This project is intended to help you tie together some important concepts and technologies from the 12-day course, including Git, Streamlit, JSON, Pandas, Requests, Heroku, and Bokeh for visualization.
The repository contains a basic template for a Streamlit configuration that will work on Heroku.
A finished example that demonstrates some basic functionality.
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Git clone the existing template repository.
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Procfile,requirements.txt, andsetup.pycontain some default settings. If you want, you can change the email address insetup.pyto your own, but it won't affect anything in the app. -
Create Heroku application with
heroku create <app_name>or leave blank to auto-generate a name. -
Deploy to Heroku:
git push heroku master -
You should be able to see your site at
https://<app_name>.herokuapp.com -
A useful reference is the Heroku quickstart guide.
- Use the
requestslibrary to grab some data from a public API. This will often be in JSON format, in which casesimplejsonwill be useful. - Build in some interactivity by having the user submit a form which determines which data is requested.
- Create a
pandasdataframe with the data.
- Create an interactive plot from the dataframe. Some recommended libraries: Altair, Bokeh, and Plotly.
- Altair provides a simple interface for creating linked and layered plots. They can even be exported and embedded in static HTML (and remain fully interactive!) See the documentation and be sure to check out the example gallery.
- Bokeh can be used in a wide range of applications, from simple charts to extensive dashboards with sophisticated backends. It's the most fully-featured library of these three, but you won't be using it for anything complicated in the Milestone Project. Here you can find the Bokeh documentation and some examples.
- Plotly provides a range of APIs in their library. Plotly express, for instance, can be used to create commonly used plots. The Graph Objects API affords more customization, but is more complicated to use. Here is the documentation for Plotly Express.