---
id: jupyter
version: "1.1.1"
license: BSD
license_treatment: permissive
maintenance: dormant
---

# jupyter — Jupyter metapackage. Install all the Jupyter components in one go.

License: permissive · Maintenance: dormant · Downloads: 18.0M/mo

## What it is and what it does

Jupyter is a convenience metapackage that bundles the most commonly used Jupyter components—notebook, jupyterlab, jupyter-console, ipykernel, nbconvert, and ipywidgets—into a single installation. It exists purely to simplify initial setup for users who want the full Jupyter ecosystem without manually specifying each component.

The package itself contains no code; it only declares dependencies on the actual Jupyter tools. The description explicitly warns that jupyter should not be used as a dependency in other packages, since it installs a large suite of tools that may be unnecessary for library use. For production or specialized workflows, installing individual components separately is recommended for efficiency and control.

Use it for:

- Setting up a complete Jupyter environment for data science work with a single install command.
- Getting started with interactive notebooks and JupyterLab without researching which components to install separately.
- Providing a standard baseline installation for educational or team environments where the full suite is expected.
- Quick prototyping and exploratory analysis where all Jupyter tools are useful and disk space is not a constraint.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Jupyter is a metapackage that installs the core Jupyter components—Notebook, JupyterLab, IPython Kernel, and related tools—in a single command for interactive computing and data science workflows.

Yes, if you are starting fresh with Jupyter and want all standard components installed together. No, if you are building a library or package—the metapackage explicitly warns against this use. Consider installing individual components (notebook, jupyterlab, ipykernel, etc.) separately if you need only specific tools or want to minimize dependencies. Note that maintenance is dormant, so rely on the underlying component packages for updates.

## Install

pip install jupyter
uv add jupyter
poetry add jupyter

## Installing jupyter

Before you install: Installation is straightforward with low friction; the package is a pure metapackage with no compiled dependencies. However, maintenance is dormant—the last release was 714 days ago—so no active updates or bug fixes are being applied to the metapackage itself, though its dependencies may be maintained separately.

License in practice: Licensed under BSD (permissive), which allows free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.

Quickstart:

pip install jupyter

import jupyter
jupyter.notebook  # Access installed components

Requires Python 3.6 or later; installing all bundled components may take time and disk space depending on your system.

Verify before relying:

- Whether the metapackage is still actively maintained or if users should install individual components directly for better control.
- Current compatibility with Python versions beyond 3.9, as classifiers only list up to 3.9.

## Package facts

- License: BSD (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: dormant
- Downloads: 18.0M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

jupyter notebook installation, interactive computing environment, data science jupyter setup, jupyterlab and notebook, ipython kernel jupyter, jupyter all-in-one install, notebook and lab together, metapackage, interactive-computing, data-science

[View on SkillFed](https://skillfed.io/packages/jupyter) · [View on PyPI](https://pypi.org/project/jupyter/)