polars 0.8.2

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Polars

Blazingly fast DataFrames in Rust & Python

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

• Lazy | eager execution
• Multi-threaded
• SIMD
• Query optimization
• Powerful expression API
• Rust | Python | ...

To learn more, read the User Guide.

Rust setup

You can take latest release from crates.io, or if you want to use the latest features/ performance improvements point to the master branch of this repo.

You can add the master like this:

polars = {version="0.13.0", git = "https://github.com/ritchie46/polars" }

Or by fixing to a specific version:

polars = {version="0.13.0", git = "https://github.com/ritchie46/polars", rev = "<optional git tag>" } 

Rust version

Required Rust version >=1.51

Python users read this!

Polars is currently transitioning from py-polars to polars. Some docs may still refer the old name.

Install the latest polars version with: $ pip3 install polars

Documentation

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

Rust

• Documentation (stable).
• Documentation (master branch).
  • DataFrame
  • Series
  • ChunkedArray
  • Traits for ChunkedArray
  • Time/ DateTime utilities
  • Groupby, aggregations and pivots
  • Lazy DataFrame
• User Guide

Python

• installation guide: $ pip3 install polars
• User Guide
• Reference guide

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.
• [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.
• POLARS_VERBOSE -> print logging info to stderr

[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. Choose any of:

• Very long compile times, fastest binary: $ cd py-polars && maturin develop --rustc-extra-args="-C target-cpu=native" --release
• Shorter compile times, fast binary: $ cd py-polars && maturin develop --rustc-extra-args="-C codegen-units=16 lto=no target-cpu=native" --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

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