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GH-49002: [Python] Fix array.to_pandas string type conversion for arrays with None #49247

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Merged
AlenkaF merged 4 commits into from
Apr 1, 2026
Merged

GH-49002: [Python] Fix array.to_pandas string type conversion for arrays with None #49247

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AlenkaF Feb 11, 2026
fec9077
Add suggestions, fix typo
AlenkaF Mar 25, 2026
600c4e4
Update python/pyarrow/array.pxi
AlenkaF Apr 1, 2026
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Apply suggestion from @jorisvandenbossche
AlenkaF Apr 1, 2026
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7 changes: 7 additions & 0 deletions python/pyarrow/array.pxi
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Original file line number Diff line number Diff line change
Expand Up @@ -2276,6 +2276,13 @@ cdef _array_like_to_pandas(obj, options, types_mapper):
dtype = original_type.to_pandas_dtype()
except NotImplementedError:
pass
elif pandas_api.uses_string_dtype() and not options["strings_to_categorical"] and (
original_type.id == _Type_STRING or
original_type.id == _Type_LARGE_STRING or
original_type.id == _Type_STRING_VIEW
):
# for pandas 3.0+, use pandas' new default string dtyp
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dtype = pandas_api.pd.StringDtype(na_value=np.nan)

# Only call __from_arrow__ for Arrow extension types or when explicitly
# overridden via types_mapper
Expand Down
48 changes: 45 additions & 3 deletions python/pyarrow/tests/test_pandas.py
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Expand Up @@ -2975,7 +2975,9 @@ def check_zero_copy_failure(self, arr):
arr.to_pandas(zero_copy_only=True)

def test_zero_copy_failure_on_object_types(self):
self.check_zero_copy_failure(pa.array(['A', 'B', 'C']))
if Version(pd.__version__) < Version("3.0.0"):
# pandas 3.0 includes default string dtype support

@raulcd raulcd Apr 1, 2026

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I am not sure I understand why this test has to have this guard now. Isn't it supposed to work with pandas > 3.0.0?
I suppose this is because we are testing object types specifically. Was this test failing on CI? I haven't seen the failure.

@AlenkaF AlenkaF Apr 1, 2026

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This should be connected to the change I made in this PR as strings are not converted to pandas object anymore. But looking at the test it might be a leftover from my previous wrong approach. Thanks for the comment, I need to check this!

@AlenkaF AlenkaF Apr 1, 2026

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OK, got it. This test checks that strings can not be zero copied to Pandas. Which has been true in the past as the C++ machinery constructed an object type from Pyarrow string type. Now, with pandas 3.0.0 we can move through __from_arrow__ where no copies are needed.

Running this test locally with pandas 3.0.0 gives following error: 
______________________________________________ TestZeroCopyConversion.test_zero_copy_failure_on_object_types _______________________________________________

self = <pyarrow.tests.test_pandas.TestZeroCopyConversion object at 0x156a0af90>

    def test_zero_copy_failure_on_object_types(self):
>       self.check_zero_copy_failure(pa.array(['A', 'B', 'C']))

python/pyarrow/tests/test_pandas.py:2978: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <pyarrow.tests.test_pandas.TestZeroCopyConversion object at 0x156a0af90>
arr = <pyarrow.lib.StringArray object at 0x15699b700>
[
  "A",
  "B",
  "C"
]

    def check_zero_copy_failure(self, arr):
>       with pytest.raises(pa.ArrowInvalid):
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E       Failed: DID NOT RAISE <class 'pyarrow.lib.ArrowInvalid'>

python/pyarrow/tests/test_pandas.py:2974: Failed

@jorisvandenbossche jorisvandenbossche Apr 1, 2026

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Yes, from what I can see this is an expected change, since string conversion will now actually be zero copy

(although, strictly speaking, it is not actually zero-copy entirely, because the test here is using string, and pandas will convert that to large_string. But I suppose that happens outside the view of pyarrow)

@jorisvandenbossche jorisvandenbossche Apr 1, 2026 •
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Essentially, the zero_copy_only keyword is ignored whenever the conversion goes through dtype.__from_arrow__ .. (same for other options), so it is not even about no longer making a copy or not in pandas 3.0, just about using an ExtensionDtype

@AlenkaF AlenkaF Apr 1, 2026

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Oh yes, I see. Should this be changed when dealing with Extension types? I know we have a list of things to work on when it comes to this topic and we can open up an umbrella issue with all possible improvements.

@jorisvandenbossche jorisvandenbossche Apr 1, 2026

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I am not sure how to easily improve this .. (since we defer to pandas for the conversion, and that method we call does not have those keywords)

(long term I would like to see this logic to be moved entirely to pandas)

self.check_zero_copy_failure(pa.array(['A', 'B', 'C']))

def test_zero_copy_failure_with_int_when_nulls(self):
self.check_zero_copy_failure(pa.array([0, 1, None]))
Expand Down Expand Up @@ -3047,6 +3049,10 @@ def test_all_none_category(self):

def test_empty_arrays(self):
for dtype_str, pa_type in self.type_pairs:
if (Version(pd.__version__) >= Version("3.0.0") and
pa_type == pa.string()):
# PyArrow backed string dtype are set by default
dtype_str = 'str'
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arr = np.array([], dtype=np.dtype(dtype_str))
_check_array_roundtrip(arr, type=pa_type)

Expand Down Expand Up @@ -3231,13 +3237,19 @@ def test_convert_empty_table(self):
empty_objects = pd.Series(np.array([], dtype=object))
tm.assert_series_equal(arr.to_pandas(),
pd.Series(np.array([], dtype=np.int64)))
arr = pa.array([], type=pa.string())
tm.assert_series_equal(arr.to_pandas(), empty_objects)
arr = pa.array([], type=pa.list_(pa.int64()))
tm.assert_series_equal(arr.to_pandas(), empty_objects)
arr = pa.array([], type=pa.struct([pa.field('a', pa.int64())]))
tm.assert_series_equal(arr.to_pandas(), empty_objects)

arr = pa.array([], type=pa.string())
if Version(pd.__version__) >= Version("3.0.0"):
# PyArrow backed string dtype are set by default
empty_str = pd.Series(np.array([], dtype=str))
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tm.assert_series_equal(arr.to_pandas(), empty_str)
else:
tm.assert_series_equal(arr.to_pandas(), empty_objects)

def test_non_natural_stride(self):
"""
ARROW-2172: converting from a Numpy array with a stride that's
Expand Down Expand Up @@ -4652,6 +4664,36 @@ def test_chunked_array_to_pandas_types_mapper():
assert result.dtype == np.dtype("int64")


@pytest.mark.parametrize(
"string_type", [pa.string(), pa.large_string(), pa.string_view()]
)
@pytest.mark.parametrize("data", [[], [None]])
def test_array_to_pandas_string_dtype(string_type, data):
# GH-49002
if Version(pd.__version__) < Version("3.0.0"):
pytest.skip("PyArrow backed string dtype missing")

arr = pa.array(data, type=string_type)
result = arr.to_pandas()
assert result.dtype == pd.StringDtype(na_value=np.nan)

arr = pa.chunked_array([data], type=string_type)
result = arr.to_pandas()
assert result.dtype == pd.StringDtype(na_value=np.nan)

# Test types_mapper takes precedence
types_mapper = {string_type: None}.get
result = arr.to_pandas(types_mapper=types_mapper)
assert result.dtype == np.dtype("object")

# Test strings_to_categorical
result = arr.to_pandas(strings_to_categorical=False)
assert result.dtype == pd.StringDtype(na_value=np.nan)
result = arr.to_pandas(strings_to_categorical=True)
assert result.dtype == pd.CategoricalDtype(categories=[],
ordered=False)


# ----------------------------------------------------------------------
# Legacy metadata compatibility tests

Expand Down
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