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 use in Lazy via UDF's

Want to know about all the features Polars supports? Check the current master docs.

Most features are described on the DataFrame, Series, and ChunkedArray structs in that order. For ChunkedArray a lot of functionality is also defined by Traits in the ops module. Other useful parts of the documentation are:

• Time/ DateTime utilities
• Groupby, aggregations and pivots
• Lazy DataFrame

Polars is written to be performant. Below are some comparisons with the (also very fast) Pandas DataFrame library.

Take a look at the 10 minutes to Polars notebook to get you started. Want to run the notebook yourself? Clone the repo and run $ cargo c && docker-compose up. This will spin up a jupyter notebook on http://localhost:8891. The notebooks are in the /examples directory.

Oh yeah.. and get a cup of coffee because compilation will take a while during the first run.

A subset of the Polars functionality is also exposed through Python bindings. You can install them with:

$ pip install py-polars

Next you can check the 10 minutes to py-polars notebook or take a look at the reference.

Additional cargo features:

• pretty (default)
  • pretty printing of DataFrames
• 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

Want to contribute? Read our contribution guideline.