Clear Jupyter Notebook Output Online (.ipynb Cleaner)
Strip saved outputs and execution counts from a Jupyter .ipynb — clean notebooks for git and smaller files, in your browser.
Why use this tool?
Use the Output Cleaner to strip saved outputs and execution counts from a notebook before you commit it. You get a clean .ipynb with readable git diffs and a much smaller file — no Python and no nbstripout install.
Tool facts
- Supported Python
- Any .ipynb (nbformat v4)
- Input limit
- Limited only by your device
- Last reviewed
- 2026-08-25
What it can't do
- •Change your code — it only strips outputs, execution counts, and metadata.
- •Recover outputs once you've cleared them and downloaded the file.
About Jupyter Notebook Output Cleaner
The Notebook Output Cleaner removes the saved outputs and execution counts from a Jupyter .ipynb file, giving you a clean version that's ideal for version control. Outputs — printed text, tables, and especially embedded images and charts — are stored inside the notebook JSON as large base64 blobs, so committing a notebook with its outputs produces enormous, noisy git diffs and bloats your repository. Clearing them first keeps history readable and files small.
It does the same job as the command-line nbstripout tool, but in your browser with nothing to install. Paste or drop your .ipynb, and it sets every code cell's outputs to empty and its execution_count to null, then hands back a cleaned notebook you can copy or download. Optionally it also removes run-state metadata (cell timing, collapsed/scrolled flags) and interactive widget state, which are the other common sources of noisy diffs.
Crucially, the cleaner never touches your code: markdown cells and the source of every code cell are preserved exactly. It only removes the generated results, which you regenerate simply by re-running the notebook. The output is a valid .ipynb that opens normally in Jupyter, JupyterLab, VS Code, or Colab.
Everything runs in your browser — the notebook is parsed and cleaned client-side. If you want to read a notebook rather than clean it, use the companion Notebook Viewer; to extract the code as a .py script, use the Jupyter-to-Script converter.
Frequently Asked Questions
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