A variety of tools are available to help you understand, debug, visualize your streaming objects:
- Most Streamz objects automatically display themselves in Jupyter notebooks, periodically updating their visual representation as text or tables by registering events with the Tornado IOLoop used by Jupyter
- The network graph underlying a stream can be visualized using dot to render a PNG using Stream.visualize(filename)
- Streaming data can be visualized using the optional separate packages hvPlot, HoloViews, and Panel (see below)
hvPlot is a separate plotting library providing Bokeh-based plots for Pandas dataframes and a variety of other object types, including streamz DataFrame and Series objects.
See hvplot.holoviz.org for instructions on how to install hvplot. Once it is installed, you can use the Pandas .plot() API to get a dynamically updating plot in Jupyter or in Bokeh/Panel Server:
import hvplot.streamz from streamz.dataframe import Random df = Random() df.hvplot(backlog=100)
See the streaming section of the hvPlot user guide for more details, and the dataframes.ipynb example that comes with streamz for a simple runnable example.
hvPlot is built on HoloViews, and you can also use HoloViews directly if you want more control over events and how they are processed. See the HoloViews user guide for more details.