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Sparse array cumsum leads to infinite recursion 62669 #62703

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813d76e
added condition to avoid max recursion depth
Oct 15, 2025
a3ff481
added condition to avoid max recursion depth
Oct 15, 2025
c550eac
added entry in rst file
Oct 15, 2025
bc75673
added test cases
Oct 15, 2025
bfeb945
Update pandas/core/arrays/sparse/array.py
santhoshbethi Oct 26, 2025
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v3.0.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -1169,6 +1169,7 @@ Sparse
- Bug in :class:`SparseDtype` for equal comparison with na fill value. (:issue:`54770`)
- Bug in :meth:`DataFrame.sparse.from_spmatrix` which hard coded an invalid ``fill_value`` for certain subtypes. (:issue:`59063`)
- Bug in :meth:`DataFrame.sparse.to_dense` which ignored subclassing and always returned an instance of :class:`DataFrame` (:issue:`59913`)
- Bug in :meth:`cumsum` for integer arrays Calling SparseArray.cumsum caused max recursion depth error. (:issue:`62669`)

ExtensionArray
^^^^^^^^^^^^^^
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3 changes: 3 additions & 0 deletions pandas/core/arrays/sparse/array.py
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Expand Up @@ -1571,6 +1571,9 @@ def cumsum(self, axis: AxisInt = 0, *args, **kwargs) -> SparseArray:
raise ValueError(f"axis(={axis}) out of bounds")

if not self._null_fill_value:
if isinstance(self, SparseArray) and self.fill_value == 0:
# special case where we can avoid max recursion depth
return SparseArray(self.to_dense().cumsum())
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@rhshadrach rhshadrach Oct 26, 2025

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Suggested change
if not self._null_fill_value:
if isinstance(self, SparseArray) and self.fill_value == 0:
# special case where we can avoid max recursion depth
return SparseArray(self.to_dense().cumsum())
if not self._null_fill_value and self.fill_value != 0:

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@rhshadrach rhshadrach Oct 30, 2025 •
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I see now this was wrong, but the previous implementation also did not handle the case

arr = pd.arrays.SparseArray([1.0, 0.0, np.nan, 3.0], fill_value=0.0)

correctly as it would have given the result [1.0, 1.0, nan, nan] whereas I believe docstring indicates the result should be [1.0, 1.0, nan, 4.0]. We can accomplish this by:

if not self._null_fill_value:
    return SparseArray(self.to_dense(), fill_value=np.nan).cumsum()

Note this changes the fill_value to NaN, but that adheres to the docstring. Anytime this method has been successful in previous versions, it was always returning an NA fill value regardless of the input fill value. So I think we should stick to that logic at least for now.

return SparseArray(self.to_dense()).cumsum()

return SparseArray(
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14 changes: 14 additions & 0 deletions pandas/tests/arrays/sparse/test_array.py
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Expand Up @@ -363,6 +363,20 @@ def test_setting_fill_value_fillna_still_works():
tm.assert_numpy_array_equal(result, expected)


def test_cumsum_integer_no_recursion():
# GH 62669: RecursionError in integer SparseArray.cumsum
arr = SparseArray([1, 2, 3])
result = arr.cumsum()
expected = SparseArray([1, 3, 6])
tm.assert_sp_array_equal(result, expected)

# Also test with some zeros interleaved
arr2 = SparseArray([0, 1, 0, 2])
result2 = arr2.cumsum()
expected2 = SparseArray([0, 1, 1, 3])
tm.assert_sp_array_equal(result2, expected2)


def test_setting_fill_value_updates():
arr = SparseArray([0.0, np.nan], fill_value=0)
arr.fill_value = np.nan
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