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Chapter 1: Introduction to Spark DataFrames

Chapter 2: Creating Spark DataFrames Using Python Collections

Chapter 3: Creating Single-Column and Multi-Column DataFrames

Chapter 4: Understanding Spark Rows and Conversions

Chapter 5: Specifying Schemas for Spark DataFrames

Chapter 6: Working with Special Data Types in Spark

Chapter 7: Manipulating Array, Map, and Struct Type Columns

Chapter 8: Selecting and Renaming Columns in Spark

Chapter 9: Transformations: Narrow vs. Wide

Chapter 10: Using select and selectExpr in Spark

Chapter 11: Exploring Column Functions: col and lit

Chapter 12: Renaming Columns and Derived Columns

Chapter 13: Advanced Column Manipulation Techniques

Chapter 14: Working with String Manipulation Functions

Chapter 15: Extracting, Padding, and Trimming Strings

Chapter 16: Performing Date and Time Operations in Spark

Chapter 17: Formatting and Parsing Dates in Spark

Chapter 18: Filtering Data in Spark DataFrames

Chapter 19: Boolean Operations and Filtering Logic

Chapter 20: Dropping Columns and Handling Nulls in DataFrames

Chapter 21: Sorting DataFrames with Composite and Prioritized Sorting

Chapter 22: Performing Aggregations and Grouped Operations

Chapter 23: Validating Data for Aggregations

Chapter 24: Overview of Joins in Spark DataFrames

Chapter 25: Performing Inner and Outer Joins

Chapter 26: Working with Broadcast and Cross Joins

Chapter 27: Best Practices for Handling Nulls in Joins and Filters

Chapter 28: Using CASE, WHEN, and Other Conditional Expressions

Chapter 29: Creating Dummy DataFrames for Testing

Chapter 30: Optimizing DataFrames for Performance

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