Page Comparison

Streamlit (https://streamlit.io/)

Python

• Open source

• Free

• No

Yes…? (https://blog.streamlit.io/how-to-build-a-real-time-live-dashboard-with-streamlit/ )

• Beginner-friendly

• Simple API with excellent documentation

• Can mix visualizations into one presentation

• Requires a bit of HTML knowledge to truly customize

• Reruns the entire app once the input is changed

• Uses a localhost for local development

• Streamlit Cloud allows you to run applications in the cloud, share with colleagues

• https://streamlit.io/security

Dash by Plotly (Dash Documentation & User Guide | Plotly)

Python

• Open source, MIT licensed

• Free

• Dash Enterprise (ie. deployment server) has an associated cost → have to fill out some form on the website to learn more about pricing

• Possible to run without internet connection, but all resources need to be bundled within the app

Yes (Live Updates | Dash for Python Documentation | Plotly)

• Pure Python → No Javascript unless you want to include Javascript assets

• Jupyter and Django integrations

• Cross-filtering/interacting with Plotly charts

• Aesthetics are more flexible, compatible with Bootstrap

• Has “callback” functions

• Robust modules library

• Difficulty with interactivity

• Difficulty to customize API

• No HTML → Must use Markdown function

• Cannot have two Python callbacks update the same element

• Dash App Manager can be installed in cloud (Azure, AWS, GCP) (Dash App Manager (plotly.com))

• Can connect Dash App to SQL Database (Connect a Dash App to an SQL Database | Dash for Python Documentation | Plotly)

• There’s an app manager that’s available with Dash Enterprise (read more: Dash App Manager (plotly.com))

Seaborn (https://seaborn.pydata.org/index.html )

Python

• Open source

• Free

• No

• Seaborn has datasets that are available on Github → Require internet to load the datasets into Seaborn

• Not really → Uses matplotlib, so you need a while loop to use “real time data”

• Can install other libraries while using Seaborn since Seaborn is based on matplotlib

• Generates engaging plot to represent our data

• Feed our data using replot() method, library computes the values and places them without us worrying it

• Default themes are aesthetically pleasing

• Visualizes information in matrices and DataFrames

• Estimators are not suited for small datasets

  • Calculating and plotting confidence intervals uses bootstraps

  • Small datasets have inaccurate intervals since bootstraps are suited for large datasets

  • Have to calculate the intervals ourselves

• May need to reformat data (ie. Three common seaborn difficulties | by Michael Waskom | Medium) since Seaborn does not plot datasets normally

• Two different plot functions and difficulty plotting data other than categorical

  • Overview of seaborn plotting functions — seaborn 0.12.2 documentation (pydata.org)

• Based on matplotlib, no connection

• Based on matplotlib

• Close integration with pandas

• Dataset oriented API for examining relationships between multiple variables

• Specialized support for categorical variables to show observations or aggregate statistics

• Concise control over matplotlib figure styling with several built-in themes.

• Tools for choosing color palettes to reveal data patterns

Bokeh (https://bokeh.org/ )

Python

• Open source

• Can add custom Javascript for advanced/specialized cases

D3.js

Javascript

Pts.js

Javascript