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Returns a new array containing the union of elements in col1 and col2, without duplicates.
Syntax
from pyspark.sql import functions as sf
sf.array_union(col1, col2)
Parameters
| Parameter | Type | Description |
|---|---|---|
col1 |
pyspark.sql.Column or str |
Name of column containing the first array. |
col2 |
pyspark.sql.Column or str |
Name of column containing the second array. |
Returns
pyspark.sql.Column: A new array containing the union of elements in col1 and col2.
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.sort_array(sf.array_union(df.c1, df.c2))).show()
+-------------------------------------+
|sort_array(array_union(c1, c2), true)|
+-------------------------------------+
| [a, b, c, d, f]|
+-------------------------------------+
Example 2: Union 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_union(df.c1, df.c2))).show()
+-------------------------------------+
|sort_array(array_union(c1, c2), true)|
+-------------------------------------+
| [a, b, c, d, e, f]|
+-------------------------------------+
Example 3: Union 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.sort_array(sf.array_union(df.c1, df.c2))).show()
+-------------------------------------+
|sort_array(array_union(c1, c2), true)|
+-------------------------------------+
| [a, b, c]|
+-------------------------------------+
Example 4: Union 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.sort_array(sf.array_union(df.c1, df.c2))).show()
+-------------------------------------+
|sort_array(array_union(c1, c2), true)|
+-------------------------------------+
| [NULL, a, b, c]|
+-------------------------------------+
Example 5: Union 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.sort_array(sf.array_union(df.c1, df.c2))).show()
+-------------------------------------+
|sort_array(array_union(c1, c2), true)|
+-------------------------------------+
| [a, b, c]|
+-------------------------------------+