Data Analysis for eBay Car Sales in Germany.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Project: Data Analysis for eBay Car Sales in Germany\n",
    "\n",
    "--By Lu Tang\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "> This project will analyze the vehicle market in Germany. The dataset used in the project was scraped and uploaded to Kaggle https://www.kaggle.com/orgesleka/used-cars-database/data, saved as 'auto_kaggle.csv'.\n",
    "\n",
    "**The data columns description as following:**\n",
    "- `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 which year the car was first registered.\n",
    "- `gearbox` - The transmission type.\n",
    "- `powerPS` - The power of the car in PS.\n",
    "- `model` - The car model name.\n",
    "- `kilometer` - How many kilometers the car has driven.\n",
    "- `monthOfRegistration` - The month in which year the car was first registered.\n",
    "- `fuelType` - What type of fuel the car uses.\n",
    "- `brand` - The brand of the car.\n",
    "- `notRepairedDamage` - If the car has a damage which is not yet repaired.\n",
    "- `dateCreated` - The date on which the eBay listing was created.\n",
    "- `nrOfPictures` - The number of pictures in the ad.\n",
    "- `postalCode` - The postal code for the location of the vehicle.\n",
    "- `lastSeenOnline` - When the crawler saw this ad last online.\n",
    "\n",
    "**The project amis to answer the following questions:**\n",
    "> - Question 1: What is the most common brands of cars in Germany and their listed average prices?\n",
    "> - Question 2: Among common brands, are there large differences on kilometer that can affect listing price?\n",
    "> - Question 3: What are the factors that can affect car prices?"
   ]
  },
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   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "### Step1_1. Initial Data Exploring and drop irrelevant columns and duplicated rows"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 371528 entries, 0 to 371527\n",
      "Data columns (total 20 columns):\n",
      "dateCrawled            371528 non-null object\n",
      "name                   371528 non-null object\n",
      "seller                 371528 non-null object\n",
      "offerType              371528 non-null object\n",
      "price                  371528 non-null int64\n",
      "abtest                 371528 non-null object\n",
      "vehicleType            333659 non-null object\n",
      "yearOfRegistration     371528 non-null int64\n",
      "gearbox                351319 non-null object\n",
      "powerPS                371528 non-null int64\n",
      "model                  351044 non-null object\n",
      "kilometer              371528 non-null int64\n",
      "monthOfRegistration    371528 non-null int64\n",
      "fuelType               338142 non-null object\n",
      "brand                  371528 non-null object\n",
      "notRepairedDamage      299468 non-null object\n",
      "dateCreated            371528 non-null object\n",
      "nrOfPictures           371528 non-null int64\n",
      "postalCode             371528 non-null int64\n",
      "lastSeen               371528 non-null object\n",
      "dtypes: int64(7), object(13)\n",
      "memory usage: 56.7+ MB\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateCrawled</th>\n",
       "      <th>name</th>\n",
       "      <th>seller</th>\n",
       "      <th>offerType</th>\n",
       "      <th>price</th>\n",
       "      <th>abtest</th>\n",
       "      <th>vehicleType</th>\n",
       "      <th>yearOfRegistration</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>powerPS</th>\n",
       "      <th>model</th>\n",
       "      <th>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</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</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</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</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</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",
       "  </tbody>\n",
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      "text/plain": [
       "           dateCrawled                            name  seller offerType  \\\n",
       "0  2016-03-24 11:52:17                      Golf_3_1.6  privat   Angebot   \n",
       "1  2016-03-24 10:58:45            A5_Sportback_2.7_Tdi  privat   Angebot   \n",
       "2  2016-03-14 12:52:21  Jeep_Grand_Cherokee_\"Overland\"  privat   Angebot   \n",
       "3  2016-03-17 16:54:04              GOLF_4_1_4__3TÜRER  privat   Angebot   \n",
       "4  2016-03-31 17:25:20  Skoda_Fabia_1.4_TDI_PD_Classic  privat   Angebot   \n",
       "\n",
       "   price abtest vehicleType  yearOfRegistration    gearbox  powerPS  model  \\\n",
       "0    480   test         NaN                1993    manuell        0   golf   \n",
       "1  18300   test       coupe                2011    manuell      190    NaN   \n",
       "2   9800   test         suv                2004  automatik      163  grand   \n",
       "3   1500   test  kleinwagen                2001    manuell       75   golf   \n",
       "4   3600   test  kleinwagen                2008    manuell       69  fabia   \n",
       "\n",
       "   kilometer  monthOfRegistration fuelType       brand notRepairedDamage  \\\n",
       "0     150000                    0   benzin  volkswagen               NaN   \n",
       "1     125000                    5   diesel        audi                ja   \n",
       "2     125000                    8   diesel        jeep               NaN   \n",
       "3     150000                    6   benzin  volkswagen              nein   \n",
       "4      90000                    7   diesel       skoda              nein   \n",
       "\n",
       "           dateCreated  nrOfPictures  postalCode             lastSeen  \n",
       "0  2016-03-24 00:00:00             0       70435  2016-04-07 03:16:57  \n",
       "1  2016-03-24 00:00:00             0       66954  2016-04-07 01:46:50  \n",
       "2  2016-03-14 00:00:00             0       90480  2016-04-05 12:47:46  \n",
       "3  2016-03-17 00:00:00             0       91074  2016-03-17 17:40:17  \n",
       "4  2016-03-31 00:00:00             0       60437  2016-04-06 10:17:21  "
      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the libraries we will use\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Loading data and check information and first 3 rows\n",
    "autos=pd.read_csv('autos_kaggle.csv', encoding='Latin-1')\n",
    "autos.info()\n",
    "autos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis**\n",
    "\n",
    "> - Column names need to be changed to be more descriptive and easier to work with. \n",
    "> - There are some columns contain null-value data.\n",
    "> - Some columns may not useful for analysis.\n",
    "> - Some columns contain non-English words and need to change to English to undertand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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       "      <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",
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       "      <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",
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       "    <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>2016-04-06 13:45:54</td>\n",
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       "      <th>freq</th>\n",
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       "      <td>30070</td>\n",
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       "      <td>223857</td>\n",
       "      <td>79640</td>\n",
       "      <td>263182</td>\n",
       "      <td>14450</td>\n",
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       "      <td>17</td>\n",
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       "      <th>mean</th>\n",
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       "      <td>1.729514e+04</td>\n",
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       "      <td>2004.577997</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>50820.66764</td>\n",
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       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>3.587954e+06</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25799.08247</td>\n",
       "      <td>NaN</td>\n",
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       "      <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": [
       "                dateCrawled         name  seller offerType         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 vehicleType  yearOfRegistration  gearbox        powerPS  \\\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      kilometer  monthOfRegistration fuelType       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",
       "       notRepairedDamage          dateCreated  nrOfPictures    postalCode  \\\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",
       "                   lastSeen  \n",
       "count                371528  \n",
       "unique               182806  \n",
       "top     2016-04-06 13:45:54  \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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis**:\n",
    "\n",
    "> - `seller` and `offerType` only have 2 unique value, with more than 370000 frequency;\n",
    "> - The following columns have *odd max and min value*: \n",
    "   `price`\n",
    "   `yearOfRegistration`\n",
    "   `powerPS`\n",
    "   `nrOfPictures`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "privat        371525\n",
       "gewerblich         3\n",
       "Name: seller, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos[\"seller\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Angebot    371516\n",
       "Gesuch         12\n",
       "Name: offerType, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos[\"offerType\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    371528\n",
       "Name: nrOfPictures, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos[\"nrOfPictures\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:**\n",
    ">`seller` and `offerType` have most of the values the same; `nrOfPictures`column has 0 for every column; `dateCrawled, abtest, nrOfPictures, monthOfRegistration, postalCode and lastSeen` are irrelevant to our analysis for car price, so we can drop these columns."
   ]
  },
  {
   "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>name</th>\n",
       "      <th>price</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>fuelType</th>\n",
       "      <th>brand</th>\n",
       "      <th>notRepairedDamage</th>\n",
       "      <th>dateCreated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Golf_3_1.6</td>\n",
       "      <td>480</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>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-24 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name  price vehicleType  yearOfRegistration  gearbox  powerPS model  \\\n",
       "0  Golf_3_1.6    480         NaN                1993  manuell        0  golf   \n",
       "\n",
       "   kilometer fuelType       brand notRepairedDamage          dateCreated  \n",
       "0     150000   benzin  volkswagen               NaN  2016-03-24 00:00:00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop unnecessary columns\n",
    "drop_col=['seller', 'offerType', 'abtest', 'dateCrawled', 'nrOfPictures', 'monthOfRegistration', 'postalCode','lastSeen',]\n",
    "autos = autos.drop(drop_col, axis=1)\n",
    "autos.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3934"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out how many rows are duplicated\n",
    "sum(autos.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop duplicated rows\n",
    "autos.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step1_2. Clean Column name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'price', 'vehicleType', 'yearOfRegistration', 'gearbox',\n",
       "       'powerPS', 'model', 'kilometer', 'fuelType', 'brand',\n",
       "       'notRepairedDamage', 'dateCreated'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis**:\n",
    ">- Change the columns from camelcase to snakecase.\n",
    ">- Change a few wordings to more accurately describe the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>price</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>kilometer</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>brand</th>\n",
       "      <th>unrepaired_damage</th>\n",
       "      <th>ad_created</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Golf_3_1.6</td>\n",
       "      <td>480</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>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-24 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name  price vehicle_type  registration_year  gearbox  power_ps model  \\\n",
       "0  Golf_3_1.6    480          NaN               1993  manuell         0  golf   \n",
       "\n",
       "   kilometer fuel_type       brand unrepaired_damage           ad_created  \n",
       "0     150000    benzin  volkswagen               NaN  2016-03-24 00:00:00  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.columns = ['name', 'price', 'vehicle_type', 'registration_year', 'gearbox', 'power_ps', 'model',\n",
    "       'kilometer','fuel_type', 'brand','unrepaired_damage', 'ad_created']\n",
    "autos.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step1_3 Investigate the columns (1.'price', 2.'registration_year', 3.'power_ps') that have abnormal values:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Investigate on \"price\" column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     10667\n",
       "1      1176\n",
       "2        12\n",
       "3         7\n",
       "4         1\n",
       "5        26\n",
       "7         3\n",
       "8         9\n",
       "9         8\n",
       "10       83\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out the rows with extreme small value on price.\n",
    "autos[\"price\"].value_counts().sort_index().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2147483647     1\n",
       "99999999      15\n",
       "99000000       1\n",
       "74185296       1\n",
       "32545461       1\n",
       "27322222       1\n",
       "14000500       1\n",
       "12345678       9\n",
       "11111111      10\n",
       "10010011       1\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out the rows with extreme large value on price\n",
    "autos[\"price\"].value_counts().sort_index(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14218"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out how many car prices are under 100\n",
    "sum(autos[\"price\"]<=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "170"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out how many car prices are over 200000\n",
    "sum(autos[\"price\"]>200000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:**\n",
    "> - As ebay is an auction site, it is possible to have listing with opening bid very low, based on common sense, we assume any price under 100 is too low. The amount of cars with price under 100 is less than 4%, so we will remove these rows. \n",
    "> - Although it is possible for luxury cars with very high price, we will limit the price within 200000 in our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the rows with price values under 100 and above 200000\n",
    "autos=autos[autos['price'].between(100,200000)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Investigate on 'registration_year' column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000    0.000065\n",
       "1001    0.000003\n",
       "1039    0.000003\n",
       "1111    0.000003\n",
       "1234    0.000011\n",
       "Name: registration_year, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out the extreme small value with percentage\n",
    "autos[\"registration_year\"].value_counts(normalize=True).sort_index().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9999    0.000037\n",
       "9450    0.000003\n",
       "9000    0.000011\n",
       "8888    0.000006\n",
       "8500    0.000003\n",
       "Name: registration_year, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out extreme large value with percentage\n",
    "autos[\"registration_year\"].value_counts(normalize=True).sort_index(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:**\n",
    ">- There are some listings with extremely small and large registration years, but the percentage is small. Based on common sense, we will cut the registration year by 1950.\n",
    ">- We will use the year of the 'ad_created' as the threshold year for the highes values for registration_year because the car can be be listed on sale before it's registered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014    0.000003\n",
       "2015    0.000082\n",
       "2016    0.999915\n",
       "Name: ad_created, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also use the following method to find out the sales year\n",
    "(autos[\"ad_created\"]\n",
    "        .str[:4]\n",
    "        .value_counts(normalize=True, dropna=False)\n",
    "        .sort_index()\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:**\n",
    ">- Most of the cars in this dataset are for sale in 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03893460555845696"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The percentage of our data that has unrealistic values in this column\n",
    "(~autos['registration_year'].between(1900,2016)).sum()/autos.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As the number ablove is below 4%, we will remove rows with value below 1900 and above 2016 \n",
    "autos=autos[autos['registration_year'].between(1900,2016)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since we have found out most of the list are in 2016, this is unrelated information with car price analysis;\n",
    "# We can drop this columns.\n",
    "autos.drop('ad_created', axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Investigate on 'power_ps' column and do the same analysis and remove the rows with unrealistic values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "autos=autos[autos['power_ps'].between(10,500)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step1_ 4 Change the values in the columns ( 1. gearbox, 2. 'unrepaired_damage')  which have only 2 unique values and are not in English "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.'gearbox'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "manuell      234851\n",
       "automatik     68018\n",
       "Name: gearbox, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.gearbox.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "manual       234851\n",
       "automatic     68018\n",
       "Name: gearbox, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping_dict2={'manuell':'manual', 'automatik':'automatic'}\n",
    "autos['gearbox']=autos['gearbox'].map(mapping_dict2)\n",
    "autos['gearbox'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.'unrepaired_damage'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nein    236921\n",
       "ja       28544\n",
       "Name: unrepaired_damage, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.unrepaired_damage.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     236921\n",
       "yes     28544\n",
       "Name: unrepaired_damage, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping_dict4={'nein':'no', 'ja':'yes'}\n",
    "autos['unrepaired_damage']=autos['unrepaired_damage'].map(mapping_dict4)\n",
    "autos['unrepaired_damage'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step1_ 5 Investigate Null-values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                     0\n",
       "price                    0\n",
       "vehicle_type         10868\n",
       "registration_year        0\n",
       "gearbox               5290\n",
       "power_ps                 0\n",
       "model                11424\n",
       "kilometer                0\n",
       "fuel_type            15431\n",
       "brand                    0\n",
       "unrepaired_damage    42694\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis**\n",
    ">- The columns with null-values are all text or boolean values, it is possible for not having complete informations in eBay, and as our focus is to analyze car 'price', we don't need to remove or fill these null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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</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>kilometer</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>brand</th>\n",
       "      <th>unrepaired_damage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>308159</td>\n",
       "      <td>308159.000000</td>\n",
       "      <td>297291</td>\n",
       "      <td>308159.000000</td>\n",
       "      <td>302869</td>\n",
       "      <td>308159.000000</td>\n",
       "      <td>296735</td>\n",
       "      <td>308159.000000</td>\n",
       "      <td>292728</td>\n",
       "      <td>308159</td>\n",
       "      <td>265465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>189396</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>BMW_318i</td>\n",
       "      <td>NaN</td>\n",
       "      <td>limousine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>golf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>616</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86094</td>\n",
       "      <td>NaN</td>\n",
       "      <td>234851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192147</td>\n",
       "      <td>65698</td>\n",
       "      <td>236921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6239.105154</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.148037</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125.968004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125418.988250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8244.307901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.865561</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.088679</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39283.109438</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1910.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7999.000000</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name          price vehicle_type  registration_year gearbox  \\\n",
       "count     308159  308159.000000       297291      308159.000000  302869   \n",
       "unique    189396            NaN            8                NaN       2   \n",
       "top     BMW_318i            NaN    limousine                NaN  manual   \n",
       "freq         616            NaN        86094                NaN  234851   \n",
       "mean         NaN    6239.105154          NaN        2003.148037     NaN   \n",
       "std          NaN    8244.307901          NaN           6.865561     NaN   \n",
       "min          NaN     100.000000          NaN        1910.000000     NaN   \n",
       "25%          NaN    1450.000000          NaN        1999.000000     NaN   \n",
       "50%          NaN    3500.000000          NaN        2003.000000     NaN   \n",
       "75%          NaN    7999.000000          NaN        2008.000000     NaN   \n",
       "max          NaN  200000.000000          NaN        2016.000000     NaN   \n",
       "\n",
       "             power_ps   model      kilometer fuel_type       brand  \\\n",
       "count   308159.000000  296735  308159.000000    292728      308159   \n",
       "unique            NaN     250            NaN         7          40   \n",
       "top               NaN    golf            NaN    benzin  volkswagen   \n",
       "freq              NaN   25147            NaN    192147       65698   \n",
       "mean       125.968004     NaN  125418.988250       NaN         NaN   \n",
       "std         60.088679     NaN   39283.109438       NaN         NaN   \n",
       "min         10.000000     NaN    5000.000000       NaN         NaN   \n",
       "25%         80.000000     NaN  100000.000000       NaN         NaN   \n",
       "50%        116.000000     NaN  150000.000000       NaN         NaN   \n",
       "75%        150.000000     NaN  150000.000000       NaN         NaN   \n",
       "max        500.000000     NaN  150000.000000       NaN         NaN   \n",
       "\n",
       "       unrepaired_damage  \n",
       "count             265465  \n",
       "unique                 2  \n",
       "top                   no  \n",
       "freq              236921  \n",
       "mean                 NaN  \n",
       "std                  NaN  \n",
       "min                  NaN  \n",
       "25%                  NaN  \n",
       "50%                  NaN  \n",
       "75%                  NaN  \n",
       "max                  NaN  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check our changes\n",
    "autos.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1: What are the common brands of vehicls in Germany and their average price ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "volkswagen       0.213195\n",
       "bmw              0.113370\n",
       "opel             0.104488\n",
       "mercedes_benz    0.097239\n",
       "audi             0.092348\n",
       "ford             0.067423\n",
       "renault          0.044789\n",
       "peugeot          0.030254\n",
       "fiat             0.024698\n",
       "seat             0.019178\n",
       "Name: brand, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of top 10 most popular brands \n",
    "brand_counts=autos['brand'].value_counts(normalize=True)\n",
    "brand_counts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'proportion in the market')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "brand_counts.head(10).sort_values().plot(kind='bar', title='Top 10 most popular brands of car in Germany')\n",
    "plt.ylabel('proportion in the market')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis**\n",
    ">- Volkswagen is the most polular choice, counting more than 20% of the market\n",
    ">- BMW, Opel, mercedes_benz and audi are the next popular one, but far from volkswagen's popularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['volkswagen', 'bmw', 'opel', 'mercedes_benz', 'audi', 'ford'], dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the brands that are more than 5% of the market to analyze\n",
    "common_brands=brand_counts[brand_counts > .05].index\n",
    "common_brands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'volkswagen': 5688,\n",
       " 'bmw': 8680,\n",
       " 'opel': 3176,\n",
       " 'mercedes_benz': 8664,\n",
       " 'audi': 9381,\n",
       " 'ford': 3942}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analyze the common brands and its average price\n",
    "brand_mean_prices = {}\n",
    "\n",
    "for brand in common_brands:\n",
    "    brand_only = autos[autos[\"brand\"] == brand]\n",
    "    mean_price = brand_only[\"price\"].mean()\n",
    "    brand_mean_prices[brand] = int(mean_price)\n",
    "\n",
    "brand_mean_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "audi             9381\n",
       "bmw              8680\n",
       "mercedes_benz    8664\n",
       "volkswagen       5688\n",
       "ford             3942\n",
       "opel             3176\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the dictionary to a pandas series and sort its value\n",
    "mean_prices=pd.Series(brand_mean_prices).sort_values(ascending=False)\n",
    "mean_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'price')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_prices.sort_values().plot(kind='bar', title='Average price of Common brand car in Germany')\n",
    "plt.ylabel('price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer 1:###  \n",
    "\n",
    ">- Volkswagen is the most popular brand, followed by Opel,BMW, Mercedes, Audi and Ford.\n",
    ">- Among these popular brands, Audi is the most expensive, average price is 9381 dollars, followed by 8680 for BMW and 8664 for Mercedes. Volkswagen is more affordable ofr most people, average price is 5688. Ford and Opel are least expensive with average price under 4000."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2: Among common brands,  are there large differences on kilometer that can affect listing price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'volkswagen': 128117,\n",
       " 'bmw': 132985,\n",
       " 'opel': 128378,\n",
       " 'mercedes_benz': 130802,\n",
       " 'audi': 129142,\n",
       " 'ford': 123575}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analyze the common brands and its average odometer_km\n",
    "brand_mean_km = {}\n",
    "\n",
    "for brand in common_brands:\n",
    "    brand_only = autos[autos[\"brand\"] == brand]\n",
    "    mean_km = brand_only[\"kilometer\"].mean()\n",
    "    brand_mean_km[brand] = int(mean_km)\n",
    "\n",
    "brand_mean_km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the dictionary to a pandas series \n",
    "mean_km=pd.Series(brand_mean_km)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert pandas series to a data frame \n",
    "common_brand_info=pd.DataFrame(mean_prices, columns=['mean_price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_price</th>\n",
       "      <th>mean_km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>audi</th>\n",
       "      <td>9381</td>\n",
       "      <td>129142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmw</th>\n",
       "      <td>8680</td>\n",
       "      <td>132985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercedes_benz</th>\n",
       "      <td>8664</td>\n",
       "      <td>130802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volkswagen</th>\n",
       "      <td>5688</td>\n",
       "      <td>128117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ford</th>\n",
       "      <td>3942</td>\n",
       "      <td>123575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opel</th>\n",
       "      <td>3176</td>\n",
       "      <td>128378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean_price  mean_km\n",
       "audi                 9381   129142\n",
       "bmw                  8680   132985\n",
       "mercedes_benz        8664   130802\n",
       "volkswagen           5688   128117\n",
       "ford                 3942   123575\n",
       "opel                 3176   128378"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add the 'mean_km' to the data frame\n",
    "common_brand_info['mean_km']=mean_km\n",
    "common_brand_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'price')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "common_brand_info['mean_km'].plot(kind='bar', title='Average odometers of Common brand cars in Germany')\n",
    "plt.ylabel('price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer 2:###\n",
    "\n",
    ">- Among these common brands of cars on sale, the average of odometers are all ablove 100000km; The range of car mileages does not vary as much as the prices do by brand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alternative ways to answering Questions 1&2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "volkswagen       0.213195\n",
       "bmw              0.113370\n",
       "opel             0.104488\n",
       "mercedes_benz    0.097239\n",
       "audi             0.092348\n",
       "ford             0.067423\n",
       "renault          0.044789\n",
       "peugeot          0.030254\n",
       "fiat             0.024698\n",
       "seat             0.019178\n",
       "Name: brand, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_counts=autos['brand'].value_counts(normalize=True)\n",
    "brand_counts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['volkswagen', 'bmw', 'opel', 'mercedes_benz', 'audi', 'ford'], dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_brands=brand_counts[brand_counts > .05].index\n",
    "common_brands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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</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>kilometer</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>brand</th>\n",
       "      <th>unrepaired_damage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A5_Sportback_2.7_Tdi</td>\n",
       "      <td>18300</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>diesel</td>\n",
       "      <td>audi</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GOLF_4_1_4__3TÜRER</td>\n",
       "      <td>1500</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>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name  price vehicle_type  registration_year gearbox  \\\n",
       "1  A5_Sportback_2.7_Tdi  18300        coupe               2011  manual   \n",
       "3    GOLF_4_1_4__3TÜRER   1500   kleinwagen               2001  manual   \n",
       "\n",
       "   power_ps model  kilometer fuel_type       brand unrepaired_damage  \n",
       "1       190   NaN     125000    diesel        audi               yes  \n",
       "3        75  golf     150000    benzin  volkswagen                no  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using query methods to select commom brands\n",
    "autos_common_brands=autos.query('brand in [\"volkswagen\", \"bmw\", \"opel\", \"mercedes_benz\", \"audi\", \"ford\"]')\n",
    "autos_common_brands.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>kilometer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brand</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>audi</th>\n",
       "      <td>9381.733959</td>\n",
       "      <td>129142.771804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmw</th>\n",
       "      <td>8680.722063</td>\n",
       "      <td>132985.029769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ford</th>\n",
       "      <td>3942.425567</td>\n",
       "      <td>123575.588391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercedes_benz</th>\n",
       "      <td>8664.469681</td>\n",
       "      <td>130802.603037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opel</th>\n",
       "      <td>3176.230069</td>\n",
       "      <td>128378.365788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volkswagen</th>\n",
       "      <td>5688.166885</td>\n",
       "      <td>128117.827027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price      kilometer\n",
       "brand                                    \n",
       "audi           9381.733959  129142.771804\n",
       "bmw            8680.722063  132985.029769\n",
       "ford           3942.425567  123575.588391\n",
       "mercedes_benz  8664.469681  130802.603037\n",
       "opel           3176.230069  128378.365788\n",
       "volkswagen     5688.166885  128117.827027"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_brands_info=autos_common_brands.groupby('brand').mean()[['price','kilometer']]\n",
    "common_brands_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3: What are the factors that affect car price?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q3_Step1: First we will analyze the columns with numerical value and see how it is correlated with the car price using correlation heatmap and scatter chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1eaf5080>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot correlation heatmap\n",
    "sns.heatmap(autos.corr(),annot=True,cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis**\n",
    ">- Prices are positively correlated with power_ps and registration_year. Power_ps has stronger correlation.\n",
    ">- Prices are negatively correlated with kilometer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f67d6a0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot scatter chart to check the relation between 'registration_year' and 'price'\n",
    "autos.plot(kind='scatter', x='registration_year', y='price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis**\n",
    ">- In general, the newer the cars, the higher the prices, but for a given registration year, there are still huge gap on prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f6e4cf8>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eJpIiItMiW240tnRaBA2g1Xo/3hvHTau24OZV+atP74eRuJgMzyJSuYZkz04xqasKlZyubBhjLcbfRPQggGf1j00AzOXmKgE063/z2lsBDCWioL66MW9vHKuJiIIAygAcFvRnGYBlgJYbLf0rk+Qbv2fi9fs/Fn6nUMAxxcllzRU/jMSpHLPQCooZeL0Gr89OsairCpWcrmyIaJTp478C2Kb/vQbAl4koonuZTQLwOoA3AEwioolEFIbmRLBGt7+8CmChvv9VAFabjnWV/vdCAGulvaawyYYB1++ZePXYocLvVBZ3FLDKpUdQKrPuxpZOrKrbj8aWzqwc08/VZKZ4vYZUnp2KQRFMHztUCpo08NP1+QkA5wMYTkRNAJYCOJ+IqqGptfYA+BYAMMYaiGgFgO0AYgCuY4yp+nGuB/AiNNfnRxhjDfoplgB4koj+C8DbAB7W2x8G8GfdyeAwNAElKVCyZcD12wWUl8rdbLMBtNmw+Tpy+ULyMuu225wWzxqH2y87I+1jFoNdx8u4FJP7cDEjSwzoyBIDuSfbac/X1B9wvPCzbaQvVG+0ZDS2dOKzv1nnaP/HD88TZgpOxpb9H+OrD21GZzSWaBscCeKxb87EdJeVYCGSi2env+K1xIBMVyPJG9muL58LnXrViMHCl3MhewSJbE71+z9OW9j0pxWBtMf4j0xXI8kbfryspE6dj8jm5GaLSkZ/89CSz46/yJWNJG8YL6t82jpOFFIpH5wKckXgTiGrVnONtNnoSJtN/iimH2Qx9ZVHsvLBEiuZ3O/+kIHBC9JmIykaCtnWYaY/vDzKS8OYNGIwykvD+e5KwZPJ/S40T71CmCRJYSOReKDQXh7p0B+EZa7I9H43tXeD2WKvWJzlLNDXTLL7nitBJB0EJBIPFHv6Fi9lniV9ZHq/S8MKoqpV2ERVhtKwkrU+eiHZfc9lUK4UNhKJB4rdzbfYhWWuyfR+H+1RMSBkHe8BoQCO9qhZ66MX3O57ricgUthIJB4odjffyvISHI9ZX3THY2rRCMtck+n9riwvQUy1CquYmvvJiZvQzPUERNpsJBKPFLubr93zVHqiupPO/TbsH6VhBUQEc8J57XNuSRZekMvVuhQ2EkkKFIvnnJ2m9m6UhIKW1DIloWBeDNbFRCr322yIj8ZUKAFCr8luMyCo5GW8RUIz13FuUtic4BSCS2Qxke545Xuci93mlAnm2KLy0rAv94HnvQY1f5nA7YiEZi5X61LYnMCYZ2I9qorrPzMJV84cV9RCx8+XeroupIXgcnyiZmuwZ7oOEFAaDnq+D16fJ16eP4Ws8mZRTWVa4+33RCVXq3WZQUDnRMsgwMu4DACRYAB3LyzO+As/X+rJMlSLzp3tzNaZciJlEBBlujZIdh9SeZ5Ev6dUzpdpH/KF1wwC0hvtBIXniQIA0Vhxxl/47caZrgtpIbkcr64/gPn31+K2Z7Zj/v213JiKbBSyKxTcqqsC7vch1efJ7r0WVsjh+pzqfe9vsVFSjXaCwtPhG+SypHG2yHa5AjteXEh55y4UW4mXiPhimEWnQrKM1m73IZ3nyWz/KA0rmH9/refzZasPhYxvKxsieoSIPiKibaa2u4loBxFtJaK/EtFQvX0CEXUTUb3+3x9M+8wgoneIqJGI7iPdf5CIhhHRy0S0U/+3XG8nfbtG/Tyf8OsaixljJhYJOt0xi9Fw7PdLXRR3AQAd3T3oUa0xLL3xOErDmvfRrfOn5D0+J9kKK9uz6HRWSNleVRmZrs0ECJ7uQ7rPk1GmoGrE4IzjsgplopIt/FzZ/AnA/QCWm9peBvBjxliMiO4C8GNo5Z0B4APGWDXnOL8HcC2ATQCeBzAPwAsAbgHwCmPsTiK6Rf+8BMDnAUzS/5up7z8zu5fWPzBmYn/ZvA/3v9qIsFK8huNMDeBejLB2z53axlbMvmstQoEA4kwrE10S0ozPi2ZUYv79tYm+3HrJFEwbU1aw3mjZnEWns0Lya1V1+2VnYPE5E1L2RqsYFMGiGZUW54JUDfyZenr1N6cOXx0EiGgCgGcZY9M43/0rgIWMsa+ItiOiUQBeZYydrn++AsD5jLFvEdF7+t8f6tu9xhg7jYj+qP/9hL5PYju3vvZnBwEvL9J8u+Zmi3SuI50XHc8gHAkG8ODiGowuG4D599cWjFOAgVvp42w5MqRznGyeO1vPcCE5dhT6b7MYSgx8A8BTps8TiehtAEcA/CdjbD2AMQCaTNs06W0AMMIQILrAOVlvHwNgP2cfh7AhomuhrZowbtw4+9f9Aq8v0mINVrST6nWkm92XtxIIKwGUlYRwtEctSF2720w7W7PodFZI2VhVra4/gJtXbYFCAagsjrsXTrcI0lRf1oVkL8nGb7MQBFZehA0R/RRADMDjetOHAMYxxtqIaAaAvxHRVAC8/A7JlmKe92GMLQOwDNBWNl76Xkz0h7T4fpPuSyWZWqpQde1uL65sBPilY2fI1DbR1hXFjSvqEYsDgGY7u2FFPWZXDUdtY2ta6rlc20vyGR+WK3Lu+kxEVwGYD+ArTNfhMcaijLE2/e83AXwAYDK0VUmlafdKAM363y26+sxQt32ktzcBGCvYp2hJx3iabbfb/uQWa5CJIVhkAM40iaOf41y3uw33vvQe6na3cb83DNzpvvDSufZMx6uh+YguaPqIxYGNH7Sl5PRgHvdsJV71ci/9TPNfSO7TOV3ZENE8aEb8TzPGjpnaTwJwmDGmEtEp0Iz7uxhjh4mok4jOAbAZwGIAv9N3WwPgKgB36v+uNrVfT0RPQnMM6Ehmryl00p2ZZHN25qamyJR8LvEzUR+5rQTSXSX4OQv96kObUNuoCZn71jZiTlUF/vzNc7JybDNer91833n7uD0X5u9Eyo7WruOOAmbxOMOrOz7CZ04/2VOWh0xWel7upd/ah0JSB/ombIjoCQDnAxhORE0AlkLzPosAeFn3YN7EGPs2gPMA3E5EMWjr4G8zxg7rh/oONM+2EmheaC/o7XcCWEFEVwPYB+Byvf15ABcDaARwDMDX/brGXJDJw5gtPbybmiLTB9btB5krIZTJS8VNLZUr+5EX6na3JQSNwfrGNtTtbkPNxIqMjs0j2bWL7ruXmB/7d7deMgUBAsxyJUDAtNFljgJmPSrDz1ZvQ3w1LFke3MY93RQzXu5lPuPDco1vwoYxdgWn+WHBtk8DeFrwXR0Ahzebrna7gNPOAFyXUmcLmEwfxmzo4UVqiobmIzhv8kkpH8/A7QeZrq49XQrBQcLPF8+6na3Cdj+EjRvJXsRu3wNwfHf7sw1QAoS4SbAoAcKx3jgGhAKOFDLHeq3H9GPcvR4zF/FhmbpwZwuZrqbAycbDmKkeXuyTkZlPhcim1NB8pGD0zLnEzxfPeZOGp9TuJ8lsiW7f875TKICQYquKGVSQ7Pn0K8tDY0sn3t7Xjh41+TGzZRvi0dYVxbr3D+GpuiZL+4q6pv5vs5GkTiEEdk0dXYaQYq3NEVIIU0eXZXRc0Y8cYAWjZ84lft7rmokVmFNVgfUmVdqcqoqcr2qA5EI1VU8/lcUBRo7tp44uS4ynEiAcjTqzPBir/Zrx5RY14yfHl3PHPZlq155lWgkQBoYU13vpR5p/Q9UYACFqV0sA/ctmI8ke+a4QWTEognsun46bVmk/WjXOcPfCzF+Copfr1NFlBaNnzjV+3us/f/Mc1O1uw7qdrThv0vC8CBoguVBN9j3vOwDc7c3jue1AB+54brtjm8aWTq49q7Gl05IZO5nBv7Gl0yJoAECNM/zy387ArFMrknrkZetem9WQPI73aqmUco0UNkVCvm0Kfr0ERcfN92oun/h5r2sm5mc1YyfZ85SOp59bwKqhSp43baRjm9pGvj2rtrE1IWy8GPxFWabfb+nErFNzN+Y8e5GZiEI42qNyv/MTKWwknvHrJcg77oLqMZgyakhWaq8UQvR0oVBIY5HseUrV08/L88nbZvigMHdbc7sXg78oy/RD63dh2fpdOQumdMvoDgAUoP7ljSbJPtbYAhTMS8MPshVvUijR04WAHAs+s04dznWdnnVqn/OEFycCI8v08o1WVZrd+83v36tdDXm0J2a5tnx5o8lKnTqFnojT/KLo7o2BiDAgqGT80sjlTNfrufKZFDIdiqH6ZSEllkyHTJ5TL/uuqT+AH63cAgYt39WvL3cGLbslMjXT2NKJNVua8dD6XQlBA2ilDR775kxMT1JnJ1totqhW/PKFHRYnAft9z/QdUAyJOCUC7Defpy8GGHrVGID0Z0y5nOmmcq5sxT3kInra7n20eNY4fP+CyQW36vQ6FoWkZjPIJHuF1+eOASACgvo5eHi1W1aNGIyrPjUBy9bvsrR7dXLJxj3w6o2Wy3eAFDYFBu/mj68odTX4pfMCzWWSzlTPla24B78D5njeR8s37sOTrzchqPR57RWCqsrLWBSimi2T7BVenztju2iMJc4hej692i3TdWPPxj3w6o2W60S9noM6iWg8EX1W/7uEiApTX1DEiJLmlYYVV4NfOi/QbCfpzOa5shXo5mfAHCD2PupR4zjWoyIai+PGlVsKIhjVGItIUIv7iATJMhaFlLDRjFv2imR4fe6a2ruh2nKoqXGW8W9hQfUYbFgyF499cyY2LJnrqUZSNu4B77rNGN5ouXwHAB5XNkR0DbS6L8MAnAotk/IfwEkXI0kfkarjaI9qmSXxbDapvkBzmTMpnXNly9Xaz7iVZDXuAaBXZWho7sB5k09Ouq3faK9T0owStiBIP1WOmamF0s9e4fW5642ploBlQLtvvbHM3YNT8eDM1j1IxRutuzdm+a67N+abp5pXNdp1AM6GlnkZjLGdpmJlkizh9uOYPnao5aUJZOaN5me0uv3lYpzLHBTq5VzZcrX2y2Vb5H3khFdiKTUy1eP3qYr6ni+zysSvyQdPLZSK8M8ke4XXZ3xP2zHu/nvajmHiSYNyZsPi3YMeVUVHd0+i7IEX7L+3aK+KQMA5OW3rikJLiNw3tnqCZF/wKmyijLEeoyNEFESmibEkDrxEVtuD1TLBzzQZdp2z9rAwfUbdfx6dGeOH4ak3mkDQXGd71bjl6oIBYOroIRmdIxt6/GSz5opBESyqqbQIzkxdZHk2gRtW1EMJBBBWvF1LptkrvDzjohXq4aM9mH3X2pwmg7VrMOIMuO7xt1M+v/n3FlQIP5s/FdPGlFnGoKm9G0rAKsiVAPmWysarsPk/IvoJgBIiuhDAfwB4Juu9keQ8NY1faTLMBscpo4Z4NsAWE7zVQkghEBiCASXhOZXtF3Y6Y5ds5dLWFcUKTsLG718wOe3+8wRcLA7E4vHEmHm5lkx/E+k+479+6T30qCynVW6Na21oPoJrltchGoun7HXKc3i447ntDjf30rDicCLwM5WNV2FzC4CrAbwD4FvQasY85EuPJHlPTZMuotlz/f6P+2ViTd71DggqeOArZ6GsJJyVyUK29PjJVs1+2GyS2Q5SOYefvwmRo4ddxZSrZ7ZiUARlJSGElYB1IuPx/F7v5Y6Dndz9dxzs9CVezKuwKQHwCGPsQQAgIkVv4ys7dYjoEWgloD9ijE3T24YBeArABAB7ACxijLWTdmd/C63w2TEA/84Ye0vf5yoA/6kf9r8YY4/q7TPQV1jteQDfZ4wx0Tk8XqskTUSz5+qxQ/tlYk3R9U4dXZa1F1I2bSluKwQ/bDZ2Adejqrqqse8FXgjPgUiNZg94z2VfM7kfXvdt7TrO3V/UnileXZ9fgfZCNygB8A8P+/0JwDxb2y0AXmGMTdKPe4ve/nlo5aAnQfN8+z2QEE5LoZV4PhvAUiIq1/f5vb6tsd+8JOc4YfCjjn1jSydW1e1HY4t1RmScCwDX1bhqxGDd7TaAgWEFkWB2XZD9wMv4+e1aneo5vPaZV9somWu0V+x9MLv//vOWC3DP5dN9Ha90MBw9zCyeNQ6/zmNfM3m2vN7Lc6v4hQ9F7ZnidWUzgDHWZXxgjHUR0cBkOzHG1hHRBFvzZdDKRQPAowBeA7BEb1+uV9rcRERDiWiUvu3LRploInoZwDwieg3AEMbYRr19OYAvQCsbLTrHCYGjbO78KZg2uiwjtQ4vUv72y87gGq83LJnrmD2n4yCtXZQtAAAgAElEQVTgNQ1MtqPekxnkzeczrxZKwwqO9qgpeQ55IZnNoq0risc378MDrzYmNby7jRUDwBiggiGdLFZupZ6Nc+W7XIaIGeOH4cnX94OIwBhDzfhhee9rJud3c3M34HlTLp41zreUS16FzVEi+oRJrTUDQLqRPyMYYx8CAGPsQ5ML9RgA+03bNeltbu1NnHa3c/R7eAbln/51G0rDClTG0vKoEUXKLzhzNNd4vWHJXEv+p1QitA1Ews1OtqPekxnkRefzs4y1m4AwUrloYwtXw7vbWFkj9bVjeY3UN/b36shQaDZJo+89KoNx7ea+p7O6y9RN3RzikM7+dseVm1ZtxdCBYUwdPcTSp9svOwOLz5mQk9x+XoXNDwCsJKJm/fMoAF/Kcl944pel0e79hETXQlPDYdy4cUm2Lg5EtSyM+hXpeNSIDKjrdrZ6MkQmM1jaf5wi4bb4nAmWH4Mf6Tbc+grA1dvOj7QfyQREnxC3Yr8PycbKLVL/vMnJ1SrZcDDIV142UcR8Os4AmU5+eAl3Q0ogpdRHvHsRjcXx7T+/iTicE86qEYNzkkDWk7BhjL1BRKcDOA3aS34HY6w3zXO2ENEofcUxCsBHensTgLGm7SoBNOvt59vaX9PbKznbu53Dfl3LACwDtKzPaV5PQZHMAygdj5oJFXyN6fRKbxU13QyWvB9nDydxIKAJPfOPIlceVEZfc+1tl0xAuBXJst+H5GOVfqQ+kLmDQT7zsmXLBTjTyY844a42Ubxx5RZPxxK9A4715jfswNVBgIjm6v/+G4BLAUyGZoi/VG9LhzUArtL/vgrAalP7YtI4B0CHrgp7EcBFRFSuOwZcBOBF/btOIjpH92RbbDsW7xz9HrNhsTTi/LGk41ETCioIKdaFZDBAaD/Wi1vnT0lqxBQZO4G+lYI5F5RIuNm9hpJFXKeDm2G2srwEPar/3naGkb2hucM1d5XopcJzwEgmDIxIfcu5PEbqA5kZtP3OyyZybDE42qMiYrv2dKpZZpprLFlOMyP1UTLM92IgR2Da+5RsfLJFspXNpwGshSZo7DAA/+u2MxE9AW1VMpyImqB5ld0JYAURXQ1gH4DL9c2fh+b23AjN9fnrAMAYO0xEdwB4Q9/udsNZAMB30Of6/IL+H1zOcULgpe56KlSWlzgijWNxhqVrGqAyhlsvmeKITnbrk7HdFsGKIBRUPBkusxlxnayvgFYmWDW9sEMKWbztspH6xzzDN1yFzZgFBM+1+PrPTMKVM8cJBb5bdoorPjnWor684uyxKV1DugZtP/OyebH9aZMI60D3qCznWca9xCV5TX3UFxzaoQeH8t3NvdpGs0HS4mlEFACwkDG2wpceFAiFXjwtE7KhCzcKRykBwtGodcaXbhGuZAW9UvFGM0dcZ9ovr32NBAn/vOUCi10kU8Ow/RzBAJKmd0nlvKJtG1s6cfHvai0qzFwVV/OrsFtjSyc++5t1jvZ//PA8y/PkdTsveC2wlmx/3u8sGAA2/+SzKY+JqE/Zuu6sFU9jjMWJ6HoA/VrY9Gey4f1jzJRe3fERlq5psKgY0i3ClWy27dVwmWnEtRd4s++woliOn+k4885REgriga98AmUlIeFYpnJe3rar6w/gppVbHLP7XEbM+5EUVuTYYrf91Ta2crerbWxFeWk4pQmEfXUHAFv2f5zW/tsOdOD2ZxssRePSGRPRitPr+GQLr95oLxPRj6BF5R81Gk3qLMkJQMWgCD5z+sn4z9XbLO2ZFOHKViyD3yUTclGSQZyRYIhvL3yr26+VXEbM+xHTIsoMYG+PBPl2kh0fHsGdf9+R8irFEOjpOj0Y+08fOxTzpo3MypjwJhki26ioPVO8ZhD4BrTkm/8HoM70n+QEwlit3HqJ2CkgHWOvKKo9Ffqipv3JUpCJAbyQzmGP8G9q70Yw4LQDhFMYv1SzVfiR3YJH1YjBmFNVYWmbU1XhmLUPHhDi7r/qraa0nRay5fSQjd+G0R/7mPMcf0IKIRTMbyLOKdCEzbnQHAPWQyueJjlBsNeB/9mlU7lZCfw09tqxq+r8LmOQyezb6KuRZUC0/4LqMRhdNgDrdrbivEnDUTOxgnO09ODNtDuPx9Blsw2EFcLz3z3Xkyol1dm7aHvt+bKWEcjU9bmtK4o39lpTIr6xt92R4WFICV/YhAIBxOLpPce5/B2Y4amvRWPOc/xRTIXVso1XYfMogCMA7tM/X6G3LfKjU5LCglcHfunqbVxjZa4qgDpS8lwyBXc8t933Mgbp2GWMvrI4Q1RlGBDSFAq8F7PZO+i+tY0peQe52cl4MRw3rdoKnlBeumCqJ0GTalyJWwmKH63cYnnpeY0pcevbM1uaQbbL473wB4b4Cp7jtnivrqiziqXmnNIBgCzqzsryEhy3Vfo8HlN9VUvaJ4R3L5yO2VXDXe+RXwUUeXgVNqcxxqabPr9KRFv86JCk8EglujwXDzDvpXXbMw0IB/kxDvlMjWLuq4Hxt/3F7DVzAo9kKwxerAdjQDAQgCGcAS3AcZrH2JpUZ++i7WsbW7llmdMtp726/oBDeCWOy5n4iCp12vdmANqP9iSu7fFNe/GzNdtghF4FA8C9i6oT4+7IGq0y/L3hIL4yc3zK15QM3oTwhhX1eOTfP+l6j3KZ/82rsHmbiM5hjG0CACKaCWCDb72SFBipRZf7/QBzX1pKwGHkLoT09W5R/vYXc7reQV5WGLwoeXuAKgCozHt8CW8VG1XFkfeV5SXcmve9nH4AwAGPwZBm2rqiuHnVVq6gEWU/TsUgXtt4CFUjBuPxTXvx079ZHWViceCmVVsSz34kqCBmCwy97ZntmDd1ZEYrNt7vSjQhPNIdS6ppyIa3qhe8OgjMBPBPItpDRHsAbATwaSJ6h4i2+ta7E5RcGVC9MrqsxGFEThZdbhg2AWT9WngvOZUxLL00eTaDXOMWqGf/0Xv1nrLjJXJdFCX/3bmT0x4zs0ODoRokxjD//lqsqT/A3cde456IhOmJooJ2N5rau8HxdwAA3HTRaVw7UCioCPexM3zQALR1RXHbs9v5GzBCQ/MR7b5zhGhIIc8ZBeysrj+A2XetxVcf2ozZd621jTF/4jekJOi704lXvK5s7DVpJD6RzxxRbv1RCIgBCCsBEMFTHXi/rkWkqltQPQbzpmbHVTRbVAyKoGZ8OWob2xJtAeJ7e6Wb8l20YjALssryEqg2tY7KGK6cOQ5XzhyX0pjZSyxMGTUEF9+3HgAQVRmgMq7tpqm9GwOCSqLMMaBVNh0vWFmcWzU8aV/sVJaXQLBQwn89vwP72o85bGC9MWemBhGzTq1AU3s3wgqhJ+b8PqrGcc3yOvzs0im4+tyJ+P3/7bJ8r8aTrxx5q5dkq9epo8sSzhUGSoAShfwKoayD10Sce/3uiMSfLMbZ6k8fDM99d07SF6Df1yJS1eVCJZBKxH5jS6dF0ABAnAGPfeNsrqdZuinf7SWM7SsIt23cxsx+rbwJxNCBYQT1dDkGPNuNSO1WEsqeq23FoAiWLpiCn/51G/d7ng1MZLPhUb+vHdXjyhFzkU7RWBw//es2DAwFEFI0ARAOBsD0Eh9uz4xogubFPka21Y35c65UZW54VaNJckCmifxy0Z9IsK9AmJt6LBfXkq0YhFRwV2U4Edlh3F5wVSMGY2HN2MQL0ctYD7DFRgwIKpaxbmrvdvzYAxCn1wec1/r45r2O2JEbVtTjmuV1iYzCBjx7mUjtdu2f3+SeXxTZn4yvzByPRTXiFbT9nqRis3lpe4vlOkojip5SyCncj/XG0asyxJmR0cxdV+cWm5PMy7OpvRuKbYKhkFhllw9VvVc1miQH5MptONP+bDvQgS8t2+iqHiu0a8kG6azWvNphRKslL6pIL4b60rCiqbgs2zChMZ/v8bcdsJ0nFoclFgUQG+IB8NVuAoZnYERfs+VD4ff2sTeCG3lOBXZmnTIMgHVl3RtTsfCPm1z36xZ4IJr7/OqOj5y2UX2CNn3sUKGXZ1tXFPsPH/N8f/OlqpcrmwLCrwjydGcxvP4Y8SzJIqNF+za1d2ccaZ7NWVkq50i2WuPtI6pvb1bjiFZLXqPQvRjqj/aoie8MBoQCwjT6vGsNBgg9SWz2A8MK7rm8GuMrSoX3p7njuO5yLUYJEGad2qdmtKfBd3sG3FL182xgRnCjFyae1LevsbIOBRWH84UI3ureuP9L1zQ4AmzNE7QF1WOwYclcPPbNmdiwZG4iGHb2XWtx8yqnnxbv/vpdzsENubIpMLLtNvz4pr247dntCCuEWDz1stDGTNSwIRztUT3HViQrdZBupHm2ZmWpniPVInBGv9zsMPxgyy0YOjCUGFuvY+1mqK8sL+EWCBOtNHnXGoszRILErQyaGA81jhtX1iOsKNz7Y2QKsHua2XM+fGVmX3kDexr8c6sqULe3XfgM8Bwm3OA5nNxw4WT84vkdjj7yxquyvAQUIMDDyigaUy2rDcNV2z4eJSEFccZw6/wpCeFk2F14TgM8eM4I+cpsAMiVTUGSLVuEEQvQE4ujK6qmNYtZXX8A8++vxW3PbMf8+2uxrbkjJfVYxSCt6JiX1ZCBaPbV2NKZtVlZOueoGBTBoppKy3GMz8n6ZbfDGPBm4dEYw7cfewvXLK9zRKH3qOKxPtqjOqwCLM7Q1N6N9e9zi9UK23kr06WXTnE4HoQUQiRIGBwJIqIbwaMxxh2HvjLWzpej/TW9oq4JbV1RbqBrbWNb0meA5yABaA4CvCJh9lXD3NOcwaRGUKcd81gZK5xIMIBwMIBFNZWW9kCALCvOxzfv445HPM6gxuNYunob1z5oqN3sdhozKseJIZ/qbbmy6ae0dUVx2zMNjnYlQJ5nMbxZ9x3Pbk+o0rxmCMhWpHk2yy+ncw5AewmaWVHXhM/+y4i0+yUs4aurP4IBWOwJMTWODY2t3NWcm13mKVu/DZ6qa8IXPjGW+519ld1+tAdfPKsSK9/cj0iwb+VibNPR3YvrHn8LndG+VYV5HJrau7kvQBFN7d3Y6aF6pH2seS7WZmobW7lefuZVw6s7+EJYFGBrHit7/rtr55yCi39XC4BZskdMGTUED7y6k3ueqMl/2xhPY5Va29iKJU9vRTDgXk00zuDIwpDrFDVmci5siOg0aKUKDE4B8DMAQwFcA+CQ3v4Txtjz+j4/BnA1tDwM32OMvai3zwPwWwAKgIcYY3fq7RMBPAlgGIC3AHyNMeackvRjmtq79ah6mw5YUIGQZ6AWvZCnjSnDhiVzPav6Up1Nibb3Un7Zq1tyOucQjQdAaffL/OMPgBxeXZGQgm7TC0Vl4rxhhl3GXoTsaI+KCRUDsXGXsyJIMk8s4wVsV2V9cnwZbrusL4eaYah2G4femOrJCA9oKr7SsJI0oNV+DiB5xcsj3fxXgfkepRNgK3Iv1gJqA5bgVWNiE1YURGPeVH6hQAANzUe4qrOIAkQ5cudIt/PYuUxRYybnajTG2HuMsWrGWDWAGdBKQP9V//o3xncmQTMFwJcBTIUWXPrfRKQQkQLgAQCfh5aV+gp9WwC4Sz/WJADt0ATVCQUviA8All46xfFwiQzUbkIiFVVfqo4PxvaRIGFgSEl4Nxnll0XHWV1/AJ+6cy2ueHATPnWnu1uyqE9u5xCNx+iyAbju/CpEguJ+ublLGyqcP3xthqO2SrQ37iwNrecNsxvJ3YT31eeewv1O1G6Gp8rasKsN8367Do9v6gvBS3aftzUfSXoug4iizdp5DhZzqipcn6WKQREsmlFpP2SCzbudQtd+j9YKVjZuiJwWUpnYuKFtyxxq19KIgsWfmsDdZ0gJfz2Rj7CBfKvRLgDwAWNsr0jHCuAyAE8yxqIAdhNRI4Cz9e8aGWO7AICIngRwGRG9C2AugCv1bR4F8HMAv/fnEgoT84xZIUKvGsfSS6c6kgAmc+fN1pI71dmU9n4l3XLc92yIjtPWFU05c7DoWG4Bo/bxWDB9NC7+XS3Cimbivva8U3DlzHGeI78NKgZFcN7kk/ClT1ZaMgiI1E6vvPsRrv3zmw4jueh+VQyKpJWdABDHCsXi0PKDERLPldt9TsWV2ZyjjedgkSzD9Yo3+WpDANi821pmgHeP7n7pPe6+ZjWauWx5w4dHHFnIp43pK8Fhvi89ahzXnV+F8tIwfvXFM3GjIGkooD3+gyLBxL2cOrrMmaopznDOxAo8uH6PY/+BHgJms1E23gv5FjZfBvCE6fP1RLQYWmG2Gxlj7QDGADA7sTfpbQCw39Y+E0AFgI8ZYzHO9icUXl7wvOJZ2cwKa3+QU7EVmQ2nyeJZGpo70socLOqTqN3snbf/8DH8dm0jACRSlzzwWiOunNk3E0/FXtXWFcUTr++3tImUTo9t2guVwSHA3O7X7ZedgU9POgkvbW/BRVNG4IIpI4XjYiaZKsueXFI0dqeP9F5q2C5j7SXC3Z4lt+Sn2rGZw8Zj3z4YCKBXdeqlDLWjXa0YIK3PxjF++rdtiVWqUZtndtVwPL55Hx54dSeWrduFB15rxK2XTHHNyxYOBvDAVz5hKV/Am1DsOMi3bW3efdi1JlIuY27yJmyIKAxgAYAf602/B3AHtN/XHQDugVYhlHcrGPgqQOayPa8P1wK4FgDGjRvH26ToSfaC33agw9W338sxRKT7ILu9oA3jqPOYol+sxwyLHjGuKRggx7gBTgeMVOxVPIEpwpZ5xiLARPfL/IJ8qq7Jc60cXs42M0GPTic8m5KIOAM2ftCGscMGpjzJSWaziTNY3I8ry0tw1Jbo7HhMdbhjB0gLAOWpFXkLUGOy9MMV2gobAP77tUZEYyxhpzFKY0RjfEN/WAmgrCRkWYWNryjFs9efa3FCeGAt39HAjVynx8qn6/PnAbzFGGsBAMZYC2NMZYzFATyIPlVZEwCzu0wlgGaX9lYAQ4koaGt3wBhbxhirYYzVnHTSSbxN+jVtXVHc8Zwze+2t8512nXSOna6bsiixZGlYER5zdNkA7rFE7elgviaeoAGcDhip2au8C0Z7sslk7quiWjk8N2Aet192Bu6/ohq82MWYILkkz6aUijfa959823NaIDPGmItebgRYvLh2H+pyCAvGnDNUQ0iJ1Ioi1DjDxg/a+EHBnNIYZsz31WxXmn9/Lfa2HU08R5+byl+litoBcaoiv9Jj5VPYXAGTCo2IRpm++1cARia9NQC+TEQR3ctsEoDXAbwBYBIRTdRXSV8GsIZpFYteBbBQ3/8qAKt9vRKfyTRiXrQ/7+FPpXiWGxs/aIXdP0GUG43XP14q+uaO48II/lQj5FPB6F9D8xFhZLoBzwFjQfUYPPaNs/H12RPw2DfOTqzuzNfd2NKJd5raeYfkYqheIgolBFj9vnYsWbUFr2w/6NjerVaOV2adOhyhoPP6f3TRZEdmCJGzhr2gmBsqg2NS4fW3sKB6DK76FL9IWSBgdaZYt5Ofg81+pSHdaUGkVgySeLrw7ocdwtIYP7poMhRobu4B/Tz2iUmyyZuXTBV2eDWODC9AP8iLGo2IBgK4EMC3TM2/IqJqaBOKPcZ3jLEGIloBYDu0LPfXMcZU/TjXA3gRmuvzI4wxI7BkCYAniei/ALwN4GHfL8onMtWpuu0vevgzDfCy67MNeLNvXv/GV5RyU9EDzFUlZZ81GxHUZkOu/ceXzDhq7l+PGufWKBkYCiAWZ1wHDPt4GKWeZ4wfljhuVzQmtM3Y08Ynrk1v6lUZVnxzJn7yt3fwfstRAJqK7LQRpXjxh+cntk+3Vo4ZntPJF84ag3tfft9y/6aMGoIbVtRbVl83rtyCh6+qQVAJOHKpeSEUCODxzfvw3681ev4tjB7Kf44v/0Sl5V6fN2k47tNtb2bsvezVY5Z4asXJI0oT48/jj+t24bSRQxz2lkUzKvsyFegn/OS4ofjJxVM8hSGY1Zebdlmzi2+2fbZztEeFYkt8oBCyMkHjkRdhwxg7Bs2Qb277msv2/w/A/+O0Pw/geU77LvSp4YqWTHWqyfb3I8CLp64BgLBCjtQbov49e/25XKEydXSZazJCnrC564V3seLNPhWM2U7Bq9lufnnx+sfjD1+rsRhwk43H8o378OTr+9GjMtfjGtfgRhzAl5ZtcmRKea/lKF7ZfjDhBPDxMX5sib09mfA1O0hMqBiIrz7yumV8blhRDyJyqvlUhgPt3Z7sNYAzfU2PGscDr+5ENMY8/RZW1x9wpJsxGGVTrQ4dGOZu5/YiNnvIlQ8M4erl/MzVBrG41t8NS+YmYtRKwwo+/9t1jm03725Hb0y1XFeymkWvbD/oEHb2Z8BOaVhxPDeqzZ6VTfLtjSZxIdM8Rl72z3aAl0gtM//MUbjj2e2OFQyvf0d7VKFQEfW3obnDoXePM1gEDdBXz6S8NMyt2W5+eSXzajI40t0rHDfReNhry2SCSOX/0vaWxItGpCpat7M14a3kZRW9uv4Ablq5BQxaShU72njyOxSNxRFRyDXTc4A0ddKXzx6HFXVNib5cd34Vlq3bZQmAdPPqu3FFvXB0d7VayzuI7lGyF3F5aRgnD4lg3fveSiEYqYOM+JYt+z+GU6xqmO+LgVvNopUCV++VbzYJhc1r7/FjiV577yPPdZRSQQqbAibTPEZe90/X24yHSC3z7NZm9KjwtIKpLC/B9LFDhUKQ31/vxvX6/R/j5CEDuDXbG5qP4LzJmrNIZXmJI6KfR2vXcctnL5Ho8TRUSalipMMHgOmVfDuc0e5lFd3WFcUPnhS/xN0gaJU3XXJ4AtAmCD2qlgbI8LgqDSto7uh2ZsMQevUdcdxbMxOGWVc2onsUCQYs7vdmG+Dq+gOOuK5kRFWG/YePJZ5nre/8/c+bZK1SKqpyagjbkhDflihqB4B3DnSk1J4pMhFnAZNq5H2290+H8tKw47VPAMK24l7mFYyof6lEOU8dPQR227Uo67v2cuH/yM2pTNqP9njynjq3qs+T0R6JvnmPM1odABZUj0563Ewxp8MfPniAI54jQFo74K3Y3UsNB9NeiwUVwsfHvI2nce4dB4/gqTf24+L71uO6x99GnGmrnuTPsvs53vuoy/KZZ1xfVDMGvDjzyvKSRKbmVASNwY0r6jHrl6/g8U17UTEognsWVTu2mVNV4VjVJJs4Vp3MX4mI2gHgM6fxPXBF7ZkiVzYFgkhXnqmaK9d5kJrauzEoErQkYxwYVhzunV5WMKlQMSiCexdV4yabDWZl3X6sN5VlnlNVgaoRg1FeGuYa329cuRVxpo2bF08ts8cPb3WwdLUzGSoArKnneuOnzIBQAPE447rP2oun8dSMxjZeVsENKaSbcfQzqAhVeTyO9sRw/RP1ic89+ow+wglytDO6zH3lP4rz/Yzxw/DUG00Jpda5VSfh3KqTuKrcLfs/dnhaAppn4OJPTcDD63cLFa+GCtGceaHzeAxL12jPSYCAy2uciVGT2Vc/N3Uk7n7pfcd+bq7P5smIl/ZMkcKmAEimK89UzZVNNZmBSDiKPNyWXjrFYbPJdp/sghXQ7DBmNu5qS7iL2mu2A5pdwVAfles1Zexo7qkBMMRRM75PVcWz8YhcfT3ayYV8ftoIXD17Iva0HcM7TR/jUY5TxnNbm/H9C08DALwhWGG9secwqkYM9uQsMm30kLT72xuPC1V5ZsIKgTEmHJ+gQpYgRx7JgkcvOWOU5bMoY4XZmG9+zkvDCno4XokMDJ+bMgIPrt+d7DIBaJkXZk4Yhp8/04CYaSYgSrPkNnEsLw0nshgYBEhrF3ljihwBpINAPyXXUbzZwE04il5aC6rHYN7UkWho7gBAmKq/uDJx7eYJPLNgXff+IaFdpqwkBIUIPKdjQ33UfqyXe944+lLA3/z0VowuG4A9bccwoWJgSokVASQtRiZiT2sXvvrI6wgFApZVpJk1W/qEjWhVYm5Ptgq+cOpI3PLXbfZDJEUhLcWKocpz06QRGNysZEejKrYd6MB0F5ftZMGjIZtK182RhqfGNeoG2c/w3bmTsaftGLwSUgi1ja0ppVkSTRxFDjI3r9qCV3YcSrQZ3phtXVHUNvJXms0dx6WDQH8kn5Xz0sGLcBS9tOypZm6dr6120hG03oSU6IXDuLVfDAz1UWvnce73ZqK9cUv9+TlVFXjDVEWSV/HRTAoB9RbePai5ubp5yn3Qegxr6g9gQfWYhHC3Y2+3v8zMAp1XOMwLy742AxdMGYnGls6k1ytIzGDhjue2Y960ka7PSMzFnmK/r6k64vTGVO6TpeqZnL2ixhk3bgsADqQcxc83UJoFDaB5Y1YOHYh7//G+i2krO16SdqSDQJ7JZ+U8IPXsBF5TXOw+1IVX3m3B7kNdifPYI6Bve2a7o9Kg2Shtrztv0NjSiZtWbuFGU9ftbsO9L72XiKK3Ow2EFMLU0WXcjAOApsYx1Ef7Pfzg7T/L9Y1teOwbZ7tWfASAAUFCWCF8c85EoSODG14VHca4XCTQ3dvbzc/D6voDmPXLV7Dojxsx65ev4H827Em5n+dMLEf1uHJs2f8xdhxM3+ZjRiHCM1uase79j7jPbUPzEdfX5UpbIbmKQc6SBItqKoXCTLR6eeC1D1BeGnY4G0wZNRg8p7Bb509BSOG/gnnVOwHt/qx7/5Dj2nkOMqKX+69e3KFN8DjnMH4ffiBXNnmGp3a69RJr8KNfpKPC8pLi4kt//Cc279ZSr9y3thFzqirwo8+d7lzBKYTeGF/Q2rMQLKqpxFdmjse2Ax247dntDoN4AISrHt6EbR/2eRopBEfGyivOHisc03AwgOe/e25ChZBKSnwze9qOYaFu5BVVfOxRGeIM+J8Ne9KaR3qN8Targ86tqkCtzVnCPBbWbAkqemJM75v2/8df5yfiFKEQMHnEYHzqzrVQAiScxafK0R4VP39Gy+kXDAD3Lqq2PbfuI/rKjhZHiYEn3rBm237i9f34/gWTAcCxQhcVnAvqY20EfP5s9Tb8cxm8pXgAACAASURBVNdhbP/QmX/OSAslso+cWzXc0ba6/oApNkz7/dxzuRaIXDEognNOsd7fs8aV4c19TjfmMCeLQ1ghEBHuXuift6pc2RQA5vrnRsnldBIQpkK6iTK1qoPWqbhR6AoAfvPSewlBY7C+sQ2tnce5dTiWXjrV4frcfrTHEXW/oq4JVyz7J376t22WiocGx3pVi6ABtCA8u+7eqG1vCPlIMICBYQWRYAC/XnimRVc9ckh6PzqzKkUYZ6N36zinOFo2OR5TEyl7zC8iQLsvxqrR/jxEE4ImfVQGLN+0D9FYHMd6vFfpTIVYHPjhU/WW5zZZDZegYnXpFpWneGj9Lm7Ru9UCT8KoPtYG/+RURjUw0kJVjRiMOVVWN+eZE8vR3NGNde8fSlyX5m69xWKD7FUZblq1NZFbz35/39zXgcuqrc4Ql00fxY11UgLZCzIWIVc2BYIxm/jSso05cRZI11ZUWV7isBLE0Rd/8MBrH3D329LUIXYcmDbS4kH26D/3cI9xrDfzH4MSILy64yN85vSTwaB5i6lxvtfY5t3i3FKGCm56ZZlFuBqu0GZbh31FkUuM63JLxFk1YrDnbAmFiMqswbjJjPTdvXY1NV+P+VDtbvSqzvQ46xv5q1XzpEE03pFgAKQ7TBhplt7Ya52cbd7djs273wDQt3IbX1EKhQKwr2mNkhY7Bdm751SdhO9+ZhLq93+Mw0d7cO8/3gfpz4TZOaW713/nJClsCohcOgtkYiuyv5iNz03t3foS3ankOW/ScNRMrHD1dvr7toO447ntiR+DHxyNqli6pgE//ds7iMWNlY92PrPLaVtXFLtbxYkV43GGGy86Db82VXRUCKgZP8yhjvJz5ZIMhbSXUbJEnMlqwBQ+fYOczEhfURq0PHuiMhR2EWSkmxkxuAR72vjOI8ZvVdSHmRPL8ZsvneU5JVIsDty0ague++4cqMy5jZFsVqSOM9ydy0vDmH3XWosKXI1rjjLmxJt+OidJYVNA5NJZIN0knE3t3SgJWYM2S0LBhABRBYJi4kmDEufl2QlExchElEYUxFQGNR53TU3CQ5TV1nA5bT/Wi5tXbYXqYmNQ4wz3vPSeRf2iMuBHK+tBpKU5cVslBANIud/pEHXJVGxfhRkq3FAggOO9sYxjgbJNAM5MzAaHUyi/cYbNAG6ohs3eiUGCwy5ojOVJg91X/m6s29mG9qM9luJ6Itd1A8a0Pt69cDpusNlsDBtLxaAI5lRVcAOYAb5QiwQV9NpmQn46J0lhU0D4kYXZjXSyC7gJROOB/4fN3RIAN27AbCfwQmlEgRpnlvruGxpbcfPTWwEGrndNqhzpjuGGFVuSplVRGRDkJNPsVYEB/FjQBCGF8IMLJnEjvrNNyGRPM2cqNma8WubrrYlsCjdeOBnDSsMYEArgu0+klwfNjD1zciZUjy3D0IFhrH3P+Xy99v4hfOETmlNGsswP18+dZPmsPdPWTopCn17YdtCRfdlg6shBJjd/Zx8NXmz4MCEEDG9NN3p0IWf8XrXYKIapo8ssTg52ddwbe9sT9klRsPVZY61q4E+OL/ftfSOFTYGRi/QyZntCqrgJxLauKP5PkAH3SHfMEYTZ1N6NmEcPpe/PrcLcfxmR6HNTezfaj/YkSuTWNh7Cz595N+XrsfNO08ee83fx+s7Qp/8WwRhL1Kf3G6MGi0HViMGW9Dr2ZJK/eGEHSsMKetV4SoLme3OrcN6k4bjyoU0wLxyz6ROw5YDYdXpCRanpb763mIG9pED70R7Pqs77X23EhGHJfzfDB4krxJpX1n9vaPF0XiPQsmJQJGGbMtPQ3IGAIIxg96EurNvZim/NOQV/XL/LNQbMcBqRQZ0nCH6klzEw2xOOx1QwxlASCqYUvS8SiE3t3Qgq5JglAtqP4UertlgE1OgyZ+ZlEXV72vHDi05L9J/FGaIqSxjqtZgF8uTxFNFdP3mb7vBYJhnw7rtjV5kFlQBaOtOrupoOL2w7iO9yXh48LywgveJZC6aPRtWIwVh66TT8/JltCFIAMRaHEhCnjUkVt0nAiCF9L/djSc5X23jI8jIVRdLzCCsBXVg57XljyvuE3OkjxS/rh9bvwukjh2BB9RhUnVQq3M6K+NqNukz2TBS98Th+8fx2y8rlnInl+LFemE3klm84jWSbvLk+E9EeInqHiOqJqE5vG0ZELxPRTv3fcr2diOg+Imokoq1E9AnTca7St99JRFeZ2mfox2/U900jdK5/YXdv7VUZYnFn6V0v8DIyV5aXcGeICgGPbNjjcLPelkJixw272lC3uy3Rf0O/frw3juO9cdzx7Hb8/NKpiAQDiAQDCLtESn5zzgTcfNFk7neTPf/4vUX/l4QUh0A93hvHOROH8XfwgftfbRTc1+z9JN7Ycxir6w9g6Zpt6FWB7lgcMTV58bdsEY2ZBaT7Oe2rjuGD+MXTePTG47j0zFHc7y4+oy9AVhQ0DGglFIzf2oVTR3q6C6Lkon153azXHAkG8K05pzjCEDaZCrNlo3prKuQ7zuYzjLFqxliN/vkWAK8wxiYBeEX/DACfBzBJ/+9aAL8HNOEEYCmAmdAqcy41BJS+zbWm/eb5fzn+k2rEvxleGnkz9pTyqVIxKIK7F56JkOlFHwDwg89ORtgWKR0KBIQ/8rPG8iOY1+1sFfY/FAig7WgP4vE44nHmusLpUePYeYjvadbU0W3pfzKCtrz99jT+ohLIoaDiiDT3CyVAek46K7yo83TZ0NiKG56qtwhWBvgSW8NjmsnoP3V0mfAeEoBZp1rjWk4f6T3B6K2XTEHzEb4n2vum0gWlYQVuzn3m35ogiYCFHQf5K27eb3pgSMGDi2uEIjeV7NvZJN/Cxs5lAB7V/34UwBdM7cuZxiYAQ4loFIDPAXiZMXaYMdYO4GUA8/TvhjDGNjLNL3e56VgFj0ig2GulpBrwmcy9NRueKAuqx+Bfz+pTxcUB7G8/xnUqmHXqcJxrC2ibU1WBuxdO5x77vEnDcTzGV/H0qHH85uX30RsHeuPuAYmPrN+DVW/xx66sJJySq7I5W+9nThuOeVNHWL4Xv2wZvn/BZJx2sveVVLoc61FxzfI6x/NSMSiCK8/OjsAjsKzaZ1LlKw+/nri+ikER4cqRSBOMZkRqwwE2SVwaUTBtTBmOdPMdBLbs1wT66voDmH9/ras90vitGd6dyWgVTC55v+k4GKaOHiLMsm0UZnOLvfKDfAobBuAlInqTiK7V20Ywxj4EAP1fw31pDABzPokmvc2tvYnTXvDwBIqRD+nmValH/JuxF1MLKeSxGJV3Gls6scKWe2pFXRMummJ9CS+q0XJR/fMDa7Djhg/EwY9DB4a5wZeRYABXfHKsZxuKm0WCqSxt1c+r77Xi+W3JDb4KEfYf7sbsu9bi/Y/EsTzZJBpjjuelrSuKFYJywqkyrDS/SWON0hBGNP16QRBtnCERdW+w+xB/1WAvI9AT070uB/KFw8YPNOO6oeoViZpIsC//ntf4prGCSaCoQGJtYyuue+Jtx/anjShNFGbLtRotnw4CsxljzUR0MoCXiUicGpevXGZptFsPqgm5awFg3LjcqDTc4GVUvnHlFgQIUCjgSM6XTgAWr+ZLNj3fRMbW59750PJ5RV0T/mXkEG5a9Htf5j8KD9fucsT4AICqxrH3cHZe2htdsgZkC5Ux3P7sdmGyRT/Z+EEbjveqqB47FEd71KxlDWj40J9Swqlg/B5E0fRmzL8bkUeY/dk0Jjqv7+XP/OPQVgV21aqZKSMH4z8+cypmnaqtLgxhYRT9O96rcu+GW1JY3m/aHsBpsPdwd8Id2i32yg/yJmwYY836vx8R0V+h2VxaiGgUY+xDXRVmuEs0ATCXr6sE0Ky3n29rf01vr+Rsb+/DMgDLAKCmpiaPSgANXuBVnxrGOR/vUdW03ZfNgiWbnm+i5JUhW1XMUCAgrLGyvZn/stjT5lTHAVpMxKvvZUcP3X4svTT6qRL3MUuCiOO9cVxvmu0uqqkUxoykytuchI+5xlBNJSv+xZg1+LJEYMi3Q3o2hqGConoA8N7BTtfg5O0HO3H9E/WWBKLak0AAAYEAIc5ZWSdzlTf/prfs/9h1EmEWtLzYK7/IixqNiEqJaLDxN4CLAGwDsAaA4VF2FYDV+t9rACzWvdLOAdChq9leBHAREZXrjgEXAXhR/66TiM7RvdAWm45VsKSaMiTOgL9s3peWs4CdTBwPzPuLklfafz+98biwxorZjdXMJdNG4ldfPBNhH2NUuntys9rIleHcDbu6MxNyfTk3fLYKN1442ZJI1VBNGVUrRXx3bpVlgjVqqLcJW6/K0BtTMXG42M72P//0VqXTSENjqN2MZKUxgQp3cLJIYRNu7xF7hnZAq+Y5SU9p4yf5stmMAFBLRFsAvA7gOcbY3wHcCeBCItoJ4EL9MwA8D2AXgEYADwL4DwBgjB0GcAeAN/T/btfbAOA7AB7S9/kAwAs5uK6M4NlU3OhVGe55+X186s5XMsoOnanjgXn/Kx9+nVufxf5yXVRTiU9O4Btx3xIYKIeWhjG+ohR/ufpsV9fmTCgAGZBjijMi4HdrG3Go8zgApivI+25cU3u364TEvkoYNtD7S3ZP2zFMGSX2XktlNBUKoH6/tyDiISXehY3xHhENgdmzLdPffirkRY3GGNsFwOFyxBhrA3ABp50BuE5wrEcAPMJprwMwLePO5pi+lBQduGZ5neP7SNBpuzGMv+lkazUKkfVwstt6ORZvfx72n9OKuibhj5wJfnw3rNiKSFALCp04fCDea8m+cb0/yZrLPzEGz7zzIUIB7ZmxG7wBCGfShU5vHKYyFJra6qZVWzF0YBgDQ+6BpL9++X18cUZfcbSowMORx4SKgXhzD790QFlJAB3d3lfGKoujfGAo6So3QM6KqslYUD0G7x08ggde2+X4rrVLc93OdUn6QnN9lkCbmZSVhBFWrMvdgeGA8GWYSoyMofJ6fNNeXPy7WkfCQa/HWl1/gLu/F0KBAGoF/v6ivFQ9ap8nnh+Cpr/x1VkTsGHJXFxz3ikgYo5Z9+STSxEszoUNl2gsjm//+U18edkm9w0Zszzfqag0Dx6J4qm6/dzvOrrjnlfcwQBw98LpaD/Wm3RbL/MBuxp8df0BPLh+D3dbIyaJly4qpsYzirVzQ6arKVC4/vMMWHrpFNz+jNOTyRwjY89BZsZI96IQibMf2+JteMczZkW8QmZe6I3HUSZwIZVkh23NHagsL8F/v9boiDAHkDO361xyrDf5KqXHli+utcu7U8iGxkPcsTSIJRFcCgHf/vQp+NezKlE1YrCj5LmIlxoO4oqZ47nf2SvuGtm7eStZoC+dT29MdWS3iMW1dj+QK5sC5rrzqxAJEgaGFYQUwg2fnYx5U0fiwcUz8J1Pn4JIkBwxMm46WPOyWSRoFNKipA2hIjpeOrOfkGLtr+rPMy3Ruf2ZBjQ0d7hmjShmLpk2Uk9NlPr1NXdoz29bVzSljBFPvNGElg5+BgEA+NQp7mmIVAY88NouzPvtOqypP4CqEYMdgc08/inIIs2ruHvbMw2u7tf1+zQ1oKjIXLLic+kip5YFiHmmElP7dMq/eGEH7nrxPQwMKeiNx/GzS6di2uiyxIojmQ7WSzVGlQFL12zD4AFBzK4aLjxeaVhJKcHif158Os4/7WSLi+WfPHruGPhfuLafwQCAkno4loQCUNU4cuSIlzXW7WwFYywtNS5AWF1/wJH12gtuzuJfP3cial3KQSeOEddqH00ZNQQbPFRxfeadFlxYf8CRKJdbcFEJuI5JvZ7p4ETLjSax0dYVxU0r6xMzFXsxMjXOEjOY259pQEd3nwqAlyfJqNQIeHetNtwyebNiw57T3HEcITf/UhuNH3Vh/v21uO2Z7Zh/fy2++tAmvJVibIYUNKkRVRkG6qtIt9l7d2/xCRpASyCbnqDRVEXpCBo3FAKGDx6AOR5WKoCWkPMHT77t+bnmZQypLC9xxEpFYyp+dOFkofu3UYKBt6oyF1zLNlLYFBj/+bdt8JrhPRpj+PZjbyXUWzxhcrRHxeZdbdiiuxP/6otnejJiavXOyaEDP9oTw+ZdbbhmeR23lICIlW82WZb6tR5mc5LMWfjHTajbexg3f+60fHeloHjqjf3Zj3ViWgLOjbu8P9vbPvRe0sLsuGM4BLQf7YE9oX2cAXe+sEPoWHClbvtp64pik62vG3e1ZSVuj4cUNgVEY0snXth2MKV9jvWoiTxpAPCtOac4tvnFCzvwpT9uxKxfvoLOaAzPf29O0sDI3ngcvTHVEQMQZ9rxUk21cuLFrxQOyzfuwy4PFSHzSQqL5Kxw+Fh6L1QFQEQwWfvarPFo7jjuW7lvw3HHbEe9+L71UAJOYSPqAgGJ4M2G5iNcBwFRZo9MkcKmQGjrimLNFkdGHc+EAgHc+rd38Nu1jdzvj8fi6FEZfvrXbdi8+zB+vbAveJT30wkA+NZjb6XdH0lh8cQb2csW4Af2KpN+c1ZlenaJi6aNwNVzJnK/0wqm+Ter+tUXzwQAi0NAj8pSsp0OigQTq6Mj3XwvPFF7pkhhUwAYM5WH1jsDsLzSo8Y9ZRwGgFtXb8PsquHYsGQufnLx6dyfR1RlRRvwJyk+cv2svSPIv5eMLfs/xt5W/irxH+8exNTRZY6Vhp1IsC9D8wCPnnAX/cvJWFA9hmuXjSjkqSYOYA1r6DrOd3UQtWeK9EbLM2YPsnQojShQ4wz/dtYY/OV1frCZnTjTlsrtx3pw6+qGtM4rkRQzm3Yn9xjj0dwRRVeUH4z8jx2t+HvDQZDL6iaiEB5cXIOykhB2H+rED1Zs9XTegREtLohnl1UZg0IENcl5KUCWMiJtR/mqRFF7pkhhk2ea2ru5NVoCBCyaUYlVbzU59KpGbqefzZ+CaWM01+fdh7o8CxtAWyoveXqrXL1ICoZicG0PADgaFU8Mb3tmu2uFzlhcK2xWMSiCv2ze6/m8qm70NPKe3ayHRvSoKuIMDq+8SDCAb8yegFmnVmB0WQmO9qiOIG9RDSK/ahNJYZMDRBH9bV1R/GXzXm5EcpwBTwqy8n5vbhU+N3VkwkUxkRUA7oXBDLSAL3IN/JJIck2hCxpAM7yXhQPoEAiceFxcNA2AxXPsoikj8JTHzNtjdXdlwFq/pqO7B9c9/jZ61T7V18CQgj98bQbOm3yS6zF3t/IzSIjaM0UKG59ZXX8AN+uFkVQWx90Lp2NB9Zi0A8oALePtfWsbcffCMy2BlyJCCgFMK4nbozIoBHzvibe5P4oAgFs+fzr2th3DX17fVxQvAIkkV1wzZyKe39qMjihf1ZTMEy0S6it4eMGUkZhQUYI9bcmzcYy2ld0w6te0dUXRZSsm2N2rekrc+c4BfpybqD1TpIOAj7R1RXHjinpEYwzHelVEYww3rKhHY0snbl61NW0//+OxOKKxOG5cuQUNzUccBsOwQggphNKIggGhAO65fDpe+P6chBtzVGXC2ZeiEO55+T2senO/FDQSiY3DR3tw3CX/WjJdgRpnlryD3z7vVE/nrRWUS999qMvxO2V6ezLOGFOWUnumyJWNj4j82GsbW7l2mlTpVRleajiIaMzpPfLEN2ciFFQSqrvL7q/1FOtSCEW9JJJC5a9vHcDAsFikuP16QorVQA8A2zzGtIwu49tR1gkyp6/b2Yqaie6ZDM6dNBzL1jtTRp07abinPqWKXNn4gBHdK/JXjwTdcxelwmOb96FH1WZURrAZEeHKh1/HtuYO/P/2zj08qupa4L81jwx5CRjkmURA8AEKkUYel8pXpQ/1quAnehFv8ba2tP1sr7a2tl7vtbZ+9N7aau2D2trKrVquVmt9FP2KLyxieRg1PCIiEYSERxBMeISQ575/nD3jZOaczCQzk8hk/b5vvpmzzt7n7HVyctbZe6+9VlFBiIodB9lQ2/dpexXlRKcDONrSwxA57YaZ4zo/yIcUJJe4bcG00a7yoR71W1rbE0YC2OUxN+MlT5VeNzYiUiIiK0Vki4hUiciNVn6HiOwWkUr7uSSqzq0iUi0iW0Xkc1Hyi6ysWkS+FyUfIyLrRGSbiPxJRDKb7zSK6NW9Nz++MW5ldNAvjBqc65rNMhUMROKoNbd10NLWwW1PbmbZ2p2ebz+KovQuD6zezqp3P4gYgnVJhLY5Y1i+Z7yynfXuEZof+Mf7CTNvPlXpvojcS54qfdGzaQNuNsacBUwHbhCRCXbfz4wxZfbzHIDdNx+YCFwE/FpE/CLiB5YAFwMTgGuijvNje6zxQD1wfaaUiU5aFBvuu7mtA79PyPFLJE/63VdNZuTA3IyEb3GbnPzBX6sYOyQvfoclQ9mVFUVx4devbGfh0vVMXfwiy9buZM2O+oR1ttY1eua9GTukwFXeaiMLuAXvDFMQcp9F8ZKnSq/P2Rhj9gJ77e8jIrIFGNVFlTnAo8aYZmCHiFQDU+2+aptiGhF5FJhjj3chsMCWeRC4A7gv3bpEpwJoaW9n3pQS/DFhNwYE/Cy59lwG5uZE5k821DQQ8gvNvTA/0t5u+PafN3nv1ykaRel12o0TycMnyWXiXF19wLV3c97orvPnABHvt1hOH17IKy6jHqcPz8KozyIyGjgXWGdFXxeRjSKyVEQGW9koIHq1Yq2VecmLgAZjTFuM3O38i0SkQkQqPvjAPTmRF/G9GMOy9bvikpI52fIk4oGyoaaB/Bw/0ktrXNo5cfPMK0o202GSMzTw0ULuWN7Z17WDwfHWjk5ZSaMxHu4MXvJU6TNvNBEpAJ4AbjLGHBaR+4A7caYf7gTuBr6Iuzehwd1Qmi7KxwuNuR+4H6C8vLxbVziZRGTgzJ98+cHXaTcGYyDg82HoYP7UUh6rqCXo83G0uU3djBVF8WTUYPeh8Dd2dj0Ml+P3Tv8+cYT7Whwvear0ibERkSCOoVlmjPkLgDGmLmr/74DldrMWKImqXgyEZ7Dc5AeAQSISsL2b6PJpI9lEZECn4bJ2mxf84TW7eOGbs2hsaedQUwtf++ObnjeFoijZhw/nLTjRi2bAh+sizacrd/PQ2l1dVxbptK4nmm373dfieMlTpS+80QR4ANhijLknSj4iqtgVwGb7+xlgvoiERGQMMB5YD7wOjLeeZzk4TgTPGGcBy0pgnq1/HfB0uvUIxyjqTv7yaAxEojyPHJirQ12K0k8ID2oFAz5PQyM4Q2c5fuHG2afH7Q8P48fmm4rl+5dNcJ2vAag7fLxb8lTpizmbmcDngQtj3JzvEpFNIrIRuAD4JoAxpgp4DHgb+BtwgzGm3fZavg6sALYAj9myAN8FvmWdCYpwjFvaubxsFI98aVqP6z9aUcv8+9fyz79czafPHJrGlrkzv7w44+dQFCUB9qnbVQJCgxNnraXdsOSV6jg3ZrdUA7GnWDz3bK61WTndy7i/KHvJU6UvvNFW4z6v8lwXdRYDi13kz7nVsx5qU2PlmSAYcELC9DRFQJMNfbHi7X0EfIljK6WCV2BPRVF6j5BfOJbESEb4kRJ+ttzyxEZmjhtCUUEo4TD+gmklXDvd29AAbKht6JY8VTSCQIoUD85N2JVNhraOzBoaRVH6HgGOtfbseRH0+SJZNsPD+F7Z3UcX5Ud+V9cdYcnL21jy8rZO63W8WpGpAX2NjZYG0hHnTFGU7CeVJ0V0lk1whvHrDh9n8XPvxJXNtwszb39qUycngp88/y4LZ5TywznnMG1MEe/ujw9NMy1BTLWeoj2bFKmtbyIn2ZysiqIoSRKOdTgg6GNA0BcXxBNgb4N7eoJtdUeorjvi6q320JpdVNcd4aRc976GlzxVtGeTIsWDc7uc6FMUpX/gl/RG5Hjsq/9Efo7fNctmmDd3uc+vvLmrgQkjvVMFVNY0sHWvewgcL3mq6Ct5ihQVhDhvzODEBRVFOeHJsXmiYgn6hRU3zeLmz5yelniDIT9MLhnEuGGFTC4Z5Om+vLfBPRDn3oZjlJUM8jx+Wckg9h11d3H2kqeKGpsUOXi0mfXbEwfTUxTlxOeeq8v4vy9NZ/HcswkFfJ0C7I4bVsg3Zo9nxU2zmFKSWgIyrxAzsYwd6h7HbOzQQsYNK2ThjNK4fQtnlDJuWCHzP1HiUhNPearoMFqKrHnvYIKANYqiZAt7Gpq4dPJIJpcM4qKzh1Nb39RpiCscnLc9xaH13FAwqXL/Ul7Mmu0fusoBfjjnHBZOH82Kqn0AfG7i8EhAz4snjeQ/n3k7ru7Fk0b2tNldosYmBZ6u3M23H9/Q181QFKWXuGvFVq78RDFFBSHqG1vYVneE/Bw/RQWhTsF5U2VykqmZvWxatHzcsELXiNFrPFJNr3nvIJdOTr/BUWPTQ8I3lqZRVpT+Q1uHoWrPIV58u66Tp9fCGaVcOaUkqeC8yXBGkmH+B+e594C85NFs2evuXLBlb0NGjI3O2fSQ2vomAr2UJkBRlI8Pu+ub4lyKH1qzi9a29oTBeUsGDUjqHOt2JM7gCVBT7+767CWP5sPG1m7JU0WNTQ8pHpxLi7o8K0q/wi/OGhY3Vm7dz11XTmJA0Ede0P3ResflE5M6z44D7l5msazb7p7y/XBTS8K6fo/Yal7yVFFj00OKCkJccW5XCUYVRelNCjOUzjiaRbPGsv2Aewj+qj2HuLxsFK9990IeWTSDq2MC3y6cUcrsCcNdPcRiyUvCG+3g0Wb+VrXfdd+uDxMbq7ll7kNlXvJU0TmbFNBRNEX5eHDZOcMoPjmf+/6+PaPnmXHaEN70SFjW1OKMdBQVhCgqCDG5ZBCLzh9LZU0DZXbNDHzkIba6+gPu+OsW12NdlsScSW19EwE/tLqkwRp6UuLhujGnFHRLniras0mBtS4uh4qi9D6nDS3kgdU7MnqOcBKzsafku+53k48bVsi88pI4b7Bxwwr5t5lj+fL5Y1yPdU4S3mjFg3M9g61deEbilCVVe9xTSnvJU0WNTQrsCA19AAAADKVJREFUOpjcuKqiKJml7vBxgmmOUXjpOcMJBYS8oJ9QQLjn6jKKCkLMHHeKa3kveVc0eWTnXbn1g4R1iwpC/LtLYrWgXwgGklkU2rtxn7N2GE1ELgJ+jpMY7/fGmP9J5/ErdhxEkzgryseD+mOttKc5+vqpRXk8+43z42KTnenhlhwtP3i0OW7BpxsDc92NQpIBBFgwrZRfrazuFJ/R7/NOBR3NxJEDCfql0/KNoF+Y2EVMtVTIyp6NiPiBJcDFwATgGhGZkM5zPFW5J52HUxQlBa76RHHEEywnTU+1B1bv4JJfrOKx12uob/zIu6uxpT0u/plfHDnAsrU7mfHfL7Hgd2vjMmzGcsW57qFhlq2vZdm6nWyoaeDg0WbP+kUFIX4yz9G7MBTwjA7tVffuqyYTEPD7ICBw91WTk6rbE7K1ZzMVqLYZOxGRR4E5OKml00Jjc2Z80RVF6R4jTsph9oThAEwYcRKX/HI1JFjvkgzH25w3/mXrd7Fs/a5IHpj8HH9cdOd248QzW7Z2J7c9tRmAlnbH+ERn2IxlcH4Ofp/EJWBsbuvgtic323MZ7rpyEpeXuXu/Xl42ipnjhiTVk4rlsYoa2gyRkbPHK2o8z5MqWdmzAUYBNVHbtVaWNjRygKIkz8iBIc4c5j6xnohEUZTrm9oib/+NLe2EEszd5If83PyZ0wn6IOiTpB+C4TwwjS3tDIhZRzMg6GPPoeP8YHn8+6xfJJJhM5ba+ibygt5jZo0t7Rxv7eCWJzYm7OF0FR3ajYodB1ld3Xnx6KvVB6lIckFpd8nWno3b7RlnHURkEbAIoLQ0se97NKcW5fWoYYrycSQvCMcy1Fm/9+pJzJ1SwsGjzUz70YsJ058H/cJ3PncGJ+flMLooj39dup72LuKNhdMlFxWEKB6cm3AVf3uHYcG0UhZMK6W2vomaDxv5+iOVSelSWdPABWd6eXoZcvxCS1tnaWt7h+ccSjLthc46potV29wXhK7adoDyDGTrzNaeTS0QPRhaDMRNshhj7jfGlBtjyk85pXueJFecW5y4kKJkiNOHpu9lJ+CD/7g4uSnNU08ewI0XjOWhL07loS+eFwm1H/4MLegck+uMYfnMneL8KxYVhLjn6jJCASE3EP/oyfH7+NU1Zay9dTaLZp3GvPISyscUReZi8nP8BP0S19OJTpdcVBCKlA/PYSycUeo6pxHuDcw4bQjBJJPQlNneQ+w57rpyEhNHDqStI37E4/uXTfQ0EtHHCveWclzaEpsSOh3MGj+kW/JUEZNmD46PAyISAN4FZgO7gdeBBcaYKq865eXlpqKiolvnuf3pTTy0Jj7tqqJ0FwGK8oNMKR3M81s+WhU+YmCIAQE/uz48hs8nCMJPr3LG7yt2HGTVtgOMHZLHg2t28lbNoUi9Qbl+Gpo6+0uWDs5lV9RwTo5fEIGfzJvM5WWjmPGjF9h7+KOJ8IBAW9Tj4fxxRTz8pelxbY/1vHrp7X08/3Ydn50wLDKX4lZ+855D3Ln8bYI+H60dHV3OS0Sf47XqA9zyxMYu68W2KZF32DOVu/nOnzdG5k9uv2wC/9h2gGc374uUCc/ZeJ0jfJxbnnCO09pu+P5lE7h22qmuOrm1N5yZc/PuQ9z5bHLXJhU+//u1vBo1lOb1N+4KEXnDGFOesFw2GhsAEbkEuBfH9XmpMWZxV+V7YmwAquuOsKJqH8da2ggFfOw/0sLEkSfx2YnDqW9sYUXVXg4cbeF//7EzUueTpxVxrKWNN6MeDkrXCPHjoAEgesQi5INmlxGJkIA/IAzKzaG9w3DgaAunFAYZP/Qk1m8/SHPMgU8bkkfJoAG8VXuIkN9HMODjyPFWxg7Jo7lDONzYzL4jLUwpOYnfLJzK81X72H6gkYkjCtndcJza+mMcONrCwNwg889z3s5/u3IbT23cy9xJI/jKBeMB5wHjLKAzTBw5MPLAcnuIJeNKW113pNNq9eq6Izz5Vi2HmlqZWzaK8jFFcQ+12OM99WYNyzft49JzhjN3SknEoM0aPyQjQyvJuginq153jxl7TXuzbZnQ0Y1U/8b93th0l54aG0VRlP5MssYmW+dsFEVRlI8RamwURVGUjKPGRlEURck4amwURVGUjKPGRlEURck46o1mEZEPgJ0Jig0B3JfdZjeqd/+iv+oN/Vf3VPQ+1RiTcFW8GptuICIVybj4ZRuqd/+iv+oN/Vf33tBbh9EURVGUjKPGRlEURck4amy6x/193YA+QvXuX/RXvaH/6p5xvXXORlEURck42rNRFEVRMo4amyQRkYtEZKuIVIvI9/q6PelERJaKyH4R2RwlO1lEXhCRbfZ7sJWLiPzCXoeNIjKl71qeGiJSIiIrRWSLiFSJyI1WntW6i8gAEVkvIhus3j+w8jEiss7q/ScRybHykN2utvtH92X7U0VE/CLylogst9tZr7eIvC8im0SkUkQqrKxX73M1NkkgIn5gCXAxMAG4RkSSyzZ1YvAH4KIY2feAl4wx44GX7DY412C8/SwC7uulNmaCNuBmY8xZwHTgBvt3zXbdm4ELjTGTgTLgIhGZDvwY+JnVux643pa/Hqg3xowDfmbLncjcCGyJ2u4vel9gjCmLcnHu3fvcGKOfBB9gBrAiavtW4Na+bleadRwNbI7a3gqMsL9HAFvt798C17iVO9E/wNPAZ/qT7kAe8CYwDWdRX8DKI/c8sAKYYX8HbDnp67b3UN9inAfrhcBynFRJ/UHv94EhMbJevc+1Z5Mco4CaqO1aK8tmhhlj9gLY73Di9ay8FnaI5FxgHf1AdzuUVAnsB14A3gMajDHhfHTRukX0tvsPAenPpNY73AvcAoTT7BXRP/Q2wPMi8oaILLKyXr3PA6keoJ/glqC8v7rxZd21EJEC4AngJmPMYRHPfPRZo7sxph0oE5FBwJPAWW7F7HdW6C0ilwL7jTFviMinwmKXolmlt2WmMWaPiAwFXhCRd7oomxG9tWeTHLVASdR2MbCnj9rSW9SJyAgA+73fyrPqWohIEMfQLDPG/MWK+4XuAMaYBuAVnDmrQSISfgGN1i2it90/EPiwd1uaFmYCl4vI+8CjOENp95L9emOM2WO/9+O8XEyll+9zNTbJ8Tow3nqt5ADzgWf6uE2Z5hngOvv7Opz5jLB8ofVYmQ4cCnfFTzTE6cI8AGwxxtwTtSurdReRU2yPBhHJBT6NM2G+Ephni8XqHb4e84CXjR3MP5EwxtxqjCk2xozG+R9+2RhzLVmut4jki0hh+DfwWWAzvX2f9/XE1YnyAS4B3sUZ276tr9uTZt0eAfYCrThvNdfjjE2/BGyz3yfbsoLjmfcesAko7+v2p6D3J3GGBzYClfZzSbbrDkwC3rJ6bwZut/KxwHqgGngcCFn5ALtdbfeP7Wsd0nANPgUs7w96W/022E9V+PnV2/e5RhBQFEVRMo4OoymKoigZR42NoiiKknHU2CiKoigZR42NoiiKknHU2CiKoigZR42NopzARC1GVJSPNWpsFCVNiMhoEXlHRB60odn/LCJ5IjLbhrTfJE46h5CITBWRv9h6c0SkSURybPj/7VZ+moj8zcazelVEzrTyP4jIPSKyEo9IxCJyh4g8LCIv2xDyX7byESKyyoaa3ywi5/fS5VH6OfpWpCjp5QzgemPMayKyFPgW8BVgtjHmXRF5CPga8CucwJ8A5+MsrjwP539ynZXfD3zVGLNNRKYBv8YJsQJwOvBp48Q482ISThiafOAtEXkWuAYnqvFimzojLy1aK0oC1NgoSnqpMca8Zn//EfgvYIcx5l0rexC4wRhzr01OdRZOnKp7gFmAH3jVBgf9J+DxqMCgoajzPJ7A0AA8bYxpAppsL2gqTuilpTYm3FPGmMqUtFWUJNFhNEVJL90JyfEqTqKqVuBFnPA5nwRW4fxvNhgn2VX4Ex2ZubEHbTHGmFU4Rm038LCILOxGexWlx6ixUZT0UioiM+zva3CMyGgRGWdlnwf+bn+vAm4C1hhjPsCJVXUmUGWMOQzsEJGrIJKqd3I32zLHzgEV4cQCe11ETsUJs/87nCCkJ2Rqa+XEQ42NoqSXLcB1IrIROBknnfAXcIbDNuEk7fqNLbsOGIZjdMAJjLnRfBSw8FrgehEJB1Cc0822rAeeBdYCdxonzPyngEoReQu4Evh5tzVUlB6ggTgVJU3YbJ/LjTFn93FTEJE7gKPGmJ/2dVsUBbRnoyiKovQC2rNRlBMYEfkCcGOM+DVjzA190R5F8UKNjaIoipJxdBhNURRFyThqbBRFUZSMo8ZGURRFyThqbBRFUZSMo8ZGURRFyThqbBRFUZSM8//4K4V3DgzsugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot scatter chart to check the relation between 'power_ps' and 'price'\n",
    "autos.plot(kind='scatter', x='power_ps', y='price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis**\n",
    ">- Most cars have pow_ps under 400, and in general the higher the power_ps, the higher the price; but there are cars with extremely high power_ps, but prices range is still very large. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q3_Step2: We will analyze the columns with catergorical string values using bar chart."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.'vehicle_type'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'price')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "type_price=autos.groupby('vehicle_type').mean()['price']\n",
    "type_price.sort_values().plot(kind='bar')\n",
    "\n",
    "plt.ylabel('price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analyze**\n",
    ">- As we can see, suv is the most expensive ones, and kleinwagen the lease expensive. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.'gearbox'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'price')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyze whether the damege is repaired can positively influnce the price\n",
    "gearbox_price=autos.groupby('gearbox')['price'].mean()\n",
    "gearbox_price.sort_values().plot(kind='bar')\n",
    "\n",
    "plt.ylabel('price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis**\n",
    ">- In general, automatic cars are more expensive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.'fuel_type'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'price')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fuel_price=autos.groupby('fuel_type').mean()['price']\n",
    "fuel_price.sort_values().plot(kind='bar')\n",
    "\n",
    "plt.ylabel('price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis**\n",
    ">- hybrid are most expensive one and tightlt followed by elektro, diesel and benzin."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4.'unrepaired_damage'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'price')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyze whether the damege is repaired can positively influnce the price\n",
    "unrepaired_price=autos.groupby('unrepaired_damage')['price'].mean()\n",
    "unrepaired_price.sort_values().plot(kind='bar')\n",
    "\n",
    "plt.ylabel('price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis**\n",
    ">- In general, when the demage is repaired, the car prices are higher"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5.'brand'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'price')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_20_expensive=autos.groupby('brand').mean().sort_values('price',ascending=False).head(20)\n",
    "top_20_expensive['price'].plot(kind='bar',title='Top 20 expensive brands', figsize=(10, 5))\n",
    "\n",
    "plt.ylabel('price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis**\n",
    ">- Porsche is the most expensive brand, the average price double the following competitor 'land_rover'. \n",
    ">- The top 6 most expensive brands are all not the most common brands. The most popular brand volkswagen has very affordable average price."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q3_Step4: Let's analyze the most popular brand 'volkswagen', and see how car model and car name length can affect prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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</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>kilometer</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>brand</th>\n",
       "      <th>unrepaired_damage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>65698</td>\n",
       "      <td>65698.000000</td>\n",
       "      <td>62552</td>\n",
       "      <td>65698.000000</td>\n",
       "      <td>64564</td>\n",
       "      <td>65698.000000</td>\n",
       "      <td>63665</td>\n",
       "      <td>65698.000000</td>\n",
       "      <td>62156</td>\n",
       "      <td>65698</td>\n",
       "      <td>55382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>40985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Volkswagen_Golf_1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>limousine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>golf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>573</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18529</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55398</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37641</td>\n",
       "      <td>65698</td>\n",
       "      <td>49797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5688.166885</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002.711148</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106.265746</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128117.827027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6409.844585</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.110533</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.209319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38422.095700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1910.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1400.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1998.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7800.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>123456.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       name          price vehicle_type  registration_year  \\\n",
       "count                 65698   65698.000000        62552       65698.000000   \n",
       "unique                40985            NaN            8                NaN   \n",
       "top     Volkswagen_Golf_1.4            NaN    limousine                NaN   \n",
       "freq                    573            NaN        18529                NaN   \n",
       "mean                    NaN    5688.166885          NaN        2002.711148   \n",
       "std                     NaN    6409.844585          NaN           7.110533   \n",
       "min                     NaN     100.000000          NaN        1910.000000   \n",
       "25%                     NaN    1400.000000          NaN        1998.000000   \n",
       "50%                     NaN    3333.000000          NaN        2003.000000   \n",
       "75%                     NaN    7800.000000          NaN        2008.000000   \n",
       "max                     NaN  123456.000000          NaN        2016.000000   \n",
       "\n",
       "       gearbox      power_ps  model      kilometer fuel_type       brand  \\\n",
       "count    64564  65698.000000  63665   65698.000000     62156       65698   \n",
       "unique       2           NaN     22            NaN         7           1   \n",
       "top     manual           NaN   golf            NaN    benzin  volkswagen   \n",
       "freq     55398           NaN  25147            NaN     37641       65698   \n",
       "mean       NaN    106.265746    NaN  128117.827027       NaN         NaN   \n",
       "std        NaN     45.209319    NaN   38422.095700       NaN         NaN   \n",
       "min        NaN     10.000000    NaN    5000.000000       NaN         NaN   \n",
       "25%        NaN     75.000000    NaN  125000.000000       NaN         NaN   \n",
       "50%        NaN    101.000000    NaN  150000.000000       NaN         NaN   \n",
       "75%        NaN    131.000000    NaN  150000.000000       NaN         NaN   \n",
       "max        NaN    500.000000    NaN  150000.000000       NaN         NaN   \n",
       "\n",
       "       unrepaired_damage  \n",
       "count              55382  \n",
       "unique                 2  \n",
       "top                   no  \n",
       "freq               49797  \n",
       "mean                 NaN  \n",
       "std                  NaN  \n",
       "min                  NaN  \n",
       "25%                  NaN  \n",
       "50%                  NaN  \n",
       "75%                  NaN  \n",
       "max                  NaN  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selet the rows that are volkswagen for analyze\n",
    "volkswagen=autos[autos['brand']=='volkswagen']\n",
    "volkswagen.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lutang/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "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>price</th>\n",
       "      <th>name_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.29155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name_length</th>\n",
       "      <td>0.29155</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price  name_length\n",
       "price        1.00000      0.29155\n",
       "name_length  0.29155      1.00000"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a new columns for the name length\n",
    "volkswagen['name_length']=volkswagen['name'].apply(len)\n",
    "volkswagen[['price','name_length']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis**\n",
    ">- name_lenth is positively correlated with price, as the longer the name is, the more features are added, so the price is higher, but the correlation is not strong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'price')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_price=volkswagen.groupby('model')['price'].mean()\n",
    "model_price.sort_values().plot(kind='bar',figsize=(10,5), title='Prices range for car models for Volkswagen')\n",
    "\n",
    "plt.ylabel('price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis**\n",
    ">- There are huge price gap on different car model. For volkswagen, amorok is the most expensive ones, and lupo the least expensive one.\n",
    ">- I have done the same analysis for other factors for volkswagen, and results are consistant compared with the whole dataset analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer 3: \n",
    "> - From the correlation heatmap and scatter chart, we can conclude `price` are positively correlated with `power_ps` and `registration_year` and are negatively correlated with `kilometer` in general, and `power_ps` is has stronger influence.\n",
    "> - The other strong catagorical factors that affect the car price are the brand and whether the damage is repaired or not; Also automic are much mroe expensive than manual\n",
    "> - vehicle_type and fuel_type have strong effects too\n",
    "> - By analyzing data for volkswagen, the most common brand in Germany, we can see the above conclusions are consistent for specific car brand. And for the same brand, different models have high price ranges too. "
   ]
  }
 ],
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   "language": "python",
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