<ul><li>Polars, a blazing-fast DataFrame library for Rust, provides a convenient way to work with tabular data.</li><li>Understanding how Polars manages ownership and borrowing of data within its DataFrame structures is crucial to write efficient and safe code.</li><li>Techniques such as using immutable operations, cloning, and breaking down operations can help overcome borrow-checker issues in Polars.</li><li>Advanced techniques like optimizing data types, leveraging lazy evaluation, and constructing efficient pipelines can enhance the performance of Polars applications.</li></ul>

Conquering Polars’ Rust NAPI Borrow Checker: A Practical Guide

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