配列を除く
col1 には存在するが col2 には存在しない要素を重複なしで含む新しい配列を返します。
構文
Python
from pyspark.sql import functions as sf
sf.array_except(col1, col2)
パラメーター
パラメーター | Type | 説明 |
|---|---|---|
|
| 最初の配列を含む列の名前。 |
|
| 2 番目の配列を含む列の名前。 |
戻り値
pyspark.sql.Column: col1 に存在するが col2 には存在しない要素を含む新しい配列。
例
例1 ：基本的な使い方
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]|
+--------------------+
例2 ：共通要素がない場合
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]|
+--------------------------------------+
例3 ：すべての共通要素を除く
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)|
+--------------------+
| []|
+--------------------+
例4 : null値を除く
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]|
+--------------------+
例5 : 空の配列を除く
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)|
+--------------------+
| []|
+--------------------+