[I] EHN: Add a `is_castable` function and/or `errors=coerce` option to `cast` [arrow]

["randolf-scholz (via GitHub)" <gi...@apache.org>]

randolf-scholz opened a new issue, #48972:
URL: https://github.com/apache/arrow/issues/48972

   ### Describe the enhancement requested
   
   I'd like to cast a `string` array to `float`, but it can contain bad entried.
   
   ```python
   import pyarrow as pa
   import pyarrow.compute as pc
   
   arr = pa.array(["1.2", "3", "10-20", None, "nan", ""])
   
   out = pc.cast(arr, pa.float64(), safe=False)  # raises ArrowInvalid
   
   print(out)  # E: [1.2, 3, null, null, nan, null]
   ```
   
   My current workaround is to export to `pandas` and use [`pandas.to_numeric(errors="coerce")`](https://pandas.pydata.org/docs/reference/api/pandas.to_numeric.html#pandas-to-numeric).
   However, it would be nice if `pyarrow` had some built-in machinery to deal with this situation:
   
   1. A `errors={"raise", "coerce"}` option like `pandas.to_numeric` that catches conversion errors other than overflow and truncation.
   
   2. Add a function that yields a boolean mask of all values that are castable.
   
       ```python
       def is_castable(arr, target_type, options=None) -> Array[bool]:
           """Returns boolean mask of values that can be cast to target_type,
           under the chosen options."""
       ```
       
       Such a function would also be useful for extracting the set of all values that cannot be cast.
       
   
   
   
   ### Component(s)
   
   C++, Python


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Re: [I] EHN: Add a `is_castable` function and/or `errors=coerce` option to `cast` [arrow]

["shashbha14 (via GitHub)" <gi...@apache.org>]

shashbha14 commented on issue #48972:
URL: https://github.com/apache/arrow/issues/48972#issuecomment-3798099827

   take


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Re: [I] EHN: Add a `is_castable` function and/or `errors=coerce` option to `cast` [arrow]

["rok (via GitHub)" <gi...@apache.org>]

rok commented on issue #48972:
URL: https://github.com/apache/arrow/issues/48972#issuecomment-3880272786

   Hey @randolf-scholz, did you consider something like:
   
   ```python
   arr = pa.array(["1.2", "abc", "ba", "c", None])
   mask = pc.match_substring_regex(arr, r"^\d+[.,]\d+")
   ```


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