Fix remaining unrecognized numpy dtypes

pandera/engines/numpy_engine.py

@@ -313,6 +313,17 @@ class Complex64(Complex128):
     bit_width: int = 64
 
 
+###############################################################################
+# bytes
+###############################################################################
+
+
+@Engine.register_dtype(equivalents=["bytes", bytes, np.bytes_])
+@immutable
+class Bytes(DataType):
+    type = np.dtype("bytes")
+
+
 ###############################################################################
 # string
 ###############################################################################

pandera/engines/pandas_engine.py

@@ -137,7 +137,10 @@ def dtype(cls, data_type: Any) -> "DataType":
                 # into a numpy or pandas dtype.
                 np_or_pd_dtype = pd.api.types.pandas_dtype(data_type)
                 if isinstance(np_or_pd_dtype, np.dtype):
-                    np_or_pd_dtype = np_or_pd_dtype.type
+                    # cast alias to platform-agnostic dtype
+                    # e.g.: np.intc -> np.int32
+                    common_np_dtype = np.dtype(np_or_pd_dtype.name)
+                    np_or_pd_dtype = common_np_dtype.type
 
             return engine.Engine.dtype(cls, np_or_pd_dtype)
 
@@ -454,13 +457,18 @@ def check(self, pandera_dtype: dtypes.DataType) -> bool:
     numpy_engine.Object,
     equivalents=[
         "object",
+        "object_",
+        "object0",
         "O",
         "bytes",
         "decimal",
         "mixed-integer",
         "mixed",
+        "bytes",
+        bytes,
         object,
         np.object_,
+        np.bytes_,
     ],
 )
 

tests/core/test_dtypes.py

@@ -397,6 +397,28 @@ def test_default_numeric_dtypes():
     )
 
 
+@pytest.mark.parametrize(
+    "alias, np_dtype",
+    [
+        (alias, np_dtype)
+        for alias, np_dtype in np.sctypeDict.items()
+        # int, uint have different bitwidth under pandas and numpy.
+        if np_dtype != np.void and alias not in ("int", "uint")
+    ],
+)
+def test_numpy_dtypes(alias, np_dtype):
+    """Test that all pandas-compatible numpy dtypes are understood."""
+    try:
+        np.dtype(alias)
+    except TypeError:
+        # not a valid alias
+        assert pandas_engine.Engine.dtype(np_dtype)
+    else:
+        assert pandas_engine.Engine.dtype(alias) == pandas_engine.Engine.dtype(
+            np_dtype
+        )
+
+
 @pytest.mark.parametrize(
     "examples",
     [