pyspark.sql.functions.transform¶

pyspark.sql.functions.transform(col: ColumnOrName, f: Union[Callable[[pyspark.sql.column.Column], pyspark.sql.column.Column], Callable[[pyspark.sql.column.Column, pyspark.sql.column.Column], pyspark.sql.column.Column]]) → pyspark.sql.column.Column[source]¶

Returns an array of elements after applying a transformation to each element in the input array.

New in version 3.1.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters

colColumn or str

name of column or expression

ffunction

a function that is applied to each element of the input array. Can take one of the following forms:

Unary (x: Column) -> Column: ...
Binary (x: Column, i: Column) -> Column..., where the second argument is
a 0-based index of the element.

and can use methods of Column, functions defined in pyspark.sql.functions and Scala UserDefinedFunctions. Python UserDefinedFunctions are not supported (SPARK-27052).

Returns

Column: a new array of transformed elements.

Examples

>>> df = spark.createDataFrame([(1, [1, 2, 3, 4])], ("key", "values"))
>>> df.select(transform("values", lambda x: x * 2).alias("doubled")).show()
+------------+
|     doubled|
+------------+
|[2, 4, 6, 8]|
+------------+

>>> def alternate(x, i):
...     return when(i % 2 == 0, x).otherwise(-x)
...
>>> df.select(transform("values", alternate).alias("alternated")).show()
+--------------+
|    alternated|
+--------------+
|[1, -2, 3, -4]|
+--------------+

pyspark.sql.functions.array_compact

pyspark.sql.functions.exists