In this chapter, we focused on how to perform parallel computation on basic data science Python libraries such as pandas, Numpy, and scikit-learn. Dask provides a complete abstraction for DataFrames and Arrays for processing moderately large datasets over single/multiple core machines or multiple nodes in a cluster.
We started this chapter by looking at Dask data types such as DataFrames, Arrays, and Bags. After that, we focused on Dask Delayed, preprocessing, and machine learning algorithms in a parallel environment.
This was the last chapter of this book, which means our learning journey ends here. We have focused on core Python libraries for data analysis and machine learning such as pandas, Numpy, Scipy, and scikit-learn. We have also focused on Python libraries that can be used for text analytics, image analytics, and parallel computation such as NLTK, spaCy, OpenCV, and Dask. Of course, your learning process doesn't need to stop here; keep learning new things and about...
