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How does dask work with pandas and pandas?


Asked by Dahlia Dougherty on Dec 09, 2021 FAQ



Dask uses existing Python APIs and data structures to make it easy to switch between Numpy, Pandas, Scikit-learn to their Dask-powered equivalents. You don't have to completely rewrite your code or retrain to scale up.
Additionally,
A Dask DataFrame is partitioned row-wise, grouping rows by index value for efficiency. These Pandas objects may live on disk or other machines. Dask DataFrames coordinate many Pandas DataFrames or Series arranged along the index
Besides, Dask is an open-source framework that enables the parallelization of Python code. This can be applied to all kinds of Python use cases, not just data science. Dask is designed to work well on single-machine setups and on multi-machine clusters. You can use Dask with not just pandas, but NumPy, scikit-learn, and other Python libraries.
In fact,
Dask isn’t a panacea, of course: Parallelism has overhead, it won’t always make things finish faster. And it doesn’t reduce the CPU time, so if you’re already saturating your CPUs it won’t speed things up on wallclock time either. Some tuning is needed.
Keeping this in consideration,
Dask is a open-source library that provides advanced parallelization for analytics, especially when you are working with large data. It is built to help you improve code performance and scale-up without having to re-write your entire code.