This section deals with using plot.ly using Python code. plot.ly can also be used through R code.
Bokeh is a service and Python interactive visualization library that lets users easily create interactive charts and dashboards, that can optionally be shared through an online service. There are several ways you can use Plot.ly in DSS:
- To display interactive (pan/zoom/…) charts within a Jupyter notebook
- To display interactive (pan/zoom/…) charts on a Dashboard
Documentation for Plot.ly is available at https://plot.ly/python/
Displaying charts in a Jupyter notebook¶
To display plot.ly charts in a Jupyter notebook, use:
import plotly.offline as py py.iplot(data_object)
For example, to display a simple line:
import plotly.offline as py import plotly.graph_objs as go scatter = go.Scatter(x =[1,2,3], y = [10, 15, 13]) data = go.Data([scatter]) py.iplot(data)
Displaying charts on a dashboard¶
plot.ly charts generated using Python code can be shared on a DSS dashboard using the “static insights” system.
Each plot.ly figure can become a single insight in the dashboard. Each chart will retain full zoom/pan/select/export capabilities;
To do so, create static insights
from dataiku import insights # f is a plot.ly figure, or any object that can be passed to iplot() insights.save_plotly("my-plotly-plot", f)
From the Dashboard, you can then add a new “Static” insight, select the
Refreshing charts on a dashboard¶
You can refresh the charts automatically on a dashboard by using a scenario to re-run the above piece of code.
This call to
dataiku.insights code can be:
- In a DSS recipe (use a regular “Build” scenario step)
- In a Jupyter notebook (use a “Export notebook” scenario step)
- As a custom Python scenario step