1. Shared library plugins for Polars

Documentation for this functionality may also be found in the Polars User Guide. This is new functionality and should be preferred over 2. as this will circumvent the GIL and will be the way we want to support extending polars.

Parallelism and optimizations are managed by the default polars runtime. That runtime will call into the plugin function. The plugin functions are compiled separately.

We can therefore keep polars more lean and maybe add support for a polars-distance, polars-geo, polars-ml, etc. Those can then have specialized expressions and don't have to worry as much for code bloat as they can be optionally installed.

The idea is that you define an expression in another Rust crate with a proc_macro polars_expr.

The macro may have one of the following attributes:

• output_type -> to define the output type of that expression
• output_type_func -> to define a function that computes the output type based on input types.
• output_type_func_with_kwargs -> to define a function that computes the output type based on input types and keyword args.

Here is an example of a String conversion expression that converts any string to pig latin:

fn pig_latin_str(value: &str, capitalize: bool, output: &mut String) {
    if let Some(first_char) = value.chars().next() {
        if capitalize {
            for c in value.chars().skip(1).map(|char| char.to_uppercase()) {
                write!(output, "{c}").unwrap()
            }
            write!(output, "AY").unwrap()
        } else {
            let offset = first_char.len_utf8();
            write!(output, "{}{}ay", &value[offset..], first_char).unwrap()
        }
    }
}

#[derive(Deserialize)]
struct PigLatinKwargs {
    capitalize: bool,
}

#[polars_expr(output_type=String)]
fn pig_latinnify(inputs: &[Series], kwargs: PigLatinKwargs) -> PolarsResult<Series> {
    let ca = inputs[0].str()?;
    let out: StringChunked =
        ca.apply_into_string_amortized(|value, output| pig_latin_str(value, kwargs.capitalize, output));
    Ok(out.into_series())
}

This can then be exposed on the Python side:

from __future__ import annotations

from typing import TYPE_CHECKING

import polars as pl
from polars.plugins import register_plugin_function

from expression_lib._utils import LIB

if TYPE_CHECKING:
    from expression_lib._typing import IntoExprColumn


def pig_latinnify(expr: IntoExprColumn, capitalize: bool = False) -> pl.Expr:
    return register_plugin_function(
        plugin_path=LIB,
        args=[expr],
        function_name="pig_latinnify",
        is_elementwise=True,
        kwargs={"capitalize": capitalize},
    )

Compile/ship and then it is ready to use:

import polars as pl
from expression_lib import language

df = pl.DataFrame({
    "names": ["Richard", "Alice", "Bob"],
})


out = df.with_columns(
   pig_latin = language.pig_latinnify("names")
)

Alternatively, you can register a custom namespace, which enables you to write:

out = df.with_columns(
   pig_latin = pl.col("names").language.pig_latinnify()
)

See the full example in [example/derive_expression]: https://github.com/pola-rs/pyo3-polars/tree/main/example/derive_expression

2. PyO3 extensions for Polars

See the example directory for a concrete example. Here we send a polars DataFrame to Rust and then compute a jaccard similarity in parallel using rayon and Rust hash sets. These PyO3 extension types are however only provided for convenience and do not have stability guarantees beyond that the latest definitions should work for the latest version of Polars.

Run example

$ cd example && make install $ venv/bin/python run.py

This will output:

shape: (2, 2)
┌───────────┬───────────────┐
│ list_a    ┆ list_b        │
│ ---       ┆ ---           │
│ list[i64] ┆ list[i64]     │
╞═══════════╪═══════════════╡
│ [1, 2, 3] ┆ [1, 2, ... 8] │
│ [5, 5]    ┆ [5, 1, 1]     │
└───────────┴───────────────┘
shape: (2, 1)
┌─────────┐
│ jaccard │
│ ---     │
│ f64     │
╞═════════╡
│ 0.75    │
│ 0.5     │
└─────────┘

Compile for release

$ make install-release

What to expect

This crate offers a PySeries and a PyDataFrame which are simple wrapper around Series and DataFrame. The advantage of these wrappers is that they can be converted to and from python as they implement FromPyObject and IntoPy.