matplotlib

name	matplotlib
description	Builds Matplotlib figures with the OO Axes API: subplots, mosaic, GridSpec, rcParams, and savefig to PNG/PDF/SVG. Use for pyplot line, scatter, bar, hist, heatmap, contour, or 3D work in Python. Never route Seaborn estimators or Plotly Dash apps through this chair.
version	1.0.1
license	https://github.com/matplotlib/matplotlib/tree/main/LICENSE
metadata	{"skill-author":"K-Dense Inc."}
risk	unknown
source	community

When to Use

Use this skill when:

• Creating any type of plot or chart (line, scatter, bar, histogram, heatmap, contour, box, violin, 3D surface, etc.)
• Generating scientific or statistical visualizations
• Customizing plot appearance (colors, styles, labels, legends, annotations)
• Creating multi-panel figures with subplots, mosaic layouts, or GridSpec
• Exporting visualizations to PNG, PDF, SVG, or other formats
• Building interactive plots or animations
• Working with 3D visualizations
• Integrating plots into Jupyter notebooks or GUI applications

Trigger keywords: plot, chart, figure, axes, subplot, matplotlib, pyplot, visualization, heatmap, contour, histogram, scatter, bar chart, savefig, rcParams, colormap.

Prerequisites

• Python 3.8+ installed and available on PATH
• Matplotlib installed: pip install matplotlib (NumPy is a required dependency and installs automatically)
• For Jupyter integration: pip install jupyter and run %matplotlib inline or %matplotlib widget in a notebook cell
• Windows host is primary (PowerShell). Use python (not python3) in PowerShell commands.

Procedure

1. Choose the Interface

Object-Oriented (RECOMMENDED for all production code):

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4])
ax.set_ylabel('some numbers')
plt.show()

• Explicit control over Figure and Axes objects
• Better for complex figures with multiple subplots
• Easier to maintain and debug

pyplot (MATLAB-style — quick exploration only):

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4])
plt.ylabel('some numbers')
plt.show()

• Stateful; convenient for simple scripts
• Avoid in production code due to implicit state confusion

2. Create a Basic Plot (OO Interface)

import matplotlib.pyplot as plt
import numpy as np

# Create figure and axes with explicit size
fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)

# 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()

3. Create Multi-Panel Figures

Regular grid:

fig, axes = plt.subplots(2, 2, figsize=(12, 10), constrained_layout=True)
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)

Mosaic layout (flexible, label-based):

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)

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

4. Select Plot Types

Plot Type	Use Case	Example Call
Line	Time series, trends	ax.plot(x, y, linewidth=2, linestyle='--', marker='o', color='blue')
Scatter	Correlations	ax.scatter(x, y, s=sizes, c=colors, alpha=0.6, cmap='viridis')
Bar	Categorical comparisons	ax.bar(categories, values, color='steelblue', edgecolor='black')
Horizontal bar	Long labels	ax.barh(categories, values)
Histogram	Distributions	ax.hist(data, bins=30, edgecolor='black', alpha=0.7)
Heatmap	Matrix data	im = ax.imshow(matrix, cmap='coolwarm', aspect='auto') then plt.colorbar(im, ax=ax)
Contour	3D data on 2D	contour = ax.contour(X, Y, Z, levels=10) then ax.clabel(contour, inline=True, fontsize=8)
Box	Statistical distributions	ax.boxplot([data1, data2, data3], labels=['A', 'B', 'C'])
Violin	Distribution densities	ax.violinplot([data1, data2, data3], positions=[1, 2, 3])

For specialized plot types beyond the table, use the official gallery: https://matplotlib.org/stable/gallery/index.html

5. Apply 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'

Style sheets:

plt.style.use('seaborn-v0_8-darkgrid')
print(plt.style.available)  # List all available styles
# Common: 'ggplot', 'bmh', 'fivethirtyeight', 'seaborn-v0_8-darkgrid'

rcParams for global defaults:

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 colormap and style-sheet options, prefer perceptually uniform maps (viridis, plasma, cividis) and the official cheatsheets: https://matplotlib.org/cheatsheets/

6. Save and Export Figures

# 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)

Key parameters:

Parameter	Purpose	Recommended Value
dpi	Resolution	300 for publications, 150 for web, 72 for screen
bbox_inches='tight'	Remove excess whitespace	Always use
facecolor='white'	Ensure white background	Use with dark themes
transparent=True	Transparent background	For overlays

7. Create 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)

ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')

8. Confirm Unfamiliar APIs

Look up method signatures on the Matplotlib API index (https://matplotlib.org/stable/api/index.html) rather than guessing Axes/Figure parameters. Gallery examples at https://matplotlib.org/stable/gallery/index.html cover specialized plot types.

9. Organize Reusable Plot Code

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')

Pitfalls

1. Overlapping elements — Use constrained_layout=True at figure creation or call fig.tight_layout() before saving. Do not use both together.
2. State confusion with pyplot interface — The pyplot state machine tracks the "current" figure/axes implicitly. Use the OO interface (fig, ax = plt.subplots()) to avoid ambiguity in production code.
3. Memory leaks with many figures — Always close figures explicitly with plt.close(fig) when generating many plots in a loop. Unclosed figures accumulate in memory.
4. Font warnings — If fonts are missing, suppress warnings by setting plt.rcParams['font.sans-serif'] = ['DejaVu Sans'] or install the required font package.
5. DPI confusion — figsize is in inches, not pixels. Final pixel dimensions: pixels = dpi * inches. A (10, 6) figure at 300 dpi produces a 3000×1800 image.
6. Rainbow colormaps (jet) — Not perceptually uniform; can misrepresent data. Use viridis, plasma, inferno, or cividis instead.
7. Large dataset file size — For scatter/line plots with many points, pass rasterized=True to reduce PDF/SVG file size. Downsample dense time series before plotting.
8. show() blocks in scripts — plt.show() blocks execution in non-interactive scripts. Call savefig() before show() to ensure the file is written.
9. Style name changes — Seaborn styles were renamed in matplotlib 3.6+ (e.g., 'seaborn-darkgrid' → 'seaborn-v0_8-darkgrid'). Use plt.style.available to check valid names.

Verification

Check Installation

python -c "import matplotlib; print(matplotlib.__version__)"

Expected output (version may differ):

3.9.0

Verify a Plot Renders and Saves

python -c "import matplotlib.pyplot as plt; fig, ax = plt.subplots(); ax.plot([1,2,3,4]); plt.savefig('test_plot.png', dpi=150, bbox_inches='tight'); print('OK')"

Expected output:

OK

Verify the file exists:

Test-Path .\test_plot.png

Expected output:

True

Verify Available Styles

python -c "import matplotlib.pyplot as plt; print(plt.style.available)"

Verify Backend (Non-Interactive / Headless)

python -c "import matplotlib; matplotlib.use('Agg'); import matplotlib.pyplot as plt; print(matplotlib.get_backend())"

Expected output:

Agg

Related Skills

• numpy — Array generation and numerical data feeding into plots
• pandas — DataFrame-based plotting and data manipulation
• seaborn — High-level statistical visualizations built on matplotlib

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

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.