Data Analysis in Python

pandas is the standard Python DataFrame. Nearly every data library accepts it, from scikit-learn and statsmodels to the plotting libraries, and there is an answer online for almost any task. It executes eagerly and organizes data around an index. Polars is a DataFrame library with a Rust core built on Apache Arrow memory. It runs multi-threaded, has no index, uses an expression-based API and offers a lazy mode that optimizes the whole query before running it, which usually means much better speed and memory use on larger data.

Ibis is a DataFrame API that compiles to SQL and runs on many backends, including DuckDB, Polars, PostgreSQL, BigQuery and Snowflake, so the same code works on a laptop and in a warehouse. A practical split: pandas when compatibility with other libraries matters most, Polars when performance on local data matters, and Ibis when the data already lives in a database. Arrow lets you move data between them cheaply.