The Future of Data Transformation: Dynamic Unpivoting with PySpark in Databricks

In this video, Mitchell Pearson from Pragmatic Works demonstrates how to perform a dynamic unpivot operation using PySpark. This tutorial focuses on converting data from a wide format with multiple columns into a more relational structure, making it easier for analysis and reporting. Mitchell explains how to handle datasets with an unspecified number of columns, ensuring that as file requirements change, the code can still dynamically unpivot the data without complicated rewrites.

What is Unpivoting?

Unpivoting refers to the process of transforming data from a wide format (with many columns) into a long format (with fewer columns and more rows). This is especially useful when working with datasets that have year-over-year data spread across multiple columns, such as economic or medical records. By unpivoting the data, each record is broken down by year, making it more suitable for aggregation and analysis.

Basic Unpivot in PySpark

To demonstrate a basic unpivot, Mitchell walks through an example where data consists of country names and BMI measurements for different years. The goal is to keep the "Country" column static while unpivoting year columns (e.g., 1980, 1981, 1982) into a single column named "Year," with corresponding BMI values in another column.

1. Use the collect function to keep certain columns static (e.g., "Country").
2. Specify the columns to unpivot, such as the years 1980, 1981, and 1982.
3. Name the two resulting columns "Year" and "BMI" to store the original year values and the BMI measurements.

Dynamic Unpivoting with PySpark

Mitchell then introduces the challenge of dynamically unpivoting data when the number of columns is unknown or changes frequently. Instead of hardcoding the columns to unpivot, he demonstrates how to dynamically generate a list of columns and pass them to the unpivot function.

Steps to Dynamic Unpivot

1. Create a list of columns from the original dataset using the columns function in PySpark.
2. Remove any static columns, such as "Country," from the list.
3. Pass the dynamically generated list of columns into the unpivot function, ensuring that the resulting data frame is structured correctly with columns for "Year" and "BMI."

Handling Large Datasets

This approach is especially useful for datasets with many columns, such as those going back to the 1800s. Instead of manually specifying each column, Mitchell's method ensures that the code adapts to new datasets without modification. He also demonstrates how to filter the data for a specific country, ensuring that each year is correctly unpivoted into a separate row.

Conclusion

This tutorial shows how to efficiently unpivot data in PySpark, particularly when the number of columns is dynamic and changes frequently. By automating the column selection and unpivoting process, users can save time and avoid complex code rewrites. Mitchell encourages viewers to try out the method in their own data environments.

Don't forget to check out the Pragmatic Works' on-demand learning platform for more insightful content and training sessions on PySpark and other Microsoft applications. Be sure to subscribe to the Pragmatic Works YouTube channel to stay up-to-date on the latest tips and tricks.