Easily Use Dask DataFrames in place of Pandas for Large Datasets

This article displays how convenient, easy and fast it is to use Dask DataFrames to read and store large datasets that Pandas is hard to handle

NumPy, pandas, and Scikit-learn are the most commonly used libraries for data computation and analysis. However, these packages are hard to process very large datasets because they were not originally designed to scale beyond a single machine.

The examples in the last article show that Pandas becomes very slow to load the data due to the large size of the dataset. Modin can easily speed up Pandas to read large dataset in most cases, but Modin also has its limitation and its speed will slow down when it meets very large data.

Therefore, we will start use some libraries for large dataset manipulation, such as Dask, Koalas (PySpark), Vaex, Polars, etc. This article will show how to use Dask DataFrame in place of Pandas when the dataset is very large.

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