02_Carsized_scrape.ipynb

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    "# Notebook Context\n",
    "\n",
    "This is 2 of 6 Jupyter Notebooks associated with the used car project.\n",
    "\n",
    "This Notebook covers web scraping of the data to be used in the project from [carsized.com](https://www.carsized.com/en/). This data was used in the second version of the modelling, described in notebook [06_Modelling_version2](https://github.com/rgdavies92/used-car-value/blob/main/06_Modelling_version2.ipynb), and was found to improve model performance!"
   ]
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    "# Import packages \n",
    "\n",
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from bs4 import BeautifulSoup\n",
    "import re\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    "# Scrape the list of cars on carsized.com. This is the pre-scrape.\n",
    "\n",
    "# I do this because I need to have a list of all different cars available on carsized.com before they\n",
    "# can be scraped. This is like a pre-scrape before I scrape the dimensions for each of them.\n",
    "carurl = []\n",
    "# A small RegEx pattern to be used later\n",
    "pattern = re.compile(r'https://www.carsized.com/en/cars[a-z-0-9]+')\n",
    "url = \"https://www.carsized.com/en/cars\"\n",
    "# This scrape uses Requests rather than Cloudscraper. No problems encountered.\n",
    "r = requests.get(url)\n",
    "\n",
    "# Check that the website responds well\n",
    "if str(r) == \"<Response [200]>\":\n",
    "    # Use BeautifulSoup to parse the HTML into some soup\n",
    "    soup = BeautifulSoup(r.text, 'html.parser')\n",
    "    # Obtain a list of all articles (cars) on this carsized.com site\n",
    "    articles = soup.find_all(\"div\", attrs={\"class\":\"indexcontainer\"})\n",
    "    \n",
    "    # Iterate over each car in the list and extract the important bit of the URL for that car, then append \n",
    "    # to carurl list\n",
    "    for car in articles:\n",
    "        y = re.findall(pattern, str(car))\n",
    "        curl = (str(y).replace('https://www.carsized.com/en/cars',''))\n",
    "        # Some small string cleaning before appending to the list\n",
    "        carurl.append(curl.replace('[','').replace(']','').replace(\"'\",\"\"))\n",
    "        "
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
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    "# Scrape the contents of carsized.com. This is the full web scrape of dimensions for each car documented.\n",
    "\n",
    "# In order to obtain the car dimensions in a scrapable format I have to review the comparison of the desired\n",
    "# car with some other car. For this purpose, I'm comparing every car to the 2008 Abarth 500. Completely arbitrary.\n",
    "\n",
    "# Initialise some empty lists to hold the outputs\n",
    "results = []\n",
    "name=[]\n",
    "wheelbase=[]\n",
    "length=[]\n",
    "width=[]\n",
    "height=[]\n",
    "groundclearance=[]\n",
    "cargovol=[]\n",
    "maxcargovol=[]\n",
    "\n",
    "# Base url with the 2008 Abarth 500 for comparison.\n",
    "site = \"https://www.carsized.com/en/cars/compare/abarth-500-2008-3-door-hatchback-vs-\"\n",
    "\n",
    "# Iterate over cars in my pre-scraped list\n",
    "for car in carurl:\n",
    "    # Make an ampty holder for all attributes of the car being iterated over. This will contain data points\n",
    "    # not worth scraping, as well as the ones that are worth scraping.\n",
    "    allatts=[]\n",
    "    # Use f-string to combine the base url and the car being iterated over. Then Requests again to return the HTML\n",
    "    # Important to standardise all units when gathering this data! I'm going metric. \n",
    "    url = f\"{site}{car}/?&units=metric\"\n",
    "    r = requests.get(url)\n",
    "\n",
    "    # Check that the site responds well before proceeding\n",
    "    if str(r) == \"<Response [200]>\":\n",
    "        # Use BeautifulSoup to parse the HTML\n",
    "        soup = BeautifulSoup(r.text, 'html.parser')\n",
    "        # The meat I'm interested in is saved in a matrix-like table. I'm going to call this an article \n",
    "        articles = soup.find_all(\"div\", attrs={\"class\":[\"dmatrixtitle\",\"dmatrixtitlesup\"]})\n",
    "        \n",
    "        # Extract the text from each entry in the matrix-like table\n",
    "        for carsoup in articles:\n",
    "            allatts.append(carsoup.text)\n",
    "        \n",
    "        # Extract important measurements from the allatts list and add them to the initialised holding lists.\n",
    "        name.append(car)\n",
    "        wheelbase.append(allatts[2])\n",
    "        length.append(allatts[5])\n",
    "        width.append(allatts[8])\n",
    "        height.append(allatts[14])\n",
    "        groundclearance.append(allatts[17])\n",
    "        cargovol.append(allatts[20])\n",
    "        maxcargovol.append(allatts[23])\n",
    "    \n",
    "    # Save the entire allatts list to the results list as a redundancy measure in case any were missed.\n",
    "    results.append(allatts)\n",
    "\n",
    "# Form a dataframe of the extracted measurements for each car.\n",
    "df = pd.DataFrame({'name': name,'wheelbase_cm':wheelbase,'length_cm': length, 'width_cm':width,\n",
    "                  'height_cm':height, 'ground_clearance_cm':groundclearance, \n",
    "                   'cargo_volume_L':cargovol, 'max_cargo_volume_L':maxcargovol})"
   ]
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    "# Convert continuous variables to float\n",
    "\n",
    "# A simple function to tidy up units on a variety of columns\n",
    "def remove_units(x):\n",
    "    x=x.replace('cm','')\n",
    "    x=x.replace('l','')\n",
    "    x=x.replace('--','0')\n",
    "    x=x.strip()\n",
    "    x=float(x)\n",
    "    return x\n",
    "\n",
    "# Clean up units on extracted data\n",
    "df.wheelbase_cm = df.wheelbase_cm.apply(lambda x: remove_units(x))\n",
    "df.length_cm = df.length_cm.apply(lambda x: remove_units(x))\n",
    "df.width_cm = df.width_cm.apply(lambda x: remove_units(x))\n",
    "df.height_cm = df.height_cm.apply(lambda x: remove_units(x))\n",
    "df.ground_clearance_cm = df.ground_clearance_cm.apply(lambda x: remove_units(x))\n",
    "df.cargo_volume_L = df.cargo_volume_L.apply(lambda x: remove_units(x))\n",
    "df.max_cargo_volume_L = df.max_cargo_volume_L.apply(lambda x: remove_units(x))\n"
   ]
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   "execution_count": 5,
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    "# Make make column - I should probably have called this brand but it's too late now. \n",
    "\n",
    "# This has been pasted from a .unique call in anorther cell.\n",
    "makes = ['Abarth','Aixam','Alfa-Romeo','Alpine','Ariel','Aston-Martin','Audi','Austin',\n",
    "        'BMW','Beauford','Bentley','Bowler','Bugatti','Buick',\n",
    "        'CUPRA','Cadillac','Carbodies','Caterham','Chesil','Chevrolet','Chrysler','Citroen','Corvette',\n",
    "        'DAF','DFSK','DS Automobiles','Dacia','Daewoo','Daihatsu','Daimler','Datsun','Delorean','Dodge',\n",
    "        'Ferrari','Fiat','Ford','GMC','Great Wall','Infinity',\n",
    "        'Hillman','Honda','Hummer','Hyundai','Infiniti','Isuzu','Iveco','Jaguar','Jeep','Jensen','KIA',\n",
    "        'LEVC','Lada','Lamborghini','Lancia','Land Rover','Lexus','Lincoln','London Taxis International','Lotus',\n",
    "        'MG','MINI','Mahindra','Maserati','Maybach','Mazda','McLaren','Mercedes-Benz','Microcar','Mitsubishi',\n",
    "        'Mitsuoka','Morgan','Morris','Nissan','Noble','Opel','Packard','Perodua','Peugeot','Pilgrim','Polestar',\n",
    "        'Pontiac','Porsche','Proton','REO','Radical','Rage','Raptor','Reliant','Renault','Replica','Reva','Riley',\n",
    "        'Rolls-Royce','Rover','SEAT','SKODA','Saab','Sebring','Singer','Smart','Spyker','Ssangyong','Subaru',\n",
    "        'Sunbeam','Suzuki','TVR','Tesla','Tiger','Toyota','Triumph','Ultima','Vauxhall','Venturi','Volkswagen',\n",
    "        'Volvo','Westfield','Wolseley','Yamaha','Zenos','Alvis','Excalibur','Fisker','Genesis','Ssang-yong',\n",
    "        'AC','BAC']\n",
    "\n",
    "df['make']=''\n",
    "\n",
    "# Try to standardise car makes between the AutoTrader data and Carsized. If the Carsized make matches the\n",
    "# AutoTrader make then use the AutoTrader one.\n",
    "for index, car in df.iterrows():\n",
    "    for make in makes:\n",
    "        if make.lower()+'-' in str(car[0]):\n",
    "            df.loc[index,'make'] = make"
   ]
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   "execution_count": 6,
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    "# Make year column using RegEx\n",
    "\n",
    "# Set all years to 0\n",
    "df['year'] = '[0]'\n",
    "\n",
    "# Extract a year from the car name from Carsized. Note that I don't care about cars with years from a long\n",
    "# time ago so was able to limit the RegEx expression. Insert NaN if no year. \n",
    "for index, car in df.iterrows():\n",
    "    if len(re.findall(r\"[12][09][0-9][0-9]\", car[0]))>0:\n",
    "        df.loc[index,'year'] = int(re.findall(r\"[12][09][0-9][0-9]\", car[0])[0])\n",
    "    else:\n",
    "        df.loc[index,'year'] = np.nan\n"
   ]
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   "execution_count": 7,
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    "# Make body column \n",
    "\n",
    "# List of Bodies from both AutoTrader and Carsized. Note that there is UK and US english being used here!\n",
    "bodies = ['Hatchback', 'SUV', 'Van', 'Saloon', 'Convertible', 'MPV', 'Coupe',\n",
    "       'Estate', 'Pickup', 'Sedan', 'Cabriolet','Liftback','Roadster','Offroader','Fastback','double-cab','pick-up']\n",
    "\n",
    "# Make the body column empty\n",
    "df['body']=''\n",
    "\n",
    "# If any of these bodies above are in the car name, then assing the UK enlish equivalent to the df.body column.\n",
    "for index, car in df.iterrows():\n",
    "    for body in bodies:\n",
    "        if body.lower() in str(car[0]):\n",
    "            df.loc[index,'body'] = body\n",
    "        if df.loc[index,'body'] == 'Sedan':\n",
    "            df.loc[index,'body'] = 'Saloon'\n",
    "        if df.loc[index,'body'] == 'Cabriolet':\n",
    "            df.loc[index,'body'] = 'Convertible'\n",
    "        if df.loc[index,'body'] == 'Roadster':\n",
    "            df.loc[index,'body'] = 'Convertible'\n",
    "        if df.loc[index,'body'] == 'Liftback':\n",
    "            df.loc[index,'body'] = 'Hatchback'\n",
    "        if df.loc[index,'body'] == 'Offroader':\n",
    "            df.loc[index,'body'] = 'SUV'\n",
    "        if df.loc[index,'body'] == 'Fastback':\n",
    "            df.loc[index,'body'] = 'Coupe'\n",
    "        if df.loc[index,'body'] == 'double-cab':\n",
    "            df.loc[index,'body'] = 'Pickup'\n",
    "        if df.loc[index,'body'] == 'pick-up':\n",
    "            df.loc[index,'body'] = 'Pickup'"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
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    "# Make name column\n",
    "\n",
    "# Save off the original name and initialise a new blank name column\n",
    "df['original_name'] = df.name\n",
    "df['name'] = ''\n",
    "\n",
    "# Start from the original_name and make some small string adjustments and translations to finish with a UK enlgish\n",
    "# name column which will correspond with the AutoTrader data\n",
    "for index, car in df.iterrows():\n",
    "    n = df.loc[index,'original_name']\n",
    "    n = n.split(str(df.loc[index,'year']))[0]\n",
    "    n = n.replace('-',' ')\n",
    "    n = re.sub('alfa romeo','Alfa-Romeo',n)\n",
    "    n = re.sub('aston martin','Aston-Martin',n)\n",
    "    n = re.sub('rolls royce','Rolls-Royce',n)\n",
    "    n = re.sub('mercedes benz','Mercedes-Benz',n)    \n",
    "    n = re.sub('opel','Vauxhall',n)\n",
    "    df.loc[index,'name']=n.title().strip().lower()\n",
    "\n",
    "# A small number of manual fixes for cars with digits in their names which look like years!    \n",
    "df.loc[756,'name'] = 'peugeot 1007'\n",
    "df.loc[758,'name'] = 'peugeot 2008'\n",
    "df.loc[759,'name'] = 'peugeot 2008'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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    "# Write to csv - note that this data aren't ready for merging yet\n",
    "\n",
    "# abspath = r'merge_data/01_carsized_dims.csv'\n",
    "# df.to_csv(abspath, index=False, header=df.columns )"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
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    "# Format the csv with ffill/bfill to be able to merge with cars dataframe on name, body, year \n",
    "\n",
    "# Make df with NaN where no volumetric data available for each year 2000-2022\n",
    "carbodies = df.groupby(by=['name','body']).mean().reset_index()[['name','body']]\n",
    "\n",
    "# Initialise some empty lists\n",
    "name=[]\n",
    "body=[]\n",
    "year=[]\n",
    "\n",
    "# For evert car name and car body combination, create 23 rows, one for each year between 2000 and 2022.\n",
    "for index,row in carbodies.iterrows():\n",
    "    for yr in range(2000,2023):\n",
    "        name.append(row[0])\n",
    "        body.append(row[1])\n",
    "        year.append(yr)\n",
    "\n",
    "# Make this into a df - still missing the measurments here        \n",
    "allyeardf = pd.DataFrame({'name':name,'body':body,'year':year})\n",
    "\n",
    "# The mesurements to be added to this dataframe\n",
    "toadd = ['wheelbase_cm', 'length_cm', 'width_cm', 'height_cm',\n",
    "       'ground_clearance_cm', 'cargo_volume_L', 'max_cargo_volume_L']\n",
    "\n",
    "# Add columns of NaN for all elements of toadd\n",
    "for name in toadd:\n",
    "    allyeardf[name]=np.nan \n",
    "    \n",
    "#Reorder the original df to match output of groupby function later\n",
    "df1 = df[['name','body','year','wheelbase_cm','length_cm','width_cm','height_cm',\n",
    "               'ground_clearance_cm','cargo_volume_L','max_cargo_volume_L']]\n",
    "\n",
    "#If df1 has an entry where allyeardf is empty, take the entry with data! Makes df_tofill with ffill/bfill.\n",
    "results=[]\n",
    "for index, row in allyeardf.iterrows():\n",
    "    name = row[0]\n",
    "    body = row[1]\n",
    "    year = row[2]\n",
    "    realcar=df1[(df1.name==name) & (df1.body==body) & (df1.year==year)]\n",
    "    if len(realcar==1):\n",
    "        results.append(realcar.iloc[0,0:10])\n",
    "    else:\n",
    "        results.append(row)\n",
    "\n",
    "df_tofill = pd.DataFrame(results, columns = allyeardf.columns)\n",
    "\n",
    "#Ffill and bfill, improtantly ffill first\n",
    "for col in toadd:\n",
    "    df_tofill[col] = df_tofill.groupby(by=['name','body'])[col].transform(lambda x: x.ffill())\n",
    "    df_tofill[col] = df_tofill.groupby(by=['name','body'])[col].transform(lambda x: x.bfill())\n",
    "    \n",
    "dfmerge=df_tofill.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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     "end_time": "2022-03-04T14:17:35.338378Z",
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   "source": [
    "# Write to csv\n",
    "\n",
    "# abspath = r'merge_data/02_carsized_dims_for_merge.csv'\n",
    "# dfmerge.to_csv(abspath, index=False, header=dfmerge.columns )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-04T14:17:35.343953Z",
     "start_time": "2022-03-04T14:17:35.341321Z"
    }
   },
   "outputs": [],
   "source": [
    "# abspath = r'merge_data/02_carsized_dims_for_merge.csv'\n",
    "# cars_size_to_merge = pd.read_csv(abspath)\n",
    "# print('all',cars_size_to_merge.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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     "end_time": "2022-03-04T14:17:35.393438Z",
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>body</th>\n",
       "      <th>year</th>\n",
       "      <th>wheelbase_cm</th>\n",
       "      <th>length_cm</th>\n",
       "      <th>width_cm</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>ground_clearance_cm</th>\n",
       "      <th>cargo_volume_L</th>\n",
       "      <th>max_cargo_volume_L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2000</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2001</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2002</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2003</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2004</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2005</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2006</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2007</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2008</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2009</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2010</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2011</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2012</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2013</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2014</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2015</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2016</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2017</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2018</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2019</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2020</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2021</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>abarth 500</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2022</td>\n",
       "      <td>230.0</td>\n",
       "      <td>365.7</td>\n",
       "      <td>162.7</td>\n",
       "      <td>148.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2000</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2001</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2002</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2003</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2004</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2005</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2006</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2007</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2008</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2009</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2010</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2012</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2013</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2014</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2015</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2016</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2017</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2018</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2019</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2020</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2021</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>acura nsx</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>2022</td>\n",
       "      <td>263.0</td>\n",
       "      <td>448.7</td>\n",
       "      <td>193.9</td>\n",
       "      <td>120.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>alfa-romeo 147</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2000</td>\n",
       "      <td>254.6</td>\n",
       "      <td>422.3</td>\n",
       "      <td>172.9</td>\n",
       "      <td>144.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>1042.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>alfa-romeo 147</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2001</td>\n",
       "      <td>254.6</td>\n",
       "      <td>422.3</td>\n",
       "      <td>172.9</td>\n",
       "      <td>144.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>1042.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>alfa-romeo 147</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2002</td>\n",
       "      <td>254.6</td>\n",
       "      <td>422.3</td>\n",
       "      <td>172.9</td>\n",
       "      <td>144.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>1042.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>alfa-romeo 147</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>2003</td>\n",
       "      <td>254.6</td>\n",
       "      <td>422.3</td>\n",
       "      <td>172.9</td>\n",
       "      <td>144.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>1042.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name       body  year  wheelbase_cm  length_cm  width_cm  \\\n",
       "0       abarth 500  Hatchback  2000         230.0      365.7     162.7   \n",
       "1       abarth 500  Hatchback  2001         230.0      365.7     162.7   \n",
       "2       abarth 500  Hatchback  2002         230.0      365.7     162.7   \n",
       "3       abarth 500  Hatchback  2003         230.0      365.7     162.7   \n",
       "4       abarth 500  Hatchback  2004         230.0      365.7     162.7   \n",
       "5       abarth 500  Hatchback  2005         230.0      365.7     162.7   \n",
       "6       abarth 500  Hatchback  2006         230.0      365.7     162.7   \n",
       "7       abarth 500  Hatchback  2007         230.0      365.7     162.7   \n",
       "0       abarth 500  Hatchback  2008         230.0      365.7     162.7   \n",
       "9       abarth 500  Hatchback  2009         230.0      365.7     162.7   \n",
       "10      abarth 500  Hatchback  2010         230.0      365.7     162.7   \n",
       "11      abarth 500  Hatchback  2011         230.0      365.7     162.7   \n",
       "12      abarth 500  Hatchback  2012         230.0      365.7     162.7   \n",
       "13      abarth 500  Hatchback  2013         230.0      365.7     162.7   \n",
       "14      abarth 500  Hatchback  2014         230.0      365.7     162.7   \n",
       "15      abarth 500  Hatchback  2015         230.0      365.7     162.7   \n",
       "16      abarth 500  Hatchback  2016         230.0      365.7     162.7   \n",
       "17      abarth 500  Hatchback  2017         230.0      365.7     162.7   \n",
       "18      abarth 500  Hatchback  2018         230.0      365.7     162.7   \n",
       "19      abarth 500  Hatchback  2019         230.0      365.7     162.7   \n",
       "20      abarth 500  Hatchback  2020         230.0      365.7     162.7   \n",
       "21      abarth 500  Hatchback  2021         230.0      365.7     162.7   \n",
       "22      abarth 500  Hatchback  2022         230.0      365.7     162.7   \n",
       "23       acura nsx      Coupe  2000         263.0      448.7     193.9   \n",
       "24       acura nsx      Coupe  2001         263.0      448.7     193.9   \n",
       "25       acura nsx      Coupe  2002         263.0      448.7     193.9   \n",
       "26       acura nsx      Coupe  2003         263.0      448.7     193.9   \n",
       "27       acura nsx      Coupe  2004         263.0      448.7     193.9   \n",
       "28       acura nsx      Coupe  2005         263.0      448.7     193.9   \n",
       "29       acura nsx      Coupe  2006         263.0      448.7     193.9   \n",
       "30       acura nsx      Coupe  2007         263.0      448.7     193.9   \n",
       "31       acura nsx      Coupe  2008         263.0      448.7     193.9   \n",
       "32       acura nsx      Coupe  2009         263.0      448.7     193.9   \n",
       "33       acura nsx      Coupe  2010         263.0      448.7     193.9   \n",
       "34       acura nsx      Coupe  2011         263.0      448.7     193.9   \n",
       "35       acura nsx      Coupe  2012         263.0      448.7     193.9   \n",
       "36       acura nsx      Coupe  2013         263.0      448.7     193.9   \n",
       "37       acura nsx      Coupe  2014         263.0      448.7     193.9   \n",
       "38       acura nsx      Coupe  2015         263.0      448.7     193.9   \n",
       "1        acura nsx      Coupe  2016         263.0      448.7     193.9   \n",
       "40       acura nsx      Coupe  2017         263.0      448.7     193.9   \n",
       "41       acura nsx      Coupe  2018         263.0      448.7     193.9   \n",
       "42       acura nsx      Coupe  2019         263.0      448.7     193.9   \n",
       "43       acura nsx      Coupe  2020         263.0      448.7     193.9   \n",
       "44       acura nsx      Coupe  2021         263.0      448.7     193.9   \n",
       "45       acura nsx      Coupe  2022         263.0      448.7     193.9   \n",
       "46  alfa-romeo 147  Hatchback  2000         254.6      422.3     172.9   \n",
       "47  alfa-romeo 147  Hatchback  2001         254.6      422.3     172.9   \n",
       "48  alfa-romeo 147  Hatchback  2002         254.6      422.3     172.9   \n",
       "49  alfa-romeo 147  Hatchback  2003         254.6      422.3     172.9   \n",
       "\n",
       "    height_cm  ground_clearance_cm  cargo_volume_L  max_cargo_volume_L  \n",
       "0       148.5                 10.4           185.0               610.0  \n",
       "1       148.5                 10.4           185.0               610.0  \n",
       "2       148.5                 10.4           185.0               610.0  \n",
       "3       148.5                 10.4           185.0               610.0  \n",
       "4       148.5                 10.4           185.0               610.0  \n",
       "5       148.5                 10.4           185.0               610.0  \n",
       "6       148.5                 10.4           185.0               610.0  \n",
       "7       148.5                 10.4           185.0               610.0  \n",
       "0       148.5                 10.4           185.0               610.0  \n",
       "9       148.5                 10.4           185.0               610.0  \n",
       "10      148.5                 10.4           185.0               610.0  \n",
       "11      148.5                 10.4           185.0               610.0  \n",
       "12      148.5                 10.4           185.0               610.0  \n",
       "13      148.5                 10.4           185.0               610.0  \n",
       "14      148.5                 10.4           185.0               610.0  \n",
       "15      148.5                 10.4           185.0               610.0  \n",
       "16      148.5                 10.4           185.0               610.0  \n",
       "17      148.5                 10.4           185.0               610.0  \n",
       "18      148.5                 10.4           185.0               610.0  \n",
       "19      148.5                 10.4           185.0               610.0  \n",
       "20      148.5                 10.4           185.0               610.0  \n",
       "21      148.5                 10.4           185.0               610.0  \n",
       "22      148.5                 10.4           185.0               610.0  \n",
       "23      120.4                 10.4           110.0                 0.0  \n",
       "24      120.4                 10.4           110.0                 0.0  \n",
       "25      120.4                 10.4           110.0                 0.0  \n",
       "26      120.4                 10.4           110.0                 0.0  \n",
       "27      120.4                 10.4           110.0                 0.0  \n",
       "28      120.4                 10.4           110.0                 0.0  \n",
       "29      120.4                 10.4           110.0                 0.0  \n",
       "30      120.4                 10.4           110.0                 0.0  \n",
       "31      120.4                 10.4           110.0                 0.0  \n",
       "32      120.4                 10.4           110.0                 0.0  \n",
       "33      120.4                 10.4           110.0                 0.0  \n",
       "34      120.4                 10.4           110.0                 0.0  \n",
       "35      120.4                 10.4           110.0                 0.0  \n",
       "36      120.4                 10.4           110.0                 0.0  \n",
       "37      120.4                 10.4           110.0                 0.0  \n",
       "38      120.4                 10.4           110.0                 0.0  \n",
       "1       120.4                 10.4           110.0                 0.0  \n",
       "40      120.4                 10.4           110.0                 0.0  \n",
       "41      120.4                 10.4           110.0                 0.0  \n",
       "42      120.4                 10.4           110.0                 0.0  \n",
       "43      120.4                 10.4           110.0                 0.0  \n",
       "44      120.4                 10.4           110.0                 0.0  \n",
       "45      120.4                 10.4           110.0                 0.0  \n",
       "46      144.2                  0.0           292.0              1042.0  \n",
       "47      144.2                  0.0           292.0              1042.0  \n",
       "48      144.2                  0.0           292.0              1042.0  \n",
       "49      144.2                  0.0           292.0              1042.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmerge.head(50)"
   ]
  }
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