How to Check Website Status Codes in Python

This script checks the HTTP status codes of multiple URLs concurrently using a thread pool and prints the results.

Easy Python 3.9+ Aug 9, 2026 Automation & scripting 12 views 0 copies

requests threading http-status concurrency monitoring
Requires third-party packages — install first
pip install requests

Python code

23 lines
Python 3.9+ 
import requests
from concurrent.futures import ThreadPoolExecutor

URLS = [
    "https://www.google.com",
    "https://www.python.org",
    "https://www.nonexistent-site-12345.com",
    "https://www.github.com",
]

def check_status(url):
    try:
        response = requests.get(url, timeout=5)
        return url, response.status_code
    except requests.RequestException:
        return url, "ERROR"

with ThreadPoolExecutor(max_workers=4) as executor:
    results = list(executor.map(check_status, URLS))

if __name__ == "__main__":
    for url, status in results:
        print(f"{url} -> {status}")

Output

stdout 
https://www.google.com -> 200
https://www.python.org -> 200
https://www.nonexistent-site-12345.com -> ERROR
https://www.github.com -> 200

How it works

The requests.get call returns a response object whose status_code attribute holds the HTTP status code. A ThreadPoolExecutor runs the check_status function concurrently for each URL, speeding up the process compared to sequential requests. The timeout=5 argument prevents the script from hanging on slow or unresponsive servers. Exceptions like connection errors are caught by requests.RequestException, so a failed request returns the string "ERROR" instead of crashing the script.

Common mistakes

  • Forgetting to set a timeout on the `requests.get` call, which can cause the script to hang.
  • Catching only `ConnectionError` instead of the broader `requests.RequestException`.
  • Not handling the case where the URL list is empty, which would produce an empty result list.
  • Using sequential requests for a large list, making the script unnecessarily slow.

Variations

  1. Use `asyncio` with `aiohttp` for async HTTP requests instead of threads.
  2. Group URLs by status code using a dictionary for easier analysis.

Real-world use cases

  • Monitoring a fleet of internal microservices to detect which ones are unhealthy after a deployment.
  • Running a periodic cron job that alerts the team when any public API endpoint returns 5xx.
  • Batch-checking a list of partner websites for link rot in a content pipeline.

Sponsored

Run locally

This sample needs third-party packages, so it cannot run in the browser IDE. Copy the code above, install the packages shown at the top, then run it in your own Python environment.

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