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REF: more distinctive names for BaseGrouper.aggregate, transform #38161

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Merged
merged 1 commit into from
Nov 30, 2020
Merged

REF: more distinctive names for BaseGrouper.aggregate, transform #38161

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4 changes: 2 additions & 2 deletions pandas/core/groupby/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1063,8 +1063,8 @@ def cast_agg_result(result, values: ArrayLike, how: str) -> ArrayLike:
def blk_func(bvalues: ArrayLike) -> ArrayLike:

try:
result, _ = self.grouper.aggregate(
bvalues, how, axis=1, min_count=min_count
result, _ = self.grouper._cython_operation(
"aggregate", bvalues, how, axis=1, min_count=min_count
)
except NotImplementedError:
# generally if we have numeric_only=False
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13 changes: 9 additions & 4 deletions pandas/core/groupby/groupby.py
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Expand Up @@ -982,16 +982,21 @@ def _transform_should_cast(self, func_nm: str) -> bool:
assert filled_series is not None
return filled_series.gt(0).any() and func_nm not in base.cython_cast_blocklist

def _cython_transform(self, how: str, numeric_only: bool = True, **kwargs):
def _cython_transform(
self, how: str, numeric_only: bool = True, axis: int = 0, **kwargs
):
output: Dict[base.OutputKey, np.ndarray] = {}

for idx, obj in enumerate(self._iterate_slices()):
name = obj.name
is_numeric = is_numeric_dtype(obj.dtype)
if numeric_only and not is_numeric:
continue

try:
result, _ = self.grouper.transform(obj.values, how, **kwargs)
result, _ = self.grouper._cython_operation(
"transform", obj.values, how, axis, **kwargs
)
except NotImplementedError:
continue

Expand Down Expand Up @@ -1067,8 +1072,8 @@ def _cython_agg_general(
if numeric_only and not is_numeric:
continue

result, agg_names = self.grouper.aggregate(
obj._values, how, min_count=min_count
result, agg_names = self.grouper._cython_operation(
"aggregate", obj._values, how, axis=0, min_count=min_count
)

if agg_names:
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10 changes: 0 additions & 10 deletions pandas/core/groupby/ops.py
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Expand Up @@ -588,16 +588,6 @@ def _cython_operation(

return result, names

def aggregate(
self, values, how: str, axis: int = 0, min_count: int = -1
) -> Tuple[np.ndarray, Optional[List[str]]]:
return self._cython_operation(
"aggregate", values, how, axis, min_count=min_count
)

def transform(self, values, how: str, axis: int = 0, **kwargs):
return self._cython_operation("transform", values, how, axis, **kwargs)

def _aggregate(
self, result, counts, values, comp_ids, agg_func, min_count: int = -1
):
Expand Down