REF: share code ArrayManager/BlockManager

pandas/core/internals/array_manager.py

@@ -22,14 +22,12 @@
 )
 from pandas._typing import (
     ArrayLike,
-    DtypeObj,
     Hashable,
 )
 from pandas.util._validators import validate_bool_kwarg
 
 from pandas.core.dtypes.cast import (
     astype_array_safe,
-    find_common_type,
     infer_dtype_from_scalar,
     soft_convert_objects,
 )
@@ -84,6 +82,7 @@
 from pandas.core.internals.base import (
     DataManager,
     SingleDataManager,
+    interleaved_dtype,
 )
 from pandas.core.internals.blocks import new_block
 
@@ -155,22 +154,11 @@ def axes(self) -> List[Index]:  # type: ignore[override]
         """Axes is BlockManager-compatible order (columns, rows)"""
         return [self._axes[1], self._axes[0]]
 
-    @property
-    def shape(self) -> Tuple[int, ...]:
-        # this still gives the BlockManager-compatible transposed shape
-        return tuple(len(ax) for ax in self.axes)
-
     @property
     def shape_proper(self) -> Tuple[int, ...]:
         # this returns (n_rows, n_columns)
         return tuple(len(ax) for ax in self._axes)
 
-    @staticmethod
-    def _normalize_axis(axis):
-        # switch axis
-        axis = 1 if axis == 0 else 0
-        return axis
-
     def set_axis(
         self, axis: int, new_labels: Index, verify_integrity: bool = True
     ) -> None:
@@ -511,9 +499,6 @@ def quantile(
         axes = [qs, self._axes[1]]
         return type(self)(new_arrs, axes)
 
-    def isna(self, func) -> ArrayManager:
-        return self.apply("apply", func=func)
-
     def where(self, other, cond, align: bool, errors: str, axis: int) -> ArrayManager:
         if align:
             align_keys = ["other", "cond"]
@@ -752,7 +737,7 @@ def as_array(
         copy = copy or na_value is not lib.no_default
 
         if not dtype:
-            dtype = _interleaved_dtype(self.arrays)
+            dtype = interleaved_dtype([arr.dtype for arr in self.arrays])
 
         if isinstance(dtype, SparseDtype):
             dtype = dtype.subtype
@@ -800,7 +785,7 @@ def fast_xs(self, loc: int) -> ArrayLike:
         -------
         np.ndarray or ExtensionArray
         """
-        dtype = _interleaved_dtype(self.arrays)
+        dtype = interleaved_dtype([arr.dtype for arr in self.arrays])
 
         values = [arr[loc] for arr in self.arrays]
         if isinstance(dtype, ExtensionDtype):
@@ -1096,26 +1081,6 @@ def unstack(self, unstacker, fill_value) -> ArrayManager:
     # TODO
     # equals
     # to_dict
-    # quantile
-
-
-def _interleaved_dtype(blocks) -> Optional[DtypeObj]:
-    """
-    Find the common dtype for `blocks`.
-
-    Parameters
-    ----------
-    blocks : List[Block]
-
-    Returns
-    -------
-    dtype : np.dtype, ExtensionDtype, or None
-        None is returned when `blocks` is empty.
-    """
-    if not len(blocks):
-        return None
-
-    return find_common_type([b.dtype for b in blocks])
 
 
 class SingleArrayManager(ArrayManager, SingleDataManager):

pandas/core/internals/base.py

@@ -4,11 +4,18 @@
 """
 from typing import (
     List,
+    Optional,
     TypeVar,
 )
 
+from pandas._typing import (
+    DtypeObj,
+    Shape,
+)
 from pandas.errors import AbstractMethodError
 
+from pandas.core.dtypes.cast import find_common_type
+
 from pandas.core.base import PandasObject
 from pandas.core.indexes.api import (
     Index,
@@ -35,6 +42,16 @@ def __len__(self) -> int:
     def ndim(self) -> int:
         return len(self.axes)
 
+    @property
+    def shape(self) -> Shape:
+        return tuple(len(ax) for ax in self.axes)
+
+    def _normalize_axis(self, axis: int) -> int:
+        # switch axis
+        if self.ndim == 2:
+            axis = 1 if axis == 0 else 0
+        return axis
+
     def reindex_indexer(
         self: T,
         new_axis,
@@ -99,6 +116,37 @@ def equals(self, other: object) -> bool:
 
         return self._equal_values(other)
 
+    def apply(
+        self: T,
+        f,
+        align_keys: Optional[List[str]] = None,
+        ignore_failures: bool = False,
+        **kwargs,
+    ) -> T:
+        raise AbstractMethodError(self)
+
+    def isna(self: T, func) -> T:
+        return self.apply("apply", func=func)
+
 
 class SingleDataManager(DataManager):
     ndim = 1
+
+
+def interleaved_dtype(dtypes: List[DtypeObj]) -> Optional[DtypeObj]:
+    """
+    Find the common dtype for `blocks`.
+
+    Parameters
+    ----------
+    blocks : List[DtypeObj]
+
+    Returns
+    -------
+    dtype : np.dtype, ExtensionDtype, or None
+        None is returned when `blocks` is empty.
+    """
+    if not len(dtypes):
+        return None
+
+    return find_common_type(dtypes)

pandas/core/internals/blocks.py

@@ -512,7 +512,7 @@ def _split(self) -> List[Block]:
         for i, ref_loc in enumerate(self.mgr_locs):
             vals = self.values[slice(i, i + 1)]
 
-            nb = self.make_block(vals, [ref_loc])
+            nb = self.make_block(vals, BlockPlacement(ref_loc))
             new_blocks.append(nb)
         return new_blocks
 

pandas/core/internals/managers.py

@@ -32,10 +32,7 @@
 from pandas.errors import PerformanceWarning
 from pandas.util._validators import validate_bool_kwarg
 
-from pandas.core.dtypes.cast import (
-    find_common_type,
-    infer_dtype_from_scalar,
-)
+from pandas.core.dtypes.cast import infer_dtype_from_scalar
 from pandas.core.dtypes.common import (
     DT64NS_DTYPE,
     is_dtype_equal,
@@ -64,6 +61,7 @@
 from pandas.core.internals.base import (
     DataManager,
     SingleDataManager,
+    interleaved_dtype,
 )
 from pandas.core.internals.blocks import (
     Block,
@@ -247,16 +245,6 @@ def __nonzero__(self) -> bool:
     # Python3 compat
     __bool__ = __nonzero__
 
-    @property
-    def shape(self) -> Shape:
-        return tuple(len(ax) for ax in self.axes)
-
-    def _normalize_axis(self, axis):
-        # switch axis to follow BlockManager logic
-        if self.ndim == 2:
-            axis = 1 if axis == 0 else 0
-        return axis
-
     def set_axis(
         self, axis: int, new_labels: Index, verify_integrity: bool = True
     ) -> None:
@@ -362,9 +350,6 @@ def _post_setstate(self) -> None:
         self._known_consolidated = False
         self._rebuild_blknos_and_blklocs()
 
-    def __len__(self) -> int:
-        return len(self.items)
-
     def __repr__(self) -> str:
         output = type(self).__name__
         for i, ax in enumerate(self.axes):
@@ -576,9 +561,6 @@ def quantile(
 
         return type(self)(blocks, new_axes)
 
-    def isna(self, func) -> BlockManager:
-        return self.apply("apply", func=func)
-
     def where(self, other, cond, align: bool, errors: str, axis: int) -> BlockManager:
         axis = self._normalize_axis(axis)
         if align:
@@ -916,7 +898,7 @@ def _interleave(
         Items must be contained in the blocks
         """
         if not dtype:
-            dtype = _interleaved_dtype(self.blocks)
+            dtype = interleaved_dtype([blk.dtype for blk in self.blocks])
 
         # TODO: https://github.com/pandas-dev/pandas/issues/22791
         # Give EAs some input on what happens here. Sparse needs this.
@@ -981,7 +963,7 @@ def fast_xs(self, loc: int) -> ArrayLike:
         if len(self.blocks) == 1:
             return self.blocks[0].iget((slice(None), loc))
 
-        dtype = _interleaved_dtype(self.blocks)
+        dtype = interleaved_dtype([blk.dtype for blk in self.blocks])
 
         n = len(self)
         if is_extension_array_dtype(dtype):
@@ -1320,7 +1302,7 @@ def reindex_indexer(
             new_blocks = [
                 blk.take_nd(
                     indexer,
-                    axis=axis,
+                    axis=1,
                     fill_value=(
                         fill_value if fill_value is not None else blk.fill_value
                     ),
@@ -1892,25 +1874,6 @@ def _stack_arrays(tuples, dtype: np.dtype):
     return stacked, placement
 
 
-def _interleaved_dtype(blocks: Sequence[Block]) -> Optional[DtypeObj]:
-    """
-    Find the common dtype for `blocks`.
-
-    Parameters
-    ----------
-    blocks : List[Block]
-
-    Returns
-    -------
-    dtype : np.dtype, ExtensionDtype, or None
-        None is returned when `blocks` is empty.
-    """
-    if not len(blocks):
-        return None
-
-    return find_common_type([b.dtype for b in blocks])
-
-
 def _consolidate(blocks: Tuple[Block, ...]) -> List[Block]:
     """
     Merge blocks having same dtype, exclude non-consolidating blocks