array_except
Returns a new array containing the elements present in col1 but not in col2, without duplicates.
Syntax
Python
from pyspark.sql import functions as sf
sf.array_except(col1, col2)
Parameters
Parameter | Type | Description |
|---|---|---|
|
| Name of column containing the first array. |
|
| Name of column containing the second array. |
Returns
pyspark.sql.Column: A new array containing the elements present in col1 but not in col2.
Examples
Example 1: Basic usage
Python
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()
Output
+--------------------+
|array_except(c1, c2)|
+--------------------+
| [b]|
+--------------------+
Example 2: Except with no common elements
Python
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()
Output
+--------------------------------------+
|sort_array(array_except(c1, c2), true)|
+--------------------------------------+
| [a, b, c]|
+--------------------------------------+
Example 3: Except with all common elements
Python
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()
Output
+--------------------+
|array_except(c1, c2)|
+--------------------+
| []|
+--------------------+
Example 4: Except with null values
Python
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()
Output
+--------------------+
|array_except(c1, c2)|
+--------------------+
| [b]|
+--------------------+
Example 5: Except with empty arrays
Python
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()
Output
+--------------------+
|array_except(c1, c2)|
+--------------------+
| []|
+--------------------+