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.

# Single plot
fig, ax = plt.subplots(figsize=(10, 6))

# Multiple subplots (2 rows, 2 columns)
fig, axes = plt.subplots(2, 2, figsize=(12, 8))
axes[0, 0].plot(x, y)   # Top-left
axes[0, 1].bar(x, y)    # Top-right
axes[1, 0].scatter(x, y)  # Bottom-left
axes[1, 1].hist(y)      # Bottom-right

plt.tight_layout()  # Prevent overlap
plt.show()

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Line Plots

Line plots are ideal for showing trends over time:

x = np.linspace(0, 10, 100)

fig, ax = plt.subplots(figsize=(10, 6))

ax.plot(x, np.sin(x), label='sin(x)', color='blue', linewidth=2)
ax.plot(x, np.cos(x), label='cos(x)', color='red', linestyle='--', linewidth=2)

ax.set_title('Trigonometric Functions', fontsize=16)
ax.set_xlabel('x', fontsize=12)
ax.set_ylabel('y', fontsize=12)
ax.legend(fontsize=12)
ax.grid(True, alpha=0.3)

plt.show()

Line Styles and Markers

Style	Description
'-'	Solid line
'--'	Dashed line
'-.'	Dash-dot line
':'	Dotted line
'o'	Circle marker
's'	Square marker
'^'	Triangle marker
'x'	X marker

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Bar Charts

Bar charts compare categories:

categories = ['Python', 'JavaScript', 'Java', 'C++', 'Go']
values = [85, 72, 65, 45, 38]

fig, ax = plt.subplots(figsize=(10, 6))
bars = ax.bar(categories, values, color=['#3776AB', '#F7DF1E', '#ED8B00', '#00599C', '#00ADD8'])

ax.set_title('Programming Language Popularity', fontsize=16)
ax.set_ylabel('Popularity Score', fontsize=12)

# Add value labels on bars
for bar, value in zip(bars, values):
    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1,
            str(value), ha='center', fontsize=11)

plt.show()

Horizontal Bar Charts

fig, ax = plt.subplots(figsize=(10, 6))
ax.barh(categories, values, color='steelblue')
ax.set_xlabel('Popularity Score')
ax.set_title('Programming Language Popularity')
plt.show()

Grouped Bar Charts

x = np.arange(len(categories))
width = 0.35

fig, ax = plt.subplots(figsize=(10, 6))
ax.bar(x - width/2, values, width, label='2024')
ax.bar(x + width/2, [80, 75, 60, 50, 45], width, label='2025')
ax.set_xticks(x)
ax.set_xticklabels(categories)
ax.legend()
ax.set_title('Language Popularity Over Time')
plt.show()

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Scatter Plots

Scatter plots show relationships between two variables:

np.random.seed(42)
x = np.random.randn(100)
y = 2 * x + np.random.randn(100) * 0.5