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.

To learn more about the inner workings of Polars read the WIP book.

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
• the book
• 10 minutes to Polars notebook

• installation guide: pip install py-polars
• the book
• Reference guide

Polars is written to be performant, and it is! But don't take my word for it, take a look at the results in h2oai's db-benchmark.

Additional cargo features:

• temporal (default)
  • Conversions between Chrono and Polars for temporal data
• simd (nightly)
  • SIMD operations
• parquet
  • Read Apache Parquet format
• json
  • Json serialization
• ipc
  • Arrow's IPC format serialization
• random
  • Generate array's with randomly sampled values
• ndarray
  • Convert from DataFrame to ndarray
• lazy
  • Lazy api
• strings
  • String utilities for Utf8Chunked
• object
  • Support for generic ChunkedArray's called ObjectChunked<T> (generic over T). These will downcastable from Series through the Any trait.
• parallel
  • ChunkedArrays can be used by rayon::par_iter()
• [plain_fmt | pretty_fmt] (mutually exclusive)
  • one of them should be chosen to fmt DataFrames. pretty_fmt can deal with overflowing cells and looks nicer but has more dependencies. plain_fmt (default) is plain formatting.

Want to contribute? Read our contribution guideline.

• POLARS_PAR_SORT_BOUND -> Sets the lower bound of rows at which Polars will use a parallel sorting algorithm. Default is 1M rows.
• POLARS_FMT_MAX_COLS -> maximum number of columns shown when formatting DataFrames.
• POLARS_FMT_MAX_ROWS -> maximum number of rows shown when formatting DataFrames.
• POLARS_TABLE_WIDTH -> width of the tables used during DataFrame formatting.
• POLARS_MAX_THREADS -> maximum number of threads used in join algorithm. Default is unbounded.

If you want a bleeding edge release or maximal performance you should compile py-polars from source.

This can be done by going through the following steps in sequence:

1. install the latest rust compiler
2. $ pip3 install maturin
3. $ cd py-polars && maturin develop --release