Data Visualization with Matplotlib and Seaborn in Python

In data analysis, data visualization is essential for understanding data, discovering patterns, and communicating insights. Python offers two powerful libraries: Matplotlib, a basic plotting library, and Seaborn, a high‑level statistical visualization library built on Matplotlib.

1. Matplotlib Basics

Matplotlib is one of the most popular Python plotting libraries, providing a MATLAB‑like interface.

1.1 Basic Chart Plotting

import matplotlib.pyplot as plt
import numpy as np

# Create data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Plot line chart
plt.plot(x, y, label='sin(x)', color='blue', linestyle='--')
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('Sine function curve')
plt.legend()
plt.grid(True)
plt.show()

1.2 Common Chart Types

Line chart: plt.plot() Scatter chart: plt.scatter() Bar chart: plt.bar() Histogram: plt.hist() Pie chart:

plt.pie()

# Scatter plot example
x = np.random.rand(50)
y = np.random.rand(50)
plt.scatter(x, y, color='red', marker='o')
plt.title('Random scatter plot')
plt.show()

2. Seaborn Advanced Statistical Visualization

Seaborn builds on Matplotlib to provide more attractive and higher‑level statistical charts, especially suited for data analysis.

2.1 Distribution Plot (Distplot & KDE)

import seaborn as sns
data = np.random.randn(1000)
sns.histplot(data, kde=True, color='green')
plt.title('Data distribution (with KDE)')
plt.show()

2.2 Boxplot

tips = sns.load_dataset('tips')  # built‑in dataset
sns.boxplot(x='day', y='total_bill', data=tips)
plt.title('Daily expenditure boxplot')
plt.show()

2.3 Heatmap

flights = sns.load_dataset('flights').pivot('month', 'year', 'passengers')
sns.heatmap(flights, annot=True, fmt='d', cmap='YlOrRd')
plt.title('Flight passengers heatmap')
plt.show()

3. Customization and Multi‑Plot Layout

3.1 Subplots

fig, axes = plt.subplots(1, 2, figsize=(10, 4))

# First subplot: line chart
axes[0].plot(x, np.sin(x), color='blue')
axes[0].set_title('Sine function')

# Second subplot: scatter chart
axes[1].scatter(x, np.cos(x), color='red')
axes[1].set_title('Cosine scatter')

plt.tight_layout()
plt.show()

3.2 Style Beautification

Seaborn provides several built‑in themes:

sns.set_style('darkgrid')  # darkgrid, whitegrid, dark, white, ticks
sns.set_palette('husl')    # husl, Set2, pastel, deep, etc.

4. Summary

Library

Applicable Scenarios

Advantages

Matplotlib

Basic plotting, high customization

Flexible, powerful

Seaborn

Statistical visualization, rapid data exploration

Clean, aesthetic, friendly API