jorisvandenbossche commented on a change in pull request #12178:
URL: https://github.com/apache/arrow/pull/12178#discussion_r798344287
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File path: python/pyarrow/tests/test_pandas.py
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@@ -4082,6 +4082,66 @@ def test_array_to_pandas():
# tm.assert_series_equal(result, expected)
+def test_to_pandas_types_mapper():
+ # https://issues.apache.org/jira/browse/ARROW-9664
+ if Version(pd.__version__) < Version("1.0.0"):
+ pytest.skip("ExtensionDtype to_pandas method missing")
+
+ data = pa.array([1, 2, 3], pa.int64())
+
+ # Test with mapper function
+ types_mapper = {pa.int64(): pd.Int64Dtype()}.get
+ result = data.to_pandas(types_mapper=types_mapper)
+ assert result.dtype == types_mapper(data.type)
+
+ # Test mapper function returning None
+ types_mapper = {pa.int64(): None}.get
+ result = data.to_pandas(types_mapper=types_mapper)
+ assert result.dtype == data.type.to_pandas_dtype()
+
+ # Test mapper function not containing the dtype
+ types_mapper = {pa.float64(): pd.Float64Dtype()}.get
+ result = data.to_pandas(types_mapper=types_mapper)
+ assert result.dtype == data.type.to_pandas_dtype()
+
+ # Test for the interval extension dtype
+ # -> ignores mapping and uses default conversion
+ types_mapper = {pa.float64(): pd.IntervalDtype()}.get
+ result = data.to_pandas(types_mapper=types_mapper)
Review comment:
> If I use the example for the intervaldtype you mentioned, it works
fine without the need for a `types_mapper`, so I am not sure what should be
done here (you can't change to any other dtype anyways?):
Ah, yes, I forgot about that this already works. That's because it is also
an extension type on the Arrow side. A way to test is would be to do
`data.storage.to_pandas(..)`, but then it is the same as the other tests with
Int64Dtype, so no need to include it.
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