Pak Wheels Car Price Calculator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 597,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "from urllib.request import Request, urlopen\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import time\n",
    "from datetime import datetime\n",
    "import itertools\n",
    "import statistics\n",
    "from datetime import date\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 598,
   "metadata": {},
   "outputs": [],
   "source": [
    "car=\"civic\"\n",
    "year_from='2018'\n",
    "year_to='2019'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 599,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "Url='https://www.pakwheels.com/used-cars/search/-/ct_karachi/rg_karachi/yr_'+year_from+'_'+year_to+'/leased_0/?page='+str(1000)+'+&q='+car+'&sortby=price%20asc'\n",
    "req = Request(Url, headers={'User-Agent': 'Mozilla/5.0'})\n",
    "webpage = urlopen(req).read()\n",
    "soup = BeautifulSoup(webpage, 'html')\n",
    "pages=int(soup.find('li', {'class':['page active']}).find('a').get_text())\n",
    "print(pages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 600,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ads_List=[]\n",
    "Ads_Dict = ['name','price','vehicleModelDate','mileageFromOdometer_km','fuelType','vehicleEngine','vehicleTransmission']\n",
    "Ads_Dict_list=[]\n",
    "Rate_List=[]\n",
    "page=1\n",
    "while page<=pages:\n",
    "    Url='https://www.pakwheels.com/used-cars/search/-/ct_karachi/rg_karachi/yr_'+year_from+'_'+year_to+'/leased_0/?page='+str(page)+'+&q='+car+'&sortby=price%20asc'\n",
    "    req = Request(Url, headers={'User-Agent': 'Mozilla/5.0'})\n",
    "    webpage = urlopen(req).read()\n",
    "    soup = BeautifulSoup(webpage, 'html')\n",
    "    len([Ads_List.append(x) for x in soup.find_all('div', {'class':['col-md-9 grid-style']})])\n",
    "    page+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 601,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://www.pakwheels.com/used-cars/search/-/ct_karachi/rg_karachi/yr_2018_2019/leased_0/?page=2+&q=civic&sortby=price%20asc'"
      ]
     },
     "execution_count": 601,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "for ad in Ads_List:\n",
    "    if ad.find('meta', {'itemprop':['price']}) is None:\n",
    "        print(ad.find('meta', {'itemprop':['price']}) is None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    for idx,ad in enumerate(Ads_List):\n",
    "        temp_dict={key: None for key in Ads_Dict}\n",
    "        temp_dict['name']=ad.find('meta', {'itemprop':['name']})['content']\n",
    "        if ad.find('meta', {'itemprop':['price']}) is not None:\n",
    "            temp_dict['price']=ad.find('meta', {'itemprop':['price']})['content']\n",
    "        else:\n",
    "            temp_dict['price']=\"Call\"\n",
    "        temp_dict['vehicleModelDate']=ad.find('li', {'itemprop':[\"vehicleModelDate\"]}).get_text()\n",
    "        temp_dict['mileageFromOdometer_km']=int(ad.find('li', {'itemprop':[\"mileageFromOdometer\"]}).get_text().replace(\" km\",\"\").replace(\",\",\"\"))        \n",
    "        temp_dict['fuelType']=ad.find('li', {'itemprop':[\"fuelType\"]}).get_text()\n",
    "        temp_dict['vehicleEngine']=ad.find('li', {'itemprop':[\"vehicleEngine\"]}).get_text()\n",
    "        temp_dict['vehicleTransmission']=ad.find('li', {'itemprop':[\"vehicleTransmission\"]}).get_text()\n",
    "        Ads_Dict_list.append(temp_dict)\n",
    "except:\n",
    "    print(\"Exception Caught\")\n",
    "# for ad in Ads_Dict_list:\n",
    "#     for header in Ads_Dict: \n",
    "#         print(ad[header])\n",
    "Rate_List=[int(x['price']) for x in Ads_Dict_list if x['price']!=\"Call\"]\n",
    "print(len(Rate_List))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fuelType</th>\n",
       "      <th>mileageFromOdometer_km</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "      <th>vehicleEngine</th>\n",
       "      <th>vehicleModelDate</th>\n",
       "      <th>vehicleTransmission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>14000</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>2975000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>6500</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3075000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>9000</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3075000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>10000</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3150000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>6500</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3175000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>10000</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3200000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>9625</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3240000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>14000</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3250000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>19000</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3250000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>30</td>\n",
       "      <td>Honda Civic  2019 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3250000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2019</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>5000</td>\n",
       "      <td>Honda Civic  2018 1.8 i-VTEC CVT</td>\n",
       "      <td>3250000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>4600</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3250000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>15500</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3250000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>16820</td>\n",
       "      <td>Honda Civic  2018</td>\n",
       "      <td>3300000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>4000</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3300000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>2500</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3300000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>5000</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3300000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>7650</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3325000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>4500</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3350000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>4000</td>\n",
       "      <td>Honda Civic  2019 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3375000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2019</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>2400</td>\n",
       "      <td>Honda Civic  2019 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3430000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2019</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>550</td>\n",
       "      <td>Honda Civic  2019 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3445000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2019</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>1205</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3470000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>4000</td>\n",
       "      <td>Honda Civic  2019 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3500000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2019</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>3500</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3560000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>100</td>\n",
       "      <td>Honda Civic  2019 1.5 RS Turbo</td>\n",
       "      <td>3700000</td>\n",
       "      <td>1500 cc</td>\n",
       "      <td>2019</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>21000</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>Call</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>15000</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>Call</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>5500</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>2985000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>4400</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3095000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>29700</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3100000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>19000</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3200000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>21000</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3200000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>1500</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3240000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>9000</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3250000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>6500</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3300000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>4000</td>\n",
       "      <td>Honda Civic  2019 VTi Oriel Prosmatec 1.8 i-VTEC</td>\n",
       "      <td>3400000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2019</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>400</td>\n",
       "      <td>Honda Civic  2019 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3450000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2019</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>1</td>\n",
       "      <td>Honda Civic  2019 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>3800000</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2019</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>10</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>Call</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>19000</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>Call</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Petrol</td>\n",
       "      <td>9000</td>\n",
       "      <td>Honda Civic  2018 Oriel 1.8 i-VTEC CVT</td>\n",
       "      <td>Call</td>\n",
       "      <td>1800 cc</td>\n",
       "      <td>2018</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fuelType  mileageFromOdometer_km  \\\n",
       "0    Petrol                   14000   \n",
       "1    Petrol                    6500   \n",
       "2    Petrol                    9000   \n",
       "3    Petrol                   10000   \n",
       "4    Petrol                    6500   \n",
       "5    Petrol                   10000   \n",
       "6    Petrol                    9625   \n",
       "7    Petrol                   14000   \n",
       "8    Petrol                   19000   \n",
       "9    Petrol                      30   \n",
       "10   Petrol                    5000   \n",
       "11   Petrol                    4600   \n",
       "12   Petrol                   15500   \n",
       "13   Petrol                   16820   \n",
       "14   Petrol                    4000   \n",
       "15   Petrol                    2500   \n",
       "16   Petrol                    5000   \n",
       "17   Petrol                    7650   \n",
       "18   Petrol                    4500   \n",
       "19   Petrol                    4000   \n",
       "20   Petrol                    2400   \n",
       "21   Petrol                     550   \n",
       "22   Petrol                    1205   \n",
       "23   Petrol                    4000   \n",
       "24   Petrol                    3500   \n",
       "25   Petrol                     100   \n",
       "26   Petrol                   21000   \n",
       "27   Petrol                   15000   \n",
       "28   Petrol                    5500   \n",
       "29   Petrol                    4400   \n",
       "30   Petrol                   29700   \n",
       "31   Petrol                   19000   \n",
       "32   Petrol                   21000   \n",
       "33   Petrol                    1500   \n",
       "34   Petrol                    9000   \n",
       "35   Petrol                    6500   \n",
       "36   Petrol                    4000   \n",
       "37   Petrol                     400   \n",
       "38   Petrol                       1   \n",
       "39   Petrol                      10   \n",
       "40   Petrol                   19000   \n",
       "41   Petrol                    9000   \n",
       "\n",
       "                                                name    price vehicleEngine  \\\n",
       "0             Honda Civic  2018 Oriel 1.8 i-VTEC CVT  2975000       1800 cc   \n",
       "1             Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3075000       1800 cc   \n",
       "2             Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3075000       1800 cc   \n",
       "3             Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3150000       1800 cc   \n",
       "4             Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3175000       1800 cc   \n",
       "5             Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3200000       1800 cc   \n",
       "6             Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3240000       1800 cc   \n",
       "7             Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3250000       1800 cc   \n",
       "8             Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3250000       1800 cc   \n",
       "9             Honda Civic  2019 Oriel 1.8 i-VTEC CVT  3250000       1800 cc   \n",
       "10                  Honda Civic  2018 1.8 i-VTEC CVT  3250000       1800 cc   \n",
       "11            Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3250000       1800 cc   \n",
       "12            Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3250000       1800 cc   \n",
       "13                                Honda Civic  2018   3300000       1800 cc   \n",
       "14            Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3300000       1800 cc   \n",
       "15            Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3300000       1800 cc   \n",
       "16            Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3300000       1800 cc   \n",
       "17            Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3325000       1800 cc   \n",
       "18            Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3350000       1800 cc   \n",
       "19            Honda Civic  2019 Oriel 1.8 i-VTEC CVT  3375000       1800 cc   \n",
       "20            Honda Civic  2019 Oriel 1.8 i-VTEC CVT  3430000       1800 cc   \n",
       "21            Honda Civic  2019 Oriel 1.8 i-VTEC CVT  3445000       1800 cc   \n",
       "22            Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3470000       1800 cc   \n",
       "23            Honda Civic  2019 Oriel 1.8 i-VTEC CVT  3500000       1800 cc   \n",
       "24            Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3560000       1800 cc   \n",
       "25                    Honda Civic  2019 1.5 RS Turbo  3700000       1500 cc   \n",
       "26            Honda Civic  2018 Oriel 1.8 i-VTEC CVT     Call       1800 cc   \n",
       "27            Honda Civic  2018 Oriel 1.8 i-VTEC CVT     Call       1800 cc   \n",
       "28            Honda Civic  2018 Oriel 1.8 i-VTEC CVT  2985000       1800 cc   \n",
       "29            Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3095000       1800 cc   \n",
       "30            Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3100000       1800 cc   \n",
       "31            Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3200000       1800 cc   \n",
       "32            Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3200000       1800 cc   \n",
       "33            Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3240000       1800 cc   \n",
       "34            Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3250000       1800 cc   \n",
       "35            Honda Civic  2018 Oriel 1.8 i-VTEC CVT  3300000       1800 cc   \n",
       "36  Honda Civic  2019 VTi Oriel Prosmatec 1.8 i-VTEC  3400000       1800 cc   \n",
       "37            Honda Civic  2019 Oriel 1.8 i-VTEC CVT  3450000       1800 cc   \n",
       "38            Honda Civic  2019 Oriel 1.8 i-VTEC CVT  3800000       1800 cc   \n",
       "39            Honda Civic  2018 Oriel 1.8 i-VTEC CVT     Call       1800 cc   \n",
       "40            Honda Civic  2018 Oriel 1.8 i-VTEC CVT     Call       1800 cc   \n",
       "41            Honda Civic  2018 Oriel 1.8 i-VTEC CVT     Call       1800 cc   \n",
       "\n",
       "   vehicleModelDate vehicleTransmission  \n",
       "0              2018           Automatic  \n",
       "1              2018           Automatic  \n",
       "2              2018           Automatic  \n",
       "3              2018           Automatic  \n",
       "4              2018           Automatic  \n",
       "5              2018           Automatic  \n",
       "6              2018           Automatic  \n",
       "7              2018           Automatic  \n",
       "8              2018           Automatic  \n",
       "9              2019           Automatic  \n",
       "10             2018           Automatic  \n",
       "11             2018           Automatic  \n",
       "12             2018           Automatic  \n",
       "13             2018           Automatic  \n",
       "14             2018           Automatic  \n",
       "15             2018           Automatic  \n",
       "16             2018           Automatic  \n",
       "17             2018           Automatic  \n",
       "18             2018           Automatic  \n",
       "19             2019           Automatic  \n",
       "20             2019           Automatic  \n",
       "21             2019           Automatic  \n",
       "22             2018           Automatic  \n",
       "23             2019           Automatic  \n",
       "24             2018           Automatic  \n",
       "25             2019           Automatic  \n",
       "26             2018           Automatic  \n",
       "27             2018           Automatic  \n",
       "28             2018           Automatic  \n",
       "29             2018           Automatic  \n",
       "30             2018           Automatic  \n",
       "31             2018           Automatic  \n",
       "32             2018           Automatic  \n",
       "33             2018           Automatic  \n",
       "34             2018           Automatic  \n",
       "35             2018           Automatic  \n",
       "36             2019           Automatic  \n",
       "37             2019           Automatic  \n",
       "38             2019           Automatic  \n",
       "39             2018           Automatic  \n",
       "40             2018           Automatic  \n",
       "41             2018           Automatic  "
      ]
     },
     "execution_count": 604,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ads_df=pd.DataFrame(Ads_Dict_list)\n",
    "Ads_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "bin_size=100000\n",
    "\n",
    "# An \"interface\" to matplotlib.axes.Axes.hist() method\n",
    "n, bins, patches = plt.hist(x=Rate_List, bins=range(min(Rate_List),max(Rate_List),+bin_size), color='#3cb371',rwidth=.9,joinstyle='bevel')\n",
    "plt.grid(axis='y')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.box(False)\n",
    "labels=n\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(20,8)\n",
    "\n",
    "for rect, label in zip(patches, n):\n",
    "    height = rect.get_height()\n",
    "    plt.text(rect.get_x() + rect.get_width() / 2, height, str(int(label))+' ('+str(round(int(label)/len(Rate_List),2)*100)+')%',fontsize=15,\n",
    "            ha='center', va='bottom')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 617,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.transforms as mtransforms\n",
    "from matplotlib.patches import FancyBboxPatch\n",
    "\n",
    "#CALCULATING STATISTICS\n",
    "\n",
    "Plots=len(Rate_List)\n",
    "Average_Price=statistics.mean(Rate_List)/100000\n",
    "Median_Price=statistics.median(Rate_List)/100000\n",
    "Min_Price=min(Rate_List)/100000\n",
    "Max_Price=max(Rate_List)/100000\n",
    "popular_price_range=str(bins[n.argmax()]/100000)+\"L -- \"+str((bins[n.argmax()]+bin_size)/100000)+\"L\"\n",
    "popular_price_range_perc=\"(\"+str(round(int(n.max())/len(Rate_List),2)*100)+\")%\"\n",
    "\n",
    "#PLOTING KPIS\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2,ncols=3, figsize=(20,6))\n",
    "\n",
    "width=3\n",
    "height=0.8\n",
    "\n",
    "from PIL import Image\n",
    "img = Image.open('car_2.png')\n",
    "img = img.resize((1500,400), Image.ANTIALIAS)\n",
    "axes[0][0].imshow(img)\n",
    "axes[0][0].text(0,600,'Displaying '+str(len(Rate_List))+\" \"+car.capitalize()+' Cars : Model '+str(year_from)+\" -- \"+str(year_to) ,size=14, color = \"#000000\")\n",
    "axes[0][0].hlines(y =500 , xmin=0, xmax=1500, linewidth=5, color = \"#000000\")\n",
    "axes[0][0].axis('off')\n",
    "# axes[0][0].ylim([-1,1])\n",
    "\n",
    "p_fancy = FancyBboxPatch(xy= (0.1, 0.1), width= width,height= height, boxstyle=\"round,pad=0.05\", fc= \"#FF6F69\", ec= \"#96CEB4\")\n",
    "axes[0][1].add_patch(p_fancy)\n",
    "axes[0][1].text(0.1, 0.4,str(format(round(Average_Price,2)))+\" Lac\",size=40, color = \"#ffffff\")\n",
    "axes[0][1].text(0.1, 0.75, \"Average Price\",size=20, color = \"#ffffff\")\n",
    "axes[0][1].text(0.1, 0.15, \"Median Price: \"+str(format(round(Median_Price,2)))+\" Lac\",size=15, color = \"#ffffff\")\n",
    "axes[0][1].hlines(y = 0.3, xmin=0.1, xmax=width, linewidth=5, color = \"#ffffff\")\n",
    "axes[0][1].axis('off')\n",
    "\n",
    "# p_fancy = FancyBboxPatch(xy= (0.1, 0.1), width= width,height= height, boxstyle=\"round,pad=0.05\", fc= \"#3cb371\", ec= \"#96CEB4\")\n",
    "# axes[0][2].add_patch(p_fancy)\n",
    "axes[0][2].boxplot([x/100000 for x in Rate_List],patch_artist=True,vert=False,notch=True)\n",
    "axes[0][2].set_title('Prices in Lacs')\n",
    "# axes[0][2].ylabel('Price in Lacs')\n",
    "\n",
    "\n",
    "p_fancy = FancyBboxPatch(xy= (0.1, 0.1), width= width,height= height, boxstyle=\"round,pad=0.05\", fc= \"#ffa500\", ec= \"#96CEB4\")\n",
    "axes[1][0].add_patch(p_fancy)\n",
    "axes[1][0].text(0.1, 0.4,str(format(round(Min_Price,2)))+\" Lac\",size=40, color = \"#ffffff\")\n",
    "axes[1][0].text(0.1, 0.75, \"Min Price\",size=20, color = \"#ffffff\")\n",
    "axes[1][0].text(0.1, 0.15,\"As at \"+str(datetime.now().strftime('%Y-%m-%d %H:%M:%S')) ,size=12, color = \"#ffffff\")\n",
    "axes[1][0].hlines(y = 0.3, xmin=0.1, xmax=width, linewidth=10, color = \"#ffffff\")\n",
    "axes[1][0].axis('off')\n",
    "\n",
    "p_fancy = FancyBboxPatch(xy= (0.1, 0.1),width= width,height= height, boxstyle=\"round,pad=0.05\", fc= \"#6a5acd\", ec= \"#96CEB4\")\n",
    "axes[1][1].add_patch(p_fancy)\n",
    "axes[1][1].text(0.1, 0.4,str(format(round(Max_Price,2)))+\" Lac\",size=40, color = \"#ffffff\")\n",
    "axes[1][1].text(0.1, 0.75, \"Max Price\",size=20, color = \"#ffffff\")\n",
    "axes[1][1].text(0.1, 0.15,\"As at \"+str(datetime.now().strftime('%Y-%m-%d %H:%M:%S')) ,size=12, color = \"#ffffff\")\n",
    "axes[1][1].hlines(y = 0.3, xmin=0.1, xmax=width, linewidth=5, color = \"#ffffff\")\n",
    "axes[1][1].axis('off')\n",
    "\n",
    "p_fancy = FancyBboxPatch(xy= (0.1, 0.1), width= width,height= height, boxstyle=\"round,pad=0.05\", fc= \"#000000\", ec= \"#96CEB4\")\n",
    "axes[1][2].add_patch(p_fancy)\n",
    "axes[1][2].text(0.1, 0.4,popular_price_range,size=30, color = \"#ffffff\")\n",
    "axes[1][2].text(0.1, 0.75, \"Popular Price Range\",size=20, color = \"#ffffff\")\n",
    "axes[1][2].text(0.1, 0.15,\"Representing \"+popular_price_range_perc+\" of the Cars\" ,size=12, color = \"#ffffff\")\n",
    "axes[1][2].hlines(y = 0.3, xmin=0.1, xmax=width, linewidth=5, color = \"#ffffff\")\n",
    "axes[1][2].axis('off')\n",
    "\n",
    "# for ax in axes:\n",
    "#     ax.xaxis.set_visible(False)\n",
    "#     ax.yaxis.set_visible(False)\n",
    "    \n",
    "#     for pos in ['top','right','left','bottom']:\n",
    "#         ax.spines[pos].set_visible(False)\n",
    "\n",
    "fig.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}