ERR: Better error msg when merging on tz-aware and tz-naive columns (pandas-dev#19525)

Author: reidy-p

doc/source/whatsnew/v0.23.0.txt

@@ -342,6 +342,7 @@ Other API Changes
 - Addition and subtraction of ``NaN`` from a :class:`Series` with ``dtype='timedelta64[ns]'`` will raise a ``TypeError` instead of treating the ``NaN`` as ``NaT`` (:issue:`19274`)
 - Set operations (union, difference...) on :class:`IntervalIndex` with incompatible index types will now raise a ``TypeError`` rather than a ``ValueError`` (:issue:`19329`)
 - :class:`DateOffset` objects render more simply, e.g. "<DateOffset: days=1>" instead of "<DateOffset: kwds={'days': 1}>" (:issue:`19403`)
+- :func:`pandas.merge` provides a more informative error message when trying to merge on timezone-aware and timezone-naive columns (:issue:`15800`)
 
 .. _whatsnew_0230.deprecations:
 

pandas/core/reshape/merge.py

@@ -940,6 +940,11 @@ def _maybe_coerce_merge_keys(self):
             elif is_dtype_equal(lk.dtype, rk.dtype):
                 continue
 
+            msg = ("You are trying to merge on {lk_dtype} and "
+                   "{rk_dtype} columns. If you wish to proceed "
+                   "you should use pd.concat".format(lk_dtype=lk.dtype,
+                                                     rk_dtype=rk.dtype))
+
             # if we are numeric, then allow differing
             # kinds to proceed, eg. int64 and int8, int and float
             # further if we are object, but we infer to
@@ -968,30 +973,18 @@ def _maybe_coerce_merge_keys(self):
                     pass
 
             # Check if we are trying to merge on obviously
-            # incompatible dtypes GH 9780
+            # incompatible dtypes GH 9780, GH 15800
             elif is_numeric_dtype(lk) and not is_numeric_dtype(rk):
-                msg = ("You are trying to merge on {lk_dtype} and "
-                       "{rk_dtype} columns. If you wish to proceed "
-                       "you should use pd.concat".format(lk_dtype=lk.dtype,
-                                                         rk_dtype=rk.dtype))
                 raise ValueError(msg)
             elif not is_numeric_dtype(lk) and is_numeric_dtype(rk):
-                msg = ("You are trying to merge on {lk_dtype} and "
-                       "{rk_dtype} columns. If you wish to proceed "
-                       "you should use pd.concat".format(lk_dtype=lk.dtype,
-                                                         rk_dtype=rk.dtype))
                 raise ValueError(msg)
             elif is_datetimelike(lk) and not is_datetimelike(rk):
-                msg = ("You are trying to merge on {lk_dtype} and "
-                       "{rk_dtype} columns. If you wish to proceed "
-                       "you should use pd.concat".format(lk_dtype=lk.dtype,
-                                                         rk_dtype=rk.dtype))
                 raise ValueError(msg)
             elif not is_datetimelike(lk) and is_datetimelike(rk):
-                msg = ("You are trying to merge on {lk_dtype} and "
-                       "{rk_dtype} columns. If you wish to proceed "
-                       "you should use pd.concat".format(lk_dtype=lk.dtype,
-                                                         rk_dtype=rk.dtype))
+                raise ValueError(msg)
+            elif is_datetime64tz_dtype(lk) and not is_datetime64tz_dtype(rk):
+                raise ValueError(msg)
+            elif not is_datetime64tz_dtype(lk) and is_datetime64tz_dtype(rk):
                 raise ValueError(msg)
 
             # Houston, we have a problem!

pandas/tests/reshape/merge/test_merge.py

@@ -1512,11 +1512,13 @@ def test_merge_on_ints_floats_warning(self):
                                                           '2011-01-02']),
         (pd.date_range('1/1/2011', periods=2, freq='D'), [0, 1]),
         (pd.date_range('1/1/2011', periods=2, freq='D'), [0.0, 1.0]),
+        (pd.date_range('20130101', periods=3),
+            pd.date_range('20130101', periods=3, tz='US/Eastern')),
         ([0, 1, 2], Series(['a', 'b', 'a']).astype('category')),
         ([0.0, 1.0, 2.0], Series(['a', 'b', 'a']).astype('category')),
     ])
     def test_merge_incompat_dtypes(self, df1_vals, df2_vals):
-        # GH 9780
+        # GH 9780, GH 15800
         # Raise a ValueError when a user tries to merge on
         # dtypes that are incompatible (e.g., obj and int/float)