mplstudio

An interactive GUI for styling matplotlib figures directly in Jupyter.

Adjust colors, fonts, axes, legends, and more in real time without touching your plot code.

Installation

pip install mplstudio

Requires Python 3.9+, Jupyter Notebook or JupyterLab, and matplotlib ≥ 3.5.

Quick Start

import matplotlib.pyplot as plt
import mplstudio

fig, ax = plt.subplots()
ax.plot([1, 2, 3], [1, 4, 9], label="y = x²")
ax.plot([1, 2, 3], [1, 2, 3], label="y = x")
ax.legend()

mplstudio.studio(fig)

For detailed usage examples, see examples/demo.ipynb.

API Reference

mplstudio.studio(fig, *, show=None, dark=False)

Display the styling control panel for a matplotlib figure.

Parameter	Type	Default	Description
fig	Figure | None	plt.gcf()	Target figure. Uses the current figure if omitted.
show	list[str] | None	None	Section names to display. None shows all sections.
dark	bool	False	Use Catppuccin Mocha dark theme.

# Show only specific sections
mplstudio.studio(fig, show=["colors", "axes", "legend"])

# Dark theme
mplstudio.studio(fig, dark=True)

mplstudio.available_sections()

Return a sorted list of all valid section names.

mplstudio.available_sections()
# ['alpha', 'axes', 'colors', 'figure_size', 'grid_spines',
#  'legend', 'palette_suggestions', 'save', 'typography']

Available Sections

Section	Controls
figure_size	Width and height sliders
typography	Font size for all elements or individually (title, labels, ticks, legend)
colors	Palette picker (with color count), manual per-series color pickers, smart CIELAB palette, colormap selector, background color
alpha	Global opacity slider + per-series opacity
axes	Title, x/y axis labels, x/y limits — supports multi-axis figures
legend	Legend title, collapsible series label editor, location dropdown, bbox position
grid_spines	Grid toggle, spine style (Box / 2-Side / None)
palette_suggestions	Colorblind-safe palette recommendations filtered by use case and background
save	Save figure with custom filename, path, DPI, and format (png, jpg, pdf, svg, eps)

Palette Utilities

mplstudio ships a curated palette library and color science tools you can use independently of the GUI: get_palette, smart_palette, recommend, palette_names, and delta_e. See examples/demo.ipynb for usage examples.

Available Palettes

Palettes for categorical, and continuous variables (sequential and diverging color maps).

Following table shows palettes for categorical values. For continuous variables, mplstudio uses matplotlib's built-in colormaps. See the matplotlib colormap reference for the full list.

Palette	Colors	Tags
Okabe-Ito	8	colorblind-safe
Paul Tol Bright	7	colorblind-safe
Paul Tol Vibrant	7	colorblind-safe
Paul Tol Muted	10	colorblind-safe
IBM Colorblind Safe	6	colorblind-safe
ColorBrewer Set1	9	categorical
ColorBrewer Set2	8	categorical
ColorBrewer Dark2	8	categorical
ColorBrewer Paired	12	categorical
Tableau 10	10	categorical
Matplotlib tab10	10	categorical
Nord	6	dark background
Catppuccin Latte	6	light background
Dracula	6	dark background
Tokyo Night	6	dark background
Pastel	6	light background
High Contrast	5	light background

Requirements

• Python ≥ 3.9
• matplotlib ≥ 3.5
• ipywidgets ≥ 8.0
• ipykernel ≥ 6.0
• Jupyter Notebook or JupyterLab

License

MIT © 2026 Seo-Yoon Moon