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Reproducible Example

import pandas as pd records = [ {"name": "A", "project": "foo"}, {"name": "A", "project": "bar"}, {"name": "A", "project": pd.NA}, ] df = pd.DataFrame.from_records(records).astype({"project": "string"}) agg_df = df.groupby("name").agg({"project": "unique"}) agg_df.to_parquet("test.parquet")

Issue Description

It seems that when aggregating on a "string" type column (potentially nullable), the resulting dataframe uses StringArray to represent the aggregated value (here project). When attempting to write to parquet, this fails with the following error:

pyarrow.lib.ArrowInvalid: ("Could not convert <StringArray>\n['foo', 'bar', <NA>]\nLength: 3, dtype: string with type StringArray: did not recognize Python value type when inferring an Arrow data type", 'Conversion failed for column project with type object') 

I'm not sure why this is happening, because StringArray clearly implements the __arrow_array__ protocol that should be allowing this conversion. This also happens if the string is of type "string[pyarrow]".

The workaround I've been using so far is to re-type the column as object prior to the groupby. This results in values of type np.ndarray, which are parquet-able. But this is suboptimal because we use read_parquet upstream that types the column as "string", which means that we have to remember to retype it every time we do this aggregation, else it will fail downstream when writing to parquet. It seems like this should work with the column typed as "string".

Expected Behavior

It should successfully write the parquet file without failures.

Installed Versions