jorisvandenbossche commented on issue #35802:
URL: https://github.com/apache/arrow/issues/35802#issuecomment-1582885252
FWIW, I think you can already relatively easily achieve the desired result
with using the `types_mapper` keyword, like
`table.to_pandas(types_mapper=pd.ArrowDtype)`. For example:
```
In [15]: table = pa.table({'a': [1, 2, 3], 'b': ["a", "b", "c"]})
In [16]: df = table.to_pandas(types_mapper=pd.ArrowDtype)
In [17]: df
Out[17]:
a b
0 1 a
1 2 b
2 3 c
In [18]: df.dtypes
Out[18]:
a int64[pyarrow]
b string[pyarrow]
dtype: object
```
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