[jira] [Updated] (ARROW-17827) [Python] Allow calling UDF kernels with field/scalar expressions

["Joris Van den Bossche (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-17827?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Joris Van den Bossche updated ARROW-17827:
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    Labels: expressions query  (was: )

> [Python] Allow calling UDF kernels with field/scalar expressions
> ----------------------------------------------------------------
>
>                 Key: ARROW-17827
>                 URL: https://issues.apache.org/jira/browse/ARROW-17827
>             Project: Apache Arrow
>          Issue Type: Improvement
>          Components: Python
>            Reporter: Joris Van den Bossche
>            Priority: Major
>              Labels: expressions, query
>
> From https://github.com/apache/arrow/pull/13687#issuecomment-1240399112, where it came up while adding documentation on how to use UDFs in Python. When just wanting to invoke a UDF with arrays, you can do {{pc.call_function("my_udf", [pc.field("a")])}}. 
> But if you want to use your UDF in a context that needs an expression (eg a dataset projection), you need to be able to call the UDF with expressions as argument. And currently, the {{pc.call_function}} doesn't work that way (it expects actual, materialized arrays/scalars as arguments). As a workaround, you can use the private {{Expression._call}}:
> {code:python}
> # doesn't work with expressions
> >>> pc.call_function("my_udf", [pc.field("col")])
> ...
> TypeError: Got unexpected argument type <class 'pyarrow._compute.Expression'> for compute function
> # workaround
> >>> pc.Expression._call("my_udf", [pc.field("col")])
> <pyarrow.compute.Expression my_udf(col)>
> {code}
> So we should try to improve the usability here. Some options:
> * See if we can change {{pc.call_function}} to also accept Expressions as arguments
> * Make the {{_call}} public, so one can do {{pc.Expression.call("my_udf", [..])}}
> cc [~westonpace] [~vibhatha]



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