• This repository has been archived on 08/Aug/2023
  • Stars
    star
    178
  • Rank 214,989 (Top 5 %)
  • Language
    TypeScript
  • License
    BSD 3-Clause "New...
  • Created almost 6 years ago
  • Updated over 1 year ago

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Repository Details

First class datasets in JupyterLab

JupyterLab Data Explorer

Stability Experimental npm npm

To experiment with the extension in a live notebook environment,

  • latest release (stable version): Binder (stable)
  • latest master (bleeding edge): Binder (latest)

Overview

  • Bring any data type you can imagine! Extensible and type safe data registry system.
  • Register conversions between the different data types.
  • Data changing on you? Use RxJS observables to represent data over time.
  • Have a new way to look at your data? Create React or lumino components to view a certain type.
  • Built-in data explorer UI to find and use available datasets.
  • Dataset in your dataset? Use the nested datatype.
  • Building another data centric application? Use the @jupyterlab/dataregistry package which can be used independently of JupyterLab.
  • Check out the project vision in the "Press Release from the Future"!

Prerequisites

When used as a JupyterLab extension,

Installation

$ jupyter labextension install @jupyterlab/dataregistry-extension

Usage

Usage docs

Contributing

This repository is in active development, and we welcome collaboration. For development guidance, please consult the development guide.

If you have ideas or questions, feel free to open an issue, or, if you feel like getting your hands dirty, feel free to tackle an existing issue by contributing a pull request.

We try to keep the current issues relevant and matched to relevant milestones.

Third Party Extenson

You can either add support by adding a new converter to this repository or creating a new JupyterLab extension that depends on the IRegistry exposed by this extension. You can access a Registry, which you can use to add your own converter.

It might also be useful to view the existing data types by looking at the source code in this repository and by using the debugger. You can open this in JupyterLab by looking for the "Data Debugger" command:

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