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Introduction

Data science typically involves data, analysis, tests and outputs. A simple way to review, verify or present results is via Visualizations.

Plotly Dash is selected to help prepare and present visualizations in a single dashboard. The key is to help present results in a organized manner to avoid

Summary

A library is under development to standardize this. See dashhtmlgrid

#TODO

  • A decidated plotly visualization library is required to help develop faster custom plotting
    • The data provided to charts should be dedicated dfs with simple configuration files

Usage

  • Prepare data
  • Define html div matrix using app layout (based on expected outputs). Some examples are:

  • Utilize standard elements to plug into each matrix divs charts, tables

The library currently supports:

  • 1 dropdown menu box (with only 1 input field)
  • multiple tables
  • multiple plots

Example codes

  • Dropdown, multiple option selection. Example is given below:

Code file: src\dashhtmlgrid\tests\test_dropdown_multiple_options.py

Drag value - Circular Callback

https://community.plotly.com/t/dash-1-19-0-circular-callbacks-drag-value-in-dcc-slider-close-button-in-dev-tools-dcc-graph-bug-fixes/49500

CSS templates

https://towardsdatascience.com/how-to-embed-bootstrap-css-js-in-your-python-dash-app-8d95fc9e599e

References

https://pypi.org/project/dash/ https://www.statworx.com/en/content-hub/blog/how-to-build-a-dashboard-in-python-plotly-dash-step-by-step-tutorial/ https://chart-studio.plotly.com/dashboard/PythonPlotBot:540/present#/

20 line code dashboard tutorial

Unexpected errors https://pyquestions.com/getting-the-error-message-r-is-null-whenever-running-a-dash-application#:~:text=The%20r%20is%20null%20also%20error%20occurs%20for,are%20no%20traces%20that%20meet%20the%20user-specified%20criteria.

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