Use PyGWalker with Plotly Dash
Overview
Embed PyGWalker
visualizations within a Plotly Dash
application to utilize Dash's hosting capabilities. This updated guide also includes steps to load a pre-existing visualization configuration.
Prerequisites
- Familiarity with
PyGWalker
andPlotly Dash
. - Python environment set up.
Tools Introduction
PyGWalker
- An interactive data visualization library.
- Enables intuitive drag-and-drop data exploration.
- Supports a feature to load predefined visualization configurations.
- Official Repository (opens in a new tab)
Plotly Dash
- A user-friendly framework to host web-based data visualizations.
- Allows data scientists to deploy interactive web applications without in-depth web development knowledge.
- Official Website (opens in a new tab)
Integration Steps
-
Environment Setup:
pip install dash pygwalker dash-dangerously-set-inner-html
-
Data Preparation:
df = pd.read_csv("data.csv")
-
PyGWalker Visualization with Predefined Config:
Use the
to_html
function to obtain the visualization, providing the path to the pre-existing configurationgw_config.json
:pyg_html_code = pyg.to_html(df, spec="./gw_config.json")
-
Dash Integration:
Embed the PyGWalker HTML within the Dash application using
dash-dangerously-set-inner-html
. Ensure the HTML content is secure:app.layout = html.Div([ dash_dangerously_set_inner_html.DangerouslySetInnerHTML(html_code), ])
-
Launch Dash App:
Execute the application to view the
PyGWalker
visualization hosted in a Dash web app:if __name__ == '__main__': app.run_server(debug=True)
Notes
- Leveraging a pre-existing visualization configuration facilitates consistent visualization setups across different datasets or platforms.
- Always ensure the security and integrity of any HTML content added using
dash-dangerously-set-inner-html
.
Experience seamless data exploration by integrating PyGWalker's predefined visualization configurations within a Dash application.