I've gone through this readme and carried out the steps to better understand Flask. In the process I built a simple app to display Quandl API queries for financial data in a Bokeh plot.
This project is intended to help you tie together some important concepts and technologies from the 12-day course, including Git, Flask, JSON, Pandas, Requests, Heroku, and Bokeh for visualization.
The repository contains a basic template for a Flask 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,conda-requirements.txt, andruntime.txtcontain some default settings. -
There is some boilerplate HTML in
templates/ -
Create Heroku application with
heroku create <app_name>or leave blank to auto-generate a name. -
(Suggested) Use the conda buildpack. If you choose not to, put all requirements into
requirements.txtheroku config:add BUILDPACK_URL=https://github.com/kennethreitz/conda-buildpack.git -
Question: What are the pros and cons of using conda vs. pip?
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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 a Bokeh plot from the dataframe.
- Consult the Bokeh documentation and examples.
- Make the plot visible on your website through embedded HTML or other methods - this is where Flask comes in to manage the interactivity and display the desired content.
- Some good references for Flask: This article, especially the links in "Starting off", and this tutorial.