Apache Spark Python - Processing Column Data - Date and Time Arithmetic

Data Engineering Spark
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Let us perform Date and Time Arithmetic using relevant functions over Spark Data Frames. Here, we will explore various functions such as date_add, date_sub, datediff, months_between, add_months, and next_day to manipulate date and time data. These functions can be useful for performing calculations on date and timestamp columns in Spark DataFrames.

Adding Days with date_add

The date_add function is used to add days to a date or timestamp. Here is an example:

from pyspark.sql.functions import date_add
date_add("date_column", 10)

Calculating Date Differences with datediff

The datediff function calculates the difference between two dates or timestamps. Here is an example:

from pyspark.sql.functions import datediff
datediff("date_column", "another_date_column")

Click here to watch the video on Date and Time Arithmetic in Spark DataFrames

Hands-On Tasks

Let’s perform some hands-on tasks related to date arithmetic:

  1. Get help on each function (date_add, date_sub, etc.) to understand their arguments.
  2. Create a DataFrame named datetimesDF with columns date and time.
  3. Add 10 days to both date and time values in the DataFrame.
  4. Subtract 10 days from both date and time values in the DataFrame.
  5. Get the difference between current_date and date values as well as current_timestamp and time values.
  6. Get the number of months between current_date and date values as well as current_timestamp and time values.
  7. Add 3 months to both date values as well as time values in the DataFrame.

Conclusion

In this article, we explored various Date and Time Arithmetic operations using Spark DataFrames. By utilizing functions like date_add, datediff, months_between, and add_months, you can efficiently perform calculations on date and timestamp data in your Spark applications. We encourage you to try out the hands-on tasks provided and further engage with the community for deeper learning.

Remember to practice and apply these concepts in your own projects to strengthen your understanding of Date and Time Arithmetic in Spark DataFrames.