dfnotebook 4.2.0b0

Dataflow Notebook for Jupyter

Dataflow Notebook for Jupyter/Python

Dataflow notebooks are Jupyter notebooks written in Python that elevate outputs to link cells. Outputs of cells are labeled by their identifiers. Referencing an output of one cell in another cell creates a dependency between between the two cells, allowing the system to ensure upstream cells are up-to-date before executing a cell. Dataflow notebooks allow identifiers to be reassigned in different cells by tracking references by the identifier and persistent cell id, ensuring each reference is not ambiguous. Identifiers are disambiguated by suffixes indicating the cell by an assigned name or unique hexadecimal identifier.

Quickstart

1. Install dfnotebook: pip install dfnotebook
2. Start JupyterLab: jupyter lab
3. Create a new notebook using the DFPython 3 kernel.

Example Notebook

Install

To install the extension and bundled kernel, execute:

pip install dfnotebook

Requirements

• JupyterLab >= 4.0.0
• IPython >= 8.0
• ipykernel >= 6.0

Uninstall

To remove the extension, execute:

pip uninstall dfnotebook

Contributing

Development install

Note: You will need NodeJS to build the extension package.

The jlpm command is JupyterLab's pinned version of yarn that is installed with JupyterLab. You may use yarn or npm in lieu of jlpm below.

# Clone the repo to your local environment
# Change directory to the dfnotebook directory
# Install package in development mode
pip install -e "."
# Link your development version of the extension with JupyterLab
jupyter labextension develop . --overwrite
# Rebuild extension Typescript source after making changes
jlpm build

You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.

# Watch the source directory in one terminal, automatically rebuilding when needed
jlpm watch
# Run JupyterLab in another terminal
jupyter lab

With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).

By default, the jlpm build command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:

jupyter lab build --minimize=False

Development uninstall

pip uninstall dfnotebook

In development mode, you will also need to remove the symlink created by jupyter labextension develop command. To find its location, you can run jupyter labextension list to figure out where the labextensions folder is located. Then you can remove the symlink named @dfnotebook/dfnotebook-extension within that folder.

Testing the extension

Frontend tests

This extension is using Jest for JavaScript code testing.

To execute them, execute:

jlpm
jlpm test

Integration tests

This extension uses Playwright for the integration tests (aka user level tests). More precisely, the JupyterLab helper Galata is used to handle testing the extension in JupyterLab.

More information are provided within the ui-tests README.

Packaging the extension

See RELEASE

Previous Versions

The dataflow notebook was originally released as dfkernel and worked with older versions of Jupyter Notebook. It was updated to support JupyterLab as a paired set of packages (dfkernel and dfnotebook(-extension)). More recently, these were unified into a single package (dfnotebook).