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What I've created here is an interface which simplifies the process of fetching data, cleaning/transforming it, and then feeding it into D-Tale. All you need to do is launch it via dtaledesktop and plug in a snippet of code which returns a pandas DataFrame. You will now have a dashboard widget that is present every time you launch dtaledesktop, and by simply clicking a button you can run that code and analyze the resulting DataFrame in dtale.
Also, if you want to you can run it as a web service -- the demo site is actually running on a kubernetes cluster. When run as a web service, it uses websocket connections to push real-time updates, ensuring that all connected users see the same thing. There are a large number of settings which can be used to configure exactly how it behaves, documented here.
(https://github.com/numba/numba/issues/6345)
jupyterlab-autoplot, a jupyterlab extension which can automagically display timeseries data in dtale without you needing to lift a finger
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