CLN: rename reduce-->do_reduce (#27706)

Author: jbrockmendel

pandas/_libs/reduction.pyx

@@ -628,7 +628,7 @@ cdef class BlockSlider:
             arr.shape[1] = 0
 
 
-def reduce(arr, f, axis=0, dummy=None, labels=None):
+def compute_reduction(arr, f, axis=0, dummy=None, labels=None):
     """
 
     Parameters

pandas/_libs/tslibs/timedeltas.pyx

@@ -1280,7 +1280,8 @@ class Timedelta(_Timedelta):
         else:
             raise ValueError(
                 "Value must be Timedelta, string, integer, "
-                "float, timedelta or convertible")
+                "float, timedelta or convertible, not {typ}"
+                .format(typ=type(value).__name__))
 
         if is_timedelta64_object(value):
             value = value.view('i8')

pandas/core/apply.py

@@ -221,7 +221,7 @@ def apply_raw(self):
         """ apply to the values as a numpy array """
 
         try:
-            result = reduction.reduce(self.values, self.f, axis=self.axis)
+            result = reduction.compute_reduction(self.values, self.f, axis=self.axis)
         except Exception:
             result = np.apply_along_axis(self.f, self.axis, self.values)
 
@@ -281,7 +281,7 @@ def apply_standard(self):
             dummy = Series(empty_arr, index=index, dtype=values.dtype)
 
             try:
-                result = reduction.reduce(
+                result = reduction.compute_reduction(
                     values, self.f, axis=self.axis, dummy=dummy, labels=labels
                 )
                 return self.obj._constructor_sliced(result, index=labels)

pandas/core/arrays/categorical.py

@@ -2703,7 +2703,7 @@ def _convert_to_list_like(list_like):
     elif is_scalar(list_like):
         return [list_like]
     else:
-        # is this reached?
+        # TODO: is this reached?
         return [list_like]
 
 

pandas/core/arrays/datetimelike.py

@@ -57,21 +57,10 @@
 class AttributesMixin:
     _data = None  # type: np.ndarray
 
-    @property
-    def _attributes(self):
-        # Inheriting subclass should implement _attributes as a list of strings
-        raise AbstractMethodError(self)
-
     @classmethod
     def _simple_new(cls, values, **kwargs):
         raise AbstractMethodError(cls)
 
-    def _get_attributes_dict(self):
-        """
-        return an attributes dict for my class
-        """
-        return {k: getattr(self, k, None) for k in self._attributes}
-
     @property
     def _scalar_type(self) -> Type[DatetimeLikeScalar]:
         """The scalar associated with this datelike

pandas/core/arrays/datetimes.py

@@ -328,7 +328,6 @@ class DatetimeArray(dtl.DatetimeLikeArrayMixin, dtl.TimelikeOps, dtl.DatelikeOps
     # -----------------------------------------------------------------
     # Constructors
 
-    _attributes = ["freq", "tz"]
     _dtype = None  # type: Union[np.dtype, DatetimeTZDtype]
     _freq = None
 

pandas/core/arrays/period.py

@@ -161,7 +161,6 @@ class PeriodArray(dtl.DatetimeLikeArrayMixin, dtl.DatelikeOps):
 
     # array priority higher than numpy scalars
     __array_priority__ = 1000
-    _attributes = ["freq"]
     _typ = "periodarray"  # ABCPeriodArray
     _scalar_type = Period
 

pandas/core/arrays/timedeltas.py

@@ -199,7 +199,6 @@ def dtype(self):
 
     # ----------------------------------------------------------------
     # Constructors
-    _attributes = ["freq"]
 
     def __init__(self, values, dtype=_TD_DTYPE, freq=None, copy=False):
         if isinstance(values, (ABCSeries, ABCIndexClass)):

pandas/core/generic.py

@@ -3556,7 +3556,7 @@ def _iget_item_cache(self, item):
     def _box_item_values(self, key, values):
         raise AbstractMethodError(self)
 
-    def _slice(self, slobj, axis=0, kind=None):
+    def _slice(self, slobj: slice, axis=0, kind=None):
         """
         Construct a slice of this container.
 
@@ -6183,8 +6183,6 @@ def fillna(
             axis = 0
         axis = self._get_axis_number(axis)
 
-        from pandas import DataFrame
-
         if value is None:
 
             if self._is_mixed_type and axis == 1:
@@ -6247,7 +6245,7 @@ def fillna(
                 new_data = self._data.fillna(
                     value=value, limit=limit, inplace=inplace, downcast=downcast
                 )
-            elif isinstance(value, DataFrame) and self.ndim == 2:
+            elif isinstance(value, ABCDataFrame) and self.ndim == 2:
                 new_data = self.where(self.notna(), value)
             else:
                 raise ValueError("invalid fill value with a %s" % type(value))

pandas/core/groupby/groupby.py

@@ -29,14 +29,16 @@ class providing the base-class of operations.
 from pandas.core.dtypes.cast import maybe_downcast_to_dtype
 from pandas.core.dtypes.common import (
     ensure_float,
+    is_datetime64_dtype,
     is_datetime64tz_dtype,
     is_extension_array_dtype,
+    is_integer_dtype,
     is_numeric_dtype,
+    is_object_dtype,
     is_scalar,
 )
 from pandas.core.dtypes.missing import isna, notna
 
-from pandas.api.types import is_datetime64_dtype, is_integer_dtype, is_object_dtype
 import pandas.core.algorithms as algorithms
 from pandas.core.arrays import Categorical
 from pandas.core.base import (
@@ -343,7 +345,7 @@ class _GroupBy(PandasObject, SelectionMixin):
 
     def __init__(
         self,
-        obj,
+        obj: NDFrame,
         keys=None,
         axis=0,
         level=None,
@@ -360,8 +362,8 @@ def __init__(
 
         self._selection = selection
 
-        if isinstance(obj, NDFrame):
-            obj._consolidate_inplace()
+        assert isinstance(obj, NDFrame), type(obj)
+        obj._consolidate_inplace()
 
         self.level = level