_get_common_dtype must check PintTypes compat

PR number 137 adds _get_common_dtype to PintType so that `PintType` operations can be performed on a mix of `PintType` and numeric values (with the later being promoted to the `PintType` for the purposes of the operation).  However, when there are multiple `PintType` elements present, it is important that all elements are in fact compatible, lest the operation attempt to combine two `PintType` elements that are not unit-compatible.

pint_pandas/pint_array.py

@@ -203,6 +203,7 @@ def _get_common_dtype(self, dtypes):
 
         In order to be able to be able to perform operation on ``PintType``
         with scalars, mix of ``PintType`` and numeric values are allowed.
+        But all ``PintType`` elements must be compatible.
 
 
         Parameters
@@ -216,7 +217,13 @@ def _get_common_dtype(self, dtypes):
         if all(
             isinstance(x, PintType) or pd.api.types.is_numeric_dtype(x) for x in dtypes
         ):
-            return self
+            PintType_list = [x for x in dtypes if isinstance(x, PintType)]
+            if len(PintType_list) < 2:
+                return self
+            if all (PintType_list[0].units.is_compatible_with(x.units) for x in PintType_list[1:]):
+                return self
+            else:
+                return None
         else:
             return None
 

pint_pandas/testsuite/test_issues.py

@@ -277,3 +277,14 @@ def test_eval(self):
         )
         tm.assert_series_equal(df.eval("a / b"), df["a"] / df["b"])
         tm.assert_series_equal(df.eval("a / c"), df["a"] / df["c"])
+
+    def test_mixed_df(self):
+        df = pd.DataFrame(
+            {
+                "a": pd.Series([1.0, 2.0, 3.0], dtype="pint[meter]"),
+                "b": pd.Series([4.0, 5.0, 6.0], dtype="pint[second]"),
+                "c": [1.0, 2.0, 3.0],
+            }
+        )
+
+        assert df["a"][0] == df.iloc[0][0]