回傳一個包含 col1 與 col2 元素交集的新陣列，且無重複。
語法
from pyspark.sql import functions as sf
sf.array_intersect(col1, col2)
參數
| 參數 | 類型 | Description |
|---|---|---|
col1 |
pyspark.sql.Column 或 str |
包含第一個陣列的欄位名稱。 |
col2 |
pyspark.sql.Column 或 str |
包含第二個陣列的欄位名稱。 |
退貨
pyspark.sql.Column：一個包含 col1 與 col2 元素交集的新陣列。
範例
範例 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.sort_array(sf.array_intersect(df.c1, df.c2))).show()
+-----------------------------------------+
|sort_array(array_intersect(c1, c2), true)|
+-----------------------------------------+
| [a, c]|
+-----------------------------------------+
範例 2：無共同元素的交點
from pyspark.sql import Row, functions as sf
df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2=["d", "e", "f"])])
df.select(sf.array_intersect(df.c1, df.c2)).show()
+-----------------------+
|array_intersect(c1, c2)|
+-----------------------+
| []|
+-----------------------+
範例 3：與所有共同元素的交集
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_intersect(df.c1, df.c2))).show()
+-----------------------------------------+
|sort_array(array_intersect(c1, c2), true)|
+-----------------------------------------+
| [a, b, c]|
+-----------------------------------------+
範例 4：與零值的交集
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_intersect(df.c1, df.c2))).show()
+-----------------------------------------+
|sort_array(array_intersect(c1, c2), true)|
+-----------------------------------------+
| [NULL, a]|
+-----------------------------------------+
範例 5：與空陣列的交集
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_intersect(df.c1, df.c2)).show()
+-----------------------+
|array_intersect(c1, c2)|
+-----------------------+
| []|
+-----------------------+