diff --git a/pandas/core/arrays/floating.py b/pandas/core/arrays/floating.py
index 1077538f6a21d..aedf6adf09db4 100644
--- a/pandas/core/arrays/floating.py
+++ b/pandas/core/arrays/floating.py
@@ -1,5 +1,5 @@
 import numbers
-from typing import TYPE_CHECKING, List, Optional, Tuple, Type, Union
+from typing import List, Optional, Tuple, Type
 import warnings
 
 import numpy as np
@@ -27,13 +27,10 @@
 from pandas.core.tools.numeric import to_numeric
 
 from .masked import BaseMaskedDtype
-from .numeric import NumericArray
+from .numeric import NumericArray, NumericDtype
 
-if TYPE_CHECKING:
-    import pyarrow
 
-
-class FloatingDtype(BaseMaskedDtype):
+class FloatingDtype(NumericDtype):
     """
     An ExtensionDtype to hold a single size of floating dtype.
 
@@ -72,34 +69,6 @@ def _get_common_dtype(self, dtypes: List[DtypeObj]) -> Optional[DtypeObj]:
             return FLOAT_STR_TO_DTYPE[str(np_dtype)]
         return None
 
-    def __from_arrow__(
-        self, array: Union["pyarrow.Array", "pyarrow.ChunkedArray"]
-    ) -> "FloatingArray":
-        """
-        Construct FloatingArray from pyarrow Array/ChunkedArray.
-        """
-        import pyarrow
-
-        from pandas.core.arrays._arrow_utils import pyarrow_array_to_numpy_and_mask
-
-        pyarrow_type = pyarrow.from_numpy_dtype(self.type)
-        if not array.type.equals(pyarrow_type):
-            array = array.cast(pyarrow_type)
-
-        if isinstance(array, pyarrow.Array):
-            chunks = [array]
-        else:
-            # pyarrow.ChunkedArray
-            chunks = array.chunks
-
-        results = []
-        for arr in chunks:
-            data, mask = pyarrow_array_to_numpy_and_mask(arr, dtype=self.type)
-            float_arr = FloatingArray(data.copy(), ~mask, copy=False)
-            results.append(float_arr)
-
-        return FloatingArray._concat_same_type(results)
-
 
 def coerce_to_array(
     values, dtype=None, mask=None, copy: bool = False
diff --git a/pandas/core/arrays/integer.py b/pandas/core/arrays/integer.py
index fa427e94fe08f..01bae9e5086a8 100644
--- a/pandas/core/arrays/integer.py
+++ b/pandas/core/arrays/integer.py
@@ -1,5 +1,5 @@
 import numbers
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Type, Union
+from typing import Dict, List, Optional, Tuple, Type
 import warnings
 
 import numpy as np
@@ -27,13 +27,10 @@
 from pandas.core.tools.numeric import to_numeric
 
 from .masked import BaseMaskedArray, BaseMaskedDtype
-from .numeric import NumericArray
+from .numeric import NumericArray, NumericDtype
 
-if TYPE_CHECKING:
-    import pyarrow
 
-
-class _IntegerDtype(BaseMaskedDtype):
+class _IntegerDtype(NumericDtype):
     """
     An ExtensionDtype to hold a single size & kind of integer dtype.
 
@@ -92,34 +89,6 @@ def _get_common_dtype(self, dtypes: List[DtypeObj]) -> Optional[DtypeObj]:
             return FLOAT_STR_TO_DTYPE[str(np_dtype)]
         return None
 
-    def __from_arrow__(
-        self, array: Union["pyarrow.Array", "pyarrow.ChunkedArray"]
-    ) -> "IntegerArray":
-        """
-        Construct IntegerArray from pyarrow Array/ChunkedArray.
-        """
-        import pyarrow
-
-        from pandas.core.arrays._arrow_utils import pyarrow_array_to_numpy_and_mask
-
-        pyarrow_type = pyarrow.from_numpy_dtype(self.type)
-        if not array.type.equals(pyarrow_type):
-            array = array.cast(pyarrow_type)
-
-        if isinstance(array, pyarrow.Array):
-            chunks = [array]
-        else:
-            # pyarrow.ChunkedArray
-            chunks = array.chunks
-
-        results = []
-        for arr in chunks:
-            data, mask = pyarrow_array_to_numpy_and_mask(arr, dtype=self.type)
-            int_arr = IntegerArray(data.copy(), ~mask, copy=False)
-            results.append(int_arr)
-
-        return IntegerArray._concat_same_type(results)
-
 
 def integer_array(values, dtype=None, copy: bool = False) -> "IntegerArray":
     """
diff --git a/pandas/core/arrays/numeric.py b/pandas/core/arrays/numeric.py
index 5447a84c86ac1..3c115ec42f6ec 100644
--- a/pandas/core/arrays/numeric.py
+++ b/pandas/core/arrays/numeric.py
@@ -1,4 +1,5 @@
 import datetime
+from typing import TYPE_CHECKING, Union
 
 import numpy as np
 
@@ -13,7 +14,46 @@
     is_list_like,
 )
 
-from .masked import BaseMaskedArray
+from .masked import BaseMaskedArray, BaseMaskedDtype
+
+if TYPE_CHECKING:
+    import pyarrow
+
+
+class NumericDtype(BaseMaskedDtype):
+    def __from_arrow__(
+        self, array: Union["pyarrow.Array", "pyarrow.ChunkedArray"]
+    ) -> BaseMaskedArray:
+        """
+        Construct IntegerArray/FloatingArray from pyarrow Array/ChunkedArray.
+        """
+        import pyarrow
+
+        from pandas.core.arrays._arrow_utils import pyarrow_array_to_numpy_and_mask
+
+        array_class = self.construct_array_type()
+
+        pyarrow_type = pyarrow.from_numpy_dtype(self.type)
+        if not array.type.equals(pyarrow_type):
+            array = array.cast(pyarrow_type)
+
+        if isinstance(array, pyarrow.Array):
+            chunks = [array]
+        else:
+            # pyarrow.ChunkedArray
+            chunks = array.chunks
+
+        results = []
+        for arr in chunks:
+            data, mask = pyarrow_array_to_numpy_and_mask(arr, dtype=self.type)
+            num_arr = array_class(data.copy(), ~mask, copy=False)
+            results.append(num_arr)
+
+        if len(results) == 1:
+            # avoid additional copy in _concat_same_type
+            return results[0]
+        else:
+            return array_class._concat_same_type(results)
 
 
 class NumericArray(BaseMaskedArray):