返回一个新数组，该数组包含 col1 中存在的元素，但不在 col2 中，不带重复项。
Syntax
from pyspark.sql import functions as sf
sf.array_except(col1, col2)
参数
| 参数 | 类型 | Description |
|---|---|---|
col1 |
pyspark.sql.Column 或 str |
包含第一个数组的列的名称。 |
col2 |
pyspark.sql.Column 或 str |
包含第二个数组的列的名称。 |
退货
pyspark.sql.Column：包含 col1 中存在的元素但不在 col2 中的元素的新数组。
例子
示例 1：基本用法
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]|
+--------------------+
示例 2：除没有常见元素外
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]|
+--------------------------------------+
示例 3：除所有常见元素外
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)|
+--------------------+
| []|
+--------------------+
示例 4：除 NULL 值外
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]|
+--------------------+
示例 5：空数组除外
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)|
+--------------------+
| []|
+--------------------+