Working with Numbers, Strings, Dates & Nulls in PySpark

In this video, we explore how to work with different data types in PySpark — from Booleans and numbers to strings, regular expressions, dates, timestamps, and null values. You’ll see practical examples in Databricks using both PySpark DataFrame APIs and Spark SQL. All code and data files are available on the below path: https://github.com/databeli/pyspark_c... PowerPoint Presentaion useed in the complete playlist(27 slides) https://topmate.io/narender_kumar_91/... What you’ll learn: Handling Boolean operations in PySpark Numeric functions and rounding examples String transformations: lower, upper, trim, substring, split Using Regex (Regular Expressions) for text extraction and replacement Date and timestamp functions (conversion, add/subtract days, casting) Managing null values using coalesce, case when, drop, fill Equivalent SQL implementations for each example Perfect for anyone learning PySpark basics or building real-world data pipelines on Databricks! #pyspark #pysparktutorial #databricks #databrickstutorial