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CLN: small readability fixes in window.pyx #27409

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CLN: small readability fixes in window.pyx #27409

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13 changes: 8 additions & 5 deletions pandas/_libs/window.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -1675,9 +1675,9 @@ def roll_generic(object obj,
return output


def roll_window(ndarray[float64_t, ndim=1, cast=True] values,
def roll_weighted_window(ndarray[float64_t, ndim=1, cast=True] values,
ndarray[float64_t, ndim=1, cast=True] weights,
int minp, bint avg=True):
int minp, name="mean"):
"""
Assume len(weights) << len(values)
"""
Expand All @@ -1686,16 +1686,19 @@ def roll_window(ndarray[float64_t, ndim=1, cast=True] values,
Py_ssize_t in_i, win_i, win_n, in_n
float64_t val_in, val_win, c, w

# currently supported ops
assert name in ("mean", "sum")

in_n = len(values)
win_n = len(weights)
output = np.zeros(in_n, dtype=float)
counts = np.zeros(in_n, dtype=float)
if avg:
if name == "mean":
tot_wgt = np.zeros(in_n, dtype=float)

minp = _check_minp(len(weights), minp, in_n)

if avg:
if name == "mean":
for win_i in range(win_n):
val_win = weights[win_i]
if val_win != val_win:
Expand All @@ -1719,7 +1722,7 @@ def roll_window(ndarray[float64_t, ndim=1, cast=True] values,
else:
output[in_i] /= tot_wgt[in_i]

else:
elif name == "sum":
for win_i in range(win_n):
val_win = weights[win_i]
if val_win != val_win:
Expand Down
10 changes: 5 additions & 5 deletions pandas/core/window.py
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Original file line number Diff line number Diff line change
Expand Up @@ -707,7 +707,7 @@ def _pop_args(win_type, arg_names, kwargs):
# GH #15662. `False` makes symmetric window, rather than periodic.
return sig.get_window(win_type, window, False).astype(float)

def _apply_window(self, mean=True, **kwargs):
def _apply_window(self, name, **kwargs):
"""
Applies a moving window of type ``window_type`` on the data.

Expand Down Expand Up @@ -750,11 +750,11 @@ def _apply_window(self, mean=True, **kwargs):

def f(arg, *args, **kwargs):
minp = _use_window(self.min_periods, len(window))
return libwindow.roll_window(
return libwindow.roll_weighted_window(
np.concatenate((arg, additional_nans)) if center else arg,
window,
minp,
avg=mean,
name=name,
)

result = np.apply_along_axis(f, self.axis, values)
Expand Down Expand Up @@ -831,13 +831,13 @@ def aggregate(self, arg, *args, **kwargs):
@Appender(_shared_docs["sum"])
def sum(self, *args, **kwargs):
nv.validate_window_func("sum", args, kwargs)
return self._apply_window(mean=False, **kwargs)
return self._apply_window(name="sum", **kwargs)

@Substitution(name="window")
@Appender(_shared_docs["mean"])
def mean(self, *args, **kwargs):
nv.validate_window_func("mean", args, kwargs)
return self._apply_window(mean=True, **kwargs)
return self._apply_window(name="mean", **kwargs)


class _GroupByMixin(GroupByMixin):
Expand Down