BUG: Raise ValueError for non numerical join columns in merge_asof

doc/source/whatsnew/v1.3.0.rst

@@ -286,7 +286,7 @@ Groupby/resample/rolling
 Reshaping
 ^^^^^^^^^
 
--
+- :meth:`merge_asof` raises ``ValueError`` instead of cryptic ``TypeError`` in case of non-numerical merge columns (:issue:`29130`)
 -
 
 Sparse

pandas/core/reshape/merge.py

@@ -1733,6 +1733,7 @@ def _get_merge_keys(self):
 
         # validate index types are the same
         for i, (lk, rk) in enumerate(zip(left_join_keys, right_join_keys)):
+
             if not is_dtype_equal(lk.dtype, rk.dtype):
                 if is_categorical_dtype(lk.dtype) and is_categorical_dtype(rk.dtype):
                     # The generic error message is confusing for categoricals.
@@ -1798,6 +1799,24 @@ def _get_merge_keys(self):
 
         return left_join_keys, right_join_keys, join_names
 
+    def _maybe_coerce_merge_keys(self):
+        """Check if the merge keys are not object and call super method."""
+        len_by_cols = 0 if self.left_by is None else len(self.left_by)
+        for i, (lk, rk) in enumerate(
+            zip(
+                self.left_join_keys,
+                self.right_join_keys,
+            )
+        ):
+            if i >= len_by_cols:
+                if is_object_dtype(rk.dtype) or is_object_dtype(lk.dtype):
+                    raise ValueError(
+                        f"Incompatible merge [{i}] dtype, {repr(lk.dtype)} and "
+                        f"{repr(rk.dtype)}, both sides must have numeric dtype"
+                    )
+
+        super()._maybe_coerce_merge_keys()
+
     def _get_join_indexers(self):
         """ return the join indexers """
 

pandas/tests/reshape/merge/test_merge_asof.py

@@ -1373,3 +1373,35 @@ def test_left_index_right_index_tolerance(self):
             tolerance=Timedelta(seconds=0.5),
         )
         tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [["2019-06-01 00:09:12", "2019-06-01 00:10:29"], [1.0, "2019-06-01 00:10:29"]],
+)
+def test_non_numerical_dtype(data):
+    # GH 29130
+    left = pd.DataFrame({"x": data})
+    right = pd.DataFrame({"x": data})
+    with pytest.raises(
+        ValueError,
+        match=r"Incompatible merge \[0\] dtype, .*, both sides must have numeric dtype",
+    ):
+        pd.merge_asof(left, right, on="x")
+
+
+def test_some_issues():
+    left = pd.DataFrame({"a": ["12", "13", "15"], "left_val1": ["a", "b", "c"]})
+    right = pd.DataFrame({"a": ["a", "b", "c"], "left_val": ["d", "e", "f"]})
+    with pytest.raises(
+        ValueError,
+        match=r"Incompatible merge \[1\] dtype, .*, both sides must have numeric dtype",
+    ):
+        pd.merge_asof(
+            left,
+            right,
+            left_on="left_val1",
+            right_on="a",
+            left_by="a",
+            right_by="left_val",
+        )