Window functions in PySpark allow you to perform operations on a subset of your data using a “window” that defines a range of rows. These functions are similar to SQL window functions and are useful for tasks like ranking, cumulative sums, and moving averages. Let’s go through various PySpark DataFrame window functions, compare them with…

String manipulation is a common task in data processing. PySpark provides a variety of built-in functions for manipulating string columns in DataFrames. Below, we explore some of the most useful string manipulation functions and demonstrate how to use them with examples. Common String Manipulation Functions Example Usage 1. Concatenation Syntax: 2. Substring Extraction Syntax: 3.…

✅ What is a DataFrame in PySpark? A DataFrame in PySpark is a distributed collection of data organized into named columns, similar to a table in a relational database or a Pandas DataFrame. It is built on top of RDDs and provides: 📊 DataFrame = RDD + Schema Under the hood: So while RDD is…