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

# What is Spatial Vectorization?

>*Spatial vectorization* refers to a situation where elements share the same
computation but are in interaction with only a subgroup of other elements.

This was already the case for the game of life example, but in some situations
there is an added difficulty because the subgroup is dynamic and needs to be
updated at each iteration. This is the case, for example, in particle systems where
particles interact mostly with local neighbors. This is also the case for
"*boids*" that simulate flocking behaviors.



This lesson discusses spatial vectorization, along with an interesting case study on Boids.

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

Spatial Vectorization

What is Spatial Vectorization?