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

For notebook interactivity:

uv add matplotlib ipympl

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

Matplotlib 3.10 requires Python 3.10+ and NumPy 1.23+. Non-interactive file output works through backends such as Agg, PDF, and SVG. For GUI windows, Matplotlib auto-selects an available backend; if TkAgg fails in a uv-managed Python, update uv and Python builds with uv self update and uv python upgrade --reinstall, or install a Qt backend with uv add pyside6.

Core Concepts

The Matplotlib Hierarchy

Matplotlib uses a hierarchical structure of objects:

Figure - The top-level container for all plot elements

Axes - The actual plotting area where data is displayed (one Figure can contain multiple Axes)

Artist - Everything visible on the figure (lines, text, ticks, etc.)

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)
ax1 = fig.add_subplot(gs[0, :])  # Top row, all columns
ax2 = fig.add_subplot(gs[1:, 0])  # Bottom two rows, first column
ax3 = fig.add_subplot(gs[1:, 1:])  # Bottom two rows, last two columns

3. Plot Types and Use Cases

Line plots - Time series, continuous data, trends

ax.plot(x, y, linewidth=2, linestyle='--', marker='o', color='blue')

Scatter plots - Relationships between variables, correlations

ax.scatter(x, y, s=sizes, c=colors, alpha=0.6, cmap='viridis')

Bar charts - Categorical comparisons

ax.bar(categories, values, color='steelblue', edgecolor='black')
# For horizontal bars:
ax.barh(categories, values)

Histograms - Distributions

ax.hist(data, bins=30, edgecolor='black', alpha=0.7)

Heatmaps - Matrix data, correlations

im = ax.imshow(matrix, cmap='coolwarm', aspect='auto')
plt.colorbar(im, ax=ax)

Contour plots - 3D data on 2D plane

contour = ax.contour(X, Y, Z, levels=10)
ax.clabel(contour, inline=True, fontsize=8)

Box plots - Statistical distributions

ax.boxplot([data1, data2, data3], tick_labels=['A', 'B', 'C'])

Violin plots - Distribution densities

ax.violinplot([data1, data2, data3], positions=[1, 2, 3])

For comprehensive plot type examples and variations, refer to references/plot_types.md.

4. Styling and Customization

Color specification methods:

Named colors: 'red', 'blue', 'steelblue'

Hex codes: '#FF5733'

RGB tuples: (0.1, 0.2, 0.3)

Colormaps: cmap='viridis', cmap='plasma', cmap='coolwarm'

Using style sheets:

plt.style.use('seaborn-v0_8-darkgrid')  # Apply predefined style
# Available styles: 'ggplot', 'bmh', 'fivethirtyeight', etc.
print(plt.style.available)  # List all available styles

Customizing with rcParams:

plt.rcParams['font.size'] = 12
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['axes.titlesize'] = 16
plt.rcParams['xtick.labelsize'] = 10
plt.rcParams['ytick.labelsize'] = 10
plt.rcParams['legend.fontsize'] = 12
plt.rcParams['figure.titlesize'] = 18

Text and annotations:

ax.text(x, y, 'annotation', fontsize=12, ha='center')
ax.annotate('important point', xy=(x, y), xytext=(x+1, y+1),
            arrowprops=dict(arrowstyle='->', color='red'))

For detailed styling options and colormap guidelines, see references/styling_guide.md.

5. Saving Figures

Export to various formats:

# High-resolution PNG for presentations/papers
fig.savefig('figure.png', dpi=300, bbox_inches='tight', facecolor='white')

# Vector format for publications (scalable)
fig.savefig('figure.pdf', bbox_inches='tight')
fig.savefig('figure.svg', bbox_inches='tight')

# Transparent background
fig.savefig('figure.png', dpi=300, bbox_inches='tight', transparent=True)

Important parameters:

dpi: Resolution (300 for publications, 150 for web, 72 for screen)

bbox_inches='tight': Removes excess whitespace

facecolor='white': Ensures white background (useful for transparent themes)

transparent=True: Transparent background

6. Working with 3D Plots

fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')

# Surface plot
ax.plot_surface(X, Y, Z, cmap='viridis')

# 3D scatter
ax.scatter(x, y, z, c=colors, marker='o')

# 3D line plot
ax.plot(x, y, z, linewidth=2)

# Labels
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')

Best Practices

1. Interface Selection

Use the object-oriented interface (fig, ax = plt.subplots()) for production code

Reserve pyplot interface for quick interactive exploration only

Always create figures explicitly rather than relying on implicit state

2. Figure Size and DPI

Set figsize at creation: fig, ax = plt.subplots(figsize=(10, 6))

Use appropriate DPI for output medium:

- Screen/notebook: 72-100 dpi
- Web: 150 dpi
- Print/publications: 300 dpi

3. Layout Management

Use constrained_layout=True or tight_layout() to prevent overlapping elements

fig, ax = plt.subplots(constrained_layout=True) is recommended for automatic spacing

4. Colormap Selection

Sequential (viridis, plasma, inferno): Ordered data with consistent progression

Diverging (coolwarm, RdBu): Data with meaningful center point (e.g., zero)

Qualitative (tab10, Set3): Categorical/nominal data

Avoid rainbow colormaps (jet) - they are not perceptually uniform

5. Accessibility

Use colorblind-friendly colormaps (viridis, cividis)

Add patterns/hatching for bar charts in addition to colors

Ensure sufficient contrast between elements

Include descriptive labels and legends

6. Performance

For large datasets, use rasterized=True in plot calls to reduce file size

Use appropriate data reduction before plotting (e.g., downsample dense time series)

For animations, use blitting for better performance

7. Code Organization

# Good practice: Clear structure
def create_analysis_plot(data, title):
    """Create standardized analysis plot."""
    fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)

    # Plot data
    ax.plot(data['x'], data['y'], linewidth=2)

    # Customize
    ax.set_xlabel('X Axis Label', fontsize=12)
    ax.set_ylabel('Y Axis Label', fontsize=12)
    ax.set_title(title, fontsize=14, fontweight='bold')
    ax.grid(True, alpha=0.3)

    return fig, ax

# Use the function
fig, ax = create_analysis_plot(my_data, 'My Analysis')
fig.savefig('analysis.png', dpi=300, bbox_inches='tight')

Quick Reference Scripts

This skill includes helper scripts in the scripts/ directory:

`plot_template.py`

Template script demonstrating various plot types with best practices. Use this as a starting point for creating new visualizations.

Usage:

uv run python scripts/plot_template.py

`style_configurator.py`

Interactive utility to configure matplotlib style preferences and generate custom style sheets.

Usage:

uv run python scripts/style_configurator.py

Detailed References

For comprehensive information, consult the reference documents:

references/plot_types.md - Complete catalog of plot types with code examples and use cases

references/styling_guide.md - Detailed styling options, colormaps, and customization

references/api_reference.md - Core classes and methods reference

references/common_issues.md - Troubleshooting guide for common problems

Integration with Other Tools

Matplotlib integrates well with:

NumPy/Pandas - Direct plotting from arrays and DataFrames

Seaborn - High-level statistical visualizations built on matplotlib

Jupyter - Interactive plotting with %matplotlib inline or %matplotlib widget

GUI frameworks - Embedding in Tkinter, Qt, wxPython applications

Common Gotchas

Overlapping elements: Use constrained_layout=True or tight_layout()

State confusion: Use OO interface to avoid pyplot state machine issues

Memory issues with many figures: Close figures explicitly with plt.close(fig)

Font warnings: Install fonts or suppress warnings with plt.rcParams['font.sans-serif']

DPI confusion: Remember that figsize is in inches, not pixels: pixels = dpi * inches

Additional Resources

Official documentation: https://matplotlib.org/

Gallery: https://matplotlib.org/stable/gallery/index.html

Cheatsheets: https://matplotlib.org/cheatsheets/

Tutorials: https://matplotlib.org/stable/tutorials/index.html