论点count
of the lag
函数采用整数而不是列对象:
psf.lag(col, count=1, default=None)
因此它不可能是一个“动态”值。
相反,您可以在列中构建滞后,然后将表与其自身连接起来。
首先让我们创建我们的数据框:
df = spark.createDataFrame(
sc.parallelize(
[[1, "2011-01-01"], [1, "2012-01-01"], [2, "2013-01-01"], [1, "2014-01-01"]]
),
["int", "date"]
)
我们想要枚举行:
from pyspark.sql import Window
import pyspark.sql.functions as psf
df = df.withColumn(
"id",
psf.monotonically_increasing_id()
)
w = Window.orderBy("id")
df = df.withColumn("rn", psf.row_number().over(w))
+---+----------+-----------+---+
|int| date| id| rn|
+---+----------+-----------+---+
| 1|2011-01-01|17179869184| 1|
| 1|2012-01-01|42949672960| 2|
| 2|2013-01-01|68719476736| 3|
| 1|2014-01-01|94489280512| 4|
+---+----------+-----------+---+
现在建立滞后:
df1 = df.select(
"int",
df.date.alias("date1"),
(df.rn - df.int).alias("rn")
)
df2 = df.select(
df.date.alias("date2"),
'rn'
)
最后我们可以加入它们并计算日期差:
df1.join(df2, "rn", "inner").withColumn(
"date_diff",
psf.datediff("date1", "date2")
).drop("rn")
+---+----------+----------+---------+
|int| date1| date2|date_diff|
+---+----------+----------+---------+
| 1|2012-01-01|2011-01-01| 365|
| 2|2013-01-01|2011-01-01| 731|
| 1|2014-01-01|2013-01-01| 365|
+---+----------+----------+---------+