Polars is a blazingly fast DataFrames library implemented in Rust. Its memory model uses Apache Arrow as backend.

It currently consists of an eager API similar to pandas and a lazy API that is somewhat similar to spark. Amongst more, Polars has the following functionalities.

Note that almost all eager operations supported by Eager on Series/ChunkedArrays can be used in Lazy via UDF's

Want to know about all the features Polars support? Read the docs!

• Documentation (stable).
• Documentation (master branch).
  • DataFrame
  • Series
  • ChunkedArray
  • Traits for ChunkedArray
  • Time/ DateTime utilities
  • Groupby, aggregations and pivots
  • Lazy DataFrame
• 10 minutes to Polars notebook

• installation guide: pip install py-polars
• Reference guide
• 10 minutes to py-polars notebook
• lazy py-polars notebook

Polars is written to be performant. Below are some comparisons with pandas and pydatatable DataFrame library (lower is better).

Additional cargo features:

• temporal (default)
  • Conversions between Chrono and Polars for temporal data
• simd (default)
  • SIMD operations
• parquet
  • Read Apache Parquet format
• random
  • Generate array's with randomly sampled values
• ndarray
  • Convert from DataFrame to ndarray
• lazy
  • Lazy api
• strings
  • String utilities for Utf8Chunked

Want to contribute? Read our contribution guideline.

• POLARS_PAR_COLUMN_BP -> breakpoint for (some) parallel operations on columns. If the number of columns exceeds this it will run in parallel