array_union
Retourne un nouveau tableau contenant l'union des éléments de col1 et col2, sans doublons.
Syntaxe
Python
from pyspark.sql import functions as sf
sf.array_union(col1, col2)
parameter
parameter | Type | Description |
|---|---|---|
|
| Nom de la colonne contenant le premier tableau. |
|
| Nom de la colonne contenant le deuxième tableau. |
Renvoie
pyspark.sql.Column: Un nouveau tableau contenant l'union des éléments dans col1 et col2.
Exemples
Exemple 1 : utilisation de base
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.sort_array(sf.array_union(df.c1, df.c2))).show()
Output
+-------------------------------------+
|sort_array(array_union(c1, c2), true)|
+-------------------------------------+
| [a, b, c, d, f]|
+-------------------------------------+
Exemple 2 : union sans éléments communs
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_union(df.c1, df.c2))).show()
Output
+-------------------------------------+
|sort_array(array_union(c1, c2), true)|
+-------------------------------------+
| [a, b, c, d, e, f]|
+-------------------------------------+
Exemple 3 : union avec tous les éléments communs
Python
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()
Output
+-------------------------------------+
|sort_array(array_union(c1, c2), true)|
+-------------------------------------+
| [a, b, c]|
+-------------------------------------+
**Exemple 4** : Union avec des valeurs nulles
Python
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()
Output
+-------------------------------------+
|sort_array(array_union(c1, c2), true)|
+-------------------------------------+
| [NULL, a, b, c]|
+-------------------------------------+
**Exemple 5** : Union avec des tableaux vides
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.sort_array(sf.array_union(df.c1, df.c2))).show()
Output
+-------------------------------------+
|sort_array(array_union(c1, c2), true)|
+-------------------------------------+
| [a, b, c]|
+-------------------------------------+