jupyter
Jupyter metapackage. Install all the Jupyter components in one go.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.6 or later; installing all bundled components may take time and disk space depending on your system.
- 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 · maintenance · safety
BSD (permissive) — Licensed under BSD (permissive), which allows free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
last release 2024-08-30 (714 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 17,992,959 downloads/mo, #1,094 on PyPI
Alternatives
Verify before relying
pip install jupyter
import jupyter
jupyter.notebook # Access installed components- 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.
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 on this page. Verify before relying on it.
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
jupyter on PyPI
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.
Requires Python 3.6 or later; installing all bundled components may take time and disk space depending on your system.
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 componentsVerify 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 | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesnotebookjupyter-consolenbconvertipykernelipywidgetsjupyterlab |
| Maintenance | Dormant 714 days since the last release |
| First released | |
| Downloads | 17,992,959 / month, #1,094 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchIntended Audience :: System AdministratorsLicense :: OSI Approved :: BSD LicenseProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: jupyter-1.1.1-py2.py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “jupyter notebook installation”
- jupyterJupyter is a metapackage that installs the core Jupyter…
- ipykernelProvides the IPython kernel backend that powers interactive Python…
- nbclassicProvides the classic Jupyter Notebook interface as a Jupyter Server…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also google-cloud-jupyter-config · ipykernel · notebook · ipyparallel · ipyfilechooser · bash_kernel · jupyter-kernel-gateway · metakernel · ipyflow-core · jupyter-console