I got tired of fighting Jupyter notebook exports, so I built JupyTools

If you work with Jupyter notebooks long enough, you eventually hit the same annoying wall:
Your notebook works perfectly… but now someone wants it in another format.
A professor asks for a PDF.
A manager wants a Word document.
A teammate needs clean Markdown for GitHub.
Someone else just wants to open the notebook without installing Jupyter.
And suddenly you’re debugging nbconvert, installing Pandoc, fighting LaTeX errors, or trying to clean giant notebook outputs before pushing to Git.
I ran into this problem constantly while working between notebooks, documentation, and reports. Sometimes I was on a locked-down work machine. Sometimes I was helping students. Sometimes I just needed a quick export without rebuilding an entire Python environment.
That frustration became JupyTools.
What JupyTools does
👉 https://jupytools.com/
JupyTools is a browser-based toolkit for working with Jupyter notebooks.
No installs.
No account.
No server-side processing for core tools.
Everything runs directly in your browser session.
You can:
• Convert .ipynb to Word (.docx)
• Export notebooks to PDF
• Generate Markdown for GitHub
• Create HTML exports
• Convert notebooks to Python scripts
• Turn Python scripts back into notebooks
• Clean notebook outputs before sharing
• Merge multiple notebooks
• Split large notebooks into smaller sections
• Open and preview notebooks directly in the browser
The main goal was simple:
Make notebook workflows feel lightweight again.
The problem with notebook exports
Most notebook tools are built for people who already have a full local Python environment.
That sounds fine until you switch machines, work with students, collaborate across teams, or need to submit something quickly.
In practice, I kept seeing the same issues:
• nbconvert dependencies breaking
• Pandoc not installed
• PDF exports failing because of LaTeX
• Huge notebooks bloated with outputs
• Reviewers unable to open .ipynb
• Git diffs polluted by execution metadata
• People asking for Word files instead of notebooks
The worst part is that none of these problems are related to the actual analysis.
They’re just workflow friction.
So instead of adding another desktop dependency, I built a tool that works directly in the browser.
Why browser-based matters
One thing I learned from working with notebooks in different environments is that setup friction kills momentum.
Sometimes you only need a quick conversion.
Sometimes you’re borrowing a machine.
Sometimes IT policies block installs entirely.
Sometimes you’re helping a non-technical teammate who just needs a readable document.
With JupyTools, you open the site, drop the file, choose the export format, and download the result.
That’s it.
No environment setup.
No login wall.
No “install these six packages first.”

The features I personally use most

1. IPYNB → DOCX This became surprisingly useful. A lot of people still review work in Microsoft Word. Researchers, professors, managers, and clients often prefer editable documents over notebooks. The DOCX export keeps: • Headings • Markdown structure • Code blocks • Inline outputs • Images and plots

That means you can hand in a real Word document without manually copying notebook content into another editor.

1. Notebook Output Cleaner If you use Git with notebooks, you already know the pain. A tiny code change creates a massive diff because outputs, execution counts, or metadata changed. Before sharing notebooks publicly, I almost always clean them first. The cleaner removes noisy outputs while keeping the notebook structure intact. It’s especially useful before: • Git commits • Pull requests • Email attachments • Sharing notebooks with students • Uploading examples publicly
2. Notebook Viewer Sometimes people just want to read a notebook. Not run it. Not install Jupyter. Just open it. The viewer lets you inspect Markdown, code cells, and outputs directly in the browser. I originally built this because I kept sending notebooks to people who couldn’t open them properly.
3. Merge + Split notebooks This came from teaching workflows. I often had: • multiple labs that needed to become one notebook • giant notebooks that needed to be split into lessons Most notebook tools don’t handle this cleanly. Now I use merge/split constantly for tutorials and documentation prep. Privacy was a big requirement One thing I strongly wanted from the beginning: Your notebook should stay local whenever possible. A lot of notebooks contain: API keys • internal datasets • experiment outputs • customer information • unreleased research That’s why the core conversion flow runs client-side in the browser. Your files stay in the browser session until you download the result. I built it this way because I personally don’t like uploading notebooks to random conversion services either. Who this is for JupyTools is especially useful if you: • Work with Jupyter regularly • Teach data science or Python • Submit notebooks for grading • Share notebooks with non-technical teams • Need Word or PDF exports often • Use GitHub heavily • Review notebooks on different machines • Want quick conversions without local setup The workflow I use now My current notebook workflow looks something like this:
4. Build analysis in Jupyter or VS Code
5. Clean outputs before commit
6. Export Markdown for docs
7. Export PDF for sharing
8. Generate DOCX when stakeholders want edits
9. Bundle everything into ZIP when handing off work That entire process now takes a couple of minutes. Before building this, I used to spend way too much time fighting tooling instead of focusing on the actual work. Things I still want to improve I’m actively improving: • export formatting • large notebook handling • syntax highlighting • table rendering • mobile experience • notebook preview performance I also want to add more workflow-focused utilities instead of turning this into “just another converter site.” The goal is to support the real day-to-day notebook workflow developers and analysts already have. Final thoughts Jupyter notebooks became the standard for experimentation, teaching, and data science work. But the surrounding tooling still feels more complicated than it should. I built JupyTools because I wanted a faster, simpler way to move between notebooks, documents, scripts, and shareable formats without constantly rebuilding environments. If that sounds useful to you, I’d genuinely love feedback from the DEV community. 👉 https://jupytools.com/ Especially: • export issues • formatting bugs • missing formats • workflow pain points • notebook utilities you wish existed I’m building this primarily for developers, analysts, researchers, and students who live inside notebooks every day.