pyspark.sql.functions.forall¶

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

Returns whether a predicate holds for every element in the 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: (x: Column) -> Column: ... returning the Boolean expression. Can use methods of Column, functions defined in pyspark.sql.functions and Scala UserDefinedFunctions. Python UserDefinedFunctions are not supported (SPARK-27052).

Returns

Column: True if “all” elements of an array evaluates to True when passed as an argument to given function and False otherwise.

Examples

>>> df = spark.createDataFrame(
...     [(1, ["bar"]), (2, ["foo", "bar"]), (3, ["foobar", "foo"])],
...     ("key", "values")
... )
>>> df.select(forall("values", lambda x: x.rlike("foo")).alias("all_foo")).show()
+-------+
|all_foo|
+-------+
|  false|
|  false|
|   true|
+-------+

pyspark.sql.functions.exists

pyspark.sql.functions.filter