您可以将函数转换为自定义项，可能类似于下面的内容。

from pyspark.sql.functions import lit, array

values = [(["EBBY" , "ABBY" , "JULIE" , "ROBERTS"],),
           (["EBBY" , "ABBY"],)]
columns = ['labels']
df = spark.createDataFrame(values, columns)

@udf
def event_list(column_to_test, input_values):
    return all(value in column_to_test for value in input_values)

steps = ["EBBY", "JULIE"]
df.withColumn("steps_present", event_list(df['labels'], array([lit(x) for x in steps]))).show(truncate=False)

你可以用 array_except 检查所提供列表中的每个元素是否都存在于labels列中。如果是，结果的大小 array_except 将是0。将大小与0进行比较将得到所需的布尔值。

import pyspark.sql.functions as F

def event_list(df, steps):
    return df.withColumn(
        'steps_present', 
        F.size(F.array_except(F.array(*[F.lit(l) for l in steps]), 'labels')) == 0
    )

df2 = event_list(df, ["EBBY", "ABBY"])

df2.show(truncate=False)
+----------------------------+-------------+
|labels                      |steps_present|
+----------------------------+-------------+
|[EBBY, ABBY, JULIE, ROBERTS]|true         |
|[EBBY, JULIE]               |false        |
+----------------------------+-------------+