Refer a Friend
Share your link — earn +15 permanent plots for every friend who joins.
Sign in to get your referral linkIs it faster to write raw Python code or to use a plain-English AI assistant to generate citable figures? Discover the best scientific plotting workflow for your research in 2026.
Free account required - works directly in the browser.
| Criteria | Plotivy | Raw Matplotlib |
|---|---|---|
| Creation Speed | Seconds (via plain-English prompt) | Minutes to hours (via manual coding) |
| Syntax Memory Required | None - use natural descriptions | Complete API knowledge needed |
| Code Output | Fully citable Python script | Yes (written by user) |
| Journal Layout Presets | Nature, Science, ACS, and PNAS | Manual size/font adjustments |
| Error Resolution | AI auto-fixes formatting conflicts | Manual traceback debugging |
Wrangling plot styles? Speed up your scientific figure pipeline.
Try Free NowRun this typical dose-response scatter regression inside the editor. Changes to parameters like color hexes, figure sizes, and marker styles render instantly.
Tip: Recreating this in Plotivy is as simple as writing "create a scatter plot with regression and 95% confidence interval".
Updating preview...
Edit the code, then press Run Code to refresh the preview
Preparing preview
Running once automatically on first load
This is a safe playground for learning! Try changing:
Edit the code, run it, then open the full data visualization tool to continue with your own dataset.
Plotivy is not a replacement for matplotlib — it is a faster way to write matplotlib. Every figure Plotivy generates comes with the complete, editable matplotlib (or Plotly/Seaborn) source code. You describe the figure in plain English, Plotivy writes the code, and you keep full control to tweak it by hand. Think of it as an AI pair-programmer for plotting rather than a closed black box.
No. You can generate publication-ready figures from natural-language prompts without writing any code. But because Plotivy always outputs readable, commented Python, it is also a practical way to learn matplotlib: you can see exactly which functions and arguments produced each part of the figure and modify them.
Yes. The generated code is standard matplotlib that runs in any Python environment — Jupyter, VS Code, or a script. You can copy it, version-control it, and integrate it into a reproducible analysis pipeline. There is no proprietary runtime or lock-in.
Both produce the same matplotlib output, so figure quality is identical. The difference is speed. Hand-writing matplotlib gives total control but costs time on boilerplate (axes, ticks, legends, DPI, journal sizing). Plotivy generates that boilerplate to journal specifications instantly, then hands you the code to refine, so you reach a submission-ready figure faster.
Get fully commented Matplotlib code alongside high-resolution images. Completely free, reproducible, and private.
Start Generating Plots