Make Pandas 3 Times Faster with PyPolars

Make Pandas 3 Times Faster with PyPolars

Learn how to speed up your Pandas workflow using the PyPolars library. If you are still wondering about it then this article is for you. Find out carefully. It will help your projects complete quickly.

Pandas is one of the most important Python packages among data scientist’s to play around with the data. Pandas library is used mostly for data explorations and visualizations as it comes with tons of inbuilt functions. Pandas fail to handle large size datasets as it does not scale or distributes its process across all the cores of the CPU.

To speed up the computations, one can utilize all the cores of the CPU and speed up the workflow. There are various open-source libraries including Dask, Vaex, Modin, Pandarallel, PyPolars, etc that parallelize the computations across multiple cores of the CPU. In this article, we will discuss the implementation and usage of the PyPolars library and compare its performance with Pandas library.

2021 may tutorials overviews pandas performance python

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