.ipynb

What is a .ipynb file?

Jupyter Notebooks combine live code, equations, visualisations, and narrative text — the standard for data science and ML in Python.

Use caution
Type Code
By Project Jupyter
MIME application/x-ipynb+json

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What is it

You have a Jupyter Notebook — an interactive document that mixes live code, equations, visualisations, and prose in a single file. It's the standard environment for data science, machine learning, and scientific computing, and the reason Python became the dominant language in those fields.

Under the hood, an .ipynb file is JSON containing an ordered list of cells — each cell is either code (executed against a kernel, usually Python) or Markdown (rendered as formatted text). Cell outputs (charts, tables, images, error messages) are stored inline, which is why notebooks can be enormous. The format was created by Fernando Pérez as IPython Notebook in 2011 and later expanded into Project Jupyter (supporting Julia, Python, R — hence Ju-Pyt-R).

JupyterLab (free, runs locally) is the native environment. VS Code has excellent Jupyter support built in. Google Colab (free) runs notebooks in the cloud with free GPU access. To open a notebook: install JupyterLab (`pip install jupyterlab`) and run `jupyter lab`. A word of caution: notebooks can execute arbitrary code. Don't run notebooks from untrusted sources without reviewing the cells first.

Technical details
Full Name
Jupyter Notebook
MIME Type
application/x-ipynb+json
Developer
Project Jupyter
Magic Bytes
N/A
Safety
.ipynb requires caution. Jupyter Notebooks can execute arbitrary code. Only open notebooks from trusted sources.
What opens it
JupyterLab
FREE Web
VS Code
FREE All
Google Colab
FREE Web
FAQ
Can I open a Jupyter Notebook without Jupyter?
VS Code opens .ipynb files with full execution support. Google Colab opens them in the browser. GitHub renders notebooks as static documents (no execution). nbviewer.jupyter.org also displays notebooks without running them.
Why are Jupyter Notebooks so large?
Notebooks store cell outputs (charts, tables, images) as embedded data in the JSON file. A notebook with many visualisations can grow to tens of megabytes. Clear outputs before committing to version control to keep file sizes manageable.
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