Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/18732#discussion_r143629848
--- Diff: python/pyspark/sql/functions.py ---
@@ -2181,30 +2187,66 @@ def udf(f=None, returnType=StringType()):
@since(2.3)
def pandas_udf(f=None, returnType=StringType()):
"""
- Creates a :class:`Column` expression representing a user defined
function (UDF) that accepts
- `Pandas.Series` as input arguments and outputs a `Pandas.Series` of
the same length.
+ Creates a vectorized user defined function (UDF).
- :param f: python function if used as a standalone function
+ :param f: user-defined function. A python function if used as a
standalone function
:param returnType: a :class:`pyspark.sql.types.DataType` object
- >>> 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() # doctest: +SKIP
- +----------+--------------+------------+
- |slen(name)|to_upper(name)|add_one(age)|
- +----------+--------------+------------+
- | 8| JOHN DOE| 22|
- +----------+--------------+------------+
+ The user-defined function can define one of the following
transformations:
+
+ 1. One or more `pandas.Series` -> A `pandas.Series`
+
+ This udf is used with :meth:`pyspark.sql.DataFrame.withColumn` and
+ :meth:`pyspark.sql.DataFrame.select`.
+ The returnType should be a primitive data type, e.g.,
`DoubleType()`.
+ The length of the returned `pandas.Series` must be of the same as
the input `pandas.Series`.
+
+ >>> 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() # doctest: +SKIP
+ +----------+--------------+------------+
+ |slen(name)|to_upper(name)|add_one(age)|
+ +----------+--------------+------------+
+ | 8| JOHN DOE| 22|
+ +----------+--------------+------------+
+
+ 2. A `pandas.DataFrame` -> A `pandas.DataFrame`
+
+ This udf is used with :meth:`pyspark.sql.GroupedData.apply`.
--- End diff --
Maybe, `This udf is used with` -> `This udf is only used with` or ..
probably we should add a `note` here. If I didn't know the context here, I'd
wonder why it does not work as normal pandas udf ..
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
