BUG: __from_arrow__ doesn't accept pyarrow null arrays for numeric ma…

doc/source/whatsnew/v2.0.0.rst

@@ -1208,6 +1208,7 @@ Numeric
 - Bug in :meth:`~arrays.ArrowExtensionArray.mode` where ``dropna=False`` was not respected when there was ``NA`` values (:issue:`50982`)
 - Bug in :meth:`DataFrame.query` with ``engine="numexpr"`` and column names are ``min`` or ``max`` would raise a ``TypeError`` (:issue:`50937`)
 - Bug in :meth:`DataFrame.min` and :meth:`DataFrame.max` with tz-aware data containing ``pd.NaT`` and ``axis=1`` would return incorrect results (:issue:`51242`)
+- Bug where the ``__from_arrow__`` method of masked ExtensionDtypes(e.g. :class:`Float64Dtype`, :class:`BooleanDtype`) would not accept pyarrow arrays of type ``pyarrow.null()`` (:issue:`52223`)
 
 Conversion
 ^^^^^^^^^^

pandas/core/arrays/arrow/_arrow_utils.py

@@ -42,6 +42,11 @@ def pyarrow_array_to_numpy_and_mask(
     """
     dtype = np.dtype(dtype)
 
+    if pyarrow.types.is_null(arr.type):
+        # No initialization of data is needed since everything is null
+        data = np.empty(len(arr), dtype=dtype)
+        mask = np.zeros(len(arr), dtype=bool)
+        return data, mask
     buflist = arr.buffers()
     # Since Arrow buffers might contain padding and the data might be offset,
     # the buffer gets sliced here before handing it to numpy.

pandas/core/arrays/boolean.py

@@ -108,7 +108,7 @@ def __from_arrow__(
         """
         import pyarrow
 
-        if array.type != pyarrow.bool_():
+        if array.type != pyarrow.bool_() and not pyarrow.types.is_null(array.type):
             raise TypeError(f"Expected array of boolean type, got {array.type} instead")
 
         if isinstance(array, pyarrow.Array):
@@ -119,6 +119,13 @@ def __from_arrow__(
 
         results = []
         for arr in chunks:
+            if pyarrow.types.is_null(arr.type):
+                mask = np.ones(len(arr), dtype=bool)
+                # No need to init data, since all null
+                data = np.empty(len(arr), dtype=bool)
+                bool_arr = BooleanArray(data, mask)
+                results.append(bool_arr)
+                continue
             buflist = arr.buffers()
             data = pyarrow.BooleanArray.from_buffers(
                 arr.type, len(arr), [None, buflist[1]], offset=arr.offset

pandas/core/arrays/numeric.py

@@ -76,7 +76,9 @@ def __from_arrow__(
         array_class = self.construct_array_type()
 
         pyarrow_type = pyarrow.from_numpy_dtype(self.type)
-        if not array.type.equals(pyarrow_type):
+        if not array.type.equals(pyarrow_type) and not pyarrow.types.is_null(
+            array.type
+        ):
             # test_from_arrow_type_error raise for string, but allow
             #  through itemsize conversion GH#31896
             rt_dtype = pandas_dtype(array.type.to_pandas_dtype())

pandas/tests/arrays/masked/test_arrow_compat.py

@@ -184,6 +184,13 @@ def test_pyarrow_array_to_numpy_and_mask(np_dtype_to_arrays):
     tm.assert_numpy_array_equal(mask, mask_expected_empty)
 
 
+def test_from_arrow_null(data):
+    arr = pa.nulls(10)
+    res = data.dtype.__from_arrow__(arr)
+    assert res.isna().all()
+    assert len(res) == 10
+
+
 def test_from_arrow_type_error(data):
     # ensure that __from_arrow__ returns a TypeError when getting a wrong
     # array type