Which of the following are recommended practices for chaining expressions in PySpark to enhance code readability?

Chaining expressions in PySpark can significantly improve code readability and maintainability. One of the recommended practices is to isolate each logical group of transformations into separate code blocks, which allows readers to easily understand the flow of operations being applied to the data. When each group of transformations is clearly defined, it enhances the overall clarity of the code.

While there is no strict rule that limits the number of statements in a chain to a maximum of five, excessive chaining can lead to complex and hard-to-read code. Thus, the idea behind limiting chains is to balance conciseness with readability. Long chains with many operations might obscure the intent of the code, making it difficult for others (or even the original author at a later date) to follow the logic.

In addition to chaining transformations in a readable manner, extracting complex logic into separate functions is also a beneficial practice. This helps to encapsulate specific logic, reduces redundancy, and makes the overall code cleaner and easier to test.

Collectively, these practices focus on organizing code in a way that aligns with good software development principles, ensuring that the PySpark codebase remains manageable and understandable as it grows.