In this episode I speak about data transformation frameworks available for the data scientist who writes Python code. The usual suspect is clearly Pandas, as the most widely used library and de-facto standard. However when data volumes increase and distributed algorithms are in place (according to a map-reduce paradigm of computation), Pandas no longer performs as expected. Other frameworks play a role in such context. 

In this episode I explain the frameworks that are the best equivalent to Pandas in bigdata contexts.

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This episode is supported by Amethix Technologies

Amethix works to create and maximize the impact of the world’s leading corporations, startups, and nonprofits, so they can create a better future for everyone they serve. Amethix is a consulting firm focused on data science, machine learning, and artificial intelligence.

References

• Pandas a fast, powerful, flexible and easy to use open source data analysis and manipulation tool - https://pandas.pydata.org/

• Modin - Scale your pandas workflows by changing one line of code - https://github.com/modin-project/modin

• Dask advanced parallelism for analytics https://dask.org/

• Ray is a fast and simple framework for building and running distributed applications https://github.com/ray-project/ray

• RAPIDS - GPU data science https://rapids.ai/