Which method adheres to the recommended PySpark style when adding new columns to a DataFrame?

Using withColumn to add each new column individually is consistent with the recommended PySpark style for several reasons. This method allows for clarity and maintainability in code. Each new column can be added explicitly through withColumn, which makes it clear to anyone reading the code what transformations are being applied to the DataFrame.

Moreover, withColumn facilitates the addition of new columns based on existing data, where you can apply transformations or computations to derive the new values. This method explicitly communicates the intent to add or modify columns, which is an essential aspect of writing clear and understandable code in data engineering practices.

While it is possible to add multiple columns in one operation using select, using withColumn typically results in more legible code in scenarios where complex transformations are required for individual columns. Additionally, the withColumnRenamed method is not appropriate for adding new columns, as it's intended solely for renaming existing columns.

Therefore, the approach of using withColumn aligns with best practices in PySpark, emphasizing clarity and the ability to track each individual transformation when working with DataFrames.