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Leverage the Power of Window Functions in PySpark
Window functions are useful in many cases. Learn how to apply them.
The traditional GROUP BY operation in PySpark (and in any other language) is probably one of the most used. Aggregating data is very important for Data Scientists to extract good information out of a dataset.
However, they are not always the best solution. Do you want to perform calculations across a set of rows that are related to the current row?
That's where Window Functions come to the scene! They're like a box that you can roll over your data and compute values within that "window".
In this article, we'll explore how to use them to perform complex data analysis tasks with ease.
What are Window Functions, Anyway?
Imagine you have a sliding window that moves across your dataset, performing calculations on the rows within its frame. It’s like looking at your data through a keyhole, but you control the size and position of that keyhole!
Window functions operate on a group of rows (a window) relative to the…
