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Polars: Blazingly fast DataFrames in Rust, Python & Node.js

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

• Lazy | eager execution
• Multi-threaded
• SIMD
• Query optimization
• Powerful expression API
• Hybrid Streaming (larger than RAM datasets)
• Rust | Python | NodeJS | ...

To learn more, read the User Guide.

>>> import polars as pl
>>> df = pl.DataFrame(
...     {
...         "A": [1, 2, 3, 4, 5],
...         "fruits": ["banana", "banana", "apple", "apple", "banana"],
...         "B": [5, 4, 3, 2, 1],
...         "cars": ["beetle", "audi", "beetle", "beetle", "beetle"],
...     }
... )

# embarrassingly parallel execution & very expressive query language
>>> df.sort("fruits").select(
...     [
...         "fruits",
...         "cars",
...         pl.lit("fruits").alias("literal_string_fruits"),
...         pl.col("B").filter(pl.col("cars") == "beetle").sum(),
...         pl.col("A").filter(pl.col("B") > 2).sum().over("cars").alias("sum_A_by_cars"),
...         pl.col("A").sum().over("fruits").alias("sum_A_by_fruits"),
...         pl.col("A").reverse().over("fruits").alias("rev_A_by_fruits"),
...         pl.col("A").sort_by("B").over("fruits").alias("sort_A_by_B_by_fruits"),
...     ]
... )
shape: (5, 8)
┌──────────┬──────────┬──────────────┬─────┬─────────────┬─────────────┬─────────────┬─────────────┐
│ fruits   ┆ cars     ┆ literal_stri ┆ B   ┆ sum_A_by_ca ┆ sum_A_by_fr ┆ rev_A_by_fr ┆ sort_A_by_B │
│ ---      ┆ ---      ┆ ng_fruits    ┆ --- ┆ rs          ┆ uits        ┆ uits        ┆ _by_fruits  │
│ str      ┆ str      ┆ ---          ┆ i64 ┆ ---         ┆ ---         ┆ ---         ┆ ---         │
│          ┆          ┆ str          ┆     ┆ i64         ┆ i64         ┆ i64         ┆ i64         │
╞══════════╪══════════╪══════════════╪═════╪═════════════╪═════════════╪═════════════╪═════════════╡
│ "apple"  ┆ "beetle" ┆ "fruits"     ┆ 11  ┆ 4           ┆ 7           ┆ 4           ┆ 4           │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ "apple"  ┆ "beetle" ┆ "fruits"     ┆ 11  ┆ 4           ┆ 7           ┆ 3           ┆ 3           │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ "banana" ┆ "beetle" ┆ "fruits"     ┆ 11  ┆ 4           ┆ 8           ┆ 5           ┆ 5           │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ "banana" ┆ "audi"   ┆ "fruits"     ┆ 11  ┆ 2           ┆ 8           ┆ 2           ┆ 2           │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ "banana" ┆ "beetle" ┆ "fruits"     ┆ 11  ┆ 4           ┆ 8           ┆ 1           ┆ 1           │
└──────────┴──────────┴──────────────┴─────┴─────────────┴─────────────┴─────────────┴─────────────┘

Performance 🚀🚀

Blazingly fast

Polars is very fast. In fact, it is one of the best performing solutions available. See the results in h2oai's db-benchmark.

In the TPCH benchmarks polars is orders of magnitudes faster than pandas, dask, modin and vaex on full queries (including IO).

Lightweight

Polars is also very lightweight. It comes with zero required dependencies, and this shows in the import times:

• polars: 70ms
• numpy: 104ms
• pandas: 520ms

Handles larger than RAM data

If you have data that does not fit into memory, polars lazy is able to process your query (or parts of your query) in a streaming fashion, this drastically reduces memory requirements so you might be able to process your 250GB dataset on your laptop. Collect with collect(streaming=True) to run the query streaming. (This might be a little slower, but it is still very fast!)

Setup

Python

Install the latest polars version with:

pip install polars

We also have a conda package (conda install polars), however pip is the preferred way to install Polars.

Install Polars with all optional dependencies.

pip install 'polars[all]'
pip install 'polars[numpy,pandas,pyarrow]'  # install a subset of all optional dependencies

You can also install the dependencies directly.

Tag	Description
all	Install all optional dependencies (all of the following)
pandas	Install with Pandas for converting data to and from Pandas Dataframes/Series
numpy	Install with numpy for converting data to and from numpy arrays
pyarrow	Reading data formats using PyArrow
fsspec	Support for reading from remote file systems
connectorx	Support for reading from SQL databases
xlsx2csv	Support for reading from Excel files
deltalake	Support for reading from Delta Lake Tables
timezone	Timezone support, only needed if 1. you are on Python < 3.9 and/or 2. you are on Windows, otherwise no dependencies will be installed

Releases happen quite often (weekly / every few days) at the moment, so updating polars regularly to get the latest bugfixes / features might not be a bad idea.

Rust

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.

polars = { git = "https://github.com/pola-rs/polars", rev = "<optional git tag>" }

Required Rust version >=1.58

Contributing

Want to contribute? Read our contribution guideline.

Python: compile polars from source

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

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

1. Install the latest Rust compiler
2. Install maturin: pip install maturin
3. Choose any of:
   • Fastest binary, very long compile times:

     $ cd py-polars && maturin develop --release -- -C target-cpu=native

   • Fast binary, Shorter compile times:

     $ cd py-polars && maturin develop --release -- -C codegen-units=16 -C lto=thin -C target-cpu=native

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.

Arrow2

Polars has transitioned to arrow2. Arrow2 is a faster and safer implementation of the Apache Arrow Columnar Format. Arrow2 also has a more granular code base, helping to reduce the compiler bloat.

Use custom Rust function in python?

See this example.

Going big...

Do you expect more than 2^32 ~4,2 billion rows? Compile polars with the bigidx feature flag.

Or for python users install pip install polars-u64-idx.

Don't use this unless you hit the row boundary as the default polars is faster and consumes less memory.

Legacy

Do you want polars to run on an old CPU (e.g. dating from before 2011)? Install pip polars-lts-cpu. This polars project is compiled without avx target features.

Acknowledgements

Development of Polars is proudly powered by

Sponsors