Data Visualization with Matplotlib

Matplotlib is a foundational plotting library in Python that provides comprehensive tools for creating static, animated, and interactive visualizations. It is highly customizable and integrates seamlessly with NumPy for numerical data manipulation. The core functionality of Matplotlib lies in its pyplot module, typically imported as plt.

Basic Plotting

Plotting Lines: Create simple line plots with customizations.

import matplotlib.pyplot as plt

# Data
x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]

# Line plot with customization
plt.plot(x, y, color='g', linestyle='--', linewidth=3, marker='x', markersize=15, label='My Line')

# Title and labels
plt.title("Plotting Lines")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")

# Add legend
plt.legend()
plt.show()

Line Plot

Trigonometric Functions

Sine and Cosine Waves: Plot trigonometric functions.

import numpy as np
import matplotlib.pyplot as plt

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

# Plot
plt.plot(x, y1, label='Sine', color='blue')
plt.plot(x, y2, label='Cosine', color='green')

plt.title("Sine and Cosine")
plt.legend()
plt.show()

Sine and Cosine Waves

Subplots

Multiple Plots in a Single Figure: Create subplots to visualize multiple datasets.

plt.figure()

# First subplot
plt.subplot(2, 1, 1)
plt.plot(x, y1)
plt.title("Sine Wave")

# Second subplot
plt.subplot(2, 1, 2)
plt.plot(x, y2)
plt.title("Cosine Wave")

plt.tight_layout()
plt.show()

Subplots

Other Visualizations

Bar Charts: Represent data with rectangular bars.

x = np.arange(1, 6)
y = np.random.randint(1, 50, 5)

plt.bar(x, y, color='purple')
plt.title("Bar Chart")
plt.xlabel("X Axis")
plt.ylabel("Values")
plt.show()

Bar Chart

Pie Charts: Visualize proportions in a dataset.

x = [4, 4, 2]
colors = ['red', 'blue', 'orange']
labels = ['A', 'B', 'C']

plt.pie(x, colors=colors, labels=labels, explode=(0, 0, 0.1), startangle=90)
plt.title("Pie Chart")
plt.show()

Pie Chart

Scatter Plots: Plot points to identify relationships between two variables.

x = np.random.normal(0, 1, 100)
y = np.random.normal(1, 10, 100)

plt.scatter(x, y, color='green', marker='o')
plt.title("Scatter Plot")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.show()

Scatter Plot

Advanced

Support Vector Machine (SVM): Visualize the decision boundary and support vectors.

import numpy as np
import matplotlib.pyplot as plt

# Select First 2 features as X and Y
X_vis = X[:, :2] 

# Find min and max of both columns
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1

# Construct a meshgrid - a list of coordinates
h = 0.01 # Step
x_coordinates, y_coordinates = np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)
xx, yy = np.meshgrid(x_coordinates, y_coordinates)

# Decision boundary
x_1d, y_1d = xx.ravel(), yy.ravel() # Convert 2D to 1D
values_1d = np.c_[x_1d, y_1d] # Concatenate
Z = model.decision_function(values_1d)
Z = Z.reshape(xx.shape)

plt.scatter(X_vis[:, 0], X_vis[:, 1], c=y, cmap='viridis', alpha=0.7)
plt.contourf(xx, yy, Z, levels=[-1, 0, 1], colors='c', alpha=0.5)
plt.contour(xx, yy, Z, levels=[-1, 0, 1], colors='r', alpha=0.5, linestyles=['--', '-', '--'])
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.title('SVM Decision Boundary')
plt.show()

SVM Decision Boundary Visualization