CLN: Clean groupby ops from unreached code paths (pandas-dev#48698)

* CLN: Clean groupby ops from unreached code paths

* Refactor

Author: phofl

pandas/core/groupby/ops.py

@@ -72,9 +72,6 @@
     PeriodArray,
     TimedeltaArray,
 )
-from pandas.core.arrays.boolean import BooleanDtype
-from pandas.core.arrays.floating import FloatingDtype
-from pandas.core.arrays.integer import IntegerDtype
 from pandas.core.arrays.masked import (
     BaseMaskedArray,
     BaseMaskedDtype,
@@ -147,26 +144,6 @@ def __init__(self, kind: str, how: str, has_dropped_na: bool) -> None:
         },
     }
 
-    # "group_any" and "group_all" are also support masks, but don't go
-    #  through WrappedCythonOp
-    _MASKED_CYTHON_FUNCTIONS = {
-        "cummin",
-        "cummax",
-        "min",
-        "max",
-        "last",
-        "first",
-        "rank",
-        "sum",
-        "ohlc",
-        "cumprod",
-        "cumsum",
-        "prod",
-        "mean",
-        "var",
-        "median",
-    }
-
     _cython_arity = {"ohlc": 4}  # OHLC
 
     # Note: we make this a classmethod and pass kind+how so that caching
@@ -220,8 +197,8 @@ def _get_cython_vals(self, values: np.ndarray) -> np.ndarray:
         """
         how = self.how
 
-        if how in ["median"]:
-            # these two only have float64 implementations
+        if how == "median":
+            # median only has a float64 implementation
             # We should only get here with is_numeric, as non-numeric cases
             #  should raise in _get_cython_function
             values = ensure_float64(values)
@@ -293,7 +270,7 @@ def _get_output_shape(self, ngroups: int, values: np.ndarray) -> Shape:
 
         out_shape: Shape
         if how == "ohlc":
-            out_shape = (ngroups, 4)
+            out_shape = (ngroups, arity)
         elif arity > 1:
             raise NotImplementedError(
                 "arity of more than 1 is not supported for the 'how' argument"
@@ -342,9 +319,6 @@ def _get_result_dtype(self, dtype: np.dtype) -> np.dtype:
                 return np.dtype(np.float64)
         return dtype
 
-    def uses_mask(self) -> bool:
-        return self.how in self._MASKED_CYTHON_FUNCTIONS
-
     @final
     def _ea_wrap_cython_operation(
         self,
@@ -358,7 +332,7 @@ def _ea_wrap_cython_operation(
         If we have an ExtensionArray, unwrap, call _cython_operation, and
         re-wrap if appropriate.
         """
-        if isinstance(values, BaseMaskedArray) and self.uses_mask():
+        if isinstance(values, BaseMaskedArray):
             return self._masked_ea_wrap_cython_operation(
                 values,
                 min_count=min_count,
@@ -367,7 +341,7 @@ def _ea_wrap_cython_operation(
                 **kwargs,
             )
 
-        elif isinstance(values, Categorical) and self.uses_mask():
+        elif isinstance(values, Categorical):
             assert self.how == "rank"  # the only one implemented ATM
             assert values.ordered  # checked earlier
             mask = values.isna()
@@ -398,7 +372,7 @@ def _ea_wrap_cython_operation(
         )
 
         if self.how in self.cast_blocklist:
-            # i.e. how in ["rank"], since other cast_blocklist methods dont go
+            # i.e. how in ["rank"], since other cast_blocklist methods don't go
             #  through cython_operation
             return res_values
 
@@ -411,12 +385,6 @@ def _ea_to_cython_values(self, values: ExtensionArray) -> np.ndarray:
             # All of the functions implemented here are ordinal, so we can
             #  operate on the tz-naive equivalents
             npvalues = values._ndarray.view("M8[ns]")
-        elif isinstance(values.dtype, (BooleanDtype, IntegerDtype)):
-            # IntegerArray or BooleanArray
-            npvalues = values.to_numpy("float64", na_value=np.nan)
-        elif isinstance(values.dtype, FloatingDtype):
-            # FloatingArray
-            npvalues = values.to_numpy(values.dtype.numpy_dtype, na_value=np.nan)
         elif isinstance(values.dtype, StringDtype):
             # StringArray
             npvalues = values.to_numpy(object, na_value=np.nan)
@@ -440,12 +408,6 @@ def _reconstruct_ea_result(
             string_array_cls = dtype.construct_array_type()
             return string_array_cls._from_sequence(res_values, dtype=dtype)
 
-        elif isinstance(values.dtype, BaseMaskedDtype):
-            new_dtype = self._get_result_dtype(values.dtype.numpy_dtype)
-            dtype = BaseMaskedDtype.from_numpy_dtype(new_dtype)
-            masked_array_cls = dtype.construct_array_type()
-            return masked_array_cls._from_sequence(res_values, dtype=dtype)
-
         elif isinstance(values, (DatetimeArray, TimedeltaArray, PeriodArray)):
             # In to_cython_values we took a view as M8[ns]
             assert res_values.dtype == "M8[ns]"
@@ -489,7 +451,8 @@ def _masked_ea_wrap_cython_operation(
         )
 
         if self.how == "ohlc":
-            result_mask = np.tile(result_mask, (4, 1)).T
+            arity = self._cython_arity.get(self.how, 1)
+            result_mask = np.tile(result_mask, (arity, 1)).T
 
         # res_values should already have the correct dtype, we just need to
         #  wrap in a MaskedArray
@@ -580,7 +543,7 @@ def _call_cython_op(
         result = maybe_fill(np.empty(out_shape, dtype=out_dtype))
         if self.kind == "aggregate":
             counts = np.zeros(ngroups, dtype=np.int64)
-            if self.how in ["min", "max", "mean", "last", "first"]:
+            if self.how in ["min", "max", "mean", "last", "first", "sum"]:
                 func(
                     out=result,
                     counts=counts,
@@ -591,18 +554,6 @@ def _call_cython_op(
                     result_mask=result_mask,
                     is_datetimelike=is_datetimelike,
                 )
-            elif self.how in ["sum"]:
-                # We support datetimelike
-                func(
-                    out=result,
-                    counts=counts,
-                    values=values,
-                    labels=comp_ids,
-                    mask=mask,
-                    result_mask=result_mask,
-                    min_count=min_count,
-                    is_datetimelike=is_datetimelike,
-                )
             elif self.how in ["var", "ohlc", "prod", "median"]:
                 func(
                     result,
@@ -615,31 +566,21 @@ def _call_cython_op(
                     **kwargs,
                 )
             else:
-                func(result, counts, values, comp_ids, min_count)
+                raise NotImplementedError(f"{self.how} is not implemented")
         else:
             # TODO: min_count
-            if self.uses_mask():
-                if self.how != "rank":
-                    # TODO: should rank take result_mask?
-                    kwargs["result_mask"] = result_mask
-                func(
-                    out=result,
-                    values=values,
-                    labels=comp_ids,
-                    ngroups=ngroups,
-                    is_datetimelike=is_datetimelike,
-                    mask=mask,
-                    **kwargs,
-                )
-            else:
-                func(
-                    out=result,
-                    values=values,
-                    labels=comp_ids,
-                    ngroups=ngroups,
-                    is_datetimelike=is_datetimelike,
-                    **kwargs,
-                )
+            if self.how != "rank":
+                # TODO: should rank take result_mask?
+                kwargs["result_mask"] = result_mask
+            func(
+                out=result,
+                values=values,
+                labels=comp_ids,
+                ngroups=ngroups,
+                is_datetimelike=is_datetimelike,
+                mask=mask,
+                **kwargs,
+            )
 
         if self.kind == "aggregate":
             # i.e. counts is defined.  Locations where count<min_count
@@ -650,7 +591,7 @@ def _call_cython_op(
                 cutoff = max(0 if self.how in ["sum", "prod"] else 1, min_count)
                 empty_groups = counts < cutoff
                 if empty_groups.any():
-                    if result_mask is not None and self.uses_mask():
+                    if result_mask is not None:
                         assert result_mask[empty_groups].all()
                     else:
                         # Note: this conversion could be lossy, see GH#40767