CLN: assorted cleanups (#38275)

Author: jbrockmendel

pandas/_libs/reduction.pyx

@@ -367,9 +367,9 @@ def apply_frame_axis0(object frame, object f, object names,
 
  try:
  piece = f(chunk)
- except Exception:
+ except Exception as err:
  # We can't be more specific without knowing something about `f`
- raise InvalidApply('Let this error raise above us')
+ raise InvalidApply("Let this error raise above us") from err
 
  # Need to infer if low level index slider will cause segfaults
  require_slow_apply = i == 0 and piece is chunk

pandas/core/groupby/generic.py

@@ -63,7 +63,7 @@
 import pandas.core.common as com
 from pandas.core.construction import create_series_with_explicit_dtype
 from pandas.core.frame import DataFrame
-from pandas.core.generic import ABCDataFrame, ABCSeries, NDFrame
+from pandas.core.generic import NDFrame
 from pandas.core.groupby import base
 from pandas.core.groupby.groupby import (
  GroupBy,
@@ -531,7 +531,7 @@ def _transform_general(self, func, *args, **kwargs):
  object.__setattr__(group, "name", name)
  res = func(group, *args, **kwargs)
 
- if isinstance(res, (ABCDataFrame, ABCSeries)):
+ if isinstance(res, (DataFrame, Series)):
  res = res._values
 
  results.append(klass(res, index=group.index))

pandas/core/groupby/groupby.py

@@ -966,22 +966,6 @@ def _cumcount_array(self, ascending: bool = True):
  rev[sorter] = np.arange(count, dtype=np.intp)
  return out[rev].astype(np.int64, copy=False)
 
- def _transform_should_cast(self, func_nm: str) -> bool:
- """
- Parameters
- ----------
- func_nm: str
- The name of the aggregation function being performed
-
- Returns
- -------
- bool
- Whether transform should attempt to cast the result of aggregation
- """
- filled_series = self.grouper.size().fillna(0)
- 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, axis: int = 0, **kwargs
  ):

pandas/core/groupby/grouper.py

@@ -18,7 +18,6 @@
  is_scalar,
  is_timedelta64_dtype,
 )
-from pandas.core.dtypes.generic import ABCSeries
 
 import pandas.core.algorithms as algorithms
 from pandas.core.arrays import Categorical, ExtensionArray
@@ -345,9 +344,7 @@ def _set_grouper(self, obj: FrameOrSeries, sort: bool = False):
  if self.key is not None:
  key = self.key
  # The 'on' is already defined
- if getattr(self.grouper, "name", None) == key and isinstance(
- obj, ABCSeries
- ):
+ if getattr(self.grouper, "name", None) == key and isinstance(obj, Series):
  # pandas\core\groupby\grouper.py:348: error: Item "None" of
  # "Optional[Any]" has no attribute "take" [union-attr]
  ax = self._grouper.take(obj.index) # type: ignore[union-attr]

pandas/core/indexes/base.py

@@ -70,7 +70,6 @@
  ABCMultiIndex,
  ABCPandasArray,
  ABCPeriodIndex,
- ABCRangeIndex,
  ABCSeries,
  ABCTimedeltaIndex,
 )
@@ -3494,12 +3493,7 @@ def reindex(self, target, method=None, level=None, limit=None, tolerance=None):
  target = ensure_has_len(target) # target may be an iterator
 
  if not isinstance(target, Index) and len(target) == 0:
- values: Union[range, ExtensionArray, np.ndarray]
- if isinstance(self, ABCRangeIndex):
- values = range(0)
- else:
- values = self._data[:0] # appropriately-dtyped empty array
- target = self._simple_new(values, name=self.name)
+ target = self[:0]
  else:
  target = ensure_index(target)
 
@@ -3829,6 +3823,7 @@ def _join_non_unique(self, other, how="left", return_indexers=False):
  else:
  return join_index
 
+ @final
  def _join_level(
  self, other, level, how="left", return_indexers=False, keep_order=True
  ):
@@ -3972,6 +3967,7 @@ def _get_leaf_sorter(labels):
  else:
  return join_index
 
+ @final
  def _join_monotonic(self, other, how="left", return_indexers=False):
  # We only get here with matching dtypes
  assert other.dtype == self.dtype

pandas/tests/series/indexing/test_xs.py

@@ -56,8 +56,8 @@ def test_series_xs_droplevel_false(self):
  mi = MultiIndex.from_tuples(
  [("a", "x"), ("a", "y"), ("b", "x")], names=["level1", "level2"]
  )
- df = Series([1, 1, 1], index=mi)
- result = df.xs("a", axis=0, drop_level=False)
+ ser = Series([1, 1, 1], index=mi)
+ result = ser.xs("a", axis=0, drop_level=False)
  expected = Series(
  [1, 1],
  index=MultiIndex.from_tuples(