Metadata-Version: 2.1
Name: jupyterlab-vega3
Version: 3.1.2
Summary: JupyterLab - Vega 3 and Vega-Lite 2 Mime Renderer Extension
Home-page: https://github.com/jupyterlab/jupyter-renderers
Author: Project Jupyter
Author-email: jupyter@googlegroups.com
License: BSD-3-Clause
Description: # jupyterlab-vega3
        
        A JupyterLab extension for rendering Vega 3 and Vega-Lite 2
        
        **Vega 3 is deprecated. The latest version comes by default with JupyterLab. Only use this extension if you have specifications that do not work with the latest version.**
        
        ![demo](http://g.recordit.co/USoTkuCOfR.gif)
        
        ## Requirements
        
        - JupyterLab >= 3.0
        
        ## Install
        
        ```bash
        pip install jupyterlab-vega3
        ```
        
        ## Usage
        
        To render Vega-Lite output in IPython:
        
        ```python
        from IPython.display import display
        
        display({
            "application/vnd.vegalite.v2+json": {
                "$schema": "https://vega.github.io/schema/vega-lite/v2.json",
                "description": "A simple bar chart with embedded data.",
                "data": {
                    "values": [
                        {"a": "A", "b": 28}, {"a": "B", "b": 55}, {"a": "C", "b": 43},
                        {"a": "D", "b": 91}, {"a": "E", "b": 81}, {"a": "F", "b": 53},
                        {"a": "G", "b": 19}, {"a": "H", "b": 87}, {"a": "I", "b": 52}
                    ]
                },
                "mark": "bar",
                "encoding": {
                    "x": {"field": "a", "type": "ordinal"},
                    "y": {"field": "b", "type": "quantitative"}
                }
            }
        }, raw=True)
        ```
        
        Using the [altair library](https://github.com/altair-viz/altair):
        
        ```python
        import altair as alt
        
        cars = alt.load_dataset('cars')
        
        chart = alt.Chart(cars).mark_point().encode(
            x='Horsepower',
            y='Miles_per_Gallon',
            color='Origin',
        )
        
        chart
        ```
        
        Provide vega-embed options via metadata:
        
        ```python
        from IPython.display import display
        
        display({
            "application/vnd.vegalite.v2+json": {
                "$schema": "https://vega.github.io/schema/vega-lite/v2.json",
                "description": "A simple bar chart with embedded data.",
                "data": {
                    "values": [
                        {"a": "A", "b": 28}, {"a": "B", "b": 55}, {"a": "C", "b": 43},
                        {"a": "D", "b": 91}, {"a": "E", "b": 81}, {"a": "F", "b": 53},
                        {"a": "G", "b": 19}, {"a": "H", "b": 87}, {"a": "I", "b": 52}
                    ]
                },
                "mark": "bar",
                "encoding": {
                    "x": {"field": "a", "type": "ordinal"},
                    "y": {"field": "b", "type": "quantitative"}
                }
            }
        }, metadata={
            "application/vnd.vegalite.v2+json": {
                "embed_options": {
                    "actions": False
                }
            }
        }, raw=True)
        ```
        
        Provide vega-embed options via altair:
        
        ```python
        import altair as alt
        
        alt.renderers.enable('default', embed_options={'actions': False})
        
        cars = alt.load_dataset('cars')
        
        chart = alt.Chart(cars).mark_point().encode(
            x='Horsepower',
            y='Miles_per_Gallon',
            color='Origin',
        )
        
        chart
        ```
        
        To render a `.vl`, `.vg`, `vl.json` or `.vg.json` file, simply open it:
        
        ## Contributing
        
        ### Development install
        
        Note: You will need NodeJS to build the extension package.
        
        The `jlpm` command is JupyterLab's pinned version of
        [yarn](https://yarnpkg.com/) that is installed with JupyterLab. You may use
        `yarn` or `npm` in lieu of `jlpm` below.
        
        ```bash
        # Clone the repo to your local environment
        # Change directory to the jupyterlab-vega3 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 run 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.
        
        ```bash
        # Watch the source directory in one terminal, automatically rebuilding when needed
        jlpm run 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 run 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:
        
        ```bash
        jupyter lab build --minimize=False
        ```
        
        ### Uninstall
        
        ```bash
        pip uninstall jupyterlab-vega3
        ```
        
Keywords: Jupyter,JupyterLab,JupyterLab3
Platform: Linux
Platform: Mac OS X
Platform: Windows
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Framework :: Jupyter
Requires-Python: >=3.6
Description-Content-Type: text/markdown
