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

*Vectorization*, in simple words, means optimizing the algorithm so that it can run multiple operations from a single instruction.
NumPy is all about vectorization. If you are familiar with Python, this is the
main difficulty you'll face because you'll need to change your way of thinking
and your new friends (among others) are named "vectors", "arrays", "views" or
"ufuncs".

>**Note:**
A custom magic command [timeit](https://www.labri.fr/perso/nrougier/from-python-to-numpy/code/tools.py) is used in all codes. It's a tool for measuring the execution time of small code snippets. 

## Object Oriented Approach

Let's take a very simple example, a random walk. One possible object-oriented
approach would be to define a `RandomWalker` class and write a walk
method that would return the current position after each (random) step. It's nice,
it's readable, but it is slow:

*Vectorization*, in simple words, means optimizing the algorithm so that it can run multiple operations from a single instruction.
NumPy is all about vectorization. If you are familiar with Python, this is the
main difficulty you'll face because you'll need to change your way of thinking
and your new friends (among others) are named "vectors", "arrays", "views" or
"ufuncs".

>**Note:**
A custom magic command [timeit](https://www.labri.fr/perso/nrougier/from-python-to-numpy/code/tools.py) is used in all codes. It's a tool for measuring the execution time of small code snippets. 

# Object Oriented Approach

Let's take a very simple example, a random walk. One possible object-oriented
approach would be to define a `RandomWalker` class and write a walk
method that would return the current position after each (random) step. It's nice,
it's readable, but it is slow:

This lesson teaches Numpy vectorization and explains it with a simple example using object-oriented, procedural and vectorized approach.  

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

NumPy Vectorization

Object Oriented Approach