如何计算pyspark中的日期差异? [英] How to calculate date difference in pyspark?

问题描述

我有这样的数据:

df = sqlContext.createDataFrame([
    ('1986/10/15', 'z', 'null'), 
    ('1986/10/15', 'z', 'null'),
    ('1986/10/15', 'c', 'null'),
    ('1986/10/15', 'null', 'null'),
    ('1986/10/16', 'null', '4.0')],
    ('low', 'high', 'normal'))

我想计算low列和2017-05-02之间的日期差，并用差值替换low列.我已经尝试过有关stackoverflow的相关解决方案，但是它们都不起作用.

I want to calculate the date difference between low column and 2017-05-02 and replace low column with the difference. I've tried related solutions on stackoverflow but neither of them works.

推荐答案

您需要将列low强制转换为课程日期，然后才能将datediff()与lit()结合使用.使用 Spark 2.2 :

You need to cast the column low to class date and then you can use datediff() in combination with lit(). Using Spark 2.2:

from pyspark.sql.functions import datediff, to_date, lit

df.withColumn("test", 
              datediff(to_date(lit("2017-05-02")),
                       to_date("low","yyyy/MM/dd"))).show()
+----------+----+------+-----+
|       low|high|normal| test|
+----------+----+------+-----+
|1986/10/15|   z|  null|11157|
|1986/10/15|   z|  null|11157|
|1986/10/15|   c|  null|11157|
|1986/10/15|null|  null|11157|
|1986/10/16|null|   4.0|11156|
+----------+----+------+-----+

使用< Spark 2.2 ，我们需要先将low列转换为类timestamp:

Using < Spark 2.2, we need to convert the the low column to class timestamp first:

from pyspark.sql.functions import datediff, to_date, lit, unix_timestamp

df.withColumn("test", 
              datediff(to_date(lit("2017-05-02")),
                       to_date(unix_timestamp('low', "yyyy/MM/dd").cast("timestamp")))).show()

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