Github user ueshin commented on a diff in the pull request:
https://github.com/apache/spark/pull/18732#discussion_r142055482
--- Diff: python/pyspark/sql/functions.py ---
@@ -2181,31 +2186,69 @@ 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 :class:`Column` expression representing a vectorized user
defined function (UDF).
+
+ 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 `DataFrame.withColumn` and `DataFrame.select`.
+ The returnType should be a primitive data type, e.g., DoubleType()
+
+ Example:
+
+ >>> 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 `GroupedData.apply`
+ The returnType should be a StructType describing the schema of the
returned
+ `pandas.DataFrame`.
+
+ Example:
+
+ >>> df = spark.createDataFrame([(1, 1.0), (1, 2.0), (2, 3.0), (2,
4.0)], ("id", "v"))
+ >>> @pandas_udf(returnType=df.schema)
+ ... def normalize(df):
+ ... v = df.v
+ ... ret = df.assign(v=(v - v.mean()) / v.std())
--- End diff --
We need `return ret`?
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