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Documentation for this functionality may also be found in thePolars User Guide.This is new functionality and should be preferred over2. as thiswill 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 apolars-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_macropolars_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 aString conversion expression that converts any string topig latin:

fnpig_latin_str(value:&str,capitalize:bool,output:&mutString){ifletSome(first_char) = value.chars().next(){if capitalize{for cin 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)]structPigLatinKwargs{capitalize:bool,}#[polars_expr(output_type=String)]fnpig_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__importannotationsfromtypingimportTYPE_CHECKINGimportpolarsasplfrompolars.pluginsimportregister_plugin_functionfromexpression_lib._utilsimportLIBifTYPE_CHECKING:fromexpression_lib._typingimportIntoExprColumndefpig_latinnify(expr:IntoExprColumn,capitalize:bool=False)->pl.Expr:returnregister_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:

importpolarsasplfromexpression_libimportlanguagedf=pl.DataFrame({"names": ["Richard","Alice","Bob"],})out=df.with_columns(pig_latin=language.pig_latinnify("names"))

Alternatively, you canregister 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

See theexample directory for a concrete example. Here we send a polarsDataFrame to rust and then compute ajaccard similarity in parallel usingrayon and rust hash sets.

$ 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     │└─────────┘

$ make install-release

This crate offers aPySeries and aPyDataFrame which are simple wrapper aroundSeries andDataFrame. Theadvantage of these wrappers is that they can be converted to and from python as they implementFromPyObject andIntoPy.