Data Visualization with Matplotlib and Seaborn

Why Visualization Matters

Numbers in a table are abstract. A well-designed chart reveals patterns, outliers, and trends that would be invisible in raw data. Visualization is not decoration — it is analysis.

Matplotlib Basics

Matplotlib is Python’s foundational plotting library. Seaborn builds on top of it with statistical visualizations and better defaults.

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

# Basic line plot
x = np.linspace(0, 10, 100)
plt.figure(figsize=(10, 6))
plt.plot(x, np.sin(x), label="sin(x)")
plt.plot(x, np.cos(x), label="cos(x)")
plt.xlabel("x")
plt.ylabel("y")
plt.title("Trigonometric Functions")
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()

Common Chart Types

Bar Charts

categories = ["Python", "JavaScript", "Rust", "Go"]
values = [85, 78, 45, 52]

plt.figure(figsize=(8, 5))
plt.bar(categories, values, color=["#306998", "#f7df1e", "#ce412b", "#00add8"])
plt.title("Language Popularity")
plt.ylabel("Score")
plt.show()

Scatter Plots

df = pd.DataFrame({
    "experience": np.random.uniform(0, 20, 100),
    "salary": np.random.uniform(30000, 150000, 100),
    "department": np.random.choice(["Engineering", "Marketing", "Sales"], 100)
})

sns.scatterplot(data=df, x="experience", y="salary", hue="department", alpha=0.7)
plt.title("Salary vs Experience")
plt.show()

Histograms and Distributions

data = np.random.normal(100, 15, 1000)
sns.histplot(data, kde=True, bins=30)
plt.title("Distribution of Scores")
plt.xlabel("Score")
plt.show()

Seaborn Statistical Plots

Heatmaps

# Correlation matrix
corr = df.select_dtypes(include="number").corr()
sns.heatmap(corr, annot=True, cmap="coolwarm", center=0)
plt.title("Feature Correlations")
plt.show()

Box Plots

sns.boxplot(data=df, x="department", y="salary")
plt.title("Salary Distribution by Department")
plt.show()

Principles of Effective Visualization

• Choose the right chart type — bar for comparison, line for trends, scatter for relationships
• Label everything — axes, title, legend. If someone cannot understand the chart without explanation, it needs more labels
• Remove clutter — no 3D effects, minimal gridlines, reasonable axis ranges
• Use color intentionally — highlight what matters, use consistent palettes
• Show the data — avoid chart types that obscure the underlying distribution (pie charts are almost always worse than bar charts)

Saving Figures

fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(x, y)
fig.savefig("chart.png", dpi=150, bbox_inches="tight")
fig.savefig("chart.svg")  # Vector format for publications

Summary

Start with Seaborn for quick statistical plots and clean defaults. Drop to Matplotlib when you need fine-grained control. The best visualization is the one that answers a question clearly.