g7OysObsCtDv7uO9F4cKF1aymXbt2IUeOHAm+X7Vq1Uowdkj/fkVFRWnK/t///odWrVqpg++kSZNgZ2eHhQsXonPnzmjevDlsbGxQrVo1BAYG4sKFC8iSJQtcXFxgbGyM6tWrY9KkSShUqJBmbZ6wsDBYWVmptVQ6d+6sQmzcrooKFSqo9Vrevn2LixcvYs6cOWrNorjvuX6BxbitpGPHjoW9vb26rQ/E9evXh5OTU4JlBeJ2ISTWFRi/m0B/cPby8oKPj4/muS9fvtQsAhgTE6OZLhtfaGgoSpUqpTmoPXv2DA0aNECzZs1w8uRJlChRAsWLF8ekSZOwY8cOODo6ol69eu/dNiMiItCpUyc0b94cFSpUQM6cOVUg8Pf3h7m5OcqWLatp1dK3Iv/8889Jnlzpy+7Zsyfc3Nxw7tw5vH37Vj3m4uKCHDlyaE4UAeC3337DsGHD3jubLDw8HBs3boSFhQVGjBiB0NBQuLu7q64kfTB5+PAhsmfPrlqf/f39cfDgQfz888+qGy/udyT+Nrpz5068evUKL1++RNu2bZE9e3Y0adIEOp1OtXqGhIRg3rx5qFixIgoVKvTe9as+5M6dO2jSpIlmvFSjRo004TKxugYHB6NHjx7o0qULJkyYgD59+qjv8s2bN2FlZYXJkycjNDQUV69eRfHixdGsWTMcOnQIderUQfv27RMcf74132SQOXPmDEqVKqUGAupbBmxsbODp6Yl9+/bhzp07MDMzw+zZszU7gqFDh8LGxkYzSyImJgbPnz9HrVq1ULVqVdUsN3v2bLi7u6NBgwaapmUnJye1fkTc/md/f38sW7YMT548QVRUFJYvX666JBLz9u1bLF++XLW4HDp0CB4eHsiYMSPy58+PU6dO4ezZs8iSJQtq1KiBoUOHwtHREYsXL050xcrXr1+rhdOOHDkCBwcHnD17Vj0eGhqK2bNnw8XFRU0xHD16NHQ6XYJ1VZLy8uVL2NjYwMrKSvOejBgxAkWLFk0w+LBIkSKoU6cOHj58iDlz5qiuobh++eUX2NnZoUSJErC3t8eiRYvUhqw/A+7UqZNmlhIA9TmFh4erFgH964KCgpA5c2b8+OOPGDVqFOrWrYv9+/dj6tSpaiaJfmfr6emJ6tWrIzw8HKGhoRg/fjxatmwJExMTtG3bVnPwjB+ybt++DZ1OB2tra80qtmvXrkWuXLnU2haTJk2CpaUlKlasqFofRowYkWirV1hYGHr37q25nlBoaCg6duyITp06qZ1g3bp1Ua5cOaxatQohISH466+/ULx4cTWVOSwsDMWLF08QeB88eIDKlSvDzMwM48ePV3X8559/1Poe3bt3x6RJk7B9+3bkzJlTHWQuXryIdevWqRaKwMBAtGnTBrVq1VI70927d6NQoUKqVWvjxo2wtLRUwUJ/8OzRowc8PT0T/P1xxcTEoHfv3jA3N0fJkiXh6empthf9FNb4CyF2794dWbJkwciRIxPdTj60+Jq+m0Afsg4fPox06dJh8eLFKqwsWbIE7u7uH33mfu3aNVhaWqr3TR+iVqxYgWLFiuH3339XyxA0b94crVu3VoO/9eIHr9evX8PX1xfu7u44ePAg1qxZg2LFiqFo0aKqm2HgwIEwNTXF4cOH8eDBAzx79gwDBw6Eg4PDB2dKPn36FLa2tqrVLCAgAC9evEBAQAA2btyITJkyYf369fj333/x9u1brFixAk5OTpg4caJmLFbceu/cuRPNmjVD3bp1VRgJDg5W3ZZxw+bz5881A3Ljt4zHNWHCBHTo0AHLly9Xn7n+ZKlRo0aoVasWrly5gnfv3qFcuXJwc3PTvP758+fJci2irVu3amYSXb58WTPbL/7+48qVK6hVq5YaLK3T6VC/fn3Nc/r3749ixYrhxIkTiI6Oxtq1a1GsWDFYW1ujWbNm7205/1Z8k0EGiN1ZNWnSBFOnTkXdunXh5+eH/fv3o2PHjqqJdsCAAShTpkyCwYf6NVv0AUf/5dq6dSsqVKiAUaNGYfny5ShcuDCmT5+OunXrwsrKSq3zol8WO+5gwZiYGEyYMAEtWrRIMMjqfTN99GdKEyZMgLW1NYYOHYrg4GBYWVmhc+fOAGKbRDt06IDSpUurs9X49Bth8+bN0aJFCzx69AhmZmZYv369ZsDm9u3bUaBAAc01jvSB8GMtWbIEJiYmmlkWb9++hbOzM3744QdN/+z+/fthYWGR6JLvYWFhGDJkCAoVKoSZM2fC398fkyZNQpkyZVTXnX4J+6JFi6rWsl27dqFAgQKaNUKAhH3N48aNQ6ZMmeDm5qb5Dvzyyy+oWLGiGtR5+vRpZMqUSe1Y9+3bh/Hjx2u69OJ69uyZWmAQiD1YxJ1Nple+fHn4+PiowHT06FF06dIFXl5easaSfrZQ/OX0W7Vqhbx582oGVHbo0AHNmjVTtx8+fIiuXbvC2NgYZcqUQebMmdG9e3fNAN5Vq1YlOKMfOHAgfHx8klxhNr7q1atrmv3jDwD97bffNINkR40apRm7dP36dTRo0EBzphoREYFy5cph+vTpSf7umzdvwtPTExUqVMC+fftw5coVVKpUCQ0aNMCVK1cQHR2Nrl27wszMDP/++y8iIiLw7t07dO3aFS1atIC9vf0nXX9IT99NYGlpqdZ8mTBhghpr5uvrCyMjI03dP4adnZ2afhx3Zk3mzJlx8OBBhIaGwtvbGzVq1NC8LqkD7L///gtzc3NNq5yfnx8qV66M9u3bq/ezXr16sLKyUktPFC1aNMHMoaQUK1YMtWrVwvfff49mzZrB3d0dGTJkwKxZs1C/fn24urqiYMGCcHNzQ65cuVQr3L1791CoUCE1OHfbtm04c+YMTp48CRsbG5iZmWlC4IoVK1C+fHm1xhQQ28JRoEAB9Rkk9R60aNECzs7OaNeuHTJmzIihQ4eqE8gDBw4gf/78Kkw8f/4cpUqVUpM5kltgYCDatm0Ld3d3zX43/jbz/PlzdOrUSV23Le5CqXEvRwHEBj1nZ2f06dNH7UuePn2a5AKC36JvNsg8ffoUbm5uKFiwoKZpPiAgAHXr1kWVKlXw9u1bdeYbt29y3759mpYK/QYfGRmJHj16wNvbG+XKlVNnIhEREdi6dataCyY6OhpeXl5wdHREp06dsHLlSnh4eMDGxuajV/+N6+HDhyhTpozaCURFRaFixYrImTPne8+aNm/enOBaNkuWLFGrijZu3BiVKlXSrAGxdu1aFC5c+IsGg4WHh8PDwwOdO3fWtDitWLEiwfoOekntjAcPHqyaeWNiYtC2bVtkypQJrq6uKmy9fPkSHh4emDp1Kr777jtkzJhRMwsorocPH2oChaurK7Jmzapp2n3z5g169uyJatWqqZ1p+/btYWJikmDmQ2L1Hjt2LGxsbFS9w8LCkDVrVgwdOlQTDk6ePImcOXNi6dKlCUKz/v9xr7q7f/9+zdo1o0ePRokSJVRg27ZtG/LkyaM5242KisK5c+ewefNmzaDA+OOO/v77b9U10KBBA7WU+V9//YX9+/dj5cqVCbphHz58iF69eqFgwYLqwBc3cN2+fRuLFi1CSEgIevfuDU9PT/j5+aFevXqqlUT//h0+fBjW1tZwcXFB586dUaJECTg5OSV60VT9OKYrV65g6NChKjDeunULhQoVQu7cuTFixAhERUXh9evXauZg1apVYWFhgSZNmuD169dInz59ggHwH+vOnTto3LixJnwdO3YMM2bMwJAhQzQnAh/r559/hrGxseZvPnXqFOzs7NSyA4cOHUKmTJlUl1XczzEkJAQ9evRQY1sOHDiAggULaqbRA7HT/QsUKKC2wzdv3uDSpUvYtm2bZhHLxMaWxHf58mX4+vqicePGWLRoEXbv3o1FixbB0tISfn5+uH79Onbu3ImlS5eiZMmSajLB27dvUbFiRVSoUAElSpSAmZmZCiT6NZniDmqPjIzEwIEDkS9fPtSvX18N/q1Ro0ai+6qQkBD069cPLVu2RNeuXdX+ffbs2ZpLC5w+fVqz2ObGjRvxww8/YMWKFe8NSF/izp07CUKM3t27d7FgwQKEhISgc+fOMDEx0QTiK1euwMPDA7169dLMaJo/fz6yZMmiuTbYf8k3G2SA2FVY06VLp2lRiI6OxsGDB5E9e3Zcu3YN8+bNg52d3QcDhv7L9tdff6FcuXLIkSNHgqmqjRs3hoeHB4DYncPEiRNRp04dVK1aVZOqP9XWrVuRLVs2zbUwOnXqBHt7e4wcOTLRcsPDw9GnTx/odDqsXLlSTdVduXIlihQpgqCgINy/fx9OTk4oW7Yspk+fjkWLFsHCwgJDhw794mbUffv2oXTp0gnWUClVqpQmIMS3YMECzJo1S02PDgwMRExMDDZu3AgrKyvUrFkTixcvRtGiRdVaM/7+/mrdnWbNmqmdVmJ/Q7Vq1VClShUV3nbt2gWdTof//e9/moPC4sWLYW9vr4LHkydP1BLqiYm7UwkJCUGFChXQqVMnNU5rxowZMDU1TTCQtEGDBnBwcNAMMI87kPzmzZsoU6YMLCwsYGNjg1KlSql6hIWF4fjx47CyskK7du0wZcoUfPfddwkWQIsrIiIiwRL1y5cvh06nU68bMWIEihQpAisrK3h6eqJs2bKws7NDjhw58PjxY/j5+aFnz56wsbFBmTJlEnQHxh0A6u3tjatXr+L48eOoXr063NzckD59eowZMwb//POP5jpLt2/fxuTJk9WS+/qy4lqyZAkaNmyIwMBABAcHIyAgAOHh4ejWrRty5cqFESNGoGPHjnByclLdNC9evMCmTZvQu3dv1dJ1+fLlJFef/lj6boL407c/V0REBKpWrQpnZ2cMGjQIu3btgpubG2rUqKG+06GhoWjbti2yZs2a4PXnzp1D0aJF1ZWSQ0NDYWJiglmzZmkCyfnz55ExY0bUrVs3ybEqn7L965+r3w9duXIFDg4OCbqjFyxYoAK7/mRPp9NprrMExLbWlC1bFj/88IMmuIeGhmLbtm1o2bIlKlWqlGAWV3xt2rSBkZGRGosGxI7/ad68OWrXro3r168jNDQU9evXR7Zs2VCkSBGYmpp+cN2nlBB/m7ly5QrOnDkDNze3BGPa9K3S+pMBvcTGRP5XfNNBJjw8HGXKlEGrVq00A+2uXLmCggULYt++fYiJiUn0GjXvM3XqVOTOnVstta3fkLds2QIrK6sEF92Ku7NOaqN7n7CwMOTKlQtVq1bFqFGjYG9vj3HjxiUIUokZNWoUypYtq87anz17hkyZMqmg8Pfff6NPnz6oWLEinJ2dEx3M/DliYmJQv359NG/eXDPo+ty5c2rQ9YfWmdEfZJ48eYKKFStqFqdyd3eHhYWFGkw4Z84cTctSXHEP3AcOHICrqyt+/PFH9VlUqlQJ5cqV03QVzZ07Fy4uLomOYYobHMPCwvDdd99hwoQJms9j1apVcHZ2VgdOAChYsCBat26tGZfx6tUr/P3332pgctz3LyoqCk2aNEH9+vXx/Plz/P3332rMUtyz7D/++EOtbWRqaqoOIPHPpuPW+9GjR5rA4+joiJYtW6rrQm3fvh1Lly7FsWPHcP36dbx48QJmZmbqbHHTpk0qKMR/r+MOAH337p16nydMmIASJUrAwcEBTZo0Qa5cueDq6opOnTph4cKFqsUosYOo/r1dtWoVLCwsNMFR3+Kpb7XQj43p0qVLooH56dOnaNKkCSpVqvRJVz+OT99NULp06c/arhPz5s0bDBkyBOXLl4ejo6MK63FdvnxZDQqO2zUWFRWFZcuWwcrKSrUWDx8+HHnz5tWM+Vu7di3KlCkDLy8vtcbPlwoMDMT169exefNm2Nvbo0OHDmpsRvzPU//9nD17NurVqwcHBwf1mP47OmPGDLi7uye47pReYu/3zp07cfr0aTUW68aNGyhRogQaN26s+b7s3r0bHh4eqtU2NDQUa9euxezZs5OcdZiS3rfNjB8/Hh4eHprWqVevXqF69eqoW7fuNz+I92N900EGiF3cqkSJEpg9e7a678yZM8iXL586GCS1Hkx8+o3s7t27qFu3LurUqaMZSDVt2jTV2qEX95o6H2qmfZ+DBw+iVatWKFmy5CedAeqvppo9e3b88MMPOHHiBJo2bYqRI0dqnve+Qcef68aNGyhatKgaCKr3MevMdO7cGXZ2drh9+zYOHjyILFmyqCb3O3fuwMvLC05OTppxMHG7YqKjo/Hw4UM0bNgwQUjt0KGDZlnxa9euQafToVy5cvj555+xePFimJqaYsSIEUm2or1+/Vp167Vu3VpzINWrU6cOfH19VYvFvn37VOtPXO/evUPt2rXRpEkTPH/+HCNHjkTXrl1x8+ZNGBsba/rDIyMjUb9+fTXLJy794m9xA198MTEx6NevH0xMTGBjY4N69eohMDAQx44dg06nw86dOxP9nv71118oXbq0pss1MUkNANWvKdKwYUPUr18fkZGRePr0KRYvXoz27dvD29sbd+7cSfB+6wfuxx37lStXLqxevRpA7ElCmzZtUK9ePRVYZ86cCVtbWxQpUkTTPXD27FkMGjRITRVOjgXB4ncTJJfg4GDNgofx1zqKiorC7NmzkT9/fs0icHfv3kXTpk01awi5uLigbNmyGD58OFasWIHSpUtj5cqVqFKlCvr37/9F+yV93TZs2IBy5crByspKM/g2bogJCwvDvHnzkC5dOnViefPmTeTIkUMtHqh/LxO7KnNSlxU4cOAA7Ozs4OTkhCJFisDBwUGNeZs2bRpKly6txrfp9enTB6VKldJcJiC1JLXNPHv2DPfu3cN3332Hhg0bakLWkiVL0L59+/fOhPsv+eaDTExMDBo2bIhs2bKhS5cuauP39fVVB+/POZvSn/m0bNkShw4dUs2Aca9InNwiIyM/u+z9+/ejatWqKFOmDEqWLKnWdPnc7q6P1adPH83UW72PWWcme/bsGDFiBHbs2AFPT0906dIFZ8+eRePGjdGtWzfNWWbczzDuOBZbW1t07NhRs7O/desWXF1dMXjwYNV0PXToUOh0OnTt2hU1a9bUtKQktqMfM2YM8ufPDyB2R2Rvb4+BAwdqmsKPHTuGjBkzYvr06apJvUqVKomeBW/atAmFChWCmZkZChQogB07diAgIADW1tZqB6f/7P/66y+kT59eBST93/7ixQvUqVNHHRTi8/Pzw/z589GyZUv4+flh27ZtcHFxUat+fvfddyhXrpwaJHjp0iW1CJ+pqSm6d+/+UddjSWwAaPr06TFt2jSMHTsWXl5eapr4h/z6669Ily4dXFxcVGth165dVfcJEDt+qXLlylizZg127doFb29v7NixI8H4mufPn2PDhg1q9da0LO4JUFJBaffu3ahQoYLmGlxAbJdXgQIFVBfk1atXMXz4cBQvXhx2dnZq7ZTq1asnmDb+uZ49e4bt27cnOhX85s2b6NChAyZNmoRHjx7BxsZGXWgxOjoaEydORLZs2TTbDhD72Zubmye5LQYGBiIkJAQeHh6aK6j7+PigYMGCePToEV6/fo2qVauiZcuWmuB6+vRpNG/ePNFZkqnhQ9uMt7d3snVhfou++SADxJ6l5MuXD5UqVULfvn3VjJf4Fi5cqM70kqLfkIKCgtChQweYmJjAy8sLXl5eaNmy5Sett/K16dcm0el0aNeu3Vf5nYkFpY9dZ2bhwoXIlSsXDhw4gB9//BHOzs7Imzcv6tSpk2Cnpzds2DBUqFBBDSrcvHkzrK2tEzRRDx06FBYWFmp1zvDwcGTNmjXBwMj49KHh8OHDsLGxUWeLM2bMgIODg2a1z6tXryJXrlxwc3NTgzOTCqI//vgjsmTJAicnJ3Udmxs3bqBq1aro06ePpuXv1KlTsLa21hyQ9fWqWbOmCiZx6afFW1tbaw4MK1euhJubG1atWoWAgADodDp1PaY9e/agcePG8Pb2TnCto/dJbADo4sWLYWFhgUOHDqFevXqoWrVqoicSkZGRmDRpkvrbTp48iSpVqmDixIkwMTHB8ePHMXbsWDRq1EgN0L527Rrq1KkDOzs75M6dW42d0PvSFoe04scff8SECRNUoIuMjMSECRNQuHBhzdir58+fw8vLC0WKFNGcxb9580YzjqV48eIJWiqS24QJE5A5c2a0bt0aa9euxcuXLxMMan706JEmUD99+lStJ5XY4NWYmBj0798fTk5O6N+/P8qUKQMgthWnX79+yJYtG7p166b2EWvXrkXp0qU1M/zSmk/dZkjrPxFkgNgVOTdu3JjogXXnzp2wsbGBq6srhg0b9t5r2AD/t2Pctm0bSpUqhZ9++klzYE3pVo4vER4e/lkzp5LLp64z4+rqiu+++w4vX75EQECAGn8UvxXt0KFDsLCwQNmyZbFs2TLN9U98fX1Rq1YttfolEDvFOmvWrGjatKkKI/qxTPEXQ7t//z5mzJihCRMHDx6Em5ubZlxLuXLl0Lx5cxWGFi5ciB49emiWIdc3iye20uju3btRunRpjBo1Sp2Fjx49Wq0mqve///0PLi4uCcZ/+Pv7w83NTbNaaVxLliyBqampJtSFh4ejVatWqFq1Kl69eoVRo0YhT548uHHjBqKjoz+7+yWxAaD29vZ4+vQpLl++jMDAwAShTr8icP369ZEvXz41ALp48eL47bffsGjRIrRp0wbdu3dH/vz5NZ9nWFgYLl269N41fQyVn58f7Ozs4Orqip49eyJv3rzo2LEjrl69ivv376NWrVqatUXCw8PVNcX0SzQAsd/vvXv3YuDAgTAzM0PDhg2TPCFIDhcuXECJEiUSdO3GxMTAzc1Ns9Ls7t27odPpUKNGDeh0OnTq1CnRlqgTJ05g7ty5qF+/Pi5fvoxly5ahdu3aWLp0KfLmzQtPT88ECx0CsQHfy8srTV9f6GO2GUrcfybIJBUuLl++jGLFiqlZEnEH5n6MuANEE1uNNi1JKzv2T11nJl++fGqdmQsXLmgWxYq7PP7gwYMT/X2nT5+Gra0txo8fr85ohg8fjoYNG6JVq1bw9/fH/PnzE4Qo/Y5l2bJlsLOzQ40aNdS02rCwMJiYmGhaYP744w94eHjAysoKFSpUQK5cuZIcgJyUXr16oVKlSmoROn9/f3Tv3h3GxsZo1KgROnTogKxZs6qrtMf9TDt06IDq1asneaG5pKbFHzhwAOXLl1frZuTMmTNZpnHGHwDarl27RLev3377DZ6enhg8eLD6ezw9PVGjRg1cvHgR69evR+XKlQEA8+bNQ6NGjaDT6ZLsHo3/vhiKuPuPuPXv1KmTug4WEDvgOVOmTGqw6rJly1CgQAFMnToVISEh6oruq1ev1rSkRUREYPXq1ahUqdJnTzv/FLNnz4adnZ3m8hf6v+/QoUPQ6XSa79nu3bsxceLERIMIoD0J0neb/fzzz7C0tESePHmwceNGzfXBpk2bplpCz549m+S6T2nJx24zpPWfCTJJjYPRX/8CiN3Qr1+/jrt372pmAyS2U4x/tpCWA0xa8ynrzOiXngdi14Bxc3ODr68vgNgxHcOGDcObN29QqFAhNYD5119/xcqVKzF06FA1jmbKlCmwt7dH+fLlUaFCBRQvXlyNm3n37h3s7OzQq1cv9VlPmjQJ1atXx+DBg3Hr1i34+/ujRIkSqFq1qgoZ9evXx4ABAzR/29WrVzFv3jyMHTtW06z/sWObbt26hbJly6Jnz56aZeM3bNiA3r17o2HDhgmutqv/7n3MLLakpsV37doVLi4u8Pf3/6hyPuR9A0D1Xr16hUaNGiFnzpyYOHEi9uzZo84679+/j2bNmsHT0xM///wzOnTogIsXLyIyMhLr1q2DTqdDkyZNkm22UGqLP5BX7969eyhatCj+/fdfvHnzBu3atYOJiQl69+6tAv+LFy8wbdo0ZMiQAc7OzjA2Nk5yKu7XfL+GDBmC4sWLJznRoV27dnBxcfmkmWNLlixB5syZVfeav78/PD09UbduXU239OHDh1GhQoUvuqTA15bUNkMf9p8JMnqbN2/G5s2b1UqO//zzjxoz4uXlBV9fX+TOnRv58+fH6tWrE2yA8c/2bt26xRDzGT5mnZnQ0FB4eXlpBvJt3LgRxsbGMDIyQr169VTImTJlCkxMTJA7d27UqlULFSpUgLOzM2xtbfHo0SOEhYVh//796NGjB4YPH55g0Or27dvh4eGBDRs2YPTo0XB2dsbo0aORO3du1K5dG3fv3sXDhw/Rt29fdQ2jGjVqqFaMpA4Qn3PgmDFjBkqXLo3JkyfjwoULqFy5coI1ORK7FtDHSGpa/J07dz659ehDAgMDkxwACvxf12xSK5CePXsWrVu3RqZMmeDp6alpMUupxcq+tvif4aRJk1C1alV07doVfn5+CAoKQtasWdG1a1fkyZMH1apV08wcu3fvnjqpOnHiBDZt2pTorMnUcPr0aaRLl04zlismJgbPnj3D5s2bcevWLWTJkuW9F+mMT7+kRtu2bdWMnfXr16NcuXKwtrZGjx490KhRIxgbG2PUqFHJ/jeltPcNmqakfXNB5tixY2o8QlwnTpyAg4MDnJycULp0aeTPn1+l9c2bN6Nx48b48ccfsXHjRgQEBKBmzZqoXbu2OuDFn0qoX1q/devWbPr7DB9aZ0Yvfh9+ly5doNPpNGtP6O3duxfHjx/H1atXERwcjIiICOh0Os2iZ0kd/KOjo+Hj44OmTZuiSZMmaizO6dOnUaFCBXTv3l21VHTr1g1NmzaFTqdDpUqV3vs3fo53796hV69ecHZ2Ru7cudGyZUvN4186Ky6pafHJ6eLFi7C3t1dX4dW/72vXrkWPHj0AxJ5d58mTB69fv8bRo0exatUqjB49WjPrKjg4GBUrVoROp0twBXrgy9+LtMLf3x+DBg2Co6Mjxo4di2LFiqnF9oYPH57gewzELi0xevToRINgWmipioiIQKtWrVCwYEGcO3cOwcHBCA4OxqhRo9CoUSMEBAQkWCn7Y+hPgvQTM2JiYvD48WMMGjQIPXv2ROfOnT9rZWUyXN9ckHF2doa3t3eCq542aNBAs8Lj9OnTodPpElydVa9Hjx7o1KkTAO0B6d69e+qy9+PGjftPXJArpbzvgBr3PX/06JFaLyYgIAAHDhyAubm5mn2W1JTgFStWwNPTUxOU3ueff/6BmZkZypYtq7l/4sSJKFu2rGo9CgkJwcGDB1GoUCE4OTl9dPmfIiwsDP/8889HX3TwUyU1Lf5L6VsDwsLCYGFhoRnbAcReX0rfdXjkyBFUq1YNmTNnhqurK+rXr48qVaogW7ZsmmvqXLp0CT169Ej2FqO0Yvz48ahduzaqVaumZvI8e/YMY8eOhZGREVasWIG8efNixowZePz4MSIiInDgwAGULl0anTt3TtA1k5bGB4WFhcHDwwOWlpbw9PSEnZ0d7O3tcfz48c8uM+5JUNzLbtB/1zcXZI4dOwYjIyO19gYQ2wxtbm4OIPbMsE+fPsiRIwfatGmjGQl++vRp/P3332jatCnMzc0TXHekb9++MDExQevWrbmiYjKJf0BN7Az78uXL0Ol0ajXT58+fo3///rCwsFBBUt+87ufnh3nz5qFmzZowNTVN8iKaSenVqxdKlSqlab5//PgxateurS62qbdz506Ym5unWNjQi4qKSvbuy5ToDh04cCBq1aqllr3fsmULMmTIoAZv/v333yhQoIBm5sizZ8+wa9cu3Lp1S51Fd+zYERUqVEj0d6Slg3RyOXv2LPLkyYOiRYtq7n/w4AFKly6N9u3bY8uWLTAzM4OdnR28vLxgbGycYFHLtCowMBCHDh3CzJkzk235/6/RqkiG45sIMvHHC7Rr1w7FihVT3RKnTp1C+fLlMXXqVOTPnx9ly5bVtMRERkbiyZMnGDBgAAoUKIDvvvtOs4AaALRo0QKOjo4JBlrSl0nqgLpgwQLMnDlTHQQnT56MrFmzqsHBp06dUld81YuMjMSuXbvQsmVLdO/e/bOWGw8MDESpUqUwZswYTd1Wr16NIkWKqNltQOx04Zw5cyaY7fRfow8XBw4cgE6nw9atW9V7V61aNXh6egKIXVPE29s7weviun//PipVqpToFde/5bFoffr0gb29fYLxIiNGjEC1atUAxHbX7dixAz/99JPmGknf8vvyPinVqkiG55sIMnpHjx5Fr169MHXqVOh0OnU2fvz4cTg7OyNXrlxYtmyZ5jUrVqxQV8e+cuWKZopeVFSU6msOCgr6z+4wUtLHXHPp999/x8OHD1G0aFG1aNbbt2+xYMECpE+fHr/99hsWL16MunXr4vjx45/V7x7X3LlzUalSJc21hMLDw9GjRw91WQMAWLduHVxcXNQ4kP8y/bZRr149uLm5qVaX8+fPI3369Ni5cycaNWqUaEB58OAB1q1bh+7duyNHjhxo2LDhB9dy+tY8e/YMJUqUSDAOqEWLFqhRo0airzHUaebJhftj0vtmgsy0adOQOXNmDB48GEOGDIGtrS0sLCzUrJYOHTrAw8NDcyD666+/4O3tjQEDBmhGiaf19WC+FR9zzaWOHTvCwcEB165dw6ZNm6DT6dQidIGBgejatSvs7e1haWn5wVWZP1ZYWBg8PT3RvXv3JFfSfPbsGfLkyZNgefj/krgL++kD/+PHj5E+fXrMmjVLjV3q1q0bdDodzM3NkTNnTowePRoHDhxQXXJ37tzBhAkTUL9+/QQzXP5L5s+fj/z582Ps2LG4cuUKjh07hsKFC2vGC+n9194bovcxuCATP2DExMQgJCQElStXxogRI9T9169fh5WVFb7//nsAsQM5mzdvDmNjY9SrVw/16tVD5syZ0b9/f4aWVPQx11wyNTXF0KFD8fjxY9StWzfBBRPjX1MnOezcuTPRrsS4a2J8yZWTDV3cWTHxt5/BgwfDwsJCreHz9OlTWFtbo2HDhli0aBEaNGigZmR5eHhg165dmhaY+DME/yvCw8Ph5uaGrFmzol69enBxcUG3bt1Su1pEaZ7BBJn442DiX1XV2NhYLYWtP9CsWLECGTNmVFdCjYyMxNKlSzF16lT069dP0yXAMPP1few1lxYtWoRs2bLhxIkTOH78OHQ6XYJrJyW3mJiYBGu3UEITJ07EDz/8gMmTJ6tLCgBA7ty50bNnTzWLac6cOciRI4d6zqtXr3DkyBHMmjVLE4r+iwEmLv0qy/PmzdMsTMj9E1HSDCbI6J09exatWrVCt27dsHr1ajUo19PTU3NdESB2qnTu3LnRunXrJMdN/Nf7mVPLp15zqUiRIqhTpw4ePnyIOXPmfLNTcdOq+NeHunz5MgoXLgwXFxf07t0buXLlgqenJ7Zu3Qog9iTCyMhIdRVFR0ejWLFi8PX1TfSgzG0wln5qcdOmTdW+LamrXxNRrHRiIADIpEmTxNPTU7JkySLR0dGyZs0a6datm4iI1KhRQ65cuSKHDx9Wr3n69KkYGRnJ+vXrZf/+/QnKjImJkfTp04tOp/tqfwfFypkzp4wcOVLevHkjGTJkUPcPHz5coqKiZPPmzfL06VN1/6xZs+Ts2bMiItKnTx8pVqzYV6/zf5lOpxOdTieBgYEiIrJlyxaxtbWVU6dOydy5c+X48eNiY2MjY8aMkZiYGOnQoYM4OzvLrFmz5NmzZ5IuXToZM2aM3L9/X968eaMpGwC3wf9Pp9PJ9OnT5dq1a7J8+XIREcmYMWMq14oobUuTQSY6OjrBfY8fP5Y9e/bI1q1bZcmSJfLzzz+LtbW17Nq1S06fPi0NGjQQc3Nz6datmxw8eFBu3Lghq1atkqFDh8qvv/4qtWvXTlBmunRp8s//z2jXrp24uLjI0aNH5eXLlyIikiVLFhk0aJAcPHhQ/vzzT/Xc6tWry+PHj8XKyiq1qvuft3PnTqlXr55cuXJFLl26JNmzZ5esWbOKiIizs7N06tRJoqOjZdasWSIisnTpUtm9e7ds375doqOjpWHDhnL58mXJmTOnplyGGC0HBwfx9vYWCwuL1K4KkUFIU0dyAKqVRETk5s2b8u7dOxERuXbtmkRHR0vVqlVl586dUrhwYTl9+rRs2bJFSpcuLa6urvLjjz9KgQIFpEuXLlK6dGk5d+6c1KlTRxo2bCiZM2cWAKn551E8mTJlknHjxsnFixdlz5496v4OHTqIqampLF26VAICAlKxhv9N8beTmJgYERExMjKSa9euiZmZmURFRYmJiYmmdaV06dJib28vL1++lKioKHF3d5f69evL06dP1TYtIhIVFfV1/hADNmvWLOnSpUtqV4PIIOiQBo/uN27ckO7du0tgYKBMmzZNateuLRs2bJDhw4eLnZ2dXLp0SQYNGiS9evWSLFmyyOvXr+XOnTvi5uYm4eHh8vjxY3n27JmUKVMmtf8U+gAAKmhOnjxZ7OzsRETk/PnzEhERIR4eHqlcw/+umzdvSuHChVXXz7t378TR0VFWrFghr169kt69e8umTZukcuXK6jVFihSRmjVrysyZM0WE3UZElPJSvUUmfjfS3r17xcvLSxwcHGTp0qXi5OQkIiJNmjQRABIYGCinT5+WIUOGSJYsWSQqKko2bdoke/bskXfv3omRkZHY2dmpEJNYNxWlHTqdTqZNmyZXrlyRtWvXqvtLlizJEJOK9u7dK66urrJgwQIJCQkREZHXr1+Ls7OzXLp0SZo2bSo2NjYydepU+fXXXyUsLEx27twpAKRWrVoJytO36hARJbdUCzL6hqC43UgiIps3bxYvLy+ZP3++lC1bVgoWLCgisQPeBg0aJPfv35cjR47IzZs35fHjxzJs2DCZNm2aFChQQIyNjRP8nrhN2pQ2FS5cmGMC0pjq1avLvHnzZM6cOTJixAiJiooSS0tLSZ8+vfz9998iIrJw4ULJkyePtG7dWipVqiRNmzaVZs2aSfXq1VU5+tYYjkcjopSSKl1LcZubr127Jo0aNZKiRYvKmjVrpFChQtK3b18ZMGCAREdHS/r06SUmJkbtCPv06SO7du2STJkySVhYmJiYmMiyZcvYjWTg4n7GlHasWrVKZs2aJXZ2drJmzRrZtWuXjBs3Tv755x/JnDmzhIeHy/nz5+XBgwfi7e0tZmZmIsIuJSL6elJtjMyLFy/kwIED4ufnJ1mzZpXBgwdLVFSUNG/eXOzs7GTp0qUi8n87xNDQUDEyMhIA4u/vLw8ePJCoqCipUqWKep4IZ0AQJbfr16+Lr6+vlClTRnLlyiWPHz+WiRMniqura4LnRkdHS7p06bgdEtFX81VOgRMbp7JixQrp2bOn7N69W9q0aSM5cuQQMzMzcXV1lXv37snevXtF5P8CytatW+X48eOSPn16sbW1FU9PTxVioqOj1ToXRJR8YmJixMnJSVatWiU5c+aUxYsXy/bt2yVTpkwJnguA6zIR0VeX4cNP+XyIXTlYjVN5+fKl5MqVS0REevToIcePHxc/Pz/NOJYffvhBBg0aJEOGDJF3796Jra2t/P7777JgwQKZMmWKVKpUKcHv4TgYopSh7+7z9PSUsmXLSlhYmGTLlk0cHBwSPJcBhohSw1fpWjpx4oSMGjVKoqKixMrKSrp06SJeXl6yY8cOGTx4sHTo0EGGDBminn/hwgWZOXOm/Pnnn5IxY0YxMjKSBQsWiLe3d0pXlYgSoR/DxLEvRJTWpHiQWbZsmQwZMkS6dOkixYoVkzNnzsjSpUvlyJEj4ubmJm3btpWnT5/K+PHjE0y3ffbsmfj7+0vJkiVFhONgiNICfUsrB2cTUVqQbHuixMbBREREyO7du2X8+PEydepUadmypVhZWcnbt2/l/PnzIiLSs2dPefHihWzfvl0iIiJE5P/WnMiTJ48KMRwHQ5Q26HQ6hhgiSjOSbW+kH6dy5coVdbG/d+/eyZkzZ6Rq1aqyb98+sba2ljVr1siOHTukc+fOIiJSpkwZqV69uuzcuVN27twZW6lEdpIcB0NERETxffZg3/jNy/fv35dWrVrJ9evXJV++fNKvXz+pWbOmODk5SbVq1SQyMlKGDh0qP/zwgxgbG8uLFy/k5MmT4uvrK99//708fPhQnJ2dk+0PIyIiom/fZ7XIxMTEqOZlfTfQrl27pFSpUrJv3z7x9fWVfv36yY0bN8Tc3FxMTExk1apV0q9fP8mcObOIiOzfv18WLFggz58/Fzs7O1m3bp24uLgk319GRERE37xPapHRz1zQt8JMnTpVzp49KwULFpRnz57JoEGDxNnZWUqXLi2nT5+WhQsXSpMmTeTBgwcydepUERExMzOTtWvXyq+//ioDBgyQnDlzJiifiIiI6GN81qylx48fy9y5c2X79u1SoUIF2bRpk+TIkUN+//13tdrnnTt3xMHBQVasWCHW1tYyY8YMuXv3ruh0OsmZM6csWrRIDeQlIiIi+hyfPEZmwoQJ4ufnJ8bGxrJ//36xsbGRFi1aSPPmzeX48ePi6OgoGTJkEHt7e+nevbtMmTJF9u7dKzt37pR3796Jv7+/6kLSd1FxJhIRERF9jk9ukTl//rzUqFFDChcuLH/99Ze6v3nz5hIQECCzZ89WLS0xMTGSIUMGGTJkiIwfP14yZsyonq+/ICQRERHR5/rkASklS5aUFi1aSFhYmJw7d07dP3HiRLl7967s3r1b3r59G1t4unTyxx9/yKBBgzQhRoTTqYmIiOjLfdbI2lGjRomIyI4dO9SspUKFCknbtm1lxYoVcvToUfVcLy8vyZUrl3oeERERUXL5rCCTJ08eadeunRw+fFgOHTqk7h8xYoRYW1tLnjx5Ev4izkYiIiKiZPbZ11oKDw+X6tWrS9GiRWXChAnqqtZEREREX8tnN5MYGRnJ4MGD5dChQ3LlyhXNY4ldd4mIiIgouX3R1a8ByLVr17giLxEREaWKLwoyRERERKmJI3CJiIjIYDHIEBERkcFikCEiIiKDxSBDREREBotBhoiIiAwWgwwREREZLAYZIiIiMlgMMkRERGSwGGSIiIjIYDHIEBERkcH6f5PUJMe/psw/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_df = brands_infos_df.sort_values(by='mean_power_ps', ascending=False)\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(_df.index, _df['mean_power_ps'])\n",
    "fig.autofmt_xdate()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_df = brands_infos_df.sort_values(by='mean_mileage_km', ascending=False)\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(_df.index, _df['mean_mileage_km'])\n",
    "fig.autofmt_xdate()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions\n",
    "As we chose to analyze the brands of the cars and their respective values of price, power and avg_mileage on the site, we could see that:\n",
    "#### Price\n",
    "- Mini, Audi, BMW and Mercedes were the most expensive brands. This was expected, since the're luxury brands.\n",
    "- Mazda, Volkswagen, Hyundai, Volvo, Toyota, Nissan, Seat and Ford were in the middle. I expected Volkswagen to be cheaper, since It's a German brand. Asian brands were also more concetrated in this category\n",
    "- Citroen, Smart, Mitsubishi, Peugeot, Opel, Fiat and Renault were the cheaper brands. It seems that french brands are popular in Germany.\n",
    "#### Power\n",
    "- BMW, Mercedes, Volvo and Mini were the most powerful brands. Expected from luxury brands.\n",
    "- Mazda, Mitsubishi, Hyundai, Toyota, Nissan, Skoda, Volkswagen and Ford were in the middle.\n",
    "- Opel, Peugeot, Seat, Citroen, Reanult, Fiat and Smart were the least powerful ones.\n",
    "#### Mileage\n",
    "- Volvo, BMW, Mercedes, Audi and Opel are the most used brands, although the difference isn't that big for other brands.\n",
    "- Fiat, Skoda, Hyundai, Smart and Mini are the least used ones."
   ]
  }
 ],
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