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Jupyter Notebook / JupyterLab
Jupyter Notebook and its successor interface JupyterLab are free, open-source tools from the nonprofit Project Jupyter, used by about 14.1% of developers. Rather than a traditional file-and-editor IDE, Jupyter runs in a browser and lets you interleave live code cells, their output (including charts and tables), and formatted markdown text in a single document — the standard way data science, machine learning, and statistics work is taught and shared.
📌 Quick facts
Type: Browser-based interactive notebook (not a traditional IDE)
Made by: Project Jupyter (open-source, nonprofit)
License: Free, open-source
Platforms: Runs in any web browser, either locally on your machine or on a cloud host such as Google Colab
Primary use case: Data science, machine learning, and statistics — exploratory analysis, visualization, and shareable, reproducible reports
Key features
- Interleaved code and output — run a cell and see its result (text, table, or chart) right below it
- Markdown cells — mix formatted explanations and math notation directly alongside code
- Stateful execution — run cells out of order and keep variables alive between them, great for iterative exploration
- Rich visualization support — plots from libraries like Matplotlib or Plotly render inline
- Cloud-hosted options — Google Colab runs Jupyter notebooks for free in the browser with no local install and optional GPU access
- Not built for large software projects — no real debugger, project structure, or version-control-friendly diffs the way normal source files have
Getting started
Install it locally with Python's package manager, or skip installation entirely with Google Colab:
# Install JupyterLab locally (requires Python)
pip install jupyterlab
# Launch it — opens automatically in your browser
jupyter lab
Or go straight to colab.research.google.com to create a notebook in the browser with zero setup.
🎯 Best for: Data science and machine learning coursework, statistical analysis, and any project where showing your work step-by-step with charts and explanations matters more than building a deployable application.