Deploy Interactive Real-Time Data Visualizations on Flask With Bokeh

Deploy Interactive Real-Time Data Visualizations on Flask With Bokeh

Deploy Interactive Real-Time Data Visualizations on Flask With Bokeh. Python has fantastic support for functional analytics tools including NumPy, SciPy, pandas, Dask, Scikit-Learn, OpenCV, and many more.

Python has fantastic support for functional analytics tools including NumPy, SciPy, pandas, Dask, Scikit-Learn, OpenCV, and many more. Of the various data visualization libraries for Python, Bokeh has prevailed as the most functional and powerful of the bunch. The library supports a handful of interfaces that cover many common use cases.

One of the great features of Bokeh is the ability to export a figure as raw HTML and JavaScript. This allows us to inject figures that are created programmatically into a Flask application’s templates. When the user connects to your Flask web app, the Bokeh figures are created and embedded into the served HTML in real time.

For our example, we are going to create an interactive explorer for movie data. Our project will feature UI widgets (sliders, menus) that, when changed, update the displayed data.

We are going to cover:

  1. How to create an interactive Bokeh figure with five data points
  2. Integrating a free cloud database with 3,000 data points (Easybase.io)
  3. How to inject a Bokeh figure into a Flask template
  4. Adding Bokeh widgets to query data with JavaScript callbacks (CustomJS)

flask bokeh python data-visualization data-science

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