Interactive Data Visualization in Python
Interactive Data Visualization in Python


When creating complex visualisations, interactivity can help communicate your core concepts. It allows the audience to familiarise themselves with the data, and makes understanding the data a step along the journey of understanding your visualisation.

However, creating interactive visualisations adds a layer of complexity to the data science workflow: during modelling and data exploration, interactivity is effectively achieved by re-running chunks of code with different parameters. Giving the reader the ability to achieve the same interactivity without having to change and re-run code therefore requires extra development from the data scientist.

In this workshop, we will go through python libraries that make this extra development as frictionless as possible, and produce interactive visualisations with as little code as possible. We will also go through options for producing interactivity for the wider public, and what steps need to be taken to achieve resilient interactive graphs.
Libraries to be used include ipywidgets, plotly, and plotly dash.


Dr Jan Freyberg is a data scientist at ASI. He has worked on data science projects in the private and public sector, and his experience ranges from geospatial to unstructured language data. He is an expert in building interactive tools for communicating complex models, and is active in developing open-source data science software.

Jan completed a PhD and a fellowship studying brain activity, vision and consciousness in autism at the University of Cambridge and King’s College London, where he taught statistics and programming at undergraduate and postgraduate level.

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