假设您想要用户的每个可能的位置组合，您只需要在USER_ID上进行连接，然后减去日期列。这里的一个技巧是使用unix_timestamp将日期时间数据解析为支持减法操作的整数。

示例代码：

from pyspark.sql.functions import unix_timestamp, col, datediff

data = [

(1, 1001, '19:11:39 5-2-2010'),
(1, 6022, '17:51:19 6-6-2010'),
(1, 1041, '11:11:39 5-2-2010'),
(2, 9483, '10:51:23 3-2-2012'),
(2, 4532, '11:11:11 4-5-2012'),
(3, 4374, '03:21:23 6-9-2013'),
(3, 4334, '04:53:13 4-5-2013')

]

df = spark.createDataFrame(data, ['USER_ID', 'location', 'timestamp'])
df = df.withColumn('timestamp', unix_timestamp('timestamp', 'HH:mm:ss dd-MM-yyyy'))

Renaming columns to avoid conflicts after join

df2 = df.selectExpr('USER_ID as USER_ID2', 'location as location2', 'timestamp as timestamp2')
cartesian = df.join(df2, col("USER_ID") == col("USER_ID2"), "inner")

Filter to get rid of reversed duplicates, and rows where location is same on both sides

pairs = cartesian.filter("location < location2") \

             .drop("USER_ID2") \
             .withColumn("diff", col("timestamp2") - col("timestamp"))

pairs.show()