ENH: Select numeric ExtensionDtypes with DataFrame.select_dtypes

pandas/core/frame.py

@@ -3547,6 +3547,7 @@ def select_dtypes(self, include=None, exclude=None) -> "DataFrame":
         4   True  1.0
         5  False  2.0
         """
+
         if not is_list_like(include):
             include = (include,) if include is not None else ()
         if not is_list_like(exclude):
@@ -3578,7 +3579,14 @@ def extract_unique_dtypes_from_dtypes_set(
                 unique_dtype
                 for unique_dtype in unique_dtypes
                 if issubclass(unique_dtype.type, tuple(dtypes_set))  # type: ignore
+                or (
+                    np.number in dtypes_set
+                    and is_extension_array_dtype(unique_dtype)
+                    and unique_dtype._is_numeric
+                )
             ]
+            if np.number in dtypes_set:
+                extracted_dtypes.extend([])
             return extracted_dtypes
 
         unique_dtypes = self.dtypes.unique()

pandas/tests/extension/test_select_dtypes_numeric.py

@@ -0,0 +1,50 @@
+import pytest
+import numpy as np
+
+import pandas as pd
+from pandas.core.arrays import ExtensionArray
+from pandas.core.dtypes.dtypes import ExtensionDtype
+
+
+class DummyDtype(ExtensionDtype):
+    type = int
+    _numeric = False
+
+    @property
+    def name(self):
+        return "Dummy"
+
+    @property
+    def _is_numeric(self):
+        return self._numeric
+
+
+class DummyArray(ExtensionArray):
+    _dtype = DummyDtype()
+
+    def __init__(self, data):
+        self.data = data
+
+    def __array__(self, dtype):
+        return self.data
+
+    @property
+    def dtype(self):
+        return self._dtype
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+    def __getitem__(self, item):
+        pass
+
+
+@pytest.mark.parametrize("numeric", [True, False])
+def test_select_dtypes_numeric(numeric):
+    da = DummyArray([1, 2])
+    da._dtype._numeric = numeric
+    df = pd.DataFrame(da)
+    if numeric:
+        assert df.select_dtypes(np.number).shape == df.shape
+    else:
+        assert df.select_dtypes(np.number).shape != df.shape