Background

Pandas is currently the dominant Python library for data manipulation. Polars, a newer alternative written in Rust, is gaining popularity due to its superior performance and memory efficiency.

Key differences between the libraries:

• Polars is significantly faster for large datasets due to its use of Apache Arrow, lazy execution, and parallel processing

• Pandas has broader ecosystem integration and a larger community

• Polars is more memory-efficient but may be slower for small datasets due to multi-threading overhead

Resolution Criteria

This market will resolve based on the monthly download statistics from PyPI (Python Package Index) for January 2028. The market resolves YES if Polars has more monthly downloads than Pandas during January 2028, and NO if Pandas maintains more monthly downloads or if the statistics are unavailable.

Considerations

• While Polars' performance advantages are significant, overcoming Pandas' established ecosystem and user base represents a major challenge

• The data science landscape could shift dramatically with new technologies or paradigms emerging by 2028

• Corporate adoption and integration into major frameworks could significantly impact download numbers

• Educational institutions' choice of teaching materials could influence adoption rates

• Download statistics may not perfectly reflect actual usage, as some downloads may be automated or part of CI/CD pipelines