matplotlib

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Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.

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npx skill4agent add microck/ordinary-claude-skills matplotlib

SKILL.md Content

Matplotlib

Overview

Matplotlib is Python's foundational visualization library for creating static, animated, and interactive plots. This skill provides guidance on using matplotlib effectively, covering both the pyplot interface (MATLAB-style) and the object-oriented API (Figure/Axes), along with best practices for creating publication-quality visualizations.

When to Use This Skill

This skill should be used when:
  • Creating any type of plot or chart (line, scatter, bar, histogram, heatmap, contour, etc.)
  • Generating scientific or statistical visualizations
  • Customizing plot appearance (colors, styles, labels, legends)
  • Creating multi-panel figures with subplots
  • Exporting visualizations to various formats (PNG, PDF, SVG, etc.)
  • Building interactive plots or animations
  • Working with 3D visualizations
  • Integrating plots into Jupyter notebooks or GUI applications

Core Concepts

The Matplotlib Hierarchy

Matplotlib uses a hierarchical structure of objects:
  1. Figure - The top-level container for all plot elements
  2. Axes - The actual plotting area where data is displayed (one Figure can contain multiple Axes)
  3. Artist - Everything visible on the figure (lines, text, ticks, etc.)
  4. Axis - The number line objects (x-axis, y-axis) that handle ticks and labels

Two Interfaces

1. pyplot Interface (Implicit, MATLAB-style)
python
import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4])
plt.ylabel('some numbers')
plt.show()
  • Convenient for quick, simple plots
  • Maintains state automatically
  • Good for interactive work and simple scripts
2. Object-Oriented Interface (Explicit)
python
import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4])
ax.set_ylabel('some numbers')
plt.show()
  • Recommended for most use cases
  • More explicit control over figure and axes
  • Better for complex figures with multiple subplots
  • Easier to maintain and debug

Common Workflows

1. Basic Plot Creation

Single plot workflow:
python
import matplotlib.pyplot as plt
import numpy as np

# Create figure and axes (OO interface - RECOMMENDED)
fig, ax = plt.subplots(figsize=(10, 6))

# Generate and plot data
x = np.linspace(0, 2*np.pi, 100)
ax.plot(x, np.sin(x), label='sin(x)')
ax.plot(x, np.cos(x), label='cos(x)')

# Customize
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('Trigonometric Functions')
ax.legend()
ax.grid(True, alpha=0.3)

# Save and/or display
plt.savefig('plot.png', dpi=300, bbox_inches='tight')
plt.show()

2. Multiple Subplots

Creating subplot layouts:
python
# Method 1: Regular grid
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
axes[0, 0].plot(x, y1)
axes[0, 1].scatter(x, y2)
axes[1, 0].bar(categories, values)
axes[1, 1].hist(data, bins=30)

# Method 2: Mosaic layout (more flexible)
fig, axes = plt.subplot_mosaic([['left', 'right_top'],
                                 ['left', 'right_bottom']],
                                figsize=(10, 8))
axes['left'].plot(x, y)
axes['right_top'].scatter(x, y)
axes['right_bottom'].hist(data)

# Method 3: GridSpec (maximum control)
from matplotlib.gridspec import GridSpec
fig = plt.figure(figsize=(12, 8))
gs = GridSpec(3, 3, figure=fig)
ax1 = fig.add_subplot(gs[0, :])  # Top row, all columns
ax2 = fig.add_subplot(gs[1:, 0])  # Bottom two rows, first column
ax3 = fig.add_subplot(gs[1:, 1:])  # Bottom two rows, last two columns

3. Plot Types and Use Cases

Line plots - Time series, continuous data, trends
python
ax.plot(x, y, linewidth=2, linestyle='--', marker='o', color='blue')
Scatter plots - Relationships between variables, correlations
python
ax.scatter(x, y, s=sizes, c=colors, alpha=0.6, cmap='viridis')
Bar charts - Categorical comparisons
python
ax.bar(categories, values, color='steelblue', edgecolor='black')
# For horizontal bars:
ax.barh(categories, values)
Histograms - Distributions
python
ax.hist(data, bins=30, edgecolor='black', alpha=0.7)
Heatmaps - Matrix data, correlations
python
im = ax.imshow(matrix, cmap='coolwarm', aspect='auto')
plt.colorbar(im, ax=ax)
Contour plots - 3D data on 2D plane
python
contour = ax.contour(X, Y, Z, levels=10)
ax.clabel(contour, inline=True, fontsize=8)
Box plots - Statistical distributions
python
ax.boxplot([data1, data2, data3], labels=['A', 'B', 'C'])
Violin plots - Distribution densities
python
ax.violinplot([data1, data2, data3], positions=[1, 2, 3])
For comprehensive plot type examples and variations, refer to 
references/plot_types.md
.

4. Styling and Customization

Color specification methods:
  • Named colors: 
    'red'
    , 
    'blue'
    , 
    'steelblue'
  • Hex codes: 
    '#FF5733'
  • RGB tuples: 
    (0.1, 0.2, 0.3)
  • Colormaps: 
    cmap='viridis'
    , 
    cmap='plasma'
    , 
    cmap='coolwarm'
Using style sheets:
python
plt.style.use('seaborn-v0_8-darkgrid')  # Apply predefined style
# Available styles: 'ggplot', 'bmh', 'fivethirtyeight', etc.
print(plt.style.available)  # List all available styles
Customizing with rcParams:
python
plt.rcParams['font.size'] = 12
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['axes.titlesize'] = 16
plt.rcParams['xtick.labelsize'] = 10
plt.rcParams['ytick.labelsize'] = 10
plt.rcParams['legend.fontsize'] = 12
plt.rcParams['figure.titlesize'] = 18
Text and annotations:
python
ax.text(x, y, 'annotation', fontsize=12, ha='center')
ax.annotate('important point', xy=(x, y), xytext=(x+1, y+1),
            arrowprops=dict(arrowstyle='->', color='red'))
For detailed styling options and colormap guidelines, see 
references/styling_guide.md
.

5. Saving Figures

Export to various formats:
python
# High-resolution PNG for presentations/papers
plt.savefig('figure.png', dpi=300, bbox_inches='tight', facecolor='white')

# Vector format for publications (scalable)
plt.savefig('figure.pdf', bbox_inches='tight')
plt.savefig('figure.svg', bbox_inches='tight')

# Transparent background
plt.savefig('figure.png', dpi=300, bbox_inches='tight', transparent=True)
Important parameters:
  • dpi
    : Resolution (300 for publications, 150 for web, 72 for screen)
  • bbox_inches='tight'
    : Removes excess whitespace
  • facecolor='white'
    : Ensures white background (useful for transparent themes)
  • transparent=True
    : Transparent background

6. Working with 3D Plots

python
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')

# Surface plot
ax.plot_surface(X, Y, Z, cmap='viridis')

# 3D scatter
ax.scatter(x, y, z, c=colors, marker='o')

# 3D line plot
ax.plot(x, y, z, linewidth=2)

# Labels
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')

Best Practices

1. Interface Selection

  • Use the object-oriented interface (fig, ax = plt.subplots()) for production code
  • Reserve pyplot interface for quick interactive exploration only
  • Always create figures explicitly rather than relying on implicit state

2. Figure Size and DPI

  • Set figsize at creation: 
    fig, ax = plt.subplots(figsize=(10, 6))
  • Use appropriate DPI for output medium:
    • Screen/notebook: 72-100 dpi
    • Web: 150 dpi
    • Print/publications: 300 dpi

3. Layout Management

  • Use 
    constrained_layout=True
    or 
    tight_layout()
    to prevent overlapping elements
  • fig, ax = plt.subplots(constrained_layout=True)
    is recommended for automatic spacing

4. Colormap Selection

  • Sequential (viridis, plasma, inferno): Ordered data with consistent progression
  • Diverging (coolwarm, RdBu): Data with meaningful center point (e.g., zero)
  • Qualitative (tab10, Set3): Categorical/nominal data
  • Avoid rainbow colormaps (jet) - they are not perceptually uniform

5. Accessibility

  • Use colorblind-friendly colormaps (viridis, cividis)
  • Add patterns/hatching for bar charts in addition to colors
  • Ensure sufficient contrast between elements
  • Include descriptive labels and legends

6. Performance

  • For large datasets, use 
    rasterized=True
    in plot calls to reduce file size
  • Use appropriate data reduction before plotting (e.g., downsample dense time series)
  • For animations, use blitting for better performance

7. Code Organization

python
# Good practice: Clear structure
def create_analysis_plot(data, title):
    """Create standardized analysis plot."""
    fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)

    # Plot data
    ax.plot(data['x'], data['y'], linewidth=2)

    # Customize
    ax.set_xlabel('X Axis Label', fontsize=12)
    ax.set_ylabel('Y Axis Label', fontsize=12)
    ax.set_title(title, fontsize=14, fontweight='bold')
    ax.grid(True, alpha=0.3)

    return fig, ax

# Use the function
fig, ax = create_analysis_plot(my_data, 'My Analysis')
plt.savefig('analysis.png', dpi=300, bbox_inches='tight')

Quick Reference Scripts

This skill includes helper scripts in the 
scripts/
directory:

plot_template.py

Template script demonstrating various plot types with best practices. Use this as a starting point for creating new visualizations.
Usage:
bash
python scripts/plot_template.py

style_configurator.py

Interactive utility to configure matplotlib style preferences and generate custom style sheets.
Usage:
bash
python scripts/style_configurator.py

Detailed References

For comprehensive information, consult the reference documents:
  • references/plot_types.md
     - Complete catalog of plot types with code examples and use cases
  • references/styling_guide.md
     - Detailed styling options, colormaps, and customization
  • references/api_reference.md
     - Core classes and methods reference
  • references/common_issues.md
     - Troubleshooting guide for common problems

Integration with Other Tools

Matplotlib integrates well with:
  • NumPy/Pandas - Direct plotting from arrays and DataFrames
  • Seaborn - High-level statistical visualizations built on matplotlib
  • Jupyter - Interactive plotting with 
    %matplotlib inline
    or 
    %matplotlib widget
  • GUI frameworks - Embedding in Tkinter, Qt, wxPython applications

Common Gotchas

  1. Overlapping elements: Use 
    constrained_layout=True
    or 
    tight_layout()
  2. State confusion: Use OO interface to avoid pyplot state machine issues
  3. Memory issues with many figures: Close figures explicitly with 
    plt.close(fig)
  4. Font warnings: Install fonts or suppress warnings with 
    plt.rcParams['font.sans-serif']
  5. DPI confusion: Remember that figsize is in inches, not pixels: 
    pixels = dpi * inches

Additional Resources