Skip to content

fix std/var with complex array #61646

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 3 commits into
base: main
Choose a base branch
from
Open

fix std/var with complex array #61646

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
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
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions doc/source/whatsnew/v3.0.0.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -716,6 +716,7 @@ 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`)

Conversion
Expand Down
6 changes: 5 additions & 1 deletion pandas/core/nanops.py
View file
Open in desktop
Original file line number Diff line number Diff line change
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":
Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

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
Expand Down
6 changes: 6 additions & 0 deletions pandas/tests/reductions/test_reductions.py
View file
Open in desktop
Original file line number Diff line number Diff line change
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
Expand Down
Loading