Customizing Plots
Making your plots clear and professional requires customization: adding labels, titles, legends, adjusting colors, and saving figures. This chapter covers the most common customizations.
Adding Labels, Title, and Legend
import matplotlib.pyplot as plt
x = [1,2,3,4,5]
y1 = [2,4,6,8,10]
y2 = [1,3,5,7,9]
plt.plot(x, y1, label='Line A', color='blue', linestyle='-')
plt.plot(x, y2, label='Line B', color='red', linestyle='--')
plt.title('Comparison of Two Lines')
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.legend()
plt.grid(True, linestyle=':', alpha=0.7)
plt.show()Adjusting Figure Size and DPI
plt.figure(figsize=(10,6), dpi=100)
plt.plot(x, y1)
plt.show()Saving Figures
plt.savefig('plot.png', dpi=300, bbox_inches='tight')Subplots (Multiple Plots in One Figure)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4))
ax1.plot(x, y1)
ax1.set_title('Line A')
ax2.bar(x, y2)
ax2.set_title('Bar Chart')
plt.tight_layout()
plt.show()Why Customization Matters in AI
Clear visualizations help you debug models (e.g., loss curves) and present results to non‑technical audiences. Customized plots are essential for reports and publications.
Two Minute Drill
plt.xlabel(),plt.ylabel(),plt.title(),plt.legend().- Use
plt.figure(figsize=(w,h))to set size. - Save with
plt.savefig(). - Subplots:
fig, axes = plt.subplots(nrows, ncols).
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