Matplotlib Mastery for Scientific Visualization

Introduction to Matplotlib

Matplotlib is the most widely used plotting library in Python, especially for scientific visualization.

import matplotlib.pyplot as plt
import numpy as np

# Generate data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Create a simple plot
plt.figure(figsize=(10, 6))
plt.plot(x, y, 'b-', label='sin(x)')
plt.xlabel('x')
plt.ylabel('sin(x)')
plt.title('Simple Sine Wave')
plt.grid(True)
plt.legend()
plt.show()
                    

Publication-Quality Figures

Learn how to create professional-looking figures suitable for academic publications.

import matplotlib.pyplot as plt
import numpy as np

# Set the style
plt.style.use('seaborn-darkgrid')

# Create figure and axis objects
fig, ax = plt.subplots(figsize=(10, 6))

# Generate data
x = np.linspace(0, 2*np.pi, 100)
y1 = np.sin(x)
y2 = np.cos(x)

# Plot with customization
ax.plot(x, y1, 'b-', label='sin(x)', linewidth=2)
ax.plot(x, y2, 'r--', label='cos(x)', linewidth=2)

# Customize the plot
ax.set_xlabel('x', fontsize=12)
ax.set_ylabel('y', fontsize=12)
ax.set_title('Trigonometric Functions', fontsize=14, pad=10)
ax.grid(True, linestyle='--', alpha=0.7)
ax.legend(fontsize=10)

# Adjust layout
plt.tight_layout()
                    

Multiple Subplots

Create complex figures with multiple subplots for comprehensive data visualization.

import matplotlib.pyplot as plt
import numpy as np

# Create a figure with subplots
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(12, 10))

# Generate data
x = np.linspace(0, 5, 100)

# Plot 1: Linear
ax1.plot(x, x)
ax1.set_title('Linear')

# Plot 2: Quadratic
ax2.plot(x, x**2)
ax2.set_title('Quadratic')

# Plot 3: Cubic
ax3.plot(x, x**3)
ax3.set_title('Cubic')

# Plot 4: Square root
ax4.plot(x, np.sqrt(x))
ax4.set_title('Square Root')

# Adjust layout
plt.tight_layout()
                    

Practice Exercises

Additional Resources

• Matplotlib Official Tutorials
• Matplotlib Gallery
• SciPy Lectures on Matplotlib