Definition · Plain-language
Jupyter Notebook
Jupyter Notebook is an open-source web application that allows researchers to create and share documents containing live code, equations, visualisations, and narrative text.
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The anatomy of a notebook
A Jupyter Notebook consists of ordered cells that can contain either executable code or formatted text. Code cells run on a background kernel, which maintains the state of variables throughout the session. Markdown cells allow researchers to document their methodology using rich formatting, equations, and images. The output of the code, including charts and tables, is displayed directly beneath the executing cell, preserving context. This layout combines code execution and documentation in a single document, making it easier for researchers to explain their analytical decisions alongside the actual computations, creating a unified narrative that benefits both peer reviewers and future collaborators.
Promoting reproducible research workflows
Jupyter Notebooks are central to the reproducibility movement in computational sciences. Instead of delivering static figures in a PDF, researchers can share the original notebook containing the exact code used to import data, run statistical models, and plot results. This allows reviewers and readers to run the analysis, modify parameters, and verify the validity of the scientific claims on their own systems. This transparency builds trust in empirical findings, as external investigators can trace every statistical result back to the specific line of code that generated it, reducing errors and accelerating the publication process. This level of verification is increasingly demanded by high-impact journals that require authors to submit their code for peer review.
Supported languages and environments
While originally designed for Python, Julia, and R (from which the name Ju-Py-teR is derived), Jupyter now supports dozens of programming language kernels. Notebooks can be run locally or in cloud-based environments like Google Colab or Binder. These cloud platforms allow users to run notebooks in a pre-configured environment without installing any software locally, further reducing barriers to reproducibility. These environments are often free, making them accessible to students and researchers globally, and they integrate with version control systems like GitHub to facilitate sharing. This enables researchers to publish interactive, executable code alongside their journals, elevating public research engagement. By eliminating local package installation obstacles, these cloud systems democratise access to high-performance computing resources for global research teams.
Key facts
At a glance
- Integrates executable code, visualisations, and narrative text in one file
- Supports multiple coding language kernels, primarily Python, R, and Julia
- Saves documents in the structured JSON-based .ipynb file format
- Facilitates computational reproducibility by showing code alongside results
- Can be shared easily via version control platforms and static web renderers
- Runs in local web browsers or remote cloud hosting environments
Common misconceptions
What people often get wrong
Often heard: A Jupyter Notebook always runs its code from top to bottom automatically.
Actually: Cells can be executed out of order, which can sometimes lead to hidden state errors; researchers should always test their notebooks by running them fresh from top to bottom before sharing.
Often heard: Jupyter Notebooks are only used by Python programmers.
Actually: Jupyter supports many languages, including R, Julia, Scala, and C++, making it a versatile tool across different statistical and scientific disciplines.
Common questions
FAQ
How do you share a Jupyter Notebook with someone who does not have Jupyter installed?+
You can export notebooks as static HTML, PDF, or Markdown files. Additionally, hosting services like GitHub render notebooks natively in the browser, and platforms like Binder can run them interactively online.
What is a Jupyter kernel?+
A kernel is the computational engine behind the notebook that executes the code. Each notebook is associated with a specific kernel (for instance, Python 3 or R), which runs the cells and returns the outputs.
Going deeper
