Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.
npxskills add davila7/claude-code-templates--skill matplotlibLoading…
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.
This skill should be used when:
Matplotlib uses a hierarchical structure of objects:
1. pyplot Interface (Implicit, MATLAB-style)
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4])
plt.ylabel('some numbers')
plt.show()
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()
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()
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
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.
Color specification methods:
'red', 'blue', 'steelblue''#FF5733'(0.1, 0.2, 0.3)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.
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 backgroundfrom 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')
fig, ax = plt.subplots(figsize=(10, 6))constrained_layout=True or tight_layout() to prevent overlapping elementsfig, ax = plt.subplots(constrained_layout=True) is recommended for automatic spacingrasterized=True in plot calls to reduce file size# 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')
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.
Usage:
python scripts/plot_template.py
style_configurator.pyInteractive utility to configure matplotlib style preferences and generate custom style sheets.
Usage:
python scripts/style_configurator.py
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 problemsMatplotlib integrates well with:
%matplotlib inline or %matplotlib widgetconstrained_layout=True or tight_layout()plt.close(fig)plt.rcParams['font.sans-serif']pixels = dpi * inches
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