This section provides access to interactive Jupyter notebooks powered by Binder.
Binder creates a temporary, cloud-based Jupyter environment so you can run and explore notebooks without installing anything locally. This is intended for deeper, more involved content where full Python packages are required.
Launch Binder session to start running notebooks in minutes!
Environment setup can cause friction when learning from or sharing your own notebooks. Binder offers a free way to streamline the process by providing a minimial, temporary, hassle-free approach to running notebooks. By using preset environments and package details, rapid exploration, testing, and sharing is possible with minimal complications.
Binder Basics
Startup Time
- Initial startup may take 1-3 minutes
- Subsequent launches are often faster due to caching
- Please be patient while the environment is prepared
Sessions are Temporary
- Sessions are ephemeral
- Files or changes you make will be lost when the session ends
- Download or copy code elsewhere to save work
Multiple Notebooks Allowed
- You can open and run any notebook in this directory
- You can explore multiple notebooks in the same session
- Outputs, variables, and files persist within the active session
Minimal Environment
- Binder provides a clean, minimal Jupyter environment
- No custom UI extensions or settings stored
- Appearance and features may differ from a local Jupyter Lab setup
Binder Quickstart
- Wait for Binder to finish starting
- Open a notebook from the file browser
- Run cells as instructed in the notebook
- Explore freely, knowing the session is temporary
Content Guide
| Topic | Location | Description |
|---|---|---|
| README.md | root | Brief text overview of Binder and Content |
| environment.yml | root | Python and package specifications |
| dataset-exploration-binder.ipynb | explore-data | Quickly assess dataset quality and summarize key metrics |
| data-viz-basics-binder.ipynb | explore-data | Overview and code for basic data visuals with matplotlib, seaborn, and plotly |
| data-viz-building-blocks-binder.ipynb | explore-data | Overview and code for basic graph elements that create data visuals with matplotlib, seaborn, and plotly |
| image-basics-binder.ipynb | explore-data | Introduction to working with image based data |
| random-forest-binder.ipynb | build-models | Learn how random forests work in this complete notebook tutorial |
| classificaiton-iris-binder.ipynb | build-models | Complete classification pipeline based on Iris dataset |
Given the nature of Binder, it is ideal for sharing and exploration. It is not a long-term building tool and is not ideal for heavy computations.
Enjoy Exploring!
Use the right tool for the right job.
Leverage tools that simplify setup when trying new packages and methods to ensure that you find the right resources before doing heavy setup.
Don’t get bogged down in the weeds before you ever start the real learning process.