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matplotlib
GitHub用于创建静态、动画及交互式图表的底层可视化库。适用于需要精细控制绘图元素、生成科学统计图、自定义样式、多面板布局或导出出版级图像的场景，支持多种格式和3D可视化。
Trigger Scenarios
需要精细控制每个绘图元素时
创建科学或统计可视化图表时
生成出版级多面板图形时
需要将图表导出为PNG/PDF/SVG格式时
Install
npx skills add synthetic-sciences/openscience --skill matplotlib -g -y
SKILL.md
Frontmatter
{
"name": "matplotlib",
"tags": [
"Visualization",
"Plotting",
"Charts",
"Figures"
],
"author": "Synthetic Sciences",
"license": "https:\/\/github.com\/matplotlib\/matplotlib\/tree\/main\/LICENSE",
"version": "1.0.0",
"category": "visualization",
"metadata": {
"skill-author": "Synthetic Sciences"
},
"description": "Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG\/PDF\/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.",
"dependencies": [
"matplotlib>=3.9.0"
]
}
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:
- Figure - The top-level container for all plot elements
- Axes - The actual plotting area where data is displayed (one Figure can contain multiple Axes)
- Artist - Everything visible on the figure (lines, text, ticks, etc.)
- Axis - The number line objects (x-axis, y-axis) that handle ticks and labels
Two Interfaces
1. pyplot Interface (Implicit, MATLAB-style)
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)
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:
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:
# 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
ax.plot(x, y, linewidth=2, linestyle='--', marker='o', color='blue')
Scatter plots - Relationships between variables, correlations
ax.scatter(x, y, s=sizes, c=colors, alpha=0.6, cmap='viridis')
Bar charts - Categorical comparisons
ax.bar(categories, values, color='steelblue', edgecolor='black')
# For horizontal bars:
ax.barh(categories, values)
Histograms - Distributions
ax.hist(data, bins=30, edgecolor='black', alpha=0.7)
Heatmaps - Matrix data, correlations
im = ax.imshow(matrix, cmap='coolwarm', aspect='auto')
plt.colorbar(im, ax=ax)
Contour plots - 3D data on 2D plane
contour = ax.contour(X, Y, Z, levels=10)
ax.clabel(contour, inline=True, fontsize=8)
Box plots - Statistical distributions
ax.boxplot([data1, data2, data3], labels=['A', 'B', 'C'])
Violin plots - Distribution densities
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:
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:
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:
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:
# 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 whitespacefacecolor='white': Ensures white background (useful for transparent themes)transparent=True: Transparent background
6. Working with 3D Plots
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=Trueortight_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=Truein 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
# 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:
python scripts/plot_template.py
style_configurator.py
Interactive utility to configure matplotlib style preferences and generate custom style sheets.
Usage:
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 casesreferences/styling_guide.md- Detailed styling options, colormaps, and customizationreferences/api_reference.md- Core classes and methods referencereferences/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 inlineor%matplotlib widget - GUI frameworks - Embedding in Tkinter, Qt, wxPython applications
Common Gotchas
- Overlapping elements: Use
constrained_layout=Trueortight_layout() - State confusion: Use OO interface to avoid pyplot state machine issues
- Memory issues with many figures: Close figures explicitly with
plt.close(fig) - Font warnings: Install fonts or suppress warnings with
plt.rcParams['font.sans-serif'] - DPI confusion: Remember that figsize is in inches, not pixels:
pixels = dpi * inches
Additional Resources
- Official documentation: https://matplotlib.org/
- Gallery: https://matplotlib.org/stable/gallery/index.html
- Cheatsheets: https://matplotlib.org/cheatsheets/
- Tutorials: https://matplotlib.org/stable/tutorials/index.html
Version History
- e9844a4 Current 2026-07-11 17:33
Dependencies
-
required
matplotlib>=3.9.0
