{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lame Cars Are a Better Deal #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "In 2019 the global used car market was estimated to be a whopping 1332 billion US dollars (1). This was prior to the ubiquitous supply chain disruptions that are now part of our daily lives and the expectations of a 13% to 32% decline in global trade (2)(3). The turnover in used car inventory for August and September was the highest in six years (4). One large sector of used car sales comes from the eBay Vehicle Market. They have over 7.4 million users per month. A car or truck is sold every 3 minutes (5). That's of lot of interactions!\n",
    "\n",
    "The main conclusion of this study is unpopular cars might be a better deal when using mileage as a metric. For the money spent they have lower mileage.\n",
    "\n",
    "Let's take a quick look at a selection of 50,000 rows from a dataset created in 2015 for the German eBay marketplace, eBay Kleinanzeigen. The original data set can be found here.\n",
    "https://data.world/data-society/used-cars-data\n",
    "\n",
    "Citations\n",
    "1. Grand View Research. Used Car Market Size, Share & Trends Analysis Report By Vehicle Type (Hybrid, Conventional, Electric), By Vendor Type, By Fuel Type, By Size, By Region, By Sales Channel, And Segment Forecasts, 2020 - 2027. Sept 2020.\n",
    "https://www.grandviewresearch.com/industry-analysis/used-car-market\n",
    "2. Li, X., Ghadami, A., Drake, J.M. et al. Mathematical model of the feedback between global supply chain disruption and COVID-19 dynamics. Sci Rep 11, 15450 (2021).\n",
    "https://doi.org/10.1038/s41598-021-94619-1\n",
    "3. World Trade Organization. Trade set to plunge as COVID-19 pandemic upends global economy. April 2020.\n",
    "https://www.wto.org/english/news_e/pres20_e/pr855_e.htm\n",
    "4. Eric Rosenbaum. The used car boom is one of the hottest, and trickiest, coronavirus markets for consumers. CNBC October 2020.\n",
    "https://www.cnbc.com/2020/10/15/used-car-boom-is-one-of-hottest-coronavirus-markets-for-consumers.html\n",
    "5. Welcome to the eBay Vehicle Seller Center. eBay. Sept 2021.\n",
    "https://pages.ebay.com/sellerinformation/vehiclesellercenter.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open and Examine Data Set\n",
    "* An initial look at the data set shows 50,000 entries with 20 columns of information.\n",
    "* Five of the columns are missing entries and in some the data needs to be converted to the correct type.\n",
    "* Some of the entries are in German and need to be translated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 20 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   dateCrawled          50000 non-null  object\n",
      " 1   name                 50000 non-null  object\n",
      " 2   seller               50000 non-null  object\n",
      " 3   offerType            50000 non-null  object\n",
      " 4   price                50000 non-null  object\n",
      " 5   abtest               50000 non-null  object\n",
      " 6   vehicleType          44905 non-null  object\n",
      " 7   yearOfRegistration   50000 non-null  int64 \n",
      " 8   gearbox              47320 non-null  object\n",
      " 9   powerPS              50000 non-null  int64 \n",
      " 10  model                47242 non-null  object\n",
      " 11  odometer             50000 non-null  object\n",
      " 12  monthOfRegistration  50000 non-null  int64 \n",
      " 13  fuelType             45518 non-null  object\n",
      " 14  brand                50000 non-null  object\n",
      " 15  notRepairedDamage    40171 non-null  object\n",
      " 16  dateCreated          50000 non-null  object\n",
      " 17  nrOfPictures         50000 non-null  int64 \n",
      " 18  postalCode           50000 non-null  int64 \n",
      " 19  lastSeen             50000 non-null  object\n",
      "dtypes: int64(5), object(15)\n",
      "memory usage: 7.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# import libraries and read csv\n",
    "import emoji\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pprint\n",
    "autos = pd.read_csv(\"autos.csv\", encoding=\"Latin-1\")\n",
    "# get info about dataset\n",
    "autos.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>dateCreated</th>\n",
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       "  <tbody>\n",
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       "      <th>count</th>\n",
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       "    <tr>\n",
       "      <th>unique</th>\n",
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       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>245</td>\n",
       "      <td>13</td>\n",
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       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2016-03-23 19:38:20</td>\n",
       "      <td>Ford_Fiesta</td>\n",
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       "      <td>Angebot</td>\n",
       "      <td>$0</td>\n",
       "      <td>test</td>\n",
       "      <td>limousine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>manuell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>golf</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>NaN</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-04-03 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-04-07 06:17:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>49999</td>\n",
       "      <td>49999</td>\n",
       "      <td>1421</td>\n",
       "      <td>25756</td>\n",
       "      <td>12859</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4024</td>\n",
       "      <td>32424</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30107</td>\n",
       "      <td>10687</td>\n",
       "      <td>35232</td>\n",
       "      <td>1946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005.073280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.355920</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.723360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50813.627300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.712813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>209.216627</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.711984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25779.747957</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30451.000000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49577.000000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71540.000000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17700.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99998.000000</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "                dateCrawled         name  seller offerType  price abtest  \\\n",
       "count                 50000        50000   50000     50000  50000  50000   \n",
       "unique                48213        38754       2         2   2357      2   \n",
       "top     2016-03-23 19:38:20  Ford_Fiesta  privat   Angebot     $0   test   \n",
       "freq                      3           78   49999     49999   1421  25756   \n",
       "mean                    NaN          NaN     NaN       NaN    NaN    NaN   \n",
       "std                     NaN          NaN     NaN       NaN    NaN    NaN   \n",
       "min                     NaN          NaN     NaN       NaN    NaN    NaN   \n",
       "25%                     NaN          NaN     NaN       NaN    NaN    NaN   \n",
       "50%                     NaN          NaN     NaN       NaN    NaN    NaN   \n",
       "75%                     NaN          NaN     NaN       NaN    NaN    NaN   \n",
       "max                     NaN          NaN     NaN       NaN    NaN    NaN   \n",
       "\n",
       "       vehicleType  yearOfRegistration  gearbox       powerPS  model  \\\n",
       "count        44905        50000.000000    47320  50000.000000  47242   \n",
       "unique           8                 NaN        2           NaN    245   \n",
       "top      limousine                 NaN  manuell           NaN   golf   \n",
       "freq         12859                 NaN    36993           NaN   4024   \n",
       "mean           NaN         2005.073280      NaN    116.355920    NaN   \n",
       "std            NaN          105.712813      NaN    209.216627    NaN   \n",
       "min            NaN         1000.000000      NaN      0.000000    NaN   \n",
       "25%            NaN         1999.000000      NaN     70.000000    NaN   \n",
       "50%            NaN         2003.000000      NaN    105.000000    NaN   \n",
       "75%            NaN         2008.000000      NaN    150.000000    NaN   \n",
       "max            NaN         9999.000000      NaN  17700.000000    NaN   \n",
       "\n",
       "         odometer  monthOfRegistration fuelType       brand notRepairedDamage  \\\n",
       "count       50000         50000.000000    45518       50000             40171   \n",
       "unique         13                  NaN        7          40                 2   \n",
       "top     150,000km                  NaN   benzin  volkswagen              nein   \n",
       "freq        32424                  NaN    30107       10687             35232   \n",
       "mean          NaN             5.723360      NaN         NaN               NaN   \n",
       "std           NaN             3.711984      NaN         NaN               NaN   \n",
       "min           NaN             0.000000      NaN         NaN               NaN   \n",
       "25%           NaN             3.000000      NaN         NaN               NaN   \n",
       "50%           NaN             6.000000      NaN         NaN               NaN   \n",
       "75%           NaN             9.000000      NaN         NaN               NaN   \n",
       "max           NaN            12.000000      NaN         NaN               NaN   \n",
       "\n",
       "                dateCreated  nrOfPictures    postalCode             lastSeen  \n",
       "count                 50000       50000.0  50000.000000                50000  \n",
       "unique                   76           NaN           NaN                39481  \n",
       "top     2016-04-03 00:00:00           NaN           NaN  2016-04-07 06:17:27  \n",
       "freq                   1946           NaN           NaN                    8  \n",
       "mean                    NaN           0.0  50813.627300                  NaN  \n",
       "std                     NaN           0.0  25779.747957                  NaN  \n",
       "min                     NaN           0.0   1067.000000                  NaN  \n",
       "25%                     NaN           0.0  30451.000000                  NaN  \n",
       "50%                     NaN           0.0  49577.000000                  NaN  \n",
       "75%                     NaN           0.0  71540.000000                  NaN  \n",
       "max                     NaN           0.0  99998.000000                  NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initial stats\n",
    "autos.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>2016-03-26 17:47:46</td>\n",
       "      <td>Peugeot_807_160_NAVTECH_ON_BOARD</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$5,000</td>\n",
       "      <td>control</td>\n",
       "      <td>bus</td>\n",
       "      <td>2004</td>\n",
       "      <td>manuell</td>\n",
       "      <td>158</td>\n",
       "      <td>andere</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>3</td>\n",
       "      <td>lpg</td>\n",
       "      <td>peugeot</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-26 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>79588</td>\n",
       "      <td>2016-04-06 06:45:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-04-04 13:38:56</td>\n",
       "      <td>BMW_740i_4_4_Liter_HAMANN_UMBAU_Mega_Optik</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$8,500</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>1997</td>\n",
       "      <td>automatik</td>\n",
       "      <td>286</td>\n",
       "      <td>7er</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>6</td>\n",
       "      <td>benzin</td>\n",
       "      <td>bmw</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-04-04 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>71034</td>\n",
       "      <td>2016-04-06 14:45:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-26 18:57:24</td>\n",
       "      <td>Volkswagen_Golf_1.6_United</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$8,990</td>\n",
       "      <td>test</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2009</td>\n",
       "      <td>manuell</td>\n",
       "      <td>102</td>\n",
       "      <td>golf</td>\n",
       "      <td>70,000km</td>\n",
       "      <td>7</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-26 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>35394</td>\n",
       "      <td>2016-04-06 20:15:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-12 16:58:10</td>\n",
       "      <td>Smart_smart_fortwo_coupe_softouch/F1/Klima/Pan...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$4,350</td>\n",
       "      <td>control</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2007</td>\n",
       "      <td>automatik</td>\n",
       "      <td>71</td>\n",
       "      <td>fortwo</td>\n",
       "      <td>70,000km</td>\n",
       "      <td>6</td>\n",
       "      <td>benzin</td>\n",
       "      <td>smart</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-12 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>33729</td>\n",
       "      <td>2016-03-15 03:16:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-04-01 14:38:50</td>\n",
       "      <td>Ford_Focus_1_6_Benzin_TÜV_neu_ist_sehr_gepfleg...</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>$1,350</td>\n",
       "      <td>test</td>\n",
       "      <td>kombi</td>\n",
       "      <td>2003</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>focus</td>\n",
       "      <td>150,000km</td>\n",
       "      <td>7</td>\n",
       "      <td>benzin</td>\n",
       "      <td>ford</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>39218</td>\n",
       "      <td>2016-04-01 14:38:50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dateCrawled                                               name  \\\n",
       "0  2016-03-26 17:47:46                   Peugeot_807_160_NAVTECH_ON_BOARD   \n",
       "1  2016-04-04 13:38:56         BMW_740i_4_4_Liter_HAMANN_UMBAU_Mega_Optik   \n",
       "2  2016-03-26 18:57:24                         Volkswagen_Golf_1.6_United   \n",
       "3  2016-03-12 16:58:10  Smart_smart_fortwo_coupe_softouch/F1/Klima/Pan...   \n",
       "4  2016-04-01 14:38:50  Ford_Focus_1_6_Benzin_TÜV_neu_ist_sehr_gepfleg...   \n",
       "\n",
       "   seller offerType   price   abtest vehicleType  yearOfRegistration  \\\n",
       "0  privat   Angebot  $5,000  control         bus                2004   \n",
       "1  privat   Angebot  $8,500  control   limousine                1997   \n",
       "2  privat   Angebot  $8,990     test   limousine                2009   \n",
       "3  privat   Angebot  $4,350  control  kleinwagen                2007   \n",
       "4  privat   Angebot  $1,350     test       kombi                2003   \n",
       "\n",
       "     gearbox  powerPS   model   odometer  monthOfRegistration fuelType  \\\n",
       "0    manuell      158  andere  150,000km                    3      lpg   \n",
       "1  automatik      286     7er  150,000km                    6   benzin   \n",
       "2    manuell      102    golf   70,000km                    7   benzin   \n",
       "3  automatik       71  fortwo   70,000km                    6   benzin   \n",
       "4    manuell        0   focus  150,000km                    7   benzin   \n",
       "\n",
       "        brand notRepairedDamage          dateCreated  nrOfPictures  \\\n",
       "0     peugeot              nein  2016-03-26 00:00:00             0   \n",
       "1         bmw              nein  2016-04-04 00:00:00             0   \n",
       "2  volkswagen              nein  2016-03-26 00:00:00             0   \n",
       "3       smart              nein  2016-03-12 00:00:00             0   \n",
       "4        ford              nein  2016-04-01 00:00:00             0   \n",
       "\n",
       "   postalCode             lastSeen  \n",
       "0       79588  2016-04-06 06:45:54  \n",
       "1       71034  2016-04-06 14:45:08  \n",
       "2       35394  2016-04-06 20:15:37  \n",
       "3       33729  2016-03-15 03:16:28  \n",
       "4       39218  2016-04-01 14:38:50  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first five rows\n",
    "autos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Begin Cleaning Data Set\n",
    "* The `price` and `odometer` columns need to be converted to integers.\n",
    "* The column titles could be more clear and use snake_case.\n",
    "* It would be nice if the `name`, `brand`, `model`, and `vehicle_type` columns were grouped together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data type for the price column is :  int64\n",
      "The data type for the odometer_km column is :  int64\n"
     ]
    }
   ],
   "source": [
    "# clean the price and odometer columns\n",
    "autos[\"price\"] = autos[\"price\"].str.replace(\"$\",\"\").str.replace(\",\",\"\")\n",
    "autos[\"price\"] = autos[\"price\"].astype(int)\n",
    "autos[\"odometer\"] = autos[\"odometer\"].str.replace(\",\",\"\").str.replace(\"km\",\"\")\n",
    "autos[\"odometer\"] = autos[\"odometer\"].astype(int)\n",
    "autos.rename(columns={\"odometer\" : \"odometer_km\"}, inplace=True)\n",
    "print(\"The data type for the price column is : \", autos[\"price\"].dtypes)\n",
    "print(\"The data type for the odometer_km column is : \", autos[\"odometer_km\"].dtypes)\n",
    "# change the column names\n",
    "new_cols = ['date_crawled', 'name', 'seller', 'offer_type', 'price', 'ab_test',\n",
    "       'vehicle_type', 'year_of_registration', 'gearbox', 'powerPS', 'model',\n",
    "       'odometer_km', 'month_of_registration', 'fuel_type', 'brand',\n",
    "       'unrepaired_damage', 'ad_created', 'num_of_pictures', 'postal_code',\n",
    "       'last_seen']\n",
    "autos.columns = new_cols\n",
    "# reorder columns\n",
    "autos = autos.reindex(columns=[\"date_crawled\", \"price\", \"name\", \"brand\", \"model\", \"vehicle_type\", \"ab_test\",\n",
    "                   \"odometer_km\", \"month_of_registration\", \"year_of_registration\", \"gearbox\",\"powerPS\",\n",
    "                   \"fuel_type\", \"unrepaired_damage\", \"ad_created\", \"postal_code\", \"last_seen\",\n",
    "                              \"seller\", \"offer_type\", \"num_of_pictures\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing outliers and unnecessary columns\n",
    "## Part 1 of 4\n",
    "Looking at the `price` and `odometer` columns.\n",
    "* The majority of the values in the price column are less than 500,000 but a small number are close to or well over 1,000,000! The rows for these prices do not appear to be unique in other ways nor are they related to the three columns being considered for removal. Two of the entries are for an expensive brand, but they are not representative of either group. Most of the expensive cars are priced a suspicious \\\\$12,345,678🤔. The high number of vehicles (1421) with a price of 0 will skew the results as well. Keeping all rows with prices between \\\\$1 and \\\\$500,00 seems to make sense.\n",
    "* The odometer column doesn't contain any outlier values. It is broken into a group of 13 buckets. Notably, 64% of the entries are in the 150,000km or more category. There doesn't seem to be any reason to remove any of the rows based on the odometer entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the index and price for the 15 most expensive vehicles:\n",
      " 39705    99999999\n",
      "42221    27322222\n",
      "39377    12345678\n",
      "47598    12345678\n",
      "27371    12345678\n",
      "2897     11111111\n",
      "24384    11111111\n",
      "11137    10000000\n",
      "47634     3890000\n",
      "7814      1300000\n",
      "22947     1234566\n",
      "43049      999999\n",
      "514        999999\n",
      "37585      999990\n",
      "36818      350000\n",
      "Name: price, dtype: int64\n",
      "Here's the index and price for the 10 least expensive vehicles:\n",
      " 31332    0\n",
      "35821    0\n",
      "8438     0\n",
      "43925    0\n",
      "38832    0\n",
      "8445     0\n",
      "29499    0\n",
      "15225    0\n",
      "43923    0\n",
      "18089    0\n",
      "Name: price, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# isolate the price column and reveal outlier values\n",
    "price_series = autos[\"price\"]\n",
    "print(\"Here's the index and price for the 15 most expensive vehicles:\\n\", price_series.sort_values(ascending=False).head(n=15))\n",
    "print(\"Here's the index and price for the 10 least expensive vehicles:\\n\", price_series.sort_values(ascending=False).tail(n=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of cars between $1 and $100:  475\n",
      "The number of cars more than $500,000:  14\n",
      "The number of cars that are free:  1421\n",
      "This chart shows the most and least expensive cars.\n"
     ]
    }
   ],
   "source": [
    "# examine rows with outlier prices\n",
    "cheap_cars = autos[autos[\"price\"].between(1,100)]\n",
    "print(\"The number of cars between $1 and $100: \", len(cheap_cars))\n",
    "expensive_cars = autos[autos[\"price\"]>500000]\n",
    "print(\"The number of cars more than $500,000: \", len(expensive_cars))\n",
    "free_cars = autos[autos[\"price\"] == 0]\n",
    "print(\"The number of cars that are free: \", len(free_cars))\n",
    "outlier_bool = (autos[\"price\"] < 1) | (autos[\"price\"] > 500000)\n",
    "outlier_rows = autos[outlier_bool]\n",
    "outlier_rows.sort_values(by=\"price\", axis=0, ascending=False)\n",
    "print(\"This chart shows the most and least expensive cars.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length:  50000\n",
      "Length:  48565\n"
     ]
    }
   ],
   "source": [
    "# remove rows with outlier prices\n",
    "print(\"Length: \", len(autos))\n",
    "autos = autos[autos[\"price\"].between(1, 500000)]\n",
    "print(\"Length: \", len(autos))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13,)\n",
      "<bound method NDFrame.describe of 0        150000\n",
      "1        150000\n",
      "2         70000\n",
      "3         70000\n",
      "4        150000\n",
      "          ...  \n",
      "49995    100000\n",
      "49996    150000\n",
      "49997      5000\n",
      "49998     40000\n",
      "49999    150000\n",
      "Name: odometer_km, Length: 48565, dtype: int64>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "150000    31414\n",
       "125000     5057\n",
       "100000     2115\n",
       "90000      1734\n",
       "80000      1415\n",
       "70000      1217\n",
       "60000      1155\n",
       "50000      1012\n",
       "40000       815\n",
       "30000       780\n",
       "20000       762\n",
       "10000       253\n",
       "5000        836\n",
       "Name: odometer_km, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# isolate the odometer column and reveal outlier values\n",
    "odometer_series = autos[\"odometer_km\"]\n",
    "print(odometer_series.unique().shape)\n",
    "print(odometer_series.describe)\n",
    "odometer_series.value_counts().sort_index(ascending=False).head(n=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing outliers and unnecessary columns\n",
    "### Part 2 of 4\n",
    "We can probably remove these three columns and make a note in the conclusion.\n",
    "* The `seller` and `offer_type` columns have only two values in each column. In each, one value represents all of the entries except for one entry.\n",
    "* The `num_of_pictures` column only has one entry, \"0\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "privat        48564\n",
      "gewerblich        1\n",
      "Name: seller, dtype: int64\n",
      "Angebot    48565\n",
      "Name: offer_type, dtype: int64\n",
      "0    48565\n",
      "Name: num_of_pictures, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# close look at value counts in three columns\n",
    "print(autos[\"seller\"].value_counts())\n",
    "print(autos[\"offer_type\"].value_counts())\n",
    "print(autos[\"num_of_pictures\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the seller, offer_type, and num_of_pictures columns\n",
    "autos = autos.drop([\"seller\", \"offer_type\", \"num_of_pictures\"], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing outliers and unnecessary columns\n",
    "### Part 3 of 4\n",
    "The date values are in string format and should be converted but an initial look shows some interesting things.\n",
    "* The `date_crawled` column shows a normal distribution for when the ads were put into the data set. There are three low points that could be due to problems with the bot or network, or some other factor.\n",
    "* The `date_created` column is heavily skewed to the left. However, the tail is very shallow and the vast majority of entries occurs under a normal distribution. Creating a frequency table shows some ads with much older `date_created` values than the rest of the set. This is the only one of the three with dates prior to March 2016. Again, there are three low points that interestingly appear to correspond to the low points in the date_crawled series. Removing any dates prior to the earliest date for the `date_crawled` column removes 203 entries. Looking at a line graph for `date_crawled` and `date_created` confirms that the dips occur at the same times.\n",
    "* The `last_seen` column aligns more closely with `date_crawled` than the `date_created` column but there is a huge increase on April 4-6. These three days represent over 50% of the values in this column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# isolate the date portion of the date_crawled column and count the number of entries per day\n",
    "date_crawled_dist = autos[\"date_crawled\"].str[:10].value_counts().sort_index()\n",
    "# set width and height for plots\n",
    "plt.rcParams['figure.figsize'] = [10,7]\n",
    "# create a line plot showing crawl volume for each day\n",
    "date_crawled_dist.plot.line()\n",
    "plt.ylabel(\"Number of entries\")\n",
    "plt.xlabel(\"Date Crawled\")\n",
    "plt.xticks(rotation=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# isolate the date portion of the ad_created column and count the number of entries per day\n",
    "ad_created_dist = autos[\"ad_created\"].str[:10].value_counts().sort_index()\n",
    "# create a line plot showing day the ad was created\n",
    "ad_created_dist.plot.line()\n",
    "plt.ylabel(\"Number of entries\")\n",
    "plt.xlabel(\"Date Ad Created\")\n",
    "plt.xticks(rotation=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48362\n",
      "48362\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# remove early entries so ad_created start date matches dates for date_crawled column\n",
    "autos = autos[autos[\"ad_created\"].str[:4] != \"2015\"]\n",
    "autos = autos[autos[\"ad_created\"].str[:7] != \"2016-01\"]\n",
    "autos = autos[autos[\"ad_created\"].str[:7] != \"2016-02\"]\n",
    "autos = autos[autos[\"ad_created\"].str[:10] != \"2016-03-01\"]\n",
    "autos = autos[autos[\"ad_created\"].str[:10] != \"2016-03-02\"]\n",
    "autos = autos[autos[\"ad_created\"].str[:10] != \"2016-03-03\"]\n",
    "autos = autos[autos[\"ad_created\"].str[:10] != \"2016-03-04\"]\n",
    "print(len(autos))\n",
    "# create a line plot showing day the ad was created\n",
    "print(len(autos))\n",
    "ad_created_dist = autos[\"ad_created\"].str[:10].value_counts().sort_index()\n",
    "ad_created_dist.plot.line()\n",
    "plt.ylabel(\"Number of entries\")\n",
    "plt.xlabel(\"Date Ad Created\")\n",
    "plt.xticks(rotation=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a line plot that shows date_crawled and ad_created\n",
    "distributions = [date_crawled_dist, ad_created_dist]\n",
    "for distribution in distributions:\n",
    "    distribution.plot.line()\n",
    "plt.legend()\n",
    "plt.xticks(rotation=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# isolate the date portion of the last_seen column and count the number of entries per day\n",
    "last_seen_dist = autos[\"last_seen\"].str[:10].value_counts().sort_index()\n",
    "# create a line plot showing day the ad was removed\n",
    "last_seen_dist.plot.line()\n",
    "plt.ylabel(\"Number of entries\")\n",
    "plt.xlabel(\"Date Ad Removed\")\n",
    "plt.xticks(rotation=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-03-05     0.097184\n",
       "2016-03-06     0.403209\n",
       "2016-03-07     0.525206\n",
       "2016-03-08     0.734047\n",
       "2016-03-09     0.942889\n",
       "2016-03-10     1.056615\n",
       "2016-03-11     1.228237\n",
       "2016-03-12     2.367561\n",
       "2016-03-13     0.876721\n",
       "2016-03-14     1.261321\n",
       "2016-03-15     1.585956\n",
       "2016-03-16     1.647988\n",
       "2016-03-17     2.799719\n",
       "2016-03-18     0.736115\n",
       "2016-03-19     1.585956\n",
       "2016-03-20     2.063604\n",
       "2016-03-21     2.065671\n",
       "2016-03-22     2.131839\n",
       "2016-03-23     1.848559\n",
       "2016-03-24     1.982962\n",
       "2016-03-25     1.922997\n",
       "2016-03-26     1.683140\n",
       "2016-03-27     1.571482\n",
       "2016-03-28     2.092552\n",
       "2016-03-29     2.243497\n",
       "2016-03-30     2.479219\n",
       "2016-03-31     2.384103\n",
       "2016-04-01     2.280716\n",
       "2016-04-02     2.501964\n",
       "2016-04-03     2.528845\n",
       "2016-04-04     2.454406\n",
       "2016-04-05    12.478806\n",
       "2016-04-06    22.221992\n",
       "2016-04-07    13.214921\n",
       "Name: last_seen, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a frequency table for the last_seen column\n",
    "# this allows for a more precise measure for the volume in the last three days\n",
    "last_seen_freq = autos[\"last_seen\"].str[:10].value_counts(normalize=True, dropna=False)*100\n",
    "last_seen_freq = last_seen_freq.sort_index()\n",
    "last_seen_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing outliers and unnecessary columns\n",
    "### Part 4 of 4\n",
    "The `year_of_registration` column has 5 entries prior to 1900 and 1879 after 2016. It's unlikely that any of these dates are correct. It would take far to long to look up the true values for these entries and using a mean value would not be appropriate for what this column represents. Removing them might allow a more accurate view of the distribution. Interestingly, while this does affect the minimum, maximum, and standard deviation values, it results in little change to the upper ends of the first, second, and third quartiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    48362.000000\n",
      "mean      2004.757371\n",
      "std         88.828659\n",
      "min       1000.000000\n",
      "25%       1999.000000\n",
      "50%       2004.000000\n",
      "75%       2008.000000\n",
      "max       9999.000000\n",
      "Name: year_of_registration, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# isolate the year_of_registration column and get initial stats\n",
    "registration_series = autos[\"year_of_registration\"]\n",
    "print(registration_series.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "1870\n",
      "The length of the data set before removing outlier registration year values is:  48362\n",
      "The length of the data set after removing outlier registration year values is:  46487\n"
     ]
    }
   ],
   "source": [
    "# remove year_of_registration outliers\n",
    "print(len(autos[autos[\"year_of_registration\"] < 1900]))\n",
    "print(len(autos[autos[\"year_of_registration\"] > 2016]))\n",
    "print(\"The length of the data set before removing outlier registration year values is: \", len(autos))\n",
    "autos = autos[autos[\"year_of_registration\"].between(1900, 2016)]\n",
    "print(\"The length of the data set after removing outlier registration year values is: \", len(autos))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    46487.000000\n",
      "mean      2002.907480\n",
      "std          7.188417\n",
      "min       1910.000000\n",
      "25%       1999.000000\n",
      "50%       2003.000000\n",
      "75%       2008.000000\n",
      "max       2016.000000\n",
      "Name: year_of_registration, dtype: float64\n",
      "The earliest year for a vehicle to be registered is: 1910\n",
      "The latest year for a vehicle to be registered is: 2016\n"
     ]
    }
   ],
   "source": [
    "# isolate the year_of_registration column and get final stats\n",
    "registration_series = autos[\"year_of_registration\"]\n",
    "print(registration_series.describe())\n",
    "print(\"The earliest year for a vehicle to be registered is:\", registration_series.min())\n",
    "print(\"The latest year for a vehicle to be registered is:\", registration_series.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Focusing on the Brand\n",
    "At this point the dataset is prepared for an initial analysis. Many consumer choices are influenced by brand. Using that as a reference for looking at mean price and mean mileage is a good way to begin grouping the entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# isolate the brand column and look at totals and relative frequencies.\n",
    "brand_series = autos[\"brand\"]\n",
    "# brand_value_counts = brand_series.value_counts()\n",
    "brand_value_counts = brand_series.value_counts(normalize=True, dropna=False)*100\n",
    "# print(brand_value_counts)\n",
    "# create a line plot showing volume of sales by brand\n",
    "# how do i get volkswagen on the last tick?\n",
    "brand_value_counts.plot.line()\n",
    "plt.ylabel(\"Number of entries\")\n",
    "plt.xlabel(\"Brand\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a function to create a dictionary and mean column value for part of a series\n",
    "def autos_converter(c_series, mean_column, start_slice=0, stop_slice=5):\n",
    "    \"\"\"\n",
    "    Takes a series index and returns that index and the mean of a column in the autos dataframe.\n",
    "    Can be sliced. Default = 0:5\n",
    "    Example:\n",
    "    >>> autos_converter(c_series=brand_value_counts, mean_column=\"price\", stop_slice=2)\n",
    "    {'volkswagen': 5404.27828123409, 'bmw': 8337.549843627834}\n",
    "    \"\"\"\n",
    "    series_slice = c_series.iloc[start_slice:stop_slice]\n",
    "    series_dict = {}\n",
    "    for index in series_slice.index:\n",
    "        index_mean = autos.loc[autos[\"brand\"] == index, mean_column].mean()\n",
    "        series_dict[index] = index_mean\n",
    "    return series_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dictionary with brand and mean price for top five selling brands\n",
    "t5_mean_price = autos_converter(c_series=brand_value_counts, mean_column=\"price\")\n",
    "# create dictionary with brand and mean odometer_km for top five selling brands\n",
    "t5_mean_odomkm = autos_converter(c_series=brand_value_counts, mean_column=\"odometer_km\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# i could have made another fundtion but i have to get onto the next project!\n",
    "# transform t5_mean_price dictionary to dataframe\n",
    "t5_mean_price_series = pd.Series(t5_mean_price)\n",
    "t5_df = pd.DataFrame(t5_mean_price_series, columns=[\"mean_price\"])\n",
    "# add columns for mean odometer_km and relative frequency in original df\n",
    "t5_mean_odomkm_series = pd.Series(t5_mean_odomkm)\n",
    "t5_df[\"mean_odomkm\"] = t5_mean_odomkm_series\n",
    "t5_df[\"%_in_autos\"] = brand_value_counts[:5]\n",
    "t5_df = t5_df.astype(int)\n",
    "t5_df = t5_df.reset_index()\n",
    "t5_df = t5_df.rename(columns={'index': 'brand'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dictionary with brand and mean price for five least selling brands\n",
    "l5_mean_price = autos_converter(c_series=brand_value_counts, mean_column=\"price\", start_slice=-5, stop_slice=None)\n",
    "# create dictionary with brand and mean odometer_km for five least selling brands\n",
    "l5_mean_odomkm = autos_converter(c_series=brand_value_counts, mean_column=\"odometer_km\", start_slice=-5, stop_slice=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform l5_mean_price dictionary to dataframe\n",
    "l5_mean_price_series = pd.Series(l5_mean_price)\n",
    "l5_df = pd.DataFrame(l5_mean_price_series, columns=[\"mean_price\"])\n",
    "# add columns for mean odometer_km and relative frequency in original df\n",
    "l5_mean_odomkm_series = pd.Series(l5_mean_odomkm)\n",
    "l5_df[\"mean_odomkm\"] = l5_mean_odomkm_series\n",
    "l5_df[\"%_in_autos\"] = brand_value_counts[-5:]\n",
    "l5_df = l5_df.astype(int)\n",
    "l5_df = l5_df.reset_index()\n",
    "l5_df = l5_df.rename(columns={'index': 'brand'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "t5_df[\"price_mileage_ratio\"] = 0\n",
    "t5_df[\"price_mileage_ratio\"] = t5_df[\"mean_price\"] / t5_df[\"mean_odomkm\"]\n",
    "l5_df[\"price_mileage_ratio\"] = 0\n",
    "l5_df[\"price_mileage_ratio\"] = l5_df[\"mean_price\"] / l5_df[\"mean_odomkm\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: center;\">\n",
       "      <th></th>\n",
       "      <th>brand</th>\n",
       "      <th>mean_price</th>\n",
       "      <th>mean_odomkm</th>\n",
       "      <th>%_in_autos</th>\n",
       "      <th>price_mileage_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>5404</td>\n",
       "      <td>128695</td>\n",
       "      <td>21</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bmw</td>\n",
       "      <td>8337</td>\n",
       "      <td>132600</td>\n",
       "      <td>11</td>\n",
       "      <td>0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>opel</td>\n",
       "      <td>2966</td>\n",
       "      <td>129425</td>\n",
       "      <td>10</td>\n",
       "      <td>0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mercedes_benz</td>\n",
       "      <td>8625</td>\n",
       "      <td>130786</td>\n",
       "      <td>9</td>\n",
       "      <td>0.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi</td>\n",
       "      <td>9341</td>\n",
       "      <td>129118</td>\n",
       "      <td>8</td>\n",
       "      <td>0.072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      brand       mean_price  mean_odomkm  %_in_autos  price_mileage_ratio\n",
       "0     volkswagen     5404       128695         21             0.042       \n",
       "1            bmw     8337       132600         11             0.063       \n",
       "2           opel     2966       129425         10             0.023       \n",
       "3  mercedes_benz     8625       130786          9             0.066       \n",
       "4           audi     9341       129118          8             0.072       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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: center;\">\n",
       "      <th></th>\n",
       "      <th>brand</th>\n",
       "      <th>mean_price</th>\n",
       "      <th>mean_odomkm</th>\n",
       "      <th>%_in_autos</th>\n",
       "      <th>price_mileage_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>daewoo</td>\n",
       "      <td>1049</td>\n",
       "      <td>121642</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>trabant</td>\n",
       "      <td>1790</td>\n",
       "      <td>54538</td>\n",
       "      <td>0</td>\n",
       "      <td>0.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rover</td>\n",
       "      <td>1602</td>\n",
       "      <td>137661</td>\n",
       "      <td>0</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lancia</td>\n",
       "      <td>3376</td>\n",
       "      <td>121900</td>\n",
       "      <td>0</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lada</td>\n",
       "      <td>2688</td>\n",
       "      <td>83518</td>\n",
       "      <td>0</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   brand    mean_price  mean_odomkm  %_in_autos  price_mileage_ratio\n",
       "0   daewoo     1049       121642          0             0.009       \n",
       "1  trabant     1790        54538          0             0.033       \n",
       "2    rover     1602       137661          0             0.012       \n",
       "3   lancia     3376       121900          0             0.028       \n",
       "4     lada     2688        83518          0             0.032       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pretty print dataframes for five most sold brands and five least sold brands\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.width', 1000)\n",
    "pd.set_option('display.colheader_justify', 'center')\n",
    "pd.set_option('display.precision', 3)\n",
    "display(t5_df)\n",
    "display(l5_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "Vehicle brand is an easy way for consumers to evaluate a purchase. Using this as a starting point leads to a few coclusions.\n",
    "* The three most popular brands (Volkswagen, BMW, and Opel respectively) represent slightly more than 50% of the vehicles in the data set. Un-commenting \"# print(brand_value_counts)\" displays the frequency table.\n",
    "* Of the five least most popular vehicles, Ladas and Trabants have very low mileage. Their unpopularity is probably due to quality perceptions.\n",
    "* The price_mileage_ratio column shows the relationship between mean_price and mean_odomkm. The closer the number to zero, the lower the vehicle mileage per dollar spent. So, amongst the five most popular brands, audis had the lowest mileage per dollar in the mean price.\n",
    "* Additionally, this allows a comparison of value compared to popularity. The mean price for the five most popular cars is \\\\$6935 and for the five least popular it is \\\\$2101. The price to mileage ratio for the popular cars is 0.05 and for the unpopular cars it is 0.114. So not only is an unpopular car less expensive it is also a better deal if mileage is the only criteria.\n",
    "* Coding a dashboard to combine different columns and row slices to create new ratios would possibly show more unexpected results.\n",
    "* Lastly, it would be interesting to examine the rows with outlier prices. If the price is controled for in some way would that group reveal similar paterns?"
   ]
  }
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