# Matplotlib Best Practices > Best practices for Matplotlib data visualization, plotting, and creating publication-quality figures in Python Use when this capability is needed. - Skill: `tomevault-io/matplotlib-best-practices` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/matplotlib-best-practices` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/matplotlib-best-practices/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/matplotlib-best-practices --- # 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 --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/mindrally) — claim your Tome and manage your conversions.