Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/18732#discussion_r142248106
--- 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())
+ >>> df.groupby('id').apply(normalize).show() # doctest: + SKIP
+ +---+-------------------+
+ | id| v|
+ +---+-------------------+
+ | 1|-0.7071067811865475|
+ | 1| 0.7071067811865475|
+ | 2|-0.7071067811865475|
+ | 2| 0.7071067811865475|
+ +---+-------------------+
+
+
+ .. note:: The user-defined functions must be deterministic.
:param f: 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|
- +----------+--------------+------------+
"""
+ import pandas as pd
+ if isinstance(returnType, pd.Series):
--- End diff --
I'm still not crazy about this option. First, you are expecting the type
to be a `Pandas.Series` of dtypes so I think you would need to check the data
type of the Series here too, just to make sure the user doesn't pass in some
other kind of Series. Second, I tend to think of a UDF declaration as being
static, especially with a decorator, so it seems a little weird to do any work
in the declaration. For instance
```
sample_df = df.filter(df.id == 1).toPandas()
@pandas_udf(returnType=foo(sample_df))
def foo(df):
ret = # Some transformation on the input pd.DataFrame
return ret
```
Is this allowed? When exactly is `foo(sample_df)` called here?
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