# How to calculate date difference in pyspark?

Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about How to calculate date difference in pyspark in Python. So Here I am Explain to you all the possible Methods here.

## How to calculate date difference in pyspark?

1. How to calculate date difference in pyspark?

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

2. calculate date difference in pyspark

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

## Method 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()```

## Method 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|
+----------+----------+------------+```

## Summery

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.