Line and Bar Charts
Visualizing data helps you understand patterns, outliers, and relationships before building AI models. Matplotlib is the foundational plotting library in Python. We’ll start with line and bar charts.
Basic Line Chart
import matplotlib.pyplot as plt
x = [1,2,3,4,5]
y = [2,4,6,8,10]
plt.plot(x, y)
plt.title('Simple Line Chart')
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.show()Bar Chart for Categories
Useful for comparing categories (e.g., model accuracy across different algorithms).
categories = ['A', 'B', 'C', 'D']
values = [15, 30, 45, 20]
plt.bar(categories, values)
plt.title('Bar Chart')
plt.xlabel('Category')
plt.ylabel('Value')
plt.show()Why Visualization Matters in AI
- Explore data: see distribution, outliers, trends.
- Model evaluation: plot loss curves, confusion matrices.
- Communicate results: present insights to stakeholders.
Line Chart for Training Loss
epochs = [1,2,3,4,5]
loss = [0.8, 0.5, 0.3, 0.2, 0.15]
plt.plot(epochs, loss, marker='o')
plt.title('Training Loss over Epochs')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.grid(True)
plt.show()Two Minute Drill
plt.plot()for line charts.plt.bar()for bar charts.- Always label axes and add a title.
- Use
plt.show()to display.
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