Bring Your Python Plots to Life: Game of Life Animation with Matplotlib

Using Matplotlib Animations

Matplotlib is the most widely used library for plotting and data visualisation in Python. Beyond static charts, it also supports simple animations, which can be demonstrated by implementing Conway's Game of Life.

Game of Life Rules

Start with an N×N grid (the example uses 50×50).

Randomly populate the grid with a number of "cells" (1500 cells chosen from 2500 possible positions).

A cell dies if it has ≤1 neighbour.

A cell dies if it has ≥4 neighbours.

A cell survives with 2 or 3 neighbours.

An empty cell becomes alive if it has exactly 3 neighbours.

Setting Up the Grid

First import the required libraries.

import time
from IPython import display
import matplotlib.pyplot as plt
import matplotlib.animation as animation

Define the input variables for the board.

# Input variables for the board
boardsize = 50  # board will be X by X where X = boardsize
pad = 2         # padded border, do not change this!
initial_cells = 1500  # number of initial cells to be placed

Generate random coordinates for the initial cells.

pos_list = []
for i in range(initial_cells):
    pos_list.append([random.randint(1, boardsize), random.randint(1, boardsize)])

Initialize the board as a NumPy array (52×52 because of the padding) and fill it using a helper function init_board() (implementation omitted, available on the linked GitHub).

# Initialize the board
my_board = np.zeros((boardsize + pad, boardsize + pad))
my_board = init_board(pos_list, my_board)

Creating the Animation

Enable notebook plotting and create a figure.

%matplotlib notebook
fig = plt.gcf()

Show the first frame.

im = plt.imshow(my_board)
plt.show()

Define the animation update function.

def animate(frame):
    im.set_data(update_board(my_board))
    return im,

Create the animation with FuncAnimation, specifying 200 frames and a 50 ms interval.

anim = animation.FuncAnimation(fig, animate, frames=200, interval=50)

The resulting animation (GIF) demonstrates the evolving Game of Life.

Conclusion

The same animation technique can be applied to many data‑science visualisations, such as illustrating Monte Carlo simulations, animating time‑series models, showing clustering evolution, or generating dynamic heatmaps.

GitHub: https://github.com/yiuhyuk/game_of_life Source: Towards Data Science article