Immowelt is a popular real estate platform where users can find listings for properties to rent or buy in various countries, primarily in Germany. While scraping Immowelt or similar websites, there are some common pitfalls that developers might encounter. Here are some mistakes to avoid:
Not Checking the Website’s Terms of Service: Before you start scraping, you should always review the website's terms of service or robots.txt file. Scraping data contrary to the terms of service can lead to legal issues or your IP being banned.
Ignoring the Robots.txt File: The robots.txt file is intended to provide guidance to web crawlers regarding which parts of the website should not be accessed. It's a good practice to respect these rules to avoid potential legal issues.
Overloading the Server: Sending too many requests in a short period can overload the server and negatively affect the website's performance. This could lead to your IP address being blocked. Implement rate limiting and use sleep intervals between your requests.
Not Handling Pagination Properly: Many listings are spread across multiple pages. Make sure to handle pagination correctly so that you can navigate through all the pages to scrape the complete data set.
Failing to Handle JavaScript-Rendered Content: If Immowelt uses JavaScript to render content dynamically, you might need to use tools like Selenium or Puppeteer that can execute JavaScript to access the content.
Ignoring Data Structure Changes: Websites often change their structure. Your scraper might stop working if it's not designed to handle changes or if you're not regularly maintaining it.
Not Managing User Agents and Headers: Websites might block requests that don't appear to come from a browser. Use legitimate user agents and proper headers to make your requests look more authentic.
Hardcoding Data Extraction Paths: Avoid using absolute paths when extracting data from the HTML document. If the website's structure changes, your scraper will break. Use relative XPaths or CSS selectors and check for unique and stable attributes.
Scraping Sensitive Information: Avoid scraping personal data or sensitive information unless you have explicit permission to do so, and always comply with data protection laws such as GDPR.
Not Using Proxies for IP Rotation: If you scrape at scale, use a rotation of proxies to prevent IP bans and mimic human behavior more closely.
Not Handling Errors and Exceptions: Your code should be robust enough to handle network issues, HTTP errors, and other exceptions that might occur during the scraping process.
Storing Data Improperly: Ensure that you have a structured way to store the scraped data, whether it's in a CSV, database, or other formats. Also, make sure to sanitize and validate the data to maintain its quality.
To avoid some of these mistakes, here's an example of careful scraping in Python using requests and BeautifulSoup, which includes handling headers and sleeping between requests (note that this is a simple example and might need adjustments for JavaScript-rendered content):
import requests
from bs4 import BeautifulSoup
import time
import random
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}
url = 'https://www.immowelt.de/liste/'
page = 1
while True:
full_url = f"{url}{page}"
response = requests.get(full_url, headers=headers)
# Check if the request was successful
if response.status_code != 200:
break
soup = BeautifulSoup(response.text, 'html.parser')
# Add logic to parse and save your data
# Random sleep to mimic human behavior
time.sleep(random.uniform(1, 5))
page += 1
Remember to adapt your scraper if you need to handle JavaScript, as you might need tools like Selenium, or if you're scraping in a language other than Python. Always scrape responsibly and ethically.
