Use Modin to scale your data explorations and visualization

Pandas is one of the most popular Python libraries used for data explorations and visualization. Pandas come up with tons of inbuilt functions that make it easier to perform data explorations. But when it comes to handling large sized-dataset, it fails, as it performs all manipulation using a single-core CPU. Pandas do not take benefit of all the available CPU cores to scale up the computations.

All the available cores of the CPU can be used to scale up the performance and time complexity of large and complex computations. There are various open-sourced libraries such as Vaex, Dask, Modin, and many more, that can use all the cores of the CPU to scale up the performance. Full utilization of CPU cores saves a lot of Data Scientist time, as it decreases the computation time numbers.

In this article, you can read how to scale up the performance of Pandas library computation using Modin, just by changing one line of code.

What is Modin?

How Modin works under the Hood?

API Coverage:

Benchmark Time Constraints:

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