Introduction

Matplotlib is the most popular Python library for data visualization. It allows creating high-quality 2D plots, histograms, bar charts, scatter plots, and more.

It provides low-level control for fine-tuned charts while also supporting quick plotting with pyplot. This makes it suitable for exploratory analysis and publication-quality graphics.

Installation

# Using pip
pip install matplotlib

# Using conda
conda install matplotlib

Use virtual environments for consistent dependencies, and consider installing seaborn for improved default styles.

Basic Plotting

import matplotlib.pyplot as plt

x = [1,2,3,4]
y = [10,20,25,30]

plt.plot(x, y)
plt.title('Basic Line Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

Basic plots are quick for exploration. For production, prefer the object-oriented API for better control.

Figure & Axes

fig, ax = plt.subplots()
ax.plot([1,2,3],[4,5,6])
ax.set_title('Axes example')
ax.set_xlabel('X')
ax.set_ylabel('Y')
plt.show()

The figure is the container, and axes are the plotting areas. This model scales cleanly to complex layouts.

Multiple Plots

plt.plot([1,2,3],[4,5,6], label='Line 1')
plt.plot([1,2,3],[6,5,4], label='Line 2')
plt.legend()
plt.show()

Use legends, labels, and consistent color palettes to keep multiple lines readable.

Bar Chart & Histogram

# Bar chart
plt.bar(['A','B','C'], [10,20,15])
plt.show()

# Histogram
data = [1,2,2,3,3,3,4]
plt.hist(data, bins=4, color='orange', edgecolor='black')
plt.show()

Histograms show distributions. Adjust bins and normalization to compare groups fairly.

Scatter & Pie Charts

# Scatter
x=[5,7,8]; y=[7,9,2]
plt.scatter(x, y, color='red')
plt.show()

# Pie
sizes=[20,30,50]; labels=['A','B','C']
plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.show()

Scatter plots reveal relationships and clusters. Pie charts are best for simple part‑to‑whole comparisons.

Customization

plt.plot(x, y, color='green', linestyle='--', marker='o')
plt.title('Custom Plot', fontsize=14, color='blue')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.grid(True)
plt.show()

Customize color, line style, markers, and annotations to improve readability and storytelling.

Subplots

fig, axs = plt.subplots(2,1)
axs[0].plot([1,2,3],[4,5,6])
axs[1].bar([1,2,3],[7,8,9])
plt.show()

Subplots help compare multiple views. Use sharex and sharey for aligned axes.

Advanced Matplotlib

• 3D plotting (mpl_toolkits.mplot3d)
• Animations (FuncAnimation)
• Styles & Themes
• Integration with Pandas & Seaborn
• Logarithmic scales, twin axes

from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter([1,2,3],[4,5,6],[7,8,9])
plt.show()

Advanced features include custom projections, animations, and fine-grained control of ticks, grids, and themes.