pyspark.sql.functions.array_except

pyspark.sql.functions.array_except(col1, col2)

Array function: returns a new array containing the elements present in col1 but not in col2, without duplicates.

New in version 2.4.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters

col1Column or str

Name of column containing the first array.

col2Column or str

Name of column containing the second array.

Returns

Column

A new array containing the elements present in col1 but not in col2.

Notes

This function does not preserve the order of the elements in the input arrays.

Examples

Example 1: Basic usage

>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2=["c", "d", "a", "f"])])
>>> df.select(sf.array_except(df.c1, df.c2)).show()
+--------------------+
|array_except(c1, c2)|
+--------------------+
|                 [b]|
+--------------------+

Example 2: Except with no common elements

>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2=["d", "e", "f"])])
>>> df.select(sf.sort_array(sf.array_except(df.c1, df.c2))).show()
+--------------------------------------+
|sort_array(array_except(c1, c2), true)|
+--------------------------------------+
|                             [a, b, c]|
+--------------------------------------+

Example 3: Except with all common elements

>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["a", "b", "c"], c2=["a", "b", "c"])])
>>> df.select(sf.array_except(df.c1, df.c2)).show()
+--------------------+
|array_except(c1, c2)|
+--------------------+
|                  []|
+--------------------+

Example 4: Except with null values

>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["a", "b", None], c2=["a", None, "c"])])
>>> df.select(sf.array_except(df.c1, df.c2)).show()
+--------------------+
|array_except(c1, c2)|
+--------------------+
|                 [b]|
+--------------------+

Example 5: Except with empty arrays

>>> from pyspark.sql import Row, functions as sf
>>> from pyspark.sql.types import ArrayType, StringType, StructField, StructType
>>> data = [Row(c1=[], c2=["a", "b", "c"])]
>>> schema = StructType([
...   StructField("c1", ArrayType(StringType()), True),
...   StructField("c2", ArrayType(StringType()), True)
... ])
>>> df = spark.createDataFrame(data, schema)
>>> df.select(sf.array_except(df.c1, df.c2)).show()
+--------------------+
|array_except(c1, c2)|
+--------------------+
|                  []|
+--------------------+