REF: combine Block _can_hold_element methods

pandas/core/dtypes/cast.py

@@ -113,6 +113,9 @@
     from pandas.core.arrays import (
         DatetimeArray,
         ExtensionArray,
+        IntervalArray,
+        PeriodArray,
+        TimedeltaArray,
     )
 
 _int8_max = np.iinfo(np.int8).max
@@ -2169,32 +2172,68 @@ def validate_numeric_casting(dtype: np.dtype, value: Scalar) -> None:
             raise ValueError(f"Cannot assign {type(value).__name__} to bool series")
 
 
-def can_hold_element(dtype: np.dtype, element: Any) -> bool:
+def can_hold_element(arr: ArrayLike, element: Any) -> bool:
     """
     Can we do an inplace setitem with this element in an array with this dtype?
 
     Parameters
     ----------
-    dtype : np.dtype
+    arr : np.ndarray or ExtensionArray
     element : Any
 
     Returns
     -------
     bool
     """
+    dtype = arr.dtype
+    if not isinstance(dtype, np.dtype) or dtype.kind in ["m", "M"]:
+        if isinstance(dtype, (PeriodDtype, IntervalDtype, DatetimeTZDtype, np.dtype)):
+            # np.dtype here catches datetime64ns and timedelta64ns; we assume
+            #  in this case that we have DatetimeArray/TimedeltaArray
+            arr = cast(
+                "PeriodArray | DatetimeArray | TimedeltaArray | IntervalArray", arr
+            )
+            try:
+                arr._validate_setitem_value(element)
+                return True
+            except (ValueError, TypeError):
+                return False
+
+        # This is technically incorrect, but maintains the behavior of
+        # ExtensionBlock._can_hold_element
+        return True
+
     tipo = maybe_infer_dtype_type(element)
 
     if dtype.kind in ["i", "u"]:
         if tipo is not None:
-            return tipo.kind in ["i", "u"] and dtype.itemsize >= tipo.itemsize
+            if tipo.kind not in ["i", "u"]:
+                # Anything other than integer we cannot hold
+                return False
+            elif dtype.itemsize < tipo.itemsize:
+                return False
+            elif not isinstance(tipo, np.dtype):
+                # i.e. nullable IntegerDtype; we can put this into an ndarray
+                #  losslessly iff it has no NAs
+                return not element._mask.any()
+            return True
 
         # We have not inferred an integer from the dtype
         # check if we have a builtin int or a float equal to an int
         return is_integer(element) or (is_float(element) and element.is_integer())
 
     elif dtype.kind == "f":
         if tipo is not None:
-            return tipo.kind in ["f", "i", "u"]
+            # TODO: itemsize check?
+            if tipo.kind not in ["f", "i", "u"]:
+                # Anything other than float/integer we cannot hold
+                return False
+            elif not isinstance(tipo, np.dtype):
+                # i.e. nullable IntegerDtype or FloatingDtype;
+                #  we can put this into an ndarray losslessly iff it has no NAs
+                return not element._mask.any()
+            return True
+
         return lib.is_integer(element) or lib.is_float(element)
 
     elif dtype.kind == "c":
@@ -2212,4 +2251,11 @@ def can_hold_element(dtype: np.dtype, element: Any) -> bool:
     elif dtype == object:
         return True
 
+    elif dtype.kind == "S":
+        # TODO: test tests.frame.methods.test_replace tests get here,
+        #  need more targeted tests.  xref phofl has a PR about this
+        if tipo is not None:
+            return tipo.kind == "S" and tipo.itemsize <= dtype.itemsize
+        return isinstance(element, bytes) and len(element) <= dtype.itemsize
+
     raise NotImplementedError(dtype)

pandas/core/indexes/base.py

@@ -4482,7 +4482,7 @@ def _validate_fill_value(self, value):
         TypeError
             If the value cannot be inserted into an array of this dtype.
         """
-        if not can_hold_element(self.dtype, value):
+        if not can_hold_element(self._values, value):
             raise TypeError
         return value
 

pandas/core/internals/blocks.py

@@ -5,7 +5,6 @@
 from typing import (
     TYPE_CHECKING,
     Any,
-    Callable,
     List,
     Optional,
     Tuple,
@@ -18,8 +17,6 @@
 import numpy as np
 
 from pandas._libs import (
-    Interval,
-    Period,
     Timestamp,
     algos as libalgos,
     internals as libinternals,
@@ -102,6 +99,7 @@
     PeriodArray,
     TimedeltaArray,
 )
+from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
 from pandas.core.base import PandasObject
 import pandas.core.common as com
 import pandas.core.computation.expressions as expressions
@@ -122,7 +120,6 @@
         Float64Index,
         Index,
     )
-    from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
 
 # comparison is faster than is_object_dtype
 _dtype_obj = np.dtype("object")
@@ -625,9 +622,11 @@ def convert(
         """
         return [self.copy()] if copy else [self]
 
+    @final
     def _can_hold_element(self, element: Any) -> bool:
         """ require the same dtype as ourselves """
-        raise NotImplementedError("Implemented on subclasses")
+        element = extract_array(element, extract_numpy=True)
+        return can_hold_element(self.values, element)
 
     @final
     def should_store(self, value: ArrayLike) -> bool:
@@ -1545,7 +1544,7 @@ def setitem(self, indexer, value):
         be a compatible shape.
         """
         if not self._can_hold_element(value):
-            # This is only relevant for DatetimeTZBlock, ObjectValuesExtensionBlock,
+            # This is only relevant for DatetimeTZBlock, PeriodDtype, IntervalDtype,
             #  which has a non-trivial `_can_hold_element`.
             # https://github.com/pandas-dev/pandas/issues/24020
             # Need a dedicated setitem until GH#24020 (type promotion in setitem
@@ -1597,10 +1596,6 @@ def take_nd(
 
         return self.make_block_same_class(new_values, new_mgr_locs)
 
-    def _can_hold_element(self, element: Any) -> bool:
-        # TODO: We may need to think about pushing this onto the array.
-        return True
-
     def _slice(self, slicer):
         """
         Return a slice of my values.
@@ -1746,54 +1741,22 @@ def _unstack(self, unstacker, fill_value, new_placement):
         return blocks, mask
 
 
-class HybridMixin:
-    """
-    Mixin for Blocks backed (maybe indirectly) by ExtensionArrays.
-    """
-
-    array_values: Callable
-
-    def _can_hold_element(self, element: Any) -> bool:
-        values = self.array_values
-
-        try:
-            # error: "Callable[..., Any]" has no attribute "_validate_setitem_value"
-            values._validate_setitem_value(element)  # type: ignore[attr-defined]
-            return True
-        except (ValueError, TypeError):
-            return False
-
-
-class ObjectValuesExtensionBlock(HybridMixin, ExtensionBlock):
-    """
-    Block providing backwards-compatibility for `.values`.
-
-    Used by PeriodArray and IntervalArray to ensure that
-    Series[T].values is an ndarray of objects.
-    """
-
-    pass
-
-
 class NumericBlock(Block):
     __slots__ = ()
     is_numeric = True
 
-    def _can_hold_element(self, element: Any) -> bool:
-        element = extract_array(element, extract_numpy=True)
-        if isinstance(element, (IntegerArray, FloatingArray)):
-            if element._mask.any():
-                return False
-        return can_hold_element(self.dtype, element)
 
-
-class NDArrayBackedExtensionBlock(HybridMixin, Block):
+class NDArrayBackedExtensionBlock(Block):
     """
     Block backed by an NDArrayBackedExtensionArray
     """
 
     values: NDArrayBackedExtensionArray
 
+    @property
+    def array_values(self) -> NDArrayBackedExtensionArray:
+        return self.values
+
     @property
     def is_view(self) -> bool:
         """ return a boolean if I am possibly a view """
@@ -1901,10 +1864,6 @@ class DatetimeLikeBlockMixin(NDArrayBackedExtensionBlock):
 
     is_numeric = False
 
-    @cache_readonly
-    def array_values(self):
-        return self.values
-
 
 class DatetimeBlock(DatetimeLikeBlockMixin):
     __slots__ = ()
@@ -1920,7 +1879,6 @@ class DatetimeTZBlock(ExtensionBlock, DatetimeLikeBlockMixin):
     is_numeric = False
 
     internal_values = Block.internal_values
-    _can_hold_element = DatetimeBlock._can_hold_element
     diff = DatetimeBlock.diff
     where = DatetimeBlock.where
     putmask = DatetimeLikeBlockMixin.putmask
@@ -1983,9 +1941,6 @@ def convert(
         res_values = ensure_block_shape(res_values, self.ndim)
         return [self.make_block(res_values)]
 
-    def _can_hold_element(self, element: Any) -> bool:
-        return True
-
 
 class CategoricalBlock(ExtensionBlock):
     # this Block type is kept for backwards-compatibility
@@ -2052,8 +2007,6 @@ def get_block_type(values, dtype: Optional[Dtype] = None):
         cls = CategoricalBlock
     elif vtype is Timestamp:
         cls = DatetimeTZBlock
-    elif vtype is Interval or vtype is Period:
-        cls = ObjectValuesExtensionBlock
     elif isinstance(dtype, ExtensionDtype):
         # Note: need to be sure PandasArray is unwrapped before we get here
         cls = ExtensionBlock

pandas/core/internals/managers.py

@@ -73,7 +73,6 @@
     CategoricalBlock,
     DatetimeTZBlock,
     ExtensionBlock,
-    ObjectValuesExtensionBlock,
     ensure_block_shape,
     extend_blocks,
     get_block_type,
@@ -1841,14 +1840,6 @@ def _form_blocks(
 
         blocks.extend(external_blocks)
 
-    if len(items_dict["ObjectValuesExtensionBlock"]):
-        external_blocks = [
-            new_block(array, klass=ObjectValuesExtensionBlock, placement=i, ndim=2)
-            for i, array in items_dict["ObjectValuesExtensionBlock"]
-        ]
-
-        blocks.extend(external_blocks)
-
     if len(extra_locs):
         shape = (len(extra_locs),) + tuple(len(x) for x in axes[1:])
 

pandas/tests/extension/test_numpy.py

@@ -18,6 +18,7 @@
 
 import pandas.util._test_decorators as td
 
+from pandas.core.dtypes.cast import can_hold_element
 from pandas.core.dtypes.dtypes import (
     ExtensionDtype,
     PandasDtype,
@@ -27,7 +28,10 @@
 import pandas as pd
 import pandas._testing as tm
 from pandas.core.arrays.numpy_ import PandasArray
-from pandas.core.internals import managers
+from pandas.core.internals import (
+    blocks,
+    managers,
+)
 from pandas.tests.extension import base
 
 # TODO(ArrayManager) PandasArray
@@ -45,6 +49,12 @@ def _extract_array_patched(obj):
     return obj
 
 
+def _can_hold_element_patched(obj, element) -> bool:
+    if isinstance(element, PandasArray):
+        element = element.to_numpy()
+    return can_hold_element(obj, element)
+
+
 @pytest.fixture(params=["float", "object"])
 def dtype(request):
     return PandasDtype(np.dtype(request.param))
@@ -70,6 +80,7 @@ def allow_in_pandas(monkeypatch):
     with monkeypatch.context() as m:
         m.setattr(PandasArray, "_typ", "extension")
         m.setattr(managers, "_extract_array", _extract_array_patched)
+        m.setattr(blocks, "can_hold_element", _can_hold_element_patched)
         yield