zero323 commented on a change in pull request #27165: 
[SPARK-28264][PYTHON][SQL] Support type hints in pandas UDF and rename/move 
inconsistent pandas UDF types

 File path: python/pyspark/sql/pandas/
 @@ -0,0 +1,114 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from pyspark.sql.pandas.utils import require_minimum_pandas_version
+def infer_eval_type(sig):
+    """
+    Infers the evaluation type in :class:`pyspark.rdd.PythonEvalType` from
+    :class:`inspect.Signature` instance.
+    """
+    from pyspark.sql.pandas.functions import PandasUDFType
+    require_minimum_pandas_version()
+    import pandas as pd
+    annotations = {}
+    for param in sig.parameters.values():
+        if param.annotation is not param.empty:
+            annotations[] = param.annotation
+    # Check if all arguments have type hints
+    parameters_sig = [annotations[parameter] for parameter
+                      in sig.parameters if parameter in annotations]
+    if len(parameters_sig) != len(sig.parameters):
+        raise ValueError(
+            "Type hints for all parameters should be specified; however, got 
%s" % sig)
+    # Check if the return has a type hint
+    return_annotation = sig.return_annotation
+    if sig.empty is return_annotation:
+        raise ValueError(
+            "Type hint for the return type should be specified; however, got 
%s" % sig)
+    # Series or Frame, ... -> Series or Frame
+    is_series_or_frame = (
+        all(a == pd.Series or a == pd.DataFrame for a in parameters_sig) and
 Review comment:
   Could we maybe
   from typing import Union
     issubclass(a, (pd.Series, pd.DataFrame)) 
     or a == Union[pd.Series, pd.DataFrame] 
     for a in parameters_sig
   ) and ...
   to support cases where user doesn't know if input is atomic or struct? 

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