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

## Breadth-First Algorithm

The breadth-first (as well as depth-first) search algorithm addresses the problem of finding a path between two nodes by examining all possibilities starting from the root node and stopping as soon as a solution has been found (destination node has been reached). This algorithm runs in linear time with complexity in **$O(|V|+|E|)$** (where **$V$** is the number of vertices, and $E$ is the number of edges).



Writing such an algorithm is not especially difficult, provided you have the right data structure. In our case, the array representation of the maze is not the most well-suited and we need to transform it into an actual graph as proposed by [Valentin Bryukhanov](http://bryukh.com/).


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### Step 1: Implement a Graph Class

# Breadth-First Algorithm

The breadth-first (as well as depth-first) search algorithm addresses the problem of finding a path between two nodes by examining all possibilities starting from the root node and stopping as soon as a solution has been found (destination node has been reached). This algorithm runs in linear time with complexity in **$O(|V|+|E|)$** (where **$V$** is the number of vertices, and $E$ is the number of edges).



Writing such an algorithm is not especially difficult, provided you have the right data structure. In our case, the array representation of the maze is not the most well-suited and we need to transform it into an actual graph as proposed by [Valentin Bryukhanov](http://bryukh.com/).


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## Step 1: Implement a Graph Class

In this lesson, we will implement the solution of finding shortest path in the maze that we build in the previous lesson. To find the shortest path, we will use the BFS strategy.  

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

Coding Example: Find shortest path in a maze (Breadth-First approach)

Breadth-First Algorithm

Step 1: Implement a Graph Class