Pygal and bqplot did not fair as well (though I suppose they were not designed to do exactly what I had in mind). mpld3, matplotlib + ipywidgets and Streamlit fulfilled most of my criteria. The table below summarizes my ability to achieve the goals I listed above with each of the tools.īokeh, Plotly and Altair all were able to fulfill each of my criteria. Format the “tooltips” to show information when you hover over the data in the plot.When the value of either of these changes, the tool uses a “callback” function to change the data shown in the plot. Create the initial figure using one country (e.g., USA) and one particular column of data (e.g., Daily Cases).In general, each plotting tool requires some version of the following workflow: I will also include some interactive figures below for you to test out within this blog post. If you download and run the notebook on your computer, you can generate the interactive figures for your own exploration. I encourage you to look at that notebook to see the different syntax and code length required to create figures with each tool. You can view the code that I wrote to create the figures for each of the tools on my GitHub repo in this Jupyter notebook. html file for use on a personal website (without needing any other service).Īfter scouring the internet for the most popular Python interactive plotting packages, I decided to test this set of tools:
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