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CLN: window/rolling.py #35982

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Merged
merged 2 commits into from
Aug 31, 2020
Merged

CLN: window/rolling.py #35982

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c32dd28
CLN: rolling.py
Aug 30, 2020
f27f104
Use obj._constructor instead
Aug 30, 2020
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23 changes: 7 additions & 16 deletions pandas/core/window/rolling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -377,23 +377,15 @@ def _prep_values(self, values: Optional[np.ndarray] = None) -> np.ndarray:

return values

def _wrap_result(self, result, block=None, obj=None):
def _wrap_result(self, result: np.ndarray) -> "Series":
"""
Wrap a single result.
Wrap a single 1D result.
"""
if obj is None:
obj = self._selected_obj
index = obj.index
obj = self._selected_obj

if isinstance(result, np.ndarray):

if result.ndim == 1:
from pandas import Series

return Series(result, index, name=obj.name)
from pandas import Series

return type(obj)(result, index=index, columns=block.columns)
return result
return Series(result, obj.index, name=obj.name)
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obj._constructor?


def _wrap_results(self, results, obj, skipped: List[int]) -> FrameOrSeriesUnion:
"""
Expand Down Expand Up @@ -454,7 +446,7 @@ def _insert_on_column(self, result: "DataFrame", obj: "DataFrame"):
# insert at the end
result[name] = extra_col

def _center_window(self, result, window) -> np.ndarray:
def _center_window(self, result: np.ndarray, window) -> np.ndarray:
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ATM _prep_values prevents us from doing anything with EAs, but in principle is that something we'd like to support?

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In the future yes, but I think a major blocker/consideration is that today rolling agg functions only operate on float64 inputs, so that will need to be addressed in tandem.

"""
Center the result in the window.
"""
Expand Down Expand Up @@ -513,7 +505,6 @@ def _apply_series(self, homogeneous_func: Callable[..., ArrayLike]) -> "Series":
Series version of _apply_blockwise
"""
_, obj = self._create_blocks(self._selected_obj)
values = obj.values

try:
values = self._prep_values(obj.values)
Expand All @@ -535,7 +526,7 @@ def _apply_blockwise(

# This isn't quite blockwise, since `blocks` is actually a collection
# of homogenenous DataFrames.
blocks, obj = self._create_blocks(self._selected_obj)
_, obj = self._create_blocks(self._selected_obj)
mgr = obj._mgr

def hfunc(bvalues: ArrayLike) -> ArrayLike:
Expand Down