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CLN: Remove _get_roll_func #38596

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Merged
merged 1 commit into from
Dec 21, 2020
Merged

CLN: Remove _get_roll_func #38596

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6 changes: 3 additions & 3 deletions pandas/core/window/ewm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -335,14 +335,14 @@ def mean(self, *args, **kwargs):
"""
nv.validate_window_func("mean", args, kwargs)
if self.times is not None:
window_func = self._get_roll_func("ewma_time")
window_func = window_aggregations.ewma_time
window_func = partial(
window_func,
times=self.times,
halflife=self.halflife,
)
else:
window_func = self._get_roll_func("ewma")
window_func = window_aggregations.ewma
window_func = partial(
window_func,
com=self.com,
Expand Down Expand Up @@ -371,7 +371,7 @@ def var(self, bias: bool = False, *args, **kwargs):
Exponential weighted moving variance.
"""
nv.validate_window_func("var", args, kwargs)
window_func = self._get_roll_func("ewmcov")
window_func = window_aggregations.ewmcov
window_func = partial(
window_func,
com=self.com,
Expand Down
54 changes: 17 additions & 37 deletions pandas/core/window/rolling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -316,26 +316,6 @@ def _insert_on_column(self, result: "DataFrame", obj: "DataFrame"):
# insert at the end
result[name] = extra_col

def _get_roll_func(self, func_name: str) -> Callable[..., Any]:
"""
Wrap rolling function to check values passed.

Parameters
----------
func_name : str
Cython function used to calculate rolling statistics

Returns
-------
func : callable
"""
window_func = getattr(window_aggregations, func_name, None)
if window_func is None:
raise ValueError(
f"we do not support this function in window_aggregations.{func_name}"
)
return window_func

@property
def _index_array(self):
# TODO: why do we get here with e.g. MultiIndex?
Expand Down Expand Up @@ -1153,21 +1133,21 @@ def aggregate(self, func, *args, **kwargs):
@Appender(_shared_docs["sum"])
def sum(self, *args, **kwargs):
nv.validate_window_func("sum", args, kwargs)
window_func = self._get_roll_func("roll_weighted_sum")
window_func = window_aggregations.roll_weighted_sum
return self._apply(window_func, name="sum", **kwargs)

@Substitution(name="window")
@Appender(_shared_docs["mean"])
def mean(self, *args, **kwargs):
nv.validate_window_func("mean", args, kwargs)
window_func = self._get_roll_func("roll_weighted_mean")
window_func = window_aggregations.roll_weighted_mean
return self._apply(window_func, name="mean", **kwargs)

@Substitution(name="window", versionadded="\n.. versionadded:: 1.0.0\n")
@Appender(_shared_docs["var"])
def var(self, ddof: int = 1, *args, **kwargs):
nv.validate_window_func("var", args, kwargs)
window_func = partial(self._get_roll_func("roll_weighted_var"), ddof=ddof)
window_func = partial(window_aggregations.roll_weighted_var, ddof=ddof)
kwargs.pop("name", None)
return self._apply(window_func, name="var", **kwargs)

Expand Down Expand Up @@ -1221,7 +1201,7 @@ class RollingAndExpandingMixin(BaseWindow):
)

def count(self):
window_func = self._get_roll_func("roll_sum")
window_func = window_aggregations.roll_sum
return self._apply(window_func, name="count")

_shared_docs["apply"] = dedent(
Expand Down Expand Up @@ -1331,7 +1311,7 @@ def _generate_cython_apply_func(
from pandas import Series

window_func = partial(
self._get_roll_func("roll_apply"),
window_aggregations.roll_apply,
args=args,
kwargs=kwargs,
raw=raw,
Expand All @@ -1347,7 +1327,7 @@ def apply_func(values, begin, end, min_periods, raw=raw):

def sum(self, *args, **kwargs):
nv.validate_window_func("sum", args, kwargs)
window_func = self._get_roll_func("roll_sum")
window_func = window_aggregations.roll_sum
return self._apply(window_func, name="sum", **kwargs)

_shared_docs["max"] = dedent(
Expand All @@ -1363,7 +1343,7 @@ def sum(self, *args, **kwargs):

def max(self, *args, **kwargs):
nv.validate_window_func("max", args, kwargs)
window_func = self._get_roll_func("roll_max")
window_func = window_aggregations.roll_max
return self._apply(window_func, name="max", **kwargs)

_shared_docs["min"] = dedent(
Expand Down Expand Up @@ -1405,12 +1385,12 @@ def max(self, *args, **kwargs):

def min(self, *args, **kwargs):
nv.validate_window_func("min", args, kwargs)
window_func = self._get_roll_func("roll_min")
window_func = window_aggregations.roll_min
return self._apply(window_func, name="min", **kwargs)

def mean(self, *args, **kwargs):
nv.validate_window_func("mean", args, kwargs)
window_func = self._get_roll_func("roll_mean")
window_func = window_aggregations.roll_mean
return self._apply(window_func, name="mean", **kwargs)

_shared_docs["median"] = dedent(
Expand Down Expand Up @@ -1451,14 +1431,14 @@ def mean(self, *args, **kwargs):
)

def median(self, **kwargs):
window_func = self._get_roll_func("roll_median_c")
window_func = window_aggregations.roll_median_c
# GH 32865. Move max window size calculation to
# the median function implementation
return self._apply(window_func, name="median", **kwargs)

def std(self, ddof: int = 1, *args, **kwargs):
nv.validate_window_func("std", args, kwargs)
window_func = self._get_roll_func("roll_var")
window_func = window_aggregations.roll_var

def zsqrt_func(values, begin, end, min_periods):
return zsqrt(window_func(values, begin, end, min_periods, ddof=ddof))
Expand All @@ -1471,7 +1451,7 @@ def zsqrt_func(values, begin, end, min_periods):

def var(self, ddof: int = 1, *args, **kwargs):
nv.validate_window_func("var", args, kwargs)
window_func = partial(self._get_roll_func("roll_var"), ddof=ddof)
window_func = partial(window_aggregations.roll_var, ddof=ddof)
return self._apply(
window_func,
name="var",
Expand All @@ -1490,7 +1470,7 @@ def var(self, ddof: int = 1, *args, **kwargs):
"""

def skew(self, **kwargs):
window_func = self._get_roll_func("roll_skew")
window_func = window_aggregations.roll_skew
return self._apply(
window_func,
name="skew",
Expand Down Expand Up @@ -1583,7 +1563,7 @@ def sem(self, ddof: int = 1, *args, **kwargs):
)

def kurt(self, **kwargs):
window_func = self._get_roll_func("roll_kurt")
window_func = window_aggregations.roll_kurt
return self._apply(
window_func,
name="kurt",
Expand Down Expand Up @@ -1646,12 +1626,12 @@ def kurt(self, **kwargs):

def quantile(self, quantile: float, interpolation: str = "linear", **kwargs):
if quantile == 1.0:
window_func = self._get_roll_func("roll_max")
window_func = window_aggregations.roll_max
elif quantile == 0.0:
window_func = self._get_roll_func("roll_min")
window_func = window_aggregations.roll_min
else:
window_func = partial(
self._get_roll_func("roll_quantile"),
window_aggregations.roll_quantile,
quantile=quantile,
interpolation=interpolation,
)
Expand Down