Polars has gained popularity for its speed, but what truly makes it stand out is its syntax, especially the use of expressions. The book Python Polars: The Definitive Guide defines an expression as "a tree of operations that describe how to construct one or more Series." In this talk, we’ll demystify this concept, explaining how expressions make Polars an elegant tool for data manipulation.

We will cover:

• Why expressions are crucial in Polars
• A formal definition of an expression and what it means in practice
• Creating expressions from existing columns or other values
• Using expressions to select, filter, sort, and aggregate data
• Applying expressions for aggregate statistics, mathematical transformations, and handling missing values
• Combining expressions with operators, comparisons, and Boolean logic
• A comparison of idiomatic vs. non-idiomatic Polars code

By the end of this talk, you’ll understand how to leverage Polars expressions to write cleaner and more efficient data manipulation code.