40 NumPy Methods That Data Scientists Use 95% of the Time
40 NumPy Methods That Data Scientists Use 95% of the Time
Applying the Pareto's principle to NumPy Library.
NumPy holds wide applicability in industry and academia due to its unparalleled potential.
Thus, being aware of its most common methods is necessary for Data Scientists.
Yet, it is important to understand that whenever you are learning a new library, mastering/practicing each and every method is not necessary.
What’s more, this may be practically infeasible and time-consuming in many cases.
Instead, put Pareto’s principle to work:
20% of your inputs contribute towards generating 80% of your outputs.
In other words, there are always some specific methods that are most widely used.
The above visual depicts the 40 most commonly used methods for NumPy.
Having used NumPy for over 4 years, I can confidently say that you will use these methods 95% of the time working with NumPy.
If you are looking for an in-depth guide, you can read my article on Medium here: Medium NumPy article.
👉 Over to you: Have I missed any commonly used method?
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Find the code for my tips here: GitHub.
I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn and Twitter.