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Fast multi-threaded DataFrame library in Rust and Python

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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 using Apache Arrow(2) as memory model.

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

To learn more, read the User Guide.

>>> 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",
    lit("fruits").alias("literal_string_fruits"),
    col("B").filter(col("cars") == "beetle").sum(),
    col("A").filter(col("B") > 2).sum().over("cars").alias("sum_A_by_cars"),       # groups by "cars"
    col("A").sum().over("fruits").alias("sum_A_by_fruits"),                        # groups by "fruits"
    col("A").reverse().over("fruits").flatten().alias("rev_A_by_fruits"),          # groups by "fruits
    col("A").sort_by("B").over("fruits").flatten().alias("sort_A_by_B_by_fruits")  # groups by "fruits"
]))
shape: (5, 8)
┌──────────┬──────────┬──────────────┬─────┬─────────────┬─────────────┬─────────────┬─────────────┐
│ fruitscarsliteral_striBsum_A_by_casum_A_by_frrev_A_by_frsort_A_by_B │
│ ------ng_fruits---rsuitsuits_by_fruits  │
│ strstr---i64------------         │
│          ┆          ┆ str          ┆     ┆ i64i64i64i64         │
╞══════════╪══════════╪══════════════╪═════╪═════════════╪═════════════╪═════════════╪═════════════╡
│ "apple""beetle""fruits"114744           │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ "apple""beetle""fruits"114733           │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ "banana""beetle""fruits"114855           │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ "banana""audi""fruits"112822           │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ "banana""beetle""fruits"114811           │
└──────────┴──────────┴──────────────┴─────┴─────────────┴─────────────┴─────────────┴─────────────┘

Performance 🚀🚀

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

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.

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

Rust version

Required Rust version >=1.52

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

Python

Contribution

Want to contribute? Read our contribution guideline.

[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 -C lto=thin -C 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).

Arrow2

Polars has transitioned to arrow2. Arrow2 is a faster and safer implementation of the arrow spec. Arrow2 also has a more granular code base, helping to reduce the compiler bloat. There is still a maintained arrow-rs branch for users who want to use another backend.

Acknowledgements

Development of Polars is proudly powered by

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