Apache Spark 3 for Data Engineering and Analytics with Python - Working with Missing or Bad Data

The video tutorial covers handling missing data in data engineering using Spark DataFrame API. It begins with creating a DataFrame with null values, then demonstrates how to drop rows with null values using the NA function. The tutorial also shows how to filter DataFrame records based on specific columns and use the describe function to obtain statistical summaries of columns. The video emphasizes practical coding steps and provides examples for each operation.

Explain how to filter a dataframe based on a specific column.

What statistical information can you obtain from a dataframe using the describe function?

How can you handle non-numeric columns when summarizing data?