Gain insights into utilizing NumPy for data manipulation and analytics. Learn about implementing concepts in both Python and NumPy through coding challenges and quizzes.

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If you're looking to grow your career in machine learning or data science in this day and age, adding a powerful library to your skill set is an important place to start. In that vein, Python has become one of the most widely used tools in the industry for serious data analytics, and NumPy is probably the most widely used data analytics library. With NumPy, you can manipulate data involving multi-dimensional arrays and matrices (think linear algebra).

Join us as we venture into the vast world of NumPy in this comprehensive course. Each lesson dive into the actual implementation of concepts in both pure Python and then NumPy, exploring how NumPy vectorization compares to traditional Python that uses a procedural and object-oriented approach.

Practice and test yourself along the way with in-browser coding challenges, quizzes, and more.

This course is intended for users who are already familiar with intermediate level Python.

From Python to Numpy

NumPy is a very versatile library but still, it does not mean you have to use it in every situation. In this chapter, we've seen some alternatives (including
Python itself) that are worth a look. As always, the choice belongs to you. You have to consider what is the best solution for you in term of development time, computation time and effort in maintenance. On one hand, if you design your own solution, you'll have to test it and to maintain it, but in exchange, you'll be free to design it the way you want. On the other hand, if you decide to rely on a third-party package, you'll save time in development and benefit from community-support even though you might have to adapt the package to your specific needs. The choice is up to you!

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As this course comes to an end, we hope it met your expectations and provided a wholesome learning experience! 💫

This lesson provides a brief summary of what we have covered so far in this chapter.

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

Conclusion