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