BeautifulSoup-scrape

import pandas as pd
from bs4 import BeautifulSoup
from urllib.request import urlopen
def car_scraping(pages):
  global Price
  Make,Model,Rest,Mileage,Price,Price_drop,Dealer,Rating,Used,Year=[],[],[],[],[],[],[],[],[],[]
  for page in range(pages):
    url=f"https://www.cars.com/shopping/results/?page={page}&page_size=20&dealer_id=&list_price_max=&list_price_min=&makes[]=&maximum_distance=20&mileage_max=&sort=best_match_desc&stock_type=all&year_max=&year_min=&zip="
    html=urlopen(url)
    soup=BeautifulSoup(html,"html.parser")
    for i in soup.findAll("div",{"class":"vehicle-card-main"}):
      # print(i.findAll("p",{"class":"stock-type"})[0].text)
      usage=i.findAll("p",{"class":"stock-type"})[0].text
      # print(i.findAll("h2",{"class":"title"})[0].text.split()[0])
      year=i.findAll("h2",{"class":"title"})[0].text.split()[0]
      car=i.findAll("h2",{"class":"title"})[0].text.split()[1:]
      make=car[0]#
      model=car[1]
      price=i.findAll("span",{"class","primary-price"})[0].text[1:]
      mileage=i.findAll("div",{"class","mileage"})[0].get_text().replace("mi.","")
      # print(mileage[0].get_text().replace("mi.",""))
      price_drop=i.findAll("span",{"class":"price-drop"})
      # print(price_drop)
      for y in price_drop:
        drop_price=y.text.replace("price drop","").strip("$")
        
        Price_drop.append(drop_price)
        
      dealer=i.findAll("strong")[0].text
      rating=i.findAll("span",class_="sds-rating__count")
      ratings=([(rate.text) for rate in rating])
      if ratings:
        
        Rating.append(ratings[0])
      else:
        Rating.append(None)
      
      Used.append(usage)
      Year.append(year)
      Make.append(make)
      Model.append(model)
      
      Mileage.append(mileage)
      Price.append(price)
     
      Dealer.append(dealer)
  Mileage=[float(i.replace(",",".")) for i in Mileage]
  cost=[]
  global df
  for i in Price:
    try:
      i=i.replace(",",".")
      i=float(i)
      cost.append(i)
    except:
      i=None
      cost.append(i) 
  Price=cost    
  drop=[]
  for i in Price_drop:
    if i:
      i=i.replace(",","")
      drop.append(int(i))
    else:
      i=0
      drop.append(i)
  Price_drop=drop

  features=Make,Model,Mileage,Price,Price_drop,Dealer,Rating,Used,Year
  
  df=pd.DataFrame(features).T
  df.Price=Price
  df.columns=["Make","Model","Mileage","Price","Price_drop","Dealer","Rating","Used","Year"] 

  return df
  
data=car_scraping(200)#First 200 pages