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