matplotlib-best-practices

SKILL.md

Matplotlib Best Practices

Expert guidelines for Matplotlib development, focusing on data visualization, plotting, and creating publication-quality figures.

Code Style and Structure

Write concise, technical Python code with accurate Matplotlib examples

Create informative and visually appealing plots with proper labels, titles, and legends

Use the object-oriented API for complex figures, pyplot for quick plots

Follow PEP 8 style guidelines

Consider color-blindness accessibility in all visualizations

API Approaches

Object-Oriented Interface (Recommended)

Use fig, ax = plt.subplots() for explicit control

Preferred for complex figures and production code

Methods are called on axes objects: ax.plot(), ax.set_xlabel()

Enables multiple subplots and fine-grained customization

Pyplot Interface

Use plt.plot(), plt.xlabel() for quick, interactive plots

Suitable for Jupyter notebooks and exploration

Use %matplotlib inline in Jupyter notebooks

Creating Effective Visualizations

Plot Types and Selection

Line plots (ax.plot()) for continuous data and trends

Scatter plots (ax.scatter()) for relationship between variables

Bar plots (ax.bar(), ax.barh()) for categorical comparisons

Histograms (ax.hist()) for distributions

Box plots (ax.boxplot()) for statistical summaries

Heatmaps (ax.imshow(), ax.pcolormesh()) for 2D data

Labels and Annotations

Always include axis labels with units

Use descriptive titles that convey the message

Add legends when multiple series are present

Use annotations (ax.annotate()) to highlight key points

Include data source attribution when appropriate

Color and Style

Use colorblind-friendly palettes (e.g., 'viridis', 'plasma', 'cividis')

Avoid red-green combinations for accessibility

Use consistent colors for the same categories across figures

Use appropriate colormaps for data type:

Sequential: 'viridis', 'plasma' for continuous data

Diverging: 'RdBu', 'coolwarm' for data with meaningful center

Qualitative: 'Set1', 'tab10' for categorical data

Figure Layout and Composition

Subplots

Use plt.subplots(nrows, ncols) for grid layouts

Use gridspec for complex, non-uniform layouts

Share axes with sharex=True, sharey=True for comparison

Use constrained_layout=True or tight_layout() to prevent overlap

Figure Size and Resolution

Set figure size explicitly: figsize=(width, height) in inches

Use appropriate DPI for intended output (72 screen, 300+ print)

Standard sizes: (10, 6) for presentations, (8, 6) for papers

Customization

Style Sheets

Use built-in styles: plt.style.use('seaborn-v0_8'), 'ggplot'

Create custom style files for consistent branding

Combine styles: plt.style.use(['seaborn-v0_8', 'custom.mplstyle'])

Text and Fonts

Use LaTeX for mathematical notation: r'$\alpha = \frac{1}{2}$'

Set font family for consistency

Adjust font sizes for readability at intended display size

Saving and Exporting

File Formats

Use vector formats (PDF, SVG, EPS) for publications

Use PNG for web and presentations with transparency

Use JPEG only for photographs (lossy compression)

Export Settings

Use bbox_inches='tight' to remove excess whitespace

Set facecolor for background color

Specify dpi appropriate for use case

Use transparent=True for overlays

Performance Optimization

Use rasterized=True for scatter plots with many points

Consider downsampling data for visualization

Close figures with plt.close() after saving

Use plt.close('all') in loops creating many figures

Key Conventions

Import as import matplotlib.pyplot as plt

Use object-oriented API for production code

Always label axes and include units

Test visualizations at intended display size

Consider accessibility in color choices