dhi.io/datascience-notebook
Jupyter Data Science Notebook
A minimal Jupyter Notebook image with Python, R, and Julia for multi-language data science workflows.
pushed 2 days ago
linux/amd64, linux/arm64
1.12 GB
for linux/amd64
664
for linux/amd64
Active support
jupyter, python, R, julia
Build Level 3
Tamper-evident proof of how and from what sources this image was built. Build Level 3 is the highest the SLSA build track defines.
CycloneDX SBOM + SPDX SBOM
A software bill of materials in both CycloneDX and SPDX formats so it drops straight into your existing tooling. Audit exactly what you're shipping.
A Vulnerability Exploitability exchange document, Docker's assessment of which CVEs actually affect this image and which don't apply, so you can focus on real risk instead of chasing false positives.
Available
A link to everything used to build the image, package source code, Git repos, and build files, so you can audit or reproduce the build and stay compliant with open source licenses.
Every known vulnerability in this image, shown in full rather than hidden. The VEX data flags which ones actually apply, so you can tell real exposure from noise before you ship.
Exactly what changed in this build, down to the package bumps and fixes behind the version you're pulling.
Verifies no keys, tokens, or credentials were accidentally baked into the image.
The image layers were scanned for known malware signatures before publishing.
About Jupyter Data Science Notebook
Jupyter Data Science Notebook is a multi-language interactive notebook environment that bundles Python, R, and Julia together with the JupyterLab and Notebook frontends. It is part of the Jupyter Docker Stacks family of community-maintained Jupyter images and targets data science, statistical computing, and numerical research workflows that need more than one language under the same kernel host.
The Docker Hardened Image variant ships the same set of conda-forge packages as upstream
quay.io/jupyter/datascience-notebook, with all downloaded binaries (micromamba, Julia) pinned to specific versions and
verified by SHA256.
What's inside
- JupyterLab and the classic Notebook frontend for interactive computing, plus
jupyterhub-singleuserfor use inside JupyterHub. - Python 3.13 with the scientific computing stack: pandas, NumPy, SciPy, scikit-learn, scikit-image, statsmodels, sympy, matplotlib, seaborn, bokeh, altair, dask, numba, h5py, and more.
- R with
r-base,tidyverse,tidymodels,caret,forecast,randomforest,shiny,rmarkdown,IRkernel(R kernel for Jupyter), andrpy2for Python-R interoperability. - Julia 1.12 with
IJulia(Julia kernel for Jupyter),HDF5, andPlutofor reactive notebooks, plusjupyter-pluto-proxy.
About Docker Hardened Images
Docker Hardened Images are built to meet the highest security and compliance standards. They provide a trusted foundation for containerized workloads by incorporating security best practices from the start.
Why use Docker Hardened Images?
These images are published with zero-known CVEs, include signed provenance, and come with a complete Software Bill of Materials (SBOM) and VEX metadata. They're designed to secure your software supply chain while fitting seamlessly into existing Docker workflows.
Trademarks
Jupyter, JupyterLab, JupyterHub, and other Jupyter word marks are trademarks of LF Charities, of which Project Jupyter is a part. All rights in those marks are reserved to LF Charities. Julia is a trademark of NumFOCUS, Inc. R is a trademark of the R Foundation for Statistical Computing. Any use by Docker is for referential purposes only and does not indicate sponsorship, endorsement, or affiliation.
This listing is prepared by Docker. Other third-party product names, logos, and trademarks are the property of their respective owners and are used solely for identification. Docker claims no interest in those marks, and no affiliation, sponsorship, or endorsement is implied.