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4 Faster Pandas Alternatives for Data Analysis
Performance Benchmark on Popular Data Analysis Tools
Pandas is no doubt one of the most popular libraries in Python. Its DataFrame is intuitive and has rich APIs for data manipulation tasks. Many Python libraries integrated with Pandas DataFrame to increase their adoption rate.
However, Pandas doesn’t shine in the land of data processing with a large dataset. It is predominantly used for data analysis on a single machine, not a cluster of machines. In this article, I will try to measure performance for Polars, DuckDB, Vaex, and Modin as alternatives to compare with Pandas.
Database-like ops benchmark published by h2oai inspires the idea of this post. The benchmark experiment was conducted in May 2021. This article is to review this field after two years with many feature and improvements.
Why is Pandas slow on large datasets?
The main reason is that Pandas wasn’t designed to run on multiple cores. Pandas uses only one CPU core at a time to perform the data manipulation tasks and takes no advantage on modern PC with multiple cores on parallelism.
How to mitigate the issue when data size is large (still can fit on one machine) but Pandas takes time to execute? One solution is to…
