fix precedence

Author: TrevorBergeron

packages/bigframes/bigframes/series.py

@@ -2065,41 +2065,32 @@ def apply(
                 " are supported."
             )
 
-        from bigframes._config import options
 
+        # Highest priority: try to map directly to an operator, for eg numpy
+        # ufuncs, or simple arithmetic/logic operators.
         bf_op = python_ops.python_callable_to_op(func)
         if bf_op and isinstance(bf_op, ops.UnaryOp):
             return self._apply_unary_op(bf_op)
 
-        if isinstance(func, bigframes.functions.Udf) or (
-            options.experiments.enable_python_transpiler and callable(func)
-        ):
-            # We are working with bigquery function at this point
-            result_series = self._apply_callable_expr(ops.func_to_expr(func), args)
-            # TODO(jialuo): Investigate why `_apply_nary_op` drops the series
-            # `name`. Manually reassigning it here as a temporary fix.
-            result_series.name = self.name
-
-            return result_series
-
-        # It is neither a remote function nor a managed function.
-        # Then it must be a vectorized function that applies to the Series
-        # as a whole.
-        if by_row:
-            raise ValueError(
-                "You have passed a function as-is. If your intention is to "
-                "apply this function in a vectorized way (i.e. to the "
-                "entire Series as a whole, and you are sure that it "
-                "performs only the operations that are implemented for a "
-                "Series (e.g. a chain of arithmetic/logical operations, "
-                "such as `def foo(s): return s % 2 == 1`), please also "
-                "specify `by_row=False`. If your function contains "
-                "arbitrary code, it can only be applied to every element "
-                "in the Series individually, in which case you must "
-                "convert it to a BigFrames BigQuery function using "
-                "`bigframes.pandas.udf`, "
-                "or `bigframes.pandas.remote_function` before passing."
-            )
+        # "compat": try by row, and then fall back to passing whole series if that fails
+        if by_row: 
+            try:
+                return self._apply_by_row(func)
+            except Exception as ex:
+                raise ValueError(
+                    "You have passed a function as-is. If your intention is to "
+                    "apply this function in a vectorized way (i.e. to the "
+                    "entire Series as a whole, and you are sure that it "
+                    "performs only the operations that are implemented for a "
+                    "Series (e.g. a chain of arithmetic/logical operations, "
+                    "such as `def foo(s): return s % 2 == 1`), please also "
+                    "specify `by_row=False`. If your function contains "
+                    "arbitrary code, it can only be applied to every element "
+                    "in the Series individually, in which case you must "
+                    "convert it to a BigFrames BigQuery function using "
+                    "`bigframes.pandas.udf`, "
+                    "or `bigframes.pandas.remote_function` before passing."
+                )
 
         try:
             return func(self)  # type: ignore
@@ -2111,6 +2102,21 @@ def apply(
                 ex.message += f"\n{_bigquery_function_recommendation_message}"
             raise
 
+    def _apply_by_row(self, func: typing.Callable, args: typing.Tuple = ()) -> Series:
+        from bigframes._config import options
+
+        if isinstance(func, bigframes.functions.Udf) or (
+            options.experiments.enable_python_transpiler and callable(func)
+        ):
+            # We are working with bigquery function at this point
+            result_series = self._apply_callable_expr(ops.func_to_expr(func), args)
+            # TODO(jialuo): Investigate why `_apply_nary_op` drops the series
+            # `name`. Manually reassigning it here as a temporary fix.
+            result_series.name = self.name
+
+            return result_series
+        raise ValueError(f"Cannot apply function {func} to Series {self}")
+
     def combine(
         self,
         other,