- Notifications
You must be signed in to change notification settings - Fork50
Plugins/extension for Polars
License
pola-rs/pyo3-polars
Folders and files
Name | Name | Last commit message | Last commit date | |
---|---|---|---|---|
Repository files navigation
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 expressionoutput_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
.