This repository of Jupyter notebooks was designed by the educators at ITHAKA's Constellate project. The project was sunset on July 1, 2025. These notebooks are Open Educational Resources (OER), free for re-use under a Creative Commons CC BY License.
These notebooks may be downloaded with your browser or cloned using git software.
Choose the green "<> Code" button at the top of the repository, then "Download ZIP."
If you have git installed on the command line, these notebooks may be cloned using:
git clone https://github.com/ithaka/constellate-notebooks
If you are using a Git Desktop or some other graphical user interface, choose "clone" and supply the URL for this repository: https://github.com/ithaka/constellate-notebooks
The notebooks are written with Python and compatible with JupyterLab as of July 1, 2025. For best compatibility, we recommend using these notebooks on a local installation of JupyterLab. They should also work well on notebook platforms that faithfully reproduce the Jupyter notebook standard. We share some additional possibilities below.
For best results, we recommend a system meeting these specifications:
- Python 3.13+
- 3.5+ GHz, 4-core CPU
- 16+ GB RAM
- 30+ GB Disk Space
- No GPU acceleration required
Local solutions can be classified into either JupyterLab or Integrated Development Environments (IDE)s. It can be difficult to install JupyterLab, so we share some recommended methods below. An IDE can be a more flexible tool than JupyterLab, and we make some recommendations of suitable IDEs below as well.
These notebooks use the Jupyter notebook (.ipynb) standard created by Project Jupyter. Their JupyterLab software remains free and open source. It is the best platform for using these notebooks.
For beginners without command line experience, we recommend Project Jupyter's JupyterLab Desktop with traditional application installers for Windows, Mac, and Linux.
For those comfortable using the command line, we offer basic instructions for:
We do not recommend Anaconda because it can be difficult to install, troubleshoot, and uninstall. Anaconda installs a large suite of data science tools; many are not necessary for using these notebooks. Moreover, Anaconda can be difficult to properly un-install which makes it hard to switch if you would like to use another solution.
A better solution is Miniconda, which installs far fewer applications on the machine. However, the difficulty of installing Miniconda is similar to JupyterLab by itself.
If you are familiar with using the VS Code or Pycharm IDEs, they offer tooling for working with Jupyter notebooks. VS Code has native support for Jupyter notebooks. PyCharm is also a good solution for Jupyter notebooks. PyCharm has free version with basic Jupyter notebook support and a Pro version with advanced Jupyter notebook functionality that is free for teachers and students.
| Installation Method | Platform Support | Package Management | Pre-installed Libraries | GPU Support | Environment Management | Learning Curve | Best For |
|---|---|---|---|---|---|---|---|
| Jupyterlab | Windows, macOS, Linux | pip | Basic | Yes | command line | Medium | Those comfortable with the command line and package management |
| JupyterLab Desktop | Windows, macOS, Linux | Built-in | Basic | No | Limited | Very Low | Non-technical users, education |
| Miniconda | Windows, macOS, Linux | Conda | Minimal (custom install) | Yes (with setup) | Conda environments | Medium | Customized environments, limited disk space |
| PyCharm + Jupyter Support | Windows, macOS, Linux | Based on Python install | None | Yes (with setup) | Based on Python install | Medium | Developers already using PyCharm |
| VSCode + Jupyter Extension | Windows, macOS, Linux | Based on Python install | None | Yes (with setup) | Based on Python install | Low | Developers already using VSCode |
There is a diverse market for Jupyter notebook cloud-based solutions including free, paid, and hosted solutions. The tradeoffs generally come in terms of monthly costs and environment management. It is a good idea to see what resources your campus offers; you may already have access to free, managed solutions with IT support.
There is no need to prioritize a cloud environment with GPU access for using these notebooks. These notebooks do not use a GPU directly, preferring to use GPU computation through APIs which are usually easier, faster, and cheaper. If you plan to train models, then an environment with GPU access may make sense.
Project Jupyter offers a cloud-based solution with integrated JupyterLab called JupyterHub. While this is the best solution, it is impractical for most users to run their own JupyterHub since it requires server management expertise. However, many higher education institutions run their own JupyterHub servers, so it is worth investigating whether a JupyterHub server is available on your campus. Check with your local IT and ask colleagues working in data science to see if JupyterHub is available.
CoCalc has a unique user interface designed for collaboration and teaching. It is especially useful for mathematics and computer science education with support for grading, LaTex, and course management. CoCalc free tier is very limited. An instructor can expect to pay about $30/month for access.
Anaconda Cloud may be a good solution if you are comfortable with Anaconda and/or conda package management. It is easy to share environments for reproducibility. There are collaboration features and version control available. A basic starter account is $15/month, but it is possible to get free access with an academic account.
If you are technically savvy, Hugging Face Spaces and Digital Ocean offer relatively easy ways to get JupyterHub running on a server. Plan on spending about $10/month for persistent storage and basic compute. The downside to this approach is you will need to be prepared to troubleshoot any platform issues. It is always a good practice to back up your notebooks to your local machine or a git repository, but this is especially important if you are new to server management.
If you would like a full, cloud-based IDE, then GitHub Codespaces offers cloud access to VS Code which has support for working with Jupyter notebooks. Choose this option if you are familiar with VS Code and you would benefit from a tight integration with GitHub.
Google Colab is a popular platform for using Jupyter notebooks because Google offers limited-access to GPU acceleration for model training. The Constellate notebooks do not include model training and do not require GPU acceleration. Instead, they use APIs to access GPU acceleration, which is often faster, easier, and cheaper.
Outside of its limited, free GPU access, Google Colab is inferior to other cloud-based Jupyter notebook solutions. The user interface and functionality is closed, non-standard, and difficult to use for file management. Moreover, it lacks support for basic notebook features, including:
- Markdown local file access (no local notebook linking, no local image rendering)
- Only one notebook at a time (no tabs for opening multiple notebooks)
- No command line access (in the free tier)
Notebooks developed for Colab often only work in Colab, locking users into the environment. Google Colab's architecture violates the FAIR principles of scientific data management.
Deepnote is a useful platform for data science professionals with some advanced functionalities for working collaboratively and accessing data repositories. The additional features make it better for working data scientists than teachers and learners.
Binder is a free platform useful for demonstrating notebooks, but it has no persistent storage. The speed and availability are not reliable.
| Platform | Free Tier | Paid Options | GPU Support | Collaboration | Key Features | Best For |
|---|---|---|---|---|---|---|
| Anaconda Cloud | Yes | Yes | No | No | Academic Accounts with 10 GB of Disk Space | Learning modules |
| CoCalc | Limited free | Various plans ($14-$49/mo) | No | Yes (real-time) | Course management, LaTeX, R | Education, math-focused notebooks |
| GitHub Codespaces | Yes | Yes | Yes | No | VS Code Jupyter environment in the cloud | VS Code users |
| Hugging Face Spaces Hosted Jupyterlab | Yes | Yes | Available for cost | Yes | Hosting Jupyterlab | Basic hosting |
Regardless of the solution you choose, you will need to become familiar with installing Python packages that these notebooks use. If you get an error that a package could not be imported when running a cell, that probably means it is not installed. You will need to install it (and may also need to restart the notebook kernel) before it will be recognized.
There are two main package managers for working with Python: pip and conda. (More advanced projects may use poetry or pipenv.)
Pip is the official Python package installer. Once Python is installed, you can generally use pip on the command line to manage packages.
pip install PACKAGENAME
pip install --upgrade PACKAGENAME
pip install --pip install --upgrade PACKAGENAME==3.2.16
See the official pip documentation and/or the Python Package Index (PyPi)
The Conda package manager is usually part of an Anaconda solution, such as Anaconda Cloud, Anaconda, or Miniconda. It can be used from the command line, but there are also graphical ways to manage packages with Conda. See the particular documentation for the version of Anaconda you are using.