Using Polars in Rust for high-performance data analysis

The article explores using Rust and the Polars high-performance DataFrame library to create a data analysis application that serves data sets and querying capabilities through a REST-based Web API. It utilizes two data sets: Deutsche Bahn train delays and German zip code demographics, to illustrate data processing and correlation analysis. The tutorial guides the reader through setting up a Rust project with necessary dependencies, loading CSV data into Polars, and building a web server with Axum and Tokio to handle REST API requests. The application allows querying of train delays, population, and area data, as well as correlation analysis using the Pearson Correlation Coefficient to explore potential relationships between various data fields. The article emphasizes Polars' performance advantages due to its Rust implementation and Apache Arrow format, highlighting its utility in analyzing and exposing data through advanced querying capabilities in web applications.