In this talk, we will report on our experiences switching from Pandas to Polars in a real-world ML project. Polars is a new high-performance dataframe library for Python based on Apache Arrow and written in Rust. We will compare the performance of polars with the popular pandas library, and show how polars can provide significant speed improvements for data manipulation and analysis tasks. We will also discuss the unique features of polars, such as its ability to handle large datasets that do not fit into memory, and how it feels in practice to make the switch from Pandas. This talk is aimed at data scientists, analysts, and anyone interested in fast and efficient data processing in Python.

Thomas Bierhance

Affiliation: BettercallPaul

Thomas passion has been working with data since 25 years: from small databases for SMEs to large distributed systems for international enterprises and intelligent systems using machine learning. He graduated from the KIT in Karlsruhe, Germany and trained his first neural network while studying at UPC, Barcelona, Spain in 2002. Today he leads the Data Science & AI practice of BettercallPaul in Stuttgart and supports his customers and teams on their journey to generate added value from data.

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