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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Repository: synthetic-sciences/openscience
Last source activity: July 4, 2026 at 06:25
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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 whitespace
facecolor='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=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

# 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.