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# Benchmarking energy usage and performance of Polars and pandas

**[Polars Blog](https://daily.dev/sources/polars)** · 7 min read · 0 upvotes · 0 comments

## Summary

Researchers from Vrije Universiteit Amsterdam benchmarked energy usage and performance of Polars versus pandas across 28 data analysis tasks, including synthetic workloads and the TPC-H benchmark, on small and large dataframes. Polars consumed roughly 8 times less energy than pandas in synthetic tasks with large dataframes and about 63% of pandas' energy usage on large TPC-H workloads, though pandas held a slight energy advantage on small dataframes. The study attributes Polars' efficiency to better multi-core CPU utilization and finds a strong correlation between energy usage and execution time. The lead researcher, Ivano Malavolta, recommends Polars for energy-efficient, fast data analysis on large datasets and urges developers to weigh energy costs when choosing libraries.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://pola.rs/posts/benchmark-energy-performance>

## Questions this post answers

### Is Polars more energy efficient than pandas for large dataframes?

Yes, a Vrije Universiteit Amsterdam study found Polars uses roughly 8 times less energy than pandas in synthetic data analysis tasks on large dataframes, and about 63% of the energy pandas requires on large TPC-H benchmark workloads. Pandas showed a slight energy advantage only on small dataframes. The efficiency gain is attributed to Polars' better utilization of multi-core CPUs.

_Comparing dataframe libraries for a data pipeline is easier with benchmark research surfaced on daily.dev._

### Why is Polars faster and more energy efficient than pandas on large datasets?

Polars better harnesses multi-core processors for concurrent data operations, which speeds up processing and reduces energy consumption compared to pandas. Researchers found a statistically significant negative correlation between energy efficiency and CPU usage for pandas, suggesting pandas underutilizes available cores while Polars' parallel processing scales more effectively on large, complex computations.

_Developers weighing performance trade-offs between data libraries can track findings like these on daily.dev._

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---

Tags: [#python](https://daily.dev/tags/python), [#pandas](https://daily.dev/tags/pandas), [#polars](https://daily.dev/tags/polars)

[View this post on daily.dev](https://daily.dev/posts/benchmarking-energy-usage-and-performance-of-polars-and-pandas-a5suovg0k)

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