K-Dense-AI/matplotlib

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

Setup

For project work, install Matplotlib with uv:

uv add matplotlib

For notebook interactivity:

uv add matplotlib ipympl

Then enable the widget backend in Jupyter with %matplotlib widget or %matplotlib ipympl.

Matplotlib 3.10 requires Python 3.10+ and NumPy 1.23+. Non-interactive file output works through backends such as Agg, PDF, and SVG. For GUI windows, Matplotlib auto-selects an available backend; if TkAgg fails in a uv-managed Python, update uv and Python builds with uv self update and uv python upgrade --reinstall, or install a Qt backend with uv add pyside6.

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)

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
fig.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], tick_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
fig.savefig('figure.png', dpi=300, bbox_inches='tight', facecolor='white')

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

# Transparent background
fig.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

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')
fig.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:

uv run python scripts/plot_template.py

style_configurator.py

Interactive utility to configure matplotlib style preferences and generate custom style sheets.

Usage:

uv run 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

• 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