Scraping API Rate Limits: Discovering Undocumented Constraints with Python

Every API has rate limits. Some document them clearly. Most don't. Whether you're building an integration, stress-testing your own service, or researching API behavior, knowing the actual rate limits saves you from mysterious 429 errors and silent throttling. This tutorial shows you how to systematically discover and document API rate limits using Python. Why Rate Limits Matter Undocumented rate limits cause real problems: Your production integration breaks at 2 AM because you hit an unknown threshold Your scraper gets silently throttled, returning stale cached data instead of errors Your competitor's API wrapper works better because they reverse-engineered the limits The Rate Limit Discovery Framework pip install requests aiohttp pandas import requests import time import json from datetime import datetime from dataclasses import dataclass , field @dataclass class RateLimitResult : endpoint : str requests_sent : int first_rejection_at : int | None rejection_code : int | None headers_fo