04_Data_cleaning_and_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "53876e26",
   "metadata": {},
   "source": [
    "# Notebook Context\n",
    "\n",
    "This is 4 of 6 Jupyter Notebooks associated with the used car project.\n",
    "\n",
    "Data cleaning, feature engineering and merging of data sources are performed before presenting some exploratory data analysis. This notebook takes input data from [01_AutoTrader_scrape](https://github.com/rgdavies92/used-car-value/blob/main/01_AutoTrader_scrape.ipynb), [02_Carsized_scrape](https://github.com/rgdavies92/used-car-value/blob/main/02_Carsized_scrape.ipynb) and [03_GeoCoding](https://github.com/rgdavies92/used-car-value/blob/main/03_GeoCoding.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "830b6570",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:28:22.573347Z",
     "start_time": "2022-03-09T21:28:19.439173Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# Import packages \n",
    "\n",
    "import pandas as pd\n",
    "import glob\n",
    "import numpy as np\n",
    "import re\n",
    "import regex\n",
    "import time\n",
    "import random\n",
    "import scipy.stats as stats\n",
    "import itertools\n",
    "from tqdm import tqdm\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import requests\n",
    "import cloudscraper\n",
    "from bs4 import BeautifulSoup\n",
    "import urllib.parse\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "from statsmodels.tools.tools import add_constant\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d23ae169",
   "metadata": {},
   "source": [
    "# Import and clean cars"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f7668a",
   "metadata": {},
   "source": [
    "The scraped cars will be cleaned in four different batches.\n",
    "\n",
    "* Used cars - not electric\n",
    "    * Starting with 374854 cars and ending with 373912\n",
    "* Used cars - electric\n",
    "    * Starting with 4887 cars and ending with 4740\n",
    "* New cars - not electric\n",
    "    * Starting with 18906 cars and ending with 18890\n",
    "* New cars - electric\n",
    "    * Starting with 2816 cars and ending with 2764\n",
    "\n",
    "The cleaning method is very similar for each batch, with differences occurring in the way engine size is handled for electric/non-electric vehicles. \n",
    "\n",
    "I have heavily commented the code for the *Used cars - not electric* cleaning section with a slightly liter code commenting approach on the subsequent 3 batches as the method is very repetitive. If ever the approach is unclear, I would suggest checking the corresponding cleaning stage in the *Used cars - not electric* section."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef393a5c",
   "metadata": {},
   "source": [
    "## Used cars - not electric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d49f0c28",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:28:33.361446Z",
     "start_time": "2022-03-09T21:28:22.577061Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame shape: (374854, 20)\n"
     ]
    }
   ],
   "source": [
    "# Import all detailed files and make cars df, including year \n",
    "\n",
    "path = r'/Users/robertdavies/Desktop/DSI/GA_P2/LargeDatasets/raw/df_detailed'\n",
    "\n",
    "li=[]\n",
    "\n",
    "# For each year between 1990 and 2021, import each used car file with glob package\n",
    "for year in range(1990,2022):\n",
    "    year_files = glob.glob(path + f\"/*{year}*used.csv\")\n",
    "    for filename in year_files:\n",
    "        # Make it into a dataframe\n",
    "        df = pd.read_csv(filename, index_col=None, header=0 )\n",
    "        # Assign YR variable - this was only possible because of the way in which they were gathered\n",
    "        df['YR'] = year\n",
    "        # Drop diplicates within a file - highly possible because of the AutoTrader searching method implemented\n",
    "        df.drop_duplicates(subset=['link'], keep='first', inplace=True)\n",
    "        # Append to a list of dataframes\n",
    "        li.append(df)\n",
    "\n",
    "# Concatenate all dataframes and drop duplicates again\n",
    "ucars = pd.concat(li, axis=0, ignore_index=True)\n",
    "ucars.drop_duplicates(subset=['link'], keep='first', inplace=True)\n",
    "print('DataFrame shape:',ucars.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0f7ca2f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:31:06.930611Z",
     "start_time": "2022-03-09T21:28:33.364491Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame shape: (374854, 20)\n"
     ]
    }
   ],
   "source": [
    "# Clean cars.body \n",
    "\n",
    "# This block of code uses label encoder to transform the body variable, then imputs the missing body data\n",
    "# with the modal value for the specified car name. \n",
    "\n",
    "# In hindsight there may be a better way to do this without using label encoder but it isn't broken so I won't\n",
    "# try to fix it here. \n",
    "\n",
    "#Create df of cars will null body\n",
    "nabody = ucars[ucars['body'].isnull()].copy()\n",
    "\n",
    "#Encode body column but don't overwrite\n",
    "to_encode = ['body']\n",
    "cars_e = ucars.copy()\n",
    "for col in to_encode:\n",
    "    le = LabelEncoder()\n",
    "    cars_e[col] = le.fit_transform(cars_e[col]) \n",
    "\n",
    "#Iterate through null-body cars and replace with corresponding modal body value based on name column grouping\n",
    "for index, car in nabody.iterrows():\n",
    "    carname=nabody.loc[index,'name']\n",
    "    body=nabody.loc[index,'body']\n",
    "    nabody.loc[index,'body'] = cars_e[cars_e['name']==carname].body.mode()[0]\n",
    "    cars_e.loc[index,'body'] = cars_e[cars_e['name']==carname].body.mode()[0]     \n",
    "\n",
    "#Reverse the encoding    \n",
    "for col in to_encode:\n",
    "    ucars[col] = le.inverse_transform(cars_e[col]) \n",
    "\n",
    "#After checking the remaining cars without a body, it is clear that they are all vans!    \n",
    "ucars.body = ['Van' if pd.Series(x).isnull()[0]==True else x for x in ucars.body]        \n",
    "\n",
    "print('DataFrame shape:',ucars.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5bb7fbd3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:31:08.654056Z",
     "start_time": "2022-03-09T21:31:06.937421Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame shape: (374590, 20)\n"
     ]
    }
   ],
   "source": [
    "# Clean cars.BHP, taking values from cars.engine where necessary. Drop remaining 264 NaN. \n",
    "\n",
    "# Unmerge some of the BHP entries which have engine size in there too.\n",
    "for index, car in ucars[ucars['BHP'].str.contains('L', regex=True, na=False)].iterrows():\n",
    "    ucars.loc[index, 'engine'] = str(ucars.loc[index, 'BHP']).split(' ')[0]\n",
    "    ucars.loc[index, 'BHP'] = str(ucars.loc[index, 'BHP']).split(' ')[1].strip('()') \n",
    "\n",
    "# Drop NaN BHP - this is only 264 cars\n",
    "ucars.dropna(subset=['BHP'],inplace=True)\n",
    "\n",
    "# This function is necessary because some BHP stats were reported in terms of PS, the German (almost) equivalent.\n",
    "# It does what it says on the tin - standardises BHP stats to be in units of BHP.\n",
    "def standardise_bhp(x):\n",
    "    lenbhp = len(str(x).split(' '))\n",
    "    bhp=x\n",
    "    if lenbhp == 1:\n",
    "        if 'BHP' in x:\n",
    "            bhp=float(re.findall('[0-9]+', x)[0])       \n",
    "        elif 'PS' in x:\n",
    "            ps=float(re.findall('[0-9]+', x)[0])\n",
    "            bhp = ps/1.014        \n",
    "    elif lenbhp == 2:\n",
    "        x=str(x.split(' ')[-1])\n",
    "        if 'BHP' in x:\n",
    "            bhp=float(re.findall('[0-9]+', x)[0])      \n",
    "        elif 'PS' in x:\n",
    "            ps=float(re.findall('[0-9]+', x)[0])\n",
    "            bhp = ps/1.014\n",
    "    else:\n",
    "        bhp=np.nan   \n",
    "    return bhp    \n",
    "\n",
    "#Standardise remaining BHP (convert from PS)\n",
    "ucars.BHP = ucars.BHP.apply(lambda x: standardise_bhp(x))\n",
    "\n",
    "print('DataFrame shape:',ucars.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd1bd36f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:31:36.637420Z",
     "start_time": "2022-03-09T21:31:08.656798Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame shape: (374590, 21)\n"
     ]
    }
   ],
   "source": [
    "# Compile the best location variable. Retain dealer_city and dealer_area. Don't drop remaining NaN. \n",
    "\n",
    "# Split href0 into city and area\n",
    "ucars['dealer_city_temp'] = [x.split('/')[3] if type(x)==str and len(x.split('/'))>3 else x for x in ucars.href0]\n",
    "ucars['dealer_area'] = [x.split('/')[2] if type(x)==str and len(x.split('/'))>3 else x for x in ucars.href0]\n",
    "\n",
    "# There are some seller ratings in seller0, move them to seller1\n",
    "# This line causes a warning. In the end I don't even use the seller ratings in the model so\n",
    "# I have left this alone. It could even be commented out with no impact on the project.\n",
    "ucars[ucars['seller0'].str.contains('[+-]?([0-9]*[.])?[0-9]+', regex=True, na=False)].seller1 = ucars[ucars['seller0'].str.contains('[+-]?([0-9]*[.])?[0-9]+', regex=True, na=False)].seller0\n",
    "\n",
    "# Make seller ratings in seller0 np.nan\n",
    "ucars.seller0 =[np.nan if '4' in str(x) or '5' in str(x) else x for x in ucars.seller0]\n",
    "\n",
    "# If the seller0 column is np.nan, use the dealer_city \n",
    "for index, car in ucars.iterrows():\n",
    "    if pd.isnull(ucars.loc[index,'seller0']):\n",
    "        ucars.loc[index,'seller0']=ucars.loc[index,'dealer_city_temp']\n",
    "        \n",
    "# Define dealer_city column and drop surplus data\n",
    "ucars['dealer_city'] = ucars['seller0']\n",
    "ucars.drop(['seller0','dealer_city_temp'],inplace=True, axis=1)\n",
    "\n",
    "print('DataFrame shape:',ucars.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c340e413",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:31:38.339311Z",
     "start_time": "2022-03-09T21:31:36.640437Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame shape: (374485, 21)\n"
     ]
    }
   ],
   "source": [
    "# Iterate over missing engine rows and use RegEx on name_subtitle to extract engine size where possible. \n",
    "\n",
    "# As mentioned above, if the engine data is null, try to recover it from the name_subtitle with RegEx\n",
    "for index, car in ucars[ucars['engine'].isnull()].iterrows():\n",
    "    car_subname = ucars.loc[index, 'name_subtitle']\n",
    "    try:\n",
    "        enginesize = re.findall('([0-9][.][0-9]+)',car_subname)[0]\n",
    "    except: \n",
    "        enginesize = np.nan\n",
    "    ucars.loc[index,'engine'] = float(enginesize)\n",
    "\n",
    "# Remove L from each engine size.\n",
    "ucars.engine= ucars.engine.apply(lambda x: float(str(x).replace('L','')))\n",
    "\n",
    "# Drop remaining 105 used cars without engine size.\n",
    "ucars.dropna(subset=['engine'],inplace=True)\n",
    "\n",
    "print('DataFrame shape:',ucars.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c2a63b83",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:32:40.592905Z",
     "start_time": "2022-03-09T21:31:38.343075Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame shape: (374231, 22)\n"
     ]
    }
   ],
   "source": [
    "# Make cars.doors \n",
    "\n",
    "# Function to pull the number of doors out of the name_subtitle column.\n",
    "# Strucutre of the door information was studied and captured in these four RegEx expressions.\n",
    "def extract_drs(x):\n",
    "    drs = re.findall('( [1-8]d[Rr]?)',x, re.I)\n",
    "    if len(drs) > 0:\n",
    "        drs = str(drs[0]).lower()\n",
    "    else:\n",
    "        drs = re.findall('([1-8][ ]?door)',x, re.I)\n",
    "        if len(drs) > 0:\n",
    "            drs = str(drs[0])[0]+'dr'\n",
    "        else:\n",
    "            drs = re.findall('([1-8]dr)',x, re.I)\n",
    "            if len(drs) > 0:\n",
    "                drs = str(drs[0])[0]+'dr'\n",
    "            else:\n",
    "                drs = re.findall('(D-4D)',x, re.I)\n",
    "                if len(drs) > 0:\n",
    "                    drs = '4dr'\n",
    "                else:\n",
    "                    drs='0dr'\n",
    "                \n",
    "    if ('r' or 'R') not in drs:\n",
    "        drs = drs.replace('d','dr')\n",
    "    return drs.strip()\n",
    "\n",
    "# Apply the extract_drs function to name_subtitle\n",
    "ucars['doors'] = ucars.name_subtitle.apply(lambda x: extract_drs(x))\n",
    "\n",
    "# Now I pursue the same logice as with car body, replacing the 0drs cars number of doors with the modal value\n",
    "# for that specific name of car.\n",
    "\n",
    "#Create df of cars with 0 doors\n",
    "zerodoors = ucars[ucars['doors']=='0dr'].copy()\n",
    "\n",
    "#Encode doors column but don't overwrite\n",
    "to_encode = ['doors']\n",
    "cars_e = ucars.copy()\n",
    "for col in to_encode:\n",
    "    le = LabelEncoder()\n",
    "    cars_e[col] = le.fit_transform(cars_e[col]) \n",
    "\n",
    "#Iterate through zero door cars and replace with corresponding num doors based on name column grouping\n",
    "for index, car in zerodoors.iterrows():\n",
    "    carname=zerodoors.loc[index,'name']\n",
    "    cars_e.loc[index,'doors'] = cars_e[cars_e['name']==carname].doors.mode()[0]     \n",
    "\n",
    "#Reverse the encoding    \n",
    "for col in to_encode:\n",
    "    ucars[col] = le.inverse_transform(cars_e[col]) \n",
    "\n",
    "#Drop the remaining 254 cars without door information    \n",
    "ucars=ucars[ucars.doors!='0dr'].copy()\n",
    "ucars.dropna(subset=['doors'],inplace=True)\n",
    "    \n",
    "print('DataFrame shape:',ucars.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bc5c34a7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:32:40.721379Z",
     "start_time": "2022-03-09T21:32:40.596846Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame shape: (373936, 22)\n"
     ]
    }
   ],
   "source": [
    "# Clean cars.mileage \n",
    "\n",
    "#Drop the NaN values - only 295 so I'm not overly worried\n",
    "ucars.dropna(subset=['mileage'],inplace=True)\n",
    "\n",
    "print('DataFrame shape:',ucars.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a623f04",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:32:40.901987Z",
     "start_time": "2022-03-09T21:32:40.726040Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame shape: (373916, 22)\n"
     ]
    }
   ],
   "source": [
    "# Clean cars.fuel \n",
    "\n",
    "#Drop the NaN values - only 20 so I'm not overly worried\n",
    "ucars.dropna(subset=['fuel'],inplace=True)\n",
    "\n",
    "print('DataFrame shape:',ucars.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "96352500",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:32:41.896261Z",
     "start_time": "2022-03-09T21:32:40.910383Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame shape: (373916, 23)\n"
     ]
    }
   ],
   "source": [
    "# Clean cars.price, make cars.ID, clean cars.year \n",
    "\n",
    "to_dummy=['drivetrain','make','body','transmission','fuel']\n",
    "\n",
    "ucars.price=ucars.price.apply(lambda x: x.replace('£',''))\n",
    "ucars.price=ucars.price.apply(lambda x: float(x.replace(',','')))\n",
    "ucars['id'] =[x.split('/')[-1] for x in ucars.link]\n",
    "ucars['year_reg']=ucars.year\n",
    "ucars['year']=ucars.YR\n",
    "ucars.drop('YR',inplace=True, axis=1)\n",
    "\n",
    "print('DataFrame shape:',ucars.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fc504bf8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:32:44.213128Z",
     "start_time": "2022-03-09T21:32:41.899333Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Have a peek at the price and mileage distribution. \n",
    "\n",
    "# This is really just a quick look. No outliers have been dropped.\n",
    "# You'll notice that I make a log-transofrm of the price and mileage data when obbserving the\n",
    "# distributions - this will be discussed later.\n",
    "\n",
    "ucars['log_price']=ucars.price.apply(lambda x: np.log(x))\n",
    "ucars['log_mileage']=ucars.mileage.apply(lambda x: np.log(x))\n",
    "\n",
    "# Logic to drop some outliers if wanted\n",
    "\n",
    "#mask = np.abs((ucars.price - ucars.price.mean(0)) / ucars.price.std(0)) > 3\n",
    "#ucars= ucars[~mask]\n",
    "#ucars.price.plot(kind='hist', bins=50, title='Car Price after outlier removal', xlabel='£');\n",
    "#mask = np.abs((ucars.mileage - ucars.mileage.mean(0)) / ucars.mileage.std(0)) > 3\n",
    "#ucars= ucars[~mask]\n",
    "#ucars.mileage.plot(kind='hist', bins=50, title='Car Mileage');\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(16,8))\n",
    "ax[0,0].hist(ucars.price, bins=60)\n",
    "ax[0,1].hist(ucars.log_price, bins=60)\n",
    "ax[1,0].hist(ucars.mileage, bins=60)\n",
    "ax[1,1].hist(ucars.log_mileage, bins=60)\n",
    "ax[0,0].set_xlabel('£')\n",
    "ax[0,1].set_xlabel('£')\n",
    "ax[1,0].set_xlabel('Miles')\n",
    "ax[1,1].set_xlabel('Miles')\n",
    "ax[0,0].set_title('Price')\n",
    "ax[0,1].set_title('Log price')\n",
    "ax[1,0].set_title('Mileage')\n",
    "ax[1,1].set_title('Log-mileage')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f8eac958",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:24.925957Z",
     "start_time": "2022-03-09T21:32:44.216206Z"
    },
    "code_folding": [
     9
    ],
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame shape: (373916, 26)\n"
     ]
    }
   ],
   "source": [
    "# Simplify the cars.name column. Probably not perfect.\n",
    "\n",
    "# Save off the original name as on car listing\n",
    "ucars['orig_name'] = ucars.name\n",
    "ucars['lname']=ucars.name.apply(lambda x: x.lower())\n",
    "\n",
    "# Lists of potential car bodies and names from Carsized.com, see associated notebook.\n",
    "body =[\"coupe\", \"convertible\", \"estate\", \"hatchback\", \"MPV\", \"pickup\", \n",
    "       \"SUV\", \"saloon\",\"cabriolet\",\"sedan\"]\n",
    "name_list_from_carsized = ['abarth 500', 'acura nsx', 'alfa-romeo 147', 'alfa-romeo 156',\n",
    "       'alfa-romeo 159', 'alfa-romeo 164', 'alfa-romeo 4c',\n",
    "       'alfa-romeo giulia', 'alfa-romeo giulietta', 'alfa-romeo mito',\n",
    "       'alfa-romeo sprint', 'alfa-romeo stelvio', 'alvis tc 108',\n",
    "       'aston-martin db11', 'aston-martin dbx', 'aston-martin rapide',\n",
    "       'aston-martin vantage', 'audi 100', 'audi a1', 'audi a2',\n",
    "       'audi a3', 'audi a4', 'audi a5', 'audi a6', 'audi a7', 'audi a8',\n",
    "       'audi e tron', 'audi e tron gt', 'audi q2', 'audi q3',\n",
    "       'audi q4 e tron', 'audi q5', 'audi q7', 'audi q8', 'audi r8',\n",
    "       'audi tt', 'austin 7', 'austin healey 3000', 'bentley 3 5 litre',\n",
    "       'bentley bentayga', 'bentley continental flying spur',\n",
    "       'bentley continental gt', 'bentley flying spur', 'bmw 1 series', 'bmw 2 series',\n",
    "       'bmw 3 series', 'bmw 4 series', 'bmw 5 series', 'bmw 6 series', 'bmw 7 series', 'bmw 8 series',\n",
    "       'bmw i3', 'bmw i8', 'bmw isetta', 'bmw ix', 'bmw x1', 'bmw x2',\n",
    "       'bmw x3', 'bmw x4', 'bmw x5', 'bmw x6', 'bmw x7', 'bmw z4',\n",
    "       'buick skylark', 'cadillac ats', 'cadillac bls', 'cadillac cts',\n",
    "       'cadillac eldorado', 'cadillac escalade', 'cadillac seville',\n",
    "       'cadillac srx', 'cadillac xt5', 'chevrolet aveo',\n",
    "       'chevrolet bel air', 'chevrolet camaro', 'chevrolet caprice',\n",
    "       'chevrolet captiva', 'chevrolet corvette', 'chevrolet cruze',\n",
    "       'chevrolet epica', 'chevrolet equinox', 'chevrolet impala',\n",
    "       'chevrolet malibu', 'chevrolet matiz', 'chevrolet spark',\n",
    "       'chevrolet tahoe', 'chrysler 300', 'chrysler pt cruiser',\n",
    "       'chrysler sebring', 'chrysler voyager', 'citroen b14',\n",
    "       'citroen berlingo', 'citroen c crosser', 'citroen c elysee',\n",
    "       'citroen c1', 'citroen c3', 'citroen c3 aircross', 'citroen c4',\n",
    "       'citroen c4 cactus', 'citroen c4 picasso', 'citroen c5',\n",
    "       'citroen c5 aircross', 'citroen c8', 'citroen ds', 'citroen ds5',\n",
    "       'citroen jumpy', 'citroen space tourer', 'cupra ateca',\n",
    "       'cupra born', 'cupra formentor', 'cupra leon', 'dacia dokker',\n",
    "       'dacia duster', 'dacia lodgy', 'dacia logan', 'dacia sandero',\n",
    "       'daewoo lacetti', 'daihatsu sirion', 'daihatsu terios',\n",
    "       'daimler 250 v8', 'daimler six', 'dodge 880', 'dodge caliber',\n",
    "       'dodge challenger', 'dodge charger', 'dodge durango',\n",
    "       'dodge journey', 'dodge nitro', 'dodge ram', 'dodge viper', 'ds 3',\n",
    "       'ds 3 crossback', 'ds 4', 'ds 7 crossback', 'excalibur series ii',\n",
    "       'ferrari 458', 'ferrari 488', 'ferrari 512', 'ferrari california',\n",
    "       'ferrari california t', 'ferrari ff', 'ferrari gtc4lusso',\n",
    "       'ferrari sf90', 'ferrari testarossa', 'fiat 1100', 'fiat 500',\n",
    "       'fiat 500l', 'fiat 500x', 'fiat 600', 'fiat bravo', 'fiat croma',\n",
    "       'fiat freemont', 'fiat fullback', 'fiat panda', 'fiat punto',\n",
    "       'fiat qubo', 'fiat tipo', 'fisker karma', 'ford b max','fiat ducato'\n",
    "       'ford c max', 'ford cougar', 'ford ecosport', 'ford edge',\n",
    "       'ford escape', 'ford explorer', 'ford f150',\n",
    "       'ford fairlane 500 skyliner', 'ford fiesta', 'ford focus',\n",
    "       'ford galaxy', 'ford ka', 'ford kuga', 'ford model 48',\n",
    "       'ford model t', 'ford mondeo', 'ford mustang',\n",
    "       'ford mustang mach e', 'ford puma', 'ford ranger', 'ford s max',\n",
    "       'ford scorpio', 'ford taurus', 'ford tourneo',\n",
    "       'ford tourneo connect', 'ford tourneo courier', 'genesis g70',\n",
    "       'genesis g80', 'genesis gv70', 'genesis gv80', 'gmc sierra',\n",
    "       'gmc terrain', 'gmc yukon xl', 'honda accord', 'honda city',\n",
    "       'honda civic', 'honda cr v', 'honda e', 'honda hr v',\n",
    "       'honda jazz fit', 'honda nsx', 'honda odyssey', 'honda s',\n",
    "       'hummer h2', 'hyundai getz', 'hyundai i10', 'hyundai i20',\n",
    "       'hyundai i30', 'hyundai i40', 'hyundai ioniq', 'hyundai ioniq 5',\n",
    "       'hyundai ix20', 'hyundai kona', 'hyundai nexo', 'hyundai santa fe',\n",
    "       'hyundai tiburon', 'hyundai tucson', 'hyundai veloster',\n",
    "       'infinity fx', 'infinity q30', 'infiniti q50 sedan',\n",
    "       'infinity qx80', 'isuzu d max', 'jac refine e s2', 'jaguar e pace',\n",
    "       'jaguar e type', 'jaguar f pace', 'jaguar f type', 'jaguar i pace',\n",
    "       'jaguar x type', 'jaguar xe', 'jaguar xf', 'jaguar xj',\n",
    "       'jaguar xk', 'jaguar xk120', 'jeep cherokee', 'jeep compass',\n",
    "       'jeep wrangler', 'jeep grand cherokee', 'jeep renegade',\n",
    "       'kia carens', 'kia ceed', 'kia ev6', 'kia magentis', 'kia niro',\n",
    "       'kia picanto', 'kia rio', 'kia sorento', 'kia soul',\n",
    "       'kia sportage', 'kia stinger', 'kia stonic', 'kia venga',\n",
    "       'lada 2102', 'lada 4x4', 'lamborghini aventador',\n",
    "       'lamborghini huracan', 'lamborghini urus', 'lancia delta',\n",
    "       'lancia phedra', 'lancia ypsilon', 'land rover defender',\n",
    "       'land rover discovery', 'land rover discovery sport',\n",
    "       'land rover freelander', 'land rover range rover',\n",
    "       'land rover range rover evoque', 'land rover range rover sport',\n",
    "       'land rover range rover velar', 'lexus ct', 'lexus gs', 'lexus is',\n",
    "       'lexus ls', 'lexus lx', 'lexus nx', 'lexus rx', 'lexus sc',\n",
    "       'lexus ux', 'lincoln mkx', 'lincoln navigator', 'lincoln town car',\n",
    "       'lotus elan', 'lotus elise', 'maserati ghibli',\n",
    "       'maserati granturismo', 'maserati levante',\n",
    "       'maserati quattroporte', 'maybach 57', 'mazda 2', 'mazda 3',\n",
    "       'mazda 5', 'mazda 6', 'mazda cx 3', 'mazda cx 30', 'mazda cx 5',\n",
    "       'mazda cx 7', 'mazda cx 9', 'mazda mx 30', 'mazda mx 5',\n",
    "       'mclaren 12c', 'mclaren 650s', 'mclaren 720s', 'mercedes-benz 123',\n",
    "       'mercedes-benz 170 s', 'mercedes-benz 220', 'mercedes-benz 220 s',\n",
    "       'mercedes-benz a class', 'mercedes-benz amg gt', 'mercedes-benz b class',\n",
    "       'mercedes-benz c class', 'mercedes-benz citan', 'mercedes-benz cl class',\n",
    "       'mercedes-benz cla class', 'mercedes-benz clk class', 'mercedes-benz cls class',\n",
    "       'mercedes-benz e class', 'mercedes-benz 3', 'mercedes-benz eqa',\n",
    "       'mercedes-benz eqc class', 'mercedes-benz eqs class', 'mercedes-benz g class',\n",
    "       'mercedes-benz gl class', 'mercedes-benz gla class', 'mercedes-benz glb class',\n",
    "       'mercedes-benz glc class', 'mercedes-benz gle class', 'mercedes-benz glk class',\n",
    "       'mercedes-benz gls class', 'mercedes-benz m class', 'mercedes-benz r class',\n",
    "       'mercedes-benz s class', 'mercedes-benz sl', 'mercedes-benz slk',\n",
    "       'mercedes-benz v class', 'mercedes-benz vito', 'mercedes-benz w114',\n",
    "       'mercedes-benz x class', 'mercury cougar', 'mg f', 'mg mgb', 'mg tf',\n",
    "       'mini clubman', 'mini cooper', 'mini countryman', 'mini paceman',\n",
    "       'mitsubishi asx', 'mitsubishi colt', 'mitsubishi i miev',\n",
    "       'mitsubishi l200', 'mitsubishi lancer', 'mitsubishi mirage',\n",
    "       'mitsubishi outlander', 'mitsubishi pajero', 'moskvitch 400',\n",
    "       'nissan 370z', 'nissan altima', 'nissan juke', 'nissan leaf',\n",
    "       'nissan micra', 'nissan murano', 'nissan navara', 'nissan note',\n",
    "       'nissan nv200', 'nissan pathfinder', 'nissan pixo',\n",
    "       'nissan qashqai', 'nissan x trail', 'oldsmobile 88',\n",
    "       'oldsmobile cutlass supreme', 'vauxhall adam', 'vauxhall agila',\n",
    "       'vauxhall ampera', 'vauxhall astra', 'vauxhall calibra', 'vauxhall cascada',\n",
    "       'vauxhall corsa', 'vauxhall crossland x', 'vauxhall grandland',\n",
    "       'vauxhall insignia', 'vauxhall karl', 'vauxhall meriva', 'vauxhall mokka',\n",
    "       'vauxhall tigra', 'vauxhall vectra', 'vauxhall zafira', 'peugeot 1007',\n",
    "       'peugeot 108', 'peugeot 206', 'peugeot 207', 'peugeot 208', 'peugeot 2008'\n",
    "       'peugeot 3008', 'peugeot 306', 'peugeot 307', 'peugeot 308',\n",
    "       'peugeot 4007', 'peugeot 4008', 'peugeot 406', 'peugeot 407',\n",
    "       'peugeot 5008', 'peugeot 508', 'peugeot 807', 'peugeot expert',\n",
    "       'peugeot partner', 'peugeot traveller', 'plymouth volare',\n",
    "       'polestar 2', 'pontiac firebird', 'pontiac gto', 'porsche 356',\n",
    "       'porsche 718', 'porsche 718 cayman', 'porsche 911', 'porsche 928',\n",
    "       'porsche cayenne 5 door coupe suv', 'porsche cayenne',\n",
    "       'porsche macan', 'porsche panamera', 'porsche taycan',\n",
    "       'renault captur', 'renault clio', 'renault espace',\n",
    "       'renault fluence', 'renault fuego', 'renault kadjar',\n",
    "       'renault kangoo', 'renault koleos', 'renault laguna',\n",
    "       'renault megane', 'renault modus', 'renault scenic',\n",
    "       'renault talisman', 'renault twingo', 'renault twizy',\n",
    "       'renault zoe', 'rolls-royce corniche', 'rolls-royce cullinan',\n",
    "       'rolls-royce ghost', 'rolls-royce phantom',\n",
    "       'rolls-royce silver shadow', 'rolls-royce silver spirit',\n",
    "       'rolls-royce wraith', 'rover sd1', 'saab 9 3', 'saab 9 5',\n",
    "       'seat alhambra', 'seat altea', 'seat arona', 'seat ateca',\n",
    "       'seat exeo', 'seat ibiza', 'seat leon', 'seat mii', 'seat tarraco',\n",
    "       'skoda citigo', 'skoda enyaq iv', 'skoda fabia', 'skoda kamiq',\n",
    "       'skoda karoq', 'skoda kodiaq', 'skoda octavia', 'skoda rapid',\n",
    "       'skoda scala', 'skoda superb', 'skoda yeti', 'smart forfour',\n",
    "       'smart fortwo', 'smart roadster', 'ssang yong korando',\n",
    "       'ssang yong kyron', 'ssang yong rexton', 'ssang yong tivoli',\n",
    "       'subaru forester', 'subaru impreza', 'subaru justy',\n",
    "       'subaru legacy', 'subaru levorg', 'subaru outback',\n",
    "       'subaru xv crosstrek', 'suzuki across', 'suzuki ignis',\n",
    "       'suzuki jimny', 'suzuki liana', 'suzuki swift', 'suzuki sx4',\n",
    "       'suzuki vitara', 'tatra 603', 'tesla model 3', 'tesla model s',\n",
    "       'tesla model x', 'tesla model y', 'toyota auris', 'toyota avensis',\n",
    "       'toyota aygo', 'toyota c hr', 'toyota celica', 'toyota corolla',\n",
    "       'toyota corolla verso', 'toyota gt 86', 'toyota highlander',\n",
    "       'toyota hilux', 'toyota iq', 'toyota land cruiser prado',\n",
    "       'toyota landcruiser', 'toyota previa', 'toyota prius',\n",
    "       'toyota prius plus', 'toyota proace verso', 'toyota rav4',\n",
    "       'toyota sienna', 'toyota supra', 'toyota tundra',\n",
    "       'toyota urban cruiser', 'toyota verso', 'toyota yaris',\n",
    "       'toyota yaris cross', 'triumph spitfire', 'triumph tr7',\n",
    "       'tvr cerbera', 'volkswagen 1600', 'volkswagen amarok',\n",
    "       'volkswagen arteon', 'volkswagen beetle', 'volkswagen bora',\n",
    "       'volkswagen caddy', 'volkswagen california',\n",
    "       'volkswagen caravelle', 'volkswagen eos', 'volkswagen fox',\n",
    "       'volkswagen golf', 'volkswagen id3', 'volkswagen id4',\n",
    "       'volkswagen jetta', 'volkswagen passat', 'volkswagen passat cc',\n",
    "       'volkswagen phaeton', 'volkswagen polo', 'volkswagen scirocco',\n",
    "       'volkswagen sharan', 'volkswagen t cross', 'volkswagen t roc',\n",
    "       'volkswagen tiguan', 'volkswagen touareg', 'volkswagen touran',\n",
    "       'volkswagen up', 'volvo 780', 'volvo 900', 'volvo c30',\n",
    "       'volvo s60', 'volvo s80', 'volvo s90', 'volvo v40', 'volvo v50',\n",
    "       'volvo v60', 'volvo v70', 'volvo v90', 'volvo xc40', 'volvo xc60',\n",
    "       'volvo xc70', 'volvo xc90', 'wartburg 353']\n",
    "\n",
    "# The aim is to standardise the car.name column a little in terms. I was just curious to see if this would work\n",
    "# and it will come in useful for the further work when I try to merge carsized.com data with autotrader data.\n",
    "\n",
    "results = []\n",
    "# Iterate over all cars, extracting the lowercase name\n",
    "for index, car in ucars.iterrows():\n",
    "    lname = car[-1]\n",
    "    # Set dummy variable to lowercase car name\n",
    "    dummy = lname\n",
    "    \n",
    "    # Check if the lowercase name matches any in the name_list_from_carsized\n",
    "    for cname in name_list_from_carsized:\n",
    "        if cname in lname:\n",
    "            dummy = cname\n",
    "    \n",
    "    # Iterate over bodies in body\n",
    "    for bod in body:\n",
    "        if bod in dummy:\n",
    "            # Remove body information from the carname saved under dummy variable\n",
    "            dummy = re.sub(' '+bod,'',dummy)\n",
    "    \n",
    "    # Append car name to results\n",
    "    results.append(dummy)\n",
    "\n",
    "# Overwrite name with this new carsized standardised name and drop the lname variable.\n",
    "ucars['name']=results\n",
    "ucars=ucars.drop('lname', axis=1)\n",
    "\n",
    "print('DataFrame shape:',ucars.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3a7080e6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:24.939413Z",
     "start_time": "2022-03-09T21:33:24.929280Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame shape: (373916, 28)\n"
     ]
    }
   ],
   "source": [
    "# Add cars.used and cars.e_engine_kWh columns to be compatible with other datasets \n",
    "\n",
    "ucars['used']=1\n",
    "ucars['e_engine_kWh']= np.nan\n",
    "\n",
    "print('DataFrame shape:',ucars.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9eee8ef1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:25.705573Z",
     "start_time": "2022-03-09T21:33:24.943422Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame shape: (373912, 28)\n"
     ]
    }
   ],
   "source": [
    "# Removing some data enrty errors which have been identified by EDA later in the project.\n",
    "\n",
    "#Remove SKODA 911 which stands as an aggressive outlier - data entry error\n",
    "ucars[ucars.make=='SKODA'].sort_values(by='price',ascending=False).head(1)\n",
    "dropindex = ucars[ucars.make=='SKODA'].sort_values(by='price',ascending=False).head(1).index.tolist()\n",
    "ucars.drop(index=dropindex, inplace=True)\n",
    "\n",
    "# Remove Mercedes missing a decimal which stands as an aggressive outlier - data entry error\n",
    "ucars[ucars.make=='Mercedes-Benz'].sort_values(by='price',ascending=False).head(1)\n",
    "dropindex = ucars[ucars.make=='Mercedes-Benz'].sort_values(by='price',ascending=False).head(1).index.tolist()\n",
    "ucars.drop(index=dropindex, inplace=True)\n",
    "\n",
    "# Remove two clear BHP errors which stand as an aggressive outlier - data entry error\n",
    "ucars.BHP.sort_values(ascending=False).head(2)\n",
    "dropindex = ucars.BHP.sort_values(ascending=False).head(2).index.tolist()\n",
    "ucars.drop(index=dropindex, inplace=True)\n",
    "\n",
    "print('DataFrame shape:',ucars.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c70104d0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:33.402222Z",
     "start_time": "2022-03-09T21:33:25.708696Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame shape: (373912, 28)\n"
     ]
    }
   ],
   "source": [
    "# Output clean used cars data \n",
    "ucars=ucars[['name','name_subtitle','year','price','body','mileage','BHP','doors'\n",
    "            ,'transmission','make','fuel','mpg','drivertrain','engine','owners'\n",
    "            ,'ULEZ','dealer_area','dealer_city','seller1','used', 'e_engine_kWh'\n",
    "            ,'log_price','log_mileage','orig_name','id','year_reg','link','href0']]\n",
    "ucars_abspath = r'/Users/robertdavies/Desktop/DSI/GA_P2/LargeDatasets/00_clean_used_cars.csv'\n",
    "ucars.to_csv(ucars_abspath, index=False, header=ucars.columns )\n",
    "\n",
    "print('DataFrame shape:',ucars.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "76d6dd19",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:34.329352Z",
     "start_time": "2022-03-09T21:33:33.405084Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 373912 entries, 0 to 594731\n",
      "Data columns (total 28 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   name           373912 non-null  object \n",
      " 1   name_subtitle  373912 non-null  object \n",
      " 2   year           373912 non-null  int64  \n",
      " 3   price          373912 non-null  float64\n",
      " 4   body           373912 non-null  object \n",
      " 5   mileage        373912 non-null  float64\n",
      " 6   BHP            373912 non-null  float64\n",
      " 7   doors          373912 non-null  object \n",
      " 8   transmission   373912 non-null  object \n",
      " 9   make           373912 non-null  object \n",
      " 10  fuel           373912 non-null  object \n",
      " 11  mpg            373912 non-null  object \n",
      " 12  drivertrain    373912 non-null  object \n",
      " 13  engine         373912 non-null  float64\n",
      " 14  owners         191966 non-null  float64\n",
      " 15  ULEZ           325815 non-null  object \n",
      " 16  dealer_area    318102 non-null  object \n",
      " 17  dealer_city    373640 non-null  object \n",
      " 18  seller1        307186 non-null  float64\n",
      " 19  used           373912 non-null  int64  \n",
      " 20  e_engine_kWh   0 non-null       float64\n",
      " 21  log_price      373912 non-null  float64\n",
      " 22  log_mileage    373912 non-null  float64\n",
      " 23  orig_name      373912 non-null  object \n",
      " 24  id             373912 non-null  object \n",
      " 25  year_reg       365722 non-null  object \n",
      " 26  link           373912 non-null  object \n",
      " 27  href0          318102 non-null  object \n",
      "dtypes: float64(9), int64(2), object(17)\n",
      "memory usage: 82.7+ MB\n"
     ]
    }
   ],
   "source": [
    "ucars.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e991632",
   "metadata": {},
   "source": [
    "Note that not all of these variables are fully populated for all cars. This is not the list of variable to go into modelling, only the list of variables after cleaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "835ee1c1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:34.571105Z",
     "start_time": "2022-03-09T21:33:34.332479Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>mileage</th>\n",
       "      <th>BHP</th>\n",
       "      <th>engine</th>\n",
       "      <th>owners</th>\n",
       "      <th>seller1</th>\n",
       "      <th>used</th>\n",
       "      <th>e_engine_kWh</th>\n",
       "      <th>log_price</th>\n",
       "      <th>log_mileage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>373912.000000</td>\n",
       "      <td>3.739120e+05</td>\n",
       "      <td>3.739120e+05</td>\n",
       "      <td>373912.000000</td>\n",
       "      <td>373912.000000</td>\n",
       "      <td>191966.000000</td>\n",
       "      <td>307186.000000</td>\n",
       "      <td>373912.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373912.000000</td>\n",
       "      <td>373912.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016.316286</td>\n",
       "      <td>1.839089e+04</td>\n",
       "      <td>4.037482e+04</td>\n",
       "      <td>155.388408</td>\n",
       "      <td>1.745712</td>\n",
       "      <td>1.865575</td>\n",
       "      <td>4.639705</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.602695</td>\n",
       "      <td>10.209382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.514855</td>\n",
       "      <td>1.240517e+04</td>\n",
       "      <td>3.293635e+04</td>\n",
       "      <td>67.717272</td>\n",
       "      <td>0.557113</td>\n",
       "      <td>1.139197</td>\n",
       "      <td>0.441332</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.718112</td>\n",
       "      <td>1.077991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1994.000000</td>\n",
       "      <td>2.500000e+02</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.521461</td>\n",
       "      <td>0.693147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.049100e+04</td>\n",
       "      <td>1.600000e+04</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.258273</td>\n",
       "      <td>9.680344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>1.669000e+04</td>\n",
       "      <td>3.059200e+04</td>\n",
       "      <td>138.067061</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.722565</td>\n",
       "      <td>10.328494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>2.349500e+04</td>\n",
       "      <td>5.660000e+04</td>\n",
       "      <td>187.376726</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.064543</td>\n",
       "      <td>10.943764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2021.000000</td>\n",
       "      <td>1.250000e+06</td>\n",
       "      <td>1.113000e+06</td>\n",
       "      <td>972.386588</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.038654</td>\n",
       "      <td>13.922570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                year         price       mileage            BHP  \\\n",
       "count  373912.000000  3.739120e+05  3.739120e+05  373912.000000   \n",
       "mean     2016.316286  1.839089e+04  4.037482e+04     155.388408   \n",
       "std         3.514855  1.240517e+04  3.293635e+04      67.717272   \n",
       "min      1994.000000  2.500000e+02  2.000000e+00       1.000000   \n",
       "25%      2015.000000  1.049100e+04  1.600000e+04     113.000000   \n",
       "50%      2017.000000  1.669000e+04  3.059200e+04     138.067061   \n",
       "75%      2019.000000  2.349500e+04  5.660000e+04     187.376726   \n",
       "max      2021.000000  1.250000e+06  1.113000e+06     972.386588   \n",
       "\n",
       "              engine         owners        seller1      used  e_engine_kWh  \\\n",
       "count  373912.000000  191966.000000  307186.000000  373912.0           0.0   \n",
       "mean        1.745712       1.865575       4.639705       1.0           NaN   \n",
       "std         0.557113       1.139197       0.441332       0.0           NaN   \n",
       "min         0.600000       1.000000       1.000000       1.0           NaN   \n",
       "25%         1.400000       1.000000       4.500000       1.0           NaN   \n",
       "50%         1.600000       2.000000       4.700000       1.0           NaN   \n",
       "75%         2.000000       2.000000       4.900000       1.0           NaN   \n",
       "max         5.500000      26.000000       5.000000       1.0           NaN   \n",
       "\n",
       "           log_price    log_mileage  \n",
       "count  373912.000000  373912.000000  \n",
       "mean        9.602695      10.209382  \n",
       "std         0.718112       1.077991  \n",
       "min         5.521461       0.693147  \n",
       "25%         9.258273       9.680344  \n",
       "50%         9.722565      10.328494  \n",
       "75%        10.064543      10.943764  \n",
       "max        14.038654      13.922570  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ucars.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bc6fab32",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:34.664335Z",
     "start_time": "2022-03-09T21:33:34.622053Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>name_subtitle</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>body</th>\n",
       "      <th>mileage</th>\n",
       "      <th>BHP</th>\n",
       "      <th>doors</th>\n",
       "      <th>transmission</th>\n",
       "      <th>make</th>\n",
       "      <th>...</th>\n",
       "      <th>seller1</th>\n",
       "      <th>used</th>\n",
       "      <th>e_engine_kWh</th>\n",
       "      <th>log_price</th>\n",
       "      <th>log_mileage</th>\n",
       "      <th>orig_name</th>\n",
       "      <th>id</th>\n",
       "      <th>year_reg</th>\n",
       "      <th>link</th>\n",
       "      <th>href0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109181</th>\n",
       "      <td>audi q7</td>\n",
       "      <td>3.0 TDI V6 S line Tiptronic quattro (s/s) 5dr</td>\n",
       "      <td>2015</td>\n",
       "      <td>29950.0</td>\n",
       "      <td>SUV</td>\n",
       "      <td>86000.0</td>\n",
       "      <td>268.244576</td>\n",
       "      <td>5dr</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Audi</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.307285</td>\n",
       "      <td>11.362103</td>\n",
       "      <td>Audi Q7</td>\n",
       "      <td>202111099396340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.autotrader.co.uk/car-details/20211...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108385</th>\n",
       "      <td>mercedes-benz slk</td>\n",
       "      <td>2.1 SLK250 CDI BlueEFFICIENCY AMG Sport 7G-Tro...</td>\n",
       "      <td>2015</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>Convertible</td>\n",
       "      <td>29847.0</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>2dr</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.615805</td>\n",
       "      <td>10.303840</td>\n",
       "      <td>Mercedes-Benz SLK</td>\n",
       "      <td>202112070309652</td>\n",
       "      <td>2015 (15 reg)</td>\n",
       "      <td>https://www.autotrader.co.uk/car-details/20211...</td>\n",
       "      <td>/dealers/stirlingshire/stirling/motorchoice-st...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19728</th>\n",
       "      <td>mini hatch</td>\n",
       "      <td>1.6 Cooper 3dr</td>\n",
       "      <td>2009</td>\n",
       "      <td>2995.0</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>84000.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>3dr</td>\n",
       "      <td>Manual</td>\n",
       "      <td>MINI</td>\n",
       "      <td>...</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.004700</td>\n",
       "      <td>11.338572</td>\n",
       "      <td>MINI Hatch</td>\n",
       "      <td>202112180642140</td>\n",
       "      <td>2009 (59 reg)</td>\n",
       "      <td>https://www.autotrader.co.uk/car-details/20211...</td>\n",
       "      <td>/dealers/warwickshire/birmingham/motor-island-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300060</th>\n",
       "      <td>mini hatch</td>\n",
       "      <td>2.0 Cooper S Steptronic (s/s) 3dr</td>\n",
       "      <td>2018</td>\n",
       "      <td>19995.0</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>25921.0</td>\n",
       "      <td>189.349112</td>\n",
       "      <td>3dr</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>MINI</td>\n",
       "      <td>...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.903238</td>\n",
       "      <td>10.162809</td>\n",
       "      <td>MINI Hatch</td>\n",
       "      <td>202112020158764</td>\n",
       "      <td>2018 (68 reg)</td>\n",
       "      <td>https://www.autotrader.co.uk/car-details/20211...</td>\n",
       "      <td>/dealers/dorset/gillingham/jason-mead-car-sale...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194420</th>\n",
       "      <td>kia sportage</td>\n",
       "      <td>CRDI 2 ISG 1.7 5dr</td>\n",
       "      <td>2017</td>\n",
       "      <td>12990.0</td>\n",
       "      <td>SUV</td>\n",
       "      <td>78702.0</td>\n",
       "      <td>114.398422</td>\n",
       "      <td>5dr</td>\n",
       "      <td>Manual</td>\n",
       "      <td>KIA</td>\n",
       "      <td>...</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.471935</td>\n",
       "      <td>11.273424</td>\n",
       "      <td>KIA Sportage</td>\n",
       "      <td>202112130464196</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.autotrader.co.uk/car-details/20211...</td>\n",
       "      <td>/dealers/northern-ireland/belfast/agnew-autoex...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name                                      name_subtitle  \\\n",
       "109181            audi q7      3.0 TDI V6 S line Tiptronic quattro (s/s) 5dr   \n",
       "108385  mercedes-benz slk  2.1 SLK250 CDI BlueEFFICIENCY AMG Sport 7G-Tro...   \n",
       "19728          mini hatch                                     1.6 Cooper 3dr   \n",
       "300060         mini hatch                  2.0 Cooper S Steptronic (s/s) 3dr   \n",
       "194420       kia sportage                                 CRDI 2 ISG 1.7 5dr   \n",
       "\n",
       "        year    price         body  mileage         BHP doors transmission  \\\n",
       "109181  2015  29950.0          SUV  86000.0  268.244576   5dr    Automatic   \n",
       "108385  2015  15000.0  Convertible  29847.0  204.000000   2dr    Automatic   \n",
       "19728   2009   2995.0    Hatchback  84000.0  120.000000   3dr       Manual   \n",
       "300060  2018  19995.0    Hatchback  25921.0  189.349112   3dr    Automatic   \n",
       "194420  2017  12990.0          SUV  78702.0  114.398422   5dr       Manual   \n",
       "\n",
       "                 make  ... seller1 used e_engine_kWh  log_price  log_mileage  \\\n",
       "109181           Audi  ...     NaN    1          NaN  10.307285    11.362103   \n",
       "108385  Mercedes-Benz  ...     3.1    1          NaN   9.615805    10.303840   \n",
       "19728            MINI  ...     4.7    1          NaN   8.004700    11.338572   \n",
       "300060           MINI  ...     4.8    1          NaN   9.903238    10.162809   \n",
       "194420            KIA  ...     2.3    1          NaN   9.471935    11.273424   \n",
       "\n",
       "                orig_name               id       year_reg  \\\n",
       "109181            Audi Q7  202111099396340            NaN   \n",
       "108385  Mercedes-Benz SLK  202112070309652  2015 (15 reg)   \n",
       "19728          MINI Hatch  202112180642140  2009 (59 reg)   \n",
       "300060         MINI Hatch  202112020158764  2018 (68 reg)   \n",
       "194420       KIA Sportage  202112130464196            NaN   \n",
       "\n",
       "                                                     link  \\\n",
       "109181  https://www.autotrader.co.uk/car-details/20211...   \n",
       "108385  https://www.autotrader.co.uk/car-details/20211...   \n",
       "19728   https://www.autotrader.co.uk/car-details/20211...   \n",
       "300060  https://www.autotrader.co.uk/car-details/20211...   \n",
       "194420  https://www.autotrader.co.uk/car-details/20211...   \n",
       "\n",
       "                                                    href0  \n",
       "109181                                                NaN  \n",
       "108385  /dealers/stirlingshire/stirling/motorchoice-st...  \n",
       "19728   /dealers/warwickshire/birmingham/motor-island-...  \n",
       "300060  /dealers/dorset/gillingham/jason-mead-car-sale...  \n",
       "194420  /dealers/northern-ireland/belfast/agnew-autoex...  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ucars.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5df3b1fe",
   "metadata": {},
   "source": [
    "## Used cars - electric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "89142ee5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:34.813021Z",
     "start_time": "2022-03-09T21:33:34.668861Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4887, 20)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import all detailed files and make cars df, including year \n",
    "\n",
    "path = r'/Users/robertdavies/Desktop/DSI/GA_P2/LargeDatasets/raw/df_detailed_ev/'\n",
    "\n",
    "li=[]\n",
    "year_files = glob.glob(path + f\"/*electric_used.csv\")\n",
    "for filename in year_files:\n",
    "    df = pd.read_csv(filename, index_col=None, header=0 )\n",
    "    df.drop_duplicates(subset=['link'], keep='first', inplace=True)\n",
    "    li.append(df)\n",
    "\n",
    "uecars = pd.concat(li, axis=0, ignore_index=True)\n",
    "uecars.drop_duplicates(subset=['link'], keep='first', inplace=True)\n",
    "uecars.shape    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fcedc1f1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:34.824775Z",
     "start_time": "2022-03-09T21:33:34.816143Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uecars.body.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e2918d4b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:34.838191Z",
     "start_time": "2022-03-09T21:33:34.828405Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4887, 20)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Body is fine. No cleaning needed.\n",
    "uecars.body.isnull().sum()\n",
    "\n",
    "uecars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3cb5e9b6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:34.878499Z",
     "start_time": "2022-03-09T21:33:34.841608Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4823, 20)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean BHP.\n",
    "\n",
    "# Drop NaN BHP\n",
    "uecars.dropna(subset=['BHP'],inplace=True)\n",
    "\n",
    "def standardise_bhp(x):\n",
    "    lenbhp = len(str(x).split(' '))\n",
    "    bhp=x\n",
    "    if lenbhp == 1:\n",
    "        if 'BHP' in x:\n",
    "            bhp=float(re.findall('[0-9]+', x)[0])       \n",
    "        elif 'PS' in x:\n",
    "            ps=float(re.findall('[0-9]+', x)[0])\n",
    "            bhp = ps/1.014        \n",
    "    elif lenbhp == 2:\n",
    "        x=str(x.split(' ')[-1])\n",
    "        if 'BHP' in x:\n",
    "            bhp=float(re.findall('[0-9]+', x)[0])      \n",
    "        elif 'PS' in x:\n",
    "            ps=float(re.findall('[0-9]+', x)[0])\n",
    "            bhp = ps/1.014\n",
    "    else:\n",
    "        bhp=np.nan   \n",
    "    return bhp    \n",
    "\n",
    "#Standardise remaining BHP (convert from PS)\n",
    "uecars.BHP = uecars.BHP.apply(lambda x: standardise_bhp(x))\n",
    "\n",
    "uecars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2ffdbd93",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:35.573959Z",
     "start_time": "2022-03-09T21:33:34.882002Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4823, 21)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compile the best location variable. Retain dealer_city and dealer_area. Don't drop remaining NaN.\n",
    "\n",
    "#split href0 into city and area\n",
    "uecars['dealer_city_temp'] = [x.split('/')[3] if type(x)==str and len(x.split('/'))>3 else x for x in uecars.href0]\n",
    "uecars['dealer_area'] = [x.split('/')[2] if type(x)==str and len(x.split('/'))>3 else x for x in uecars.href0]\n",
    "\n",
    "#If there are some seller ratings in seller0, move them to seller1\n",
    "uecars[uecars['seller0'].str.contains('[+-]?([0-9]*[.])?[0-9]+', regex=True, na=False)].seller1 = uecars[uecars['seller0'].str.contains('[+-]?([0-9]*[.])?[0-9]+', regex=True, na=False)].seller0\n",
    "\n",
    "#Make seller ratings in seller0 np.nan\n",
    "uecars.seller0 =[np.nan if '4' in str(x) or '5' in str(x) else x for x in uecars.seller0]\n",
    "\n",
    "#If the seller0 column is np.nan, use the dealer_city \n",
    "for index, car in uecars.iterrows():\n",
    "    if pd.isnull(uecars.loc[index,'seller0']):\n",
    "        uecars.loc[index,'seller0']=uecars.loc[index,'dealer_city_temp']\n",
    "    #Tiny number of cars don't have a location, and they're all from one dealer in Oldbury.    \n",
    "    if pd.isnull(uecars.loc[index,'seller0']):\n",
    "        uecars.loc[index,'seller0']= 'Oldbury'\n",
    "        \n",
    "uecars['dealer_city'] = uecars['seller0']\n",
    "uecars.drop(['seller0','dealer_city_temp'],inplace=True, axis=1)\n",
    "\n",
    "uecars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f08c26d6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:36.566394Z",
     "start_time": "2022-03-09T21:33:35.577535Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4823, 22)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Populate uecars.e_engine_kWh from name_subtitle ~20% NaN. Not great but BHP supersedes it.\n",
    "\n",
    "# For electric cars we can't measure engline volume in litres. This is a pain but not too problematic\n",
    "# because fortunately BHP supersedes it. This won't be used in modelling. \n",
    "uecars['e_engine_kWh']=''\n",
    "uecars['engine']=np.nan\n",
    "\n",
    "for index, car in uecars.iterrows():\n",
    "    car_subname = uecars.loc[index, 'name_subtitle']\n",
    "    try:\n",
    "        eenginesize = re.findall('[\\d]+[.]?[\\d]+[ ]?kwh',car_subname, flags=re.I)[0]\n",
    "    except: \n",
    "        eenginesize = np.nan\n",
    "    uecars.loc[index,'e_engine_kWh'] = eenginesize\n",
    "    \n",
    "uecars.e_engine_kWh= uecars.e_engine_kWh.apply(lambda x: float(str(x).lower().replace('kwh','')))\n",
    "#uecars.dropna(subset=['engine'],inplace=True)\n",
    "\n",
    "uecars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "08b66e15",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:36.604042Z",
     "start_time": "2022-03-09T21:33:36.570050Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4770, 23)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make doors column. Drop 53 without info. \n",
    "     \n",
    "uecars['doors'] = uecars.name_subtitle.apply(lambda x: extract_drs(x))\n",
    "\n",
    "# Drop the remaining 53 uecars without door information    \n",
    "uecars.drop(uecars[uecars.doors=='0dr'].index, inplace=True)\n",
    "\n",
    "uecars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d268752c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:36.660745Z",
     "start_time": "2022-03-09T21:33:36.608350Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4770, 24)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean uecars.price, make uecars.ID, clean uecars.year. Fix uecars.ULEZ \n",
    "to_dummy=['mpg','drivetrain','make','body','transmission','fuel']\n",
    "\n",
    "uecars.price=uecars.price.apply(lambda x: x.replace('£',''))\n",
    "uecars.price=uecars.price.apply(lambda x: float(x.replace(',','')))\n",
    "uecars['id'] =[x.split('/')[-1] for x in uecars.link]\n",
    "uecars['year_reg']=uecars['year']\n",
    "uecars['year']=uecars['YR']\n",
    "uecars.drop('YR',inplace=True, axis=1)\n",
    "uecars.owners=int(0)\n",
    "uecars.ULEZ='ULEZ'\n",
    "\n",
    "\n",
    "uecars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7bb2f945",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:36.702642Z",
     "start_time": "2022-03-09T21:33:36.697421Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# Set uecars.used = 1.\n",
    "\n",
    "uecars['used']=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ef2e8645",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:37.412901Z",
     "start_time": "2022-03-09T21:33:36.706152Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Have a peek at the price and mileage distribution\n",
    "\n",
    "uecars.drop(uecars[uecars.mileage.isnull()].index, inplace=True)\n",
    "uecars['log_price']=uecars.price.apply(lambda x: np.log(x))\n",
    "uecars['mileage']=uecars.mileage.apply(lambda x: float(x))\n",
    "uecars['log_mileage']=uecars.mileage.apply(lambda x: np.log(x))\n",
    "\n",
    "#mask = np.abs((ncars.price - ncars.price.mean(0)) / ncars.price.std(0)) > 3\n",
    "#ncars= ncars[~mask]\n",
    "#ncars.price.plot(kind='hist', bins=50, title='Car Price after outlier removal', xlabel='£');\n",
    "\n",
    "fig, ax = plt.subplots(ncols=2, figsize=(16,4))\n",
    "ax[0].hist(uecars.price, bins=60)\n",
    "ax[1].hist(uecars.log_price, bins=60)\n",
    "ax[0].set_xlabel('£')\n",
    "ax[1].set_xlabel('£')\n",
    "ax[0].set_title('Price')\n",
    "ax[1].set_title('Log price')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "362aad5b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:37.927627Z",
     "start_time": "2022-03-09T21:33:37.416151Z"
    },
    "code_folding": [
     5
    ],
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4740, 28)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Simplify the name column a little. Probably not perfect \n",
    "\n",
    "uecars['orig_name'] = uecars.name\n",
    "uecars['lname']=uecars.name.apply(lambda x: x.lower())\n",
    "\n",
    "body =[\"coupe\", \"convertible\", \"estate\", \"hatchback\", \"MPV\", \"pickup\", \n",
    "        \"SUV\", \"saloon\",\"cabriolet\",\"sedan\"]\n",
    "\n",
    "results = []\n",
    "for index, car in uecars.iterrows():\n",
    "    lname = car[-1]\n",
    "    dummy = lname\n",
    "    for cname in name_list_from_carsized:\n",
    "        if cname in lname:\n",
    "            dummy = cname\n",
    "    for bod in body:\n",
    "        if bod in dummy:\n",
    "            dummy = re.sub(' '+bod,'',dummy)\n",
    "    results.append(dummy)\n",
    "\n",
    "uecars['name']=results\n",
    "uecars=uecars.drop('lname', axis=1)\n",
    "\n",
    "uecars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0cf6c621",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:38.039690Z",
     "start_time": "2022-03-09T21:33:37.931408Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# Output clean used cars data \n",
    "uecars=uecars[['name','name_subtitle','year','price','body','mileage','BHP','doors'\n",
    "            ,'transmission','make','fuel','mpg','drivertrain','engine','owners'\n",
    "            ,'ULEZ','dealer_area','dealer_city','seller1','used', 'e_engine_kWh'\n",
    "            ,'log_price','log_mileage','orig_name','id','year_reg','link','href0']]\n",
    "uecars_abspath = r'/Users/robertdavies/Desktop/DSI/GA_P2/LargeDatasets/00_clean_used_electric_cars.csv'\n",
    "uecars.to_csv(uecars_abspath, index=False, header=uecars.columns )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cd57b1d3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:38.052680Z",
     "start_time": "2022-03-09T21:33:38.042263Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Electric    4740\n",
       "Name: mpg, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uecars.mpg.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "70a6e100",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:38.086515Z",
     "start_time": "2022-03-09T21:33:38.056088Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4740 entries, 0 to 4886\n",
      "Data columns (total 28 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   name           4740 non-null   object \n",
      " 1   name_subtitle  4740 non-null   object \n",
      " 2   year           4740 non-null   int64  \n",
      " 3   price          4740 non-null   float64\n",
      " 4   body           4740 non-null   object \n",
      " 5   mileage        4740 non-null   float64\n",
      " 6   BHP            4740 non-null   float64\n",
      " 7   doors          4740 non-null   object \n",
      " 8   transmission   4740 non-null   object \n",
      " 9   make           4740 non-null   object \n",
      " 10  fuel           4740 non-null   object \n",
      " 11  mpg            4740 non-null   object \n",
      " 12  drivertrain    4740 non-null   object \n",
      " 13  engine         0 non-null      float64\n",
      " 14  owners         4740 non-null   int64  \n",
      " 15  ULEZ           4740 non-null   object \n",
      " 16  dealer_area    3880 non-null   object \n",
      " 17  dealer_city    4740 non-null   object \n",
      " 18  seller1        3777 non-null   float64\n",
      " 19  used           4740 non-null   int64  \n",
      " 20  e_engine_kWh   3046 non-null   float64\n",
      " 21  log_price      4740 non-null   float64\n",
      " 22  log_mileage    4740 non-null   float64\n",
      " 23  orig_name      4740 non-null   object \n",
      " 24  id             4740 non-null   object \n",
      " 25  year_reg       4636 non-null   object \n",
      " 26  link           4740 non-null   object \n",
      " 27  href0          3880 non-null   object \n",
      "dtypes: float64(8), int64(3), object(17)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "uecars.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08ac1231",
   "metadata": {},
   "source": [
    "## New cars - not electric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "345796f1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:39.912458Z",
     "start_time": "2022-03-09T21:33:38.090318Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18906, 20)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import all detailed files and make new ncars df, including year\n",
    "\n",
    "path = r'/Users/robertdavies/Desktop/DSI/GA_P2/LargeDatasets/raw/df_detailed/'\n",
    "\n",
    "li=[]\n",
    "year_files = glob.glob(path + f\"/*2023_new.csv\")\n",
    "#print(len(year_files),'file(s) to load from', year)\n",
    "for filename in year_files:\n",
    "    df = pd.read_csv(filename, index_col=None, header=0 )\n",
    "    df['YR'] = '2022 new'\n",
    "    df.drop_duplicates(subset=['link'], keep='first', inplace=True)\n",
    "    li.append(df)\n",
    "\n",
    "ncars = pd.concat(li, axis=0, ignore_index=True)\n",
    "ncars.drop_duplicates(subset=['link'], keep='first', inplace=True)\n",
    "ncars.shape  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "75fe09ae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:44.082002Z",
     "start_time": "2022-03-09T21:33:39.916887Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18906, 20)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Clean ncars.body\n",
    "\n",
    "#Create df of ncars will null body\n",
    "nabody = ncars[ncars['body'].isnull()].copy()\n",
    "\n",
    "#Encode body column but don't overwrite\n",
    "to_encode = ['body']\n",
    "ncars_e = ncars.copy()\n",
    "for col in to_encode:\n",
    "    le = LabelEncoder()\n",
    "    ncars_e[col] = le.fit_transform(ncars_e[col]) \n",
    "\n",
    "#Iterate through null-body ncars and replace with corresponding modal body value based on name column grouping\n",
    "for index, car in nabody.iterrows():\n",
    "    carname=nabody.loc[index,'name']\n",
    "    body=nabody.loc[index,'body']\n",
    "    nabody.loc[index,'body'] = ncars_e[ncars_e['name']==carname].body.mode()[0]\n",
    "    ncars_e.loc[index,'body'] = ncars_e[ncars_e['name']==carname].body.mode()[0]     \n",
    "\n",
    "#Reverse the encoding    \n",
    "for col in to_encode:\n",
    "    ncars[col] = le.inverse_transform(ncars_e[col]) \n",
    "\n",
    "#After checking the remaining ncars without a body, it is clear that they are all vans!    \n",
    "ncars.body = ['Van' if pd.Series(x).isnull()[0]==True else x for x in ncars.body]        \n",
    "\n",
    "ncars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6cbfadc9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:44.181765Z",
     "start_time": "2022-03-09T21:33:44.084991Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18890, 20)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Clean ncars.BHP, taking values from ncars.engine where necessary. Drop remaining NaN.\n",
    "\n",
    "# Unmerge some of the BHP entries which have engine size in there too.\n",
    "for index, car in ncars[ncars['BHP'].str.contains('L', regex=True, na=False)].iterrows():\n",
    "    ncars.loc[index, 'engine'] = str(ncars.loc[index, 'BHP']).split(' ')[0]\n",
    "    ncars.loc[index, 'BHP'] = str(ncars.loc[index, 'BHP']).split(' ')[1].strip('()') \n",
    "\n",
    "# Drop NaN BHP\n",
    "ncars.dropna(subset=['BHP'],inplace=True)\n",
    "\n",
    "def standardise_bhp(x):\n",
    "    lenbhp = len(str(x).split(' '))\n",
    "    bhp=x\n",
    "    if lenbhp == 1:\n",
    "        if 'BHP' in x:\n",
    "            bhp=float(re.findall('[0-9]+', x)[0])       \n",
    "        elif 'PS' in x:\n",
    "            ps=float(re.findall('[0-9]+', x)[0])\n",
    "            bhp = ps/1.014        \n",
    "    elif lenbhp == 2:\n",
    "        x=str(x.split(' ')[-1])\n",
    "        if 'BHP' in x:\n",
    "            bhp=float(re.findall('[0-9]+', x)[0])      \n",
    "        elif 'PS' in x:\n",
    "            ps=float(re.findall('[0-9]+', x)[0])\n",
    "            bhp = ps/1.014\n",
    "    else:\n",
    "        bhp=np.nan   \n",
    "    return bhp    \n",
    "\n",
    "#Standardise remaining BHP (convert from PS)\n",
    "ncars.BHP = ncars.BHP.apply(lambda x: standardise_bhp(x))\n",
    "\n",
    "ncars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d24877d4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:45.538231Z",
     "start_time": "2022-03-09T21:33:44.184944Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18890, 21)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compile the best location variable. Retain dealer_city and dealer_area. Don't drop remaining NaN.\n",
    "\n",
    "#split href0 into city and area\n",
    "ncars['dealer_city_temp'] = [x.split('/')[3] if type(x)==str and len(x.split('/'))>3 else x for x in ncars.href0]\n",
    "ncars['dealer_area'] = [x.split('/')[2] if type(x)==str and len(x.split('/'))>3 else x for x in ncars.href0]\n",
    "\n",
    "#If there are some seller ratings in seller0, move them to seller1\n",
    "ncars[ncars['seller0'].str.contains('[+-]?([0-9]*[.])?[0-9]+', regex=True, na=False)].seller1 = ncars[ncars['seller0'].str.contains('[+-]?([0-9]*[.])?[0-9]+', regex=True, na=False)].seller0\n",
    "\n",
    "#Make seller ratings in seller0 np.nan\n",
    "ncars.seller0 =[np.nan if '4' in str(x) or '5' in str(x) else x for x in ncars.seller0]\n",
    "\n",
    "#If the seller0 column is np.nan, use the dealer_city \n",
    "for index, car in ncars.iterrows():\n",
    "    if pd.isnull(ncars.loc[index,'seller0']):\n",
    "        ncars.loc[index,'seller0']=ncars.loc[index,'dealer_city_temp']\n",
    "        \n",
    "ncars['dealer_city'] = ncars['seller0']\n",
    "ncars.drop(['seller0','dealer_city_temp'],inplace=True, axis=1)\n",
    "\n",
    "ncars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "14e72b1a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:45.578337Z",
     "start_time": "2022-03-09T21:33:45.540985Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18890, 21)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Iterate over missing engine rows and use regex name_subtitle to extract engine size where possible. Convert to float\n",
    "\n",
    "for index, car in ncars[ncars['engine'].isnull()].iterrows():\n",
    "    car_subname = ncars.loc[index, 'name_subtitle']\n",
    "    try:\n",
    "        enginesize = re.findall('([0-9][.][0-9]+)',car_subname)[0]\n",
    "    except: \n",
    "        enginesize = np.nan\n",
    "    ncars.loc[index,'engine'] = float(enginesize)\n",
    "    \n",
    "ncars.engine= ncars.engine.apply(lambda x: float(str(x).replace('L','')))\n",
    "ncars.dropna(subset=['engine'],inplace=True)\n",
    "\n",
    "ncars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f0bc39d0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:45.873609Z",
     "start_time": "2022-03-09T21:33:45.581190Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18890, 22)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make doors column\n",
    "\n",
    "def extract_drs(x):\n",
    "    drs = re.findall('( [1-8]d[Rr]?)',x, re.I)\n",
    "    if len(drs) > 0:\n",
    "        drs = str(drs[0]).lower()\n",
    "    else:\n",
    "        drs = re.findall('([1-8][ ]?door)',x, re.I)\n",
    "        if len(drs) > 0:\n",
    "            drs = str(drs[0])[0]+'dr'\n",
    "        else:\n",
    "            drs = re.findall('([1-8]dr)',x, re.I)\n",
    "            if len(drs) > 0:\n",
    "                drs = str(drs[0])[0]+'dr'\n",
    "            else:\n",
    "                drs = re.findall('(D-4D)',x, re.I)\n",
    "                if len(drs) > 0:\n",
    "                    drs = '4dr'\n",
    "                else:\n",
    "                    drs='0dr'\n",
    "                \n",
    "    if ('r' or 'R') not in drs:\n",
    "        drs = drs.replace('d','dr')\n",
    "    return drs.strip()\n",
    "     \n",
    "ncars['doors'] = ncars.name_subtitle.apply(lambda x: extract_drs(x))\n",
    "\n",
    "#Create df of ncars with 0 doors\n",
    "zerodoors = ncars[ncars['doors']=='0dr'].copy()\n",
    "\n",
    "#Encode doors column but don't overwrite\n",
    "to_encode = ['doors']\n",
    "ncars_e = ncars.copy()\n",
    "for col in to_encode:\n",
    "    le = LabelEncoder()\n",
    "    ncars_e[col] = le.fit_transform(ncars_e[col]) \n",
    "\n",
    "#Iterate through zero door ncars and replace with corresponding num doors based on name column grouping\n",
    "for index, car in zerodoors.iterrows():\n",
    "    carname=zerodoors.loc[index,'name']\n",
    "    ncars_e.loc[index,'doors'] = ncars_e[ncars_e['name']==carname].doors.mode()[0]     \n",
    "\n",
    "#Reverse the encoding    \n",
    "for col in to_encode:\n",
    "    ncars[col] = le.inverse_transform(ncars_e[col]) \n",
    "\n",
    "#Drop the remaining 226 ncars without door information    \n",
    "ncars.dropna(subset=['doors'],inplace=True)\n",
    "    \n",
    "ncars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "67849e75",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:45.884514Z",
     "start_time": "2022-03-09T21:33:45.877034Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18890, 22)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean ncars.mileage\n",
    "\n",
    "#Drop them - only 283\n",
    "ncars.mileage = '0'\n",
    "\n",
    "ncars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7b29f9bc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:45.912073Z",
     "start_time": "2022-03-09T21:33:45.887724Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18890, 22)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean ncars.fuel\n",
    "\n",
    "#Drop them - only 12\n",
    "ncars.dropna(subset=['fuel'],inplace=True)\n",
    "\n",
    "ncars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2d763202",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:45.968900Z",
     "start_time": "2022-03-09T21:33:45.914721Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18890, 23)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean ncars.price, make ncars.ID, clean ncars.year \n",
    "\n",
    "to_dummy=['mpg','drivetrain','make','body','transmission','fuel']\n",
    "\n",
    "ncars.price=ncars.price.apply(lambda x: x.replace('£',''))\n",
    "ncars.price=ncars.price.apply(lambda x: float(x.replace(',','')))\n",
    "ncars['id'] =[x.split('/')[-1] for x in ncars.link]\n",
    "ncars['year_reg']=ncars.year\n",
    "ncars['year']= 2022\n",
    "ncars.drop('YR',inplace=True, axis=1)\n",
    "\n",
    "ncars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "83bbf9e6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:45.976298Z",
     "start_time": "2022-03-09T21:33:45.971654Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "#Obtain ncars.ll_search <-- Doesn't actually work with nominatim openstreetmap. Probably not needed.\n",
    "\n",
    "# ncars['ll_search']=''\n",
    "# ll_search_series = []\n",
    "# for index, car in tqdm(ncars.iterrows()):\n",
    "#     dc = str(car[19])\n",
    "#     da = str(car[18])\n",
    "#     if dc == 'nan':\n",
    "#         dc = ''\n",
    "#     if da == 'nan':\n",
    "#         da = ''\n",
    "#     car[23] = dc+' '+da\n",
    "#     ll_search_series.append(car[23])\n",
    "# ncars['ll_search'] = ll_search_series\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a2544e6d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:45.983963Z",
     "start_time": "2022-03-09T21:33:45.980219Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "#Iterate over ncars to convert dealer_city to lat/lon. 2s per car makes this a 2+day cell. Running in other workbook. \n",
    "\n",
    "# lat_series = []\n",
    "# lon_series = []\n",
    "\n",
    "# for index, car in tqdm(ncars.iterrows()):\n",
    "#     address = car[19] + ' UK'\n",
    "#     url = 'https://nominatim.openstreetmap.org/search/' + urllib.parse.quote(address) +'?format=json'\n",
    "#     response = requests.get(url).json()\n",
    "#     lat_series.append(response[0][\"lat\"])\n",
    "#     lon_series.append(response[0][\"lon\"])\n",
    "    \n",
    "# ncars['seller_lat']=lat_series\n",
    "# ncars['seller_lon']=lon_series\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e6285246",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:46.738653Z",
     "start_time": "2022-03-09T21:33:45.987843Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Have a peek at the price and mileage distribution\n",
    "\n",
    "ncars['log_price']=ncars.price.apply(lambda x: np.log(x))\n",
    "ncars['mileage']=ncars.mileage.apply(lambda x: float(x))\n",
    "ncars['log_mileage']=ncars.mileage.apply(lambda x: np.log(x))\n",
    "\n",
    "#mask = np.abs((ncars.price - ncars.price.mean(0)) / ncars.price.std(0)) > 3\n",
    "#ncars= ncars[~mask]\n",
    "#ncars.price.plot(kind='hist', bins=50, title='Car Price after outlier removal', xlabel='£');\n",
    "\n",
    "fig, ax = plt.subplots(ncols=2, figsize=(16,4))\n",
    "ax[0].hist(ncars.price, bins=60)\n",
    "ax[1].hist(ncars.log_price, bins=60)\n",
    "ax[0].set_xlabel('£')\n",
    "ax[1].set_xlabel('£')\n",
    "ax[0].set_title('Price')\n",
    "ax[1].set_title('Log price')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "804350d9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:48.726844Z",
     "start_time": "2022-03-09T21:33:46.742145Z"
    },
    "code_folding": [
     0,
     5
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18890, 26)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Simplify the name column a little. Probably not perfect \n",
    "\n",
    "ncars['orig_name'] = ncars.name\n",
    "ncars['lname']=ncars.name.apply(lambda x: x.lower())\n",
    "\n",
    "body =[\"coupe\", \"convertible\", \"estate\", \"hatchback\", \"MPV\", \"pickup\", \n",
    "       \"SUV\", \"saloon\",\"cabriolet\",\"sedan\"]\n",
    "\n",
    "results = []\n",
    "for index, car in ncars.iterrows():\n",
    "    lname = car[-1]\n",
    "    dummy = lname\n",
    "    for cname in name_list_from_carsized:\n",
    "        if cname in lname:\n",
    "            dummy = cname\n",
    "    for bod in body:\n",
    "        if bod in dummy:\n",
    "            dummy = re.sub(' '+bod,'',dummy)\n",
    "    results.append(dummy)\n",
    "results\n",
    "\n",
    "ncars['name']=results\n",
    "ncars=ncars.drop('lname', axis=1)\n",
    "\n",
    "ncars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3eb0f40a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:48.739132Z",
     "start_time": "2022-03-09T21:33:48.730263Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18890, 28)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add ncars.used and ncars.e_engine_kWh columns to be compatible with other datasets \n",
    "\n",
    "ncars['used']=0\n",
    "ncars['e_engine_kWh']= np.nan\n",
    "\n",
    "ncars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b2dc1bd4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:49.126025Z",
     "start_time": "2022-03-09T21:33:48.743318Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# Output clean used cars data \n",
    "ncars=ncars[['name','name_subtitle','year','price','body','mileage','BHP','doors'\n",
    "            ,'transmission','make','fuel','mpg','drivertrain','engine','owners'\n",
    "            ,'ULEZ','dealer_area','dealer_city','seller1','used', 'e_engine_kWh'\n",
    "            ,'log_price','log_mileage','orig_name','id','year_reg','link','href0']]\n",
    "ncars_abspath = r'/Users/robertdavies/Desktop/DSI/GA_P2/LargeDatasets/00_clean_new_cars.csv'\n",
    "ncars.to_csv(ncars_abspath, index=False, header=ncars.columns )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c7c6e2fb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:49.187606Z",
     "start_time": "2022-03-09T21:33:49.128769Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 18890 entries, 0 to 50386\n",
      "Data columns (total 28 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   name           18890 non-null  object \n",
      " 1   name_subtitle  18890 non-null  object \n",
      " 2   year           18890 non-null  int64  \n",
      " 3   price          18890 non-null  float64\n",
      " 4   body           18890 non-null  object \n",
      " 5   mileage        18890 non-null  float64\n",
      " 6   BHP            18890 non-null  float64\n",
      " 7   doors          18890 non-null  object \n",
      " 8   transmission   18890 non-null  object \n",
      " 9   make           18890 non-null  object \n",
      " 10  fuel           18890 non-null  object \n",
      " 11  mpg            18890 non-null  object \n",
      " 12  drivertrain    18890 non-null  object \n",
      " 13  engine         18890 non-null  float64\n",
      " 14  owners         0 non-null      float64\n",
      " 15  ULEZ           18780 non-null  object \n",
      " 16  dealer_area    16203 non-null  object \n",
      " 17  dealer_city    18686 non-null  object \n",
      " 18  seller1        16203 non-null  float64\n",
      " 19  used           18890 non-null  int64  \n",
      " 20  e_engine_kWh   0 non-null      float64\n",
      " 21  log_price      18890 non-null  float64\n",
      " 22  log_mileage    18890 non-null  float64\n",
      " 23  orig_name      18890 non-null  object \n",
      " 24  id             18890 non-null  object \n",
      " 25  year_reg       0 non-null      float64\n",
      " 26  link           18890 non-null  object \n",
      " 27  href0          16203 non-null  object \n",
      "dtypes: float64(10), int64(2), object(16)\n",
      "memory usage: 4.7+ MB\n"
     ]
    }
   ],
   "source": [
    "ncars.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a024e410",
   "metadata": {},
   "source": [
    "## New cars - electric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0d8e8ea2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:49.261128Z",
     "start_time": "2022-03-09T21:33:49.192794Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2816, 20)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import all detailed files and make new ncars df, including year\n",
    "\n",
    "path = r'/Users/robertdavies/Desktop/DSI/GA_P2/LargeDatasets/raw/df_detailed_ev/'\n",
    "\n",
    "li=[]\n",
    "year_files = glob.glob(path + f\"/*2023_electric_new.csv\")\n",
    "#print(len(year_files),'file(s) to load from', year)\n",
    "for filename in year_files:\n",
    "    df = pd.read_csv(filename, index_col=None, header=0 )\n",
    "    df['YR'] = '2022 new'\n",
    "    df.drop_duplicates(subset=['link'], keep='first', inplace=True)\n",
    "    li.append(df)\n",
    "\n",
    "necars = pd.concat(li, axis=0, ignore_index=True)\n",
    "necars.drop_duplicates(subset=['link'], keep='first', inplace=True)\n",
    "necars.shape  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d409000f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:49.273506Z",
     "start_time": "2022-03-09T21:33:49.264191Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2816, 20)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Body is fine. No cleaning needed.\n",
    "\n",
    "necars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1362f097",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:49.304186Z",
     "start_time": "2022-03-09T21:33:49.279273Z"
    },
    "code_folding": [
     0,
     5
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2768, 20)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean BHP.\n",
    "\n",
    "# Drop NaN BHP\n",
    "necars.dropna(subset=['BHP'],inplace=True)\n",
    "\n",
    "def standardise_bhp(x):\n",
    "    lenbhp = len(str(x).split(' '))\n",
    "    bhp=x\n",
    "    if lenbhp == 1:\n",
    "        if 'BHP' in x:\n",
    "            bhp=float(re.findall('[0-9]+', x)[0])       \n",
    "        elif 'PS' in x:\n",
    "            ps=float(re.findall('[0-9]+', x)[0])\n",
    "            bhp = ps/1.014        \n",
    "    elif lenbhp == 2:\n",
    "        x=str(x.split(' ')[-1])\n",
    "        if 'BHP' in x:\n",
    "            bhp=float(re.findall('[0-9]+', x)[0])      \n",
    "        elif 'PS' in x:\n",
    "            ps=float(re.findall('[0-9]+', x)[0])\n",
    "            bhp = ps/1.014\n",
    "    else:\n",
    "        bhp=np.nan   \n",
    "    return bhp    \n",
    "\n",
    "#Standardise remaining BHP (convert from PS)\n",
    "necars.BHP = necars.BHP.apply(lambda x: standardise_bhp(x))\n",
    "\n",
    "necars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "761771a6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:49.568608Z",
     "start_time": "2022-03-09T21:33:49.307835Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2768, 21)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compile the best location variable. Retain dealer_city and dealer_area. Don't drop remaining NaN.\n",
    "\n",
    "#split href0 into city and area\n",
    "necars['dealer_city_temp'] = [x.split('/')[3] if type(x)==str and len(x.split('/'))>3 else x for x in necars.href0]\n",
    "necars['dealer_area'] = [x.split('/')[2] if type(x)==str and len(x.split('/'))>3 else x for x in necars.href0]\n",
    "\n",
    "#If there are some seller ratings in seller0, move them to seller1\n",
    "necars[necars['seller0'].str.contains('[+-]?([0-9]*[.])?[0-9]+', regex=True, na=False)].seller1 = necars[necars['seller0'].str.contains('[+-]?([0-9]*[.])?[0-9]+', regex=True, na=False)].seller0\n",
    "\n",
    "#Make seller ratings in seller0 np.nan\n",
    "necars.seller0 =[np.nan if '4' in str(x) or '5' in str(x) else x for x in necars.seller0]\n",
    "\n",
    "#If the seller0 column is np.nan, use the dealer_city \n",
    "for index, car in necars.iterrows():\n",
    "    if pd.isnull(necars.loc[index,'seller0']):\n",
    "        necars.loc[index,'seller0']=necars.loc[index,'dealer_city_temp']\n",
    "    #Tiny number of cars don't have a location, and they're all from one dealer in Oldbury.    \n",
    "    if pd.isnull(necars.loc[index,'seller0']):\n",
    "        necars.loc[index,'seller0']= 'Oldbury'\n",
    "        \n",
    "necars['dealer_city'] = necars['seller0']\n",
    "necars.drop(['seller0','dealer_city_temp'],inplace=True, axis=1)\n",
    "\n",
    "necars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "41c01111",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:50.072985Z",
     "start_time": "2022-03-09T21:33:49.571539Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2768, 22)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Populate necars.e_engine_kWh from name_subtitle ~20% NaN. Not great but BHP supersedes it.\n",
    "\n",
    "necars['e_engine_kWh']=''\n",
    "necars['engine']=np.nan\n",
    "\n",
    "for index, car in necars.iterrows():\n",
    "    car_subname = necars.loc[index, 'name_subtitle']\n",
    "    try:\n",
    "        eenginesize = re.findall('[\\d]+[.]?[\\d]+[ ]?kwh',car_subname, flags=re.I)[0]\n",
    "    except: \n",
    "        eenginesize = np.nan\n",
    "    necars.loc[index,'e_engine_kWh'] = eenginesize\n",
    "    \n",
    "necars.e_engine_kWh= necars.e_engine_kWh.apply(lambda x: float(str(x).lower().replace('kwh','')))\n",
    "#necars.dropna(subset=['engine'],inplace=True)\n",
    "\n",
    "necars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7f80bc3e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:50.098341Z",
     "start_time": "2022-03-09T21:33:50.076484Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2764, 23)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make doors column. Drop 4 without info. \n",
    "     \n",
    "necars['doors'] = necars.name_subtitle.apply(lambda x: extract_drs(x))\n",
    "\n",
    "# Drop the remaining 4 necars without door information    \n",
    "necars.drop(necars[necars.doors=='0dr'].index, inplace=True)\n",
    "\n",
    "necars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "df65e9e0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:50.127261Z",
     "start_time": "2022-03-09T21:33:50.101457Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2764, 24)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean necars.price, make necars.ID, clean necars.year \n",
    "to_dummy=['mpg','drivetrain','make','body','transmission','fuel']\n",
    "\n",
    "necars.price=necars.price.apply(lambda x: x.replace('£',''))\n",
    "necars.price=necars.price.apply(lambda x: float(x.replace(',','')))\n",
    "necars['id'] =[x.split('/')[-1] for x in necars.link]\n",
    "necars['year_reg']=necars.YR\n",
    "necars['year']=2022\n",
    "necars.drop('YR',inplace=True, axis=1)\n",
    "necars.owners=int(0)\n",
    "necars.ULEZ='ULEZ'\n",
    "\n",
    "\n",
    "necars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2ab3b5d4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:50.136224Z",
     "start_time": "2022-03-09T21:33:50.131004Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# Set necars.mileage = 0. Set necars.used = 0.\n",
    "\n",
    "necars['mileage']= 0\n",
    "necars['used']=0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b04d8a9d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:50.766869Z",
     "start_time": "2022-03-09T21:33:50.140287Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Have a peek at the price and mileage distribution\n",
    "\n",
    "necars['log_price']=necars.price.apply(lambda x: np.log(x))\n",
    "necars['mileage']=necars.mileage.apply(lambda x: float(x))\n",
    "necars['log_mileage']=necars.mileage.apply(lambda x: np.log(x))\n",
    "\n",
    "#mask = np.abs((ncars.price - ncars.price.mean(0)) / ncars.price.std(0)) > 3\n",
    "#ncars= ncars[~mask]\n",
    "#ncars.price.plot(kind='hist', bins=50, title='Car Price after outlier removal', xlabel='£');\n",
    "\n",
    "fig, ax = plt.subplots(ncols=2, figsize=(16,4))\n",
    "ax[0].hist(necars.price, bins=60)\n",
    "ax[1].hist(necars.log_price, bins=60)\n",
    "ax[0].set_xlabel('£')\n",
    "ax[1].set_xlabel('£')\n",
    "ax[0].set_title('Price')\n",
    "ax[1].set_title('Log price')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "35b40bd8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:51.081430Z",
     "start_time": "2022-03-09T21:33:50.770713Z"
    },
    "code_folding": [
     5
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2764, 28)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Simplify the name column a little. Probably some room for improvement here but it will be used when merging \n",
    "\n",
    "necars['orig_name'] = necars.name\n",
    "necars['lname']=necars.name.apply(lambda x: x.lower())\n",
    "\n",
    "body =[\"coupe\", \"convertible\", \"estate\", \"hatchback\", \"MPV\", \"pickup\", \n",
    "        \"SUV\", \"saloon\",\"cabriolet\",\"sedan\"]\n",
    "\n",
    "results = []\n",
    "for index, car in necars.iterrows():\n",
    "    lname = car[-1]\n",
    "    dummy = lname\n",
    "    for cname in name_list_from_carsized:\n",
    "        if cname in lname:\n",
    "            dummy = cname\n",
    "    for bod in body:\n",
    "        if bod in dummy:\n",
    "            dummy = re.sub(' '+bod,'',dummy)\n",
    "    results.append(dummy)\n",
    "\n",
    "necars['name']=results\n",
    "necars=necars.drop('lname', axis=1)\n",
    "\n",
    "necars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a02c6ef3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:51.151243Z",
     "start_time": "2022-03-09T21:33:51.085053Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# Output clean used cars data \n",
    "\n",
    "# Sort the order of the columns.\n",
    "necars=necars[['name','name_subtitle','year','price','body','mileage','BHP','doors'\n",
    "            ,'transmission','make','fuel','mpg','drivertrain','engine','owners'\n",
    "            ,'ULEZ','dealer_area','dealer_city','seller1','used', 'e_engine_kWh'\n",
    "            ,'log_price','log_mileage','orig_name','id','year_reg','link','href0']]\n",
    "necars_abspath = r'/Users/robertdavies/Desktop/DSI/GA_P2/LargeDatasets/00_clean_new_electric_cars.csv'\n",
    "necars.to_csv(necars_abspath, index=False, header=necars.columns )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1d37e1aa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:51.179805Z",
     "start_time": "2022-03-09T21:33:51.153938Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2764 entries, 0 to 2815\n",
      "Data columns (total 28 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   name           2764 non-null   object \n",
      " 1   name_subtitle  2764 non-null   object \n",
      " 2   year           2764 non-null   int64  \n",
      " 3   price          2764 non-null   float64\n",
      " 4   body           2764 non-null   object \n",
      " 5   mileage        2764 non-null   float64\n",
      " 6   BHP            2764 non-null   float64\n",
      " 7   doors          2764 non-null   object \n",
      " 8   transmission   2764 non-null   object \n",
      " 9   make           2764 non-null   object \n",
      " 10  fuel           2764 non-null   object \n",
      " 11  mpg            2764 non-null   object \n",
      " 12  drivertrain    2764 non-null   object \n",
      " 13  engine         0 non-null      float64\n",
      " 14  owners         2764 non-null   int64  \n",
      " 15  ULEZ           2764 non-null   object \n",
      " 16  dealer_area    2415 non-null   object \n",
      " 17  dealer_city    2764 non-null   object \n",
      " 18  seller1        2415 non-null   float64\n",
      " 19  used           2764 non-null   int64  \n",
      " 20  e_engine_kWh   2297 non-null   float64\n",
      " 21  log_price      2764 non-null   float64\n",
      " 22  log_mileage    2764 non-null   float64\n",
      " 23  orig_name      2764 non-null   object \n",
      " 24  id             2764 non-null   object \n",
      " 25  year_reg       2764 non-null   object \n",
      " 26  link           2764 non-null   object \n",
      " 27  href0          2415 non-null   object \n",
      "dtypes: float64(8), int64(3), object(17)\n",
      "memory usage: 626.2+ KB\n"
     ]
    }
   ],
   "source": [
    "necars.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "097f1bcb",
   "metadata": {},
   "source": [
    "# Combine dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6439786d",
   "metadata": {},
   "source": [
    "OK, so we have cleaned the data in four batches, now I want to combine it into a single DataFrame.\n",
    "\n",
    "The four batches so far have already been engineered to have the same series of variables in the same order, so I can concatenate them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83abfbb2",
   "metadata": {},
   "source": [
    "## Combine input data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45af462a",
   "metadata": {},
   "source": [
    "One large file is easier to work with than four small ones, so now it's time to concatenate them all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "59be26c3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:55.371354Z",
     "start_time": "2022-03-09T21:33:51.183414Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame shape: (400245, 28)\n"
     ]
    }
   ],
   "source": [
    "# Read and cat clean cars data \n",
    "\n",
    "# Each of my absolute paths for  the datasets cleaned so far. If you're trying to recreate this project\n",
    "# yourself then this will need to be tailored.\n",
    "car_dataframes = [ucars_abspath, ncars_abspath, necars_abspath, uecars_abspath]\n",
    "li=[]\n",
    "for filename in car_dataframes:\n",
    "    df = pd.read_csv(filename, index_col=None, header=0 )\n",
    "    # Drop duplicates\n",
    "    df.drop_duplicates(subset=['link'], keep='first', inplace=True)\n",
    "    li.append(df)\n",
    "\n",
    "# Concatenate the four dataframes into one.     \n",
    "cars = pd.concat(li, axis=0, ignore_index=True)\n",
    "cars.drop_duplicates(subset=['id'], keep='first', inplace=True)\n",
    "print('DataFrame shape:',cars.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9348f659",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:56.505939Z",
     "start_time": "2022-03-09T21:33:55.374327Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 400245 entries, 0 to 400305\n",
      "Data columns (total 28 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   name           400245 non-null  object \n",
      " 1   name_subtitle  400245 non-null  object \n",
      " 2   year           400245 non-null  int64  \n",
      " 3   price          400245 non-null  float64\n",
      " 4   body           400245 non-null  object \n",
      " 5   mileage        400245 non-null  float64\n",
      " 6   BHP            400245 non-null  float64\n",
      " 7   doors          400245 non-null  object \n",
      " 8   transmission   400245 non-null  object \n",
      " 9   make           400245 non-null  object \n",
      " 10  fuel           400245 non-null  object \n",
      " 11  mpg            400245 non-null  object \n",
      " 12  drivertrain    400245 non-null  object \n",
      " 13  engine         392741 non-null  float64\n",
      " 14  owners         199447 non-null  float64\n",
      " 15  ULEZ           352088 non-null  object \n",
      " 16  dealer_area    340545 non-null  object \n",
      " 17  dealer_city    399769 non-null  object \n",
      " 18  seller1        329526 non-null  float64\n",
      " 19  used           400245 non-null  int64  \n",
      " 20  e_engine_kWh   5343 non-null    float64\n",
      " 21  log_price      400245 non-null  float64\n",
      " 22  log_mileage    400245 non-null  float64\n",
      " 23  orig_name      400245 non-null  object \n",
      " 24  id             400245 non-null  int64  \n",
      " 25  year_reg       373067 non-null  object \n",
      " 26  link           400245 non-null  object \n",
      " 27  href0          340545 non-null  object \n",
      "dtypes: float64(9), int64(3), object(16)\n",
      "memory usage: 88.6+ MB\n"
     ]
    }
   ],
   "source": [
    "cars.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "67e7c9a6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:56.842109Z",
     "start_time": "2022-03-09T21:33:56.510855Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>mileage</th>\n",
       "      <th>BHP</th>\n",
       "      <th>engine</th>\n",
       "      <th>owners</th>\n",
       "      <th>seller1</th>\n",
       "      <th>used</th>\n",
       "      <th>e_engine_kWh</th>\n",
       "      <th>log_price</th>\n",
       "      <th>log_mileage</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>400245.000000</td>\n",
       "      <td>4.002450e+05</td>\n",
       "      <td>4.002450e+05</td>\n",
       "      <td>400245.000000</td>\n",
       "      <td>392741.000000</td>\n",
       "      <td>199447.000000</td>\n",
       "      <td>329526.000000</td>\n",
       "      <td>400245.000000</td>\n",
       "      <td>5343.000000</td>\n",
       "      <td>400245.000000</td>\n",
       "      <td>4.002450e+05</td>\n",
       "      <td>4.002450e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016.663406</td>\n",
       "      <td>1.930479e+04</td>\n",
       "      <td>3.787096e+04</td>\n",
       "      <td>156.053268</td>\n",
       "      <td>1.734427</td>\n",
       "      <td>1.795444</td>\n",
       "      <td>4.636698</td>\n",
       "      <td>0.945908</td>\n",
       "      <td>55.656410</td>\n",
       "      <td>9.647099</td>\n",
       "      <td>-inf</td>\n",
       "      <td>2.021445e+14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.652162</td>\n",
       "      <td>1.319212e+04</td>\n",
       "      <td>3.327608e+04</td>\n",
       "      <td>69.251285</td>\n",
       "      <td>0.556279</td>\n",
       "      <td>1.172630</td>\n",
       "      <td>0.442237</td>\n",
       "      <td>0.226199</td>\n",
       "      <td>19.113811</td>\n",
       "      <td>0.723010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.517598e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1994.000000</td>\n",
       "      <td>2.500000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>5.521461</td>\n",
       "      <td>-inf</td>\n",
       "      <td>2.015062e+14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.099100e+04</td>\n",
       "      <td>1.347300e+04</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>9.304832</td>\n",
       "      <td>9.508443e+00</td>\n",
       "      <td>2.021110e+14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>1.724900e+04</td>\n",
       "      <td>2.833300e+04</td>\n",
       "      <td>138.067061</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>9.755509</td>\n",
       "      <td>1.025178e+01</td>\n",
       "      <td>2.021121e+14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>2.450000e+04</td>\n",
       "      <td>5.400000e+04</td>\n",
       "      <td>187.376726</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>10.106428</td>\n",
       "      <td>1.089674e+01</td>\n",
       "      <td>2.022011e+14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2022.000000</td>\n",
       "      <td>1.250000e+06</td>\n",
       "      <td>1.113000e+06</td>\n",
       "      <td>972.386588</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>14.038654</td>\n",
       "      <td>1.392257e+01</td>\n",
       "      <td>2.022021e+14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                year         price       mileage            BHP  \\\n",
       "count  400245.000000  4.002450e+05  4.002450e+05  400245.000000   \n",
       "mean     2016.663406  1.930479e+04  3.787096e+04     156.053268   \n",
       "std         3.652162  1.319212e+04  3.327608e+04      69.251285   \n",
       "min      1994.000000  2.500000e+02  0.000000e+00       1.000000   \n",
       "25%      2015.000000  1.099100e+04  1.347300e+04     113.000000   \n",
       "50%      2018.000000  1.724900e+04  2.833300e+04     138.067061   \n",
       "75%      2019.000000  2.450000e+04  5.400000e+04     187.376726   \n",
       "max      2022.000000  1.250000e+06  1.113000e+06     972.386588   \n",
       "\n",
       "              engine         owners        seller1           used  \\\n",
       "count  392741.000000  199447.000000  329526.000000  400245.000000   \n",
       "mean        1.734427       1.795444       4.636698       0.945908   \n",
       "std         0.556279       1.172630       0.442237       0.226199   \n",
       "min         0.600000       0.000000       1.000000       0.000000   \n",
       "25%         1.400000       1.000000       4.500000       1.000000   \n",
       "50%         1.600000       1.000000       4.700000       1.000000   \n",
       "75%         2.000000       2.000000       4.900000       1.000000   \n",
       "max         5.500000      26.000000       5.000000       1.000000   \n",
       "\n",
       "       e_engine_kWh      log_price   log_mileage            id  \n",
       "count   5343.000000  400245.000000  4.002450e+05  4.002450e+05  \n",
       "mean      55.656410       9.647099          -inf  2.021445e+14  \n",
       "std       19.113811       0.723010           NaN  4.517598e+10  \n",
       "min       11.000000       5.521461          -inf  2.015062e+14  \n",
       "25%       42.000000       9.304832  9.508443e+00  2.021110e+14  \n",
       "50%       50.000000       9.755509  1.025178e+01  2.021121e+14  \n",
       "75%       71.000000      10.106428  1.089674e+01  2.022011e+14  \n",
       "max      125.000000      14.038654  1.392257e+01  2.022021e+14  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "214d6e8f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:33:56.898121Z",
     "start_time": "2022-03-09T21:33:56.849153Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>name_subtitle</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>body</th>\n",
       "      <th>mileage</th>\n",
       "      <th>BHP</th>\n",
       "      <th>doors</th>\n",
       "      <th>transmission</th>\n",
       "      <th>make</th>\n",
       "      <th>...</th>\n",
       "      <th>seller1</th>\n",
       "      <th>used</th>\n",
       "      <th>e_engine_kWh</th>\n",
       "      <th>log_price</th>\n",
       "      <th>log_mileage</th>\n",
       "      <th>orig_name</th>\n",
       "      <th>id</th>\n",
       "      <th>year_reg</th>\n",
       "      <th>link</th>\n",
       "      <th>href0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>160338</th>\n",
       "      <td>ford fiesta</td>\n",
       "      <td>Titanium Hatchback 5dr Petrol Manual 97 g/km 9...</td>\n",
       "      <td>2017</td>\n",
       "      <td>12991.0</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>27480.0</td>\n",
       "      <td>98.619329</td>\n",
       "      <td>5dr</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Ford</td>\n",
       "      <td>...</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.472012</td>\n",
       "      <td>10.221214</td>\n",
       "      <td>Ford Fiesta</td>\n",
       "      <td>202201201567638</td>\n",
       "      <td>2017 (67 reg)</td>\n",
       "      <td>https://www.autotrader.co.uk/car-details/20220...</td>\n",
       "      <td>/dealers/berkshire/wokingham/think-ford-woking...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392314</th>\n",
       "      <td>volkswagen t-cross</td>\n",
       "      <td>T-Cross SE 1.0 TSI 110PS 6-spe 5dr</td>\n",
       "      <td>2022</td>\n",
       "      <td>23095.0</td>\n",
       "      <td>SUV</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108.481262</td>\n",
       "      <td>5dr</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.047371</td>\n",
       "      <td>-inf</td>\n",
       "      <td>Volkswagen T-Cross</td>\n",
       "      <td>202201281867246</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.autotrader.co.uk/classified/advert...</td>\n",
       "      <td>/dealers/west-yorkshire/bradford/jct600-volksw...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105124</th>\n",
       "      <td>peugeot 308</td>\n",
       "      <td>1.6 THP GT (s/s) 5dr</td>\n",
       "      <td>2015</td>\n",
       "      <td>11990.0</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>38210.0</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>5dr</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Peugeot</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.391828</td>\n",
       "      <td>10.550853</td>\n",
       "      <td>Peugeot 308</td>\n",
       "      <td>202201091161974</td>\n",
       "      <td>2015 (65 reg)</td>\n",
       "      <td>https://www.autotrader.co.uk/car-details/20220...</td>\n",
       "      <td>/dealers/buckinghamshire/high-wycombe/autohave...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236602</th>\n",
       "      <td>mercedes-benz cla class</td>\n",
       "      <td>CLA 220d AMG Line Night Edition 2.1 4dr</td>\n",
       "      <td>2018</td>\n",
       "      <td>22990.0</td>\n",
       "      <td>Saloon</td>\n",
       "      <td>57404.0</td>\n",
       "      <td>167.652860</td>\n",
       "      <td>4dr</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>...</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.042815</td>\n",
       "      <td>10.957869</td>\n",
       "      <td>Mercedes-Benz Cla Class</td>\n",
       "      <td>202201191510217</td>\n",
       "      <td>2018 (68 reg)</td>\n",
       "      <td>https://www.autotrader.co.uk/car-details/20220...</td>\n",
       "      <td>/dealers/norwich/heigham-street/mercedes-benz-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320180</th>\n",
       "      <td>hyundai kona</td>\n",
       "      <td>1.6T GDi Blue Drive Premium GT 4WD DCT 5dr</td>\n",
       "      <td>2019</td>\n",
       "      <td>20998.0</td>\n",
       "      <td>SUV</td>\n",
       "      <td>19500.0</td>\n",
       "      <td>174.556213</td>\n",
       "      <td>5dr</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.952182</td>\n",
       "      <td>9.878170</td>\n",
       "      <td>Hyundai Kona</td>\n",
       "      <td>202201211593395</td>\n",
       "      <td>2019 (19 reg)</td>\n",
       "      <td>https://www.autotrader.co.uk/car-details/20220...</td>\n",
       "      <td>/dealers/north-humberside/hessle/carsupermarke...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           name  \\\n",
       "160338              ford fiesta   \n",
       "392314       volkswagen t-cross   \n",
       "105124              peugeot 308   \n",
       "236602  mercedes-benz cla class   \n",
       "320180             hyundai kona   \n",
       "\n",
       "                                            name_subtitle  year    price  \\\n",
       "160338  Titanium Hatchback 5dr Petrol Manual 97 g/km 9...  2017  12991.0   \n",
       "392314                 T-Cross SE 1.0 TSI 110PS 6-spe 5dr  2022  23095.0   \n",
       "105124                               1.6 THP GT (s/s) 5dr  2015  11990.0   \n",
       "236602            CLA 220d AMG Line Night Edition 2.1 4dr  2018  22990.0   \n",
       "320180         1.6T GDi Blue Drive Premium GT 4WD DCT 5dr  2019  20998.0   \n",
       "\n",
       "             body  mileage         BHP doors transmission           make  ...  \\\n",
       "160338  Hatchback  27480.0   98.619329   5dr       Manual           Ford  ...   \n",
       "392314        SUV      0.0  108.481262   5dr       Manual     Volkswagen  ...   \n",
       "105124  Hatchback  38210.0  205.000000   5dr       Manual        Peugeot  ...   \n",
       "236602     Saloon  57404.0  167.652860   4dr    Automatic  Mercedes-Benz  ...   \n",
       "320180        SUV  19500.0  174.556213   5dr    Automatic        Hyundai  ...   \n",
       "\n",
       "       seller1 used e_engine_kWh  log_price  log_mileage  \\\n",
       "160338     4.7    1          NaN   9.472012    10.221214   \n",
       "392314     3.0    0          NaN  10.047371         -inf   \n",
       "105124     5.0    1          NaN   9.391828    10.550853   \n",
       "236602     4.7    1          NaN  10.042815    10.957869   \n",
       "320180     NaN    1          NaN   9.952182     9.878170   \n",
       "\n",
       "                      orig_name               id       year_reg  \\\n",
       "160338              Ford Fiesta  202201201567638  2017 (67 reg)   \n",
       "392314       Volkswagen T-Cross  202201281867246            NaN   \n",
       "105124              Peugeot 308  202201091161974  2015 (65 reg)   \n",
       "236602  Mercedes-Benz Cla Class  202201191510217  2018 (68 reg)   \n",
       "320180             Hyundai Kona  202201211593395  2019 (19 reg)   \n",
       "\n",
       "                                                     link  \\\n",
       "160338  https://www.autotrader.co.uk/car-details/20220...   \n",
       "392314  https://www.autotrader.co.uk/classified/advert...   \n",
       "105124  https://www.autotrader.co.uk/car-details/20220...   \n",
       "236602  https://www.autotrader.co.uk/car-details/20220...   \n",
       "320180  https://www.autotrader.co.uk/car-details/20220...   \n",
       "\n",
       "                                                    href0  \n",
       "160338  /dealers/berkshire/wokingham/think-ford-woking...  \n",
       "392314  /dealers/west-yorkshire/bradford/jct600-volksw...  \n",
       "105124  /dealers/buckinghamshire/high-wycombe/autohave...  \n",
       "236602  /dealers/norwich/heigham-street/mercedes-benz-...  \n",
       "320180  /dealers/north-humberside/hessle/carsupermarke...  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "9b3ac440",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:07.379485Z",
     "start_time": "2022-03-09T21:33:56.901528Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# Output all cars data - still containsa a few NaNs\n",
    "cars=cars[['name','name_subtitle','year','price','body','mileage','BHP','doors'\n",
    "            ,'transmission','make','fuel','mpg','drivertrain','engine','owners'\n",
    "            ,'ULEZ','dealer_area','dealer_city','seller1','used', 'e_engine_kWh'\n",
    "            ,'log_price','log_mileage','orig_name','id','year_reg','link','href0']]\n",
    "cars_abspath = r'/Users/robertdavies/Desktop/DSI/GA_P2/LargeDatasets/00_all_cars.csv'\n",
    "cars.to_csv(cars_abspath, index=False, header=cars.columns )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3977f1a8",
   "metadata": {},
   "source": [
    "This file (above) is the input data to the [EDA](#EDA-on-used-cars) in section in 4 of this Notebook, ans also is the input data to the modelling in Notebook 5 of 6."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b33de7d",
   "metadata": {},
   "source": [
    "## Join county, latitude and longitude"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "642f0d43",
   "metadata": {},
   "source": [
    "Merging latitude and longitude information first, then county information second to the dataframe. The lat/lon merge is a one-one merge based on car ID. The county merge is then a many-to-one merge based on rounded lat/lon figures.\n",
    "\n",
    "All GeoCoding is performed in Notebook 03_GeoCoding, where .csv files are created/condition for merging. \n",
    "\n",
    "Note that this location information was not included in the first version of the model. It was part of the further work which I undertook when experimenting with ways to improve model performance after the first version. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "13d65bee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:07.971352Z",
     "start_time": "2022-03-09T21:34:07.382655Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(398983, 3)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import all latlon files and make ll dataframe \n",
    "\n",
    "path = r'/Users/robertdavies/Desktop/DSI/GA_P2/LargeDatasets/latlon/'\n",
    "\n",
    "li=[]\n",
    "files = glob.glob(path + f\"/*.csv\")\n",
    "for filename in files:\n",
    "    df = pd.read_csv(filename, index_col=None, header=0 )\n",
    "    li.append(df)\n",
    "\n",
    "ll = pd.concat(li, axis=0, ignore_index=True)\n",
    "ll.drop_duplicates(subset=['id'], keep='first', inplace=True)\n",
    "ll = ll[['id','dealer_lat','dealer_lon']]\n",
    "ll.shape    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9b122372",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:08.581681Z",
     "start_time": "2022-03-09T21:34:07.973924Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400245, 30)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge cars and lat/lon df on 'id'. One-to-one merge.\n",
    "\n",
    "cars = pd.merge(\n",
    "    cars,\n",
    "    ll,\n",
    "    how=\"left\",\n",
    "    on='id',\n",
    "    sort=False,\n",
    "    suffixes=(\"_x\", \"_y\"),\n",
    "    copy=True,\n",
    "    indicator=False,\n",
    "    validate=\"one_to_one\"\n",
    ")\n",
    "\n",
    "cars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "59543522",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:09.507696Z",
     "start_time": "2022-03-09T21:34:08.585202Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 400245 entries, 0 to 400244\n",
      "Data columns (total 30 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   name           400245 non-null  object \n",
      " 1   name_subtitle  400245 non-null  object \n",
      " 2   year           400245 non-null  int64  \n",
      " 3   price          400245 non-null  float64\n",
      " 4   body           400245 non-null  object \n",
      " 5   mileage        400245 non-null  float64\n",
      " 6   BHP            400245 non-null  float64\n",
      " 7   doors          400245 non-null  object \n",
      " 8   transmission   400245 non-null  object \n",
      " 9   make           400245 non-null  object \n",
      " 10  fuel           400245 non-null  object \n",
      " 11  mpg            400245 non-null  object \n",
      " 12  drivertrain    400245 non-null  object \n",
      " 13  engine         392741 non-null  float64\n",
      " 14  owners         199447 non-null  float64\n",
      " 15  ULEZ           352088 non-null  object \n",
      " 16  dealer_area    340545 non-null  object \n",
      " 17  dealer_city    399769 non-null  object \n",
      " 18  seller1        329526 non-null  float64\n",
      " 19  used           400245 non-null  int64  \n",
      " 20  e_engine_kWh   5343 non-null    float64\n",
      " 21  log_price      400245 non-null  float64\n",
      " 22  log_mileage    400245 non-null  float64\n",
      " 23  orig_name      400245 non-null  object \n",
      " 24  id             400245 non-null  int64  \n",
      " 25  year_reg       373067 non-null  object \n",
      " 26  link           400245 non-null  object \n",
      " 27  href0          340545 non-null  object \n",
      " 28  dealer_lat     398695 non-null  float64\n",
      " 29  dealer_lon     398695 non-null  float64\n",
      "dtypes: float64(11), int64(3), object(16)\n",
      "memory usage: 94.7+ MB\n"
     ]
    }
   ],
   "source": [
    "cars.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "19a2b263",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:09.544266Z",
     "start_time": "2022-03-09T21:34:09.512265Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['original_ll', 'geocode', 'county', 'postcode', 'postcode_short',\n",
       "       'dealer_lat', 'dealer_lon', 'dealer_lat_rnd', 'dealer_lon_rnd'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read counties csv file prepared in 03_GeoCoding\n",
    "\n",
    "counties = pd.read_csv('/Users/robertdavies/Desktop/DSI/GA_P2/LargeDatasets/04_merge_counties.csv')\n",
    "# Observe counties columns\n",
    "counties.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f801faf3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:09.557154Z",
     "start_time": "2022-03-09T21:34:09.548661Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'name_subtitle', 'year', 'price', 'body', 'mileage', 'BHP',\n",
       "       'doors', 'transmission', 'make', 'fuel', 'mpg', 'drivertrain', 'engine',\n",
       "       'owners', 'ULEZ', 'dealer_area', 'dealer_city', 'seller1', 'used',\n",
       "       'e_engine_kWh', 'log_price', 'log_mileage', 'orig_name', 'id',\n",
       "       'year_reg', 'link', 'href0', 'dealer_lat', 'dealer_lon'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Observe cars columns\n",
    "cars.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2dea998e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:10.225272Z",
     "start_time": "2022-03-09T21:34:09.561038Z"
    }
   },
   "outputs": [],
   "source": [
    "# Generate rounded lat/lon in cars dataframe to match counties. This rounding is approx accurate to 1 m\n",
    "cars['dealer_lat_rnd']=cars.dealer_lat.apply(lambda x: round(x,5))\n",
    "cars['dealer_lon_rnd']=cars.dealer_lon.apply(lambda x: round(x,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3e07e4cc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:10.909938Z",
     "start_time": "2022-03-09T21:34:10.228384Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# Left merge counties data into cars dataframe based on rounded lat/lon. Many-to-one merge. \n",
    "cars = pd.merge(\n",
    "    cars,\n",
    "    counties,\n",
    "    how=\"left\",\n",
    "    left_on=['dealer_lat_rnd','dealer_lon_rnd'],\n",
    "    right_on=['dealer_lat_rnd','dealer_lon_rnd'],\n",
    "    sort=False,\n",
    "    suffixes=(\"\", \"_y\"),\n",
    "    copy=True,\n",
    "    indicator=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8de30b93",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:12.203717Z",
     "start_time": "2022-03-09T21:34:10.913516Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 400245 entries, 0 to 400244\n",
      "Data columns (total 39 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   name            400245 non-null  object \n",
      " 1   name_subtitle   400245 non-null  object \n",
      " 2   year            400245 non-null  int64  \n",
      " 3   price           400245 non-null  float64\n",
      " 4   body            400245 non-null  object \n",
      " 5   mileage         400245 non-null  float64\n",
      " 6   BHP             400245 non-null  float64\n",
      " 7   doors           400245 non-null  object \n",
      " 8   transmission    400245 non-null  object \n",
      " 9   make            400245 non-null  object \n",
      " 10  fuel            400245 non-null  object \n",
      " 11  mpg             400245 non-null  object \n",
      " 12  drivertrain     400245 non-null  object \n",
      " 13  engine          392741 non-null  float64\n",
      " 14  owners          199447 non-null  float64\n",
      " 15  ULEZ            352088 non-null  object \n",
      " 16  dealer_area     340545 non-null  object \n",
      " 17  dealer_city     399769 non-null  object \n",
      " 18  seller1         329526 non-null  float64\n",
      " 19  used            400245 non-null  int64  \n",
      " 20  e_engine_kWh    5343 non-null    float64\n",
      " 21  log_price       400245 non-null  float64\n",
      " 22  log_mileage     400245 non-null  float64\n",
      " 23  orig_name       400245 non-null  object \n",
      " 24  id              400245 non-null  int64  \n",
      " 25  year_reg        373067 non-null  object \n",
      " 26  link            400245 non-null  object \n",
      " 27  href0           340545 non-null  object \n",
      " 28  dealer_lat      398695 non-null  float64\n",
      " 29  dealer_lon      398695 non-null  float64\n",
      " 30  dealer_lat_rnd  398695 non-null  float64\n",
      " 31  dealer_lon_rnd  398695 non-null  float64\n",
      " 32  original_ll     398695 non-null  object \n",
      " 33  geocode         398695 non-null  object \n",
      " 34  county          398695 non-null  object \n",
      " 35  postcode        398695 non-null  object \n",
      " 36  postcode_short  386212 non-null  object \n",
      " 37  dealer_lat_y    398695 non-null  float64\n",
      " 38  dealer_lon_y    398695 non-null  float64\n",
      "dtypes: float64(15), int64(3), object(21)\n",
      "memory usage: 122.1+ MB\n"
     ]
    }
   ],
   "source": [
    "cars.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "296ffaf0",
   "metadata": {},
   "source": [
    "Note that only 398695/400245 cars have lat/lon/county data. Pretty good. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6db5a0eb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:12.590998Z",
     "start_time": "2022-03-09T21:34:12.206342Z"
    }
   },
   "outputs": [],
   "source": [
    "# Drop extra columns no longer needed\n",
    "cars.drop(labels=['dealer_lat_rnd', 'dealer_lon_rnd', 'original_ll',\n",
    "       'dealer_lat_y', 'dealer_lon_y'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "820ceeed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:12.619006Z",
     "start_time": "2022-03-09T21:34:12.593312Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Greater London             32808\n",
       "West Midlands              32761\n",
       "West Yorkshire             18519\n",
       "Greater Manchester         17016\n",
       "Surrey                     12955\n",
       "                           ...  \n",
       "Argyll and Bute Council       33\n",
       "Isle of Wight                 26\n",
       "Na h-Eileanan an Iar           3\n",
       "Orkney Islands                 1\n",
       "Shetland Islands               1\n",
       "Name: county, Length: 136, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quick look at car counts in each county. It looks as expected - Most in London, least in Shetland and Orkney!\n",
    "cars.county.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "22afc053",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:24.648677Z",
     "start_time": "2022-03-09T21:34:12.622205Z"
    }
   },
   "outputs": [],
   "source": [
    "# Output all cars data with lat&lon and county - still containsa a few NaNs\n",
    "cars=cars[['name','name_subtitle','year','price','body','mileage','BHP','doors'\n",
    "            ,'transmission','make','fuel','mpg','drivertrain','engine','owners'\n",
    "            ,'ULEZ','county','dealer_area','dealer_city'\n",
    "            ,'dealer_lat','dealer_lon','geocode'\n",
    "            ,'county', 'postcode', 'postcode_short','seller1','used', 'e_engine_kWh'\n",
    "            ,'log_price','log_mileage','orig_name','id','year_reg','link','href0']]\n",
    "cars_abspath = r'/Users/robertdavies/Desktop/DSI/GA_P2/LargeDatasets/00_all_cars_ll.csv'\n",
    "cars.to_csv(cars_abspath, index=False, header=cars.columns )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "42b20a83",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:25.882681Z",
     "start_time": "2022-03-09T21:34:24.651702Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 400245 entries, 0 to 400244\n",
      "Data columns (total 35 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   name            400245 non-null  object \n",
      " 1   name_subtitle   400245 non-null  object \n",
      " 2   year            400245 non-null  int64  \n",
      " 3   price           400245 non-null  float64\n",
      " 4   body            400245 non-null  object \n",
      " 5   mileage         400245 non-null  float64\n",
      " 6   BHP             400245 non-null  float64\n",
      " 7   doors           400245 non-null  object \n",
      " 8   transmission    400245 non-null  object \n",
      " 9   make            400245 non-null  object \n",
      " 10  fuel            400245 non-null  object \n",
      " 11  mpg             400245 non-null  object \n",
      " 12  drivertrain     400245 non-null  object \n",
      " 13  engine          392741 non-null  float64\n",
      " 14  owners          199447 non-null  float64\n",
      " 15  ULEZ            352088 non-null  object \n",
      " 16  county          398695 non-null  object \n",
      " 17  dealer_area     340545 non-null  object \n",
      " 18  dealer_city     399769 non-null  object \n",
      " 19  dealer_lat      398695 non-null  float64\n",
      " 20  dealer_lon      398695 non-null  float64\n",
      " 21  geocode         398695 non-null  object \n",
      " 22  county          398695 non-null  object \n",
      " 23  postcode        398695 non-null  object \n",
      " 24  postcode_short  386212 non-null  object \n",
      " 25  seller1         329526 non-null  float64\n",
      " 26  used            400245 non-null  int64  \n",
      " 27  e_engine_kWh    5343 non-null    float64\n",
      " 28  log_price       400245 non-null  float64\n",
      " 29  log_mileage     400245 non-null  float64\n",
      " 30  orig_name       400245 non-null  object \n",
      " 31  id              400245 non-null  int64  \n",
      " 32  year_reg        373067 non-null  object \n",
      " 33  link            400245 non-null  object \n",
      " 34  href0           340545 non-null  object \n",
      "dtypes: float64(11), int64(3), object(21)\n",
      "memory usage: 109.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# A final look at the cars data frame info before I move onto merging the car size data from \n",
    "# Notebook 02_Carsized_scrape\n",
    "cars.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec097b60",
   "metadata": {},
   "source": [
    "## Merging the car size details"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01b227e4",
   "metadata": {},
   "source": [
    "Now I merge in the car dimension information from Notebook 02_Carsized_scrape.\n",
    "\n",
    "Note that this cars size data isn't actually used in the primary modelling, it was considered further work which was used to improve the model after the first version had been finished. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2d1ba2c2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:51.113446Z",
     "start_time": "2022-03-09T21:34:25.885941Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# Read all car data \n",
    "\n",
    "abspath = r'/Users/robertdavies/Desktop/DSI/GA_P2/LargeDatasets/00_all_cars_ll.csv'\n",
    "dfcars = pd.read_csv(abspath)\n",
    "\n",
    "abspath = r'/Users/robertdavies/Desktop/DSI/GA_P2/LargeDatasets/02_carsized_dims_for_merge.csv'\n",
    "dfsize = pd.read_csv(abspath)\n",
    "\n",
    "# A small number of string manipulations to improve merging of two dataframes using name column as one of the labels.\n",
    "# Note that most of these are simple american/english naming conventions. This doesn't capture them all - there are\n",
    "# still a number of failed matches between the dataframes as you'll see later, but it's OK for now. If I had more\n",
    "# time I would go through it in more detail.\n",
    "\n",
    "# Iterate over rows in the dfcars dataframe and replace some string elements, creating a match_name columns at the \n",
    "# end\n",
    "match_name=[]\n",
    "for index, car in dfcars.iterrows():\n",
    "    name = car[0]\n",
    "    name = name.replace('series','')\n",
    "    name = name.replace('-',' ')\n",
    "    name = name.replace('class','')\n",
    "    name = name.replace('hatch','cooper')\n",
    "    name = name.replace('mini','mini cooper')\n",
    "    name = name.replace('cooper cooper','cooper')\n",
    "    name = name.replace('cooper clubman','clubman')\n",
    "    name = name.replace('cooper countryman','countryman')\n",
    "    name = name.replace('jazz','jazz fit')\n",
    "    name = name.strip()\n",
    "    name = name.replace('mercedes benz','mercedes-benz')\n",
    "    match_name.append(name)\n",
    "dfcars['match_name']=match_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "bee16a1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:51.122486Z",
     "start_time": "2022-03-09T21:34:51.116282Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'name_subtitle', 'year', 'price', 'body', 'mileage', 'BHP',\n",
       "       'doors', 'transmission', 'make', 'fuel', 'mpg', 'drivertrain', 'engine',\n",
       "       'owners', 'ULEZ', 'county', 'dealer_area', 'dealer_city', 'dealer_lat',\n",
       "       'dealer_lon', 'geocode', 'county.1', 'postcode', 'postcode_short',\n",
       "       'seller1', 'used', 'e_engine_kWh', 'log_price', 'log_mileage',\n",
       "       'orig_name', 'id', 'year_reg', 'link', 'href0', 'match_name'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Observe columns\n",
    "dfcars.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f06eb321",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:52.168701Z",
     "start_time": "2022-03-09T21:34:51.125606Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All data size (400245, 44)\n",
      "Null cargo data size (48402, 44)\n"
     ]
    }
   ],
   "source": [
    "# Merge dfcars with dfsize on match_name, body and year. Many-to-one merge.\n",
    "\n",
    "cars_size = pd.merge(\n",
    "    dfcars,\n",
    "    dfsize,\n",
    "    how=\"left\",\n",
    "    left_on=['match_name','body','year'],\n",
    "    right_on=['name','body','year'],\n",
    "    sort=False,\n",
    "    suffixes=(\"_x\", \"_y\"),\n",
    "    copy=True,\n",
    "    indicator=False,\n",
    ")\n",
    "\n",
    "# Drop duplicates as a precaution. Likely not needed.\n",
    "cars_size.drop_duplicates(subset='id', keep='first',inplace=True)\n",
    "print('All data size',cars_size.shape)\n",
    "\n",
    "# Evaluate the number of failed matches. \n",
    "failed_match = cars_size[cars_size.cargo_volume_L.isnull()].copy()\n",
    "failed_match.drop_duplicates(subset='id', keep='first',inplace=True)\n",
    "print('Null cargo data size',failed_match.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba45f88a",
   "metadata": {},
   "source": [
    "Around 12.5% of cars do not have corresponding dimensions. This isn't great but it isn't criminal either.\n",
    "\n",
    "Below I have a look at which sort of cars are not matched, but I don't have the time to fix them here. It's just for awareness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "5b3aba43",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:52.207367Z",
     "start_time": "2022-03-09T21:34:52.191184Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bmw 2 series         1876\n",
       "vauxhall insignia    1419\n",
       "vauxhall zafira      1324\n",
       "skoda octavia        1192\n",
       "ford mondeo           992\n",
       "ford c-max            871\n",
       "abarth 595            789\n",
       "citroen c4 cactus     757\n",
       "ford s-max            733\n",
       "audi s3               702\n",
       "Name: name_x, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Failed match value counts\n",
    "failed_match['name_x'].value_counts(dropna=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e9d7c8e0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:52.267606Z",
     "start_time": "2022-03-09T21:34:52.210807Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name_x</th>\n",
       "      <th>name_subtitle</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>body</th>\n",
       "      <th>mileage</th>\n",
       "      <th>BHP</th>\n",
       "      <th>doors</th>\n",
       "      <th>transmission</th>\n",
       "      <th>make</th>\n",
       "      <th>...</th>\n",
       "      <th>href0</th>\n",
       "      <th>match_name</th>\n",
       "      <th>name_y</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>191866</th>\n",
       "      <td>bmw 2 series</td>\n",
       "      <td>220d M Sport 5dr [Nav] Step Auto 2.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>21745.0</td>\n",
       "      <td>MPV</td>\n",
       "      <td>24908.0</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>5dr</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>BMW</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bmw 2</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306385</th>\n",
       "      <td>bmw 2 series</td>\n",
       "      <td>220d xDrive M Sport Active Tourer 2.0 5dr</td>\n",
       "      <td>2019</td>\n",
       "      <td>26850.0</td>\n",
       "      <td>MPV</td>\n",
       "      <td>20386.0</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>5dr</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>BMW</td>\n",
       "      <td>...</td>\n",
       "      <td>/dealers/oxfordshire/oxford/north-oxford-bmw-2...</td>\n",
       "      <td>bmw 2</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202767</th>\n",
       "      <td>bmw 2 series</td>\n",
       "      <td>2.0 220D xDrive SE Active Tourer 5dr</td>\n",
       "      <td>2018</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>MPV</td>\n",
       "      <td>12060.0</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>5dr</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>BMW</td>\n",
       "      <td>...</td>\n",
       "      <td>/dealers/greater-london/london/cargiant-13713?...</td>\n",
       "      <td>bmw 2</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204536</th>\n",
       "      <td>bmw 2 series</td>\n",
       "      <td>1.5 218i Sport Gran Tourer Auto (s/s) 5dr</td>\n",
       "      <td>2018</td>\n",
       "      <td>19999.0</td>\n",
       "      <td>MPV</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>134.122288</td>\n",
       "      <td>5dr</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>BMW</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bmw 2</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202779</th>\n",
       "      <td>bmw 2 series</td>\n",
       "      <td>220d Xdrive Luxury Gran Tourer 2 2.0 5dr</td>\n",
       "      <td>2018</td>\n",
       "      <td>21499.0</td>\n",
       "      <td>MPV</td>\n",
       "      <td>35630.0</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>5dr</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>BMW</td>\n",
       "      <td>...</td>\n",
       "      <td>/dealers/northumberland/newcastle-upon-tyne/di...</td>\n",
       "      <td>bmw 2</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              name_x                              name_subtitle  year  \\\n",
       "191866  bmw 2 series       220d M Sport 5dr [Nav] Step Auto 2.0  2018   \n",
       "306385  bmw 2 series  220d xDrive M Sport Active Tourer 2.0 5dr  2019   \n",
       "202767  bmw 2 series       2.0 220D xDrive SE Active Tourer 5dr  2018   \n",
       "204536  bmw 2 series  1.5 218i Sport Gran Tourer Auto (s/s) 5dr  2018   \n",
       "202779  bmw 2 series   220d Xdrive Luxury Gran Tourer 2 2.0 5dr  2018   \n",
       "\n",
       "          price body  mileage         BHP doors transmission make  ...  \\\n",
       "191866  21745.0  MPV  24908.0  190.000000   5dr    Automatic  BMW  ...   \n",
       "306385  26850.0  MPV  20386.0  190.000000   5dr    Automatic  BMW  ...   \n",
       "202767  23000.0  MPV  12060.0  190.000000   5dr    Automatic  BMW  ...   \n",
       "204536  19999.0  MPV  30000.0  134.122288   5dr    Automatic  BMW  ...   \n",
       "202779  21499.0  MPV  35630.0  190.000000   5dr    Automatic  BMW  ...   \n",
       "\n",
       "                                                    href0 match_name name_y  \\\n",
       "191866                                                NaN      bmw 2    NaN   \n",
       "306385  /dealers/oxfordshire/oxford/north-oxford-bmw-2...      bmw 2    NaN   \n",
       "202767  /dealers/greater-london/london/cargiant-13713?...      bmw 2    NaN   \n",
       "204536                                                NaN      bmw 2    NaN   \n",
       "202779  /dealers/northumberland/newcastle-upon-tyne/di...      bmw 2    NaN   \n",
       "\n",
       "        wheelbase_cm  length_cm width_cm height_cm ground_clearance_cm  \\\n",
       "191866           NaN        NaN      NaN       NaN                 NaN   \n",
       "306385           NaN        NaN      NaN       NaN                 NaN   \n",
       "202767           NaN        NaN      NaN       NaN                 NaN   \n",
       "204536           NaN        NaN      NaN       NaN                 NaN   \n",
       "202779           NaN        NaN      NaN       NaN                 NaN   \n",
       "\n",
       "       cargo_volume_L  max_cargo_volume_L  \n",
       "191866            NaN                 NaN  \n",
       "306385            NaN                 NaN  \n",
       "202767            NaN                 NaN  \n",
       "204536            NaN                 NaN  \n",
       "202779            NaN                 NaN  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There are a few failed matches, but at this stage I have to let it go. If I was being paid I could fix these.\n",
    "failed_match[failed_match.cargo_volume_L.isnull()].match_name.value_counts(dropna=False).head(50)\n",
    "failed_match[failed_match.match_name=='bmw 2'].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "55133be1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:52.277661Z",
     "start_time": "2022-03-09T21:34:52.271351Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400245, 44)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cars_size shape before dropping nans\n",
    "cars_size.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "288ca2fc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:53.059657Z",
     "start_time": "2022-03-09T21:34:52.282157Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(335824, 44)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cars_size shape after dropping nans\n",
    "\n",
    "cars_size.dropna(subset = ['cargo_volume_L'], inplace=True)\n",
    "cars_size.dropna(subset = ['dealer_lat'], inplace=True)\n",
    "cars_size.dropna(subset = ['dealer_lon'], inplace=True)\n",
    "cars_size.dropna(subset = ['county'], inplace=True)\n",
    "\n",
    "cars_size = cars_size[cars_size.cargo_volume_L!=0.0].copy()\n",
    "cars_size.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "961f0326",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:34:53.081987Z",
     "start_time": "2022-03-09T21:34:53.067743Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name_x', 'name_subtitle', 'year', 'price', 'body', 'mileage', 'BHP',\n",
       "       'doors', 'transmission', 'make', 'fuel', 'mpg', 'drivertrain', 'engine',\n",
       "       'owners', 'ULEZ', 'county', 'dealer_area', 'dealer_city', 'dealer_lat',\n",
       "       'dealer_lon', 'geocode', 'county.1', 'postcode', 'postcode_short',\n",
       "       'seller1', 'used', 'e_engine_kWh', 'log_price', 'log_mileage',\n",
       "       'orig_name', 'id', 'year_reg', 'link', 'href0', 'match_name', 'name_y',\n",
       "       'wheelbase_cm', 'length_cm', 'width_cm', 'height_cm',\n",
       "       'ground_clearance_cm', 'cargo_volume_L', 'max_cargo_volume_L'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars_size.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "6128e403",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:35:03.966900Z",
     "start_time": "2022-03-09T21:34:53.085846Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# Output all cars data with lat&lon and boot size stats - still containsa a few NaNs but 335824 is\n",
    "# a good number of car listings to use for modelling in further work! \n",
    "\n",
    "cars_size.columns = ['name', 'name_subtitle', 'year', 'price', 'body', 'mileage', 'BHP',\n",
    "       'doors', 'transmission', 'make', 'fuel', 'mpg', 'drivertrain', 'engine',\n",
    "       'owners', 'ULEZ', 'county', 'dealer_area', 'dealer_city', 'dealer_lat',\n",
    "       'dealer_lon', 'geocode', 'county', 'postcode', 'postcode_short',\n",
    "       'seller1', 'used', 'e_engine_kWh', 'log_price', 'log_mileage',\n",
    "       'orig_name', 'id', 'year_reg', 'link', 'href0', 'match_name', 'name_y',\n",
    "       'wheelbase_cm', 'length_cm', 'width_cm', 'height_cm',\n",
    "       'ground_clearance_cm', 'cargo_volume_L', 'max_cargo_volume_L']\n",
    "\n",
    "cars_size=cars_size[['name','name_subtitle','year','price','body','mileage','BHP','doors'\n",
    "            ,'transmission','make','fuel','mpg','drivertrain','engine','owners'\n",
    "            ,'ULEZ','dealer_area','dealer_city','geocode', 'county', 'postcode', 'postcode_short'\n",
    "            ,'dealer_lat','dealer_lon','seller1','used', 'e_engine_kWh'\n",
    "            ,'log_price','log_mileage','orig_name','id','year_reg','link','href0'\n",
    "            ,'wheelbase_cm', 'length_cm', 'width_cm'\n",
    "            ,'height_cm', 'ground_clearance_cm', 'cargo_volume_L']]\n",
    "cars_abspath = r'/Users/robertdavies/Desktop/DSI/GA_P2/LargeDatasets/02_cars_with_location_and_size.csv'\n",
    "cars_size.to_csv(cars_abspath, index=False, header=cars_size.columns )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3ee5a163",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:35:05.109818Z",
     "start_time": "2022-03-09T21:35:03.969235Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 335824 entries, 171 to 400243\n",
      "Data columns (total 41 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   name                 335824 non-null  object \n",
      " 1   name_subtitle        335824 non-null  object \n",
      " 2   year                 335824 non-null  int64  \n",
      " 3   price                335824 non-null  float64\n",
      " 4   body                 335824 non-null  object \n",
      " 5   mileage              335824 non-null  float64\n",
      " 6   BHP                  335824 non-null  float64\n",
      " 7   doors                335824 non-null  object \n",
      " 8   transmission         335824 non-null  object \n",
      " 9   make                 335824 non-null  object \n",
      " 10  fuel                 335824 non-null  object \n",
      " 11  mpg                  335824 non-null  object \n",
      " 12  drivertrain          335824 non-null  object \n",
      " 13  engine               330407 non-null  float64\n",
      " 14  owners               165740 non-null  float64\n",
      " 15  ULEZ                 300816 non-null  object \n",
      " 16  dealer_area          288075 non-null  object \n",
      " 17  dealer_city          335824 non-null  object \n",
      " 18  geocode              335824 non-null  object \n",
      " 19  county               335824 non-null  object \n",
      " 20  county               335824 non-null  object \n",
      " 21  postcode             335824 non-null  object \n",
      " 22  postcode_short       325290 non-null  object \n",
      " 23  dealer_lat           335824 non-null  float64\n",
      " 24  dealer_lon           335824 non-null  float64\n",
      " 25  seller1              278140 non-null  float64\n",
      " 26  used                 335824 non-null  int64  \n",
      " 27  e_engine_kWh         3643 non-null    float64\n",
      " 28  log_price            335824 non-null  float64\n",
      " 29  log_mileage          335824 non-null  float64\n",
      " 30  orig_name            335824 non-null  object \n",
      " 31  id                   335824 non-null  int64  \n",
      " 32  year_reg             314334 non-null  object \n",
      " 33  link                 335824 non-null  object \n",
      " 34  href0                288075 non-null  object \n",
      " 35  wheelbase_cm         335824 non-null  float64\n",
      " 36  length_cm            335824 non-null  float64\n",
      " 37  width_cm             335824 non-null  float64\n",
      " 38  height_cm            335824 non-null  float64\n",
      " 39  ground_clearance_cm  335824 non-null  float64\n",
      " 40  cargo_volume_L       335824 non-null  float64\n",
      "dtypes: float64(17), int64(3), object(21)\n",
      "memory usage: 107.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# One final look at the dataframe info which will be used for the 2nd version of modelling\n",
    "cars_size.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c60b123",
   "metadata": {
    "tags": []
   },
   "source": [
    "# EDA on used cars"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b03ea02a",
   "metadata": {},
   "source": [
    "This section contains a general exploration of the **used car** data, before the lat/lon, county and boot size information were added. This is the data which have been used for modelling in the first version of the model. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d961271",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Pretty general interest plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a29a811",
   "metadata": {},
   "source": [
    "The aim of this section is to provide a general overview of the used car data which has been scraped. I'll look at some distributions of the continuous variables and try to evaluate how some of the categorical variables might be impacting used car price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "99696302",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:35:08.704527Z",
     "start_time": "2022-03-09T21:35:05.113437Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All cars (400245, 28)\n",
      "Used cars (378595, 28)\n"
     ]
    }
   ],
   "source": [
    "# Read clean used cars data \n",
    "\n",
    "abspath = r'/Users/robertdavies/Desktop/DSI/GA_P2/LargeDatasets/00_all_cars.csv'\n",
    "df = pd.read_csv(abspath)\n",
    "print('All cars',df.shape)\n",
    "# Drop new cars - not part of my model\n",
    "df = df[df['used']==1]\n",
    "print('Used cars',df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "04b7f554-9c5c-49cc-99d6-54c7fbeaa7d7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:35:10.007564Z",
     "start_time": "2022-03-09T21:35:08.707622Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of make frequency. Note that car brand==car make. With hindsight I wish I used the term brand but it's too\n",
    "# late to change it now.\n",
    "\n",
    "# Groupby car make sort the counted values. \n",
    "df.groupby(by='make')['name'].count().sort_values(ascending=False).head(30).reset_index()\n",
    "\n",
    "# Plot with seaborn\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize=(16,6))\n",
    "sns.barplot(x=\"make\", y=\"name\", data=df.groupby(by='make')['name'].count().sort_values(ascending=False).head(30).reset_index()\n",
    "            ,palette='husl').set_title('Top 30 most common used cars brands for sale on AutoTrader')\n",
    "ax.set_xlabel('Make')\n",
    "ax.set_ylabel('Count')\n",
    "ax.tick_params(axis='x', rotation=60)\n",
    "plt.tight_layout()\n",
    "# Save to pdf\n",
    "plt.savefig('carbrandcount.pdf',bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "093a23a7",
   "metadata": {},
   "source": [
    "A BMW is the most common used car for sale in the UK at the start of February 2022, with over 35,000 used BMWs on the market. This makes up just under 10% of all used cars on AutoTrader. German brands in general are rather popular with 4/5 of the most common brands being German! \n",
    "\n",
    "94% of used cars can be described by the top 23 brands. At this stage I do not drop data from the car brands which are poorly represented, but later just before the modelling (in Notebook 05_modelling) I drop brands with less than 200 used cars recorded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "7f973748-d1b0-4652-b1c2-02488b1fa4a6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:35:14.451602Z",
     "start_time": "2022-03-09T21:35:10.010711Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of pridce distributions by categorical variables using Seaborn - remove outliers first in log space \n",
    "# X-axis is presented in log form because it can show better distribution differentiation. \n",
    "\n",
    "dfo=df.copy()\n",
    "mask = np.abs((dfo.log_price - dfo.log_price.mean(0)) / dfo.log_price.std(0)) > 3\n",
    "dfo= dfo[~mask]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(nrows=3, figsize=(16,15))\n",
    "sns.histplot(data=dfo[dfo.year>2020], x=\"log_price\",hue=\"body\",palette='husl',bins=50, legend=True, ax=ax[0]).set_title('Distribution of log-price by body type for cars < 2 years old')\n",
    "ax[0].set_xlabel('Log-price (£)')\n",
    "ax[0].set_ylabel('Count')\n",
    "sns.histplot(data=dfo[dfo.year>2020], x=\"log_price\",hue=\"transmission\",palette='husl',bins=50, legend=True, ax=ax[1]).set_title('Distribution of log-price by transmission type for cars < 2 years old')\n",
    "ax[1].set_xlabel('Log-price (£)')\n",
    "ax[1].set_ylabel('Count')\n",
    "sns.histplot(data=dfo[dfo.year>2020], x=\"log_price\",hue=\"fuel\",palette='husl',bins=50, legend=True, ax=ax[2]).set_title('Distribution of log-price by fuel type for cars < 2 years old')\n",
    "ax[2].set_xlabel('Log-price (£)')\n",
    "ax[2].set_ylabel('Count')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62a092d5",
   "metadata": {},
   "source": [
    "It is clear that some of these categorical variables will be useful in predicting the price of a used car - manual gearboxes are clearly cheaper than automatic ones.\n",
    "\n",
    "It's a little harder to draw conclusions from the other two plots. It can be seen that hatchback cars are generally cheaper, with SUV cars more expensive than hatchbacks, but other body types are generally less represented and generally overlay on the distribution plot. In terms of fuel type, it looks like petrol cars are the cheapest, with electric cars being more expensive, less represented. \n",
    "\n",
    "None of these insights are surprising. I'm happy with the start of this data analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "058ce230",
   "metadata": {},
   "source": [
    "In these three categorical variables presented it looks like transmission variable provides the best information on car price. Automatic cars are clearly more expensive than manual cars."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "956a7300",
   "metadata": {},
   "source": [
    "In the plots below I try to capture the average mileage, average price and count of cars when divided by different categorical variables. It has mostly worked well, although I haven't been able to make the size of the dots in the key look neat. The general idea is still conveyed by the plot so I have retained these. Interpretation will be below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "8d9e5625-04fb-462c-81a1-f6da559288e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:35:16.319394Z",
     "start_time": "2022-03-09T21:35:14.454958Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of avergae price and mileage figures for categorical variables\n",
    "\n",
    "plotdf1 = df[df.year>2020].groupby(by='fuel')[['price','mileage','doors']].agg({'price':'mean', 'mileage':'mean','doors':'count'}).reset_index()\n",
    "plotdf1 = plotdf1[plotdf1.fuel!='Bi Fuel']\n",
    "plotdf1.columns = ['Fuel','price','mileage','Count']\n",
    "plotdf2 = df[df.year>2020].groupby(by='transmission')[['price','mileage','doors']].agg({'price':'mean', 'mileage':'mean','doors':'count'}).reset_index()\n",
    "plotdf2.columns = ['Transmission','price','mileage','Count']\n",
    "\n",
    "plotdf3 = df[df.year>2020].groupby(by='body')[['price','mileage','doors']].agg({'price':'mean', 'mileage':'mean','doors':'count'}).reset_index()\n",
    "plotdf3.columns = ['Body','price','mileage','Count']\n",
    "\n",
    "fig, ax = plt.subplots(nrows=3,figsize=(10,15))\n",
    "sns.scatterplot(data=plotdf1, x=\"price\", y=\"mileage\", hue=\"Fuel\", size='Count',\n",
    "                sizes=(300, 1500), alpha=.5,palette='husl', ax=ax[2], \n",
    "                ).set_title('Average mileage and average price by fuel type for cars < 2 years old')\n",
    "ax[2].set_xlabel('Mean price (£)')\n",
    "ax[2].set_ylabel('Mean mileage')\n",
    "sns.scatterplot(data=plotdf2, x=\"price\", y=\"mileage\", hue=\"Transmission\",\n",
    "                size='Count',sizes=(300, 1500), alpha=.5,palette='husl', ax=ax[1], \n",
    "                ).set_title('Average mileage and average price by transmission type for cars < 2 years old')\n",
    "ax[1].set_xlabel('Mean price (£)')\n",
    "ax[1].set_ylabel('Mean mileage')\n",
    "sns.scatterplot(data=plotdf3, x=\"price\", y=\"mileage\", hue=\"Body\",size='Count',\n",
    "                sizes=(300, 1500), alpha=.5,palette='husl', ax=ax[0], \n",
    "                ).set_title('Average mileage and average price by body type for cars < 2 years old')\n",
    "ax[0].set_xlabel('Mean price (£)')\n",
    "ax[0].set_ylabel('Mean mileage')\n",
    "for i in range(0,3):\n",
    "    ax[i].legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "    ax[i].set_ylim(3000,7000)\n",
    "    ax[i].set_xlim(15000,60000)\n",
    "    ax[i].tick_params(axis='x', rotation=30)   \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74fadacd",
   "metadata": {},
   "source": [
    "From the top plot where data are divided by car body type:\n",
    "* Saloons less than 2 years old have run up the most miles, around 6000 on average.\n",
    "* Hatchbacks less than 2 years old are the cheapest on average of all body types, with Coupe cars being the most expensive.\n",
    "* SUVs are the most populous used car less than 2 years old on the market.\n",
    "\n",
    "From the mid plot where data are divided by transmission type:\n",
    "* Used automatic cars under 2 years old are more common than used manual cars under 2 years old.\n",
    "* Used automatic cars under 2 years old are more expensive than used manual cars under 2 years old.\n",
    "* Used automatic cars under 2 years old generally have more miles on them than used manual cars under 2 years old.\n",
    "\n",
    "From the bottom plot where data are divided by fuel type:\n",
    "* Used electric cars under 2 years old generally have fewer miles on them than other fuel types. Diesel hybrid cars are the other end of the spectrum. \n",
    "* Used petrol cars are the most populous fuel type for cars less than 2 years old.\n",
    "* Petrol is the cheapest fuel type and Diesel plug-in hybrid cars are the most expensive fuel types for used cars less than 2 years old."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8bb5977",
   "metadata": {},
   "source": [
    "The plot below begins to look at the distribution of car price by each make/brand. This is done for the 16 most popular brands. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "3fe5dd40",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:35:21.595454Z",
     "start_time": "2022-03-09T21:35:16.324596Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot top 16 car brands - outliers removed in log-space\n",
    "\n",
    "# Obtain top 16 brands\n",
    "top16makes = list(df.make.value_counts().head(16).reset_index()['index'])\n",
    "# Save to a dataframe\n",
    "plotdf1 = df[df.make.isin(top16makes)]\n",
    "# Remove outliers from log_price data. Outliers are defined as being outside of 3 standard deviations from the mean.\n",
    "# There was no major work put into evaluating whether or not this definition is good/true. It could be an area\n",
    "# for improvement in future if required.\n",
    "mask = np.abs((plotdf1.log_price - plotdf1.log_price.mean(0))) / plotdf1.log_price.std(0) > 3\n",
    "plotdf1= plotdf1[~mask]\n",
    "\n",
    "# Use Seaborn to plot a series of plots.\n",
    "# Row 1 presents boxplots with a y-axis in terms of price\n",
    "# Row 2 presents boxplots with a y-axis in terms of log-price\n",
    "# Row 3 presents barplot of average price with y-axis in terms of price\n",
    "fig, ax = plt.subplots(nrows = 2, ncols = 1, figsize=(16,14))\n",
    "sns.boxplot(x='make', y='price', data=plotdf1, ax=ax[0],palette='husl').set_title('Make vs Price for 16 most common used cars')\n",
    "ax[0].set_xlabel('Make')\n",
    "ax[0].set_ylabel('Price (£)')\n",
    "# sns.boxplot(x='make', y='log_price', data=plotdf1,ax=ax[1],palette='husl').set_title('Make vs Log-Price for 16 most common used cars')\n",
    "# ax[1].set_xlabel('Make')\n",
    "# ax[1].set_ylabel('Log-Price (£)')\n",
    "sns.barplot(x=\"make\", y=\"price\", data=plotdf1, ax=ax[1],palette='husl').set_title('Make vs average Price for 16 most common used cars')\n",
    "ax[1].set_xlabel('Make')\n",
    "ax[1].set_ylabel('Price (£)')\n",
    "for i in range(0,2):\n",
    "    ax[i].tick_params(axis='x', rotation=30)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fad4a5a5",
   "metadata": {},
   "source": [
    "These plots tell the same story in a number of different ways:\n",
    "* Land Rover, Mercedes-Benz and Volvo stand out as the most expensive brands, with Vauxhall and Renault the cheapest. This can be identified by the average prices in the bottom bar plot or by the median prices in each box plot.\n",
    "* The skew in the top series of box plots, where the y-axis is in units of price tells of which brands also cater to the high-end car market --> Greater skew means more high performance cars.\n",
    "    * For each brand, their prime market price range will lie between the 1st and 3rd interquartile ranges.\n",
    "    * Land Rover, Mercedes-Benz, Audi, and BMW clearly have a greater skew and so also have a high end car offering besides their prime market. The same cannot be said for brands like Mini."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "946614b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:35:21.756805Z",
     "start_time": "2022-03-09T21:35:21.597598Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name                                                  toyota hilux\n",
      "name_subtitle            3.0 D-4D Invincible Double Cab Pickup 4dr\n",
      "year                                                          2014\n",
      "price                                                     100000.0\n",
      "body                                                        Pickup\n",
      "mileage                                                   190000.0\n",
      "BHP                                                          171.0\n",
      "doors                                                          4dr\n",
      "transmission                                             Automatic\n",
      "make                                                        Toyota\n",
      "fuel                                                        Diesel\n",
      "mpg                                                        OVER_30\n",
      "drivertrain                                       Four Wheel Drive\n",
      "engine                                                         3.0\n",
      "owners                                                         1.0\n",
      "ULEZ                                                           NaN\n",
      "dealer_area                                         leicestershire\n",
      "dealer_city                                              leicester\n",
      "seller1                                                        4.2\n",
      "used                                                             1\n",
      "e_engine_kWh                                                   NaN\n",
      "log_price                                                11.512925\n",
      "log_mileage                                              12.154779\n",
      "orig_name                                             Toyota Hilux\n",
      "id                                                 202112160583633\n",
      "year_reg                                             2014 (14 reg)\n",
      "link             https://www.autotrader.co.uk/car-details/20211...\n",
      "href0            /dealers/leicestershire/leicester/auto-investm...\n",
      "Name: 84789, dtype: object\n",
      "https://www.autotrader.co.uk/car-details/202112160583633\n"
     ]
    }
   ],
   "source": [
    "# Checking outlier code. This code can be used to filter on a variable and sort to obtain a printout of the details\n",
    "# for the most expensive car with that search criteria.\n",
    "\n",
    "# Unfortunately over time some of the car listings have been removed from AutoTrader, as they sell, so I can't\n",
    "# always return the listing. Stale data!\n",
    "f= df[df.make=='Toyota'].sort_values(by='log_price',ascending=False).head(1)\n",
    "for index, car in f.iterrows():\n",
    "    print(car)\n",
    "    print(car[-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "a9340cdb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:35:28.099691Z",
     "start_time": "2022-03-09T21:35:21.760393Z"
    },
    "code_folding": [],
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot used car price by year - outliers removed in log-space \n",
    "\n",
    "# Remove outliers from log_price data. Outliers are defined as being outside of 3 standard deviations from the mean.\n",
    "# There was no major work put into evaluating whether or not this definition is good/true. It could be an area\n",
    "# for improvement in future if required.\n",
    "mask = np.abs((df.log_price - df.log_price.mean(0))) / df.log_price.std(0) > 3\n",
    "plotdf2= df[~mask]\n",
    "\n",
    "# Use Seaborn to plot a series of plots.\n",
    "# Row 1 presents boxplots with a y-axis in terms of price\n",
    "# Row 2 presents boxplots with a y-axis in terms of log-price\n",
    "# Row 3 presents barplot of average price with y-axis in terms of price\n",
    "fig, ax = plt.subplots(nrows = 2, ncols = 1, figsize=(16,14))\n",
    "sns.boxplot(x='year', y='price', data=plotdf2, ax=ax[0]).set_title('Year vs Price')\n",
    "ax[0].set_xlabel('Year')\n",
    "ax[0].set_ylabel('Price (£)')\n",
    "# sns.boxplot(x='year', y='log_price', data=plotdf2,ax=ax[1]).set_title('Year vs Log-Price')\n",
    "# ax[1].set_xlabel('Year')\n",
    "# ax[1].set_ylabel('Log-Price (£)')\n",
    "sns.barplot(x=\"year\", y=\"price\", data=plotdf2, ax=ax[1]).set_title('Year vs average Price')\n",
    "ax[1].set_xlabel('Year')\n",
    "ax[1].set_ylabel('Price (£)')\n",
    "for i in range(0,2):\n",
    "    ax[i].tick_params(axis='x', rotation=30)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e14a97a4",
   "metadata": {},
   "source": [
    "These plots describe the relationship between price and age of a car. \n",
    "* Price can be seen to decease as the car year approaches 2003, before a slight increase in average price. This must be the vintage car factor being introduced. \n",
    "* The relationship between age and price is non-linear. This is already causing alarm bells in terms of lienar regression assumptions.\n",
    "* Note that confidence bounds (black bar at top of bars in bar plot) are wider for newer cars and older cars as the sample size within each year is greatly reduced. There is a single car from 1994."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a63e1f9b",
   "metadata": {},
   "source": [
    "This concludes the high-level EDA of the dataset. None of the plots have been particularly surprising. Some of my personal pieces of interest are bulleted below:\n",
    "* It was interesting to see the non-linear relationship between used car price and age.\n",
    "* It was interesting to see that used electric vehicles have the least miles on them after two years of use.\n",
    "* It was interesting to identify the car bands which also offer high-end cars outside of their main market.\n",
    "\n",
    "From here I'm going to have a look at some of the distributions of the continuous variables in the dataset, and work on justifying some feature selection for the modelling going forward."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d749c5e",
   "metadata": {},
   "source": [
    "## Distribution and collinearity evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16ea3858",
   "metadata": {},
   "source": [
    "The continuous variables in the dataset are price, mileage, BHP, year and engine size. I'm going to look at some distributions and evaluate them for multicollinearity. \n",
    "\n",
    "Multicollinearity and the problems associated are explained really nicely in this [link.](https://blog.minitab.com/en/understanding-statistics/handling-multicollinearity-in-regression-analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4503a877",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:35:33.034137Z",
     "start_time": "2022-03-09T21:35:28.103319Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x1152 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn plots of continuous variables\n",
    "\n",
    "# Remove some outliers from these distributions to observe trends better on the plots below.\n",
    "mask1 = np.abs((df.log_price - df.log_price.mean())) / df.log_price.std() > 3\n",
    "dfplot= df[~mask1]\n",
    "mask2 = np.abs((dfplot.log_mileage - dfplot.log_mileage.mean())) / dfplot.log_mileage.std() > 3\n",
    "dfplot= dfplot[~mask2]\n",
    "mask3 = np.abs((dfplot.BHP - dfplot.BHP.mean())) / dfplot.BHP.std() > 6\n",
    "dfplot= dfplot[~mask3]\n",
    "\n",
    "# Plot a series of histograms using Seaborn and subplots.\n",
    "fig, ax = plt.subplots(nrows=4, ncols=2, figsize=(12,16))\n",
    "sns.histplot(data=dfplot.price, bins=60, color='skyblue',ax=ax[0,0]).set_title('Price distribution after outlier removal')\n",
    "sns.histplot(data=dfplot.log_price, bins=60, color='skyblue',ax=ax[0,1]).set_title('Log of Price distribution after outlier removal')\n",
    "sns.histplot(data=dfplot.mileage, bins=60, color='skyblue',ax=ax[1,0]).set_title('Mileage distribution after outlier removal')\n",
    "sns.histplot(data=dfplot.log_mileage, bins=60, color='skyblue',ax=ax[1,1]).set_title('Log of Mileage distribution after outlier removal')\n",
    "sns.histplot(data=dfplot.BHP, bins=60, color='skyblue',ax=ax[2,0]).set_title('BHP distribution')\n",
    "sns.histplot(data=dfplot.BHP.apply(lambda x: np.log(x)), bins=60, color='skyblue',ax=ax[2,1]).set_title('Log of BHP distribution')\n",
    "sns.histplot(data=dfplot.engine, bins=20, color='skyblue',ax=ax[3,0]).set_title('Engine size distribution (L)')\n",
    "sns.histplot(data=dfplot.year, bins=22, color='skyblue',ax=ax[3,1]).set_title('Year distribution')\n",
    "ax[3,1].set_xlim(2000,2022)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "251f0b17",
   "metadata": {},
   "source": [
    "It's clear that none of these continuous variables have a particularly normal distribution. For this reason, although they will be tested, I don't expect linear regression methods to be the best option for modelling within this project.\n",
    "\n",
    "Taking the log of year, mileage and BHP serves to bring them closer to a normal distribution, with maybe th eexception of BHP. I will used log-price and log-mileage variables in the linear modelling methods. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d916837",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-08T15:24:52.070455Z",
     "start_time": "2022-03-08T15:24:52.067154Z"
    }
   },
   "source": [
    "Below I have generated some heatmaps of Pearson correlation stats between different variables within the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c2deea96",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:35:33.521478Z",
     "start_time": "2022-03-09T21:35:33.037396Z"
    },
    "code_folding": [],
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap before dummies\n",
    "\n",
    "# Select variabls I want to compute correlation statistics for\n",
    "tocorr=['log_price','log_mileage','year','engine','BHP','owners']\n",
    "plt.rcParams[\"figure.figsize\"] = (8,7)\n",
    "# Mask the diagonals\n",
    "mask =np.zeros_like(df[tocorr].corr())\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "# Seaborn used to make the heatmap of the correlation matrix\n",
    "sns.heatmap(df[tocorr].corr(), vmin=-1, vmax=1, cmap='vlag', annot=True, mask=mask)\n",
    "plt.title('Heatmap before dummies added')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e96ef9c",
   "metadata": {},
   "source": [
    "It looks like there's some significant correlation between engine and BHP. I don't want to use highly correlated predictors, and I don't want to venture into Principal Component Analysis (PCA) so I'm going to have to drop one - Engine. \n",
    "\n",
    "This is fortunate actually because engine units are inconsistent between electric cars and conventional cars. Teslas don't have 3.0L engines! I will drop the engine column and it won't be used in modelling.\n",
    "\n",
    "Whilst PCA is a really interesting avenue to go down, it also makes interpretation of the resulting predictors/model much more difficult and would not help with my project objectives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "85429bf7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:35:33.559008Z",
     "start_time": "2022-03-09T21:35:33.524754Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "log_price           0\n",
       "log_mileage         0\n",
       "year                0\n",
       "engine           4740\n",
       "BHP                 0\n",
       "owners         181912\n",
       "dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for null values \n",
    "df[tocorr].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cde26e3b",
   "metadata": {},
   "source": [
    "The 4740 null engine values are the electric cars in the dataset.\n",
    "\n",
    "As well as dropping the engine column, I'm going to have to drop the owners column. This just wasn't populated enough on AutoTrader and has 45% NaN values. I believe that this isn't something I'd be willing to share if the car I was trying to sell had many owners, thus the data, or presence of data, would be biased and highly unreliable anyway. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "efede237",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:35:33.915015Z",
     "start_time": "2022-03-09T21:35:33.567579Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap after dropping engine and owners\n",
    "\n",
    "tocorr=['log_price','log_mileage','year','BHP']\n",
    "plt.rcParams[\"figure.figsize\"] = (5,4)\n",
    "mask =np.zeros_like(df[tocorr].corr())\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "sns.heatmap(df[tocorr].corr(), vmin=-1, vmax=1, cmap='vlag', annot=True, mask=mask)\n",
    "plt.title('Heatmap with final continuous variables for modelling')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55a54dee",
   "metadata": {},
   "source": [
    "Some of these correlation statistics are still quite high, but there are none >0.8 or <-0.8 now so I'll proceed.\n",
    "\n",
    "Below I assess the Variance Inflation Factor for my remaining continuous variables to evaluate multicollinearity.\n",
    "\n",
    "The Variance Inflation Factor (VIF) is a measure of colinearity among predictor variables within a multiple regression. It is calculated by taking the the ratio of the variance of all a given model's betas divide by the variane of a single beta if it were fit alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "30c0d526",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:35:34.261880Z",
     "start_time": "2022-03-09T21:35:33.918904Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VIF stats: \n",
      " const          585118.638545\n",
      "log_mileage         1.733264\n",
      "year                1.745992\n",
      "BHP                 1.014354\n",
      "dtype: float64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Modelling without dummies  - assess VIF \n",
    "\n",
    "# Define X and y\n",
    "X = df[['log_mileage','year','BHP']]\n",
    "\n",
    "# Great link on multicollinearity\n",
    "# https://blog.minitab.com/en/understanding-statistics/handling-multicollinearity-in-regression-analysis\n",
    "\n",
    "# If VIF=1 then there's no multicollinearity\n",
    "# If VIF>5 then there's significant multicollinearity and it will be a problem\n",
    "\n",
    "Xvif = add_constant(X)\n",
    "VIF = pd.Series([variance_inflation_factor(Xvif.values, i) \n",
    "               for i in range(Xvif.shape[1])], \n",
    "              index=Xvif.columns)\n",
    "print('VIF stats: \\n',VIF,'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ddb37cc",
   "metadata": {},
   "source": [
    "Since the VIFs are all <5, specifically <2, I conclude that the remaining continuous variables are not overly correlated. This bodes well for model building. \n",
    "\n",
    "The continuous variables being progressed to modelling are `price` (target variable),`year`,`BHP` and `mileage`. Note that for the linear models the target variable will be `log_price` and the `log_mileage` will be used.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "74bf5e90-8b34-4360-929a-3c12da3082a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-09T21:35:35.718719Z",
     "start_time": "2022-03-09T21:35:34.265431Z"
    },
    "code_folding": [],
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>categorical1</th>\n",
       "      <th>categorical2</th>\n",
       "      <th>chi2_statistic</th>\n",
       "      <th>p-value</th>\n",
       "      <th>degrees_of_freedom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>drivertrain</td>\n",
       "      <td>make</td>\n",
       "      <td>300836.176103</td>\n",
       "      <td>0.0</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>drivertrain</td>\n",
       "      <td>body</td>\n",
       "      <td>213636.324160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>drivertrain</td>\n",
       "      <td>transmission</td>\n",
       "      <td>94245.449378</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>drivertrain</td>\n",
       "      <td>fuel</td>\n",
       "      <td>57479.277298</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>drivertrain</td>\n",
       "      <td>doors</td>\n",
       "      <td>137283.344829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>make</td>\n",
       "      <td>body</td>\n",
       "      <td>293153.567222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>make</td>\n",
       "      <td>transmission</td>\n",
       "      <td>108665.291193</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>make</td>\n",
       "      <td>fuel</td>\n",
       "      <td>298334.596129</td>\n",
       "      <td>0.0</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>make</td>\n",
       "      <td>doors</td>\n",
       "      <td>206251.379448</td>\n",
       "      <td>0.0</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>body</td>\n",
       "      <td>transmission</td>\n",
       "      <td>41010.800991</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>body</td>\n",
       "      <td>fuel</td>\n",
       "      <td>64327.647838</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>body</td>\n",
       "      <td>doors</td>\n",
       "      <td>763536.539002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>transmission</td>\n",
       "      <td>fuel</td>\n",
       "      <td>46845.218990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>transmission</td>\n",
       "      <td>doors</td>\n",
       "      <td>30123.217817</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>fuel</td>\n",
       "      <td>doors</td>\n",
       "      <td>33016.210586</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    categorical1  categorical2  chi2_statistic  p-value  degrees_of_freedom\n",
       "0    drivertrain          make   300836.176103      0.0                 122\n",
       "1    drivertrain          body   213636.324160      0.0                  16\n",
       "2    drivertrain  transmission    94245.449378      0.0                   2\n",
       "3    drivertrain          fuel    57479.277298      0.0                  14\n",
       "4    drivertrain         doors   137283.344829      0.0                   8\n",
       "5           make          body   293153.567222      0.0                 488\n",
       "6           make  transmission   108665.291193      0.0                  61\n",
       "7           make          fuel   298334.596129      0.0                 427\n",
       "8           make         doors   206251.379448      0.0                 244\n",
       "9           body  transmission    41010.800991      0.0                   8\n",
       "10          body          fuel    64327.647838      0.0                  56\n",
       "11          body         doors   763536.539002      0.0                  32\n",
       "12  transmission          fuel    46845.218990      0.0                   7\n",
       "13  transmission         doors    30123.217817      0.0                   4\n",
       "14          fuel         doors    33016.210586      0.0                  28"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chi2 independence tests have been calculated for categorical variables\n",
    "\n",
    "# In hindsight, with the domain knowldge that I have, I might be able to just state that I know these categorical\n",
    "# variables to be independent of each other. \n",
    "\n",
    "# Great link on chi2 test for independence https://www.jmp.com/en_be/statistics-knowledge-portal/chi-square-test/chi-square-test-of-independence.html\n",
    "\n",
    "results=[]\n",
    "categoricals = ['drivertrain','make','body','transmission','fuel','doors']\n",
    "for x in itertools.combinations(('drivertrain','make','body','transmission','fuel','doors'), 2):\n",
    "    categoricalv1 = x[0]\n",
    "    categoricalv2 = x[1]\n",
    "    cross_tab = pd.crosstab(df[categoricalv1], df[categoricalv2])\n",
    "    chi2 = stats.chi2_contingency(cross_tab)\n",
    "    results.append([x[0],x[1],chi2[0],round(chi2[1],4),chi2[2]])\n",
    "    chi2df = pd.DataFrame(results, columns = ['categorical1','categorical2','chi2_statistic','p-value','degrees_of_freedom'])  \n",
    "    \n",
    "chi2df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "623fa10f",
   "metadata": {},
   "source": [
    "I'll have to admit that this is hard work for me. I don't use statistics and hypothesis testing frequently enough to be completely comfortable with it, but I'm aware that it can be done, how it can be done and that it can be valuable.\n",
    "\n",
    "The requirements for a valid chi2 independence test are :\n",
    "* *Data values that are a simple random sample from the population of interest.*\n",
    "* *Two categorical or nominal variables. Don't use the independence test with continuous variables that define the category combinations. However, the counts for the combinations of the two categorical variables will be continuous.*\n",
    "* *For each combination of the levels of the two variables, we need at least five expected values. When we have fewer than five for any one combination, the test results are not reliable.*\n",
    "\n",
    "These requirements have been met in this circumstance so we can use the test to determine independence of the categorical variables within this project.\n",
    "\n",
    "For each row in the dataframe above, I understand the null hypothesis to be that 'categorical1 and categorical2 are unrelated'. With the returned p-value of 0.0 each time, I am able to accept the null hypothesis with great confidence, i.e. the categorical variables are indeed unrelated. Hooray!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bef0435",
   "metadata": {},
   "source": [
    "You may have noticed that I've only computed chi<sup>2</sup> stats for a handful of categorical variables. \n",
    "\n",
    "These are the categorical variables that I intend to use in the modelling stage of this project: `fuel`,`doors`,`transmission`,`body`,`make`,`drivetrain`. \n",
    "\n",
    "Below is a list of the additional variables in my dataframe, both continuous and categorical, which will not be used in the fist modelling phase, and the associated reason as to why I've chosen not to use them.\n",
    "\n",
    "* `name` - 1206 unique elements in the name variable but it would require extensive cleaning and is likely captured by other more measurable statistics.\n",
    "* `name_subtitle` - Free form text box which has been used extensively in data cleaning, now I have all the good information from this unstructured field\n",
    "* `mpg` - This measurement doesn't translate from conventional to electric vehicles and so has been rejected\n",
    "* `engine` - This measurement doesn't translate from conventional to electric vehicles and so has been rejected\n",
    "* `owners` - Not a reliably entered field on AutoTrader.co.uk, better to leave it out\n",
    "* `ULEZ` - Again, this field is not fully populated and I have little confidence in it as ULEZ is not important to a car dealer in Glasgow (Yet!).\n",
    "* `dealer_area` - This has been scraped together from the dealer href below. It's not used in the first version of modelling but after some extensive geocoding (see Notebook 3/5) it has been used to obtain Dealer County. This will feature in future work.\n",
    "* `dealer_city` - This has been scraped together from the dealer href below. It's not used in the first version of modelling but after some extensive geocoding (see Notebook 3/5) it has been used to obtain Dealer County. This will feature in future work.\n",
    "* `seller1` - Dealer rating was not populated enough to be useful. \n",
    "* `e_engine_kW` - This measurement doesn't translate from conventional to electric vehicles and so has been rejected\n",
    "* `id` - Just a unique identifier from AutoTrader. Not useful in modelling.\n",
    "* `year_reg` - The information is captured in the year variable.\n",
    "* `link` - Main purpose is for me to check car listings against the website if they are still on there. Not useful for modelling.\n",
    "* `href` - This contained the dealer city/area as discussed above. Not useful in modelling."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5955eec6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-08T20:01:18.262275Z",
     "start_time": "2022-03-08T20:01:17.245631Z"
    },
    "code_folding": []
   },
   "source": [
    "I think that concludes my distribution and collinearity evaluation. I've confirmed that there is not substantial multicollinearity between the continuous variables, and I've confirmed that there is no substantial multicollinearity between the categorical variables. \n",
    "\n",
    "I'm aware that for completeness that I should confirm that there's no multicollinearity between the continuous and categorical variables, but that becomes messy. \n",
    "* I've looked into the *Point Biserial* correlation, but opted not to pursue this because only one of my categorical variables (Transmission) is dichotomous. \n",
    "* The alternative to the *Point Biserial* correlation is to look at implementing Logistic Regression to understand if there is a relationship between the categorical and the continuous variable. I simply have too many continuous/categorical variables to implement this in my project time frame and so have opted not to. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fe0405f",
   "metadata": {},
   "source": [
    "This concludes Notebook 4 of 5. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "used_cars_env",
   "language": "python",
   "name": "used_cars_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "377.969px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  },
  "toc-autonumbering": true,
  "toc-showtags": false
 },
 "nbformat": 4,
 "nbformat_minor": 5
}