diff --git a/python-package/xgboost/data.py b/python-package/xgboost/data.py
index 7e0ae793ba6e..bd196e2e59f9 100644
--- a/python-package/xgboost/data.py
+++ b/python-package/xgboost/data.py
@@ -458,7 +458,7 @@ def pandas_pa_type(ser: Any) -> np.ndarray:
     # combine_chunks takes the most significant amount of time
     chunk: pa.Array = aa.combine_chunks()
     # When there's null value, we have to use copy
-    zero_copy = chunk.null_count == 0
+    zero_copy = chunk.null_count == 0 and not pa.types.is_boolean(chunk.type)
     # Alternately, we can use chunk.buffers(), which returns a list of buffers and
     # we need to concatenate them ourselves.
     # FIXME(jiamingy): Is there a better way to access the arrow buffer along with
@@ -825,37 +825,9 @@ def _arrow_transform(data: DataType) -> Any:
 
     data = cast(pa.Table, data)
 
-    def type_mapper(dtype: pa.DataType) -> Optional[str]:
-        """Maps pyarrow type to pandas arrow extension type."""
-        if pa.types.is_int8(dtype):
-            return pd.ArrowDtype(pa.int8())
-        if pa.types.is_int16(dtype):
-            return pd.ArrowDtype(pa.int16())
-        if pa.types.is_int32(dtype):
-            return pd.ArrowDtype(pa.int32())
-        if pa.types.is_int64(dtype):
-            return pd.ArrowDtype(pa.int64())
-        if pa.types.is_uint8(dtype):
-            return pd.ArrowDtype(pa.uint8())
-        if pa.types.is_uint16(dtype):
-            return pd.ArrowDtype(pa.uint16())
-        if pa.types.is_uint32(dtype):
-            return pd.ArrowDtype(pa.uint32())
-        if pa.types.is_uint64(dtype):
-            return pd.ArrowDtype(pa.uint64())
-        if pa.types.is_float16(dtype):
-            return pd.ArrowDtype(pa.float16())
-        if pa.types.is_float32(dtype):
-            return pd.ArrowDtype(pa.float32())
-        if pa.types.is_float64(dtype):
-            return pd.ArrowDtype(pa.float64())
-        if pa.types.is_boolean(dtype):
-            return pd.ArrowDtype(pa.bool_())
-        return None
-
     # For common cases, this is zero-copy, can check with:
     # pa.total_allocated_bytes()
-    df = data.to_pandas(types_mapper=type_mapper)
+    df = data.to_pandas(types_mapper=pd.ArrowDtype)
     return df
 
 
diff --git a/python-package/xgboost/testing/data.py b/python-package/xgboost/testing/data.py
index 0c4f290086d1..f4e97e59d363 100644
--- a/python-package/xgboost/testing/data.py
+++ b/python-package/xgboost/testing/data.py
@@ -164,10 +164,6 @@ def pd_arrow_dtypes() -> Generator:
 
     # Integer
     dtypes = pandas_pyarrow_mapper
-    Null: Union[float, None, Any] = np.nan
-    orig = pd.DataFrame(
-        {"f0": [1, 2, Null, 3], "f1": [4, 3, Null, 1]}, dtype=np.float32
-    )
     # Create a dictionary-backed dataframe, enable this when the roundtrip is
     # implemented in pandas/pyarrow
     #
@@ -190,24 +186,33 @@ def pd_arrow_dtypes() -> Generator:
     # pd_catcodes = pd_cat_df["f1"].cat.codes
     # assert pd_catcodes.equals(pa_catcodes)
 
-    for Null in (None, pd.NA):
+    for Null in (None, pd.NA, 0):
         for dtype in dtypes:
             if dtype.startswith("float16") or dtype.startswith("bool"):
                 continue
+            # Use np.nan is a baseline
+            orig_null = Null if not pd.isna(Null) and Null == 0 else np.nan
+            orig = pd.DataFrame(
+                {"f0": [1, 2, orig_null, 3], "f1": [4, 3, orig_null, 1]},
+                dtype=np.float32,
+            )
+
             df = pd.DataFrame(
                 {"f0": [1, 2, Null, 3], "f1": [4, 3, Null, 1]}, dtype=dtype
             )
             yield orig, df
 
-    orig = pd.DataFrame(
-        {"f0": [True, False, pd.NA, True], "f1": [False, True, pd.NA, True]},
-        dtype=pd.BooleanDtype(),
-    )
-    df = pd.DataFrame(
-        {"f0": [True, False, pd.NA, True], "f1": [False, True, pd.NA, True]},
-        dtype=pd.ArrowDtype(pa.bool_()),
-    )
-    yield orig, df
+    # If Null is `False`, then there's no missing value.
+    for Null in (pd.NA, False):
+        orig = pd.DataFrame(
+            {"f0": [True, False, Null, True], "f1": [False, True, Null, True]},
+            dtype=pd.BooleanDtype(),
+        )
+        df = pd.DataFrame(
+            {"f0": [True, False, Null, True], "f1": [False, True, Null, True]},
+            dtype=pd.ArrowDtype(pa.bool_()),
+        )
+        yield orig, df
 
 
 def check_inf(rng: RNG) -> None: