Most Python scripts in data science are synchronous — fetching one record at a time, waiting for APIs, or slowly scraping websites. In this talk, we’ll introduce Python’s asyncio ecosystem and show how it transforms IO - heavy data workflows. You'll see how httpx , aiofiles , and async constructs speed up tasks like web scraping and batch API calls. We’ll compare async vs threading, walk through a real - world case study, and wrap with performance benchmarks that demonstrate async's value.

Async Python for Data Science: Speeding Up IO - Bound Workflows\nMost Python scripts in data science are synchronous — fetching one record at a time, waiting for APIs, or slowly scraping websites. In this talk, we’ll introduce Python’s asyncio ecosystem and show how it transforms IO - heavy data workflows. You'll see how httpx , aiofiles , and async constructs speed up tasks like web scraping and batch API calls. We’ll compare async vs threading, walk through a real - world case study, and wrap with performance benchmarks that demonstrate async's value.\nKeywords: p ython 3.x , AsyncIO, Web Scraping, API, Concurrency, Performance, Optimization

Most Python scripts in data science are synchronous — fetching one record at a time, waiting for APIs, or slowly scraping websites. In this talk, we’ll introduce Python’s asyncio ecosystem and show how it transforms IO-heavy data workflows. You'll see how httpx, aiofiles, and async constructs speed up tasks like web scraping and batch API calls. We’ll compare async vs threading, walk through a real-world case study, and wrap with performance benchmarks that demonstrate async's value.