Scott Routledge
Scott is a Software Engineer at Bodo.ai, where he has worked on the performance and reliability of the BodoSQL engine, contributed to the Bodo Just-In-Time Python Compiler, and is currently working on Bodo DataFrames. He earned his undergraduated in computer science from Carnegie Mellon University.
Session
Pandas is a popular library for data scientists but it struggles with large datasets; programs either become too slow or run out of memory. In this talk, we introduce Bodo DataFrames (https://github.com/bodo-ai/Bodo) as a drop-in replacement for the Pandas library that uses high performance computing (HPC) based techniques such as Message Passing Interface (MPI) and JIT compilation for acceleration and scaling. We give an overview of its architecture and explain how it avoids the problems of Pandas (while keeping user code the same), go over concrete examples, and finally discuss current limitations. This talk is for Pandas users who would like to run their code on larger data while avoiding frustrating code rewrites to other APIs. Basic knowledge of Pandas and Python is recommended.