Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/19325#discussion_r140624294
--- Diff: python/pyspark/sql/functions.py ---
@@ -2183,14 +2183,29 @@ def pandas_udf(f=None, returnType=StringType()):
:param f: python function if used as a standalone function
:param returnType: a :class:`pyspark.sql.types.DataType` object
- # TODO: doctest
+ >>> from pyspark.sql.types import IntegerType, StringType
+ >>> slen = pandas_udf(lambda s: s.str.len(), IntegerType())
+ >>> @pandas_udf(returnType=StringType())
+ ... def to_upper(s):
+ ... return s.str.upper()
+ ...
+ >>> @pandas_udf(returnType="integer")
+ ... def add_one(x):
+ ... return x + 1
+ ...
+ >>> df = spark.createDataFrame([(1, "John Doe", 21)], ("id", "name",
"age"))
+ >>> df.select(slen("name").alias("slen(name)"), to_upper("name"),
add_one("age")).show()
+ +----------+--------------+------------+
+ |slen(name)|to_upper(name)|add_one(age)|
+ +----------+--------------+------------+
+ | 8| JOHN DOE| 22|
+ +----------+--------------+------------+
"""
+ wrapped_udf = _create_udf(f, returnType=returnType, vectorized=True)
import inspect
- # If function "f" does not define the optional kwargs, then wrap with
a kwargs placeholder
- if inspect.getargspec(f).keywords is None:
- return _create_udf(lambda *a, **kwargs: f(*a),
returnType=returnType, vectorized=True)
- else:
- return _create_udf(f, returnType=returnType, vectorized=True)
+ if not inspect.getargspec(wrapped_udf.func).args:
--- End diff --
It looks `wrapped_udf.func` could be `_udf` within `_create_udf`, that
takes a single argument, for example:
```python
@pandas_udf(returnType=LongType())
def add_one():
return 1
```
I tried a rough idea to solve this:
```diff
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -2124,11 +2124,14 @@ class UserDefinedFunction(object):
return wrapper
-def _create_udf(f, returnType, vectorized):
+def _create_udf(f, returnType, vectorized, checker=None):
def _udf(f, returnType=StringType(), vectorized=vectorized):
udf_obj = UserDefinedFunction(f, returnType, vectorized=vectorized)
- return udf_obj._wrapped()
+ wrapped = udf_obj._wrapped()
+ if checker is not None:
+ checker(wrapped)
+ return wrapped
# decorator @udf, @udf(), @udf(dataType()), or similar with @pandas_udf
if f is None or isinstance(f, (str, DataType)):
@@ -2201,10 +2204,14 @@ def pandas_udf(f=None, returnType=StringType()):
| 8| JOHN DOE| 22|
+----------+--------------+------------+
"""
- wrapped_udf = _create_udf(f, returnType=returnType, vectorized=True)
- import inspect
- if not inspect.getargspec(wrapped_udf.func).args:
- raise NotImplementedError("0-parameter pandas_udfs are not
currently supported")
+
+ def checker(wrapped):
+ import inspect
+ if not inspect.getargspec(wrapped.func).args:
+ raise NotImplementedError("0-parameter pandas_udfs are not
currently supported")
+
+ wrapped_udf = _create_udf(f, returnType=returnType, vectorized=True,
checker=checker)
+
return wrapped_udf
```
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