Matplotlib Best Practices

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