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

As explained on the NumPy website, NumPy is the fundamental package for
scientific computing with Python. However, as illustrated in this chapter, the usage of NumPy strengths goes far beyond a mere *multi-dimensional container of
generic data*. Using `ndarray` as a private property in one case (`TypedList`) or
directly subclassing the `ndarray` class (`GPUData`) to keep track of memory in
another case, we've seen how it is possible to extend NumPy's capabilities to
suit very specific needs. The limit is only your imagination and your experience.
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The next chapter will give a brief overview of some other useful libraries that can be used in Python.  

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