ipykernel
IPython Kernel for Jupyter
Decision gist · record as of 2026-08-14
Yes. ipykernel is essential infrastructure for Jupyter notebook users and is actively maintained with no known vulnerabilities. Install it if you use Jupyter notebooks for interactive Python work. It typically comes pre-installed with standard Jupyter distributions, but you may need to explicitly install or register it if you're working with a custom Python environment or multiple kernel versions.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; Jupyter must be installed separately to use the kernel.
- Low install friction with a pure-wheel distribution.
- Active maintenance with a recent release 65 days ago and ongoing repository activity.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause (permissive) allows commercial and private use with minimal restrictions; you must include a copy of the license and copyright notice.
last release 2026-06-10 (65 days) · last repo commit 2026-08-13 · 737 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 81,799,814 downloads/mo, #413 on PyPI
Alternatives
Verify before relying
pip install ipykernel
python -m ipykernel install --user
# Then launch Jupyter and select the ipykernel in a notebook- Whether the package works with all current Jupyter versions or has specific compatibility constraints.
- Performance characteristics when handling large datasets or long-running computations in notebooks.
What it is and what it does
ipykernel is the execution backend that allows Jupyter notebooks and JupyterLab to run interactive Python code. It sits between the Jupyter frontend (the notebook interface) and the Python interpreter, handling code execution, output capture, and communication with the notebook server. The package depends on IPython for the interactive shell experience, jupyter-client for protocol communication, and several supporting libraries like pyzmq for messaging and debugpy for debugging support.
When you run a cell in a Jupyter notebook, ipykernel receives the code, executes it in an isolated Python environment, captures output and errors, and sends results back to the frontend. It's the standard kernel for interactive Python work in Jupyter environments and is typically installed automatically when you set up Jupyter, though you may need to explicitly register it with the `ipython kernel install` command.
Use it for
- Running interactive Python analysis and visualization in Jupyter notebooks for data science workflows.
- Teaching Python interactively with live code execution and immediate feedback in notebook environments.
- Debugging Python code step-by-step using the integrated debugger in Jupyter notebooks.
- Creating reproducible computational documents that mix code, output, and narrative text.
- Prototyping and experimenting with code interactively before moving it to production scripts.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
ipykernel is essential infrastructure for Jupyter notebook users and is actively maintained with no known vulnerabilities. Install it if you use Jupyter notebooks for interactive Python work. It typically comes pre-installed with standard Jupyter distributions, but you may need to explicitly install or register it if you're working with a custom Python environment or multiple kernel versions.
Install
ipykernel on PyPI
Before you install
Low install friction with a pure-wheel distribution. Active maintenance with a recent release 65 days ago and ongoing repository activity. Requires Python 3.10 or later.
Requires Python 3.10 or later; Jupyter must be installed separately to use the kernel.
License in practice
BSD-3-Clause (permissive) allows commercial and private use with minimal restrictions; you must include a copy of the license and copyright notice.
Quickstart
pip install ipykernel
python -m ipykernel install --user
# Then launch Jupyter and select the ipykernel in a notebookVerify before relying
- Whether the package works with all current Jupyter versions or has specific compatibility constraints.
- Performance characteristics when handling large datasets or long-running computations in notebooks.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 13 packagesappnopecommdebugpyipythonjupyter-clientjupyter-corematplotlib-inlinenest-asyncio2packagingpsutilpyzmqtornadotraitlets |
| Maintenance | Actively maintained 65 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 81,799,814 / month, #413 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 AdministratorsProgramming Language :: PythonProgramming Language :: Python :: 3 |
Evidence: ipykernel-7.3.0-py3-none-any.whl
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Similar packages
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.
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Install it if you are building Jupyter frontends, managing kernels programmatically, or integrating interactive computation into applications.
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Jupyter Kernel Gateway is a web server that exposes Jupyter kernels over HTTP and WebSocket, enabling remote code execution and kernel management without a notebook interface.
See also bash_kernel · jupyter-server · metakernel · ipython