Apache Spark Python - Processing Column Data - Categories of Functions

Learn how to leverage pyspark.sql.functions for efficient data analysis in PySpark. This article provides a comprehensive guide on key concepts, hands-on tasks, and practical examples to help you master essential functions for manipulating and analyzing data.

Categories of Functions

There are approximately 300 functions under pyspark.sql.functions. These functions can be categorized into:

  • String Manipulation Functions
  • Date Manipulation Functions
  • Aggregate Functions
  • Other Functions for various use cases in data analysis.

String Manipulation Functions

Master functions like lower, upper, length, substring, split, trim, ltrim, rtrim, lpad, rpad, concat, and concat_ws for string manipulation tasks.

Date Manipulation Functions

Learn to work with functions like current_date, current_timestamp, date_add, date_sub, datediff, months_between, add_months, next_day, last_day, trunc, date_trunc, date_format, dayofyear, dayofmonth, dayofweek, year, and month for effective date manipulation.

Aggregate Functions

Understand the usage of count, countDistinct, sum, avg, min, and max for aggregating data in PySpark.

Other Functions

Explore functions like CASE and WHEN, CAST for type casting, and handling special column types like ARRAY, MAP, and STRUCT.

Watch the video tutorial here

Hands-On Tasks

Practice the following tasks to reinforce your understanding of pyspark.sql.functions:

  1. Perform string concatenation using concat and concat_ws.
  2. Calculate the average value of a column using avg.
  3. Extract the day of the week from a date column using dayofweek.

Conclusion

In this article, we covered key concepts and practical tasks related to pyspark.sql.functions for data analysis in PySpark. Dive deeper into these functions and explore their applications to enhance your data manipulation skills. Happy coding!