Data visualization with Matplotlib

Data Visualization with Matplotlib

Data without visualization is blind numbers. Matplotlib transforms those numbers into charts that instantly reveal patterns.

Core Concept: Figure and Axes

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(8, 5))
ax.plot([1, 2, 3, 4], [10, 20, 15, 25])
ax.set_title("Chart Title")
ax.set_xlabel("X Axis")
ax.set_ylabel("Y Axis")
plt.tight_layout()
plt.show()

Line Plot — Training History

epochs     = range(1, 11)
train_loss = [2.5, 1.8, 1.3, 1.0, 0.8, 0.65, 0.55, 0.48, 0.43, 0.40]
val_loss   = [2.6, 2.0, 1.5, 1.2, 1.0, 0.90, 0.85, 0.82, 0.80, 0.79]

fig, ax = plt.subplots(figsize=(9, 5))
ax.plot(epochs, train_loss, label="Train",      color="blue",   lw=2)
ax.plot(epochs, val_loss,   label="Validation", color="orange", lw=2, ls="--")
ax.set_title("Training vs Validation Loss")
ax.legend(); ax.grid(True, alpha=0.3)
plt.show()

Bar Chart

models = ["GPT-4", "Claude", "Gemini", "Llama 3"]
scores = [91.2, 93.7, 90.5, 88.9]

fig, ax = plt.subplots(figsize=(8, 5))
ax.bar(models, scores,
       color=["#4285F4","#FF6B35","#34A853","#EA4335"])
ax.set_title("AI Model Performance Comparison")
ax.set_ylim(85, 97)
plt.show()

Scatter Plot

import numpy as np
np.random.seed(42)
experience = np.random.randint(1, 15, 50)
salary = experience * 1200 + np.random.randn(50) * 2000

fig, ax = plt.subplots(figsize=(8, 5))
ax.scatter(experience, salary, alpha=0.7, s=80)
ax.set_title("Experience vs Salary")
plt.show()

Histogram

scores = np.random.normal(loc=75, scale=12, size=200)

fig, ax = plt.subplots(figsize=(8, 5))
ax.hist(scores, bins=20, color="#4A90E2", edgecolor="white")
ax.axvline(scores.mean(), color="red", ls="--", lw=2)
ax.set_title("Student Score Distribution")
plt.show()

Multiple Charts (Subplots)

fig, axes = plt.subplots(1, 2, figsize=(12, 5))
axes[0].plot(range(10), [x**2 for x in range(10)])
axes[1].bar(["A","B","C"], [30,50,20])
plt.tight_layout(); plt.show()

Saving a Chart

fig.savefig("chart.png", dpi=150, bbox_inches="tight")

Lesson Summary

• plt.subplots(): the professional way to create charts
• plot(): for time-series and continuous data
• bar(): for category comparison
• scatter(): for exploring variable relationships
• hist(): for understanding data distribution
• grid + legend + title: required for every professional chart