Python Data Frame Benchmark — Pandas, Polars, and Dask

In the ever-evolving landscape of data science and analysis, Python has emerged as a powerhouse programming language, and its versatility is exemplified by the widespread adoption of libraries such as Pandas for handling tabular data. As the volume and complexity of data continue to grow, the efficiency and performance of data manipulation operations become paramount.

In this article, I did some benchmark on Python data frame libraries. It seeks to explore and evaluate the performance of various Python data frame libraries, shedding light on their strengths and limitations.

This benchmark aims to provide valuable insights for data scientists, analysts, and developers who rely on Python for data manipulation, helping them make informed decisions when selecting the most suitable tools for their specific requirements.

All the data and code detail is open here https://github.com/andreaschandra/df-benchmark