Polars vs pandas: What’s the Difference?
Polars and pandas both provide DataFrame-based data analysis in Python, but they differ in syntax, performance, and features. In this tutorial on Polars vs pandas, you’ll compare their method chaining styles, run timed performance tests, explore LazyFrame optimizations in Polars, convert data between the two libraries, and create plots with their built-in tools. You’ll also examine scenarios where each library’s strengths make it the better choice.
By the end of this tutorial, you’ll understand that:
- Polars expressions and contexts let you build clear, optimized query pipelines without mutating your original data.
- LazyFrames with query optimization in Polars can outperform pandas for grouped and aggregated workloads.
- Streaming in Polars enables processing datasets that don’t fit in memory, which pandas can’t