Allen Y
I’m Allen, Associate Software Engineer at Red Hat, focused on building scalable, high-performance backend systems using Python, GraphQL, Kubernetes, and cloud native tooling. I work across distributed systems, payments, event-driven pipelines, and async architectures with experience in GCP, AWS, PostgreSQL, and MongoDB.
I’m an active speaker in the Python community, with talks delivered at
PyCascades 2025 (Portland, USA) — Unlocking Concurrency in Python with AsyncIO and ASGI
FOSSASIA Summit 2025 (Bangkok, Thailand) — Kubernetes Kung Fu: Mastering Containers and Microservices
PyCon India 2025 (Banglore, India) — From Stress to Success: Load Testing Python Apps & Visualizing Performance
Session
Pandas is one of the most widely used tools in Python, yet many developers unintentionally write slow or memory heavy DataFrame code. This talk covers practical performance techniques that can significantly speed up Pandas workflows: vectorization, avoiding apply, optimizing data types, reducing memory usage, minimizing DataFrame copies, improving joins and groupbys, and using chunked loading for large files. We also look at when to extend Pandas with Polars, Apache Arrow, or DuckDB for faster execution. If you work with data at any scale, this session gives you simple, actionable tricks to make your Pandas pipelines faster and more production ready.