Spark Reference

Understanding the date_sub Function in PySpark

The date_sub function in PySpark is a handy tool for manipulating dates. It allows you to subtract a specified number of days from a given date. Interestingly, if you provide a negative number of days, it will add those days to the date instead. This guide will walk you through how to use date_sub effectively in your PySpark applications.

Syntax and Parameters

The date_sub function follows a simple syntax:

  • Syntax: F.date_sub(start, days)

  • Parameters:

    • start: The starting date. This is the reference date from which days will be subtracted or added.
    • days: The number of days to subtract from the start date. If a negative value is provided, the days will be added to the start date.

How to Use date_sub

To use date_sub, you'll first need to import the PySpark SQL functions module. Here's how you can do it:

import pyspark.sql.functions as F

Now, let's dive into some examples to see date_sub in action.

Subtracting Days from a Date

To subtract 7 days from dates in a DataFrame column:

df.select(F.date_sub(df.date_column, 7).alias('new_date')).show()

Adding Days to a Date

You can also add days by providing a negative value, although we would not recommend this for most use cases - consider using date_add. To add 30 days:

df.select(F.date_sub(df.date_column, -30).alias('new_date')).show()

Working with Specific Dates

Calculating the date 90 days before January 1, 2022:

df.select(F.date_sub(F.lit('2022-01-01'), 90).alias('new_date')).show()

Tips and Common Pitfalls

  • Syntax and Data Types: Always ensure you're using the correct syntax and data types. The start date should be a valid date or timestamp type, and days should be an integer.

  • Negative Values: Using a negative number for days will add days instead of subtracting. This can be a useful feature but double-check to avoid confusion.

By following this guide, you should now have a good understanding of how to use the date_sub function in PySpark to manipulate dates in your data processing tasks.