Skip to content

CLN: nanops #37396

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

Merged
merged 4 commits into from
Oct 26, 2020
Merged

CLN: nanops #37396

Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
f6e7b53
BUG: DataFrame.std(skipna=False) with td64 dtype
jbrockmendel Oct 25, 2020
384e3e8
GH ref
jbrockmendel Oct 25, 2020
28fd045
CLN: remove unnecessary branches
jbrockmendel Oct 25, 2020
a44ca73
Merge branch 'master' of https://github.com/pandas-dev/pandas into cl…
jbrockmendel Oct 26, 2020
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
14 changes: 5 additions & 9 deletions pandas/core/nanops.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -381,18 +381,16 @@ def _na_for_min_count(
if is_numeric_dtype(values):
values = values.astype("float64")
fill_value = na_value_for_dtype(values.dtype)
if fill_value is NaT:
fill_value = values.dtype.type("NaT", "ns")

if values.ndim == 1:
return fill_value
else:
assert axis is not None # assertion to make mypy happy
result_shape = values.shape[:axis] + values.shape[axis + 1 :]
# calling np.full with dtype parameter throws an ValueError when called
# with dtype=np.datetime64 and and fill_value=pd.NaT
try:
result = np.full(result_shape, fill_value, dtype=values.dtype)
except ValueError:
result = np.full(result_shape, fill_value)

result = np.full(result_shape, fill_value, dtype=values.dtype)
return result


Expand Down Expand Up @@ -526,11 +524,9 @@ def nansum(
def _mask_datetimelike_result(
result: Union[np.ndarray, np.datetime64, np.timedelta64],
axis: Optional[int],
mask: Optional[np.ndarray],
mask: np.ndarray,
orig_values: np.ndarray,
):
if mask is None:
mask = isna(orig_values)
if isinstance(result, np.ndarray):
# we need to apply the mask
result = result.astype("i8").view(orig_values.dtype)
Expand Down