Add tests for common dtypes, raises check for pandas ones.

Author: sstanovnik

pandas/tests/types/test_cast.py

@@ -15,7 +15,10 @@
                                _possibly_convert_objects,
                                _infer_dtype_from_scalar,
                                _maybe_convert_string_to_object,
-                               _maybe_convert_scalar)
+                               _maybe_convert_scalar,
+                               _find_common_type)
+from pandas.types.dtypes import (CategoricalDtype,
+                                 DatetimeTZDtype)
 from pandas.util import testing as tm
 
 _multiprocess_can_split_ = True
@@ -188,6 +191,42 @@ def test_possibly_convert_objects_copy(self):
         self.assertTrue(values is not out)
 
 
+class TestCommonTypes(tm.TestCase):
+    def setUp(self):
+        super(TestCommonTypes, self).setUp()
+
+    def test_numpy_dtypes(self):
+        self.assertEqual(_find_common_type([np.int64]), np.int64)
+        self.assertEqual(_find_common_type([np.uint64]), np.uint64)
+        self.assertEqual(_find_common_type([np.float32]), np.float32)
+        self.assertEqual(_find_common_type([np.object]), np.object)
+
+        self.assertEqual(_find_common_type([np.int16, np.int64]),
+                         np.int64)
+        self.assertEqual(_find_common_type([np.int32, np.uint32]),
+                         np.int64)
+        self.assertEqual(_find_common_type([np.object, np.float32]),
+                         np.object)
+        self.assertEqual(_find_common_type([np.object, np.int16]),
+                         np.object)
+        self.assertEqual(_find_common_type([np.int16, np.float64]),
+                         np.float64)
+        self.assertEqual(_find_common_type([np.float16, np.int16]),
+                         np.float32)
+        self.assertEqual(_find_common_type([np.float16, np.int64]),
+                         np.float64)
+        self.assertEqual(_find_common_type([np.complex128, np.int32]),
+                         np.complex128)
+        self.assertEqual(_find_common_type([np.uint64, np.int64]),
+                         np.float64)
+
+    def test_pandas_dtypes(self):
+        with self.assertRaises(TypeError):
+            self.assertEqual(_find_common_type([CategoricalDtype]),
+                             CategoricalDtype)
+            self.assertEqual(_find_common_type([DatetimeTZDtype]),
+                             DatetimeTZDtype)
+
 if __name__ == '__main__':
     nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
                    exit=False)

pandas/types/cast.py

@@ -19,6 +19,7 @@
                      _ensure_int32, _ensure_int64,
                      _NS_DTYPE, _TD_DTYPE, _INT64_DTYPE,
                      _DATELIKE_DTYPES, _POSSIBLY_CAST_DTYPES)
+from .dtypes import ExtensionDtype
 from .generic import ABCDatetimeIndex, ABCPeriodIndex, ABCSeries
 from .missing import isnull, notnull
 from .inference import is_list_like
@@ -865,5 +866,9 @@ def _possibly_cast_to_datetime(value, dtype, errors='raise'):
 
 def _find_common_type(types):
     """Find a common data type among the given dtypes."""
-    # TODO: enable using pandas specific types
+    # TODO: enable using pandas-specific types
+    if any(issubclass(t, ExtensionDtype) or isinstance(t, ExtensionDtype)
+           for t in types):
+        raise TypeError("Common type discovery is currently only "
+                        "supported for pure numpy dtypes.")
     return np.find_common_type(types, [])