Polars versus pandas in 2025: the real-world practice

Polars runs 3 to 10 times faster than pandas on aggregations, joins, and filters over parquet datasets of 1 to 20 GB, and holds 40 to 60 percent less memory thanks to native Arrow columns. With the 1.x API frozen and Arrow interoperability, both libraries can coexist in one pipeline.

June 17, 2025 6 min 359 4.3

Architecture

DuckDB: Fast Analytics Without Moving Data

DuckDB is an embedded, columnar, vectorized SQL engine that runs inside your own process and queries Parquet, CSV, JSON, S3, and URLs in place. For DuckDB analytics up to a hundred gigabytes it beats pandas on speed and a cloud warehouse on friction cost. It is not a transactional engine. Version 1.0 landed in June 2024.

November 1, 2024 6 min 205 4.4