August 2019
Beginner
482 pages
12h 56m
English
Content preview from Learn Python by Building Data Science Applications
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Summary
In this chapter, we covered the foundation of Python's data science stack—the NumPy, pandas, SciPy, scikit-learn, and Jupyter libraries. By doing so, we were able to gather an understanding of this ecosystem, why and when we need all of these packages, and how they relate to each other. Understanding their relationships helps to navigate and search for a specific functionality or tool to use.
We also touched upon the reasons why NumPy-based computations are so fast, and why this leads to a somewhat different philosophy of data-driven development. We further showcased how pandas complements NumPy arrays by supporting plenty of data formats and types, and SciPy and scikit-learn build upon those data structures, allowing us to quickly ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.