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

# Problem Description

Path finding is all about finding the shortest path in a graph. This can be split in two distinct problems:

- To find a path between two nodes in a graph
- To find the shortest path.

We'll illustrate this through path finding in a
maze. The first task is thus to build a maze!

$$$$

# Building a maze
There exist many [maze generation algorithms](https://en.wikipedia.org/wiki/Maze_generation_algorithm) but I tend to prefer the one I've been using for several years but whose origin is unknown to me. I've added the code in the cited Wikipedia entry. Feel free to complete it if you know the original author.


This algorithm works by creating `n` (density) islands of length `p` (complexity). An island is created by choosing a random starting point with odd coordinates, then a random direction is chosen. If the cell two steps away in the given direction is free, then a wall is added at both one and two steps in this direction. The process is iterated for `n` steps for this island. `p` islands are created. `n` and `p` are expressed as a float to adapt them to the size of the maze. With a low complexity, islands are very small and the maze is easy to solve. With a low density, the maze has more "big empty rooms".



In this lesson, we will look how to find the shortest path in a maze. 

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

Coding Example: Find shortest path in a maze

Problem Description