• You write code.

• Maybe you're generating or displaying data.

• How do you get others to interface with it?

Why do something different?

• Reporting tools often are locked to vendors
• often fall behind users' expectations of what they get elsewhere
• code to create dashboard is different that remaining area of expertise

• It is.
• plotly made something that allow you to just write python so you can be like the cool kids

• I did.
• That package is called shiny
• It allows you do a lot of cool things
• I like R
• I like the community that builds packages
• But maybe the rest of your server logic doesn't use R to run...
• also plotly claims that dash is not a replica of Shiny. ¯\_(ツ)_/¯
  • "Idioms and philosophies are different."
  • I believe 'em

Dash. what is it?

History

2017-06

2017-08

2018-04

2017-09

Editable and sortable tables

Today

You know about dash

Inside a Dash app

• 1 file for [your-app].py, import modules like normal py scripts

Core Components

• the cool stuff
  • dropdowns, graphs, markdown, tabs, upload, etc.

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash()

app.layout = html.Div([

    # Your code

])

if __name__ == '__main__':
    app.run_server(debug=True)

HTML Components

• the needed stuff
  • HTML tags, HTML attributes

What do good packages need?

• Support from a community
• Documentation
• Good License; Ability to Contribute
• Active support for experiments
• Corporate sponsors to keep the OSS dream alive

• community.plot.ly
• dash.plot.ly
• MIT, github.com/plotly/dash
• github.com/plotly/dash-table-experiments
• plot.ly/dash/pricing/

They're serious about community

One of my favorite Dash apps

@adrianomy

What I am most excited about

Interactive tables (experimental)

Cross-filtering

A science example

• Resonant Ultrasound Spectroscopy is a technique to apply vibrations to a material to determine the stiffness of the material
  • provides some sense of material properties w/o destroying a sample
  • reverse solution is robustly available (known stiffness, unknown peaks)
  • forward solution (known peaks, unknown stiffness) does not exist
  • can only be solved through iteration
    • local minima can be a $@#&*#@^
    • visualization is helpful

A science demo

plotly's guide to deploy

Isn't the title of this talk, Dash - using your Python beyond a dashboard in the galaxy?

Glad you asked.