Paginating and Collecting Large Datasets
Handling pagination, next_cursor patterns, rate limiting with time.sleep, progress bars.
Why Pagination Exists
APIs rarely return millions of records in one response. They split results into pages so each response stays small and fast.
To collect a full dataset you must loop through every page and combine the results. This lesson covers the common pagination styles plus rate limiting and progress tracking.
Page-Number Pagination
The simplest scheme uses a page parameter. You increment it until the API returns an empty page.
import requests
all_items = []
page = 1
while True:
resp = requests.get(url, params={"page": page, "per_page": 100}, timeout=10)
resp.raise_for_status()
items = resp.json()
if not items:
break
all_items.extend(items)
page += 1
print(len(all_items), "items collected")All lessons in this course
- REST API Fundamentals for Data Collection
- Paginating and Collecting Large Datasets
- Storing Collected Data Efficiently
- Working with Public Data APIs