https://github.com/bendavidaaron/bdamilestone

Milestone Project for the Data Incubator
https://github.com/bendavidaaron/bdamilestone

bokeh flask heroku pandas

Last synced: 11 days ago
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Milestone Project for the Data Incubator

• Host: GitHub
• URL: https://github.com/bendavidaaron/bdamilestone
• Owner: BenDavidAaron
• Created: 2017-09-10T21:39:29.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-11-18T21:55:41.000Z (about 6 years ago)
• Last Synced: 2024-11-17T08:36:50.015Z (2 months ago)
• Topics: bokeh, flask, heroku, pandas
• Language: Python
• Homepage:
• Size: 16.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# final edit:

the app is [here](https://bda-stock-month.herokuapp.com/mile)

# Flask on Heroku

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](https://lemurian.herokuapp.com) that demonstrates some basic functionality.

## Step 1: Setup and deploy

- Git clone the existing template repository.

- `Procfile`, `requirements.txt`, `conda-requirements.txt`, and `runtime.txt`

  contain some default settings.

- There is some boilerplate HTML in `templates/`

- Create Heroku application with `heroku create ` or leave blank to

  auto-generate a name.

- (Suggested) Use the [conda buildpack](https://github.com/kennethreitz/conda-buildpack).

  If you choose not to, put all requirements into `requirements.txt`

  `heroku config:add BUILDPACK_URL=https://github.com/kennethreitz/conda-buildpack.git`

  The advantages of conda include easier virtual environment management and fast package installation from binaries (as compared to the compilation that pip-installed packages sometimes require).

  One disadvantage is that binaries take up a lot of memory, and the slug pushed to Heroku is limited to 300 MB. Another note is that the conda buildpack is being deprecated in favor of a Docker solution.

- Deploy to Heroku: `git push heroku master`

- You should be able to see your site at `https://.herokuapp.com`

- A useful reference is the Heroku [quickstart guide](https://devcenter.heroku.com/articles/getting-started-with-python-o).

## Step 2: Get data from API and put it in pandas

- Use the `requests` library to grab some data from a public API. This will

  often be in JSON format, in which case `simplejson` will be useful.

- Build in some interactivity by having the user submit a form which determines which data is requested.

- Create a `pandas` dataframe with the data.

## Step 3: Use Bokeh to plot pandas data

- Create a Bokeh plot from the dataframe.

- Consult the Bokeh [documentation](http://bokeh.pydata.org/en/latest/docs/user_guide/embed.html)

  and [examples](https://github.com/bokeh/bokeh/tree/master/examples/embed).

- 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](https://realpython.com/blog/python/python-web-applications-with-flask-part-i/), especially the links in "Starting off", and [this tutorial](https://github.com/bev-a-tron/MyFlaskTutorial).