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 Code vectorization?
Code vectorization means that the problem you're trying to solve is inherently
vectorizable and only requires a few NumPy tricks to make it faster. Of course, it does not mean it is easy or straightforward, but at least it does not
necessitate totally rethinking your problem (as it will be the case in the
**Problem vectorization** chapter). Still, it may require some experience to see
where code can be vectorized.

## Example
Let's illustrate this through a simple example
where we want to **sum up two lists of integers**.
## Solution 1: Pure Python 
One simple way using pure Python
is:

This lesson gives a brief introduction to code vectorization and explains an example using pure Python and NumPy.

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

Introduction

What is Code vectorization?