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

# Problem Description

The Game of Life is a cellular automaton devised by the British mathematician
John Horton Conway in 1970. It is the best-known example of a cellular
automaton. The "game" is actually a zero-player game, meaning that its
evolution is determined by its initial state, needing no input from human
players. One interacts with the Game of Life by creating an initial
configuration and observing how it evolves.

The universe of the Game of Life is an infinite two-dimensional orthogonal grid
of square cells, each of which is in one of two possible states, live or
dead. Every cell interacts with its eight neighbors, which are the cells that
are directly horizontally, vertically, or diagonally adjacent. 

## Rules
At each step in
time, the following transitions occur:

1. Any live cell with fewer than two live neighbors dies, as if by 
   underpopulation.
2. Any live cell with more than three live neighbors dies, as if by overcrowding.
3. Any live cell with two or three live neighbors lives, unchanged, to the
   next generation.
4. Any dead cell with exactly three live neighbors becomes a live cell.


The initial pattern constitutes the 'seed' of the system. The first generation
is created by applying the above rules simultaneously to every cell in the seed
– births and deaths happen simultaneously, and the discrete moment at which
this happens is sometimes called a tick. (In other words, each generation is a
pure function of the one before.) The rules continue to be applied repeatedly
to create further generations.

*(Excerpt from the Wikipedia entry on the
[Game of Life](https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life))*
$$$$
## Python implementation

In pure Python, we can code the Game of Life using a list of lists representing
the board where cells are supposed to evolve. Such a board will be equipped with a border of 0 that allows accelerating things a bit by avoiding having specific
tests for borders when counting the number of neighbors. We could have used the more efficient python [array interface](http://docs.python.org/3/library/array.html) but it is more convenient to use the familiar list object.


```python
# generate a two-dimensional grid Z
# Each index value indicates one of two possible states
# 1 means active, 0 means dead 
   Z = [[0,0,0,0,0,0],
        [0,0,0,1,0,0],
        [0,1,0,1,0,0],
        [0,0,1,1,0,0],
        [0,0,0,0,0,0],
        [0,0,0,0,0,0]]
```
Taking the border into account, counting neighbors then is straightforward:

This lesson discusses the case study Game of life and explains its solution using Python implementation.

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

Coding Example: Game of life (Python approach)

Problem Description