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Data Science & AI Workbench makes it easy for you to create and share interactive data visualizations, live notebooks or machine learning models built using popular libraries such as Bokeh and HoloViews. To get you started quickly, Workbench provides sample projects of Bokeh applications for clustering and cross filtering data. There are also several examples of Workbench projects that use HoloViz here.

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Create an interactive plot

  1. From the Projects view, create a new project using the minimal Python templates:
  2. Open a session for your newly created project.
  3. Open a terminal.
  4. Run the following command to install hvplot and panel: 
    anaconda-project add-packages hvplot panel
  5. Create a new Notebook and rename it to tips.ipynb.
  6. Add the following code to tips.ipynb: 
    import pandas as pd
import hvplot.pandas
import panel

panel.extension()

df = pd.read_csv('http://bit.ly/tips-csv')
p = df.hvplot.scatter(x='total_bill', y='tip', hover_cols=['sex','day','size'])
pn.panel(p).servable()
    In this example, the data is being read from the Internet. Alternatively, you could download the .csv and upload it to the project.
  7. Open the project’s anaconda-project.yml file, and add the following deployment command: 
    commands:
  scatter-plot:
  unix: panel serve tips.ipynb
  supports_http_options: True
  8. Save and commit your changes.
  9. Deploy the project.
For a deeper dive into data visualization, follow this HoloViz tutorial.
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