fix issue

pint_pandas/pint_array.py

@@ -214,19 +214,18 @@ def _get_common_dtype(self, dtypes):
         -------
         returns self for acceptable cases or None otherwise
         """
+        # Return self (PintType with same units) if possible
         if all(
-            isinstance(x, PintType) or pd.api.types.is_numeric_dtype(x) for x in dtypes
+            isinstance(dtype, PintType) and dtype.units == self.units
+            for dtype in dtypes
         ):
-            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
+            return self
+        # Otherwise return PintType with undefined units
+        elif all(
+            isinstance(dtype, PintType) or pd.api.types.is_numeric_dtype(dtype)
+            for dtype in dtypes
+        ):
+            return PintType
         else:
             return None
 

pint_pandas/testsuite/test_issues.py

@@ -277,6 +277,7 @@ 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"])
+        tm.assert_series_equal(df.eval("a / a"), df["a"] / df["a"])
 
     def test_mixed_df(self):
         df = pd.DataFrame(
@@ -287,4 +288,26 @@ def test_mixed_df(self):
             }
         )
 
-        assert df["a"][0] == df.iloc[0][0]
+        assert df["a"][0] == df.iloc[0, 0]
+
+
+class TestIssue246(BaseExtensionTests):
+    def test_issue246(self):
+        df = pd.DataFrame(
+            {
+                "a": [1, 2, 3],
+                "b": [4, 5, 6],
+                "c": [7, 8, 9],
+            }
+        )
+
+        df = df.astype(
+            {
+                "a": "pint[m]",
+                "b": "pint[m/s]",
+                "c": "pint[kN]",
+            }
+        )
+
+        # now an operation where each cell is independent from each other
+        df.apply(lambda x: x * 2, axis=1)