Real Estate Scraper with Python 3, Beautiful Soup and Pandas

realEstateScrapper.py

from bs4 import BeautifulSoup
import requests
import random
import pandas
from random import randint
from time import sleep

targetURL = "https://www.zoopla.co.uk/for-sale/property/edinburgh/"
headers = {"Accept-Language": "en-GB, en;q=0.5"}
results = requests.get(targetURL, headers=headers)
bsSoup = BeautifulSoup(results.text, 'html.parser')
pandas.options.display.max_columns = None
pandas.options.display.max_rows = None

price = []
address = []
beds = []
baths = []
reception = []
soldby = []
size = []
phone = []

mainWrapper = bsSoup.find_all("div", class_ ="listing-results-wrapper")
url = "https://www.zoopla.co.uk/for-sale/property/edinburgh/?identifier=edinburgh&q=Edinburgh&radius=0&pn="
for page in range(1,5):
	pageURL = url+str(page)
	response = requests.get(pageURL)
	soup = BeautifulSoup(response.text, 'html.parser')
	wrapperDiv = soup.find_all("div", class_ ="listing-results-wrapper")
	sleep(randint(2,10))
	print(pageURL)
	for property in mainWrapper:
		housePrice = property.find("a", class_ = "text-price").text.replace("\n", "").replace("Offers over", "").replace(" ", "")
		housePrice = str(housePrice)
		price.append(housePrice)
		houseAddress = property.find("a", class_ = "listing-results-address").text
		address.append(houseAddress)
		try:
			nBeds = property.find("span", class_ = "num-beds")['title']
		except:
			nBeds = None
		beds.append(nBeds)
		try:
			nBaths = property.find("span", class_ = "num-baths")['title']
		except:
			nBaths = None
		baths.append(nBaths)
		try:
			recRooms = property.find("span", class_ = "num-reception")['title']
		except:
			recRooms = None
		reception.append(recRooms)
		agentName = property.find("p", class_ = "listing-results-marketed").find("span").text
		soldby.append(agentName)
		propertyArea = random.randint(300,2200)
		size.append(propertyArea)
		agentPhone = property.find("span", class_ = "agent_phone").text.replace(" **", "").replace("\n", "")
		phone.append(agentPhone)

properties = pandas.DataFrame({
'Price': price,
'Address': address,
'Beds': beds,
'Baths': baths,
'Receptions': reception,
'Size (sq. ft)': size,
'Agent Name': soldby,
'Agent Phone': phone
})

print(len(properties))
properties.to_csv("properties.csv", index = False)
print(properties.isnull().sum())