TYP: follow-ups to recent PRs

pandas/core/dtypes/concat.py

@@ -89,7 +89,7 @@ def _cast_to_common_type(arr: ArrayLike, dtype: DtypeObj) -> ArrayLike:
         # wrap datetime-likes in EA to ensure astype(object) gives Timestamp/Timedelta
         # this can happen when concat_compat is called directly on arrays (when arrays
         # are not coming from Index/Series._values), eg in BlockManager.quantile
-        arr = array(arr)
+        arr = ensure_wrapped_if_datetimelike(arr)
 
     if is_extension_array_dtype(dtype):
         if isinstance(arr, np.ndarray):

pandas/core/frame.py

@@ -9600,15 +9600,15 @@ def _from_nested_dict(data) -> collections.defaultdict:
     return new_data
 
 
-def _reindex_for_setitem(value, index: Index):
+def _reindex_for_setitem(value: FrameOrSeriesUnion, index: Index) -> ArrayLike:
     # reindex if necessary
 
     if value.index.equals(index) or not len(index):
         return value._values.copy()
 
     # GH#4107
     try:
-        value = value.reindex(index)._values
+        reindexed_value = value.reindex(index)._values
     except ValueError as err:
         # raised in MultiIndex.from_tuples, see test_insert_error_msmgs
         if not value.index.is_unique:
@@ -9618,7 +9618,7 @@ def _reindex_for_setitem(value, index: Index):
         raise TypeError(
             "incompatible index of inserted column with frame index"
         ) from err
-    return value
+    return reindexed_value
 
 
 def _maybe_atleast_2d(value):

pandas/core/internals/blocks.py

@@ -1274,22 +1274,6 @@ def shift(self, periods: int, axis: int = 0, fill_value=None):
 
         return [self.make_block(new_values)]
 
-    def _maybe_reshape_where_args(self, values, other, cond, axis):
-        transpose = self.ndim == 2
-
-        cond = _extract_bool_array(cond)
-
-        # If the default broadcasting would go in the wrong direction, then
-        # explicitly reshape other instead
-        if getattr(other, "ndim", 0) >= 1:
-            if values.ndim - 1 == other.ndim and axis == 1:
-                other = other.reshape(tuple(other.shape + (1,)))
-            elif transpose and values.ndim == self.ndim - 1:
-                # TODO(EA2D): not neceesssary with 2D EAs
-                cond = cond.T
-
-        return other, cond
-
     def where(self, other, cond, errors="raise", axis: int = 0) -> List["Block"]:
         """
         evaluate the block; return result block(s) from the result
@@ -1319,7 +1303,7 @@ def where(self, other, cond, errors="raise", axis: int = 0) -> List["Block"]:
         if transpose:
             values = values.T
 
-        other, cond = self._maybe_reshape_where_args(values, other, cond, axis)
+        cond = _extract_bool_array(cond)
 
         if cond.ravel("K").all():
             result = values
@@ -2072,7 +2056,7 @@ def where(self, other, cond, errors="raise", axis: int = 0) -> List["Block"]:
         # TODO(EA2D): reshape unnecessary with 2D EAs
         arr = self.array_values().reshape(self.shape)
 
-        other, cond = self._maybe_reshape_where_args(arr, other, cond, axis)
+        cond = _extract_bool_array(cond)
 
         try:
             res_values = arr.T.where(cond, other).T
@@ -2572,23 +2556,20 @@ def _block_shape(values: ArrayLike, ndim: int = 1) -> ArrayLike:
     return values
 
 
-def safe_reshape(arr, new_shape: Shape):
+def safe_reshape(arr: ArrayLike, new_shape: Shape) -> ArrayLike:
     """
-    If possible, reshape `arr` to have shape `new_shape`,
-    with a couple of exceptions (see gh-13012):
-
-    1) If `arr` is a ExtensionArray or Index, `arr` will be
-       returned as is.
-    2) If `arr` is a Series, the `_values` attribute will
-       be reshaped and returned.
+    Reshape `arr` to have shape `new_shape`, unless it is an ExtensionArray,
+    in which case it will be returned unchanged (see gh-13012).
 
     Parameters
     ----------
-    arr : array-like, object to be reshaped
-    new_shape : int or tuple of ints, the new shape
+    arr : np.ndarray or ExtensionArray
+    new_shape : Tuple[int]
+
+    Returns
+    -------
+    np.ndarray or ExtensionArray
     """
-    if isinstance(arr, ABCSeries):
-        arr = arr._values
     if not is_extension_array_dtype(arr.dtype):
         # Note: this will include TimedeltaArray and tz-naive DatetimeArray
         # TODO(EA2D): special case will be unnecessary with 2D EAs

pandas/core/internals/concat.py

@@ -5,7 +5,7 @@
 import numpy as np
 
 from pandas._libs import NaT, internals as libinternals
-from pandas._typing import DtypeObj, Shape
+from pandas._typing import ArrayLike, DtypeObj, Shape
 from pandas.util._decorators import cache_readonly
 
 from pandas.core.dtypes.cast import maybe_promote
@@ -29,11 +29,12 @@
 from pandas.core.internals.managers import BlockManager
 
 if TYPE_CHECKING:
+    from pandas import Index
     from pandas.core.arrays.sparse.dtype import SparseDtype
 
 
 def concatenate_block_managers(
-    mgrs_indexers, axes, concat_axis: int, copy: bool
+    mgrs_indexers, axes: List["Index"], concat_axis: int, copy: bool
 ) -> BlockManager:
     """
     Concatenate block managers into one.
@@ -96,7 +97,7 @@ def concatenate_block_managers(
     return BlockManager(blocks, axes)
 
 
-def _get_mgr_concatenation_plan(mgr, indexers):
+def _get_mgr_concatenation_plan(mgr: BlockManager, indexers: Dict[int, np.ndarray]):
     """
     Construct concatenation plan for given block manager and indexers.
 
@@ -235,7 +236,7 @@ def is_na(self) -> bool:
 
         return isna_all(values_flat)
 
-    def get_reindexed_values(self, empty_dtype: DtypeObj, upcasted_na):
+    def get_reindexed_values(self, empty_dtype: DtypeObj, upcasted_na) -> ArrayLike:
         if upcasted_na is None:
             # No upcasting is necessary
             fill_value = self.block.fill_value
@@ -307,7 +308,9 @@ def get_reindexed_values(self, empty_dtype: DtypeObj, upcasted_na):
         return values
 
 
-def _concatenate_join_units(join_units, concat_axis, copy):
+def _concatenate_join_units(
+    join_units: List[JoinUnit], concat_axis: int, copy: bool
+) -> ArrayLike:
     """
     Concatenate values from several join units along selected axis.
     """
@@ -513,7 +516,7 @@ def _is_uniform_reindex(join_units) -> bool:
     )
 
 
-def _trim_join_unit(join_unit, length):
+def _trim_join_unit(join_unit: JoinUnit, length: int) -> JoinUnit:
     """
     Reduce join_unit's shape along item axis to length.
 
@@ -540,7 +543,7 @@ def _trim_join_unit(join_unit, length):
     return JoinUnit(block=extra_block, indexers=extra_indexers, shape=extra_shape)
 
 
-def _combine_concat_plans(plans, concat_axis):
+def _combine_concat_plans(plans, concat_axis: int):
     """
     Combine multiple concatenation plans into one.
 

pandas/core/tools/numeric.py

@@ -171,7 +171,7 @@ def to_numeric(arg, errors="raise", downcast=None):
     if is_numeric_dtype(values_dtype):
         pass
     elif is_datetime_or_timedelta_dtype(values_dtype):
-        values = values.astype(np.int64)
+        values = values.view(np.int64)
     else:
         values = ensure_object(values)
         coerce_numeric = errors not in ("ignore", "raise")

pandas/tests/plotting/frame/test_frame.py

@@ -156,8 +156,8 @@ def test_nullable_int_plot(self):
                 "A": [1, 2, 3, 4, 5],
                 "B": [1.0, 2.0, 3.0, 4.0, 5.0],
                 "C": [7, 5, np.nan, 3, 2],
-                "D": pd.to_datetime(dates, format="%Y"),
-                "E": pd.to_datetime(dates, format="%Y", utc=True),
+                "D": pd.to_datetime(dates, format="%Y").view("i8"),
+                "E": pd.to_datetime(dates, format="%Y", utc=True).view("i8"),
             },
             dtype=np.int64,
         )