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Polars

rust docs Build and test Gitter

Blazingly fast DataFrames in Rust & Python

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.

Python users read this!

Polars is currently transitioning from py-polars to polars. Some docs may still refer the old name. We're working towards a new 0.7.0 release. For the mean time install a pre-release version. This will likely be more stable than 0.6.7.

Install the latest pre-release version with: $ pip install polars==0.7.0-beta.1

Functionality Eager Lazy (DataFrame) Lazy (Series)
Filters ✔ ✔ ✔
Shifts ✔ ✔ ✔
Joins ✔ ✔
GroupBys + aggregations ✔ ✔
Comparisons ✔ ✔ ✔
Arithmetic ✔ ✔
Sorting ✔ ✔ ✔
Reversing ✔ ✔ ✔
Closure application (User Defined Functions) ✔ ✔
SIMD ✔ ✔
Pivots ✔ ✗
Melts ✔ ✗
Filling nulls + fill strategies ✔ ✗ ✔
Aggregations ✔ ✔ ✔
Moving Window aggregates ✔ ✗ ✗
Find unique values ✔ ✗
Rust iterators ✔ ✔
IO (csv, json, parquet, Arrow IPC ✔ ✗
Query optimization: (predicate pushdown) ✗ ✔
Query optimization: (projection pushdown) ✗ ✔
Query optimization: (type coercion) ✗ ✔
Query optimization: (simplify expressions) ✗ ✔
Query optimization: (aggregate pushdown) ✗ ✔

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

Documentation

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

Rust

Python

Performance

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.

Cargo Features

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.

Contribution

Want to contribute? Read our contribution guideline.

ENV vars

  • 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.

[Python] compile py-polars from source

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

Note that the Rust crate implementing the Python bindings is called py-polars to distinguish from the wrapped Rust crate polars itself. However, both the Python package and the Python module are named polars, so you can pip install polars and import polars (previously, these were called py-polars and pypolars).

Acknowledgements

Development of Polars is proudly powered by

Xomnia