Jupyter Notebook
Open SourceFree software, open standards, and web services for interactive computing across all programming languages.
Published 29 May 2026 · Last updated 27 September 2026
Scores
Popularity4/5
Standard for Python data science and ML; widely used in academia and industry.
Learning Curve2/5
Cell-by-cell execution is intuitive for Python users; kernels and widgets add gradual depth.
Flexibility4/5
Any Python library, widgets, custom kernels, and nbextensions for advanced workflows.
Performance3/5
Cell execution is interactive; kernel startup adds time; not optimized for production.
Portability4/5
Open .ipynb format widely supported; skills transfer to JupyterHub, Colab, and VS Code notebooks.
About Jupyter Notebook
Jupyter Notebook is an open-source web application for creating and sharing computational documents called notebooks. A notebook mixes live code, Markdown text, LaTeX equations, tables, charts, and interactive widgets, with each cell's output stored right under the code that produced it. That makes it a standard tool for exploratory data analysis, teaching, scientific computing, and machine learning experiments.
It started as the IPython notebook and became the core product of Project Jupyter, a community project under NumFOCUS. Jupyter Notebook 7, the current major version, is built on the same components as JupyterLab: the Notebook keeps a simple one-document-per-tab interface, while JupyterLab adds a multi-panel IDE-style workspace. Both open the same files.
Code runs in a kernel, a separate process for a given language. Python (through IPython) is the default, and community kernels cover R, Julia, Scala, and dozens more. Notebooks are saved in the .ipynb JSON format, so they render on GitHub and nbviewer, open in VS Code and hosted services such as Google Colab, and convert to HTML, PDF, slides, or Python scripts with nbconvert. The jupyter-ai extension adds an LLM chat and code assistant.
Jupyter Notebook is free under the BSD licence and runs locally with pip install notebook, or on a shared server through JupyterHub. Its weak points are the ones notebooks share: out-of-order execution hides state bugs, .ipynb diffs are noisy in Git, and notebook code is hard to test and deploy as is.
Key Features
- Cell-based execution mixing code, Markdown, equations, and visualisations
- Kernels for Python, R, Julia, Scala, and dozens of other languages
- Rich output: HTML, images, video, and interactive widgets (ipywidgets)
- Portable .ipynb format that renders on GitHub and nbviewer
- Runs locally in the browser, or multi-user through JupyterHub
- Export to HTML, PDF, LaTeX, slides, or scripts with nbconvert
- jupyter-ai extension for LLM chat and code assistance
Pros
- Cell-by-cell execution makes exploratory analysis fast
- Code, narrative, and results in one shareable document
- Huge ecosystem of shared notebooks, kernels, and tutorials
- The same interface works across many languages
- Free and open source, with a file format every major tool opens
Cons
- .ipynb JSON diffs are noisy and hard to review in Git
- Out-of-order execution leaves hidden state and stale variables
- Weak linting, refactoring, and debugging compared with a full IDE
- Large outputs make notebooks slow in the browser
- Notebook code is hard to test and deploy to production
Jupyter Notebook Pricing
Open SourceTech Stacks with Jupyter Notebook
Jupyter Data Analysis
ProjectExploratory data analysis environment with Jupyter Notebook, Pandas and NumPy.
ML Exploration Starter
ProjectGet started with machine learning in Jupyter Notebooks. scikit-learn provides simple APIs for classification, regression, and clustering; Pandas handles data wrangling. No GPU required: it runs entirely on your laptop.
PyTorch ML Training
ProjectTrain deep learning models with PyTorch, with scikit-learn baselines to compare against, Pandas for data preparation, and Jupyter for experimentation. MLflow or Weights & Biases can be added to track experiments and model versions once runs need comparing.
Tools Related to Jupyter Notebook
Works well with Jupyter Notebook(7)
Databricks notebooks are Jupyter-compatible — Jupyter kernels can connect to Databricks clusters.
Jupyter renders Pandas DataFrames as styled HTML tables natively — the combination is the standard Python exploratory data analysis environment.
NumPy is a first-class citizen in Jupyter — inline array output, rich repr, and used in nearly every scientific computing notebook.
Jupyter Notebooks are the standard environment for Keras model development — the cell-by-cell workflow matches how practitioners iterate on layers, hyperparameters, and training curves.
Jupyter Notebooks are the standard interactive environment for TensorFlow experimentation — cell-by-cell execution lets practitioners iterate on model architectures and visualise training curves inline.
PyTorch development and experimentation happens almost exclusively in Jupyter — training loops, loss curves, and model inspection all live in notebooks.
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Jupyter Notebook is a Python-based interactive computing environment.