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matplotlib 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.
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7 fichiers name matplotlib 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. category visualization license https://github.com/matplotlib/matplotlib/tree/main/LICENSE metadata {"skill-author":"Synthetic Sciences"} version 1.0.0 author Synthetic Sciences tags ["Visualization","Plotting","Charts","Figures"] 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 elementsAxes - 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 import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(10 , 6 ))
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)' )
ax.set_xlabel('x' )
ax.set_ylabel('y' )
ax.set_title('Trigonometric Functions' )
ax.legend()
ax.grid(True , alpha=0.3 )
plt.savefig('plot.png' , dpi=300 , bbox_inches='tight' )
plt.show()
2. Multiple Subplots Creating subplot layouts:
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 )
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)
from matplotlib.gridspec import GridSpec
fig = plt.figure(figsize=(12 , 8 ))
gs = GridSpec(3 , 3 , figure=fig)
ax1 = fig.add_subplot(gs[0 , :])
ax2 = fig.add_subplot(gs[1 :, 0 ])
ax3 = fig.add_subplot(gs[1 :, 1 :])
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' )
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'
plt.style.use('seaborn-v0_8-darkgrid' )
print (plt.style.available)
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
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:
plt.savefig('figure.png' , dpi=300 , bbox_inches='tight' , facecolor='white' )
plt.savefig('figure.pdf' , bbox_inches='tight' )
plt.savefig('figure.svg' , bbox_inches='tight' )
plt.savefig('figure.png' , dpi=300 , bbox_inches='tight' , transparent=True )
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' )
ax.plot_surface(X, Y, Z, cmap='viridis' )
ax.scatter(x, y, z, c=colors, marker='o' )
ax.plot(x, y, z, linewidth=2 )
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 codeReserve 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 progressionDiverging (coolwarm, RdBu): Data with meaningful center point (e.g., zero)Qualitative (tab10, Set3): Categorical/nominal dataAvoid 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
def create_analysis_plot (data, title ):
"""Create standardized analysis plot."""
fig, ax = plt.subplots(figsize=(10 , 6 ), constrained_layout=True )
ax.plot(data['x' ], data['y' ], linewidth=2 )
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
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.pyTemplate script demonstrating various plot types with best practices. Use this as a starting point for creating new visualizations.
python scripts/plot_template.py
style_configurator.pyInteractive utility to configure matplotlib style preferences and generate custom style sheets.
python scripts/style_configurator.py
Detailed References For comprehensive information, consult the reference documents:
references/plot_types.mdreferences/styling_guide.mdreferences/api_reference.mdreferences/common_issues.md
Integration with Other Tools Matplotlib integrates well with:
NumPy/Pandas - Direct plotting from arrays and DataFramesSeaborn - High-level statistical visualizations built on matplotlibJupyter - Interactive plotting with %matplotlib inline or %matplotlib widgetGUI frameworks - Embedding in Tkinter, Qt, wxPython applications
Common Gotchas
Overlapping elements : Use constrained_layout=True or tight_layout()State confusion : Use OO interface to avoid pyplot state machine issuesMemory 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 