Gain insights into utilizing NumPy for data manipulation and analytics. Learn about implementing concepts in both Python and NumPy through coding challenges and quizzes.

python.tar.gz

python3.6

Live3.6

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

# NumPy Implementation
Starting from the Python version, the vectorization of the Game of Life requires two parts,
- one responsible for counting the neighbors
-  one responsible for enforcing the rules

Neighbor-counting is relatively easy if we remember we took care of adding a null border around the arena. By considering partial views of the arena we can actually access neighbors quite intuitively as illustrated below for the one-dimensional case:

```
               ┏━━━┳━━━┳━━━┓───┬───┐
        Z[:-2] ┃ 0 ┃ 1 ┃ 1 ┃ 1 │ 0 │ (left neighbors)
               ┗━━━┻━━━┻━━━┛───┴───┘
                     ↓︎
           ┌───┏━━━┳━━━┳━━━┓───┐
   Z[1:-1] │ 0 ┃ 1 ┃ 1 ┃ 1 ┃ 0 │ (actual cells)
           └───┗━━━┻━━━┻━━━┛───┘
                     ↑
       ┌───┬───┏━━━┳━━━┳━━━┓
Z[+2:] │ 0 │ 1 ┃ 1 ┃ 1 ┃ 0 ┃ (right neighbors)
       └───┴───┗━━━┻━━━┻━━━┛
```
Going to the two dimensional case requires just a bit of arithmetic to make sure to consider all the eight neighbors.


This lesson discusses the case study Game of life and explains its solution using NumPy approach.

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

Coding Example: Game of life (NumPy approach)

NumPy Implementation