Matplotlib

Overview

Matplotlib is Python's foundational visualization library for creating static, animated, and interactive plots. This skill provides guidance on using matplotlib effectively, covering both the pyplot interface (MATLAB-style) and the object-oriented API (Figure/Axes), along with best practices for creating publication-quality visualizations.

When to Use This Skill

This skill should be used when:

• Creating any type of plot or chart (line, scatter, bar, histogram, heatmap, contour, etc.)
• Generating scientific or statistical visualizations
• Customizing plot appearance (colors, styles, labels, legends)
• Creating multi-panel figures with subplots
• Exporting visualizations to various formats (PNG, PDF, SVG, etc.)
• Building interactive plots or animations
• Working with 3D visualizations
• Integrating plots into Jupyter notebooks or GUI applications

Setup

For project work, install Matplotlib with uv:

uv add "matplotlib==3.11.2" numpy scipy

For notebook interactivity:

uv add "matplotlib==3.11.2" ipympl

Then enable the widget backend in Jupyter with %matplotlib widget or %matplotlib ipympl.

Targets Matplotlib 3.11.2 (Python 3.11+), reviewed 2026-10-01. The bundled scripts and representative examples were executed using Agg and PNG/PDF/SVG output. GUI windows, Jupyter widgets, and external LaTeX are environment-dependent and were not exercised. Fragment examples assume imports and named data; adapt and validate them before use. Check the 3.11 API changes when migrating older code: use tick_labels and orientation for box plots, mpl.colormaps[name] for colormaps, and label contour lines rather than contourf.

File output needs no GUI. Use MPLBACKEND=Agg for batch scripts, or select Agg before importing pyplot. Interactive output requires an installed GUI toolkit such as PySide6 (QtAgg) or working Tk (TkAgg); plt.ioff() does not remove GUI thread requirements. See backends.

Core Concepts

The Matplotlib Hierarchy

Matplotlib uses a hierarchical structure of objects:

1. Figure - The top-level container for all plot elements
2. Axes - The actual plotting area where data is displayed (one Figure can contain multiple Axes)
3. Artist - Everything visible on the figure (lines, text, ticks, etc.)
4. Axis - The number line objects (x-axis, y-axis) that handle ticks and labels

Two Interfaces

1. pyplot Interface (Implicit, MATLAB-style)

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4])
plt.ylabel('some numbers')
plt.show()

• Convenient for quick, simple plots
• Maintains state automatically
• Good for interactive work and simple scripts

2. Object-Oriented Interface (Explicit)

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4])
ax.set_ylabel('some numbers')
plt.show()

• Recommended for most use cases
• More explicit control over figure and axes
• Better for complex figures with multiple subplots
• Easier to maintain and debug

Common Workflows

1. Basic Plot Creation

Single plot workflow:

import matplotlib.pyplot as plt
import numpy as np

# Create figure and axes (OO interface - RECOMMENDED)
fig, ax = plt.subplots(figsize=(10, 6))

# Generate and plot data
x = np.linspace(0, 2*np.pi, 100)
ax.plot(x, np.sin(x), label='sin(x)')
ax.plot(x, np.cos(x), label='cos(x)')

# Customize
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('Trigonometric Functions')
ax.legend()
ax.grid(True, alpha=0.3)

# Save and/or display
fig.savefig('plot.png', dpi=300, bbox_inches='tight')
plt.show()

2. Multiple Subplots

Creating subplot layouts:

# Method 1: Regular grid
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
axes[0, 0].plot(x, y1)
axes[0, 1].scatter(x, y2)
axes[1, 0].bar(categories, values)
axes[1, 1].hist(data, bins=30)

# Method 2: Mosaic layout (more flexible)
fig, axes = plt.subplot_mosaic([['left', 'right_top'],
                                 ['left', 'right_bottom']],
                                figsize=(10, 8))
axes['left'].plot(x, y)
axes['right_top'].scatter(x, y)
axes['right_bottom'].hist(data)

# Method 3: GridSpec (maximum control)
from matplotlib.gridspec import GridSpec
fig = plt.figure(figsize=(12, 8))
gs = GridSpec(3, 3, figure=fig)

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