How to calculate date difference in pyspark?

Question

I have data like this:

df = sqlContext.createDataFrame([
    (\'1986/10/15\', \'z\', \'null\'), 
    (\'1986/10/15\', \'z\', \'null\'),
    (\'1986/10/15\'         


        

Answer 1

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|
+----------+----+------+-----+

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()

Answer 2

Alternatively, how to find the number of days passed between two subsequent user's actions using pySpark:

import pyspark.sql.functions as funcs
from pyspark.sql.window import Window

window = Window.partitionBy('user_id').orderBy('action_date')

df = df.withColumn("days_passed", funcs.datediff(df.action_date, 
                                  funcs.lag(df.action_date, 1).over(window)))



+----------+-----------+-----------+
|   user_id|action_date|days_passed| 
+----------+-----------+-----------+
|623       |2015-10-21|        null|
|623       |2015-11-19|          29|
|623       |2016-01-13|          59|
|623       |2016-01-21|           8|
|623       |2016-03-24|          63|
+----------+----------+------------+