Data Visualisation with Matplotlib

Matplotlib is the foundational plotting library in Python. Created by John D. Hunter in 2003, it provides a MATLAB-like interface for creating static, animated, and interactive visualisations. While there are many visualisation libraries in Python (Seaborn, Plotly, Altair), Matplotlib is the one they are all built on — understanding it gives you complete control over your plots.

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The Two Interfaces

Matplotlib offers two ways to create plots:

1. Pyplot Interface (Quick and Simple)

import matplotlib.pyplot as plt
import numpy as np

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

plt.plot(x, y)
plt.title('Simple Line Plot')
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.show()

2. Object-Oriented Interface (Recommended)

fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Simple Line Plot')
ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
plt.show()

The object-oriented interface gives you more control and is the recommended approach for anything beyond quick plots.

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Figure and Axes

Every Matplotlib plot has two key objects:

• Figure — the entire window or page. Think of it as the canvas.
• Axes — an individual plot within the figure. A figure can have multiple axes.