Using NumPy to perform calculations quickly
As we talked about in the How OSS and Anaconda create the data science landscape section in Chapter 2, Analyzing Open Source Software, open source builds on itself. One library uses another to do some basic operations, and that library then itself can be used by something else in order to accomplish a different task or do the same thing in a more abstract way. NumPy is one of those base libraries that is used by a huge number of tools and frameworks to handle fast mathematical operations for arrays.
Created by Travis Oliphant (who later went on to help found Anaconda, Inc), NumPy is used by scikit-learn, SciPy, and pandas in order to focus on the respective problems they are trying to solve and lets NumPy do what it's good at. Getting a good grasp of NumPy allows you to better understand those other higher abstractions that use NumPy later on, as well as being able to use it directly when you are cleaning and creating datasets.
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