Data Visualization with Matplotlib and Seaborn — Complete Guide

In this tutorial, you will learn data visualization with Python using Matplotlib and Seaborn to create publication-quality plots, customize styles, and communicate insights visually.

What You'll Learn

Build line charts, bar plots, scatter plots, heatmaps, and complex subplot layouts with Matplotlib and Seaborn using both pyplot and object-oriented interfaces.

Why It Matters

Visualization is the most effective way to find patterns and communicate findings. A well-designed chart reveals insights that summary statistics alone cannot show.

Real-World Use

A data analyst at a logistics company visualizes delivery times across regions, overlays traffic data, and identifies that 30 percent of delays occur in two specific zones, leading to rerouted配送 routes.

Visualization Decision Flow

flowchart TD
  A[What is your data?] --> B{Numeric vs Categorical}
  B -->|One Numeric| C[Histogram or Box Plot]
  B -->|Two Numeric| D[Scatter Plot]
  B -->|One Cat, One Num| E[Bar Chart or Box Plot]
  B -->|Two Categorical| F[Count Plot or Heatmap]
  B -->|Time Series| G[Line Chart]
  C --> H[Seaborn: histplot / boxplot]
  D --> I[Seaborn: scatterplot]
  E --> J[Seaborn: barplot / boxplot]
  F --> K[Seaborn: countplot / heatmap]
  G --> L[Matplotlib: plot]

Setting Up Styles and Themes

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

sns.set_theme(style="whitegrid", palette="muted")
plt.rcParams.update({
    "figure.figsize": (10, 6),
    "font.size": 12,
    "axes.titlesize": 14,
})

tips = sns.load_dataset("tips")
print(tips.head())

Output:

   total_bill   tip     sex smoker  day    time  size
0       16.99  1.01  Female     No  Sun  Dinner     2
1       10.34  1.66    Male     No  Sun  Dinner     3
2       21.01  3.50    Male     No  Sun  Dinner     3

Multi-Panel Figures

fig, axes = plt.subplots(2, 2, figsize=(14, 10))

sns.histplot(tips["total_bill"], bins=30, kde=True, ax=axes[0, 0])
axes[0, 0].set_title("Total Bill Distribution")

sns.boxplot(x="day", y="total_bill", data=tips, ax=axes[0, 1])
axes[0, 1].set_title("Bill by Day")

sns.scatterplot(x="total_bill", y="tip", hue="time", data=tips, ax=axes[1, 0])
axes[1, 0].set_title("Tip vs Total Bill by Time")

sns.heatmap(tips.corr(numeric_only=True), annot=True, cmap="coolwarm",
            center=0, ax=axes[1, 1])
axes[1, 1].set_title("Correlation Heatmap")

plt.tight_layout()
plt.show()

Output: A 2x2 grid showing histogram, box plot, scatter plot, and heatmap in one figure.

Customizing with the Object-Oriented API

fig, ax = plt.subplots(figsize=(10, 6))
sns.barplot(x="day", y="total_bill", hue="sex", data=tips,
            estimator=np.mean, ci=95, ax=ax)
ax.set_title("Average Bill by Day and Gender", fontweight="bold")
ax.set_xlabel("Day of Week")
ax.set_ylabel("Average Total Bill ($)")
ax.legend(title="Gender")
sns.despine()
plt.show()

Output: A grouped bar chart showing average bills by day split by gender, with confidence interval whiskers and cleaned spines.

Practice Questions

1. What are the advantages of using Seaborn over plain Matplotlib for statistical plots?
2. How do you create a 2x3 grid of subplots and control individual axes?
3. What is the purpose of sns.despine() and when should you call it?

Answers:

1. Seaborn provides built-in statistical aggregation, automatic grouping via hue, attractive default styles, and single-line complex plot functions.
2. Use plt.subplots(2, 3, figsize=(w, h)) to get a 2D array of Axes objects and index like axes[row, col] to access each subplot.
3. sns.despine() removes top and right spines for a cleaner look. Call it after plotting on the relevant axes or globally.

Challenge

Load the Iris dataset from Seaborn and create a figure with four panels: a scatter matrix (pairplot), a box plot of petal length by species, a violin plot of sepal width by species, and a heatmap of numeric correlations.

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