lovelyplots
Format Matplotlib Plots for thesis, scientific papers and reports.
Decision gist · record as of 2026-08-14
Yes, if you regularly produce scientific plots for papers or theses. The package is low-friction to install and solves a real workflow problem—consistent, publication-ready formatting with LaTeX and Adobe Illustrator compatibility. However, note the dormant maintenance status: the last release was 891 days ago, so verify compatibility with your current matplotlib version before relying on it for critical work.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- As of version 1.0.0, the import lovelyplots statement must come before calling plt.style.use().
- Low install friction with a single runtime dependency on matplotlib.
- The package is dormant (last release 891 days ago), though the repository remains active with recent commits and moderate community interest (926 stars).
License · maintenance · safety
MIT (permissive) — MIT license is permissive, allowing free use, modification, and distribution with minimal restrictions—suitable for academic and commercial projects.
last release 2024-03-06 (891 days) · last repo commit 2024-03-25 · 926 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 522,711 downloads/mo, #6,201 on PyPI
Alternatives
Verify before relying
pip install lovelyplots
import lovelyplots
import matplotlib.pyplot as plt
plt.style.use('ipynb')
# Create and display plots normally
fig, ax = plt.subplots()
ax.plot(x, y)- Whether the package works with current matplotlib versions (last release was 891 days ago).
- Compatibility with modern Python versions beyond what the classifiers indicate.
What it is and what it does
LovelyPlots is a collection of matplotlib style sheets designed to produce publication-quality scientific figures. It installs predefined .mplstyle files into matplotlib's configuration directory, allowing you to apply consistent formatting across plots with a single line of code. The main 'ipynb' style sets figure dimensions, enables scientific notation, and configures output for Adobe Illustrator editability.
The package is particularly useful for academic writing: it includes color-blind-safe palettes, line styles, markers, and colormaps, plus utilities for font customization and retina display support. A key feature is SVG export without embedded fonts, allowing figures imported into LaTeX documents to automatically adopt the document's native font without manual editing.
Use it for
- Format plots for inclusion in academic papers or thesis documents with consistent styling across all figures.
- Create publication-ready plots that remain fully editable in Adobe Illustrator for final design adjustments.
- Generate SVG figures that automatically inherit fonts from LaTeX documents when imported.
- Apply color-blind-safe color cycles to ensure accessibility across scientific visualizations.
- Quickly switch between multiple predefined styles (e.g., combining 'ipynb' with 'colorsblind34') for different output contexts.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you regularly produce scientific plots for papers or theses.
The package is low-friction to install and solves a real workflow problem—consistent, publication-ready formatting with LaTeX and Adobe Illustrator compatibility. However, note the dormant maintenance status: the last release was 891 days ago, so verify compatibility with your current matplotlib version before relying on it for critical work.
Install
lovelyplots on PyPI
Before you install
Low install friction with a single runtime dependency on matplotlib. The package is dormant (last release 891 days ago), though the repository remains active with recent commits and moderate community interest (926 stars).
As of version 1.0.0, the import lovelyplots statement must come before calling plt.style.use().
License in practice
MIT license is permissive, allowing free use, modification, and distribution with minimal restrictions—suitable for academic and commercial projects.
Quickstart
pip install lovelyplots
import lovelyplots
import matplotlib.pyplot as plt
plt.style.use('ipynb')
# Create and display plots normally
fig, ax = plt.subplots()
ax.plot(x, y)Verify before relying
- Whether the package works with current matplotlib versions (last release was 891 days ago).
- Compatibility with modern Python versions beyond what the classifiers indicate.
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagematplotlib |
| Maintenance | Dormant 891 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 522,711 / month, #6,201 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Framework :: MatplotlibLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3 |
Evidence: lovelyplots-1.0.2-py3-none-any.whl
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