Summary  
This chapter covers techniques for detecting and managing missing values in data structures, including removing null entries and applying various imputation methods (mean, median, forward/backward fill) to maintain data consistency.

General domain of usage  
Data preprocessing

Learn the essentials of PySpark for big data analysis using real-world flights data. Explore Spark architecture, RDDs, DataFrames, and practical data processing techniques.

Core concepts and hands-on practice with PySpark using flights data.

Handling Missing Data