Fix int inference in select_dtypes.

Author: Oleh Kozynets

pandas/core/dtypes/common.py

@@ -1796,11 +1796,6 @@ def pandas_dtype(dtype) -> DtypeObj:
     # try a numpy dtype
     # raise a consistent TypeError if failed
     try:
-        # int is mapped to different types (int32, in64) on Windows and Linux
-        # see https://github.com/numpy/numpy/issues/9464
-        if (isinstance(dtype, str) and dtype == "int") or (dtype is int):
-            dtype = "int64"
-
         npdtype = np.dtype(dtype)
     except SyntaxError as err:
         # np.dtype uses `eval` which can raise SyntaxError

pandas/core/frame.py

@@ -3633,8 +3633,21 @@ def select_dtypes(self, include=None, exclude=None) -> DataFrame:
             raise ValueError("at least one of include or exclude must be nonempty")
 
         # convert the myriad valid dtypes object to a single representation
-        include = frozenset(infer_dtype_from_object(x) for x in include)
-        exclude = frozenset(infer_dtype_from_object(x) for x in exclude)
+        def check_int_infer_dtype(dtypes):
+            converted_dtypes = []
+            for dtype in dtypes:
+                # Numpy maps int to different types (int32, in64) on Windows and Linux
+                # see https://github.com/numpy/numpy/issues/9464
+                if (isinstance(dtype, str) and dtype == "int") or (dtype is int):
+                    converted_dtypes.append(np.int32)
+                    converted_dtypes.append(np.int64)
+                else:
+                    converted_dtypes.append(infer_dtype_from_object(dtype))
+            return frozenset(converted_dtypes)
+
+        include = check_int_infer_dtype(include)
+        exclude = check_int_infer_dtype(exclude)
+
         for dtypes in (include, exclude):
             invalidate_string_dtypes(dtypes)
 

pandas/tests/dtypes/test_common.py

@@ -43,22 +43,20 @@ def test_invalid_dtype_error(self, box):
             com.pandas_dtype(box)
 
     @pytest.mark.parametrize(
-        "dtype_input,dtype_output",
+        "dtype",
         [
-            (object, object),
-            ("float64", np.float64),
-            (np.object_, np.object_),
-            (np.dtype("object"), np.object_),
-            ("O", object),
-            (np.float64, np.float64),
-            (float, float),
-            (np.dtype("float64"), np.float64),
-            ("int", np.int64),
-            (int, np.int64),
+            object,
+            "float64",
+            np.object_,
+            np.dtype("object"),
+            "O",
+            np.float64,
+            float,
+            np.dtype("float64"),
         ],
     )
-    def test_pandas_dtype_valid(self, dtype_input, dtype_output):
-        assert com.pandas_dtype(dtype_input) == dtype_output
+    def test_pandas_dtype_valid(self, dtype):
+        assert com.pandas_dtype(dtype) == dtype
 
     @pytest.mark.parametrize(
         "dtype", ["M8[ns]", "m8[ns]", "object", "float64", "int64"]