General Web Scraping

What property data can I scrape from Zoopla.co.uk using Python?

Posted by Danijel Niobe on 12/21/2024 at 8:09 am

Scraping property data from Zoopla.co.uk using Python allows for the collection of details such as property names, prices, and locations. Zoopla is a prominent property website in the UK, offering a wealth of data for real estate market analysis. By using Python’s capabilities, you can automate the process of gathering data efficiently. The first step involves inspecting the site’s HTML to identify elements containing property details, such as names, prices, and descriptions. Pagination handling ensures that all listings across multiple pages are captured.
Python allows you to send HTTP requests to Zoopla’s pages and parse the HTML to extract relevant details. Adding random delays between requests can mimic human browsing behavior and reduce detection risks. Once collected, storing the data in structured formats such as CSV or JSON enables easier analysis. Below is an example script for scraping Zoopla.

import requests
from html.parser import HTMLParser
class ZooplaParser(HTMLParser):
    def __init__(self):
        super().__init__()
        self.in_property_name = False
        self.in_property_price = False
        self.properties = []
        self.current_property = {}
    def handle_starttag(self, tag, attrs):
        attrs = dict(attrs)
        if tag == "h2" and "class" in attrs and "property-name" in attrs["class"]:
            self.in_property_name = True
        if tag == "span" and "class" in attrs and "property-price" in attrs["class"]:
            self.in_property_price = True
    def handle_endtag(self, tag):
        if self.in_property_name and tag == "h2":
            self.in_property_name = False
        if self.in_property_price and tag == "span":
            self.in_property_price = False
    def handle_data(self, data):
        if self.in_property_name:
            self.current_property["name"] = data.strip()
        if self.in_property_price:
            self.current_property["price"] = data.strip()
            self.properties.append(self.current_property)
            self.current_property = {}
url = "https://www.zoopla.co.uk/"
response = requests.get(url)
parser = ZooplaParser()
parser.feed(response.text)
for property in parser.properties:
    print(f"Property: {property['name']}, Price: {property['price']}")

This script extracts property names and prices from Zoopla’s property listing pages. Pagination logic ensures data is collected across all pages. Adding random delays between requests ensures smoother operations and reduces detection risks.

Danijel Niobe replied 1 day, 5 hours ago 1 Member · 0 Replies

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