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



You've almost reached the end of the course and, hopefully, you've learned that
NumPy is a very versatile and powerful library. However in the meantime,
remember that Python is also quite a powerful language. In fact, in some
specific cases, it might be more powerful than NumPy.

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# Allocations 

Let's consider, for example, an interesting exercise that has been proposed by Tucker Balch in his Computational Investing course. The exercise
is written as:
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##  

>*Write the most succinct code possible to compute all "legal" allocations to 4 stocks such that the allocations are in 1.0 chunks, and the allocations sum to 10.0.*
$$$$
Yaser Martinez collected the different answers from the community and the proposed solutions yield surprising results. But let's start with the most obvious Python solution:

Python is quite a powerful language! Let's find out how by looking at an interesting example proposed by Tucker Balch. 

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

Back to Python

Allocations