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IPYNB converter

An .ipynb file is a Jupyter notebook: a JSON document holding an ordered list of cells, each either markdown prose or executable code, with each code cell's stored outputs — text, tables, images, HTML — saved alongside it. It is a record of an analysis, not just its source.

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Drop IPYNB files here

Batch files can each use a different output. Nothing uploads for local conversions.

Working inputs include camera RAW, browser-local audio/video, PDF, CBZ/CBR comics, office documents, ebooks, markup, 3D models, structured text, images, and archives.

Where IPYNB comes from

Fernando Pérez started IPython in 2001; the browser-based notebook arrived in 2011 and was split out as Project Jupyter in 2014 to reflect that it had outgrown Python. The nbformat schema has been stable at version 4 since 2015. Notebooks became the default medium for data science and machine-learning work, and are now rendered directly by GitHub, GitLab, Kaggle, Google Colab and every major cloud platform.

How IPYNB works

  • The file is JSON with a cells array and an nbformat version field; anything claiming to be a notebook without both is not one.
  • Cell source is stored as an array of strings, one per line with trailing newlines retained, which is what makes notebooks diff badly.
  • Outputs are embedded — including base64 PNGs — so a notebook's size is dominated by results rather than code.
  • Stored outputs may include text/html, which must never be trusted: it is arbitrary HTML saved by whoever ran the notebook.
  • Kernel and language metadata record what executed the notebook, which is how a converter knows the code cells are Python rather than R or Julia.

When to use IPYNB

  • Data analysis and machine-learning experiments kept alongside their results
  • Teaching material where prose, code and output belong together
  • Publishing a reproducible analysis as HTML
  • Extracting the code from a notebook as a runnable script

Strengths and limitations

Strengths

  • Code, explanation and results live in one artefact
  • Rendered natively by every major code-hosting and data platform
  • Language-agnostic through the kernel abstraction

Limitations

  • JSON with embedded outputs makes version control and code review painful
  • Out-of-order execution means a notebook can show results no fresh run reproduces
  • Stored HTML outputs are an injection risk in any tool that renders them naively

Compatibility

JupyterLab, VS Code, PyCharm, Google Colab, GitHub and nbviewer all open notebooks. Novus Convert exports source cells locally to HTML, Markdown, plain text, Python and PDF without executing anything and without trusting stored HTML outputs — the two properties that make notebook conversion safe rather than convenient.

Kernel and language metadata are read; the Python export uses it to decide the script it writes.

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