Data Transformation Techniques

In this chapter, we will look at how aggregations, window functions, and User-Defined Functions (UDFs) are essential tools in data analysis, data science, and data engineering workflows. We’ll also cover how we can use SQL in Python Polars.

We will understand how aggregations involve combining and summarizing data to gain insights. They are commonly used in data analysis to perform operations such as sum, average, count, or maximum on a dataset. They help summarize your data and compute the necessary parts to further your data transformations.

We will also understand how window functions, on the other hand, allow you to perform calculations across a specific window or subset of data within a dataset. They are valuable in data analysis for tasks such as ranking and identifying trends within a partition of data.

Furthermore, we will learn about UDFs that provide flexibility by allowing you to define custom functions to process and transform...