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fix std/var with complex array #61646

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Merged
merged 3 commits into from
Jun 16, 2025
Merged

fix std/var with complex array #61646

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c9389eb
fix std/var with complex array
randolf-scholz Jun 13, 2025
9261960
added whatsnew entry
randolf-scholz Jun 13, 2025
69be10c
Merge branch 'main' into fix_complex_variance
randolf-scholz Jun 14, 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 @@ -715,6 +715,7 @@ Numeric
- Bug in :meth:`DataFrame.cov` raises a ``TypeError`` instead of returning potentially incorrect results or other errors (:issue:`53115`)
- Bug in :meth:`DataFrame.quantile` where the column type was not preserved when ``numeric_only=True`` with a list-like ``q`` produced an empty result (:issue:`59035`)
- Bug in :meth:`Series.dot` returning ``object`` dtype for :class:`ArrowDtype` and nullable-dtype data (:issue:`61375`)
- Bug in :meth:`Series.std` and :meth:`Series.var` when using complex-valued data (:issue:`61645`)
- Bug in ``np.matmul`` with :class:`Index` inputs raising a ``TypeError`` (:issue:`57079`)

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6 changes: 5 additions & 1 deletion pandas/core/nanops.py
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Expand Up @@ -1014,7 +1014,11 @@ def nanvar(
avg = _ensure_numeric(values.sum(axis=axis, dtype=np.float64)) / count
if axis is not None:
avg = np.expand_dims(avg, axis)
sqr = _ensure_numeric((avg - values) ** 2)
if values.dtype.kind == "c":
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I used an if-clause here to minimize performance impact for non-complex data.

# Need to use absolute value for complex numbers.
sqr = _ensure_numeric(abs(avg - values) ** 2)
else:
sqr = _ensure_numeric((avg - values) ** 2)
if mask is not None:
np.putmask(sqr, mask, 0)
result = sqr.sum(axis=axis, dtype=np.float64) / d
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6 changes: 6 additions & 0 deletions pandas/tests/reductions/test_reductions.py
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Expand Up @@ -780,6 +780,12 @@ def test_var_masked_array(self, ddof, exp):
assert result == result_numpy_dtype
assert result == exp

def test_var_complex_array(self):
# GH#61645
ser = Series([-1j, 0j, 1j], dtype=complex)
assert ser.var(ddof=1) == 1.0
assert ser.std(ddof=1) == 1.0

@pytest.mark.parametrize("dtype", ("m8[ns]", "M8[ns]", "M8[ns, UTC]"))
def test_empty_timeseries_reductions_return_nat(self, dtype, skipna):
# covers GH#11245
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